Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "from sklearn.preprocessing import Imputer, StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv',sep=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891221, 85)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the structure of the data after it's loaded (e.g. print the number of\n",
    "# rows and columns, print the first few rows).\n",
    "\n",
    "# The number of rows and columns of the demographics data\n",
    "azdias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of rows and columns for the feature attributes summary\n",
    "feat_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
       "2        -1                     3          2            3.0   \n",
       "3         2                     4          2            2.0   \n",
       "4        -1                     3          1            5.0   \n",
       "5         3                     1          2            2.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
       "5                  3              1                 5               2   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3    ...            NaN         NaN   \n",
       "1                      4                 5    ...            2.0         3.0   \n",
       "2                      3                 5    ...            3.0         3.0   \n",
       "3                      1                 2    ...            2.0         2.0   \n",
       "4                      3                 2    ...            2.0         4.0   \n",
       "5                      2                 5    ...            2.0         3.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
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       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
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       "5         1.0         1.0          1.0       5.0       5.0     2.0   \n",
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "#The first 6 rows of the demographics data\n",
    "azdias.head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "AGER_TYP                   int64\n",
       "ALTERSKATEGORIE_GROB       int64\n",
       "ANREDE_KZ                  int64\n",
       "CJT_GESAMTTYP            float64\n",
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       "FINANZTYP                  int64\n",
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       "                          ...   \n",
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       "BALLRAUM                 float64\n",
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       "MOBI_REGIO               float64\n",
       "ONLINE_AFFINITAET        float64\n",
       "REGIOTYP                 float64\n",
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       "PLZ8_ANTG1               float64\n",
       "PLZ8_ANTG2               float64\n",
       "PLZ8_ANTG3               float64\n",
       "PLZ8_ANTG4               float64\n",
       "PLZ8_BAUMAX              float64\n",
       "PLZ8_HHZ                 float64\n",
       "PLZ8_GBZ                 float64\n",
       "ARBEIT                   float64\n",
       "ORTSGR_KLS9              float64\n",
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       "Length: 85, dtype: object"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The data types of the demographics data\n",
    "azdias.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>2.253330</td>\n",
       "      <td>2.801858</td>\n",
       "      <td>1.595426</td>\n",
       "      <td>0.699166</td>\n",
       "      <td>1.943913</td>\n",
       "      <td>3.612821</td>\n",
       "      <td>3.381087</td>\n",
       "      <td>3.167854</td>\n",
       "      <td>5.293002</td>\n",
       "      <td>3.07222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.198724</td>\n",
       "      <td>1.068775</td>\n",
       "      <td>0.499512</td>\n",
       "      <td>1.595021</td>\n",
       "      <td>1.321055</td>\n",
       "      <td>1.464749</td>\n",
       "      <td>1.322134</td>\n",
       "      <td>1.529603</td>\n",
       "      <td>1.486731</td>\n",
       "      <td>1.353248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972008</td>\n",
       "      <td>0.920309</td>\n",
       "      <td>0.986736</td>\n",
       "      <td>0.727137</td>\n",
       "      <td>1.459654</td>\n",
       "      <td>0.973967</td>\n",
       "      <td>1.111598</td>\n",
       "      <td>1.002376</td>\n",
       "      <td>2.303739</td>\n",
       "      <td>1.36298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AGER_TYP  ALTERSKATEGORIE_GROB      ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "count  891221.000000         891221.000000  891221.000000  886367.000000   \n",
       "mean       -0.358435              2.777398       1.522098       3.632838   \n",
       "std         1.198724              1.068775       0.499512       1.595021   \n",
       "min        -1.000000              1.000000       1.000000       1.000000   \n",
       "25%        -1.000000              2.000000       1.000000       2.000000   \n",
       "50%        -1.000000              3.000000       2.000000       4.000000   \n",
       "75%        -1.000000              4.000000       2.000000       5.000000   \n",
       "max         3.000000              9.000000       2.000000       6.000000   \n",
       "\n",
       "       FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "count      891221.000000  891221.000000     891221.000000   891221.000000   \n",
       "mean            3.074528       2.821039          3.401106        3.033328   \n",
       "std             1.321055       1.464749          1.322134        1.529603   \n",
       "min             1.000000       1.000000          1.000000        1.000000   \n",
       "25%             2.000000       1.000000          3.000000        2.000000   \n",
       "50%             3.000000       3.000000          3.000000        3.000000   \n",
       "75%             4.000000       4.000000          5.000000        5.000000   \n",
       "max             5.000000       5.000000          5.000000        5.000000   \n",
       "\n",
       "       FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER      ...          PLZ8_ANTG1  \\\n",
       "count          891221.000000     891221.000000      ...       774706.000000   \n",
       "mean                2.874167          3.075121      ...            2.253330   \n",
       "std                 1.486731          1.353248      ...            0.972008   \n",
       "min                 1.000000          1.000000      ...            0.000000   \n",
       "25%                 2.000000          2.000000      ...            1.000000   \n",
       "50%                 3.000000          3.000000      ...            2.000000   \n",
       "75%                 4.000000          4.000000      ...            3.000000   \n",
       "max                 5.000000          5.000000      ...            4.000000   \n",
       "\n",
       "          PLZ8_ANTG2     PLZ8_ANTG3     PLZ8_ANTG4    PLZ8_BAUMAX  \\\n",
       "count  774706.000000  774706.000000  774706.000000  774706.000000   \n",
       "mean        2.801858       1.595426       0.699166       1.943913   \n",
       "std         0.920309       0.986736       0.727137       1.459654   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         2.000000       1.000000       0.000000       1.000000   \n",
       "50%         3.000000       2.000000       1.000000       1.000000   \n",
       "75%         3.000000       2.000000       1.000000       3.000000   \n",
       "max         4.000000       3.000000       2.000000       5.000000   \n",
       "\n",
       "            PLZ8_HHZ       PLZ8_GBZ         ARBEIT    ORTSGR_KLS9  \\\n",
       "count  774706.000000  774706.000000  794005.000000  794005.000000   \n",
       "mean        3.612821       3.381087       3.167854       5.293002   \n",
       "std         0.973967       1.111598       1.002376       2.303739   \n",
       "min         1.000000       1.000000       1.000000       0.000000   \n",
       "25%         3.000000       3.000000       3.000000       4.000000   \n",
       "50%         4.000000       3.000000       3.000000       5.000000   \n",
       "75%         4.000000       4.000000       4.000000       7.000000   \n",
       "max         5.000000       5.000000       9.000000       9.000000   \n",
       "\n",
       "           RELAT_AB  \n",
       "count  794005.00000  \n",
       "mean        3.07222  \n",
       "std         1.36298  \n",
       "min         1.00000  \n",
       "25%         2.00000  \n",
       "50%         3.00000  \n",
       "75%         4.00000  \n",
       "max         9.00000  \n",
       "\n",
       "[8 rows x 81 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The description of the demographics data\n",
    "azdias.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINANZ_MINIMALIST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FINANZ_SPARER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FINANZ_VORSORGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FINANZ_ANLEGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FINANZ_UNAUFFAELLIGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANZ_HAUSBAUER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>person</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RETOURTYP_BK_S</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SEMIO_SOZ</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SEMIO_FAM</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SEMIO_REL</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SEMIO_MAT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEMIO_VERT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SEMIO_LUST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>KBA05_ANTG1</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>KBA05_ANTG2</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>KBA05_ANTG3</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>KBA05_ANTG4</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>KBA05_BAUMAX</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>KBA05_GBZ</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>BALLRAUM</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>EWDICHTE</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>INNENSTADT</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>GEBAEUDETYP_RASTER</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>KKK</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MOBI_REGIO</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ONLINE_AFFINITAET</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>REGIOTYP</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>KBA13_ANZAHL_PKW</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>PLZ8_ANTG1</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PLZ8_ANTG2</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>PLZ8_ANTG3</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>PLZ8_ANTG4</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>PLZ8_HHZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>PLZ8_GBZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ARBEIT</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>RELAT_AB</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                attribute information_level         type missing_or_unknown\n",
       "0                AGER_TYP            person  categorical             [-1,0]\n",
       "1    ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]\n",
       "2               ANREDE_KZ            person  categorical             [-1,0]\n",
       "3           CJT_GESAMTTYP            person  categorical                [0]\n",
       "4       FINANZ_MINIMALIST            person      ordinal               [-1]\n",
       "5           FINANZ_SPARER            person      ordinal               [-1]\n",
       "6        FINANZ_VORSORGER            person      ordinal               [-1]\n",
       "7          FINANZ_ANLEGER            person      ordinal               [-1]\n",
       "8   FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]\n",
       "9        FINANZ_HAUSBAUER            person      ordinal               [-1]\n",
       "10              FINANZTYP            person  categorical               [-1]\n",
       "11            GEBURTSJAHR            person      numeric                [0]\n",
       "12        GFK_URLAUBERTYP            person  categorical                 []\n",
       "13       GREEN_AVANTGARDE            person  categorical                 []\n",
       "14             HEALTH_TYP            person      ordinal             [-1,0]\n",
       "15    LP_LEBENSPHASE_FEIN            person        mixed                [0]\n",
       "16    LP_LEBENSPHASE_GROB            person        mixed                [0]\n",
       "17        LP_FAMILIE_FEIN            person  categorical                [0]\n",
       "18        LP_FAMILIE_GROB            person  categorical                [0]\n",
       "19         LP_STATUS_FEIN            person  categorical                [0]\n",
       "20         LP_STATUS_GROB            person  categorical                [0]\n",
       "21       NATIONALITAET_KZ            person  categorical             [-1,0]\n",
       "22  PRAEGENDE_JUGENDJAHRE            person        mixed             [-1,0]\n",
       "23         RETOURTYP_BK_S            person      ordinal                [0]\n",
       "24              SEMIO_SOZ            person      ordinal             [-1,9]\n",
       "25              SEMIO_FAM            person      ordinal             [-1,9]\n",
       "26              SEMIO_REL            person      ordinal             [-1,9]\n",
       "27              SEMIO_MAT            person      ordinal             [-1,9]\n",
       "28             SEMIO_VERT            person      ordinal             [-1,9]\n",
       "29             SEMIO_LUST            person      ordinal             [-1,9]\n",
       "..                    ...               ...          ...                ...\n",
       "55            OST_WEST_KZ          building  categorical               [-1]\n",
       "56               WOHNLAGE          building        mixed               [-1]\n",
       "57        CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]\n",
       "58         CAMEO_DEU_2015     microcell_rr4  categorical               [XX]\n",
       "59        CAMEO_INTL_2015     microcell_rr4        mixed            [-1,XX]\n",
       "60            KBA05_ANTG1     microcell_rr3      ordinal               [-1]\n",
       "61            KBA05_ANTG2     microcell_rr3      ordinal               [-1]\n",
       "62            KBA05_ANTG3     microcell_rr3      ordinal               [-1]\n",
       "63            KBA05_ANTG4     microcell_rr3      ordinal               [-1]\n",
       "64           KBA05_BAUMAX     microcell_rr3        mixed             [-1,0]\n",
       "65              KBA05_GBZ     microcell_rr3      ordinal             [-1,0]\n",
       "66               BALLRAUM          postcode      ordinal               [-1]\n",
       "67               EWDICHTE          postcode      ordinal               [-1]\n",
       "68             INNENSTADT          postcode      ordinal               [-1]\n",
       "69     GEBAEUDETYP_RASTER        region_rr1      ordinal                 []\n",
       "70                    KKK        region_rr1      ordinal             [-1,0]\n",
       "71             MOBI_REGIO        region_rr1      ordinal                 []\n",
       "72      ONLINE_AFFINITAET        region_rr1      ordinal                 []\n",
       "73               REGIOTYP        region_rr1      ordinal             [-1,0]\n",
       "74       KBA13_ANZAHL_PKW    macrocell_plz8      numeric                 []\n",
       "75             PLZ8_ANTG1    macrocell_plz8      ordinal               [-1]\n",
       "76             PLZ8_ANTG2    macrocell_plz8      ordinal               [-1]\n",
       "77             PLZ8_ANTG3    macrocell_plz8      ordinal               [-1]\n",
       "78             PLZ8_ANTG4    macrocell_plz8      ordinal               [-1]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8        mixed             [-1,0]\n",
       "80               PLZ8_HHZ    macrocell_plz8      ordinal               [-1]\n",
       "81               PLZ8_GBZ    macrocell_plz8      ordinal               [-1]\n",
       "82                 ARBEIT         community      ordinal             [-1,9]\n",
       "83            ORTSGR_KLS9         community      ordinal             [-1,0]\n",
       "84               RELAT_AB         community      ordinal             [-1,9]\n",
       "\n",
       "[85 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the feature attributes summary\n",
    "feat_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make a copy of feat_info dataframe to prevent any future unwanted change\n",
    "azdias_copy = azdias.copy()\n",
    "feat_info_copy = feat_info.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of naturally missing data: 4,896,838\n"
     ]
    }
   ],
   "source": [
    "# print the Number of naturally missing data\n",
    "print('Number of naturally missing data: {:,}'.format(azdias_copy.isnull().sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in range(feat_info_copy.shape[0]):\n",
    "    col=azdias_copy.columns[index]\n",
    "    null_list= feat_info_copy.iloc[index,3]\n",
    "    null_list= null_list.strip('[]').split(',')\n",
    "    \n",
    "    if null_list != ['']:\n",
    "        for null_value in null_list:\n",
    "            if null_value!='X' and null_value!='XX' and null_value!='':\n",
    "                null_value= int(null_value)\n",
    "            else:\n",
    "                null_value=null_value\n",
    "            \n",
    "            azdias_copy.loc[azdias_copy[col]==null_value,col]=np.nan                             \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data takes on a missing or unknown codes: 8,373,929\n"
     ]
    }
   ],
   "source": [
    "# print the Number of data takes on a missing or unknown codes\n",
    "print('Number of data takes on a missing or unknown codes: {:,}'.format(azdias_copy.isnull().sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,   3.,   2.,   4.,   1.,   5.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get unique values to check if the null values are converted to NaN\n",
    "azdias_copy.ARBEIT.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0       NaN                   2.0        1.0            2.0   \n",
       "1       NaN                   1.0        2.0            5.0   \n",
       "2       NaN                   3.0        2.0            3.0   \n",
       "3       2.0                   4.0        2.0            2.0   \n",
       "4       NaN                   3.0        1.0            5.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                3.0            4.0               3.0             5.0   \n",
       "1                1.0            5.0               2.0             5.0   \n",
       "2                1.0            4.0               1.0             2.0   \n",
       "3                4.0            2.0               5.0             2.0   \n",
       "4                4.0            3.0               4.0             1.0   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                    5.0               3.0    ...            NaN         NaN   \n",
       "1                    4.0               5.0    ...            2.0         3.0   \n",
       "2                    3.0               5.0    ...            3.0         3.0   \n",
       "3                    1.0               2.0    ...            2.0         2.0   \n",
       "4                    3.0               2.0    ...            2.0         4.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
       "1         2.0         1.0          1.0       5.0       4.0     3.0   \n",
       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN       NaN  \n",
       "1          5.0       4.0  \n",
       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display 5 records to check if the null values are converted to NaN\n",
    "azdias_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\n",
    "\n",
    "For the remaining features, are there any patterns in which columns have, or share, missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZABEOTYP                  0.000000\n",
       "SEMIO_TRADV               0.000000\n",
       "SEMIO_PFLICHT             0.000000\n",
       "SEMIO_KAEM                0.000000\n",
       "SEMIO_DOM                 0.000000\n",
       "SEMIO_KRIT                0.000000\n",
       "SEMIO_RAT                 0.000000\n",
       "SEMIO_KULT                0.000000\n",
       "SEMIO_ERL                 0.000000\n",
       "SEMIO_LUST                0.000000\n",
       "SEMIO_VERT                0.000000\n",
       "SEMIO_MAT                 0.000000\n",
       "SEMIO_REL                 0.000000\n",
       "SEMIO_SOZ                 0.000000\n",
       "SEMIO_FAM                 0.000000\n",
       "FINANZ_VORSORGER          0.000000\n",
       "ANREDE_KZ                 0.000000\n",
       "FINANZ_MINIMALIST         0.000000\n",
       "FINANZ_SPARER             0.000000\n",
       "FINANZ_ANLEGER            0.000000\n",
       "FINANZ_UNAUFFAELLIGER     0.000000\n",
       "FINANZ_HAUSBAUER          0.000000\n",
       "GREEN_AVANTGARDE          0.000000\n",
       "FINANZTYP                 0.000000\n",
       "ALTERSKATEGORIE_GROB      0.323264\n",
       "GFK_URLAUBERTYP           0.544646\n",
       "LP_STATUS_GROB            0.544646\n",
       "LP_STATUS_FEIN            0.544646\n",
       "ONLINE_AFFINITAET         0.544646\n",
       "RETOURTYP_BK_S            0.544646\n",
       "                           ...    \n",
       "CAMEO_INTL_2015          11.147852\n",
       "ANZ_HAUSHALTE_AKTIV      11.176913\n",
       "KBA13_ANZAHL_PKW         11.871354\n",
       "PRAEGENDE_JUGENDJAHRE    12.136608\n",
       "NATIONALITAET_KZ         12.153551\n",
       "HEALTH_TYP               12.476816\n",
       "VERS_TYP                 12.476816\n",
       "SHOPPER_TYP              12.476816\n",
       "PLZ8_ANTG2               13.073637\n",
       "PLZ8_ANTG3               13.073637\n",
       "PLZ8_ANTG1               13.073637\n",
       "PLZ8_ANTG4               13.073637\n",
       "PLZ8_BAUMAX              13.073637\n",
       "PLZ8_HHZ                 13.073637\n",
       "PLZ8_GBZ                 13.073637\n",
       "KBA05_ANTG3              14.959701\n",
       "KBA05_ANTG2              14.959701\n",
       "KBA05_ANTG1              14.959701\n",
       "MOBI_REGIO               14.959701\n",
       "KBA05_GBZ                14.959701\n",
       "KBA05_ANTG4              14.959701\n",
       "W_KEIT_KIND_HH           16.605084\n",
       "KKK                      17.735668\n",
       "REGIOTYP                 17.735668\n",
       "ALTER_HH                 34.813699\n",
       "GEBURTSJAHR              44.020282\n",
       "KBA05_BAUMAX             53.468668\n",
       "KK_KUNDENTYP             65.596749\n",
       "AGER_TYP                 76.955435\n",
       "TITEL_KZ                 99.757636\n",
       "Length: 85, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "missing_data_col_percent= ((azdias_copy.isnull().sum(axis=0))/len(azdias_copy))*100\n",
    "missing_data_col_percent.sort_values(inplace=True)\n",
    "missing_data_col_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c1abcdb70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the distribution of missing data in each column\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.title('Distribution of missing data in each column')\n",
    "plt.hist(missing_data_col_percent, bins=np.linspace(0,100,10))\n",
    "plt.ylabel('Number of Columns')\n",
    "plt.xlabel('Percentage of Missing data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "\n",
    "#divide data into 2 subset based on the quantity of missing data \n",
    "\n",
    "Low_missing_data_col_percent = missing_data_col_percent[missing_data_col_percent <= 30]\n",
    "High_missing_data_col_percent = missing_data_col_percent[missing_data_col_percent > 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Number of Low_missing_value columns is: 79 \n",
      "\n",
      "The Number of High_missing_value columns is: 6 \n",
      "\n",
      "The Low_missing_value columns are:\n",
      " ['ZABEOTYP', 'SEMIO_TRADV', 'SEMIO_PFLICHT', 'SEMIO_KAEM', 'SEMIO_DOM', 'SEMIO_KRIT', 'SEMIO_RAT', 'SEMIO_KULT', 'SEMIO_ERL', 'SEMIO_LUST', 'SEMIO_VERT', 'SEMIO_MAT', 'SEMIO_REL', 'SEMIO_SOZ', 'SEMIO_FAM', 'FINANZ_VORSORGER', 'ANREDE_KZ', 'FINANZ_MINIMALIST', 'FINANZ_SPARER', 'FINANZ_ANLEGER', 'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER', 'GREEN_AVANTGARDE', 'FINANZTYP', 'ALTERSKATEGORIE_GROB', 'GFK_URLAUBERTYP', 'LP_STATUS_GROB', 'LP_STATUS_FEIN', 'ONLINE_AFFINITAET', 'RETOURTYP_BK_S', 'CJT_GESAMTTYP', 'HH_EINKOMMEN_SCORE', 'WOHNDAUER_2008', 'ANZ_TITEL', 'SOHO_KZ', 'ANZ_PERSONEN', 'KONSUMNAEHE', 'LP_FAMILIE_GROB', 'LP_FAMILIE_FEIN', 'OST_WEST_KZ', 'WOHNLAGE', 'GEBAEUDETYP', 'MIN_GEBAEUDEJAHR', 'GEBAEUDETYP_RASTER', 'BALLRAUM', 'EWDICHTE', 'INNENSTADT', 'LP_LEBENSPHASE_GROB', 'ANZ_HH_TITEL', 'ORTSGR_KLS9', 'ARBEIT', 'RELAT_AB', 'LP_LEBENSPHASE_FEIN', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015', 'CAMEO_INTL_2015', 'ANZ_HAUSHALTE_AKTIV', 'KBA13_ANZAHL_PKW', 'PRAEGENDE_JUGENDJAHRE', 'NATIONALITAET_KZ', 'HEALTH_TYP', 'VERS_TYP', 'SHOPPER_TYP', 'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG1', 'PLZ8_ANTG4', 'PLZ8_BAUMAX', 'PLZ8_HHZ', 'PLZ8_GBZ', 'KBA05_ANTG3', 'KBA05_ANTG2', 'KBA05_ANTG1', 'MOBI_REGIO', 'KBA05_GBZ', 'KBA05_ANTG4', 'W_KEIT_KIND_HH', 'KKK', 'REGIOTYP'] \n",
      "\n",
      "The High_missing_value columns are:\n",
      " ['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP', 'TITEL_KZ']\n"
     ]
    }
   ],
   "source": [
    "#print the information\n",
    "print('The Number of Low_missing_value columns is:', Low_missing_data_col_percent.count(),'\\n')\n",
    "print('The Number of High_missing_value columns is:', High_missing_data_col_percent.count(),'\\n')\n",
    "print('The Low_missing_value columns are:\\n', Low_missing_data_col_percent.index.tolist(),'\\n')\n",
    "print('The High_missing_value columns are:\\n', High_missing_data_col_percent.index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c18884780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "plt.figure(figsize=(20, 25))\n",
    "plt.suptitle('Patterns in the amount of missing data in each column', fontsize=30)\n",
    "plt.subplots_adjust(hspace = 1, wspace=.3)\n",
    "plt.subplot(311)\n",
    "missing_data_col_percent.plot.bar()\n",
    "plt.title('Missing data')\n",
    "plt.xlabel('Columns Names')\n",
    "plt.ylabel('% of missing values')\n",
    "plt.subplot(312)\n",
    "Low_missing_data_col_percent.plot.bar()\n",
    "plt.title('Low Missing data')\n",
    "plt.xlabel('Columns Names')\n",
    "plt.ylabel('% of missing values')\n",
    "plt.subplot(313)\n",
    "High_missing_data_col_percent.plot.bar()\n",
    "plt.title('High Missing data')\n",
    "plt.xlabel('Columns Names')\n",
    "plt.ylabel('% of missing values')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000     24\n",
       "13.073637     7\n",
       "0.544646      6\n",
       "14.959701     6\n",
       "10.451729     4\n",
       "8.247000      4\n",
       "10.518154     3\n",
       "11.147852     3\n",
       "12.476816     3\n",
       "10.926022     2\n",
       "8.728699      2\n",
       "17.735668     2\n",
       "16.605084     1\n",
       "12.136608     1\n",
       "53.468668     1\n",
       "2.058749      1\n",
       "10.914689     1\n",
       "12.153551     1\n",
       "10.452514     1\n",
       "10.954859     1\n",
       "0.323264      1\n",
       "99.757636     1\n",
       "10.611509     1\n",
       "10.884842     1\n",
       "34.813699     1\n",
       "76.955435     1\n",
       "44.020282     1\n",
       "65.596749     1\n",
       "8.299737      1\n",
       "11.176913     1\n",
       "11.871354     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_col_percent_values= missing_data_col_percent.value_counts()\n",
    "missing_data_col_percent_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([   0.5446460529992,  8.247000463409188,  8.728699166648902,\n",
       "              10.451728583594866, 10.518154307405233,  10.92602171627464,\n",
       "              11.147852216229195, 12.476815514894735, 13.073637178657146,\n",
       "              14.959701353536328,  17.73566825736826],\n",
       "             dtype='float64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Shared_missing_data_col_values= missing_data_col_percent_values.index[1:12]\n",
