Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, I 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 I use has been provided by Bertelsmann Arvato Analytics.\n"
   ]
  },
  {
   "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 collections\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import 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",
    "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."
   ]
  },
  {
   "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/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</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>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</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>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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        -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",
       "\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",
       "\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",
       "\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": 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",
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>count</th>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>886367.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>774706.000000</td>\n",
       "      <td>794005.000000</td>\n",
       "      <td>794005.000000</td>\n",
       "      <td>794005.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.358435</td>\n",
       "      <td>2.777398</td>\n",
       "      <td>1.522098</td>\n",
       "      <td>3.632838</td>\n",
       "      <td>3.074528</td>\n",
       "      <td>2.821039</td>\n",
       "      <td>3.401106</td>\n",
       "      <td>3.033328</td>\n",
       "      <td>2.874167</td>\n",
       "      <td>3.075121</td>\n",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891221, 85)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\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. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "def define_missing(string):\n",
    "    def_missing = string.replace('[', '').replace(']', '').split(',')\n",
    "\n",
    "    if(def_missing != None):\n",
    "        for i in range(len(def_missing)):\n",
    "            try: def_missing[i] = int(def_missing[i])\n",
    "            except ValueError: continue #we are trying to convert string to int ie int('XX') -> error  \n",
    "\n",
    "    return def_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    677503\n",
       " 2     98472\n",
       " 1     79802\n",
       " 3     27104\n",
       " 0      8340\n",
       "Name: AGER_TYP, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(azdias.AGER_TYP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "  </tbody>\n",