    "Shared_missing_data_col_values= Shared_missing_data_col_values.sort_values()\n",
    "Shared_missing_data_col_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ZABEOTYP                  0.000000\n",
       "FINANZTYP                 0.000000\n",
       "GREEN_AVANTGARDE          0.000000\n",
       "FINANZ_HAUSBAUER          0.000000\n",
       "FINANZ_UNAUFFAELLIGER     0.000000\n",
       "FINANZ_SPARER             0.000000\n",
       "FINANZ_MINIMALIST         0.000000\n",
       "ANREDE_KZ                 0.000000\n",
       "FINANZ_VORSORGER          0.000000\n",
       "SEMIO_FAM                 0.000000\n",
       "SEMIO_SOZ                 0.000000\n",
       "SEMIO_REL                 0.000000\n",
       "FINANZ_ANLEGER            0.000000\n",
       "SEMIO_VERT                0.000000\n",
       "SEMIO_LUST                0.000000\n",
       "SEMIO_ERL                 0.000000\n",
       "SEMIO_KULT                0.000000\n",
       "SEMIO_RAT                 0.000000\n",
       "SEMIO_KRIT                0.000000\n",
       "SEMIO_DOM                 0.000000\n",
       "SEMIO_KAEM                0.000000\n",
       "SEMIO_PFLICHT             0.000000\n",
       "SEMIO_TRADV               0.000000\n",
       "SEMIO_MAT                 0.000000\n",
       "ALTERSKATEGORIE_GROB      0.323264\n",
       "CJT_GESAMTTYP             0.544646\n",
       "RETOURTYP_BK_S            0.544646\n",
       "ONLINE_AFFINITAET         0.544646\n",
       "LP_STATUS_GROB            0.544646\n",
       "GFK_URLAUBERTYP           0.544646\n",
       "                           ...    \n",
       "CAMEO_INTL_2015          11.147852\n",
       "ANZ_HAUSHALTE_AKTIV      11.176913\n",
       "KBA13_ANZAHL_PKW         11.871354\n",
       "PRAEGENDE_JUGENDJAHRE    12.136608\n",
       "NATIONALITAET_KZ         12.153551\n",
       "VERS_TYP                 12.476816\n",
       "SHOPPER_TYP              12.476816\n",
       "HEALTH_TYP               12.476816\n",
       "PLZ8_ANTG2               13.073637\n",
       "PLZ8_ANTG3               13.073637\n",
       "PLZ8_ANTG1               13.073637\n",
       "PLZ8_ANTG4               13.073637\n",
       "PLZ8_BAUMAX              13.073637\n",
       "PLZ8_HHZ                 13.073637\n",
       "PLZ8_GBZ                 13.073637\n",
       "MOBI_REGIO               14.959701\n",
       "KBA05_ANTG4              14.959701\n",
       "KBA05_GBZ                14.959701\n",
       "KBA05_ANTG1              14.959701\n",
       "KBA05_ANTG2              14.959701\n",
       "KBA05_ANTG3              14.959701\n",
       "W_KEIT_KIND_HH           16.605084\n",
       "KKK                      17.735668\n",
       "REGIOTYP                 17.735668\n",
       "ALTER_HH                 34.813699\n",
       "GEBURTSJAHR              44.020282\n",
       "KBA05_BAUMAX             53.468668\n",
       "KK_KUNDENTYP             65.596749\n",
       "AGER_TYP                 76.955435\n",
       "TITEL_KZ                 99.757636\n",
       "Length: 85, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_col_percent_sorted= missing_data_col_percent.sort_values()\n",
    "missing_data_col_percent_sorted_index= missing_data_col_percent_sorted.index\n",
    "missing_data_col_percent_sorted_index\n",
    "missing_data_col_percent_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There're (6) features with (0.5446460529992) percent of missing data: \n",
      "\n",
      "['CJT_GESAMTTYP', 'RETOURTYP_BK_S', 'ONLINE_AFFINITAET', 'LP_STATUS_GROB', 'GFK_URLAUBERTYP', 'LP_STATUS_FEIN']\n",
      "\n",
      "            attribute information_level         type missing_or_unknown\n",
      "3       CJT_GESAMTTYP            person  categorical                [0]\n",
      "12    GFK_URLAUBERTYP            person  categorical                 []\n",
      "19     LP_STATUS_FEIN            person  categorical                [0]\n",
      "20     LP_STATUS_GROB            person  categorical                [0]\n",
      "23     RETOURTYP_BK_S            person      ordinal                [0]\n",
      "72  ONLINE_AFFINITAET        region_rr1      ordinal                 []\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (4) features with (8.247000463409188) percent of missing data: \n",
      "\n",
      "['WOHNDAUER_2008', 'ANZ_TITEL', 'SOHO_KZ', 'ANZ_PERSONEN']\n",
      "\n",
      "         attribute information_level         type missing_or_unknown\n",
      "39         SOHO_KZ            person  categorical               [-1]\n",
      "44    ANZ_PERSONEN         household      numeric                 []\n",
      "45       ANZ_TITEL         household      numeric                 []\n",
      "49  WOHNDAUER_2008         household      ordinal             [-1,0]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (2) features with (8.728699166648902) percent of missing data: \n",
      "\n",
      "['LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB']\n",
      "\n",
      "          attribute information_level         type missing_or_unknown\n",
      "17  LP_FAMILIE_FEIN            person  categorical                [0]\n",
      "18  LP_FAMILIE_GROB            person  categorical                [0]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (4) features with (10.451728583594866) percent of missing data: \n",
      "\n",
      "['GEBAEUDETYP', 'MIN_GEBAEUDEJAHR', 'OST_WEST_KZ', 'WOHNLAGE']\n",
      "\n",
      "           attribute information_level         type missing_or_unknown\n",
      "52       GEBAEUDETYP          building  categorical             [-1,0]\n",
      "54  MIN_GEBAEUDEJAHR          building      numeric                [0]\n",
      "55       OST_WEST_KZ          building  categorical               [-1]\n",
      "56          WOHNLAGE          building        mixed               [-1]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (3) features with (10.518154307405233) percent of missing data: \n",
      "\n",
      "['BALLRAUM', 'EWDICHTE', 'INNENSTADT']\n",
      "\n",
      "     attribute information_level     type missing_or_unknown\n",
      "66    BALLRAUM          postcode  ordinal               [-1]\n",
      "67    EWDICHTE          postcode  ordinal               [-1]\n",
      "68  INNENSTADT          postcode  ordinal               [-1]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (2) features with (10.92602171627464) percent of missing data: \n",
      "\n",
      "['RELAT_AB', 'ARBEIT']\n",
      "\n",
      "   attribute information_level     type missing_or_unknown\n",
      "82    ARBEIT         community  ordinal             [-1,9]\n",
      "84  RELAT_AB         community  ordinal             [-1,9]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (3) features with (11.147852216229195) percent of missing data: \n",
      "\n",
      "['CAMEO_DEUG_2015', 'CAMEO_DEU_2015', 'CAMEO_INTL_2015']\n",
      "\n",
      "          attribute information_level         type missing_or_unknown\n",
      "57  CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]\n",
      "58   CAMEO_DEU_2015     microcell_rr4  categorical               [XX]\n",
      "59  CAMEO_INTL_2015     microcell_rr4        mixed            [-1,XX]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (3) features with (12.476815514894735) percent of missing data: \n",
      "\n",
      "['VERS_TYP', 'SHOPPER_TYP', 'HEALTH_TYP']\n",
      "\n",
      "      attribute information_level         type missing_or_unknown\n",
      "14   HEALTH_TYP            person      ordinal             [-1,0]\n",
      "38  SHOPPER_TYP            person  categorical               [-1]\n",
      "41     VERS_TYP            person  categorical               [-1]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (7) features with (13.073637178657146) percent of missing data: \n",
      "\n",
      "['PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG1', 'PLZ8_ANTG4', 'PLZ8_BAUMAX', 'PLZ8_HHZ', 'PLZ8_GBZ']\n",
      "\n",
      "      attribute information_level     type missing_or_unknown\n",
      "75   PLZ8_ANTG1    macrocell_plz8  ordinal               [-1]\n",
      "76   PLZ8_ANTG2    macrocell_plz8  ordinal               [-1]\n",
      "77   PLZ8_ANTG3    macrocell_plz8  ordinal               [-1]\n",
      "78   PLZ8_ANTG4    macrocell_plz8  ordinal               [-1]\n",
      "79  PLZ8_BAUMAX    macrocell_plz8    mixed             [-1,0]\n",
      "80     PLZ8_HHZ    macrocell_plz8  ordinal               [-1]\n",
      "81     PLZ8_GBZ    macrocell_plz8  ordinal               [-1]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (6) features with (14.959701353536328) percent of missing data: \n",
      "\n",
      "['MOBI_REGIO', 'KBA05_ANTG4', 'KBA05_GBZ', 'KBA05_ANTG1', 'KBA05_ANTG2', 'KBA05_ANTG3']\n",
      "\n",
      "      attribute information_level     type missing_or_unknown\n",
      "60  KBA05_ANTG1     microcell_rr3  ordinal               [-1]\n",
      "61  KBA05_ANTG2     microcell_rr3  ordinal               [-1]\n",
      "62  KBA05_ANTG3     microcell_rr3  ordinal               [-1]\n",
      "63  KBA05_ANTG4     microcell_rr3  ordinal               [-1]\n",
      "65    KBA05_GBZ     microcell_rr3  ordinal             [-1,0]\n",
      "71   MOBI_REGIO        region_rr1  ordinal                 []\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "There're (2) features with (17.73566825736826) percent of missing data: \n",
      "\n",
      "['KKK', 'REGIOTYP']\n",
      "\n",
      "   attribute information_level     type missing_or_unknown\n",
      "70       KKK        region_rr1  ordinal             [-1,0]\n",
      "73  REGIOTYP        region_rr1  ordinal             [-1,0]\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for value in Shared_missing_data_col_values:\n",
    "    col_names=(missing_data_col_percent_sorted[missing_data_col_percent_sorted==value].index.tolist()) \n",
    "    print('There\\'re ({}) features with ({}) percent of missing data: \\n'.format(len(col_names), value))\n",
    "    print('{}\\n'.format(col_names))\n",
    "    print('{}\\n{}\\n\\n'.format(feat_info_copy.loc[feat_info_copy['attribute'].isin(col_names)],'-'*120))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove high missing features from azdias dataframe\n",
    "azdias_copy.drop(High_missing_data_col_percent.index.tolist(), axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ALTERSKATEGORIE_GROB</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "0                   2.0        1.0            2.0                3.0   \n",
       "1                   1.0        2.0            5.0                1.0   \n",
       "2                   3.0        2.0            3.0                1.0   \n",
       "3                   4.0        2.0            2.0                4.0   \n",
       "4                   3.0        1.0            5.0                4.0   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0            4.0               3.0             5.0                    5.0   \n",
       "1            5.0               2.0             5.0                    4.0   \n",
       "2            4.0               1.0             2.0                    3.0   \n",
       "3            2.0               5.0             2.0                    1.0   \n",
       "4            3.0               4.0             1.0                    3.0   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP    ...     PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0               3.0        4.0    ...            NaN         NaN         NaN   \n",
       "1               5.0        1.0    ...            2.0         3.0         2.0   \n",
       "2               5.0        1.0    ...            3.0         3.0         1.0   \n",
       "3               2.0        6.0    ...            2.0         2.0         2.0   \n",
       "4               2.0        5.0    ...            2.0         4.0         2.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  RELAT_AB  \n",
       "0         NaN          NaN       NaN       NaN     NaN          NaN       NaN  \n",
       "1         1.0          1.0       5.0       4.0     3.0          5.0       4.0  \n",
       "2         0.0          1.0       4.0       4.0     3.0          5.0       2.0  \n",
       "3         0.0          1.0       3.0       4.0     2.0          3.0       3.0  \n",
       "4         1.0          2.0       3.0       3.0     4.0          6.0       5.0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the columns are removed\n",
    "azdias_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove high missing features from feat_info dataframe\n",
    "feat_info_copy = feat_info_copy.set_index('attribute')\n",
    "feat_info_copy= feat_info_copy.drop(High_missing_data_col_percent.index.tolist(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>person</td>\n",
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       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
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       "    <tr>\n",
       "      <th>GFK_URLAUBERTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN_GEBAEUDEJAHR</th>\n",
       "      <td>building</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEU_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_INTL_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG2</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG3</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKK</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGIOTYP</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANZAHL_PKW</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      information_level         type missing_or_unknown\n",
       "attribute                                                              \n",
       "ALTERSKATEGORIE_GROB             person      ordinal           [-1,0,9]\n",
       "ANREDE_KZ                        person  categorical             [-1,0]\n",
       "CJT_GESAMTTYP                    person  categorical                [0]\n",
       "FINANZ_MINIMALIST                person      ordinal               [-1]\n",
       "FINANZ_SPARER                    person      ordinal               [-1]\n",
       "FINANZ_VORSORGER                 person      ordinal               [-1]\n",
       "FINANZ_ANLEGER                   person      ordinal               [-1]\n",
       "FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]\n",
       "FINANZ_HAUSBAUER                 person      ordinal               [-1]\n",
       "FINANZTYP                        person  categorical               [-1]\n",
       "GFK_URLAUBERTYP                  person  categorical                 []\n",
       "GREEN_AVANTGARDE                 person  categorical                 []\n",
       "HEALTH_TYP                       person      ordinal             [-1,0]\n",
       "LP_LEBENSPHASE_FEIN              person        mixed                [0]\n",
       "LP_LEBENSPHASE_GROB              person        mixed                [0]\n",
       "LP_FAMILIE_FEIN                  person  categorical                [0]\n",
       "LP_FAMILIE_GROB                  person  categorical                [0]\n",
       "LP_STATUS_FEIN                   person  categorical                [0]\n",
       "LP_STATUS_GROB                   person  categorical                [0]\n",
       "NATIONALITAET_KZ                 person  categorical             [-1,0]\n",
       "PRAEGENDE_JUGENDJAHRE            person        mixed             [-1,0]\n",
       "RETOURTYP_BK_S                   person      ordinal                [0]\n",
       "SEMIO_SOZ                        person      ordinal             [-1,9]\n",
       "SEMIO_FAM                        person      ordinal             [-1,9]\n",
       "SEMIO_REL                        person      ordinal             [-1,9]\n",
       "SEMIO_MAT                        person      ordinal             [-1,9]\n",
       "SEMIO_VERT                       person      ordinal             [-1,9]\n",
       "SEMIO_LUST                       person      ordinal             [-1,9]\n",
       "SEMIO_ERL                        person      ordinal             [-1,9]\n",
       "SEMIO_KULT                       person      ordinal             [-1,9]\n",
       "...                                 ...          ...                ...\n",
       "MIN_GEBAEUDEJAHR               building      numeric                [0]\n",
       "OST_WEST_KZ                    building  categorical               [-1]\n",
       "WOHNLAGE                       building        mixed               [-1]\n",
       "CAMEO_DEUG_2015           microcell_rr4  categorical             [-1,X]\n",
       "CAMEO_DEU_2015            microcell_rr4  categorical               [XX]\n",
       "CAMEO_INTL_2015           microcell_rr4        mixed            [-1,XX]\n",
       "KBA05_ANTG1               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG2               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG3               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG4               microcell_rr3      ordinal               [-1]\n",
       "KBA05_GBZ                 microcell_rr3      ordinal             [-1,0]\n",
       "BALLRAUM                       postcode      ordinal               [-1]\n",
       "EWDICHTE                       postcode      ordinal               [-1]\n",
       "INNENSTADT                     postcode      ordinal               [-1]\n",
       "GEBAEUDETYP_RASTER           region_rr1      ordinal                 []\n",
       "KKK                          region_rr1      ordinal             [-1,0]\n",
       "MOBI_REGIO                   region_rr1      ordinal                 []\n",
       "ONLINE_AFFINITAET            region_rr1      ordinal                 []\n",
       "REGIOTYP                     region_rr1      ordinal             [-1,0]\n",
       "KBA13_ANZAHL_PKW         macrocell_plz8      numeric                 []\n",
       "PLZ8_ANTG1               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG2               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG3               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG4               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_BAUMAX              macrocell_plz8        mixed             [-1,0]\n",
       "PLZ8_HHZ                 macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_GBZ                 macrocell_plz8      ordinal               [-1]\n",
       "ARBEIT                        community      ordinal             [-1,9]\n",
       "ORTSGR_KLS9                   community      ordinal             [-1,0]\n",
       "RELAT_AB                      community      ordinal             [-1,9]\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the features are removed\n",
    "feat_info_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)\n",
    "\n",
    "- The majority of the columns are missing less than 200,000 data. So, columns with more than 200,000 data were considered as <font color=darkred> outliers.</font> \n",
    "- Hence, 6 columns were removed: <font color=darkred>['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP', 'TITEL_KZ'] </font> \n",
    "<br>  \n",
    "\n",
    "#### <font color=blue>There are 11 missing value patterns:</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (1)</font>\n",
    "***There're (6) features with (0.54) percent of missing data:***\n",
    "1. 'CJT_GESAMTTYP'\n",
    "2. 'RETOURTYP_BK_S'\n",
    "3. 'ONLINE_AFFINITAET'\n",
    "4. 'LP_STATUS_GROB'\n",
    "5. 'GFK_URLAUBERTYP'\n",
    "6. 'LP_STATUS_FEIN'\n",
    "<br> \n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'CJT_GESAMTTYP', 'RETOURTYP_BK_S', 'LP_STATUS_GROB', 'GFK_URLAUBERTYP', and 'LP_STATUS_FEIN' are <font color=darkgreen>Person-level features.</font> \n",
    "- 'LP_STATUS_FEIN' and 'LP_STATUS_GROB' <font color=darkgreen> contain the same information about Social status, but they differ in the scale type either fine scale or rough scale.</font> \n",
    "\n",
    "\n",
    "##### <font color=darkblue>Pattern (2)</font>\n",
    "***There're (4) features with (8.24) percent of missing data:***\n",
    "1. 'WOHNDAUER_2008'\n",
    "2. 'ANZ_TITEL'\n",
    "3. 'SOHO_KZ'\n",
    "4. 'ANZ_PERSONEN'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'WOHNDAUER_2008', 'ANZ_TITEL', and 'ANZ_PERSONEN' are <font color=darkgreen> Household-level features. </font>\n",
    "- 'ANZ_TITEL' and 'ANZ_PERSONEN' are <font color=darkgreen> numeric features. </font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (3)</font>\n",
    "***There're (2) features with (8.72) percent of missing data:*** \n",
    "1. 'LP_FAMILIE_FEIN'\n",
    "2. 'LP_FAMILIE_GROB'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:***  \n",
    "- 'LP_FAMILIE_FEIN' and 'LP_FAMILIE_GROB' are <font color=darkgreen> Person-level features. </font>\n",
    "- 'LP_FAMILIE_FEIN' and 'LP_FAMILIE_GROB' are <font color=darkgreen> categorical features. </font> \n",
    "- 'LP_FAMILIE_FEIN' and 'LP_FAMILIE_GROB' <font color=darkgreen> hold the same information about the Family type, but they differ in the scale type either fine scale or rough scale.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (4)</font>\n",
    "***There're (4) features with (10.45) percent of missing data:***\n",
    "1. 'GEBAEUDETYP'\n",
    "2. 'MIN_GEBAEUDEJAHR'\n",
    "3. 'OST_WEST_KZ'\n",
    "4. 'WOHNLAGE'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'GEBAEUDETYP', 'MIN_GEBAEUDEJAHR', 'OST_WEST_KZ', and 'WOHNLAGE' are <font color=darkgreen>  Building-level features.</font>\n",
    "- 'GEBAEUDETYP' and 'OST_WEST_KZ' are <font color=darkgreen> categorical features.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (5)</font>\n",
    "***There're (3) features with (10.51) percent of missing data:*** \n",
    "1. 'BALLRAUM'\n",
    "2. 'EWDICHTE'\n",
    "3. 'INNENSTADT'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:***\n",
    "- 'BALLRAUM', 'EWDICHTE', and 'INNENSTADT' are <font color=darkgreen> Postcode-level features.</font>\n",
    "- 'BALLRAUM', 'EWDICHTE', and 'INNENSTADT' are <font color=darkgreen> ordinal features.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (6)</font>\n",
    "***There're (2) features with (10.92) percent of missing data:*** \n",
    "1. 'RELAT_AB'\n",
    "2. 'ARBEIT'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'RELAT_AB' and 'ARBEIT' are <font color=darkgreen> Community-level features.</font> \n",
    "- 'RELAT_AB' and 'ARBEIT' are <font color=darkgreen> ordinal features.</font> \n",
    "- 'RELAT_AB' and 'ARBEIT' <font color=darkgreen> hold the same information about the share of unemployment in community.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (7)</font>\n",
    "***There're (3) features with (11.14) percent of missing data:*** \n",
    "1. 'CAMEO_DEUG_2015'\n",
    "2. 'CAMEO_DEU_2015'\n",
    "3. 'CAMEO_INTL_2015'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015', and  'CAMEO_INTL_2015' are <font color=darkgreen> RR4 micro-cell features.</font>\n",
    "- 'CAMEO_DEUG_2015' and  'CAMEO_DEU_2015' are <font color=darkgreen> categorical features.</font> \n",
    "- 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015', and 'CAMEO_INTL_2015' <font color=darkgreen> hold the same information about the German CAMEO: Wealth / Life Stage Typology. But they differ in the representative code either rough scale, detailed scale or mapped to international code.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (8)</font>\n",
    "***There're (3) features with (12.47) percent of missing data:*** \n",
    "1. 'VERS_TYP'\n",
    "2. 'SHOPPER_TYP'\n",
    "3. 'HEALTH_TYP'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:***\n",
    "- 'VERS_TYP', 'SHOPPER_TYP', and  'HEALTH_TYP' are <font color=darkgreen> Person-level features.</font> \n",
    "- 'SHOPPER_TYP', and  'HEALTH_TYP' are <font color=darkgreen> categorical features. </font> \n",
    "\n",
    "\n",
    "##### <font color=darkblue>Pattern (9)</font>\n",
    "***There're (7) features with (13.07) percent of missing data:*** \n",
    "1. 'PLZ8_ANTG2'\n",
    "2. 'PLZ8_ANTG3'\n",
    "3. 'PLZ8_ANTG1'\n",
    "4. 'PLZ8_ANTG4'\n",
    "5. 'PLZ8_BAUMAX'\n",
    "6. 'PLZ8_HHZ'\n",
    "7. 'PLZ8_GBZ']\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG1', 'PLZ8_ANTG4', 'PLZ8_BAUMAX', 'PLZ8_HHZ', and 'PLZ8_GBZ' are <font color=darkgreen> PLZ8 macro-cell features.</font> \n",
    "- 'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG1', 'PLZ8_ANTG4', 'PLZ8_HHZ', and 'PLZ8_GBZ' are <font color=darkgreen>  ordinal features.</font> \n",
    "\n",
    "##### <font color=darkblue>Pattern (10)</font>\n",
    "***There're (6) features with (14.95) percent of missing data:*** \n",
    "1. 'MOBI_REGIO'\n",
    "2. 'KBA05_ANTG4'\n",
    "3. 'KBA05_GBZ'\n",
    "4. 'KBA05_ANTG1'\n",
    "5. 'KBA05_ANTG2'\n",
    "6. 'KBA05_ANTG3']\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'KBA05_ANTG4', 'KBA05_GBZ', 'KBA05_ANTG1', 'KBA05_ANTG2', 'KBA05_ANTG3' are <font color=darkgreen> RR3 micro-cell features.</font>\n",
    "- 'MOBI_REGIO', 'KBA05_ANTG4', 'KBA05_GBZ', 'KBA05_ANTG1', 'KBA05_ANTG2',  and 'KBA05_ANTG3' are <font color=darkgreen> ordinal features.</font>\n",
    "\n",
    "##### <font color=darkblue>Pattern (11)</font>\n",
    "***There're (2) features with (17.73) percent of missing data:*** \n",
    "1. 'KKK'\n",
    "2. 'REGIOTYP'\n",
    "<br>\n",
    "\n",
    "***Common characteristics of the features:*** \n",
    "- 'KKK' and 'REGIOTYP' are <font color=darkgreen> RR1 neighborhood features.</font>\n",
    "- 'KKK' and 'REGIOTYP' are <font color=darkgreen> ordinal features. </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         43\n",
       "1          0\n",
       "2          0\n",
       "3          7\n",
       "4          0\n",
       "5          0\n",
       "6          0\n",
       "7          0\n",
       "8          0\n",
       "9          0\n",
       "10         0\n",
       "11        47\n",
       "12         6\n",
       "13         8\n",
       "14        47\n",
       "15         8\n",
       "16         6\n",
       "17        47\n",
       "18         3\n",
       "19         0\n",
       "20        10\n",
       "21         0\n",
       "22         0\n",
       "23         8\n",
       "24        47\n",
       "25         5\n",
       "26        19\n",
       "27         0\n",
       "28         0\n",
       "29         2\n",
       "          ..\n",
       "891191     0\n",
       "891192     0\n",
       "891193     0\n",
       "891194     0\n",
       "891195     0\n",
       "891196     0\n",
       "891197     0\n",
       "891198     0\n",
       "891199     0\n",
       "891200     0\n",
       "891201     0\n",
       "891202     0\n",
       "891203    14\n",
       "891204     0\n",
       "891205     0\n",
       "891206     0\n",
       "891207     0\n",
       "891208     3\n",
       "891209     0\n",
       "891210     0\n",
       "891211     0\n",
       "891212     0\n",
       "891213     0\n",
       "891214     0\n",
       "891215     0\n",
       "891216     3\n",
       "891217     4\n",
       "891218     5\n",
       "891219     0\n",
       "891220     0\n",
       "Length: 891221, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_rows= azdias_copy.isnull().sum(axis=1)\n",
    "missing_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891221.000000\n",
       "mean          5.649894\n",
       "std          13.234687\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           3.000000\n",
       "max          49.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_rows.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c18621400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the distribution of missing data in each row\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.title('Distribution of missing data in each row')\n",
    "plt.hist(missing_rows / len(azdias_copy.columns) * 100, bins=np.linspace(0,100,10))\n",
    "plt.ylabel('Number of Rows')\n",
    "plt.xlabel('Percentage of Missing data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "\n",
    "Low_missing_data_row= missing_rows[missing_rows<=5]\n",
    "High_missing_data_row= missing_rows[missing_rows>5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Low_missing_data_row_df=azdias_copy.iloc[Low_missing_data_row.index,:]\n",
    "High_missing_data_row_df=azdias_copy.iloc[High_missing_data_row.index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "1                   1.0        2.0            5.0                1.0   \n",
       "2                   3.0        2.0            3.0                1.0   \n",
       "4                   3.0        1.0            5.0                4.0   \n",
       "5                   1.0        2.0            2.0                3.0   \n",
       "6                   2.0        2.0            5.0                1.0   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "1            5.0               2.0             5.0                    4.0   \n",
       "2            4.0               1.0             2.0                    3.0   \n",
       "4            3.0               4.0             1.0                    3.0   \n",
       "5            1.0               5.0             2.0                    2.0   \n",
       "6            5.0               1.0             5.0                    4.0   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP    ...     PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "1               5.0        1.0    ...            2.0         3.0         2.0   \n",
       "2               5.0        1.0    ...            3.0         3.0         1.0   \n",
       "4               2.0        5.0    ...            2.0         4.0         2.0   \n",
       "5               5.0        2.0    ...            2.0         3.0         1.0   \n",
       "6               3.0        4.0    ...            3.0         3.0         1.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  RELAT_AB  \n",
       "1         1.0          1.0       5.0       4.0     3.0          5.0       4.0  \n",
       "2         0.0          1.0       4.0       4.0     3.0          5.0       2.0  \n",
       "4         1.0          2.0       3.0       3.0     4.0          6.0       5.0  \n",
       "5         1.0          1.0       5.0       5.0     2.0          3.0       3.0  \n",
       "6         0.0          1.0       5.0       5.0     4.0          6.0       3.0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Low_missing_data_row_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
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       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
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       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>6.0</td>\n",
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       "<p>5 rows × 79 columns</p>\n",
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      ],
      "text/plain": [
       "    ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "0                    2.0        1.0            2.0                3.0   \n",
       "3                    4.0        2.0            2.0                4.0   \n",
       "11                   2.0        1.0            6.0                3.0   \n",
       "12                   3.0        1.0            6.0                5.0   \n",
       "13                   1.0        2.0            5.0                1.0   \n",
       "\n",
       "    FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0             4.0               3.0             5.0                    5.0   \n",
       "3             2.0               5.0             2.0                    1.0   \n",
       "11            4.0               3.0             5.0                    5.0   \n",
       "12            3.0               4.0             2.0                    4.0   \n",
       "13            4.0               3.0             5.0                    5.0   \n",
       "\n",
       "    FINANZ_HAUSBAUER  FINANZTYP    ...     PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0                3.0        4.0    ...            NaN         NaN         NaN   \n",
       "3                2.0        6.0    ...            2.0         2.0         2.0   \n",
       "11               3.0        4.0    ...            NaN         NaN         NaN   \n",
       "12               1.0        3.0    ...            3.0         3.0         1.0   \n",
       "13               2.0        1.0    ...            2.0         1.0         1.0   \n",
       "\n",
       "    PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN          NaN       NaN       NaN     NaN          NaN       NaN  \n",
       "3          0.0          1.0       3.0       4.0     2.0          3.0       3.0  \n",
       "11         NaN          NaN       NaN       NaN     NaN          NaN       NaN  \n",
       "12         0.0          1.0       5.0       5.0     3.0          6.0       4.0  \n",
       "13         1.0          1.0       3.0       3.0     3.0          6.0       4.0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "High_missing_data_row_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records of the demographics data: 891,221\n",
      "\n",
      "Number of records of the Low_missing_data_row: 719,624\n",
      "\n",
      "The percent of the Low_missing_data_row:  80.74585316100047 \n",
      "\n",
      "Number of records of the High_missing_data_row: 171,597\n",
      "\n",
      "The percent of the High_missing_data_row:  19.25414683899953 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The number of records of the the demographics data\n",
    "total_num= azdias_copy.shape[0]\n",
    "print('Number of records of the demographics data: {:,}\\n'.format(total_num))\n",
    "\n",
    "# The number of records of the Low_missing_data_row\n",
    "Low_missing_data_row_num= Low_missing_data_row_df.shape[0]\n",
    "print('Number of records of the Low_missing_data_row: {:,}\\n'.format(Low_missing_data_row_num))\n",
    "\n",
    "# The percentage of the Low_missing_data_row\n",
    "print('The percent of the Low_missing_data_row: ',(Low_missing_data_row_num/total_num)*100,'\\n')\n",
    "\n",
    "\n",
    "# The number of records of the High_missing_data_row\n",
    "High_missing_data_row_num= High_missing_data_row_df.shape[0]\n",
    "print('Number of records of the High_missing_data_row: {:,}\\n'.format(High_missing_data_row_num))\n",
    "\n",
    "# The percentage of the High_missing_data_row\n",
    "print('The percent of the High_missing_data_row: ',(High_missing_data_row_num/total_num)*100,'\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare the distribution of values for at least five columns where there are\n",
    "# no or few missing values, between the two subsets.\n",
    "\n",
    "#this function compares the missing values of rows with no-missing vale column\n",
    "def compare_missing_values(col, lm, hm):\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.subplots_adjust(hspace = 1, wspace=.3)\n",
    "    plt.subplot(121)\n",
    "    sns.countplot(x=col, data=lm)\n",
    "    plt.title('Low Missing data')\n",
    "    plt.subplot(122)\n",
    "    sns.countplot(x=col, data=hm)\n",
    "    plt.title('High Missing data')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c186cb748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c16bcbac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c16b013c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c18724dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c18401588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "No_missing_data_col_name= Low_missing_data_col_percent[Low_missing_data_col_percent ==0].index.tolist()\n",
    "No_missing_data_columns= np.random.choice(No_missing_data_col_name, 5)\n",
    "for col in No_missing_data_columns:\n",
    "    compare_missing_values(col,Low_missing_data_row_df,High_missing_data_row_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop High missing values row\n",
    "azdias_copy= azdias_copy.drop(High_missing_data_row_df.index.tolist(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(719624, 79)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the high missing records are removed\n",
    "azdias_copy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)\n",
    "\n",
    "<font color=darkblue>Based on the graph above, data with lots of missing values are qualitatively different from data with few or no missing values. Also, it has a small percentage with only 19%. So, it can be removed without any negative effect.</font>\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFK_URLAUBERTYP</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIN_GEBAEUDEJAHR</th>\n",
       "      <td>building</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEU_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_INTL_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG2</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG3</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKK</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGIOTYP</th>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANZAHL_PKW</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      information_level         type missing_or_unknown\n",
       "attribute                                                              \n",
       "ALTERSKATEGORIE_GROB             person      ordinal           [-1,0,9]\n",
       "ANREDE_KZ                        person  categorical             [-1,0]\n",
       "CJT_GESAMTTYP                    person  categorical                [0]\n",
       "FINANZ_MINIMALIST                person      ordinal               [-1]\n",
       "FINANZ_SPARER                    person      ordinal               [-1]\n",
       "FINANZ_VORSORGER                 person      ordinal               [-1]\n",
       "FINANZ_ANLEGER                   person      ordinal               [-1]\n",
       "FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]\n",
       "FINANZ_HAUSBAUER                 person      ordinal               [-1]\n",
       "FINANZTYP                        person  categorical               [-1]\n",
       "GFK_URLAUBERTYP                  person  categorical                 []\n",
       "GREEN_AVANTGARDE                 person  categorical                 []\n",
       "HEALTH_TYP                       person      ordinal             [-1,0]\n",
       "LP_LEBENSPHASE_FEIN              person        mixed                [0]\n",
       "LP_LEBENSPHASE_GROB              person        mixed                [0]\n",
       "LP_FAMILIE_FEIN                  person  categorical                [0]\n",
       "LP_FAMILIE_GROB                  person  categorical                [0]\n",
       "LP_STATUS_FEIN                   person  categorical                [0]\n",
       "LP_STATUS_GROB                   person  categorical                [0]\n",
       "NATIONALITAET_KZ                 person  categorical             [-1,0]\n",
       "PRAEGENDE_JUGENDJAHRE            person        mixed             [-1,0]\n",
       "RETOURTYP_BK_S                   person      ordinal                [0]\n",
       "SEMIO_SOZ                        person      ordinal             [-1,9]\n",
       "SEMIO_FAM                        person      ordinal             [-1,9]\n",
       "SEMIO_REL                        person      ordinal             [-1,9]\n",
       "SEMIO_MAT                        person      ordinal             [-1,9]\n",
       "SEMIO_VERT                       person      ordinal             [-1,9]\n",
       "SEMIO_LUST                       person      ordinal             [-1,9]\n",
       "SEMIO_ERL                        person      ordinal             [-1,9]\n",
       "SEMIO_KULT                       person      ordinal             [-1,9]\n",
       "...                                 ...          ...                ...\n",
       "MIN_GEBAEUDEJAHR               building      numeric                [0]\n",
       "OST_WEST_KZ                    building  categorical               [-1]\n",
       "WOHNLAGE                       building        mixed               [-1]\n",
       "CAMEO_DEUG_2015           microcell_rr4  categorical             [-1,X]\n",
       "CAMEO_DEU_2015            microcell_rr4  categorical               [XX]\n",
       "CAMEO_INTL_2015           microcell_rr4        mixed            [-1,XX]\n",
       "KBA05_ANTG1               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG2               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG3               microcell_rr3      ordinal               [-1]\n",
       "KBA05_ANTG4               microcell_rr3      ordinal               [-1]\n",
       "KBA05_GBZ                 microcell_rr3      ordinal             [-1,0]\n",
       "BALLRAUM                       postcode      ordinal               [-1]\n",
       "EWDICHTE                       postcode      ordinal               [-1]\n",
       "INNENSTADT                     postcode      ordinal               [-1]\n",
       "GEBAEUDETYP_RASTER           region_rr1      ordinal                 []\n",
       "KKK                          region_rr1      ordinal             [-1,0]\n",
       "MOBI_REGIO                   region_rr1      ordinal                 []\n",
       "ONLINE_AFFINITAET            region_rr1      ordinal                 []\n",
       "REGIOTYP                     region_rr1      ordinal             [-1,0]\n",
       "KBA13_ANZAHL_PKW         macrocell_plz8      numeric                 []\n",
       "PLZ8_ANTG1               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG2               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG3               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_ANTG4               macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_BAUMAX              macrocell_plz8        mixed             [-1,0]\n",
       "PLZ8_HHZ                 macrocell_plz8      ordinal               [-1]\n",
       "PLZ8_GBZ                 macrocell_plz8      ordinal               [-1]\n",
       "ARBEIT                        community      ordinal             [-1,9]\n",
       "ORTSGR_KLS9                   community      ordinal             [-1,0]\n",
       "RELAT_AB                      community      ordinal             [-1,9]\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    18\n",
       "mixed           6\n",
       "numeric         6\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "feat_info_copy['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binary categoricals:\n",
      "\n",
      "Numeric values:\n",
      "\n",
      "ANREDE_KZ : [ 2.  1.] \n",
      "\n",
      "Numeric values:\n",
      "\n",
      "GREEN_AVANTGARDE : [0 1] \n",
      "\n",
      "Numeric values:\n",
      "\n",
      "SOHO_KZ : [ 1.  0.] \n",
      "\n",
      "Numeric values:\n",
      "\n",
      "VERS_TYP : [ 2.  1.] \n",
      "\n",
      "Non-numeric values:\n",
      "\n",
      "OST_WEST_KZ : ['W' 'O'] \n",
      "\n",
      "\n",
      "\n",
      "multi-level categoricals:\n",
      "\n",
      "CJT_GESAMTTYP : [ 5.  3.  2.  4.  1.  6.] \n",
      "\n",
      "FINANZTYP : [ 1.  5.  2.  4.  6.  3.] \n",
      "\n",
      "GFK_URLAUBERTYP : [ 10.   5.   1.  12.   9.   3.   8.  11.   4.   7.   2.   6.] \n",
      "\n",
      "LP_FAMILIE_FEIN : [  5.   1.  10.   2.   7.  11.   8.   4.   6.   9.   3.] \n",
      "\n",
      "LP_FAMILIE_GROB : [ 3.  1.  5.  2.  4.] \n",
      "\n",
      "LP_STATUS_FEIN : [  2.   3.   4.   1.  10.   8.   9.   5.   6.   7.] \n",
      "\n",
      "LP_STATUS_GROB : [ 1.  2.  5.  4.  3.] \n",
      "\n",
      "NATIONALITAET_KZ : [ 1.  2.  3.] \n",
      "\n",
      "SHOPPER_TYP : [ 3.  2.  0.  1.] \n",
      "\n",
      "ZABEOTYP : [ 5.  4.  1.  6.  3.  2.] \n",
      "\n",
      "GEBAEUDETYP : [ 8.  1.  3.  6.  2.  4.  5.] \n",
      "\n",
      "CAMEO_DEUG_2015 : ['8' '4' '6' '2' '1' '9' '5' '3' '7'] \n",
      "\n",
      "CAMEO_DEU_2015 : ['8A' '4C' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5D' '9E' '5C' '9B' '2A' '1B'\n",
      " '8B' '3D' '7A' '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C' '9C' '7D'\n",
      " '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F' '5F' '1C'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "categorical_cols= feat_info_copy[feat_info_copy['type']=='categorical'].index.tolist()\n",
    "\n",
    "binary_cols_dict={}\n",
    "multi_level_cols_dict={}\n",
    "\n",
    "for col in categorical_cols:\n",
    "    unique_values=azdias_copy[col].dropna().unique()\n",
    "    unique_num= len(unique_values)\n",
    "    if  unique_num == 2:\n",
    "        binary_cols_dict.update({col:unique_values})\n",
    "    else:\n",
    "        multi_level_cols_dict.update({col:unique_values})\n",
    "        \n",
    "print('Binary categoricals:\\n')\n",
    "\n",
    "for key,value in binary_cols_dict.items():\n",
    "    if azdias_copy[key].dtypes != object:\n",
    "        print('Numeric values:\\n')\n",
    "    else:\n",
    "        print('Non-numeric values:\\n')\n",
    "    print(key,':', value,'\\n') \n",
    "\n",
    "    \n",
    "\n",
    "print('\\n')\n",
    "print('multi-level categoricals:\\n')\n",
    "\n",
    "for key,value in multi_level_cols_dict.items():\n",
    "    print(key,':', value ,'\\n')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "azdias_copy= pd.get_dummies(data=azdias_copy, columns=['CJT_GESAMTTYP','FINANZTYP','LP_FAMILIE_GROB',\n",
    "                                                                         'LP_STATUS_GROB','NATIONALITAET_KZ','SHOPPER_TYP','ZABEOTYP','OST_WEST_KZ'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GFK_URLAUBERTYP  GREEN_AVANTGARDE      ...        SHOPPER_TYP_2.0  \\\n",
       "1             10.0                 0      ...                      0   \n",
       "2             10.0                 1      ...                      1   \n",
       "4              5.0                 0      ...                      1   \n",
       "5              1.0                 0      ...                      0   \n",
       "6             12.0                 0      ...                      0   \n",
       "\n",
       "   SHOPPER_TYP_3.0  ZABEOTYP_1.0  ZABEOTYP_2.0  ZABEOTYP_3.0  ZABEOTYP_4.0  \\\n",
       "1                1             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "4                0             0             0             0             1   \n",
       "5                0             0             0             0             1   \n",
       "6                0             0             0             0             1   \n",
       "\n",
       "   ZABEOTYP_5.0  ZABEOTYP_6.0  OST_WEST_KZ_O  OST_WEST_KZ_W  \n",
       "1             1             0              0              1  \n",
       "2             1             0              0              1  \n",
       "4             0             0              0              1  \n",
       "5             0             0              0              1  \n",
       "6             0             0              0              1  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_encode= azdias_copy.drop(['GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN','LP_STATUS_FEIN',\n",
    "                                'GEBAEUDETYP','CAMEO_DEUG_2015','CAMEO_DEU_2015'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_info_copy= feat_info_copy.drop(['GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN','LP_STATUS_FEIN',\n",
    "                                'GEBAEUDETYP','CAMEO_DEUG_2015','CAMEO_DEU_2015'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP      ...        SHOPPER_TYP_2.0  \\\n",
       "1                 0         3.0      ...                      0   \n",
       "2                 1         3.0      ...                      1   \n",
       "4                 0         3.0      ...                      1   \n",
       "5                 0         3.0      ...                      0   \n",
       "6                 0         2.0      ...                      0   \n",
       "\n",
       "   SHOPPER_TYP_3.0  ZABEOTYP_1.0  ZABEOTYP_2.0  ZABEOTYP_3.0  ZABEOTYP_4.0  \\\n",
       "1                1             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "4                0             0             0             0             1   \n",
       "5                0             0             0             0             1   \n",
       "6                0             0             0             0             1   \n",
       "\n",
       "   ZABEOTYP_5.0  ZABEOTYP_6.0  OST_WEST_KZ_O  OST_WEST_KZ_W  \n",
       "1             1             0              0              1  \n",
       "2             1             0              0              1  \n",
       "4             0             0              0              1  \n",
       "5             0             0              0              1  \n",
       "6             0             0              0              1  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info_copy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    " ##### <font color=darkblue>Features with more than 6 levels were removed:</font>\n",
    " ['GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN','LP_STATUS_FEIN','GEBAEUDETYP','CAMEO_DEUG_2015','CAMEO_DEU_2015']\n",
    "\n",
    "\n",
    "##### <font color=darkblue>Features with less than 6 levels are kept:</font> \n",
    "###### All the binary categories variables:\n",
    "'ANREDE_KZ' ,  'GREEN_AVANTGARDE', 'SOHO_KZ' ,  'VERS_TYP', and  'OST_WEST_KZ'. \n",
    "\n",
    "###### 7 of the multi-level categoricals:\n",
    "['CJT_GESAMTTYP', 'FINANZTYP', 'LP_FAMILIE_GROB',                                                                      'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\n",
    "  \n",
    "######  <font color=darkblue>These variables were encoded using multiple dummy variables.</font> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_INTL_2015</th>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      information_level   type missing_or_unknown\n",
       "attribute                                                        \n",
       "LP_LEBENSPHASE_FEIN              person  mixed                [0]\n",
       "LP_LEBENSPHASE_GROB              person  mixed                [0]\n",
       "PRAEGENDE_JUGENDJAHRE            person  mixed             [-1,0]\n",
       "WOHNLAGE                       building  mixed               [-1]\n",
       "CAMEO_INTL_2015           microcell_rr4  mixed            [-1,XX]\n",
       "PLZ8_BAUMAX              macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info_copy.loc[feat_info_copy['type']=='mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2]),'Decade']='40s'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([3,4]),'Decade']='50s'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]),'Decade']='60s'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([8,9]),'Decade']='70s'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]),'Decade']='80s'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([14,15]),'Decade']='90s'\n",
    "\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'Movement']='Mainstream'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'Movement']='Avantgarde'\n",
    "\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2,3,4,5,8,9,14,15]),'Nation']='E+W'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([7,12,13]),'Nation']='E'\n",
    "azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([6,10,11]),'Nation']='W'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decade count\n",
      "40s:  25968\n",
      "50s:  70443\n",
      "60s:  108519\n",
      "70s:  161946\n",
      "80s:  138348\n",
      "90s:  207036\n",
      "\n",
      "\n",
      "\n",
      "Movement count\n",
      "Mainstream:  550217\n",
      "Avantgarde:  550217\n",
      "\n",
      "\n",
      "\n",
      "Nation count\n",
      "E+W:  545457\n",
      "E:  31710\n",
      "W:  135093\n"
     ]
    }
   ],
   "source": [
    "print('Decade count')\n",
    "print('40s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2]),'Decade'].count())\n",
    "print('50s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([3,4]),'Decade'].count())\n",
    "print('60s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]),'Decade'].count())\n",
    "print('70s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([8,9]),'Decade'].count())\n",
    "print('80s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]),'Decade'].count())\n",
    "print('90s: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([14,15]),'Decade'].count())\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Movement count')\n",
    "print('Mainstream: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'Movement'].count())\n",
    "print('Avantgarde: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'Movement'].count())\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Nation count')\n",
    "print('E+W: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2,3,4,5,8,9,14,15]),'Nation'].count())\n",
    "print('E: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([7,12,13]),'Nation'].count())\n",
    "print('W: ',azdias_encode.loc[azdias_encode['PRAEGENDE_JUGENDJAHRE'].isin([6,10,11]),'Nation'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90s    207036\n",
      "70s    161946\n",
      "80s    138348\n",
      "60s    108519\n",
      "50s     70443\n",
      "40s     25968\n",
      "Name: Decade, dtype: int64 \n",
      "\n",
      "Mainstream    550217\n",
      "Avantgarde    162043\n",
      "Name: Movement, dtype: int64 \n",
      "\n",
      "E+W    545457\n",
      "W      135093\n",
      "E       31710\n",
      "Name: Nation, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "assert('Decade' in azdias_encode.columns)\n",
    "assert('Movement' in azdias_encode.columns)\n",
    "assert('Nation' in azdias_encode.columns)\n",
    "\n",
    "print(azdias_encode.Decade.value_counts(),'\\n')\n",
    "print(azdias_encode.Movement.value_counts(),'\\n')\n",
    "print(azdias_encode.Nation.value_counts(),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>ZABEOTYP_2.0</th>\n",
       "      <th>ZABEOTYP_3.0</th>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <th>ZABEOTYP_6.0</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>Decade</th>\n",
       "      <th>Movement</th>\n",
       "      <th>Nation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90s</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>E+W</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90s</td>\n",
       "      <td>Avantgarde</td>\n",
       "      <td>E+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70s</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>E+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50s</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>E+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80s</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP   ...    ZABEOTYP_2.0  ZABEOTYP_3.0  \\\n",
       "1                 0         3.0   ...               0             0   \n",
       "2                 1         3.0   ...               0             0   \n",
       "4                 0         3.0   ...               0             0   \n",
       "5                 0         3.0   ...               0             0   \n",
       "6                 0         2.0   ...               0             0   \n",
       "\n",
       "   ZABEOTYP_4.0  ZABEOTYP_5.0  ZABEOTYP_6.0  OST_WEST_KZ_O  OST_WEST_KZ_W  \\\n",
       "1             0             1             0              0              1   \n",
       "2             0             1             0              0              1   \n",
       "4             1             0             0              0              1   \n",
       "5             1             0             0              0              1   \n",
       "6             1             0             0              0              1   \n",
       "\n",
       "   Decade    Movement  Nation  \n",
       "1     90s  Mainstream     E+W  \n",
       "2     90s  Avantgarde     E+W  \n",
       "4     70s  Mainstream     E+W  \n",
       "5     50s  Mainstream     E+W  \n",
       "6     80s  Mainstream       W  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_encode=pd.get_dummies(azdias_encode, columns=['Decade','Movement','Nation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
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       "\n",
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       "6                 0         2.0    ...              0           0           0   \n",
       "\n",
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       "2           0           1                    1                    0         0   \n",
       "4           0           0                    0                    1         0   \n",
       "5           0           0                    0                    1         0   \n",
       "6           1           0                    0                    1         0   \n",
       "\n",
       "   Nation_E+W  Nation_W  \n",
       "1           1         0  \n",
       "2           1         0  \n",
       "4           1         0  \n",
       "5           1         0  \n",
       "6           0         1  \n",
       "\n",
       "[5 rows x 113 columns]"
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     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_encode= azdias_encode.drop('PRAEGENDE_JUGENDJAHRE',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_info_copy= feat_info_copy.drop('PRAEGENDE_JUGENDJAHRE', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "                    information_level   type missing_or_unknown\n",