       "</table>\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]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total NaN: 4896838 \n",
      "\n",
      "NaN per column\n",
      " count        85.000000\n",
      "mean      57609.858824\n",
      "std       78323.462873\n",
      "min           0.000000\n",
      "25%           0.000000\n",
      "50%       18348.000000\n",
      "75%       98979.000000\n",
      "max      584612.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#num previously missing values from whole df\n",
    "print('total NaN: %s \\n'%sum(azdias.isnull().sum()))\n",
    "print('NaN per column\\n %s'%azdias.isnull().sum().describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Another way to do the above to replace all the missing or incomplete \n",
    "#in azdias with NaN based on the info in feat_info\n",
    "for col in azdias:\n",
    "    missing_rep = define_missing(feat_info[feat_info['attribute']==col]['missing_or_unknown'].values[0])\n",
    "    \n",
    "    if(missing_rep[0] == ''): \n",
    "        azdias[col] = azdias[col].replace('', np.nan)\n",
    "        i+=1\n",
    "        continue\n",
    "    i=0\n",
    "    replace_with = list(np.nan for i in missing_rep) #create list of None same length as missing_rep\n",
    "    azdias[col] = azdias[col].replace(missing_rep, replace_with);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to clean missing data\n",
    "```python\n",
    "# turn missing_or_unknown to list \n",
    "feat_info['missing_or_unknown'] = feat_info['missing_or_unknown'].apply(lambda x: x[1:-1].split(','))\n",
    "\n",
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "for attrib, missing_values in zip(feat_info['attribute'], feat_info['missing_or_unknown']):\n",
    "    if missing_values[0] != '':\n",
    "        for value in missing_values:\n",
    "            if value.isnumeric() or value.lstrip('-').isnumeric():\n",
    "                value = int(value)\n",
    "            azdias.loc[azdias[attrib] == value, attrib] = np.nan\n",
    "```"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[685843   2881      0   4854      0      0      0      0      0      0\n",
      "      0 392318   4854      0 111196  97632  94572  77792  77792   4854\n",
      "   4854 108315 108164   4854      0      0      0      0      0      0\n",
      "      0      0      0      0      0      0      0      0 111196  73499\n",
      " 889061 111196      0 310267  73499  73499  18348 584612 147988  73499\n",
      "  99611  97008  93148  73969  93148  93148  93148  99352  99352  99352\n",
      " 133324 133324 133324 133324 476524 133324  93740  93740  93740  93155\n",