       "attribute                                                      \n",
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       "LP_LEBENSPHASE_GROB            person  mixed                [0]\n",
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       "PLZ8_BAUMAX            macrocell_plz8  mixed             [-1,0]"
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     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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    "feat_info_copy.loc[feat_info_copy['type']=='mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
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       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
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       "   Decade_80s  Decade_90s  Movement_Avantgarde  Movement_Mainstream  Nation_E  \\\n",
       "1           0           1                    0                    1         0   \n",
       "2           0           1                    1                    0         0   \n",
       "4           0           0                    0                    1         0   \n",
       "5           0           0                    0                    1         0   \n",
       "6           1           0                    0                    1         0   \n",
       "\n",
       "   Nation_E+W  Nation_W  \n",
       "1           1         0  \n",
       "2           1         0  \n",
       "4           1         0  \n",
       "5           1         0  \n",
       "6           0         1  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "azdias_encode['CAMEO_INTL_2015']=azdias_encode['CAMEO_INTL_2015'].astype(float)\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==1.0,'Wealth']='Wealthy Households'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==2.0,'Wealth']='Prosperous Households'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==3.0,'Wealth']='Comfortable Households'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==4.0,'Wealth']='Less Affluent Households'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==5.0,'Wealth']='Poorer Households'\n",
    "\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==1.0,'Life_Stage']='Pre-Family Couples & Singles'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==2.0,'Life_Stage']='Young Couples With Children'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==3.0,'Life_Stage']='Families With School Age Childre'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==4.0,'Life_Stage']='Older Families &  Mature Couples'\n",
    "azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==5.0,'Life_Stage']='Elders In Retirement'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wealth count\n",
      "Wealthy Households:  107293\n",
      "Prosperous Households:  166282\n",
      "Comfortable Households:  60007\n",
      "Less Affluent Households:  174832\n",
      "Poorer Households:  208153\n",
      "\n",
      "\n",
      "\n",
      "Life_stage count\n",
      "Pre-Family Couples & Singles:  224778\n",
      "Young Couples With Children:  68854\n",
      "Families With School Age Childre:  103356\n",
      "Older Families &  Mature Couples:  211632\n",
      "Elders In Retirement:  107947\n"
     ]
    }
   ],
   "source": [
    "print('Wealth count')\n",
    "print('Wealthy Households: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==1.0,'Wealth'].count())\n",
    "print('Prosperous Households: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==2.0,'Wealth'].count())\n",
    "print('Comfortable Households: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==3.0,'Wealth'].count())\n",
    "print('Less Affluent Households: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==4.0,'Wealth'].count())\n",
    "print('Poorer Households: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] //10==5.0,'Wealth'].count())\n",
    "print('\\n\\n')\n",
    "\n",
    "print('Life_stage count')\n",
    "print('Pre-Family Couples & Singles: ',azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==1.0,'Life_Stage'].count())\n",
    "print('Young Couples With Children: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==2.0,'Life_Stage'].count())\n",
    "print('Families With School Age Childre: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==3.0,'Life_Stage'].count())\n",
    "print('Older Families &  Mature Couples: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==4.0,'Life_Stage'].count())\n",
    "print('Elders In Retirement: ', azdias_encode.loc[azdias_encode['CAMEO_INTL_2015'] % 10==5.0,'Life_Stage'].count())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poorer Households           208153\n",
      "Less Affluent Households    174832\n",
      "Prosperous Households       166282\n",
      "Wealthy Households          107293\n",
      "Comfortable Households       60007\n",
      "Name: Wealth, dtype: int64 \n",
      "\n",
      "Pre-Family Couples & Singles        224778\n",
      "Older Families &  Mature Couples    211632\n",
      "Elders In Retirement                107947\n",
      "Families With School Age Childre    103356\n",
      "Young Couples With Children          68854\n",
      "Name: Life_Stage, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "assert('Wealth' in azdias_encode.columns)\n",
    "assert('Life_Stage' in azdias_encode.columns)\n",
    "\n",
    "print(azdias_encode.Wealth.value_counts(),'\\n')\n",
    "print(azdias_encode.Life_Stage.value_counts(),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_encode=pd.get_dummies(azdias_encode, columns=['Wealth','Life_Stage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_encode= azdias_encode.drop('CAMEO_INTL_2015',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_info_copy= feat_info_copy.drop('CAMEO_INTL_2015', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attribute</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_GROB</th>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    information_level   type missing_or_unknown\n",
       "attribute                                                      \n",
       "LP_LEBENSPHASE_FEIN            person  mixed                [0]\n",
       "LP_LEBENSPHASE_GROB            person  mixed                [0]\n",
       "WOHNLAGE                     building  mixed               [-1]\n",
       "PLZ8_BAUMAX            macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info_copy.loc[feat_info_copy['type']=='mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>Wealth_Comfortable Households</th>\n",
       "      <th>Wealth_Less Affluent Households</th>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <th>Life_Stage_Elders In Retirement</th>\n",
       "      <th>Life_Stage_Families With School Age Childre</th>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
       "      <th>Life_Stage_Pre-Family Couples &amp; Singles</th>\n",
       "      <th>Life_Stage_Young Couples With Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "1                 0         3.0                   ...                     \n",
       "2                 1         3.0                   ...                     \n",
       "4                 0         3.0                   ...                     \n",
       "5                 0         3.0                   ...                     \n",
       "6                 0         2.0                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "1                              0                                0   \n",
       "2                              0                                0   \n",
       "4                              0                                1   \n",
       "5                              0                                0   \n",
       "6                              0                                0   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "1                         1                             0   \n",
       "2                         0                             1   \n",
       "4                         0                             0   \n",
       "5                         1                             0   \n",
       "6                         0                             1   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "1                          0                                0   \n",
       "2                          0                                0   \n",
       "4                          0                                0   \n",
       "5                          0                                0   \n",
       "6                          0                                0   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "1                                            0   \n",
       "2                                            0   \n",
       "4                                            1   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
       "1                                            0   \n",
       "2                                            1   \n",
       "4                                            0   \n",
       "5                                            1   \n",
       "6                                            0   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "1                                        1   \n",
       "2                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "1                                       0  \n",
       "2                                       0  \n",
       "4                                       0  \n",
       "5                                       0  \n",
       "6                                       1  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove LP_LEBENSPHASE_FEIN \n",
    "azdias_encode= azdias_encode.drop('LP_LEBENSPHASE_FEIN',axis=1)\n",
    "feat_info_copy= feat_info_copy.drop('LP_LEBENSPHASE_FEIN', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove LP_LEBENSPHASE_GROB\n",
    "azdias_encode= azdias_encode.drop('LP_LEBENSPHASE_GROB',axis=1)\n",
    "feat_info_copy= feat_info_copy.drop('LP_LEBENSPHASE_GROB', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "            information_level   type missing_or_unknown\n",
       "attribute                                              \n",
       "WOHNLAGE             building  mixed               [-1]\n",
       "PLZ8_BAUMAX    macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info_copy.loc[feat_info_copy['type']=='mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>Wealth_Comfortable Households</th>\n",
       "      <th>Wealth_Less Affluent Households</th>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <th>Life_Stage_Elders In Retirement</th>\n",
       "      <th>Life_Stage_Families With School Age Childre</th>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
       "      <th>Life_Stage_Pre-Family Couples &amp; Singles</th>\n",
       "      <th>Life_Stage_Young Couples With Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>...</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "1                 0         3.0                   ...                     \n",
       "2                 1         3.0                   ...                     \n",
       "4                 0         3.0                   ...                     \n",
       "5                 0         3.0                   ...                     \n",
       "6                 0         2.0                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "1                              0                                0   \n",
       "2                              0                                0   \n",
       "4                              0                                1   \n",
       "5                              0                                0   \n",
       "6                              0                                0   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "1                         1                             0   \n",
       "2                         0                             1   \n",
       "4                         0                             0   \n",
       "5                         1                             0   \n",
       "6                         0                             1   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "1                          0                                0   \n",
       "2                          0                                0   \n",
       "4                          0                                0   \n",
       "5                          0                                0   \n",
       "6                          0                                0   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "1                                            0   \n",
       "2                                            0   \n",
       "4                                            1   \n",
       "5                                            0   \n",
       "6                                            0   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
       "1                                            0   \n",
       "2                                            1   \n",
       "4                                            0   \n",
       "5                                            1   \n",
       "6                                            0   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "1                                        1   \n",
       "2                                        0   \n",
       "4                                        0   \n",
       "5                                        0   \n",
       "6                                        0   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "1                                       0  \n",
       "2                                       0  \n",
       "4                                       0  \n",
       "5                                       0  \n",
       "6                                       1  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(719624, 119)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    "- <font color=darkblue>No need to engineer WOHNLAGE and PLZ8_BAUMAX features, because they already have numeric values and each number represent one status.</font>\n",
    "<br>\n",
    "\n",
    "- <font color=darkblue> Removed Features are: LP_LEBENSPHASE_FEIN and LP_LEBENSPHASE_GROB, because they're repeatable ('CAMEO_INTL_2015' feature has similar kind of information).</font>\n",
    "<br>\n",
    "\n",
    "- <font color=darkblue> The Engineered features are PRAEGENDE_JUGENDJAHRE and CAMEO_INTL_2015</font>\n",
    "<br>\n",
    "\n",
    "\n",
    "#####  <font color=darkgreen>Performed Engineering steps for 'PRAEGENDE_JUGENDJAHRE' feature:</font>\n",
    "1.\tThree new variables were created to capture the three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west).\n",
    "2.\tThese variables were encoded using multiple dummy variables\n",
    "3.\t'PRAEGENDE_JUGENDJAHRE' feature was deleted.\n",
    "\n",
    "##### <font color=darkgreen> Performed Engineering steps for 'CAMEO_INTL_2015' feature:</font>\n",
    "1.\tTwo new ordinal variables were created to capture the two axes: wealth and life stage, by breaking up the two-digit codes by their 'tens'-place and 'ones'-place digits. \n",
    "2.\tThese variables were encoded using multiple dummy variables\n",
    "3.\t'CAMEO_INTL_2015' feature was deleted.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    12\n",
       "numeric         6\n",
       "mixed           2\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n",
    "feat_info_copy['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do whatever you need to in order to ensure that the dataframe only contains\n",
    "# the columns that should be passed to the algorithm functions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    \n",
    "    \"\"\"\n",
    "    Perform feature trimming, re-encoding, and engineering for demographics\n",
    "    data\n",
    "    \n",
    "    INPUT: Demographics DataFrame\n",
    "    OUTPUT: Trimmed and cleaned demographics DataFrame\n",
    "    \"\"\"\n",
    "    feat_info_copy=feat_info.copy()\n",
    "    \n",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs, ...\n",
    "    for index in range(df.shape[1]):\n",
    "        col=df.columns[index]\n",
    "        null_list= feat_info_copy.iloc[index,3]\n",
    "        null_list= null_list.strip('[]').split(',')\n",
    "        if null_list != ['']:\n",
    "            for null_value in null_list:\n",
    "                if null_value!='X' and null_value!='XX' and null_value!='':\n",
    "                    null_value= int(null_value)\n",
    "                else:\n",
    "                    null_value=null_value\n",
    "            \n",
    "                df.loc[df[col]==null_value,col]=np.nan\n",
    "    \n",
    "    # remove selected columns and rows ....\n",
    "    High_missing_data_col_percent = ['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP', 'TITEL_KZ']\n",
    "\n",
    "    #remove high missing features from azdias dataframe\n",
    "    df.drop(High_missing_data_col_percent, axis=1, inplace=True)\n",
    "   \n",
    "    \n",
    "    #remove high missing features from feat_info dataframe\n",
    "    feat_info_copy = feat_info_copy.set_index('attribute')\n",
    "    feat_info_copy= feat_info_copy.drop(High_missing_data_col_percent, axis=0)\n",
    "    \n",
    "    #remove high missing records from the dataframe\n",
    "    missing_rows= df.isnull().sum(axis=1)\n",
    "\n",
    "    # Write code to divide the data into two subsets based on the number of missing values in each row.\n",
    "\n",
    "    Low_missing_data_row= missing_rows[missing_rows<=5]\n",
    "    High_missing_data_row= missing_rows[missing_rows>5]\n",
    "\n",
    "    Low_missing_data_row_df=df.iloc[Low_missing_data_row.index,:]\n",
    "    High_missing_data_row_df=df.iloc[High_missing_data_row.index,:]\n",
    "\n",
    "    #drop High missing values row\n",
    "    df= df.drop(High_missing_data_row_df.index.tolist(), axis=0)\n",
    "\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    \n",
    "    # Re-encode categorical variable(s) to be kept in the analysis.\n",
    "    df= pd.get_dummies(data=df, columns=['CJT_GESAMTTYP','FINANZTYP','LP_FAMILIE_GROB','LP_STATUS_GROB',\n",
    "                                         'NATIONALITAET_KZ','SHOPPER_TYP','ZABEOTYP','OST_WEST_KZ'])\n",
    "    df_encode= df.drop(['GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN','LP_STATUS_FEIN','GEBAEUDETYP',\n",
    "                 'CAMEO_DEUG_2015','CAMEO_DEU_2015'],axis=1)\n",
    "\n",
    "    feat_info_copy= feat_info_copy.drop(['GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN','LP_STATUS_FEIN', 'GEBAEUDETYP',\n",
    "                                         'CAMEO_DEUG_2015','CAMEO_DEU_2015'], axis=0)\n",
    "                    \n",
    "    # Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2]),'Decade']='40s'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([3,4]),'Decade']='50s'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]),'Decade']='60s'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([8,9]),'Decade']='70s'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]),'Decade']='80s'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([14,15]),'Decade']='90s'\n",
    "\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'Movement']='Mainstream'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'Movement']='Avantgarde'\n",
    "\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([1,2,3,4,5,8,9,14,15]),'Nation']='E+W'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([7,12,13]),'Nation']='E'\n",
    "    df_encode.loc[df_encode['PRAEGENDE_JUGENDJAHRE'].isin([6,10,11]),'Nation']='W'\n",
    "\n",
    "    df_encode=pd.get_dummies(df_encode, columns=['Decade','Movement','Nation'])\n",
    "    df_encode= df_encode.drop('PRAEGENDE_JUGENDJAHRE',axis=1)\n",
    "    feat_info_copy= feat_info_copy.drop('PRAEGENDE_JUGENDJAHRE', axis=0)\n",
    "\n",
    "    df_encode['CAMEO_INTL_2015']=df_encode['CAMEO_INTL_2015'].astype(float)\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] //10==1.0,'Wealth']='Wealthy Households'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] //10==2.0,'Wealth']='Prosperous Households'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] //10==3.0,'Wealth']='Comfortable Households'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] //10==4.0,'Wealth']='Less Affluent Households'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] //10==5.0,'Wealth']='Poorer Households'\n",
    "\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] % 10==1.0,'Life_Stage']='Pre-Family Couples & Singles'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] % 10==2.0,'Life_Stage']='Young Couples With Children'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] % 10==3.0,'Life_Stage']='Families With School Age Childre'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] % 10==4.0,'Life_Stage']='Older Families &  Mature Couples'\n",
    "    df_encode.loc[df_encode['CAMEO_INTL_2015'] % 10==5.0,'Life_Stage']='Elders In Retirement'\n",
    "\n",
    "    df_encode=pd.get_dummies(df_encode, columns=['Wealth','Life_Stage'])\n",
    "    df_encode= df_encode.drop('CAMEO_INTL_2015',axis=1)\n",
    "    feat_info_copy= feat_info_copy.drop('CAMEO_INTL_2015', axis=0)\n",
    "    \n",
    "    #remove LP_LEBENSPHASE_FEIN \n",
    "    df_encode= df_encode.drop('LP_LEBENSPHASE_FEIN',axis=1)\n",
    "    feat_info_copy= feat_info_copy.drop('LP_LEBENSPHASE_FEIN', axis=0)\n",
    "\n",
    "    #remove LP_LEBENSPHASE_GROB\n",
    "    df_encode= df_encode.drop('LP_LEBENSPHASE_GROB',axis=1)\n",
    "    feat_info_copy= feat_info_copy.drop('LP_LEBENSPHASE_GROB', axis=0)\n",
    "\n",
    "    \n",
    "    # Return the cleaned dataframe.\n",
    "    return df_encode\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164427"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "\n",
    "imp= Imputer(missing_values='NaN', strategy='most_frequent')\n",
    "azdias_encode_imputed= pd.DataFrame(data=imp.fit_transform(azdias_encode),index=azdias_encode.index, columns=azdias_encode.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode_imputed.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ALTERSKATEGORIE_GROB</th>\n",
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       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
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       "      <th>Wealth_Less Affluent Households</th>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <th>Life_Stage_Elders In Retirement</th>\n",
       "      <th>Life_Stage_Families With School Age Childre</th>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
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       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0        2.0                1.0            5.0   \n",
       "2                   3.0        2.0                1.0            4.0   \n",
       "4                   3.0        1.0                4.0            3.0   \n",
       "5                   1.0        2.0                3.0            1.0   \n",
       "6                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1               2.0             5.0                    4.0               5.0   \n",
       "2               1.0             2.0                    3.0               5.0   \n",
       "4               4.0             1.0                    3.0               2.0   \n",
       "5               5.0             2.0                    2.0               5.0   \n",
       "6               1.0             5.0                    4.0               3.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "1               0.0         3.0                   ...                     \n",
       "2               1.0         3.0                   ...                     \n",
       "4               0.0         3.0                   ...                     \n",
       "5               0.0         3.0                   ...                     \n",
       "6               0.0         2.0                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "1                            0.0                              0.0   \n",
       "2                            0.0                              0.0   \n",
       "4                            0.0                              1.0   \n",
       "5                            0.0                              0.0   \n",
       "6                            0.0                              0.0   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "1                       1.0                           0.0   \n",
       "2                       0.0                           1.0   \n",
       "4                       0.0                           0.0   \n",
       "5                       1.0                           0.0   \n",
       "6                       0.0                           1.0   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "1                        0.0                              0.0   \n",
       "2                        0.0                              0.0   \n",
       "4                        0.0                              0.0   \n",
       "5                        0.0                              0.0   \n",
       "6                        0.0                              0.0   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "4                                          1.0   \n",
       "5                                          0.0   \n",
       "6                                          0.0   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
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       "2                                          1.0   \n",
       "4                                          0.0   \n",
       "5                                          1.0   \n",
       "6                                          0.0   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "1                                      1.0   \n",
       "2                                      0.0   \n",
       "4                                      0.0   \n",
       "5                                      0.0   \n",
       "6                                      0.0   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "1                                     0.0  \n",
       "2                                     0.0  \n",
       "4                                     0.0  \n",
       "5                                     0.0  \n",
       "6                                     1.0  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode_imputed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "st_scaler= StandardScaler()\n",
    "azdias_encode_scaler= st_scaler.fit_transform(azdias_encode_imputed)\n",
    "azdias_encode_scaler_df= pd.DataFrame(data=azdias_encode_scaler, index=azdias_encode.index, columns=azdias_encode.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.074321</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 119 columns</p>\n",
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      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1             -1.741337   0.967618          -1.517013       1.580959   \n",
       "2              0.200259   0.967618          -1.517013       0.902619   \n",
       "4              0.200259  -1.033466           0.686636       0.224279   \n",
       "5             -1.741337   0.967618          -0.047914      -1.132401   \n",
       "6             -0.770539   0.967618          -1.517013       1.580959   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1         -1.051251        1.514759               1.045335          1.331721   \n",
       "2         -1.772031       -0.544028               0.318260          1.331721   \n",
       "4          0.390308       -1.230290               0.318260         -0.845814   \n",
       "5          1.111087       -0.544028              -0.408814          1.331721   \n",
       "6         -1.772031        1.514759               1.045335         -0.119969   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "1         -0.539090    1.019923                   ...                     \n",
       "2          1.854978    1.019923                   ...                     \n",
       "4         -0.539090    1.019923                   ...                     \n",
       "5         -0.539090    1.019923                   ...                     \n",
       "6         -0.539090   -0.303645                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "1                      -0.301616                        -0.566494   \n",
       "2                      -0.301616                        -0.566494   \n",
       "4                      -0.301616                         1.765245   \n",
       "5                      -0.301616                        -0.566494   \n",
       "6                      -0.301616                        -0.566494   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "1                  1.567542                     -0.548183   \n",
       "2                 -0.637941                      1.824207   \n",
       "4                 -0.637941                     -0.548183   \n",
       "5                  1.567542                     -0.548183   \n",
       "6                 -0.637941                      1.824207   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "1                  -0.418594                        -0.420092   \n",
       "2                  -0.418594                        -0.420092   \n",
       "4                  -0.418594                        -0.420092   \n",
       "5                  -0.418594                        -0.420092   \n",
       "6                  -0.418594                        -0.420092   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "1                                    -0.409527   \n",
       "2                                    -0.409527   \n",
       "4                                     2.441839   \n",
       "5                                    -0.409527   \n",
       "6                                    -0.409527   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
       "1                                    -0.645449   \n",
       "2                                     1.549308   \n",
       "4                                    -0.645449   \n",
       "5                                     1.549308   \n",
       "6                                    -0.645449   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "1                                 1.483741   \n",
       "2                                -0.673972   \n",
       "4                                -0.673972   \n",
       "5                                -0.673972   \n",
       "6                                -0.673972   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "1                               -0.325275  \n",
       "2                               -0.325275  \n",
       "4                               -0.325275  \n",
       "5                               -0.325275  \n",
       "6                                3.074321  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_encode_scaler_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)\n",
    "- <font color=darkblue>an Imputer was applied to replace all missing values with the most frequent values.</font>\n",
    "- <font color=darkblue>Feature Scaling was applied to scale each feature to mean 0 and standard deviation 1.</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "pca = PCA()\n",
    "azdias_encode_pca= pca.fit_transform(azdias_encode_scaler_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tX9IigRw9enYz2x1wRaDeX0REpCElFdVk55eRvaeMrd6v2wvK2J5fzvbCMnIKKqis2bczfohBclwEKd0jGdgzmilDetA7LpLe8ZH07h5J7+4R9IqLJCFKQUwCJ5iXR0VERFpdaWU1WXllZOWVkrWnlK17yti6p3RvUNtTr4UsLNRIiY+kT3wUkwb28NzvHklKfBQp8ZGkdI8kKTacbqFaREiCS6FNREQ6lNpaR05ROZm7S9mSV0pWXimZ3ltWXim7iiv32T8qLJR+PaLo3yOKg/sn0K9HFP0SPI/7JUSTHBdBqFrHpANQaBMRkXanuqaWbfnlZOwuYcvuErbsLmXL7hI27/aEs8rq7y5fhoYYfRMiGZgYzTGjejMgMdpz6xHFgMRoesaEawoK6RQU2kREJCicc+wsqmBTbjHpuSVk7PLcNu8qIWtPKVU13w25jAoLZVDPaA5KjuHokb0YmBjNoJ7RDEqMoU9CJGG6dCldgEKbiIgEVFllDem7PMGsLqCl7yomI7eEEp8JYSPDQhjcM4YRKXEcPzaFIT1jGNQzmiFJMSTHRai1TLo8hTYREWkVu4sr2LizmI25xZ6vOz0BLTu/bO8+ZtAvIYrU5FjSBiVyUHIMqcmxDEmKIaV7pEZeijRBoU1ERFokr6SS9TlFbMgpYn1Osef+zmLySr4bABAdHkpqcgxTBvfgzOQBHJQcS2pyDEOSYogM0wSxIvtDoU1ERBpUWlnN+pxivt1eyLc7itiws4h1O4rZVVyxd5+4iG4M6x3LsaN6M6x3LEN7xTKsdxx91Gom0uoU2kREujjnHNn5ZazdXsSabYWs3V7ItzsK2ZJXivOOBYgOD2VY7ziOGpHM8N5xDE+JY3jvWFK6R6qvmUgbUWgTEelCnHPsKCxnxdYCVmzNZ8XWAlZmF+xdkskMBiVGM6pPd06d2J+RfeIYmRLHgB7RajkTCTKFNhGRTqyovIrlWQUs35rPN1n5LM/KZ2eR5/JmaIgxonccM8ekMLZfPKP6dGdkShwxEfrTINIe6V+miEgn4ZxjU24JX23JY1lmPl9n7mHDzuK9lzhTk2I4dGgS4/vHc/CABEb36a5BASIdiEKbiEgHVV1Ty+pthXy5OY8lGXks3bJn7wjO+KgwJg5M4KTxfZkwIIGDByQQHxUW5IpF5EAotImIdBAV1TUszypgScZuvsjI4+ste/ZOTjswMZqjRvRi6pAepA1OJDUpRgMERDoZhTYRkXaqLqQtTt/N4vTdfLVlDxXeNTdHpsRx+uT+TBmcyNQhifTuHhnkakUk0BTaRETaicrqWpZvzefzTfuGNDMYldKdc6cNYlpqIlMHJ9IjJjzY5YpIG1NoExEJEucc63OK+XhDLp9u3MUXGXmUVtbsE9KmpyYybUhP4qPVH02kq1NoExFpQ3kllXy8IZeP1u/i4w25e6ffSE2K4fRJ/Tl0aBLTUxNJiFZLmojsS6FNRCSAqms8lzwXrcvlw/W5rMwuwDnP6M4Zw5I4fFgSM4Yl0y8hKtilikg7p9AmItLKCkqreH9dDu+u2cnHG3IpLK8mxGDiwB5cd8xwDh+ezLh+8YRqhQERaQGFNhGRVrC9oIwFq3awcE0OX2TkUVPr6BUXwcyxKRwxvBczhiapX5qIHBCFNhGR/bS9oIz5K3cwf+V2vtqyB4ChvWL52eGpHDcmhfH94rVep4i0GoU2EZEW2F1cwfyV25m3fBtfbvYEtVF9unPDccM5YVwfDkqODXKFItJZKbSJiDSjtLKahatzePObbD7esIuaWsewXrFcf+xwfjReQU1E2oZCm4hIA2pqHZ9t2sUbX2fzv9U7KK2soV9CFJcdlsrsCX0ZmRKnZaJEpE0ptImI+NiUW8yrS7fyxrKt5BRWEBfZjZMP7supE/sxZXCi+qiJSNAotIlIl1dcUc1/VmzjlaVb+WrLHkJDjCOHJ3PrSf354aheRIaFBrtEERGFNhHpmpxzfJ25h5eWZPGfldspraxhaK9YfnvCSE6d1I9ecVqAXUTaF4U2EelS8koq+dfXW3npyyw27iwmJjyUkw/uyxlTBjBxQIL6qYlIu6XQJiKdXm2t4/P03by4JJMFq3dQVeOYNDCB+04fz4nj+xAToV+FItL+6TeViHRau4srePWrrby4JJMtu0uJjwrj/OmDOWvqAIb3jgt2eSIiLaLQJiKdinOOpVv28PziLfx35Q4qa2qZOiSRXx07nOPHpGhQgYh0WAptItIpFFdU8+aybJ5fvIVvdxQRF9GNc6YN5NxpAxmmVjUR6QQU2kSkQ9u4s4jnPt/C619nU1xRzeg+3bn7tHHMntCX6HD9ihORzkO/0USkw3HOM7Dg8Y/S+WBdLuGhIZw4vg/nTR/EpIEaASoinZNCm4h0GFU1tcxfuZ1/fpTO6m2F9IwJ59pjhnHe9EEkxUYEuzwRkYBSaBORdq+4opqXlmTy9Kebyc4vIzU5hrtPG8epE/tpYIGIdBkKbSLSbu0sLOepTzcz94stFJVXM3VIInecPIajR/bSGqAi0uUotIlIu7Mpt5h/fpjOG8uyqa6t5YRxfbjssFQmDEgIdmkiIkGj0CYi7cY3Wfk8tmgjC9fkEB4awplTBnDpYUMY1DMm2KWJiASdQpuIBN0X6bt55IONfLxhF/FRYVx51FAuPGSwBheIiPhQaBORoHDO8fGGXTzy/kaWbM4jKTac354wknOnDyJWa4GKiHyPfjOKSJtyzrFoXS4PvbeBb7Ly6RMfyR0nj+HMKQM0ElREpAkKbSLSJpxzvLt2J399bwMrswvolxDFH08dx+mT+xHRTWFNRKQ5Cm0iElB1Ye0v765n9bZCBiZGc9/p4zl1Uj/CQkOCXZ6ISIeh0CYiAeGc4721O/nLe+tZle0Ja3/68XhOmaiwJiKyPxTaRKRVOedYtD6XB99Zz4qtBQxIjOK+H4/nVIU1EZEDotAmIq3COcdnm3bzwMJ1fJ2ZT7+EKO49fRynTeqvsCYi0goU2kTkgH2duYf7/vcti9PzSOkeyV2njOWMtAGEd1NYExFpLQptIrLf1u0o4k8L1vHu2hySYsO5bdZozp46UFN3iIgEgEKbiLRYVl4pD76znje+ySY2vBs3HDecnx46hBhNiisiEjD6DSsifsstquDRDzYy94sthJhx+WGp/PyIg+gREx7s0kREOj2FNhFpVlF5FY9/lM4Tn2RQUV3LmVMGcPXRw0iJjwx2aSIiXUZAQ5uZzQQeAkKBJ5xz99TbPhCYAyR497nJOTc/kDWJiP8qqmuYuziTRz7YSF5JJSeN78Ovjh1OanJssEsTEelyAhbazCwUeBQ4FtgKfGlm85xza3x2uwV4xTn3mJmNBuYDgwNVk4j4p7bW8e8V27h/4Tqy8so4dGhPbpo5inH944NdmohIlxXIlrapwEbnXDqAmb0EzAZ8Q5sDunvvxwPbGjqQmbmWvPHkyZNbXKyIeCxO380f/rOWldkFjOrTnWcvHsdhw5Iws2CXJiLSpQUytPUDsnwebwWm1dvndmChmV0FxADHBLAeEWlCxq4S7p6/loVrcugTH8mfzziYUyb0IyREYU1EpD0IZGhr6Dd9/Razs4FnnHMPmNkPgOfMbKxzrnafFznXor8aaWlpLWqZE+nKCkqreOi9DTz7+WYiuoVww3HDuWRGKlHhmmtNRKQ9CWRo2woM8Hncn+9f/rwEmAngnPvczCKBJGBnAOsSEaC6ppYXl2Ty53fWU1BWxZlTBnDdscPpFacRoSIi7VEgQ9uXwDAzGwJkA2cB59TbJxP4IfCMmY0CIoHcANYkIsCnG3dx57/XsC6niOmpidx60hhG9+3e/AtFRCRoAhbanHPVZnYlsADPdB5POedWm9mdwFLn3DzgeuBxM7sOz6XTi5xzurQpEiBZeaXc9Z81LFidw8DEaP5+3mSOH9NbgwxERDqAgM7T5p1zbX695271ub8GODSQNYgIlFZW89iiTfzjo3S6hRi/Pn4El8wYojVCRUQ6EK2IINKJOed4e8V2/jh/LdsLyjllQl9uOmGUVjIQEemAFNpEOqm12wu5fd5qvsjIY0zf7jx89kTSBicGuywREdlPCm0inUxBaRUPvrueZz/fTHxUGH84dSxnTRlIqOZbExHp0BTaRDoJ5xz/+jqbP85fy57SSs6dNojrjxtOQnR4sEsTEZFWoNAm0gmszyniljdXsSQjj4kDE5hz8VTG9tM6oSIinYlCm0gHVlpZzUPvbeDJjzOIjezGPaeN44y0AVp6SkSkE1JoE+mg3l2Tw23zVpOdX8YZaf256YRRJMboUqiISGel0CbSwWTnl3HHvNUsXJPD8N6xvPrzHzBFo0JFRDo9hTaRDqK6ppanP93Mg++up9Y5bjphJJfMGEJYaEiwSxMRkTag0CbSASzL3MPNb6xi7fZCfjiyF7efPIYBidHBLktERNqQQptIO1ZYXsWf/reO57/YQu+4SP5+3iSOH5OitUJFRLoghTaRdsg5x/yVO7j936vZXVzBRYcM5vrjRhAboX+yIiJdlf4CiLQzW/eUcutbq3n/252M7dedpy6cwrj+mnNNRKSrU2gTaSeqa2p55rPNPLBwPWZwy4mjuOiQwXTTQAMREUGhTaRdWJVdwE3/WsGq7EKOHtmLO2ePoX8PDTQQEZHvKLSJBFFpZTUPvrOeJz/JIDEmgkfPmcSPxmmggYiIfJ9Cm0iQfLQ+l5vfWMnWPWWcPXUAN80cRXx0WLDLEhGRdkqhTaSN7Smp5Pdvr+Ffy7JJTY7h5cunMy21Z7DLEhGRdk6hTaSNOOeYt3wbd/57DQVlVVx19FCuOGookWGhwS5NREQ6AIU2kTawLb+MW95cxfvf7uTgAQnMPX0cI1O6B7ssERHpQBTaRAKottYxd0km9/73W2pqHbecOIqfHjqE0BANNBARkZZRaBMJkPTcYm56fSVLNucxY2gSd582TuuFiojIflNoE2ll1TW1PPFJBg++s56IbiHc9+Px/GRyf03jISIiB0ShTaQVrd1eyI2vrWBldgHHj+nN72ePpVf3yGCXJSIinUCToc3M+gNnAYcBfYEyYBXwH+C/zrnagFco0gFUVtfyyAcb+dsHG0mIDtMkuSIi0uoaDW1m9jTQD3gbuBfYCUQCw4GZwP+Z2U3OuY/aolCR9mpVdgE3vLqcb3cUccqEvtw2aww9YsKDXZaIiHQyTbW0PeCcW9XA86uAf5lZODAwMGWJtH8V1TU88v5G/rZoEz1jwnnigjSOGd072GWJiEgn1WhoayiwmdlBQLRzbqVzrhLYGMjiRNqrVdkFXP/KctblFHH6pP7cetJoLUElIiIB5fdABDO7GRgH1JpZrXPu/MCVJdI+VdXU8ugHG3nk/Y0kxoTz1EVpHD1SrWsiIhJ4TfVpuwr4m3OuxvvUwc65M73bVrRFcSLtybodRVz/6jesyi7klAl9uf3kMSREq++aiIi0jaZa2vYA/zOzvzrn/g0sNLMPgRBgQZtUJ9IO1NQ6Hv84nT8vXE9cZDf+ft4kZo7tE+yyRESki2mqT9vzZvYa8GszuxS4FXgRCHPOFbRVgSLBlLGrhOtf+YavM/M5fkxv/nDqOJJiI4JdloiIdEHN9Wk7CHgZeBz4PeDwhDeFNunUamsdzy3ewt3/XUt4aAh/OXMCsyf01bxrIiISNE31aXvGuz0K2OScu8zMJgKPm9kS59zv26hGkTa1Lb+MX7+2nE837uaI4cnce/p4UuK1qoGIiARXUy1tE51zBwOY2TIA59wyYJaZzW6L4kTaknOO17/O5o55q6lxjj+cOpZzpg5U65qIiLQLTYW2/3kHHoQDL/hucM69FdCqRNrYruIKbv7XShauyWHK4B7c/5ODGdQzJthliYiI7NXUQITfmFl3oNY5V9yGNYm0qfe/zeHG11ZQWFbNzT8aySUzUgkNUeuaiIi0L031aTsPeKGxReG9qyP0cc59EqjiRAKprLKGP85fy3OLtzAyJY65l05nREpcsMsSERFpUFOXR3sCy8zsK+ArIBfPgvFDgSOAXcBNAa9QJABWZRdwzUvL2JRbwqUzhnDD8SOIDAsNdlkiIiKNaury6ENm9ghwNHAoMB4oA9YC5zvnMtumRJEm8KwHAAAgAElEQVTWU1vreOKTdP60YB2JMeE8f8k0ZgxLCnZZIiIizWpynjbvElbveG8iHdqOgnKuf/UbPt24m+PH9Oae08bTI0bLUImISMfg94LxIh3ZgtU7+M3rK6ioquWe08Zx5pQBmspDREQ6FIU26dTKq2r4/dtrmPtFJuP6xfPQWRNITY4NdlkiIiItptAmndb6nCKuemEZ63KK+NnhqVx/3AjCu4UEuywREZH90tSUH79q6oXOuT+3fjkiB845x4tLsrjz7dXERnRjzsVTOWJ4crDLEhEROSBNtbTVTVg1ApgCzPM+ngV8FMiiRPZXUXkVN/1rJf9ZsZ3DhiXxwBkH0ytO64aKiEjH19SUH3cAmNlCYJJzrsj7+Hbg1TapTqQFVm8r4Iq5X5O1p4wbZ47g54cfRIhWNhARkU7Cnz5tA4FKn8eVwOCAVCOyH5xzvLAkkzv+vYbE6HBeunw6UwYnBrssERGRVuVPaHsOWGJmbwAOOBV4NqBVifipuKKa/3tjJW99s43Dhyfz4BkH0zM2IthliYiItLpmQ5tz7g9m9l/gMO9TP3XOLQtsWSLNW7ejiF/M/YrNu0q44bjh/PLIobocKiIinZa/U35EA4XOuafNLNnMhjjnMgJZmEhTXvtqK7e8uZLYiDCev3QahxykpahERKRzaza0mdltQBqeUaRPA2HA83jWIxVpU+VVNdz21mpeXprF9NRE/nr2RI0OFRGRLsGflrZTgYnA1wDOuW1mFtf0S0RaX1ZeKT9//itWbyvkyqOGcu0xw+gWqslyRUSka/DnL16lc87hGYSAmcX4e3Azm2lm68xso5nd1Mg+Z5jZGjNbbWYv+Hts6Vo+WLeTkx7+hKy8Up68MI0bjh+hwCYiIl2KPy1tr5jZP4AEM7sMuBh4vLkXmVko8ChwLLAV+NLM5jnn1vjsMwz4LXCoc26PmfXan5OQzqu21vHw+xv5y3vrGZnSnb+fN4lBPf3+f4OIiEin4c/o0fvN7FigEE+/tludc+/4ceypwEbnXDqAmb0EzAbW+OxzGfCoc26P9712NnQgM3N+vN9ekydPbsnu0k4Vlldx3Uvf8N63OzltYj/+cOo4osJDg12WiIhIUPg1etQb0vwJar76AVk+j7cC0+rtMxzAzD4FQoHbnXP/a+H7SCe0PqeInz33FVl5pdw5ewznTx+EmabzEBGRrqvZTkFmdpqZbTCzAjMrNLMiMyv049gN/YWt32LWDRgGHAmcDTxhZgnfe5Fz1pKbH7VJOzZ/5XZOefRTisqrefHy6Vzwg8EKbCIi0uX509J2HzDLObe2hcfeCgzwedwf2NbAPoudc1VAhpmtwxPivmzhe0knUFPreGDhOv62aBMTBybw2LmTSYnXdB4iIiLg3+jRnP0IbOAJXsPMbIiZhQNnAfPq7fMmcBSAmSXhuVyavh/vJR1cQVkVl8z5kr8t2sTZUwfw0uXTFdhERER8+NPSttTMXsYTsCrqnnTO/aupFznnqs3sSmABnv5qTznnVpvZncBS59w877bjzGwNUAP82jm3ez/PRTqojTuLuPzZr8jMK+WuU8Zy3vRBwS5JRESk3THPFGxN7GD2dANPO+fcxYEp6cClpaW5pUuXBrsM8cN7a3O45qVviOgWwmPnTWbqkMRglyQiItKmzOwr51xac/v5M+XHT1unJJHvOOd47MNN/GnBOsb07c4/zk+jX0JUsMsSERFptxoNbWZ2o3PuPjN7mO+P+sQ5d3VAK5NOq7yqht+8voK3vtnGrIP7ct/p4zX/moiISDOaammrG3yg64zSanIKy7n82aUs31rADccN54qjhmo6DxERET80Gtqcc//2fp3TduVIZ7Y8K5/Ln1tKUXk1/zh/MsePSQl2SSIiIh1Gs33azCwZ+A0wGtg7B4Nz7ugA1iWdzLzl2/j1q8tJio3g9V8cwqg+3YNdkoiISIfizzxtc/FcKh0C3AFsRpPfip9qvRPmXv3iMsb3j2felYcqsImIiOwHf+Zp6+mce9LMrnHOfQh8aGYfBrow6fhKK6v51cvL+d/qHZyR1p+7ThlHeDd//p8gIiIi9fkT2qq8X7eb2Yl4lqLqH7iSpDPYWVjOxXO+ZM22Qn530mguPlTrh4qIiBwIf0LbXWYWD1wPPAx0B64LaFXSoX27o5CLn/6S/LIqnrgwjaNH9g52SSIiIh2eP5Prvu29W4B3nVCRxny0Ppdfzv2amIhQXvnZDxjbLz7YJYmIiHQKTU2u2+CkunU0ua7U99KSTP7vzVUM6xXL0z+dQp94rXAgIiLSWppqadOkuuIX5xz3L1zHox9s4ojhyTx67iRiI/y58i4iIiL+ampy3X0m1TWz7p6nXVHAq5IOo6K6hhtf8yxJdfbUgfx+9hi6hWqEqIiISGvzZ3LdNOBpIM7z0PKBi51zXwW6OGnf8ksrufy5r1iSkceNM0fwiyMO0ghRERGRAPHnGtZTwC+dcx8DmNkMPCFufCALk/ZtR0E55z6xmKy8Mh46awKzJ/QLdkkiIiKdmj+hragusAE45z4xM10i7cKy88s45/HF7C6u5NlLpjI9tWewSxIREen0/AltS8zsH8CLeEaTngksMrNJAM65rwNYn7QzWXmlnPXPxRSWV/HcJVOZOLBHsEsSERHpEvwJbRO8X2+r9/wheEKcFo7vIjbvKuHsxxdTWlnDC5dOZ1x/zcEmIiLSVvyZXFcT6gqbcos5+5+Lqa51vHjZdEb31aLvIiIibanZuRnM7DnvMlZ1jweZ2XuBLUvakw05RZz5j8XUOgU2ERGRYPFnQq1PgC/M7EdmdhnwDvCXwJYl7cW6HUWc/fhizOCly6czIiUu2CWJiIh0Sf5cHv2Hma0GPgB2AROdczsCXpkE3drthZz7xBd0CzFevHw6ByXHBrskERGRLsufy6Pn45mr7QLgGWC+mR0c4LokyNZsK+ScxxcTHhrCyz/7gQKbiIhIkPkzevR0YIZzbifwopm9Aczhu1Gl0sl8u6OQc59YTGRYKC9eNp3BSTHBLklERKTL8+fy6Cn1Hi8xs6mBK0mCaX1OEec+/gUR3RTYRERE2pNGL4+a2Ss+9++tt/ntgFUkQbNxZxHnPL6Y0BDjhcumKbCJiIi0I031aRvmc//YetuSA1CLBNGm3GLOfvwLwHjhsumkqg+biIhIu9JUaHP7uU06mKy8Us59/Atqax0vXjaNob0U2ERERNqbpvq0RZvZRDzBLsp737y3qLYoTgJvR0E55zyxmLKqGl7+2XSG9dY8bCIiIu1RU6FtO/Bn7/0dPvfrHksHt7u4gnOfWMyekirmXjqNkSla6UBERKS9ajS0ac3Rzq2grIrzn1xCdn4Zc346lYMHJAS7JBEREWmCP8tYSSdTVlnDxc98yYadRfzj/DSmpfYMdkkiIiLSDIW2Lqam1nH1S8tYlrmHv541kSOGayCwiIhIR6DQ1sX8/u01vLMmh9tmjeGEcX2CXY6IiIj4yZ+1R83MzjOzW72PB2pFhI7pyU8yeOazzVwyYwgXHjI42OWIiIhIC/jT0vY34AfA2d7HRcCjAatIAuJ/q7Zz13/WMHNMCv/3o1HBLkdERERayJ8F46c55yaZ2TIA59weMwsPcF3SipZl7uGal75hwoAE/nLWBEJCLNgliYiISAv509JWZWaheFdBMLNkoDagVUmrycor5bJnl9K7eySPX5BGZFhosEsSERGR/eBPaPsr8AbQy8z+AHwC/DGgVUmrKCqv4tI5S6moruWpi6aQFBsR7JJERERkPzV7edQ5N9fMvgJ+iGcJq1Occ2sDXpkckOqaWq58YRmbcouZc/FUrScqIiLSwTUb2sxsOrDaOfeo93GcmU1zzn0R8Opkv/3+7TV8uD6Xu08bx6FDk4JdjoiIiBwgfy6PPgYU+zwu8T4n7dSzn29mzudbuPzwVM6eOjDY5YiIiEgr8Ce0mXPO1T1wztXi36hTCYIv0ndzx7/XcMyoXvxm5shglyMiIiKtxJ/Qlm5mV5tZmPd2DZAe6MKk5bYXlHHFC18zKDGaP585gVBN7SEiItJp+BPafg4cAmQDW4FpwOWBLEparqK6hl88/zVllTX884LJdI8MC3ZJIiIi0or8GT26EzirDWqRA3D7vNV8k5XP38+bxNBeccEuR0RERFqZP6NHk4HLgMG++zvnLg5cWdISLy7J5MUlWfzyyIOYOVaLwIuIiHRG/gwoeAv4GHgXqAlsOdJSq7ILuO2t1Rw2LInrjxsR7HJEREQkQPwJbdHOud8EvBJpscLyKn4592t6xobz0FkTNfBARESkE/NnIMLbZvajgFciLeKc46bXV5CdX8bDZ08kMSY82CWJiIhIAPkT2q7BE9zKzKzQzIrMrDDQhUnTnv18C/NX7uDG40eQNjgx2OWIiIhIgPkzelRDEduZFVvzues/azh6ZC8uOyw12OWIiIhIG/BrZQMz6wEMAyLrnnPOfRSooqRxheVVXPHC1yTHRvDATw4mRP3YREREuoRmL4+a2aXAR8AC4A7v19v9ObiZzTSzdWa20cxuamK/H5uZM7M0/8ruun735iq25Zfz8DkT6aF+bCIiIl2Gv33apgBbnHNHAROB3OZeZGahwKPACcBo4GwzG93AfnHA1cAXLai7S3pzWTZvfbONa384jMmD1I9NRESkK/EntJU758oBzCzCOfct4M+EYFOBjc65dOdcJfASMLuB/X4P3AeU+1lzl5SVV8rv3lzFlME9+OVRQ4NdjoiIiLQxf0LbVjNLAN4E3jGzt4BtfryuH5Dlexzvc3uZ2URggHPu7aYO5L106vfNj9o6lOqaWq57+RsA/nyGFoIXERHpivwZPXqq9+7tZvYBEA/8z49jN5Qs9gYqMwsBHgQu8uNYXdrfFm1i6ZY9PHTWBAYkRge7HBEREQmCRlvazKy792ti3Q1YCXwCxPpx7K3AAJ/H/dm3hS4OGAssMrPNwHRgXkODEZxz1pKbH7V1GMsy9/DQexs4ZUJfZk/o1/wLREREpFNqqqXtBeAk4Cs8LWRW72tzE4R9CQwzsyFANnAWcE7dRudcAZBU99jMFgE3OOeWtvgsOqnqmlpufmMVveIiuPOUscEuR0RERIKo0dDmnDvJzAw4wjmX2dIDO+eqzexKPFOEhAJPOedWm9mdwFLn3Lz9rrqLeG7xFtZuL+SxcyfRPTIs2OWIiIhIEDXZp80558zsDWDy/hzcOTcfmF/vuVsb2ffI/XmPzmpnUTl/Xriew4YlMXNsSrDLERERkSDzZ/ToYjObEvBKZB/3zP+Wiupa7pw9Fk+Dp4iIiHRl/ixjdRTwMzPbApTg7dPmnBsf0Mq6sC/Sd/OvZdlcedRQhiTFBLscERERaQf8CW0nBLwK2auqppZb31pNv4QortAkuiIiIuLlzzxtWwDMrBc+C8ZLYDz7+RbW5RTxj/MnExUeGuxyREREpJ3wZ8H4k81sA5ABfAhsBv4b4Lq6pKLyKh5+fwOHD0/muNG9g12OiIiItCP+DET4PZ6Jb9c754YAPwQ+DWhVXdSczzaTX1rFDccN1+ADERER2Yc/oa3KObcbCDGzEOfcB8CEANfV5RSVV/H4xxn8cGQvxvdPCHY5IiIi0s74MxAh38xigY+AuWa2E6gObFldzzOfbqagrIprjxke7FJERESkHfKnpW02UAZch2eh+E3ArEAW1dUUllfxxCcZHDOqF+P6xwe7HBEREWmHGm1pM7NHgBecc5/5PD0n8CV1PWplExERkeY01dK2AXjAzDab2b1mpn5sAVBYXsUTH6dzzKjejO2nVjYRERFpWKOhzTn3kHPuB8ARQB7wtJmtNbNbzUxNQq3k6U82U1hezbXHDAt2KSIiItKONdunzTm3xTl3r3NuInAOcCqwNuCVdQHFFdU8+Uk6x45WK5uIiIg0zZ/JdcPMbJaZzcUzqe564PSAV9YFvLo0i8Lyan555EHBLkVERETauaYGIhwLnA2cCCwBXgIud86VtFFtnVpNrePpTzczaWACEwf2CHY5IiIi0s41NU/bzcALwA3Oubw2qqfLeHdtDpl5pfxm5shglyIiIiIdQKOhzTl3VFsW0tU8+UkG/RKiOH6M1hgVERGR5vkzua60slXZBSzJyOOiQwbTLVTfAhEREWmeEkMQPPVJBtHhoZwxZUCwSxEREZEOQqGtje0sLOffK7ZxRtoA4qPCgl2OiIiIdBAKbW3s2c+3UF3r+Omhg4NdioiIiHQgCm1tqLyqhrlfbOGYUb0Z1DMm2OWIiIhIB6LQ1oYWrN7BntIqtbKJiIhIiym0taGFq3NIjotg+pCewS5FREREOhiFtjZSUV3DonU7OWZUb0JCLNjliIiISAej0NZGPtu0m5LKGo7TZLoiIiKyHxTa2sjC1TnEhIdyyEG6NCoiIiItp9DWBmprHe+uzeHIEb2I6BYa7HJERESkA1JoawPfbM0nt6hCl0ZFRERkvym0tYGFq3PoFmIcOaJXsEsRERGRDkqhrQ0sXLODHxzUU8tWiYiIyH5TaAuwjTuLSc8t4djRujQqIiIi+0+hLcDeWZMDwDGjFNpERERk/ym0BdjCNTsY3z+evglRwS5FREREOjCFtgDaWVjOssx8jtOlURERETlACm0B9M5az6XRY0enBLkSERER6egU2gLokw276JcQxfDescEuRURERDo4hbYAcc6xJCOPaamJmGmBeBERETkwCm0Bsim3mN0llUwforVGRURE5MAptAXIFxl5AEwdkhjkSkRERKQzUGgLkCUZefSKi2BQz+hglyIiIiKdgEJbADjn+CI9j6lD1J9NREREWodCWwBs3VPGjsJypunSqIiIiLQShbYA+K4/mwYhiIiISOtQaAuAJRm7SYgOY1gvzc8mIiIirUOhLQCWZOQxZXAiISHqzyYiIiKtQ6GtleUUlrN5d6n6s4mIiEirUmhrZUs0P5uIiIgEgEJbK1uSkUdMeCij+3QPdikiIiLSiSi0tbIlGXlMHpxIt1B9tCIiItJ6lCxa0Z6SStblFKk/m4iIiLQ6hbZWtGSz+rOJiIhIYCi0taIlGXmEdwthfP/4YJciIiIinUxAQ5uZzTSzdWa20cxuamD7r8xsjZmtMLP3zGxQIOsJtKWb85gwIIGIbqHBLkVEREQ6mYCFNjMLBR4FTgBGA2eb2eh6uy0D0pxz44HXgPsCVU+gOefYsLOYMX01alRERERaXyBb2qYCG51z6c65SuAlYLbvDs65D5xzpd6Hi4H+AawnoHIKKyitrCE1WUtXiYiISOsLZGjrB2T5PN7qfa4xlwD/bWiDmbmW3FrxHPyWnlsMQGpSTDDeXkRERDq5bgE8dkMLbzYYqMzsPCANOCKA9QRU+q4SAFKTFdpERESk9QWypW0rMMDncX9gW/2dzOwY4P+Ak51zFQ0dyDlnLbkF5GyakbGrhKiwUHrHRQbj7UVERKSTC2Ro+xIYZmZDzCwcOAuY57uDmU0E/oEnsO0MYC0Bl55bzOCkGEJCgpIZRUREpJMLWGhzzlUDVwILgLXAK8651WZ2p5md7N3tT0As8KqZfWNm8xo5XLuXsatEl0ZFREQkYALZpw3n3Hxgfr3nbvW5f0wg37+tVFbXkrWnjFkH9w12KSIiItJJaUWEVpCZV0pNrVNLm4iIiASMQlsrqJvuY0iS5mgTERGRwFBoawUZ3uk+hmiONhEREQkQhbZWkJ5bQlJsOPFRYcEuRURERDophbZWkLGrhFRdGhUREZEAUmhrBem7inVpVERERAJKoe0AFZRVsau4kiEaOSoiIiIBpNB2gOoGIWiheBEREQkkhbYDlLHLM92H5mgTERGRQOrSoe2RRx4hLS2NiIgILrroor3PV1ZW8uMf/5jBgwdjZixatKjRY2TklpD55x8zIbUPsbGxxMbGEhoaylVXXQVAVlYW06dPJzExkeuvv36f186cOZOlS5cG4tRERESkk+nSoa1v377ccsstXHzxxd/bNmPGDJ5//nlSUlKaPMamXSUc9of5FBcXU1xcTE5ODlFRUfzkJz8B4O677+bCCy8kIyODN998c29Ie/nll0lNTSUtLa31T0xEREQ6nYCuPdrenXbaaQAsXbqUrVu37n0+PDyca6+9FoDQ0NAmj5GRW7JPf7bXXnuNXr16cdhhh3m2Z2RwzTXXEB8fz5QpU0hPT2f48OHcc889fPDBB619SiIiItJJdemWtgNVW+vI2FWyz/JVc+bM4YILLsDMABg7dizvvPMO+fn5LF26lNGjR/O73/2Oa6+9loSEhGCVLiIiIh2MQtsByCkqp6yqZu90H5mZmXz44YdceOGFe/f57W9/y8cff8wRRxzBFVdcQVVVFStWrGDWrFmcc845HH744TzyyCPBOgURERHpILr05dEDlZ7rme7jIO/l0WeffZYZM2YwZMiQvfskJiby8ssvA1BbW8vhhx/O3//+d+655x7Gjh3LM888w6RJkzj66KMZPXp025+EiIiIdAhqaTsA6XULxSd/F9p8W9nq++c//8n06dMZO3YsK1euJC0tjfDwcMaNG8eqVavapGYRERHpmLp0aKuurqa8vJyamhpqamooLy+nuroagIqKCsrLywHPFCDl5eU45/Z5fXpuMVFhoaR0j+Szzz4jOzt776jR+nbu3Mmjjz7K7bffDsCQIUP44IMPKC4uZunSpaSmpgbuREVERKTD69Kh7a677iIqKop77rmH559/nqioKK655hqOPvpooqKiiIqKIjs7m+OPP56oqCi2bNkCwB//+EdmzJjBP397CZsePIuQkBDmzJnDaaedRlxcHABHHnkkkZGRe+duGzZsGLfeeiuxsbEsX76cd999l/vvv5/k5GROPvlk0tLSqKqqYtq0aWRlZQXzYxEREZF2yOq3HnUGaWlpbn8mra2urmb06NH8/Oc/55prruHDDz9k1qxZLFu2jOHDh++z77p165j1f08wdGAf/vvg9d9rhTvyyCM577zzuPTSS7/3Pj/60Y+46qqrGD9+POPHj2f16tWkpKRw7733YmbceOONLa5dREREOiYz+8o51+zErV26pa2+b7/9lm3btnHdddcRGhrK0UcfzaGHHspzzz33vX0HHzSU6mFHMmHc2Ba/T0ZGBkcffTT9+vVj2LBhZGZmkpmZyeuvv851113XGqciIiIinYxCm4+GWh2dcw0OEhhxy/+odTBn8ZZGj/fb3/6WpKQkDj300H2Wwho7diwLFy5k69atbN68mYMOOoirr76a++67j7CwsFY5FxEREelcFNp8jBw5kl69evGnP/2JqqoqFi5cyIcffkhpaWmLj3XvvfeSnp5OdnY2l19+ObNmzWLTpk0A3H///Tz22GOcfPLJPPjgg3z66afExcWRmprK7NmzOeKII3j11Vdb+/RERESkA1OftnpWrFjBVVddxapVq0hLSyM5OZmIiAiefPLJffYbfNN/AKjas41t/7y8wVY6XzNnzuTEE0/cu5B8ndLSUg455BAWLFjAVVddxSmnnMKJJ57I2LFjWb58OYmJift1HiIiItIxqE/bfho/fjwffvghu3fvZsGCBaSnpzN16tQDPq6ZNRjs7rzzTi699FJ69+69d+62+Ph4+vfvz8aNGw/4fUVERKRzUGirZ8WKFZSXl1NaWsr999/P9u3bueiii763n3MOV12Jq/HM61ZeXk5FRQUA+fn5LFiwYO+8b3PnzuWjjz7i+OOP3+cYa9asYdGiRfziF78APHO3vf/+++Tk5LBhwwYGDhwY2JMVERGRDkOhrZ7nnnuOPn360KtXL9577z3eeecdIiIiyMzMJDY2lszMTABqCneS+cBpbH/ylwBERUUxYsQIAKqqqrjllltITk4mKSmJhx9+mDfffHPv9jpXXHEFDz30EKGhoQDcfffd/PWvf2XMmDHcfPPNpKSktOGZi4iISHumPm37qa5PG8Dme04M6HuJiIhI56U+bSIiIiKdiEJbM+ovR1X/EmedwTf9hz4XPUTkgLGEhEf9f3tnHl5Fke7/79t99iV7crKvBAIBwhIIm4LCyOISFBAEFYafOALqzKDjMt474KAzckWdcVwfvagwMOOIGzOgiCKoiIgoAUGQRTYJAQIhZD3JOe/vj3Oq7A5hX+Otz/P0k1Sf6uqq6uqqb71VXQWfz4e//vWvACCHVo0HEeGJJ54AAJSUlCA/Px9xcXF46qmnZJhqWyuFQqFQKBQCJdpOgWeeeQZVVVWoqqrC5s2bm/UTqDmC/W9MhafTIKTd/Q9s3boVV111FQAgPT1dXl9VVYX169dD0zQMGzYMQGgR3pkzZ6KkpASPPPII9u3bBwB48sknMWzYMKSlpV2YhCoUCoVCobhksVzsCPxcqFz9DpxZXeDJvwIA4PV60bZt22b9zp49G5dffjkyMzMB/LStld1ul9ta+f1+vPnmm1ixYsWFSoJCoVAoFIpLGGVpOwWOtx2Vkfq9m6E5PNg3517s/tsYXHvttfJL06bMnj0bY8eOlW61rZVCoVAoFIqToUTbSTjedlQNh37EzpnX4+C/ZwIAAkcPourbpYgecDtSJ76CrKws3HTTTfD7/cjLy0NqaioA4NNPP0VZWRmWL1+ONm3aQNM0FBUVmba1eu655/Dhhx/ixhtvRGFhodzWqqKiAl26dMHRo0cvZpYoFAqFQqG4CKjh0ZNQVFQk/x87diz+8Y9/YNGiRTi05BXYk3Llb2SxwZXbE/ak1gCAqQ9ORVxcHKZPn46EhARUVVUBAF577TUMGzYMhYWFuPnmm3H//fcjLi4OixYtAhDa1io2Nhbvvfce/vSnP+Hzzz/Hrl27UFBQgAEDBuCBBx6A1+u9gDmgUCgUCoXiUkBZ2k4TIsKaNWug2d1wZBTI87aELIDM/gDgX//6Fx588EEAQG1tLd544w2MHTsWkydPRv/+/eFwOEzh//GPf4Tb7TwYc+oAACAASURBVEa/fv2we/duOJ1ONDY2IioqCps2bcKNN954/hOpUCgUCoXikkOJthPQ3HZUy5cvx7JlyxB95f8z+XV3GIDa71fCX7YdHGjE9OnTER0djcceewxOpxMA8PbbbyMqKgpXXHFFs/cT21rl5OSgpKQEsbGxaGhogN/vx6ZNmzBz5szznmaFQqFQKBSXJmpHhBNw4MABDBkyBJs2bYKu68jLy0NSUhJ69OiBp9YBZf98CLo3FvFODWWHjoAsNnCgAQRC5/Z58Hg8WLZsGZYtW4aRI0ciEAigpqYGtbW1cvP4Pn364LbbbkNVVRUefPBB1NfXY9CgQaisrER5eTkqKyuxf/9+2Gw2AEB0dDSysrIwbdo09O3bF4MGDcIjjzyCwsKTLqSsUCgUCoXiEuRUd0RQc9pOQHx8PFavXi3da9euxZgxY/D666/j+T8sQWTPG+Ev3YodGz9Fm6kfoqF8N0rnPQBbYi5KStbCbrejc+fOGDVqFIDQRw1xcXEYOnQoAOCOO+7AypUr8dVXX4GI0NjYCCJCTEwMFixYAAAoLS1Fu3btUFVVBa/Xi4aGBvz2t7/FyJEjYbFYUF5ejk8//VSKtoaGBvTp0wfz589X67spFAqFQvEzQg2PngbLli3Djh07kJ6ejt3P3IzKL99G3a4S9OzZM+yDQCBwoAHMDLfbjR9++AEPPPAA9u/fjwkTJuDee++V4b3wwgvo2bMnXnjhBdTW1qKoqAgWiwXz5s1Damoq7rvvPvz6179GIBBAQUEBKioqEAwGMXr0aJSVlaGyshKrVq3CI488gu+//x4DBw5EREQE/H4/kpOT5X0mTJiAt99++wLnlkKhUCgUinOJEm2nwe23345t27Zh7dq1SB73N3g7DYYzuxAFBQXY9cQw7H35Dli8sYgf+iCS7ngVthFPwD1yJsjmgtfrxZ49e3DPPfcACK3NZrfb8f3336OhoQHl5eX46quvcNlll2HkyJFYtWoV3nrrLSxatAg1NTUoKSkBEJpnN2vWLABAY2MjLrvsMlRUVKB79+6or69Hfn4+nE4nXnrpJfTo0QMRERH46KOPcP3118t0DBo0COdi+FihUCgUCsWFQ81pO0MyH1iIis/movFwKao2fAzfTX/G/jemIbLXjYgsGg7SQyPPgerD2PPcL5EQF4OysjJs3boVubm5SE1NxZ49e44Jt0OHDvKL0a+//hpAaG23G264AQcOHDD5JSI5N07TNASDQRARLBYLAoEAmFn+Lr5mZWZYrVYQEdxuNw4fPgxN02Cz2aBpGmpqaqBpGkaNGoX77rtPWvXi4uLw97//HYWFhWoIVqFQKBSKc8ipzmlTlrazIKrPGMRdGxrudGZ0RMa9byFw9CCOfhNac40DjTj475nwdBiAsrIyAMDBgwcBAEuWLMFDDz2EsWPH4s0330SHDh3QtWtXEBFcLhf++7//G5qmITIyEn369MEVV1yByMhIAKGPEe6++24AITHWp08f084JDQ0Nofszg4hgtVoxZMgQWCwWWCwWNDY2wmKxoLa2FgAQDAbR2NiIQCAAAOjSpQtef/11dOrUCVu3bkVNTQ02b96M7t27Q9M02O12rF69Gnl5edB1HbquIyEhAd988w3sdjuioqJgs9kwZcoUlJSUID8/H3FxccjIyMDu3btlHIuKiqRboVAoFArFiVGi7VwTDKKxohTMQRz8zxOAbkHML+4AEBIqYng0Ly9PXtKtWzds3LhRLuSbkZGBkpISBINBuX9p27Zt4XK5AACHDx/G008/jbi4ODAzVq5cifr6egAhEWez2dC3b18AQGpqKhobG7FixQoEAgEMGDAABQUF8Pv9sFhC1sCUlBT07t0bbrcbAPD1119D0zRomoZAIIDa2lrY7XYkJiZC0zRpwaurq0MwGERERAQOHDiArl27wu/3o66uDg0NDXjqqafQqVMnbNy4EZWVldi1axfS09NBRLDb7fjyyy+Rk5MDIpKHuK/xnMPhOObciQ5d18/oN3H/5vxZrVZERUWZfrPb7cjJyZFWSk3TkJ2djfT0dMTGxiI1NRWff/65Eq0KhUKhOCco0XYWBKorUL1xOaqqqsDBAGq3r0H1d8thT++I8kVPI1BTgfihvwfpoeHKqA5XYM2uIwAg134DQnuR9uzZE16vF8FgEDt27MDMmTNBRFi1ahUSEhLwP//zPygtLQUA9O/fH6Wlpaivr4fD4UDr1q3hdDrh9XphsVjg8/nw7bffAgB2794NZpYfMdjtdmzatEl+qQoA1dXViIyMxJEjR+D1euHxeAAAbrcbgUAAcXFxaGhogM1mg8vlkmIvIiICAJCUlASv1yt3avD7/ejQoQM8Ho+0Duq6Do/Hg9jYWHmtEGMAkJmZCZvNBovFgszMTFgsFvlbQkKCaVHhpKQkpKenS/e0adOg6zoAwOFwIBgMwuFwSBEmwgBCVkUAiIqKkr8BQE5ODjRNk2kLBoNwu93o378/gND8werqatjtdjAzNE2D3+/H9u3bTZbNXbt2Yffu3Thy5Ah+/PFH9O7dG5s2bUJNTY1JtNpsNnz55ZdIT09vVqieirA80W+nE96FPiwWy2mJbPEsjQLa6XSazuXn5yMrKwsulws2mw26riM+Pt4klktKStCuXTtYrVZMnTpVPnsloBUKRYtBWE1+TkfXrl35fJNx/3849a65bE9rz5GRkUw2J1vjMjhm4J3s6TSIrQnZDIudUybO4vT7/s3Dhw9nAKYjIiKCx44dy61bt+brrrvumN+7devGrVq14sTERPZ6vabfdF3n2NhY1jSNiYjT0tK4a9eubLVaGQDHxsayxWLhuLg4tlqtnJeXx1FRUUxEbLPZOC4uToZl/F/TNAbADoeDiUied7vdx8RPHE899RRrmsa9e/dmAExEfNddd7HD4eArr7ySAXCvXr3Y7XZzfHw8A+BrrrmGU1JS2G63MwCOjo7mmJgYTkxM5KysLLZarRwdHc0AOD4+nh999FEmIiYijoqKYovFIu/1+uuvy3RrmsZut5tdLtcxeWWMc0pKikwfEXFubi67XC6Zn5qmcZcuXaQfm83GvXr1kuHFxcXJZyLiZTxEODabzZSPM2bMkP9HRUWxpmms67op7wsLCxmATOPVV18tfwPAMTExMmyRbovFwm3btpXpdjqdpjBfffVVGQ9RJkQZdDqdbLPZWNd1U/rz8/Nl3ADw008/zZqmscVi4aKiIhk/r9fLbrebNU3jmJgYmUcAODs7W8aBiDgiIkKGl5+fL89brVZOSEhgANylSxe22WzSn9fr5bZt23JiYqIMKyYmhjVNM4V3vg8i4tTUVNO5hIQE07O51A5j2bPZbKb8IiLTM4+JieGoqCiOjY1lh8PBbrebrVYrOxwOjo+P58rKSt61a5d89qNHjzbViQMHDuTVq1ef97pXofg5AuArPgV9c9EF1vk4LpRoE4fRnXLHrFClqFuZrA4mq4Oh6ZyTk8NJtz3HZHVwysRZzMz80EMP8ZAhQ9jlcnFlZSWPGTOGbTYbL1++XP4+duxYZmaeOnWqrGCHDRvGBw4c4M6dOzMA7tu3L+u6ziNGjGCPxyMbzUcffZSvuOIKJiIeM2aMFH+isRNiraCggB0OB0dHR7PH4+GcnBy++eabWdM0djqdHBERwW3atOHk5GRZ2Xfs2JFbt24tGwCHw8FRUVHyd7vdzhEREXzNNddIUeHz+WRD/+c//5kBSNFWVFQkBY8QcUK05ebmckZGhrw2KSnJJHY+/fRTU+Pk8XjY5/M124iJa7p27crAT2LU7XZLoSAEzV133WW6dtmyZQzAJAiFfyGSxNGhQwcmIk5KSjI1kjt37pRuu93OSUlJUlDqus5Wq5U7dOhgEjNC+Ip87NOnj/y/devWbLVapQB0u92s67rMVxGuiLvIv8TERFN8hwwZIp8fEBLZiYmJMl+joqJMYUyZMoXz8vJkgy/SbAzXbrdzTk6O/D02NpYHDBhguocQCCJfhdAXeRwfH88PP/ywfK6TJ0/miIgIWVZsNpsU++JZxMTEsMPhkOU8MTGRi4qK5H1TU1PZ6/UyEXH79u3ZZrOxxWKR5ctqtbKmafzhhx/KayZMmMCZmZnSLd4NozCyWCymshEZGSnLJhHx4MGD5e9ExA6HQ8bT2Am57rrrTELw4YcflmUhLy9PluGEhAROS0uT/pKTk9lms7GmaZyVlWUSvmlpabLDJO6TkpJyUtHXtFw3JwZPRTieyL8x/c29q8bydLI4NL3mVAT16aTleIeoM84k/ceLhzHuxvfMGGbTa04UxukeJ3tmp+r3XOTvuTg0TTOVIWPHXcRT0zR5uFwudrlc7PF4ODExkZcvX852u10aREaPHs1r167ldu3acWxsLLdp00Z2Wvx+P3fv3p137dp1ypoCSrSdX44n2poeQsTZ7fafRJzFzrNmzeJJkyaxruu8efNmbmho4PT0dCYidrvd7Ha7pdAJBoO8cuVKttls7PF4pBWqqKiIbTYbt27dmtu3b88ul4udTicTER88eJCZmSdOnMhAqEEUFbnVauWIiAgpWETje9lll7Hdbufo6Gju0aMHR0RE8JVXXikr/7y8PM7IyJB+RK+diLiwsFA2+larlbOzs3nkyJGyMm5aKVutVvb5fNI6ZxSTMTExnJOTI/0mJSWZLBxNRZOxMQXAiYmJUiyJSryppVJYwE5UqXXp0sXkFiJVvOg+n09aK4TwEpWCeH5CkIg8SE9PPyYOovG0Wq2s6zpfdtllDISEsK7rPGrUKFPajQ28+F/cp02bNgz8ZMUS6RfPUNM0vvXWW03xat++Pefk5HC7du1kPKOiorh79+6m+4pGX9dDnZCsrCz5LHJyctjn83FWVpa8j8vlMll2LBYL33DDDdKt67pMIxASCE6n0yS4haAS+dPUshYXF8d2u10K2fT0dHY4HPL59+/fn91uNw8dOlRe88tf/pIdDoeM+9ChQ1nXdVl+7XY7OxwOXrlypbxm586dJhHkcrlMFmogJGztdrtJxFVVVcm0TZw4kQsKCkxh6LpuKs92u5379u0rw/B6vfzaa6/J/CosLJTlMC8vj202GzudTtmou91udjqdnJ6eLq2eNpuNFyxYIO9x7bXXcrt27UyWZmFhE+ccDgd36tRJvh8ul4t/9atfMRHJjpzID/G8bDYbZ2Zmymt0XeePPvpI3jcvL88kdMVzF+JZpEH4sVqtTEQ8YcIEGUZRUZGp4Y2OjpYjCqLcaZpmErMA+O6775Zpu+KKK2QnB4C08IryLcrByJEjZZh9+vSR74zNZjO9k7quc1paGlssFtP7ZrVa+Z577pH+Ro8ebSofsbGxphGFyMhI1nVdpj8+Pp4tFgtnZ2fLMEQbIdJprCON7n/+85+m34wCT/xtWp+JZyCuE/nicrmkH03TTP6Mz9BYFiwWC+fl5cl31zjiYLFYpAFAhCnqHiA0cmOsM0Q8RGfU5/OZrPdAqA3Iy8tju93OVquVJ02aJNs3YWTo16+ftPaLTlNaWpo0enTr1o2tVit7vV62WCx866238sSJE9lms3FkZCQXFRVxYmIi5+Tk8Pjx4zk7O5t79erFixYt4ueee47tdjuXlpYyM/Njjz3GM2bMOC1NASXazi+nKtqa8xPZ+yZThQKAH3roISniNmzYwEePHuUxY8aw2+1mh8PBrVq14uLiYk5JSeHhw4fz1q1buVWrVvL65ORkLi4u5oiICNY0jf/1r3/x3LlzZUPy73//+5ieqrFXarfbOTk52fQyiApUVCIVFRXscDjkCyesVSIs47XCLRoku93OBQUFUkxomsYTJkyQDUtmZia73W6Oi4vj9PR07tKli3zhdV3nefPmHSPWxO9xcXHHDOOJ+IuKuOnworBIisrJ2ONq166d6V4iH4wVss1m4549ezIQEmjGylg0OEJIiKOpZUPTNDkU2txBRBwZGWk6J4S78ZwxrpqmmQSZSK8xTK/XKysuUann5+fLhs9isZgEimjIjA2hEOZCrKampnK3bt1Mz8EohIxlStO0Zi0T4hk1TbO4r7HhEM+1aVqN5VCUO6PF0BiOONq2bWtyN7Uueb1eXrBggSnfhVgUcdV1nXVdN+W1MR66rvPixYtN0wysVqvJEmu1WtlqtZo6OE3f2dTU1GPy1Bimx+M5xtoUGxvLhw8flu4//OEP7PP55HssGiqRD06nky0WC0+aNEle43A4eNWqVQyAW7Vqxd27d5dWPQDcs2dPdjqdnJqaKvPXbrfz9u3bZRgjRowwDf0Li6jFYpFD6wkJCaY0d+3alWfOnCndzzzzDBcVFck0pqSksM/nM+Vjhw4dZEMPhISE0Uo8ffp0HjlypGlqQe/eveWzIyJu3bq1vC8R8XvvvSfj7nK5ODU11VSuOnTowB6PR5ZHYe2fPXu2vO+qVavY5/PJd6tjx45cVFQk4yo6SiL9cXFx3L17d5N1ulu3bqbOkqgTjHEhIlO+OxwO9nq9Mm5CXIqOsbBYGzuUmqbxlClTTO+gEJRGi5VxdAUItWO6rrPH45EdV5Fe8cyM1mZRH4wbN07ea/369bI+FXnqcrmk0Bbl0yjuExISpEEhLi6OY2JiuFOnTgyExGibNm140KBB7HQ62W63y46Z2+3mgoICTkhI4NzcXO7SpQt7PB6OjIzknj178tKlS9npdHJubi736NGDs7KyOCIigg8fPswjR47k5ORk3r9/P3fq1IkLCwt51apVvHPnTu7WrRv7/f7T0hRQou38cjai7VREXNOjb9++J/XTtKFoekRHR5t6OMaKn4j4qquu4k6dOskXKTc3l4uLi2WDZLfb2e12y56bseF1u91yzld2djbn5+dzbGysFCqZmZkcGRnJgwcPbraBFcOdERERPGrUKE5OTpYVQ1JSEs+YMUPOOWvbtq2sJHRdl9YGY+NnzA+n02mq+BMTE/mNN96Q7szMTNM8qvT0dNn4GHuHRoEQHR1tsiqJPDKmz263m3qzYtjY6G4aV5fLxbm5udIt5hMZK2ghrnw+nxRGIhzRgxQ9V+EWcbVYLBwbG8s33fRTmbv++ut5zpw5JsFxww03mHr4RUVF0topxOahQ4fk75MmTeKMjAxZIYueq8gz8ZyNAqOpIDE2eMbzrVq1Ms2HGz58OOu6zkQk0+l2u2WejhkzxtQwjBgxwpQ2q9XK06ZNM92jW7duJitgWlqaLOcirkYrkaZp7PV6TRbcpmKwe/fuJuuVsIAJv03FVURExDFi1ul0moaYLRaLyaoSERFhmsvq8/m4a9eups5Kx44dTR0Gj8fDbdu2PcaSbCyHTS3jxviKhjomJkaeE8+oaRjGuaaPPvqonI8p3jNh1RXvodfrlULY4XCwz+fjxx9/XIYxbtw49vl88vkOGDCAk5KSZAMt5rO2b9/e9A598MEHMm4fffQR5+TkmPJfDHkZ80jEQ9M0k1VVTHuIj4+X6XvsscekxU3UWXa7XYYZERFxTCfu448/Zp/PJ9/3goIC9vl88r5dunRhn8/H/fv3l9d4vV6OiYmR77M4mnY8hMAWR0xMjLToiTiJ5y/KknFOsriXcBufo6iHAJgsZAC4pqaGgWM7qEIYNvd+u1wuk3V9/fr1sjwAP40eAKERBPFsjdY7ES/xvhlFbXJyMhcVFUmjhqifRJpE2yTyRdM0joqK4smTJ0trocVi4SVLlnBiYiJnZmby4cOHOScnh3/xi1/wNddcw08++ST7fD4+ePAgFxcX88cff3zamgKnKNrU16OXAFF9xoCZkXH/f0yH8UEtW7bshA9SfA0nvo5sjo4dO2Lv3r3HnBdhfPDBBwBCC/wGg0Fs3boVCxcuRCAQQCAQQENDAxwOB/bt2wdd10OqH0B+fj4CgQDWrFkDAEhOTsbmzZtRUVEhv9qsra1FMBjEhx9+CADo3r07gsGg/IJTfM1ZV1eHDz74AHv37pVf19bW1sLtdiMYDCIYDCI+Pl5++RoIBOByuVBXVwer1QqXy4VgMCgXEKbwl5S7du2S6a2urkZVVZV079ixA4MGDYLf7wcA7NmzR34lKoiOjsacOXOkW+w2Ycxzu92O/fv3AwgtdlxfX4+UlBTpZmZ88sknAACLxQJmRt++fU1fsdbU1MjlWoLBIOrq6uRyLkDoy1yxoLHf7wcRoaKiQi7XwhxaimX79u0m97hx46S7vLwcmzZtkmGWlJRg27Ztcp0+ILQMjNPplO4tW7bIrdGCwSDWrVuHzMxM+ftLL70EZkb79u3luXXr1qFr166mPGrdurX8PScnBwDkl6Ai/8XXuIIuXbqAmeX5jRs3IiYmBsyMLVu2AADGjx8PIPSV8ooVK2TcdF3H6tWr5RfRADB27FjMmzdPfu2s6zpcLhfWr18v3ampqXA4HLIMMLNcckd85f3KK6+Ynt3kyZNNedK7d2/4fD4ZRvv27WUaNU1DRkYG2rVrh/j4eABAZWUlcnJykJqaKsOIiYkxfbHsdDrx1ltvIRgMwmaz4fLLL0d1dbWMh8/nQ2NjIzwej3xHamtrsW/fPgChJYEWL16MN998Uy7eHRsbi5deekmWq2AwiBtvvNGUlokTJyI7OxtAaFeWiIgIbN++XZaZHTt24NZbb8Xw4cNlGB06dJDvKjPjz3/+s8xDIkJZWRk8Ho9cL7K0tBQ5OTnS7ff7kZKSgpkzZ8ow58yZg6ioKNjtdgDAF198gfT0dGzevFmmtbGxUX49L+591VVXybRcf/31qKmpMZX3hoYGxMTESHcgEIDNZpPPvqqqCqmpqfI9JiKkpaXJcv2nP/0J2dnZqKysBBBaj7N9+/by6/m6ujqsW7cOHTt2lHXixIkTERsbK9/VLVu2ICUlRaZ/06ZNSElJwRdffCHjdfToUURGRsq6R1BRUWFyDx06VD5/ADh06BAOHz4MALJeFc9fpGHjxo0yvQBkGTK+e6IOEOW46