      " 158064 133324   4854 158064 105800 116515 116515 116515 116515 116515\n",
      " 116515 116515  97375  97274  97375]\n"
     ]
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "null_count = np.asarray(azdias.isnull().sum().tolist())\n",
    "print(null_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        85.000000\n",
       "mean      98516.811765\n",
       "std      146604.203317\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%       93148.000000\n",
       "75%      116515.000000\n",
       "max      889061.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "azdias.isnull().sum().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 24),\n",
       " (116515, 7),\n",
       " (4854, 6),\n",
       " (133324, 6),\n",
       " (73499, 4),\n",
       " (93148, 4),\n",
       " (111196, 3),\n",
       " (99352, 3),\n",
       " (93740, 3),\n",
       " (77792, 2),\n",
       " (158064, 2),\n",
       " (97375, 2)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most common # of NaN: from `https://docs.python.org/2/library/collections.html#collections.Counter`\n",
    "collections.Counter(null_count).most_common(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61862cb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(null_count, bins=30)\n",
    "plt.xlabel(\"# of NaN\")\n",
    "plt.ylabel('# of features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num null > 200,000: 6  200k-150k: 2  150k-100k: 20  100k-50k: 25\n"
     ]
    }
   ],
   "source": [
    "i_5, i_10, i_15, i_20 = 0,0,0,0\n",
    "for value in null_count:\n",
    "    if(value > 200000):\n",
    "        i_20+=1\n",
    "    elif(value > 150000):\n",
    "        i_15 +=1\n",
    "    elif(value>100000):\n",
    "        i_10+=1\n",
    "    elif(value>50000):\n",
    "        i_5+=1\n",
    "\n",
    "print('num null > 200,000: %s' % i_20, ' 200k-150k: %s'%i_15,' 150k-100k: %s'%i_10, ' 100k-50k: %s'%i_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "labels_to_clean = []\n",
    "for col in azdias:\n",
    "    if(azdias[col].isnull().sum()>200000):\n",
    "        labels_to_clean.append(col)\n",
    "azdias_cleaned = azdias.drop(labels_to_clean, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891221, 79)\n",
      "  attribute information_level         type\n",
      "0  AGER_TYP            person  categorical\n",
      "\n",
      "\n",
      "      attribute information_level     type\n",
      "11  GEBURTSJAHR            person  numeric\n",
      "\n",
      "\n",
      "   attribute information_level         type\n",
      "40  TITEL_KZ            person  categorical\n",
      "\n",
      "\n",
      "   attribute information_level      type\n",
      "43  ALTER_HH         household  interval\n",
      "\n",
      "\n",