YLvYnF4UQ+Kr7ldLpe8pqqqyrQKga7rMt+A0BJYKSkpMmzxbIlC+3Nv3bpVflkuEGk4dOgQLBaLzHcA2LdvH7Zt24Y1a9bIujQQCCA6OlpeU19fj6ioKBmfhIQEfPjhh9i+fTs0TUNMTAzuv/9+NDQ0oLGxEVlZWWBmZGdn4/PPP8err76K7OxsXH755di7dy+ys7NRXFyMvn374o033sA55VSUXUs7Wpql7XhhNOfnfDJ16tRme97G41QtfhfiMPaMdV3n4uLiE1oaXS7XSSczG8M0WkiM/ppaIZoOJSYnJ8tevJiXI673eDzHzKUz9vAA8LPPPmu6f9MPJIRVwTj0IO4hwnruuedMcTJaFu12u7RuREZGcnx8vOyFi7CMX+CKsIVb0zQeOHCgtNrabDZ2OByckJBg8gOAn3zySVO4RouocQhLDOuLOBnT/8wzz0h/Ypha5IXVamW73c5btmxhAHJOl/gARkwvMOZ37969OTIy0mTVMlryxHXGPGuurDRXFoyWN03T+M033zTlu5hSYLVaOTExkZ1OJw8cOFD6iYyM5Hnz5km31Wrlu++++7j3MJYtER+v18t/+ctfTH6M8Tx48KDJIuzz+XjChAkmi86QIUN42LBh0p2fn88333yzdE+ZMoWff/55Wc779evH8+bNM1n4fv/735u+Jp4+fbq0zDgcDr766qu5R48eMm5XXXUVz5o1S1pcdV3nWbNmyaFP8XGTcXhYDJ0ZreD333+/yXLTq1cvk9V43LhxprQA4PHjx5usV+np6bJ8u91uLiws5MWLF5vK0KxZs0xTNJ555hkZV5vNxu+++678QMZisfBjjz1msoBlZ2dzx44dZdyTkpJM6ff5fDxr1iyTZcrpdPLChQuPmYphnANsfP/E8fLLL5vKg81mM1nDxHCh8Rrj72JI1Hhfm81myjObzcZt27aVox8AOCsri4nIZC1zOBycnp4upwI0LcNut5uTk5Nl3or64Pnnn5fTO8S0DIvFwsXFxTJ8Xdc5OjpajkIAIUudGNXJy8uTdY/b7eYJEybIdIq5mh6Ph1u1aiUt316vl1955RXu0aMHu91ubtOmDT/66KNcW1vLLpeL33nnHf7d737HU6dO5Y4dO3KbNm144MCBPHfuXK6oqODU1FQuLy8/afsLZWn7eZL5wEJ5NHU3d5wO06ZNO2mBOZnF70IeDQ0N8v/Gxka88847pu27mh7V1dWoq6sznQsGgyZ3WVkZiouL4XK5kJqaihdffBFXXXUV7HY7iAgvvvgirr76arkDhcVigcPhkG6v14ujR4/KXq4xjgCkhc+4gwUzSwshELLYAJA9Sb/fD13XpTVl7dq1AEI9ZtF7ZWY4nU55n0mTJpme7YgRI2R86+vrZc/8yJEjOHDggOyFDxs2DF6vF9XV1aipqYHFYoHVagUzo6amBjabDenp6Vi8eDH27t0r16urq6vD/v37UVNTA+Cnnu+UKVNM6RRWBE3TZG8cgDwv4kTh9dgA4M477wQQ6mnX1tZKq6/I3/r6euTm5gIIWVFra2vxn//8R7qN1i23240VK1agpqZG9tY1TYOu6yYrdVRUFI4cOSLd8fHxuPzyy2UeAkCbNm1M1ozMzExMnz5durOyskyWCJfLJddaDAaDKCsrg8vlwooVK0zP6scff5T/NzQ0oFWrVtIdEREhLYQxMTGwWq2wWq1yXcPGxkZpKTDm++zZs2Ue9O3bF0uWLJFxt9lseOutt7Bu3Tp5zRdffIHPPvtMXrNnzx5pZQGAZ599FpMmTZLWqi1btuCee+6RVpxgMCjvCYQsG3PmzEF5eTmA0P7H27ZtM1nrNmzYgJUrV0pLYyAQwPLly3HttdfKvJgyZQp+9atfSUtbZGQk+vXrJxctF9cVFxdL9+7du03vw7Zt2zB//nyTpaaiogIFBQXSXVNTg2uuuQZA6P2LiorC+++/L39fuXIlJk+eLNN78OBBTJkyRZYZv9+PiRMnYsKEg22lygAAEAVJREFUCQBC76qmaXjyySflfbdv3w6/349bbrkFQKisGtNfVVWFlStXyrINALm5uXj66adNVnC73Y6JEydKt9Vqxeuvvy7dLpcLL7zwgqw/mBkWiwV/+ctfpB+bzWay6FssFvTs2VO6nU6nXGtT4Pf7cejQIZO7tLQUgUBAjgJUVlaC+SeLuLj/rl270NjYiIiICFx55ZXyNyLCa6+9hkAgINcSZWZER0fj/fffBzPj6NGj0nJqtVpRVlaGwYMHAwg9+4qKChw4cEDWhTt37oTX60V8fDzKyspgt9tRW1uLhIQEWCwWtG/fHrquY8aMGbBaraiqqsL27dvh8/lgt9tRVVWFO++8E+vWrUNtbS2+//57vPLKK3j11VeRlpaG+vp6rF+/Ht999x0mTJiAzp07Y/369SgsLERkZCRSU1OxdetWnCuUaPs/wImEXlP32Qq/lk5MTAzeeecdVFdXY9euXbj99tvxzjvvyOFd4fb7/VI4HjlyRLorKytRWVlpEmtNDzHsaRSc1dXVJxWlTQVnc6K0sbHxlIVuc8f8+fNx5MgRk//6+nrprq+vxw8//CDdJxLJJzqaXuf3+03nDh48iMGDB8PlciEtLQ0vvvgirrvuOrnQ7ty5c1FeXo64uDgAocre5/OZhkV8Pp8c6g8Gg3JIGggJAL/fj+joaNMzEA3qjz/+iNtuu02GdeDAASxfvhy6rkuRsXnzZlkmgNAQ4W9+8xsp6rZt24Y77rhDhnH48GE5PUGktby8HFVVVVKgHjlyBL/73e9MZVJsWRcZGYnKykoZRmVlJfx+P/x+vxQFQEiAjB492hSGcAeDQWzYsEGWEyLC7t27UV5ejgMHDsjFjw8dOiS33tN1HQ0NDXj88cdNiyOL65OTk9HY2IjS0lLU1dVJMVhaWmoSUxUVFXIbv9raWjQ0NGD//v1ye72amhosWrRICjJN0zB//nw5bBkMBlFaWoo33ngD9fX1ICLU19dj69atctoFEFqsXEwXAICysjLTkPO+fftQX1+PQCAAu90Ol8uFd999F23atJF+ysvLpfAVwmzBggXy93bt2sHlcsny4vF4kJGRIZ83ESEmJsY0DUM07KL8iCkUQoQTERYuXGhK/8KFC+WQssjDpUuXoqamRt67qqrKlN5AIGCaBpOQkID169ebOiV+vx8lJSXSffToUdNwODPj888/l+6amhr4/X4cPXrUNKx57733Sj+6rpued0ZGBurr62Gz2eS9NU1DSkoKMjIy5Hv/2WefybQQETwejywnRIR+/fqhurpaTl+Ii4vD8uXLYbVaUVdXh/Xr16Nz587yHXI6nXKBbSBUbvLy8lBRUYHDhw8jPj4eR48ele+M6KyUl5ejoqJCvkezZ8+W4tXj8WDp0qWy09qvXz/87W9/Q0NDA7KzsxEZGYnPPvsMt9xyC7766itkZmZi6dKlKCsrw5YtW0yLwZ8t53XDeCIaBOCvAHQALzPzY01+twOYDaArgHIAI5l5x9ne90JtGC/Y8djVJxU3Tf2czH2m15yL+16MuJ9pGAqFonkOHTqE8ePHY8mSJYiNjcV//dd/YdGiRVi8eDHq6+vx97//HYMGDUJycrIUQVarVVomAci9io1ommZqhInIND9McWHweDymnXWaIyoqyjTfrenz1MK7wIhnLubxGcuA8XkDIVEkOlnH83MxaFp+4+LipPgTxMfHHzMPz4jT6ZRWf6LQzjXCIqhpGrp27Qq3241PPvkEtbW1uOOOO/DEE0+gV69eOHLkCPbu3Yvx48fj1ltvxU033YT9+/fj97//vWnE4QTxP6UN4y0nDekMISIdwLMAfgFgD4DVRLSAmTcavP0/AIeZuRURjQIwA8DI8xUnxc+Pn4tobWlxV1z6CKuxEWEVNFJXV3ehoqRQ/CwxWiUF4sOOc815E20AugPYyszbAYCI/gmgGIAxJcUApoX/nw/gGSIiPp/mP4VCcda0ZMF5sQS3QqFQnC3nU7SlADDuwLwHQNHx/DBzIxEdARALwGTTDH9ZcsqIZQYUCoXiUuFSFK3n6r4XI+6XUvovpbgrft6ctzltRDQCwEBmvi3svgVAd2a+y+BnQ9jPnrB7W9hPeZOwTjeSBwHsPJv4KxQKhUKhUFwgMpg5/mSezqelbQ+ANIM7FUDTlV2Fnz1EZAEQCeBQEz9gZmp6TqFQKBQKheL/EudzyY/VAHKJKIuIbABGAVjQxM8CAGPD/w8HsFTNZ1MoFAqFQqE4lvNmaQvPUbsTwGKElvyYxcwbiOiPCK38uwDA/wKYQ0RbEbKwjTpf8VEoFAqFQqFoyZzXddoUCoVCoVAoFOcGtSOCQqFQKBQKRQtAiTaFQqFQKBSKFoASbWcIEQ0ios1EtJWIHrjY8WmJEFEaEX1MRN8R0QYi+nX4fAwRLSGiLeG/0Rc7ri0RItKJ6Bsi+k/YnUVEq8L5+nr4AyHFKUJEUUQ0n4g2hctsT1VWzx4i+m34/f+WiP5BRA5VVk8PIppFRPuJ6FvDuWbLJoV4Otx2rSOiLhcv5pc2x8nXx8N1wDoiepuIogy/PRjO181ENPB8xEmJtjPAsEXXYADtANxERO0ubqxaJI0A7mHmtgB6AJgczscHAHzEzLkAPgq7FafPrwF8Z3DPAPBUOF8PI7SNnOLU+SuA95k5D0ABQnmryupZQEQpAO4GUMjM7RH6aE1saajK6qnzKoBBTc4dr2wOBpAbPm4H8PwFimNL5FUcm69LALRn5o4AvgfwIACE265RAPLD1zwX1grnFCXazgy5RRcz+wGILboUpwEzlzLz1+H/jyLUCKYglJevhb29BmDoxYlhy4WIUgFcDeDlsJsAXInQdnGAytfTgogiAFyO0BfvYGY/M1dAldVzgQWAM7xWpwtAKVRZPS2Y+RMcu8bp8cpmMYDZHOILAFFElHRhYtqyaC5fmfkDZm4MO79AaA1aIJSv/2Tmemb+AcBWhLTCOUWJtjOjuS26Ui5SXH4WEFEmgM4AVgHwMXMpEBJ2ABIuXsxaLH8BcB+AYNgdC6DCUNmoMnt6ZAM4AOCV8JDzy0TkhiqrZwUz/whgJoBdCIm1IwDWQJXVc8HxyqZqv84d4wG8F/7/guSrEm1nRnM7NKi1U84QIvIAeBPAb5i58mLHp6VDRNcA2M/Ma4ynm/GqyuypYwHQBcDzzNwZQDXUUOhZE55nVQwgC0AyADdCw3dNUWX13KHqgnMAET2E0BSfueJUM97Oeb4q0XZmnMoWXYpTgIisCAm2ucz8Vvh0mTDXh//uv1jxa6H0BnAdEe1AaOj+SoQsb1HhIShAldnTZQ+APcy8Kuyej5CIU2X17BgA4AdmPsDMDQDeAtALqqyeC45XNlX7dZYQ0VgA1wAYY9jF6YLkqxJtZ8apbNGlOAnheVb/C+A7Zn7S8JNxe7OxAN690HFryTDzg8ycysyZCJXNpcw8BsDHCG0XB6h8PS2YeR+A3UTUJnyqP4CNUGX1bNkFoAcRucL1gchXVVbPnuOVzQUAbg1/RdoDwBExjKo4OUQ0CMD9AK5j5hrDTwsAjCIiOxFlIfShx5fn/P5qR4Qzg4iGIGS9EFt0PXqRo9TiIKI+AD4FsB4/zb36PULz2v4FIB2hSn0EMzedZKs4BYioH4B7mfkaIspGyPIWA+AbADczc/3FjF9Lgog6IfRhhw3AdgC/RKjjq8rqWUBEDwMYidBQ0zcAbkNoLpAqq6cIEf0DQD8AcQDKAEwF8A6aKZthcfwMQl841gD4JTN/dTHifalznHx9EIAdQHnY2xfMfEfY/0MIzXNrRGi6z3tNwzzrOCnRplAoFAqFQnHpo4ZHFQqFQqFQKFoASrQpFAqFQqFQtACUaFMoFAqFQqFoASjRplAoFAqFQtECUKJNoVAoFAqFogWgRJtCoTivEFGAiNYS0bdE9AYRuY7jbxERRZ1B+MlENP/kPo97/Q4iimvmvIeIXiSibUS0gYg+IaKiM73PpQARdQovV6RQKFogSrQpFIrzTS0zd2Lm9gD8AO4w/hhe5FNj5iHhTdhPC2bey8zDT+7ztHkZoc2ic5k5H8A4hNZrasl0AqBEm0LRQlGiTaFQXEg+BdCKiDKJ6Dsieg7A1wDShMXL8NtLYQvXB0TkBAAiakVEHxJRCRF9TUQ5Yf/fhn8fR0TvEtH7RLSZiKaKGxPRO0S0Jhzm7SeKJBHlACgC8F/MHAQAZt7OzAvDv08JWw6/JaLfhM9lEtGm8Gby3xLRXCIaQEQriGgLEXUP+5tGRHOIaGn4/ITweSKix8PXrieikeHz/YhoGRHND4c/N7xAKoioKxEtD6drsWHbomVENIOIviSi74nosvDuLX8EMDJs+Rx5jp6pQqG4QCjRplAoLggU2ktyMEI7YABAGwCzmbkzM+9s4j0XwLNhC1cFgGHh83PD5wsQ2qOyue13ugMYg5BVaQQRFYbPj2fmrgAKAdxNRLEniG4+gLXMHGgmHV0R2g2hCEAPABOIqHP451YA/gqgI4A8AKMB9AFwL0K7fQg6ArgaQE8AfyCiZAA3hONcgNCenI8LEQagM4DfAGgHIBtAbwrt2/s3AMPD6ZoFwLgzi4WZu4evm8rMfgB/APB62PL5+gnSr1AoLkEsJ/eiUCgUZ4WTiNaG//8Uof1mkwHsZOYvjnPND8wsrlkDIJOIvABSmPltAGDmOgAIG52MLGHm8vBvbyEkmr5CSKhdH/aThpAwLG968SnQB8DbzFxtuMdlCO09+AMzrw+f3wDgI2ZmIloPINMQxrvMXAuglog+Rkho9gHwj7BQLCOi5QC6AagE8CUz7wmHuzYcVgWA9gCWhPNAh1nEvhX+u6bJvRUKRQtFiTaFQnG+qWXmTsYTYZFRfYJrjPtMBgA4ARyjzo5D0735mEJ7sA4A0JOZa4hoGQDHCcLYAKAgPNcu2OS3E8XDGO+gwR2Eub49Jo6nEW4gHBYB2MDMPU9yjfCvUChaOGp4VKFQtAiYuRLAHiIaCgBEZD/Ol6i/IKKY8Dy4oQBWAIgEcDgs2PIQGtY80b22IWSde9gwfyyXiIoBfAJgKBG5iMgN4HqELIinQzEROcJDtP0ArA6HO5KIdCKKB3A5gC9PEMZmAPFE1DMcPysR5Z/kvkcBeE8zrgqF4hJBiTaFQtGSuAWhYc51AD4HkNiMn88AzAGwFsCbzPwVgPcBWMLXTQdwvGFZI7eFw98aHt58CcBeZv4awKsICapVAF5m5m9OMx1fAlgYjsd0Zt4L4G0A6wCUAFgK4D5m3ne8AMJz1IYDmEFEJeH09jrJfT8G0E59iKBQtEyIuamVXqFQKFomRDQOQCEz33mx43I8iGgagCpmnnmx46JQKFoWytKmUCgUCoVC0QJQljaFQqFQKBSKFoCytCkUCoVCoVC0AJRoUygUCoVCoWgBKNGmUCgUCoVC0QJQok2hUCgUCoWiBaBEm0KhUCgUCkULQIk2hUKhUCgUihbA/wecS3fcng3psAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c186bfc18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "def scree_plot(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT:\n",
    "            None\n",
    "    '''\n",
    "    num_components = len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "    for i in range(num_components):\n",
    "        ax.annotate(r\"%s%%\" % ((str(vals[i]*100)[:4])), (ind[i]+0.2, vals[i]), va=\"bottom\", ha=\"center\", fontsize=12)\n",
    " \n",
    "    ax.xaxis.set_tick_params(width=0)\n",
    "    ax.yaxis.set_tick_params(width=2, length=12)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')\n",
    "    \n",
    "\n",
    "scree_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca_40 = PCA(n_components=40)\n",
    "azdias_encode_pca_40= pca_40.fit_transform(azdias_encode_scaler_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)\n",
    "<br>\n",
    "\n",
    "<font color=darkblue> 40 principal components are kept, because they represent about 30 % of the features and 80% of variance. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca_map_weights(pca, i):\n",
    "    \n",
    "    weights_df= pd.DataFrame(data=pca.components_[i], index=list(azdias_encode.columns), columns=['PC '+str(i+1)]) \n",
    "    sorted_weights_df=weights_df.sort_values(by=['PC '+str(i+1)], ascending=False)\n",
    "\n",
    "\n",
    "    return sorted_weights_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC 1</th>\n",
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       "      <th>LP_STATUS_GROB_1.0</th>\n",
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       "      <td>0.189142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>0.184627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.184231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>0.180563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <td>0.160799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.157980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <td>0.147491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>0.142004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>0.140744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life_Stage_Pre-Family Couples &amp; Singles</th>\n",
       "      <td>0.139730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>0.129727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.126188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_1.0</th>\n",
       "      <td>0.126136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.119490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG3</th>\n",
       "      <td>0.119200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <td>0.115905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>0.109135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Movement_Mainstream</th>\n",
       "      <td>0.108577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>0.105754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>0.101362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_90s</th>\n",
       "      <td>0.097821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>0.092366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>0.090937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>0.088873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>0.077021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB_1.0</th>\n",
       "      <td>0.074719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>0.074022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>0.073163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>0.068144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_60s</th>\n",
       "      <td>-0.058293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB_5.0</th>\n",
       "      <td>-0.060390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <td>-0.062189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
       "      <td>-0.063178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>-0.064512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANZAHL_PKW</th>\n",
       "      <td>-0.066351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATIONALITAET_KZ_1.0</th>\n",
       "      <td>-0.069977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>-0.073997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>-0.077484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>-0.079876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_2.0</th>\n",
       "      <td>-0.091561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>-0.094735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>-0.096493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>-0.100635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <td>-0.100817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <td>-0.102746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_GROB_4.0</th>\n",
       "      <td>-0.108237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>-0.111331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Movement_Avantgarde</th>\n",
       "      <td>-0.111331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>-0.112101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>-0.115907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_GROB_5.0</th>\n",
       "      <td>-0.120525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.133490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.137774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.142049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>-0.185893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.190647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>-0.195803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>-0.204573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>-0.208804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 PC 1\n",
       "LP_STATUS_GROB_1.0                           0.197748\n",
       "PLZ8_ANTG3                                   0.189142\n",
       "HH_EINKOMMEN_SCORE                           0.184627\n",
       "PLZ8_ANTG4                                   0.184231\n",
       "PLZ8_BAUMAX                                  0.180563\n",
       "ORTSGR_KLS9                                  0.160799\n",
       "EWDICHTE                                     0.157980\n",
       "Wealth_Poorer Households                     0.147491\n",
       "FINANZ_HAUSBAUER                             0.142004\n",
       "FINANZ_SPARER                                0.140744\n",
       "Life_Stage_Pre-Family Couples & Singles      0.139730\n",
       "KBA05_ANTG4                                  0.129727\n",
       "PLZ8_ANTG2                                   0.126188\n",
       "FINANZTYP_1.0                                0.126136\n",
       "ARBEIT                                       0.119490\n",
       "KBA05_ANTG3                                  0.119200\n",
       "ANZ_HAUSHALTE_AKTIV                          0.115905\n",
       "RELAT_AB                                     0.109135\n",
       "Movement_Mainstream                          0.108577\n",
       "SEMIO_PFLICHT                                0.105754\n",
       "SEMIO_REL                                    0.101362\n",
       "Decade_90s                                   0.097821\n",
       "ZABEOTYP_5.0                                 0.092366\n",
       "SEMIO_RAT                                    0.090937\n",
       "SEMIO_TRADV                                  0.088873\n",
       "FINANZ_UNAUFFAELLIGER                        0.077021\n",
       "LP_FAMILIE_GROB_1.0                          0.074719\n",
       "SEMIO_MAT                                    0.074022\n",
       "FINANZ_ANLEGER                               0.073163\n",
       "SEMIO_FAM                                    0.068144\n",
       "...                                               ...\n",
       "Decade_60s                                  -0.058293\n",
       "LP_FAMILIE_GROB_5.0                         -0.060390\n",
       "WOHNDAUER_2008                              -0.062189\n",
       "Life_Stage_Older Families &  Mature Couples -0.063178\n",
       "WOHNLAGE                                    -0.064512\n",
       "KBA13_ANZAHL_PKW                            -0.066351\n",
       "NATIONALITAET_KZ_1.0                        -0.069977\n",
       "SEMIO_ERL                                   -0.073997\n",
       "ANZ_PERSONEN                                -0.077484\n",
       "SEMIO_LUST                                  -0.079876\n",
       "FINANZTYP_2.0                               -0.091561\n",
       "ZABEOTYP_1.0                                -0.094735\n",
       "GEBAEUDETYP_RASTER                          -0.096493\n",
       "BALLRAUM                                    -0.100635\n",
       "Wealth_Wealthy Households                   -0.100817\n",
       "Wealth_Prosperous Households                -0.102746\n",
       "LP_STATUS_GROB_4.0                          -0.108237\n",
       "GREEN_AVANTGARDE                            -0.111331\n",
       "Movement_Avantgarde                         -0.111331\n",
       "FINANZ_VORSORGER                            -0.112101\n",
       "ALTERSKATEGORIE_GROB                        -0.115907\n",
       "LP_STATUS_GROB_5.0                          -0.120525\n",
       "INNENSTADT                                  -0.133490\n",
       "KONSUMNAEHE                                 -0.137774\n",
       "PLZ8_GBZ                                    -0.142049\n",
       "KBA05_GBZ                                   -0.185893\n",
       "PLZ8_ANTG1                                  -0.190647\n",
       "KBA05_ANTG1                                 -0.195803\n",
       "FINANZ_MINIMALIST                           -0.204573\n",
       "MOBI_REGIO                                  -0.208804\n",
       "\n",
       "[119 rows x 1 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "\n",
    "weights_pc1 = pca_map_weights(pca_40,0)\n",
    "weights_pc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>0.223451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>0.208035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_3.0</th>\n",
       "      <td>0.203779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>0.186802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>0.156967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>0.156326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <td>0.117105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>0.111326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_60s</th>\n",
       "      <td>0.108991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_2.0</th>\n",
       "      <td>0.101829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_50s</th>\n",
       "      <td>0.100425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>0.094282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_5.0</th>\n",
       "      <td>0.092243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.090912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>0.089451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>0.086738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <td>0.086102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.085680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_2.0</th>\n",
       "      <td>0.078109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_3.0</th>\n",
       "      <td>0.077019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>0.076247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>0.069906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.067912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_1.0</th>\n",
       "      <td>0.067632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_6.0</th>\n",
       "      <td>0.065161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <td>0.064781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_40s</th>\n",
       "      <td>0.063419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.062199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_70s</th>\n",
       "      <td>0.061607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <td>0.061580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_80s</th>\n",
       "      <td>-0.059614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_0.0</th>\n",
       "      <td>-0.060531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB_4.0</th>\n",
       "      <td>-0.063182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.067477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>-0.069093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.070188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>-0.072119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.074241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>-0.077131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_3.0</th>\n",
       "      <td>-0.080936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>-0.084411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>-0.085522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>-0.091138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_4.0</th>\n",
       "      <td>-0.093870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.093988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>-0.094207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <td>-0.100236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_1.0</th>\n",
       "      <td>-0.117658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>-0.127154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>-0.138297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>-0.155543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <td>-0.164527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>-0.173541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>-0.190901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_90s</th>\n",
       "      <td>-0.191250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>-0.201271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>-0.203814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>-0.210647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>-0.213039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>-0.215714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              PC 2\n",
       "ALTERSKATEGORIE_GROB      0.223451\n",
       "FINANZ_VORSORGER          0.208035\n",
       "ZABEOTYP_3.0              0.203779\n",
       "SEMIO_ERL                 0.186802\n",
       "SEMIO_LUST                0.156967\n",
       "RETOURTYP_BK_S            0.156326\n",
       "W_KEIT_KIND_HH            0.117105\n",
       "FINANZ_HAUSBAUER          0.111326\n",
       "Decade_60s                0.108991\n",
       "CJT_GESAMTTYP_2.0         0.101829\n",
       "Decade_50s                0.100425\n",
       "PLZ8_ANTG3                0.094282\n",
       "FINANZTYP_5.0             0.092243\n",
       "PLZ8_ANTG4                0.090912\n",
       "SEMIO_KRIT                0.089451\n",
       "PLZ8_BAUMAX               0.086738\n",
       "ORTSGR_KLS9               0.086102\n",
       "EWDICHTE                  0.085680\n",
       "FINANZTYP_2.0             0.078109\n",
       "SHOPPER_TYP_3.0           0.077019\n",
       "SEMIO_KAEM                0.076247\n",
       "KBA05_ANTG4               0.069906\n",
       "ARBEIT                    0.067912\n",
       "CJT_GESAMTTYP_1.0         0.067632\n",
       "FINANZTYP_6.0             0.065161\n",
       "Wealth_Poorer Households  0.064781\n",
       "Decade_40s                0.063419\n",
       "PLZ8_ANTG2                0.062199\n",
       "Decade_70s                0.061607\n",
       "ANZ_HAUSHALTE_AKTIV       0.061580\n",
       "...                            ...\n",
       "Decade_80s               -0.059614\n",
       "SHOPPER_TYP_0.0          -0.060531\n",
       "LP_FAMILIE_GROB_4.0      -0.063182\n",
       "KONSUMNAEHE              -0.067477\n",
       "ANZ_PERSONEN             -0.069093\n",
       "INNENSTADT               -0.070188\n",
       "SEMIO_SOZ                -0.072119\n",
       "PLZ8_GBZ                 -0.074241\n",
       "ZABEOTYP_1.0             -0.077131\n",
       "FINANZTYP_3.0            -0.080936\n",
       "ZABEOTYP_5.0             -0.084411\n",
       "KBA05_ANTG1              -0.085522\n",
       "KBA05_GBZ                -0.091138\n",
       "FINANZTYP_4.0            -0.093870\n",
       "PLZ8_ANTG1               -0.093988\n",
       "MOBI_REGIO               -0.094207\n",
       "ZABEOTYP_4.0             -0.100236\n",
       "FINANZTYP_1.0            -0.117658\n",
       "SEMIO_MAT                -0.127154\n",
       "SEMIO_FAM                -0.138297\n",
       "SEMIO_RAT                -0.155543\n",
       "ONLINE_AFFINITAET        -0.164527\n",
       "SEMIO_KULT               -0.173541\n",
       "FINANZ_ANLEGER           -0.190901\n",
       "Decade_90s               -0.191250\n",
       "SEMIO_PFLICHT            -0.201271\n",
       "SEMIO_TRADV              -0.203814\n",
       "FINANZ_UNAUFFAELLIGER    -0.210647\n",
       "FINANZ_SPARER            -0.213039\n",
       "SEMIO_REL                -0.215714\n",
       "\n",
       "[119 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "weights_pc2 = pca_map_weights(pca_40,1)\n",
    "weights_pc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>0.321095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>0.255004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>0.252806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>0.237095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_5.0</th>\n",
       "      <td>0.145641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>0.135822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_0.0</th>\n",
       "      <td>0.120548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>0.114798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>0.106330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>0.092737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Movement_Avantgarde</th>\n",
       "      <td>0.089479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>0.089479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>0.078805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <td>0.074153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.073902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <td>0.068393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_GROB_5.0</th>\n",
       "      <td>0.062505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>0.062081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <td>0.059180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>0.058137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.057548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_6.0</th>\n",
       "      <td>0.054077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_1.0</th>\n",
       "      <td>0.042283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.042018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>0.040966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>0.040843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.040030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_60s</th>\n",
       "      <td>0.037664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>0.034987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_50s</th>\n",
       "      <td>0.034382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <td>-0.034407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>-0.035819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>-0.036322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>-0.038144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.039779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>-0.040986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_3.0</th>\n",
       "      <td>-0.041303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>-0.042659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKK</th>\n",
       "      <td>-0.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_FAMILIE_GROB_3.0</th>\n",
       "      <td>-0.049417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <td>-0.050951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.054199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.054329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>-0.055641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>-0.055775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>-0.057236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade_90s</th>\n",
       "      <td>-0.061278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.064847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>-0.067593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <td>-0.068207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Movement_Mainstream</th>\n",
       "      <td>-0.089086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_2.0</th>\n",
       "      <td>-0.102667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_1.0</th>\n",
       "      <td>-0.106383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>-0.165329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>-0.165513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>-0.191980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>-0.258978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>-0.280442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>-0.311710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>-0.342596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  PC 3\n",
       "SEMIO_VERT                    0.321095\n",
       "SEMIO_SOZ                     0.255004\n",
       "SEMIO_FAM                     0.252806\n",
       "SEMIO_KULT                    0.237095\n",
       "FINANZTYP_5.0                 0.145641\n",
       "FINANZ_MINIMALIST             0.135822\n",
       "SHOPPER_TYP_0.0               0.120548\n",
       "ZABEOTYP_1.0                  0.114798\n",
       "SEMIO_REL                     0.106330\n",
       "RETOURTYP_BK_S                0.092737\n",
       "Movement_Avantgarde           0.089479\n",
       "GREEN_AVANTGARDE              0.089479\n",
       "SEMIO_MAT                     0.078805\n",
       "ORTSGR_KLS9                   0.074153\n",
       "EWDICHTE                      0.073902\n",
       "W_KEIT_KIND_HH                0.068393\n",
       "LP_STATUS_GROB_5.0            0.062505\n",
       "FINANZ_VORSORGER              0.062081\n",
       "PLZ8_BAUMAX                   0.059180\n",
       "PLZ8_ANTG3                    0.058137\n",
       "PLZ8_ANTG4                    0.057548\n",
       "ZABEOTYP_6.0                  0.054077\n",
       "SHOPPER_TYP_1.0               0.042283\n",
       "PLZ8_ANTG2                    0.042018\n",
       "RELAT_AB                      0.040966\n",
       "SEMIO_LUST                    0.040843\n",
       "ARBEIT                        0.040030\n",
       "Decade_60s                    0.037664\n",
       "ALTERSKATEGORIE_GROB          0.034987\n",
       "Decade_50s                    0.034382\n",
       "...                                ...\n",
       "Wealth_Prosperous Households -0.034407\n",
       "SEMIO_TRADV                  -0.035819\n",
       "SEMIO_PFLICHT                -0.036322\n",
       "ZABEOTYP_5.0                 -0.038144\n",
       "PLZ8_GBZ                     -0.039779\n",
       "GEBAEUDETYP_RASTER           -0.040986\n",
       "SHOPPER_TYP_3.0              -0.041303\n",
       "HH_EINKOMMEN_SCORE           -0.042659\n",
       "KKK                          -0.044109\n",
       "LP_FAMILIE_GROB_3.0          -0.049417\n",
       "WOHNLAGE                     -0.050951\n",
       "KONSUMNAEHE                  -0.054199\n",
       "PLZ8_ANTG1                   -0.054329\n",
       "FINANZ_UNAUFFAELLIGER        -0.055641\n",
       "FINANZ_HAUSBAUER             -0.055775\n",
       "BALLRAUM                     -0.057236\n",
       "Decade_90s                   -0.061278\n",
       "INNENSTADT                   -0.064847\n",
       "FINANZ_SPARER                -0.067593\n",
       "ZABEOTYP_4.0                 -0.068207\n",
       "Movement_Mainstream          -0.089086\n",
       "SHOPPER_TYP_2.0              -0.102667\n",
       "FINANZTYP_1.0                -0.106383\n",
       "FINANZ_ANLEGER               -0.165329\n",
       "SEMIO_RAT                    -0.165513\n",
       "SEMIO_ERL                    -0.191980\n",
       "SEMIO_KRIT                   -0.258978\n",
       "SEMIO_DOM                    -0.280442\n",
       "SEMIO_KAEM                   -0.311710\n",
       "ANREDE_KZ                    -0.342596\n",
       "\n",
       "[119 rows x 1 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "weights_pc3 = pca_map_weights(pca_40,2)\n",
    "weights_pc3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)\n",
    "\n",
    "#### <font color = darkred> The first principal component</font>\n",
    "It focuses on the number of buildings and Financial condition.\n",
    "\n",
    "##### <font color = darkblue> The features with positive relationships</font>\n",
    "- **(LP_STATUS_GROB_1.0 - HH_EINKOMMEN_SCORE):** whenever the number of low-income earners increases, the value of Estimated household net income increases. \n",
    "- **(PLZ8_ANTG3 - PLZ8_ANTG4):** in the PLZ8 region, the more 6-10 family houses, the more 10+ family houses.\n",
    "- **(PLZ8_ANTG3, PLZ8_ANTG4 -  PLZ8_BAUMAX):** each increase in the number of houses, leads to an increment of a number of buildings.\n",
    " \n",
    "##### <font color = darkblue> The features with negative relationships </font> \n",
    "- **(PLZ8_ANTG3, PLZ8_ANTG4 - PLZ8_ANTG1):** in the PLZ8 region, each increase in the number of large capacity family houses, leads to a decrease in the number of small capacity family houses.\t\n",
    "- **(PLZ8 macro-cell features - RR3 micro-cell features)**: the more of large capacity family houses in PLZ8 region, the less in microcell. \n",
    " \n",
    "#### <font color = darkred> The second principal component</font>\n",
    "It focuses on the financial and Personality typologies. \n",
    "\n",
    "##### <font color = darkblue> The features with positive relationships</font>\n",
    "- **FINANZ_VORSORGER, ZABEOTYP_3.0,** and **SEMIO_ERL** increase and decrease together. \n",
    "- The personality typologies **SEMIO_PFLICHT, SEMIO_TRADV** and **SEMIO_REL** increase and decrease together. \n",
    "- The financial typologies **FINANZ_UNAUFFAELLIGER** and **FINANZ_SPARER** increase and decrease together. \n",
    " \n",
    "##### <font color = darkblue> The features with negative relationships </font> \n",
    "- When **SEMIO_ERL** increases , **SEMIO_PFLICHT, SEMIO_TRADV** and **SEMIO_REL** decrease and vice versa.\n",
    "- When **FINANZ_VORSORGER** increases ,  **FINANZ_UNAUFFAELLIGER** and **FINANZ_SPARER** decrease and vice versa.\n",
    "\n",
    "#### <font color = darkred> The third principal component</font>\n",
    "\n",
    "It focuses more on the gender and Personality typologies.  \n",
    "##### <font color = darkblue> The features with positive and negative relationships </font> \n",
    "- The Personality typologies **SEMIO_VERT, SEMIO_SOZ, SEMIO_FAM,** and **SEMIO_KULT**  increase and decrease together. \n",
    "- On the other hand, they have a negative relationship with **SEMIO_ERL, SEMIO_KRIT, SEMIO_DOM,** and **SEMIO_KAEM.** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_kmeans_score(data, center):\n",
    "    '''\n",
    "    returns the kmeans score regarding SSE for points to centers\n",
    "    INPUT:\n",
    "        data - the dataset you want to fit kmeans to\n",
    "        center - the number of centers you want (the k value)\n",
    "    OUTPUT:\n",
    "        score - the SSE score for the kmeans model fit to the data\n",
    "    '''\n",
    "    #instantiate kmeans\n",
    "    kmeans = KMeans(n_clusters=center)\n",
    "\n",
    "    # Then fit the model to your data using the fit method\n",
    "    model = kmeans.fit(data)\n",
    "    \n",
    "    # Obtain a score related to the model fit\n",
    "    score = np.abs(model.score(data))\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Over a number of different cluster counts...\n",
    "\n",
    "\n",
    "    # run k-means clustering on the data and...\n",
    "\n",
    "   \n",
    "    # compute the average within-cluster distances.\n",
    "scores = []\n",
    "centers = list(range(1,20,2))\n",
    "\n",
    "for center in centers:\n",
    "    scores.append(get_kmeans_score(azdias_encode_pca_40, center))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c14984400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "\n",
    "plt.plot(centers, scores, linestyle='--', marker='o', color='b');\n",
    "plt.xlabel('K');\n",
    "plt.ylabel('SSE');\n",
    "plt.title('SSE vs. K');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "kmeans = KMeans(n_clusters = 15)\n",
    "model_15 = kmeans.fit(azdias_encode_pca_40)\n",
    "azdias_pred = model_15.predict(azdias_encode_pca_40)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)\n",
    "<br>\n",
    "\n",
    "<font color=darkblue> The data was segmented into 15 clusters, because 15 seems to be the elbow.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_copy=customers.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "customers_clean = clean_data(customers_copy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128989, 119)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_imputed= imp.transform(customers_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_scaler= st_scaler.transform(customers_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_pca_40= pca_40.transform(customers_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_pred = model_15.predict(customers_pca_40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c148593c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.subplots_adjust(hspace = 1, wspace=.3)\n",
    "plt.subplot(121)\n",
    "sns.countplot(customers_pred)\n",
    "plt.title('Customers Data')\n",
    "plt.subplot(122)\n",
    "sns.countplot(azdias_pred)\n",
    "plt.title('General Population Data')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What kinds of people are part of a cluster that is overrepresented in the\n",
    "# customer data compared to the general population?\n",
    "c_o = st_scaler.inverse_transform(pca_40.inverse_transform(customers_pca_40[np.where(customers_pred ==8)])).round()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
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       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>Wealth_Comfortable Households</th>\n",
       "      <th>Wealth_Less Affluent Households</th>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <th>Life_Stage_Elders In Retirement</th>\n",
       "      <th>Life_Stage_Families With School Age Childre</th>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
       "      <th>Life_Stage_Pre-Family Couples &amp; Singles</th>\n",
       "      <th>Life_Stage_Young Couples With Children</th>\n",
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       "<p>5 rows × 119 columns</p>\n",
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      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "0                   4.0        1.0                5.0            1.0   \n",
       "1                   3.0        1.0                5.0            1.0   \n",
       "2                   3.0        1.0                5.0            1.0   \n",
       "3                   4.0        1.0                5.0            1.0   \n",
       "4                   3.0        1.0                5.0            2.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "0               5.0             1.0                    2.0               3.0   \n",
       "1               4.0             1.0                    2.0               3.0   \n",
       "2               5.0             1.0                    2.0               3.0   \n",
       "3               5.0             0.0                    2.0               3.0   \n",
       "4               4.0             2.0                    3.0               1.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "0               1.0         1.0                   ...                     \n",
       "1               1.0         3.0                   ...                     \n",
       "2               1.0         2.0                   ...                     \n",
       "3               1.0         1.0                   ...                     \n",
       "4               1.0         3.0                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "0                            0.0                              0.0   \n",
       "1                            1.0                             -0.0   \n",
       "2                            0.0                              0.0   \n",
       "3                            0.0                              0.0   \n",
       "4                           -0.0                              0.0   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "0                      -0.0                          -0.0   \n",
       "1                      -0.0                           0.0   \n",
       "2                      -0.0                           1.0   \n",
       "3                       0.0                           0.0   \n",
       "4                       0.0                           0.0   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "0                        1.0                              0.0   \n",
       "1                        0.0                             -0.0   \n",
       "2                        0.0                              0.0   \n",
       "3                        1.0                              1.0   \n",
       "4                        1.0                              1.0   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "0                                          1.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
       "0                                          0.0   \n",
       "1                                          1.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                         -0.0   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                     -0.0   \n",
       "3                                      0.0   \n",
       "4                                     -0.0   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "0                                     0.0  \n",
       "1                                     0.0  \n",
       "2                                     0.0  \n",
       "3                                     0.0  \n",
       "4                                     0.0  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_o_df = pd.DataFrame(data=c_o, index=np.array(range(0, c_o.shape[0])),columns=customers_clean.columns)\n",
    "c_o_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0    22491\n",
      "3.0    18591\n",
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      "5.0      347\n",
      "1.0       32\n",
      "Name: ALTERSKATEGORIE_GROB, dtype: int64\n",
      "1.0    42693\n",
      "2.0       10\n",
      "Name: ANREDE_KZ, dtype: int64\n",
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      "4.0     4901\n",
      "3.0      377\n",
      "6.0      255\n",
      "2.0        2\n",
      "Name: FINANZ_MINIMALIST, dtype: int64\n",
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      "2.0     7559\n",
      "3.0      975\n",
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      "4.0       66\n",
      "Name: FINANZ_SPARER, dtype: int64\n",
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      "Name: FINANZ_VORSORGER, dtype: int64\n",
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      "Name: FINANZ_ANLEGER, dtype: int64\n",
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      "5.0       29\n",
      "Name: FINANZ_UNAUFFAELLIGER, dtype: int64\n",
      "2.0    20108\n",
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      "Name: FINANZ_HAUSBAUER, dtype: int64\n",
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      "3.0     6442\n",
      "4.0       36\n",
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      "Name: HEALTH_TYP, dtype: int64\n",
      "4.0    31019\n",
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      "8.0        4\n",
      "Name: SEMIO_FAM, dtype: int64\n",
      "4.0    20532\n",
      "3.0    14728\n",
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      "7.0      346\n",
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      "Name: SEMIO_KRIT, dtype: int64\n",
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      "Name: SEMIO_DOM, dtype: int64\n",
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      "5.0        5\n",
      "6.0        2\n",
      "Name: SEMIO_KAEM, dtype: int64\n",
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      "6.0      423\n",
      "7.0       32\n",
      "Name: SEMIO_PFLICHT, dtype: int64\n",
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      "2.0    12373\n",
      "4.0     8089\n",
      "5.0     2613\n",
      "1.0     2319\n",
      "6.0      364\n",
      "0.0       15\n",
      "Name: SEMIO_TRADV, dtype: int64\n",
      "0.0    42324\n",
      "1.0      379\n",
      "Name: SOHO_KZ, dtype: int64\n",
      "1.0    25605\n",
      "2.0    17049\n",
      "0.0       39\n",
      "3.0       10\n",
      "Name: VERS_TYP, dtype: int64\n",
      "3.0    13560\n",
      "2.0    10616\n",
      "4.0     9872\n",
      "1.0     8213\n",
      "5.0      370\n",
      "0.0       64\n",
      "6.0        8\n",
      "Name: ANZ_PERSONEN, dtype: int64\n",
      "0.0    41280\n",
      "1.0     1422\n",
      "2.0        1\n",
      "Name: ANZ_TITEL, dtype: int64\n",
      "2.0    22366\n",
      "3.0    11562\n",
      "1.0     6468\n",
      "4.0     2167\n",
      "5.0       90\n",
      "0.0       50\n",
      "Name: HH_EINKOMMEN_SCORE, dtype: int64\n",
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      "4.0     8610\n",
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      "7.0      935\n",
      "1.0      478\n",
      "0.0        3\n",
      "Name: W_KEIT_KIND_HH, dtype: int64\n",
      "9.0     18081\n",
      "8.0     11302\n",
      "10.0     9282\n",
      "7.0      2297\n",
      "11.0     1442\n",
      "6.0       211\n",
      "12.0       67\n",
      "5.0        21\n",
      "Name: WOHNDAUER_2008, dtype: int64\n",
      " 1.0     4979\n",
      " 2.0     4869\n",
      " 0.0     4714\n",
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      "-1.0     3972\n",
      " 4.0     3387\n",
      "-2.0     3053\n",
      " 5.0     2479\n",
      "-3.0     2164\n",
      " 6.0     1683\n",
      "-4.0     1306\n",
      " 7.0      970\n",
      "-5.0      834\n",
      " 8.0      600\n",
      "-6.0      453\n",
      " 9.0      337\n",
      "-7.0      258\n",
      " 19.0     188\n",
      " 20.0     179\n",
      " 10.0     174\n",
      " 18.0     159\n",
      " 21.0     140\n",
      "-8.0      134\n",
      " 16.0     132\n",
      " 17.0     132\n",
      " 11.0     118\n",
      " 15.0     114\n",
      " 22.0     111\n",
      " 12.0      90\n",
      " 14.0      89\n",
      "         ... \n",
      "-11.0      17\n",
      " 31.0      16\n",
      " 34.0      14\n",
      " 32.0      10\n",
      "-12.0       9\n",
      " 33.0       7\n",
      "-13.0       6\n",
      " 39.0       5\n",
      " 36.0       5\n",
      " 38.0       5\n",
      "-14.0       4\n",
      " 37.0       4\n",
      " 42.0       3\n",
      " 40.0       3\n",
      " 35.0       3\n",
      " 41.0       3\n",
      " 48.0       2\n",
      " 45.0       2\n",
      " 52.0       2\n",
      " 46.0       1\n",
      "-15.0       1\n",
      " 43.0       1\n",
      " 58.0       1\n",
      " 75.0       1\n",
      " 69.0       1\n",
      " 63.0       1\n",
      " 73.0       1\n",
      " 44.0       1\n",
      " 53.0       1\n",
      " 50.0       1\n",
      "Name: ANZ_HAUSHALTE_AKTIV, Length: 71, dtype: int64\n",
      "-0.0    41043\n",
      " 2.0     1321\n",
      " 1.0      209\n",
      " 3.0      119\n",
      " 4.0        6\n",
      " 5.0        4\n",
      " 7.0        1\n",
      "Name: ANZ_HH_TITEL, dtype: int64\n",
      "3.0    17739\n",
      "4.0    15027\n",
      "2.0     6074\n",
      "5.0     2663\n",
      "1.0     1046\n",
      "0.0      103\n",
      "6.0       51\n",
      "Name: KONSUMNAEHE, dtype: int64\n",
      "1992.0    23525\n",
      "1993.0    10071\n",
      "1991.0     5094\n",
      "1994.0     2588\n",
      "1995.0      960\n",
      "1996.0      292\n",
      "1997.0       74\n",
      "1990.0       73\n",
      "1998.0       19\n",
      "1999.0        3\n",
      "1989.0        3\n",
      "2000.0        1\n",
      "Name: MIN_GEBAEUDEJAHR, dtype: int64\n",
      " 3.0    12518\n",
      " 2.0    11623\n",
      " 4.0     8214\n",
      " 1.0     4441\n",
      " 5.0     4136\n",
      " 6.0     1312\n",
      " 0.0      377\n",
      " 7.0       73\n",
      "-1.0        7\n",
      " 8.0        2\n",
      "Name: WOHNLAGE, dtype: int64\n",
      "3.0    27714\n",
      "2.0    11205\n",
      "4.0     2246\n",
      "1.0     1458\n",
      "0.0       80\n",
      "Name: KBA05_ANTG1, dtype: int64\n",
      " 1.0    24686\n",
      " 2.0    12413\n",
      " 0.0     4212\n",
      " 3.0     1333\n",
      " 4.0       37\n",
      "-1.0       21\n",
      " 6.0        1\n",
      "Name: KBA05_ANTG2, dtype: int64\n",
      "-0.0    33033\n",
      " 1.0     6967\n",
      "-1.0     2277\n",
      " 2.0      411\n",
      " 3.0       14\n",
      " 4.0        1\n",
      "Name: KBA05_ANTG3, dtype: int64\n",
      " 0.0    41604\n",
      "-1.0      919\n",
      " 1.0      171\n",
      " 2.0        6\n",
      "-2.0        2\n",
      "-3.0        1\n",
      "Name: KBA05_ANTG4, dtype: int64\n",
      "4.0    35574\n",
      "5.0     4059\n",
      "3.0     2861\n",
      "2.0      206\n",
      "1.0        3\n",
      "Name: KBA05_GBZ, dtype: int64\n",
      " 4.0     9136\n",
      " 3.0     9071\n",
      " 5.0     8278\n",
      " 2.0     7139\n",
      " 6.0     4795\n",
      " 1.0     2461\n",
      " 7.0     1394\n",
      " 0.0      203\n",
      " 8.0      195\n",
      " 9.0       26\n",
      " 10.0       4\n",
      "-1.0        1\n",
      "Name: BALLRAUM, dtype: int64\n",
      "4.0    12537\n",
      "5.0    11164\n",
      "3.0     9249\n",
      "6.0     5926\n",
      "2.0     3126\n",
      "7.0      365\n",
      "1.0      335\n",
      "0.0        1\n",
      "Name: EWDICHTE, dtype: int64\n",
      "4.0    10575\n",
      "5.0    10364\n",
      "3.0     8277\n",
      "6.0     7680\n",
      "2.0     3050\n",
      "7.0     2229\n",
      "1.0      260\n",
      "8.0      250\n",
      "9.0       17\n",
      "0.0        1\n",
      "Name: INNENSTADT, dtype: int64\n",
      "4.0    25887\n",
      "3.0     8341\n",
      "5.0     7298\n",
      "2.0     1138\n",
      "1.0       35\n",
      "6.0        4\n",
      "Name: GEBAEUDETYP_RASTER, dtype: int64\n",
      "1.0    14841\n",
      "3.0    12834\n",
      "2.0    12803\n",
      "4.0     1788\n",
      "0.0      435\n",
      "5.0        2\n",
      "Name: KKK, dtype: int64\n",
      "4.0    28413\n",
      "5.0     9656\n",
      "3.0     4267\n",
      "2.0      365\n",
      "1.0        2\n",
      "Name: MOBI_REGIO, dtype: int64\n",
      "3.0    16728\n",
      "4.0    13224\n",
      "2.0     8388\n",
      "5.0     2546\n",
      "1.0     1736\n",
      "6.0       45\n",
      "0.0       36\n",
      "Name: ONLINE_AFFINITAET, dtype: int64\n",
      " 2.0    9731\n",
      " 3.0    8225\n",
      " 5.0    7813\n",
      " 1.0    6176\n",
      " 4.0    4862\n",
      " 6.0    4614\n",
      " 7.0     735\n",
      " 0.0     511\n",
      " 8.0      33\n",
      "-1.0       2\n",
      " 9.0       1\n",
      "Name: REGIOTYP, dtype: int64\n",
      " 574.0     86\n",
      " 457.0     84\n",
      " 480.0     83\n",
      " 396.0     81\n",
      " 440.0     80\n",
      " 417.0     80\n",
      " 448.0     79\n",
      " 464.0     79\n",
      " 472.0     79\n",
      " 427.0     79\n",
      " 465.0     78\n",
      " 478.0     78\n",
      " 504.0     78\n",
      " 561.0     78\n",
      " 424.0     77\n",
      " 483.0     77\n",
      " 452.0     77\n",
      " 591.0     76\n",
      " 481.0     76\n",
      " 493.0     76\n",
      " 453.0     76\n",
      " 459.0     75\n",
      " 608.0     75\n",
      " 446.0     74\n",
      " 460.0     74\n",
      " 415.0     74\n",
      " 500.0     74\n",
      " 571.0     74\n",
      " 437.0     74\n",
      " 499.0     73\n",
      "           ..\n",
      "-92.0       1\n",
      "-68.0       1\n",
      "-199.0      1\n",
      "-146.0      1\n",
      "-182.0      1\n",
      " 1629.0     1\n",
      " 1411.0     1\n",
      " 1626.0     1\n",
      "-5.0        1\n",
      " 1502.0     1\n",
      "-80.0       1\n",
      " 1476.0     1\n",
      "-9.0        1\n",
      " 1544.0     1\n",
      "-173.0      1\n",
      " 7.0        1\n",
      "-123.0      1\n",
      "-36.0       1\n",
      " 1524.0     1\n",
      "-219.0      1\n",
      " 1513.0     1\n",
      " 1552.0     1\n",
      "-51.0       1\n",
      "-90.0       1\n",
      "-209.0      1\n",
      "-176.0      1\n",
      "-38.0       1\n",
      " 9.0        1\n",
      " 26.0       1\n",
      " 1543.0     1\n",
      "Name: KBA13_ANZAHL_PKW, Length: 1664, dtype: int64\n",
      "3.0    27394\n",
      "2.0    10492\n",
      "4.0     3814\n",
      "1.0     1001\n",
      "0.0        2\n",
      "Name: PLZ8_ANTG1, dtype: int64\n",
      "3.0    22450\n",
      "2.0    16810\n",
      "4.0     3020\n",
      "1.0      379\n",
      "5.0       43\n",
      "6.0        1\n",
      "Name: PLZ8_ANTG2, dtype: int64\n",
      "1.0    26364\n",
      "2.0    11469\n",
      "0.0     4001\n",
      "3.0      868\n",
      "4.0        1\n",
      "Name: PLZ8_ANTG3, dtype: int64\n",
      " 0.0    29157\n",
      " 1.0    13416\n",
      " 2.0      128\n",
      "-1.0        2\n",
      "Name: PLZ8_ANTG4, dtype: int64\n",
      " 1.0    22414\n",
      " 2.0    10955\n",
      " 0.0     6979\n",
      " 3.0     1958\n",
      " 4.0      372\n",
      "-1.0       17\n",
      " 5.0        8\n",
      "Name: PLZ8_BAUMAX, dtype: int64\n",
      "3.0    18145\n",
      "4.0    13568\n",
      "5.0     7820\n",
      "2.0     2410\n",
      "6.0      622\n",
      "1.0      138\n",
      "Name: PLZ8_HHZ, dtype: int64\n",
      "4.0    17088\n",
      "3.0    14241\n",
      "5.0     9408\n",
      "2.0     1142\n",
      "6.0      784\n",
      "1.0       40\n",
      "Name: PLZ8_GBZ, dtype: int64\n",
      "3.0    16630\n",
      "4.0    12371\n",
      "2.0    10582\n",
      "1.0     2501\n",
      "5.0      612\n",
      "6.0        4\n",
      "0.0        3\n",
      "Name: ARBEIT, dtype: int64\n",
      "5.0     8823\n",
      "6.0     8587\n",
      "7.0     7145\n",
      "4.0     6812\n",
      "8.0     5176\n",
      "3.0     3504\n",
      "2.0     1589\n",
      "9.0      873\n",
      "1.0      190\n",
      "10.0       4\n",
      "Name: ORTSGR_KLS9, dtype: int64\n",
      " 3.0    12074\n",
      " 4.0    11906\n",
      " 2.0     9696\n",
      " 1.0     4685\n",
      " 5.0     3251\n",
      " 0.0      986\n",
      " 6.0       92\n",
      "-1.0       12\n",
      " 7.0        1\n",
      "Name: RELAT_AB, dtype: int64\n",
      " 0.0    35556\n",
      " 1.0     7098\n",
      "-1.0       49\n",
      "Name: CJT_GESAMTTYP_1.0, dtype: int64\n",
      " 0.0    32073\n",
      " 1.0    10571\n",
      "-1.0       57\n",
      " 2.0        2\n",
      "Name: CJT_GESAMTTYP_2.0, dtype: int64\n",
      "-0.0    33503\n",
      " 1.0     9200\n",
      "Name: CJT_GESAMTTYP_3.0, dtype: int64\n",
      "-0.0    34352\n",
      " 1.0     8351\n",
      "Name: CJT_GESAMTTYP_4.0, dtype: int64\n",
      " 0.0    37415\n",
      " 1.0     5285\n",
      "-1.0        3\n",
      "Name: CJT_GESAMTTYP_5.0, dtype: int64\n",
      "-0.0    40526\n",
      " 1.0     2168\n",
      "-1.0        9\n",
      "Name: CJT_GESAMTTYP_6.0, dtype: int64\n",
      "0.0    42696\n",
      "1.0        7\n",
      "Name: FINANZTYP_1.0, dtype: int64\n",
      " 0.0    31978\n",
      " 1.0    10722\n",
      "-1.0        3\n",
      "Name: FINANZTYP_2.0, dtype: int64\n",
      "-0.0    42569\n",
      " 1.0      134\n",
      "Name: FINANZTYP_3.0, dtype: int64\n",
      " 0.0    42646\n",
      " 1.0       55\n",
      "-1.0        2\n",
      "Name: FINANZTYP_4.0, dtype: int64\n",
      "0.0    22265\n",
      "1.0    20438\n",
      "Name: FINANZTYP_5.0, dtype: int64\n",
      " 0.0    34179\n",
      " 1.0     8499\n",
      "-1.0       25\n",
      "Name: FINANZTYP_6.0, dtype: int64\n",
      " 0.0    34248\n",
      " 1.0     8256\n",
      "-1.0      199\n",
      "Name: LP_FAMILIE_GROB_1.0, dtype: int64\n",
      "0.0    32681\n",
      "1.0    10022\n",
      "Name: LP_FAMILIE_GROB_2.0, dtype: int64\n",
      "0.0    42452\n",
      "1.0      251\n",
      "Name: LP_FAMILIE_GROB_3.0, dtype: int64\n",
      "-0.0    40986\n",
      " 1.0     1717\n",
      "Name: LP_FAMILIE_GROB_4.0, dtype: int64\n",
      " 1.0    22279\n",
      "-0.0    20373\n",
      " 2.0       50\n",
      "-1.0        1\n",
      "Name: LP_FAMILIE_GROB_5.0, dtype: int64\n",
      "-0.0    42607\n",
      " 1.0       51\n",
      "-1.0       45\n",
      "Name: LP_STATUS_GROB_1.0, dtype: int64\n",
      " 0.0    40406\n",
      " 1.0     2251\n",
      "-1.0       46\n",
      "Name: LP_STATUS_GROB_2.0, dtype: int64\n",
      "0.0    35666\n",
      "1.0     5643\n",
      "2.0     1287\n",
      "3.0      105\n",
      "5.0        1\n",
      "4.0        1\n",
      "Name: LP_STATUS_GROB_3.0, dtype: int64\n",
      " 0.0    42430\n",
      " 1.0      166\n",
      "-1.0      107\n",
      "Name: LP_STATUS_GROB_4.0, dtype: int64\n",
      " 1.0    34382\n",
      " 0.0     8315\n",
      "-1.0        6\n",
      "Name: LP_STATUS_GROB_5.0, dtype: int64\n",
      " 1.0    41892\n",
      "-0.0      811\n",