      "       attribute information_level         type\n",
      "47  KK_KUNDENTYP         household  categorical\n",
      "\n",
      "\n",
      "       attribute information_level   type\n",
      "64  KBA05_BAUMAX     microcell_rr3  mixed\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(azdias_cleaned.shape)\n",
    "\n",
    "for label in labels_to_clean:\n",
    "    print(feat_info[feat_info['attribute'] == label][['attribute', 'information_level', 'type']])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "There were some columns with significant amounts of missing data. Out of the total of 891,221 observations, 6 of the features had more than 200,000 missing values. I decided that these were the outliers since close to 25% of the data was missing. Most of these were supposed to hold information on a personal or household level. Possibly people were uncomfortable disclosing this kind of information. The columns excluded from the dataset are shown above.\n",
    "\n",
    "It is also interesting to note that many columns had identical numbers of missing values. The most common was 116515 missing values which occurred 7 time, including the following key/value paris of (#NaN,#occurences): `(4854, 6), (133324, 6), (73499, 4), (93148, 4), (111196, 3), (99352, 3), (93740, 3), (77792, 2), (158064, 2), (97375, 2)`. Since so many features had identical numbers of missing values it is most likely that it was the same entries (rows) that didn't fill in particular information (perhaps because it was sensitive). This might also mean that these columns contain similar data and it might not be necessary to keep all the features in this case.\n",
    "\n",
    "For example, the 7 features that all have 116515 are all from the `PLZ8 macro_cell` features. This is all to do with information regarding the building types and family houses of the region a person lives in. Maybe all the people with mssing data live in the same region for which there is no data.\n",
    "\n",
    "The features that each have 4845 missing values mostly come from different feature groupings/categories: `CJT_GESAMTTYP`, `GFK_URLAUBERTYP`, `RETOURTYP_BK_S`, `ONLINE_AFFINITAET`, apart from `LP_STATUS_FEIN` and `LP_STATUS_GROB` which are the same feature on a fine and rough scale. Since they are mostly different features describing different things maybe certain people here didn't feel comfortable disclosing this information.\n",
    "\n",
    "This means that there are feasibly two reasons columns would have identical numer of NaN's:\n",
    "1. they have similar meaning and if it is hard to get data on one then it is hard to get data for all features in that category (like `RR3 micro-cell`)\n",
    "\n",