      "Name: NATIONALITAET_KZ_1.0, dtype: int64\n",
      "0.0    42243\n",
      "1.0      460\n",
      "Name: NATIONALITAET_KZ_2.0, dtype: int64\n",
      "-0.0    42504\n",
      " 1.0      199\n",
      "Name: NATIONALITAET_KZ_3.0, dtype: int64\n",
      "-0.0    29831\n",
      " 1.0    12851\n",
      "-1.0       21\n",
      "Name: SHOPPER_TYP_0.0, dtype: int64\n",
      "0.0    33544\n",
      "1.0     9159\n",
      "Name: SHOPPER_TYP_1.0, dtype: int64\n",
      "0.0    38696\n",
      "1.0     4007\n",
      "Name: SHOPPER_TYP_2.0, dtype: int64\n",
      "0.0    26418\n",
      "1.0    16285\n",
      "Name: SHOPPER_TYP_3.0, dtype: int64\n",
      " 1.0    30687\n",
      "-0.0    11957\n",
      "-1.0       59\n",
      "Name: ZABEOTYP_1.0, dtype: int64\n",
      "0.0    41032\n",
      "1.0     1671\n",
      "Name: ZABEOTYP_2.0, dtype: int64\n",
      " 0.0    31791\n",
      " 1.0    10911\n",
      "-1.0        1\n",
      "Name: ZABEOTYP_3.0, dtype: int64\n",
      "-0.0    41406\n",
      " 1.0     1235\n",
      "-1.0       62\n",
      "Name: ZABEOTYP_4.0, dtype: int64\n",
      " 0.0    42677\n",
      "-1.0       16\n",
      " 1.0       10\n",
      "Name: ZABEOTYP_5.0, dtype: int64\n",
      "-0.0    42588\n",
      " 1.0      114\n",
      "-1.0        1\n",
      "Name: ZABEOTYP_6.0, dtype: int64\n",
      "-0.0    41067\n",
      " 1.0     1634\n",
      " 2.0        2\n",
      "Name: OST_WEST_KZ_O, dtype: int64\n",
      " 1.0    41067\n",
      "-0.0     1634\n",
      "-1.0        2\n",
      "Name: OST_WEST_KZ_W, dtype: int64\n",
      "-0.0    35043\n",
      " 1.0     7658\n",
      "-1.0        2\n",
      "Name: Decade_40s, dtype: int64\n",
      "0.0    27309\n",
      "1.0    15394\n",
      "Name: Decade_50s, dtype: int64\n",
      "0.0    32082\n",
      "1.0    10621\n",
      "Name: Decade_60s, dtype: int64\n",
      " 0.0    35907\n",
      " 1.0     6759\n",
      "-1.0       37\n",
      "Name: Decade_70s, dtype: int64\n",
      "-0.0    40548\n",
      " 1.0     2155\n",
      "Name: Decade_80s, dtype: int64\n",
      "0.0    42473\n",
      "1.0      230\n",
      "Name: Decade_90s, dtype: int64\n",
      "1.0    42403\n",
      "0.0      299\n",
      "2.0        1\n",
      "Name: Movement_Avantgarde, dtype: int64\n",
      " 0.0    42403\n",
      " 1.0      299\n",
      "-1.0        1\n",
      "Name: Movement_Mainstream, dtype: int64\n",
      "-0.0    42304\n",
      " 1.0      399\n",
      "Name: Nation_E, dtype: int64\n",
      "1.0    30085\n",
      "0.0    12618\n",
      "Name: Nation_E+W, dtype: int64\n",
      "0.0    30633\n",
      "1.0    12070\n",
      "Name: Nation_W, dtype: int64\n",
      " 0.0    39016\n",
      " 1.0     3659\n",
      "-1.0       28\n",
      "Name: Wealth_Comfortable Households, dtype: int64\n",
      " 0.0    36563\n",
      " 1.0     6133\n",
      "-1.0        7\n",
      "Name: Wealth_Less Affluent Households, dtype: int64\n",
      "-0.0    41221\n",
      " 1.0     1296\n",
      "-1.0      186\n",
      "Name: Wealth_Poorer Households, dtype: int64\n",
      "-0.0    31129\n",
      " 1.0    11529\n",
      "-1.0       45\n",
      "Name: Wealth_Prosperous Households, dtype: int64\n",
      "0.0    22923\n",
      "1.0    19754\n",
      "2.0       26\n",
      "Name: Wealth_Wealthy Households, dtype: int64\n",
      " 0.0    32006\n",
      " 1.0    10696\n",
      "-1.0        1\n",
      "Name: Life_Stage_Elders In Retirement, dtype: int64\n",
      " 0.0    36538\n",
      " 1.0     6149\n",
      "-1.0       16\n",
      "Name: Life_Stage_Families With School Age Childre, dtype: int64\n",
      "0.0    24086\n",
      "1.0    18617\n",
      "Name: Life_Stage_Older Families &  Mature Couples, dtype: int64\n",
      " 0.0    39343\n",
      " 1.0     2908\n",
      "-1.0      451\n",
      " 2.0        1\n",
      "Name: Life_Stage_Pre-Family Couples & Singles, dtype: int64\n",
      " 0.0    39448\n",
      " 1.0     3248\n",
      "-1.0        7\n",
      "Name: Life_Stage_Young Couples With Children, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in c_o_df.columns:\n",
    "    print(c_o_df[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What kinds of people are part of a cluster that is underrepresented in the\n",
    "# customer data compared to the general population?\n",
    "c_u = st_scaler.inverse_transform(pca_40.inverse_transform(customers_pca_40[np.where(customers_pred ==4)])).round()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>Wealth_Comfortable Households</th>\n",
       "      <th>Wealth_Less Affluent Households</th>\n",
       "      <th>Wealth_Poorer Households</th>\n",
       "      <th>Wealth_Prosperous Households</th>\n",
       "      <th>Wealth_Wealthy Households</th>\n",
       "      <th>Life_Stage_Elders In Retirement</th>\n",
       "      <th>Life_Stage_Families With School Age Childre</th>\n",
       "      <th>Life_Stage_Older Families &amp;  Mature Couples</th>\n",
       "      <th>Life_Stage_Pre-Family Couples &amp; Singles</th>\n",
       "      <th>Life_Stage_Young Couples With Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "0                   3.0        2.0                1.0            4.0   \n",
       "1                   1.0        2.0                1.0            4.0   \n",
       "2                   2.0        2.0                1.0            5.0   \n",
       "3                   2.0        2.0                1.0            5.0   \n",
       "4                   2.0        2.0                1.0            5.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "0               2.0             4.0                    4.0               4.0   \n",
       "1               2.0             5.0                    3.0               5.0   \n",
       "2               1.0             5.0                    3.0               4.0   \n",
       "3               2.0             4.0                    4.0               4.0   \n",
       "4               2.0             4.0                    4.0               4.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP                   ...                    \\\n",
       "0               0.0         1.0                   ...                     \n",
       "1              -0.0         2.0                   ...                     \n",
       "2               0.0         3.0                   ...                     \n",
       "3              -0.0         3.0                   ...                     \n",
       "4               0.0         1.0                   ...                     \n",
       "\n",
       "   Wealth_Comfortable Households  Wealth_Less Affluent Households  \\\n",
       "0                            0.0                              1.0   \n",
       "1                            0.0                              0.0   \n",
       "2                           -0.0                             -0.0   \n",
       "3                           -0.0                             -0.0   \n",
       "4                           -0.0                              1.0   \n",
       "\n",
       "   Wealth_Poorer Households  Wealth_Prosperous Households  \\\n",
       "0                      -0.0                          -0.0   \n",
       "1                       0.0                           0.0   \n",
       "2                       0.0                           1.0   \n",
       "3                       1.0                           0.0   \n",
       "4                       0.0                          -0.0   \n",
       "\n",
       "   Wealth_Wealthy Households  Life_Stage_Elders In Retirement  \\\n",
       "0                       -0.0                             -0.0   \n",
       "1                        0.0                              0.0   \n",
       "2                        0.0                              0.0   \n",
       "3                        0.0                             -0.0   \n",
       "4                        0.0                              0.0   \n",
       "\n",
       "   Life_Stage_Families With School Age Childre  \\\n",
       "0                                          1.0   \n",
       "1                                         -0.0   \n",
       "2                                         -0.0   \n",
       "3                                         -0.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   Life_Stage_Older Families &  Mature Couples  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          1.0   \n",
       "3                                          1.0   \n",
       "4                                         -0.0   \n",
       "\n",
       "   Life_Stage_Pre-Family Couples & Singles  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                     -0.0   \n",
       "3                                      0.0   \n",
       "4                                      1.0   \n",
       "\n",
       "   Life_Stage_Young Couples With Children  \n",
       "0                                    -0.0  \n",
       "1                                    -0.0  \n",
       "2                                     0.0  \n",
       "3                                     0.0  \n",
       "4                                     0.0  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_u_df = pd.DataFrame(data=c_u, index=np.array(range(0, c_u.shape[0])),columns=customers_clean.columns)\n",
    "c_u_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0    307\n",
      "3.0    185\n",
      "2.0    168\n",
      "4.0     10\n",
      "0.0      1\n",
      "Name: ALTERSKATEGORIE_GROB, dtype: int64\n",
      "2.0    671\n",
      "Name: ANREDE_KZ, dtype: int64\n",
      "1.0    540\n",
      "2.0     91\n",
      "0.0     34\n",
      "3.0      6\n",
      "Name: FINANZ_MINIMALIST, dtype: int64\n",
      "5.0    325\n",
      "4.0    302\n",
      "3.0     41\n",
      "2.0      3\n",
      "Name: FINANZ_SPARER, dtype: int64\n",
      "2.0    453\n",
      "3.0    100\n",
      "1.0     71\n",
      "4.0     27\n",
      "0.0     19\n",
      "5.0      1\n",
      "Name: FINANZ_VORSORGER, dtype: int64\n",
      "4.0    460\n",
      "5.0    122\n",
      "3.0     87\n",
      "2.0      2\n",
      "Name: FINANZ_ANLEGER, dtype: int64\n",
      "4.0    465\n",
      "5.0    101\n",
      "3.0     80\n",
      "2.0     23\n",
      "1.0      2\n",
      "Name: FINANZ_UNAUFFAELLIGER, dtype: int64\n",
      "4.0    483\n",
      "5.0    155\n",
      "3.0     28\n",
      "2.0      3\n",
      "1.0      1\n",
      "6.0      1\n",
      "Name: FINANZ_HAUSBAUER, dtype: int64\n",
      "0.0    607\n",
      "1.0     64\n",
      "Name: GREEN_AVANTGARDE, dtype: int64\n",
      "2.0    366\n",
      "3.0    273\n",
      "1.0     31\n",
      "4.0      1\n",
      "Name: HEALTH_TYP, dtype: int64\n",
      "2.0    473\n",
      "3.0    130\n",
      "1.0     37\n",
      "4.0     31\n",
      "Name: RETOURTYP_BK_S, dtype: int64\n",
      "3.0    288\n",
      "4.0    225\n",
      "2.0    119\n",
      "5.0     35\n",
      "1.0      4\n",
      "Name: SEMIO_SOZ, dtype: int64\n",
      "4.0    355\n",
      "3.0    179\n",
      "2.0    103\n",
      "5.0     33\n",
      "1.0      1\n",
      "Name: SEMIO_FAM, dtype: int64\n",
      "6.0    242\n",
      "5.0    169\n",
      "3.0    139\n",
      "2.0     60\n",
      "4.0     51\n",
      "7.0      7\n",
      "1.0      3\n",
      "Name: SEMIO_REL, dtype: int64\n",
      "4.0    148\n",
      "3.0    146\n",
      "5.0    114\n",
      "2.0     99\n",
      "6.0     86\n",
      "7.0     63\n",
      "1.0     14\n",
      "0.0      1\n",
      "Name: SEMIO_MAT, dtype: int64\n",
      "2.0    451\n",
      "3.0    190\n",
      "1.0     26\n",
      "4.0      4\n",
      "Name: SEMIO_VERT, dtype: int64\n",
      "2.0    185\n",
      "3.0    177\n",
      "4.0    128\n",
      "5.0     90\n",
      "1.0     55\n",
      "6.0     34\n",
      "7.0      2\n",
      "Name: SEMIO_LUST, dtype: int64\n",
      "4.0    315\n",
      "6.0    161\n",
      "3.0     87\n",
      "5.0     84\n",
      "7.0     20\n",
      "2.0      4\n",
      "Name: SEMIO_ERL, dtype: int64\n",
      "4.0    196\n",
      "5.0    175\n",
      "3.0    145\n",
      "2.0    137\n",
      "1.0     17\n",
      "6.0      1\n",
      "Name: SEMIO_KULT, dtype: int64\n",
      "6.0    247\n",
      "7.0    197\n",
      "4.0    159\n",
      "5.0     34\n",
      "3.0     31\n",
      "2.0      2\n",
      "8.0      1\n",
      "Name: SEMIO_RAT, dtype: int64\n",
      "6.0    354\n",
      "5.0    282\n",
      "4.0     20\n",
      "7.0     15\n",
      "Name: SEMIO_KRIT, dtype: int64\n",
      "6.0    401\n",
      "7.0    216\n",
      "5.0     46\n",
      "8.0      8\n",
      "Name: SEMIO_DOM, dtype: int64\n",
      "6.0    430\n",
      "7.0    200\n",
      "5.0     41\n",
      "Name: SEMIO_KAEM, dtype: int64\n",
      "7.0    222\n",
      "6.0    197\n",
      "4.0    131\n",
      "5.0     54\n",
      "3.0     51\n",
      "2.0     12\n",
      "8.0      4\n",
      "Name: SEMIO_PFLICHT, dtype: int64\n",
      "6.0    372\n",
      "3.0    142\n",
      "4.0     57\n",
      "5.0     49\n",
      "7.0     32\n",
      "2.0     19\n",
      "Name: SEMIO_TRADV, dtype: int64\n",
      "-0.0    664\n",
      " 1.0      7\n",
      "Name: SOHO_KZ, dtype: int64\n",
      "2.0    399\n",
      "1.0    272\n",
      "Name: VERS_TYP, dtype: int64\n",
      "1.0    391\n",
      "2.0    122\n",
      "3.0    107\n",
      "4.0     40\n",
      "0.0      7\n",
      "5.0      3\n",
      "6.0      1\n",
      "Name: ANZ_PERSONEN, dtype: int64\n",
      "0.0    668\n",
      "1.0      3\n",
      "Name: ANZ_TITEL, dtype: int64\n",
      "5.0    328\n",
      "6.0    275\n",
      "4.0     62\n",
      "3.0      5\n",
      "7.0      1\n",
      "Name: HH_EINKOMMEN_SCORE, dtype: int64\n",
      "5.0    220\n",
      "4.0    136\n",
      "6.0    113\n",
      "2.0     91\n",
      "3.0     61\n",
      "1.0     48\n",
      "0.0      2\n",
      "Name: W_KEIT_KIND_HH, dtype: int64\n",
      "7.0     209\n",
      "6.0     204\n",
      "8.0     131\n",
      "5.0      78\n",
      "9.0      33\n",
      "10.0      9\n",
      "4.0       5\n",
      "11.0      1\n",
      "3.0       1\n",
      "Name: WOHNDAUER_2008, dtype: int64\n",
      " 3.0     52\n",
      " 5.0     48\n",
      " 4.0     44\n",
      " 6.0     42\n",
      " 7.0     31\n",
      " 9.0     26\n",
      " 8.0     25\n",
      " 2.0     24\n",
      " 1.0     20\n",
      " 18.0    20\n",
      " 28.0    16\n",
      " 17.0    16\n",
      " 10.0    15\n",
      " 27.0    15\n",
      " 0.0     14\n",
      " 29.0    14\n",
      "-1.0     14\n",
      " 16.0    14\n",
      " 13.0    12\n",
      " 30.0    12\n",
      " 19.0    12\n",
      " 32.0    12\n",
      " 25.0    11\n",
      " 11.0    11\n",
      " 31.0    11\n",
      " 20.0    11\n",
      " 12.0    10\n",
      " 15.0    10\n",
      " 21.0     9\n",
      " 26.0     9\n",
      "         ..\n",
      " 14.0     6\n",
      " 34.0     6\n",
      " 24.0     6\n",
      " 41.0     4\n",
      " 36.0     4\n",
      " 44.0     4\n",
      " 40.0     3\n",
      " 38.0     3\n",
      " 37.0     3\n",
      " 39.0     3\n",
      "-2.0      3\n",
      " 35.0     2\n",
      "-4.0      2\n",
      " 43.0     2\n",
      " 52.0     2\n",
      "-8.0      1\n",
      " 51.0     1\n",
      "-5.0      1\n",
      " 58.0     1\n",
      " 62.0     1\n",
      "-3.0      1\n",
      " 87.0     1\n",
      " 42.0     1\n",
      " 50.0     1\n",
      " 47.0     1\n",
      " 75.0     1\n",
      " 56.0     1\n",
      " 91.0     1\n",
      " 60.0     1\n",
      " 45.0     1\n",
      "Name: ANZ_HAUSHALTE_AKTIV, Length: 63, dtype: int64\n",
      "-0.0    637\n",
      " 1.0     30\n",
      " 3.0      2\n",
      " 2.0      1\n",
      " 6.0      1\n",
      "Name: ANZ_HH_TITEL, dtype: int64\n",
      "1.0    287\n",
      "2.0    223\n",
      "3.0    126\n",
      "0.0     23\n",
      "4.0     12\n",
      "Name: KONSUMNAEHE, dtype: int64\n",
      "1992.0    341\n",
      "1993.0    202\n",
      "1991.0     69\n",
      "1994.0     41\n",
      "1990.0     10\n",
      "1995.0      6\n",
      "1996.0      2\n",
      "Name: MIN_GEBAEUDEJAHR, dtype: int64\n",
      "3.0    321\n",
      "4.0    205\n",
      "2.0    106\n",
      "5.0     21\n",
      "1.0     11\n",
      "6.0      6\n",
      "7.0      1\n",
      "Name: WOHNLAGE, dtype: int64\n",
      " 0.0    382\n",
      " 1.0    251\n",
      " 2.0     32\n",
      "-1.0      4\n",
      " 3.0      2\n",
      "Name: KBA05_ANTG1, dtype: int64\n",
      " 2.0    230\n",
      " 1.0    152\n",
      " 0.0    143\n",
      " 3.0    121\n",
      "-1.0     24\n",
      " 4.0      1\n",
      "Name: KBA05_ANTG2, dtype: int64\n",
      " 1.0    345\n",
      " 2.0    181\n",
      " 0.0    134\n",
      " 3.0      6\n",
      "-1.0      5\n",
      "Name: KBA05_ANTG3, dtype: int64\n",
      "-0.0    371\n",
      " 1.0    229\n",
      " 2.0     71\n",
      "Name: KBA05_ANTG4, dtype: int64\n",
      "2.0    314\n",
      "3.0    220\n",
      "1.0    107\n",
      "4.0     30\n",
      "Name: KBA05_GBZ, dtype: int64\n",
      "2.0    199\n",
      "3.0    149\n",
      "1.0    123\n",
      "4.0     93\n",
      "5.0     52\n",
      "6.0     28\n",
      "0.0     14\n",
      "7.0     13\n",
      "Name: BALLRAUM, dtype: int64\n",
      "6.0    319\n",
      "5.0    176\n",
      "4.0     73\n",
      "7.0     71\n",
      "3.0     30\n",
      "2.0      2\n",
      "Name: EWDICHTE, dtype: int64\n",
      "2.0    227\n",
      "3.0    180\n",
      "4.0    114\n",
      "5.0     58\n",
      "1.0     55\n",
      "6.0     32\n",
      "7.0      5\n",
      "Name: INNENSTADT, dtype: int64\n",
      "3.0    355\n",
      "4.0    214\n",
      "2.0     89\n",
      "5.0     11\n",
      "1.0      2\n",
      "Name: GEBAEUDETYP_RASTER, dtype: int64\n",
      "3.0    364\n",
      "2.0    169\n",
      "1.0     70\n",
      "4.0     68\n",
      "Name: KKK, dtype: int64\n",
      "2.0    315\n",
      "1.0    230\n",
      "3.0    120\n",
      "4.0      5\n",
      "0.0      1\n",
      "Name: MOBI_REGIO, dtype: int64\n",
      "3.0    254\n",
      "4.0    211\n",
      "5.0    112\n",
      "2.0     77\n",
      "6.0      9\n",
      "1.0      8\n",
      "Name: ONLINE_AFFINITAET, dtype: int64\n",
      "6.0    207\n",
      "5.0    155\n",
      "3.0    105\n",
      "4.0     78\n",
      "7.0     54\n",
      "1.0     35\n",
      "2.0     35\n",
      "0.0      2\n",
      "Name: REGIOTYP, dtype: int64\n",
      " 400.0     6\n",
      " 459.0     5\n",
      " 412.0     5\n",
      " 495.0     5\n",
      " 769.0     4\n",
      " 713.0     4\n",
      " 258.0     4\n",
      " 535.0     4\n",
      " 836.0     3\n",
      " 554.0     3\n",
      " 432.0     3\n",
      " 192.0     3\n",
      " 519.0     3\n",
      " 667.0     3\n",
      " 564.0     3\n",
      " 241.0     3\n",
      " 761.0     3\n",
      " 600.0     3\n",
      " 279.0     3\n",
      " 389.0     3\n",
      " 646.0     3\n",
      " 423.0     3\n",
      " 434.0     3\n",
      " 746.0     3\n",
      " 473.0     3\n",
      " 310.0     3\n",
      " 227.0     3\n",
      " 278.0     3\n",
      " 229.0     3\n",
      " 677.0     3\n",
      "          ..\n",
      " 297.0     1\n",
      " 770.0     1\n",
      " 191.0     1\n",
      " 477.0     1\n",
      " 628.0     1\n",
      " 239.0     1\n",
      " 620.0     1\n",
      " 413.0     1\n",
      " 197.0     1\n",
      " 964.0     1\n",
      " 1098.0    1\n",
      " 729.0     1\n",
      " 1295.0    1\n",
      " 1065.0    1\n",
      " 277.0     1\n",
      " 756.0     1\n",
      " 334.0     1\n",
      " 597.0     1\n",
      " 497.0     1\n",
      " 948.0     1\n",
      " 637.0     1\n",
      " 167.0     1\n",
      " 658.0     1\n",
      " 219.0     1\n",
      "-197.0     1\n",
      " 262.0     1\n",
      " 937.0     1\n",
      " 233.0     1\n",
      " 598.0     1\n",
      " 463.0     1\n",
      "Name: KBA13_ANZAHL_PKW, Length: 466, dtype: int64\n",
      "1.0    426\n",
      "2.0    207\n",
      "3.0     25\n",
      "0.0     13\n",
      "Name: PLZ8_ANTG1, dtype: int64\n",
      "4.0    343\n",
      "3.0    285\n",
      "2.0     38\n",
      "5.0      5\n",
      "Name: PLZ8_ANTG2, dtype: int64\n",
      "3.0    399\n",
      "2.0    241\n",
      "1.0     30\n",
      "4.0      1\n",
      "Name: PLZ8_ANTG3, dtype: int64\n",
      "1.0    351\n",
      "2.0    304\n",
      "0.0     16\n",
      "Name: PLZ8_ANTG4, dtype: int64\n",
      "4.0    268\n",
      "3.0    153\n",
      "2.0    152\n",
      "5.0     74\n",
      "1.0     24\n",
      "Name: PLZ8_BAUMAX, dtype: int64\n",
      "4.0    280\n",
      "3.0    244\n",
      "5.0    116\n",
      "2.0     26\n",
      "6.0      5\n",
      "Name: PLZ8_HHZ, dtype: int64\n",
      "3.0    259\n",
      "2.0    257\n",
      "4.0     84\n",
      "1.0     58\n",
      "5.0     13\n",
      "Name: PLZ8_GBZ, dtype: int64\n",
      "4.0    356\n",
      "3.0    275\n",
      "2.0     28\n",
      "5.0     12\n",
      "Name: ARBEIT, dtype: int64\n",
      "8.0    219\n",
      "7.0    163\n",
      "9.0    120\n",
      "6.0    100\n",
      "5.0     42\n",
      "4.0     21\n",
      "3.0      6\n",
      "Name: ORTSGR_KLS9, dtype: int64\n",
      "4.0    360\n",
      "3.0    209\n",
      "5.0     58\n",
      "2.0     37\n",
      "1.0      5\n",
      "6.0      2\n",
      "Name: RELAT_AB, dtype: int64\n",
      "-0.0    648\n",
      " 1.0     21\n",
      "-1.0      2\n",
      "Name: CJT_GESAMTTYP_1.0, dtype: int64\n",
      "-0.0    630\n",
      " 1.0     40\n",
      "-1.0      1\n",
      "Name: CJT_GESAMTTYP_2.0, dtype: int64\n",
      "0.0    501\n",
      "1.0    170\n",
      "Name: CJT_GESAMTTYP_3.0, dtype: int64\n",
      "0.0    478\n",
      "1.0    193\n",
      "Name: CJT_GESAMTTYP_4.0, dtype: int64\n",
      "0.0    559\n",
      "1.0    112\n",
      "Name: CJT_GESAMTTYP_5.0, dtype: int64\n",
      "0.0    558\n",
      "1.0    113\n",
      "Name: CJT_GESAMTTYP_6.0, dtype: int64\n",
      "1.0    568\n",
      "0.0    103\n",
      "Name: FINANZTYP_1.0, dtype: int64\n",
      "-0.0    670\n",
      " 1.0      1\n",
      "Name: FINANZTYP_2.0, dtype: int64\n",
      "0.0    670\n",
      "1.0      1\n",
      "Name: FINANZTYP_3.0, dtype: int64\n",
      " 0.0    603\n",
      " 1.0     67\n",
      "-1.0      1\n",
      "Name: FINANZTYP_4.0, dtype: int64\n",
      "0.0    671\n",
      "Name: FINANZTYP_5.0, dtype: int64\n",
      "-0.0    614\n",
      " 1.0     57\n",
      "Name: FINANZTYP_6.0, dtype: int64\n",
      " 1.0    382\n",
      "-0.0    287\n",
      "-1.0      2\n",
      "Name: LP_FAMILIE_GROB_1.0, dtype: int64\n",
      "-0.0    590\n",
      " 1.0     81\n",
      "Name: LP_FAMILIE_GROB_2.0, dtype: int64\n",
      "0.0    646\n",
      "1.0     25\n",
      "Name: LP_FAMILIE_GROB_3.0, dtype: int64\n",
      "0.0    618\n",
      "1.0     53\n",
      "Name: LP_FAMILIE_GROB_4.0, dtype: int64\n",
      "0.0    539\n",
      "1.0    131\n",
      "2.0      1\n",
      "Name: LP_FAMILIE_GROB_5.0, dtype: int64\n",
      " 1.0    603\n",
      "-0.0     67\n",
      "-1.0      1\n",
      "Name: LP_STATUS_GROB_1.0, dtype: int64\n",
      "-0.0    609\n",
      " 1.0     62\n",
      "Name: LP_STATUS_GROB_2.0, dtype: int64\n",
      "0.0    654\n",
      "1.0     15\n",
      "2.0      2\n",
      "Name: LP_STATUS_GROB_3.0, dtype: int64\n",
      "-0.0    663\n",
      "-1.0      5\n",
      " 1.0      3\n",
      "Name: LP_STATUS_GROB_4.0, dtype: int64\n",
      "-0.0    668\n",
      "-1.0      2\n",
      " 1.0      1\n",
      "Name: LP_STATUS_GROB_5.0, dtype: int64\n",
      " 1.0    554\n",
      "-0.0    117\n",
      "Name: NATIONALITAET_KZ_1.0, dtype: int64\n",
      "0.0    621\n",
      "1.0     50\n",
      "Name: NATIONALITAET_KZ_2.0, dtype: int64\n",
      "-0.0    621\n",
      " 1.0     50\n",
      "Name: NATIONALITAET_KZ_3.0, dtype: int64\n",
      "-0.0    623\n",
      " 1.0     46\n",
      "-1.0      2\n",
      "Name: SHOPPER_TYP_0.0, dtype: int64\n",
      "0.0    559\n",
      "1.0    112\n",
      "Name: SHOPPER_TYP_1.0, dtype: int64\n",
      "1.0    337\n",
      "0.0    334\n",
      "Name: SHOPPER_TYP_2.0, dtype: int64\n",
      "-0.0    511\n",
      " 1.0    160\n",
      "Name: SHOPPER_TYP_3.0, dtype: int64\n",
      "-0.0    660\n",
      "-1.0      7\n",
      " 1.0      4\n",
      "Name: ZABEOTYP_1.0, dtype: int64\n",
      "0.0    663\n",
      "1.0      8\n",
      "Name: ZABEOTYP_2.0, dtype: int64\n",
      "0.0    652\n",
      "1.0     19\n",
      "Name: ZABEOTYP_3.0, dtype: int64\n",
      "0.0    355\n",
      "1.0    316\n",
      "Name: ZABEOTYP_4.0, dtype: int64\n",
      "0.0    378\n",
      "1.0    293\n",
      "Name: ZABEOTYP_5.0, dtype: int64\n",
      "-0.0    669\n",
      " 1.0      2\n",
      "Name: ZABEOTYP_6.0, dtype: int64\n",
      "0.0    537\n",
      "1.0    134\n",
      "Name: OST_WEST_KZ_O, dtype: int64\n",
      "1.0    537\n",
      "0.0    134\n",
      "Name: OST_WEST_KZ_W, dtype: int64\n",
      "0.0    671\n",
      "Name: Decade_40s, dtype: int64\n",
      "0.0    669\n",
      "1.0      2\n",
      "Name: Decade_50s, dtype: int64\n",
      "-0.0    669\n",
      " 1.0      2\n",
      "Name: Decade_60s, dtype: int64\n",
      "0.0    628\n",
      "1.0     43\n",
      "Name: Decade_70s, dtype: int64\n",
      "0.0    671\n",
      "Name: Decade_80s, dtype: int64\n",
      "1.0    622\n",
      "0.0     49\n",
      "Name: Decade_90s, dtype: int64\n",
      "0.0    607\n",
      "1.0     64\n",
      "Name: Movement_Avantgarde, dtype: int64\n",
      "1.0    607\n",
      "0.0     64\n",
      "Name: Movement_Mainstream, dtype: int64\n",
      "-0.0    671\n",
      "Name: Nation_E, dtype: int64\n",
      "1.0    671\n",
      "Name: Nation_E+W, dtype: int64\n",
      "0.0    671\n",
      "Name: Nation_W, dtype: int64\n",
      "0.0    598\n",
      "1.0     73\n",
      "Name: Wealth_Comfortable Households, dtype: int64\n",
      "0.0    415\n",
      "1.0    256\n",
      "Name: Wealth_Less Affluent Households, dtype: int64\n",
      "-0.0    365\n",
      " 1.0    306\n",
      "Name: Wealth_Poorer Households, dtype: int64\n",
      "-0.0    644\n",
      " 1.0     26\n",
      "-1.0      1\n",
      "Name: Wealth_Prosperous Households, dtype: int64\n",
      "-0.0    658\n",
      " 1.0     13\n",
      "Name: Wealth_Wealthy Households, dtype: int64\n",
      "-0.0    606\n",
      " 1.0     65\n",
      "Name: Life_Stage_Elders In Retirement, dtype: int64\n",
      "-0.0    631\n",
      " 1.0     39\n",
      "-1.0      1\n",
      "Name: Life_Stage_Families With School Age Childre, dtype: int64\n",
      "0.0    546\n",
      "1.0    125\n",
      "Name: Life_Stage_Older Families &  Mature Couples, dtype: int64\n",
      " 1.0    419\n",
      " 0.0    251\n",
      "-1.0      1\n",
      "Name: Life_Stage_Pre-Family Couples & Singles, dtype: int64\n",
      "-0.0    645\n",
      " 1.0     25\n",
      "-1.0      1\n",
      "Name: Life_Stage_Young Couples With Children, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in c_u_df.columns:\n",
    "    print(c_u_df[col].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\n",
    "\n",
    "#### <font color = darkblue> Overrepresented Cluster</font>\n",
    "**Most people of this cluster have the following characteristics:**\n",
    "- males aged more than 45 years old\n",
    "- very high money-savers, investors, and inconspicuous\n",
    "- average to high home ownership\n",
    "- member of green avantgarde\n",
    "- their Health typologies are sanitary affine or critical reserved\n",
    "- very high estimated household net income\n",
    "- high share of 1-2 family homes\n",
    "- high share of 1-2 family homes\n",
    "- high share of 3-5 family homes\n",
    "- 5 - 10 km to city center\n",
    "- low movement\n",
    "- conservatives neighborhood\n",
    "- average share of unemployment in community\n",
    "\n",
    "\n",
    "#### <font color = darkblue> underrepresented Cluster</font>\n",
    "**Most people of this cluster have the following characteristics:**\n",
    "- females aged more than 30 years old\n",
    "- very high low financial interest and be prepared\n",
    "- not a member of green avantgarde\n",
    "- their Health typologies are sanitary affine or jaunty hedonists\n",
    "- lower estimated household net income\n",
    "- average share of 3-5 family homes\n",
    "- very high share of 3-5 family homes\n",
    "- high share of 6-10 family homes\n",
    "- less than 3 km to city center\n",
    "- high movement\n",
    "- traditional workers neighborhood\n",
    "- high Share of unemployment in community\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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