    "2. certain people didn't feel comfortable disclosing certain information and these people are likely to be more withdrawn in answering among various features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891221.000000\n",
       "mean          9.396018\n",
       "std          13.971829\n",
       "min           0.000000\n",
       "25%           3.000000\n",
       "50%           4.000000\n",
       "75%           6.000000\n",
       "max          54.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "azdias.isnull().sum(axis=1).describe() #count isnull along axis=1 --> along each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAEKCAYAAADghJ9DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xu0JGV57/HvT0ZUVARlUORyQDMaLzGjTtAcoxLxMmAiYEBhnQhRDIKg0cQc4bgSjMZEo8aoUYwKAmq4CCqjgkiIqMmRyyAIAyqMhIQRwkUUTUj0AM/5o2pLs+murp7pns3MfD9r9drVb79PvW91P7u7nl3VtVNVSJIkSZKm434LPQFJkiRJ2phYZEmSJEnSFFlkSZIkSdIUWWRJkiRJ0hRZZEmSJEnSFFlkSZIkSdIUWWRJkiRJ0hRZZEmSJEnSFFlkSZIkSdIULVroCdxXbLPNNrXzzjsv9DQkSZIk3UddfPHFt1TV4nH9LLJaO++8MytXrlzoaUiSJEm6j0ryr336ebqgJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRN0aKFnoDW3r9/+OjefR/12j+b4UwkSZIkzfFIliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE3RzIqsJMcluSnJqoG2U5Jc2t6uTXJp275zkv8aeOwjAzFPT3J5ktVJPpAkbfvDk5yT5Or259Zte9p+q5NcluRps9pGSZIkSZpvlkeyjgeWDzZU1curamlVLQVOBz478PD35x6rqkMH2o8BDgGWtLe5dR4JnFtVS4Bz2/sAewz0PaSNlyRJkqT1YmZFVlV9Hbh12GPt0aiXASd1rSPJdsCWVfXNqirgRGDv9uG9gBPa5RPmtZ9YjfOBrdr1SJIkSdLMLdR3sp4N3FhVVw+07ZLkkiRfS/Lstm17YM1AnzVtG8Ajq+oGgPbntgMx142IkSRJkqSZWrRA4x7APY9i3QDsVFU/TPJ04PNJngRkSGyNWXfvmCSH0JxSyE477TR20pIkSZI0zno/kpVkEfBS4JS5tqr6WVX9sF2+GPg+8Diao1A7DITvAFzfLt84dxpg+/Omtn0NsOOImHuoqo9W1bKqWrZ48eJ13TRJkiRJWpDTBZ8PfLeqfnEaYJLFSTZrlx9Dc9GKa9rTAH+a5Jnt97gOBM5ow1YAB7XLB81rP7C9yuAzgdvmTiuUJEmSpFmb5SXcTwK+CTw+yZokB7cP7c+9L3jxHOCyJN8GTgMOraq5i2YcBnwcWE1zhOustv2dwAuSXA28oL0PcCZwTdv/Y8Brp71tkiRJkjTKzL6TVVUHjGj/vSFtp9Nc0n1Y/5XAk4e0/xDYfUh7AYdPOF1JkiRJmoqFurqgJEmSJG2ULLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKZlZkJTkuyU1JVg20vTXJD5Jc2t72HHjsqCSrk3wvyYsG2pe3bauTHDnQvkuSC5JcneSUJJu37Q9o769uH995VtsoSZIkSfPN8kjW8cDyIe3vq6ql7e1MgCRPBPYHntTGfDjJZkk2Az4E7AE8ETig7QvwrnZdS4AfAQe37QcDP6qqXwLe1/aTJEmSpPViZkVWVX0duLVn972Ak6vqZ1X1L8BqYNf2trqqrqmqnwMnA3slCfA84LQ2/gRg74F1ndAunwbs3vaXJEmSpJlbiO9kHZHksvZ0wq3btu2B6wb6rGnbRrU/AvhxVd0xr/0e62ofv63tfy9JDkmyMsnKm2++ed23TJIkSdImb30XWccAjwWWAjcA723bhx1pqrVo71rXvRurPlpVy6pq2eLFi7vmLUmSJEm9rNciq6purKo7q+ou4GM0pwNCcyRqx4GuOwDXd7TfAmyVZNG89nusq338YfQ/bVGSJEmS1sl6LbKSbDdwdx9g7sqDK4D92ysD7gIsAS4ELgKWtFcS3Jzm4hgrqqqArwL7tvEHAWcMrOugdnlf4B/b/pIkSZI0c4vGd1k7SU4CdgO2SbIGOBrYLclSmtP3rgVeA1BVVyQ5FbgSuAM4vKrubNdzBHA2sBlwXFVd0Q7xZuDkJH8OXAIc27YfC3wyyWqaI1j7z2obJUmSJGm+mRVZVXXAkOZjh7TN9X8H8I4h7WcCZw5pv4a7TzccbP9vYL+JJitJkiRJU7IQVxeUJEmSpI2WRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE2RRZYkSZIkTZFFliRJkiRNkUWWJEmSJE3RooWewKbupo+8b6L+2x76xhnNRJIkSdI0eCRLkiRJkqbII1mboDV/+5qJ+u9wxN/NaCaSJEnSxscjWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFMyuykhyX5KYkqwba3p3ku0kuS/K5JFu17Tsn+a8kl7a3jwzEPD3J5UlWJ/lAkrTtD09yTpKr259bt+1p+61ux3narLZRkiRJkuab5ZGs44Hl89rOAZ5cVU8BrgKOGnjs+1W1tL0dOtB+DHAIsKS9za3zSODcqloCnNveB9hjoO8hbbwkSZIkrRczK7Kq6uvArfPavlJVd7R3zwd26FpHku2ALavqm1VVwInA3u3DewEntMsnzGs/sRrnA1u165EkSZKkmVvI72S9Cjhr4P4uSS5J8rUkz27btgfWDPRZ07YBPLKqbgBof247EHPdiJh7SHJIkpVJVt58883rtjWSJEmSxAIVWUneAtwBfLptugHYqaqeCvwh8PdJtgQyJLzGrb5vTFV9tKqWVdWyxYsX95u8JEmSJHVYtL4HTHIQ8FvA7u0pgFTVz4CftcsXJ/k+8Diao1CDpxTuAFzfLt+YZLuquqE9HfCmtn0NsOOIGEmSJEmaqfV6JCvJcuDNwEuq6vaB9sVJNmuXH0Nz0Ypr2tMAf5rkme1VBQ8EzmjDVgAHtcsHzWs/sL3K4DOB2+ZOK5QkSZKkWZvZkawkJwG7AdskWQMcTXM1wQcA57RXYj+/vZLgc4C3JbkDuBM4tKrmLppxGM2VCh9E8x2uue9xvRM4NcnBwL8B+7XtZwJ7AquB24FXzmobJUmSJGm+mRVZVXXAkOZjR/Q9HTh9xGMrgScPaf8hsPuQ9gIOn2iykiRJkjQlC3l1QUmSJEna6FhkSZIkSdIUWWRJkiRJ0hRZZEmSJEnSFFlkSZIkSdIUWWRJkiRJ0hRZZEmSJEnSFFlkSZIkSdIUWWRJkiRJ0hRZZEmSJEnSFPUqspKc26dNkiRJkjZ1i7oeTPJAYAtgmyRbA2kf2hJ49Iznpvug1X+7V+++v3TEGTOciSRJknTf1FlkAa8B3kBTUF3M3UXWT4APzXBekiRJkrRB6iyyqur9wPuTvK6qPrie5iRJkiRJG6xxR7IAqKoPJvmfwM6DMVV14ozmJUmSJEkbpF5FVpJPAo8FLgXubJsLsMiSJEmSpAG9iixgGfDEqqpZTkaSJEmSNnR9/0/WKuBRs5yIJEmSJG0M+hZZ2wBXJjk7yYq527igJMcluSnJqoG2hyc5J8nV7c+t2/Yk+UCS1UkuS/K0gZiD2v5XJzlooP3pSS5vYz6QJF1jSJIkSdKs9S2y3grsDfwF8N6B2zjHA8vntR0JnFtVS4Bz2/sAewBL2tshwDHQFEzA0cAzgF2BoweKpmPavnNxy8eMIUmSJEkz1ffqgl9bm5VX1deT7DyveS9gt3b5BOA84M1t+4nt977OT7JVku3avudU1a0ASc4Blic5D9iyqr7Ztp9IUwie1TGGJEmSJM1U36sL/pTmaoIAmwP3B/6zqrZcizEfWVU3AFTVDUm2bdu3B64b6LembetqXzOkvWsMSZIkSZqpvkeyHjp4P8neNKfuTVOGDb0W7f0HTA6hOd2QnXbaaZJQSZIkSRqq73ey7qGqPg88by3HvLE9DZD2501t+xpgx4F+OwDXj2nfYUh71xjzt+OjVbWsqpYtXrx4LTdHkiRJku7Wq8hK8tKB275J3smER40GrADmrhB4EHDGQPuB7VUGnwnc1p7ydzbwwiRbtxe8eCFwdvvYT5M8s72q4IHz1jVsDEmSJEmaqb7/jPi3B5bvAK6lubhEpyQn0VyAYpska2iuEvhO4NQkBwP/BuzXdj8T2BNYDdwOvBKgqm5N8nbgorbf2+YuggEcRnMFwwfRXPDirLZ91BiSJEmSNFN9v5P1yrVZeVUdMOKh3Yf0LeDwEes5DjhuSPtK4MlD2n84bAxJkiRJmrW+pwvukORz7T8WvjHJ6Ul2GB8pSZIkSZuWvhe++ATN95weTXOZ9C+0bZIkSZKkAX2LrMVV9YmquqO9HQ94OT5JkiRJmqdvkXVLkt9Nsll7+13gh7OcmCRJkiRtiPoWWa8CXgb8O3ADsC/t1f8kSZIkSXfrewn3twMHVdWPAJI8HHgPTfElSZIkSWr1PZL1lLkCC5r/XQU8dTZTkiRJkqQNV98i635Jtp670x7J6nsUTJIkSZI2GX0LpfcC/zfJaUDRfD/rHTOblSRJkiRtoHoVWVV1YpKVwPOAAC+tqitnOjNJkiRJ2gD1PuWvLaosrCRJkiSpQ9/vZEmSJEmSerDIkiRJkqQpssiSJEmSpCmyyJIkSZKkKbLIkiRJkqQpssiSJEmSpCmyyJIkSZKkKVrvRVaSxye5dOD2kyRvSPLWJD8YaN9zIOaoJKuTfC/Jiwbal7dtq5McOdC+S5ILklyd5JQkm6/v7ZQkSZK0aVrvRVZVfa+qllbVUuDpwO3A59qH3zf3WFWdCZDkicD+wJOA5cCHk2yWZDPgQ8AewBOBA9q+AO9q17UE+BFw8PraPkmSJEmbtoU+XXB34PtV9a8dffYCTq6qn1XVvwCrgV3b2+qquqaqfg6cDOyVJMDzgNPa+BOAvWe2BZIkSZI0YKGLrP2BkwbuH5HksiTHJdm6bdseuG6gz5q2bVT7I4AfV9Ud89olSZIkaeYWrMhqvyf1EuAzbdMxwGOBpcANwHvnug4Jr7VoHzaHQ5KsTLLy5ptvnmD2kiRJkjTcQh7J2gP4VlXdCFBVN1bVnVV1F/AxmtMBoTkSteNA3A7A9R3ttwBbJVk0r/1equqjVbWsqpYtXrx4SpslSZIkaVO2kEXWAQycKphku4HH9gFWtcsrgP2TPCDJLsAS4ELgImBJeyXBzWlOPVxRVQV8Fdi3jT8IOGOmWyJJkiRJrUXju0xfki2AFwCvGWj+qyRLaU7tu3busaq6IsmpwJXAHcDhVXVnu54jgLOBzYDjquqKdl1vBk5O8ufAJcCxM98oSZIkSWKBiqyqup3mAhWDba/o6P8O4B1D2s8EzhzSfg13n26o+4DLjnnJRP2fctiKGc1EkiRJmq2FvrqgJEmSJG1ULLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKLLIkSZIkaYossiRJkiRpiiyyJEmSJGmKFqzISnJtksuTXJpkZdv28CTnJLm6/bl1254kH0iyOsllSZ42sJ6D2v5XJzlooP3p7fpXt7FZ/1spSZIkaVOz0EeyfrOqllbVsvb+kcC5VbUEOLe9D7AHsKS9HQIcA01RBhwNPAPYFTh6rjBr+xwyELd89psjSZIkaVO30EXWfHsBJ7TLJwB7D7SfWI3zga2SbAe8CDinqm6tqh8B5wDL28e2rKpvVlUBJw6sS5IkSZJmZiGLrAK+kuTiJIe0bY+sqhsA2p/btu3bA9cNxK5p27ra1wxpv4ckhyRZmWTlzTffPIVNkiRJkrSpW7SAYz+rqq5Psi1wTpLvdvQd9n2qWov2ezZUfRT4KMCyZcvu9bgkSZIkTWrBjmRV1fXtz5uAz9F8p+rG9lQ/2p83td3XADsOhO8AXD+mfYch7ZIkSZI0UwtSZCV5cJKHzi0DLwRWASuAuSsEHgSc0S6vAA5srzL4TOC29nTCs4EXJtm6veDFC4Gz28d+muSZ7VUFDxxYlyRJkiTNzEKdLvhI4HPtVdUXAX9fVV9OchFwapKDgX8D9mv7nwnsCawGbgdeCVBVtyZ5O3BR2+9tVXVru3wYcDzwIOCs9iZJkiRJM7UgRVZVXQP86pD2HwK7D2kv4PAR6zoOOG5I+0rgyes8WUmSJEn3WTf97Zd69932iBfPcCZ3u69dwl2SJEmSNmgWWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFFlmSJEmSNEWLFnoCG4ubP/KxifovPvT3ZzQTSZIkSQvJI1mSJEmSNEXrvchKsmOSryb5TpIrkvxB2/7WJD9Icml723Mg5qgkq5N8L8mLBtqXt22rkxw50L5LkguSXJ3klCSbr9+tlCRJkrSpWojTBe8A/qiqvpXkocDFSc5pH3tfVb1nsHOSJwL7A08CHg38Q5LHtQ9/CHgBsAa4KMmKqroSeFe7rpOTfAQ4GDhm5lsmSZIkbeBu/JsLJ+r/yDfsOqOZbLjW+5Gsqrqhqr7VLv8U+A6wfUfIXsDJVfWzqvoXYDWwa3tbXVXXVNXPgZOBvZIEeB5wWht/ArD3bLZGkiRJku5pQb+TlWRn4KnABW3TEUkuS3Jckq3btu2B6wbC1rRto9ofAfy4qu6Y1y5JkiRJM7dgRVaShwCnA2+oqp/QnM73WGApcAPw3rmuQ8JrLdqHzeGQJCuTrLz55psn3AJJkiRJurcFKbKS3J+mwPp0VX0WoKpurKo7q+ou4GM0pwNCcyRqx4HwHYDrO9pvAbZKsmhe+71U1UerallVLVu8ePF0Nk6SJEnSJm0hri4Y4FjgO1X11wPt2w102wdY1S6vAPZP8oAkuwBLgAuBi4Al7ZUEN6e5OMaKqirgq8C+bfxBwBmz3CZJkiRJmrMQVxd8FvAK4PIkl7Zt/wc4IMlSmlP7rgVeA1BVVyQ5FbiS5sqEh1fVnQBJjgDOBjYDjquqK9r1vRk4OcmfA5fQFHWSJEmSNHPrvciqqn9i+PemzuyIeQfwjiHtZw6Lq6pruPt0Q0mSJElabxb06oKSJEmStLGxyJIkSZKkKbLIkiRJkqQpWogLX0gTueDvfqt332e85ou/WP7ax1480TjP/f0vTdRfkiRJGsYjWZIkSZI0RR7JkoY4+9g9e/d90cEjL4w5kc98Ynnvvvu98stTGVOSJEnT55EsSZIkSZoiiyxJkiRJmiKLLEmSJEmaIr+TJU3RiuP2mKj/S1511jqP+cnjXzRR/1f83tm/WP74if1jX33g2eM7SZIkySNZkiRJkjRNFlmSJEmSNEUWWZIkSZI0RRZZkiRJkjRFXvhC0sQ+9KnJLrZx+O960QxJkrTp8EiWJEmSJE2RRZYkSZIkTZGnC0qSJM3QX3zuht59/88+2/1i+SOfvXGicQ596SMn6i9pdjbaIivJcuD9wGbAx6vqnQs8JUkbmFd+bnnvvp/Y58sznIkkSdqQbJRFVpLNgA8BLwDWABclWVFVVy7szCS996T+F834owPuvmDG20+Z7GIbf/LyJvaPT+tfKAG8e991L5b2OOPlE/U/a69T1nlMSbP1+s9dN1H/D+yz44xm0s9Jp9/cu+8Bv7P4F8srPnPLROO8ZL9tJuovbSo2yiIL2BVYXVXXACQ5GdgLsMiSdJ+2x+f/oHffs/Z+/y+W9/z8n0w0zpl7v/3u2M/9Zf+4fY6aaBxJm45/OKl/Yff8AxaP7yRtwDbWImt7YPBPTmuAZyzQXCRpo/Tiz75vov5feukbB2I/PEHcaycaR+ry8tOv7t33lN9ZMsOZSP38+3v75yzAo/5ow8zbmz74DxP13/Z1z5/RTKYjVbXQc5i6JPsBL6qqV7f3XwHsWlWvm9fvEOCQ9u7jge+NWOU2wGTHz9c91jEdc13iHNMxHXO2cY7pmI452zjHdMz76pj/o6rGH4qtqo3uBvw6cPbA/aOAo9ZhfSvXd6xjOuaGNlfHdMwNccwNaa6O6Zgb4pgb0lwd0zGnMebcbWP9P1kXAUuS7JJkc2B/YMUCz0mSJEnSJmCj/E5WVd2R5AjgbJpLuB9XVVcs8LQkSZIkbQI2yiILoKrOBM6c0uo+ugCxjumY6xLnmI7pmLONc0zHdMzZxjmmY26oYwIb6YUvJEmSJGmhbKzfyZIkSZKkBWGRNUaS5Um+l2R1kiMniDsuyU1JVk043o5JvprkO0muSNLrP5MmeWCSC5N8u437swnH3SzJJUm+OGHctUkuT3JpkpUTxm6V5LQk322399d7xDy+HWvu9pMkb5hgzDe2z8+qJCcleWDPuD9oY64YN96w1z7Jw5Ock+Tq9ufWPeP2a8e8K8myCcd8d/vcXpbkc0m26hn39jbm0iRfSfLovmMOPPamJJVkm55jvjXJDwZe1z0nGTPJ69rf0yuS/FXPMU8ZGO/aJJf2HTPJ0iTnz+V9kl17xv1qkm+2vzNfSLLlkLih7wHjcqgjbmwOdcR25lBH3NgcGhU78PjQHOoYc2wOdY3ZlUMdY47NoY7YzhzqiOuTQ0M/D9JcDOqCNodOSXNhqD5xR6T5DBz6Oz0m9tPt87oqze/E/XvGHdu2XZbmc+IhfcccePyDSf5jgrken+RfBl7TpRPEJsk7klzVvmav7xn3jYHxrk/y+QnG3D3Jt9rYf0rySz3jntfGrUpyQpKhXx3JvP2CcfkzJnZsDo2I68yfMbFjc2hY3ED70PzpGG9s/nTEduZPR9zY/OmI7cyfjri++XOv/cP02BfqiO3zWTYsru8+zcj92XTs03Ra18sTbsw3motmfB94DLA58G3giT1jnwM8DVg14ZjbAU9rlx8KXNVnTCDAQ9rl+wMXAM+cYNw/BP4e+OKE870W2GYtn98TgFe3y5sDW63F6/PvNP+voE//7YF/AR7U3j8V+L0ecU8GVgFb0HyP8R+AJZO89sBfAUe2y0cC7+oZ9wSa/+F2HrBswjFfCCxql981wZhbDiy/HvjIJDkO7Ehz0Zl/HZYbI8Z8K/CmHq/FsNjfbF+TB7T3t+0714HH3wv86QRjfgXYo13eEzivZ9xFwHPb5VcBbx8SN/Q9YFwOdcSNzaGO2M4c6ogbm0OjYsflUMeYY3OoI7Yzh7rmOi6HOsbszKGOuD45NPTzgOY9b/+2/SPAYT3jngrsTMf7fUfsnu1jAU6aYMzBHPpr2tzvE9veXwZ8EviPCeZ6PLDvmBwaFftK4ETgfiNyaOxnNHA6cOAEY14FPKFtfy1wfI+4/wlcBzyubX8bcPCIbb3HfsG4/BkTOzaHRsR15s+Y2LE5NCxuXP50jDc2fzpiO/Ona67j8qdjzM78GRZHc3Cmb/7c67Wmx75QR2yfz7JhcX33aYbmJmP2abpuHsnqtiuwuqquqaqfAycDe/UJrKqvA7dOOmBV3VBV32qXfwp8h6Y4GBdXVTX3F5f7t7fqM2aSHYAXAx+fdL5rK81fX58DHAtQVT+vqh9PuJrdge9X1b9OELMIeFD7l5ctgOt7xDwBOL+qbq+qO4CvAfuM6jzitd+Lpqik/bl3n7iq+k5Vjfon2eNiv9LOF+B8YIeecT8ZuPtgRuRRR46/D/jfaxE31ojYw4B3VtXP2j43TTJmkgAvo/kA7ztmAXNHEB7GkDwaEfd44Ovt8jnA7wyJG/Ue0JlDo+L65FBHbGcOdcSNzaEx73Ujc2ht3yPHxHbm0Lgxu3KoI7Yzhzri+uTQqM+D5wGnte3DcmhoXFVdUlXXzh+nZ+yZ7WMFXMi9c2hU3E/gF8/tgxieC0Njk2wGvJsmh3rPtWv7esQeBrytqu5q+83Poc4xkzyU5vW515GIjthxOTQs7k7gZ1V1Vds+NIfm7xe0r0Nn/oyKbecyNodGxHXmz5jYsTk0LG5c/oyK62tEbGf+jBuzK386Ysd+jg2JewQ98qfD2H2hUfp8lo2I67VP06Fzn6aLRVa37Wkq9jlr6PlhPg1Jdqb5688FPftvluZ0lZuAc6qqVxzwNzQJdNdaTLOAryS5OMkhE8Q9BrgZ+ER7GPrjSR484dj7M2LHeOhEq34AvAf4N+AG4Laq+kqP0FXAc5I8IskWNH9Z23HCuT6yqm5o53EDsO2E8evqVcBZfTu3py1cB/wv4E8niHsJ8IOq+vbkU+SI9pD+caNOIRjhccCz05zG8rUkvzbhuM8GbqyqqyeIeQPw7vY5eg/NPzzvYxXwknZ5P8bk0bz3gN45NOl7R8/YzhyaHzdJDg3GTpJDQ+baO4fmxfbOoRHPT68cmhfbO4fmxfXKofmfBzRnZfx4oGge+nm2Dp8jnbFpTvN6BfDlvnFJPkFztsIvAx+cYMwjgBVzvy8TzvUdbQ69L8kDJoh9LPDyNKd+npVkySTPD80f7s6dt0M4LvbVwJlJ1tA8t+8cF0dTqNx/4HSrfRmeQ/P3Cx5Bj/wZEdvXyLiu/OmK7ZFDw+LG5k/HXMfmz4jYsfnTMSaMyZ8RsWPzZ0jcLfTLHxi+f9j3c2xt9y2HxvX8PLpX7Dru01hkjZEhbRNXsms1cHPu8OnAGzp+ae6hqu6sqqU0f+nZNcmTe4zzW8BNVXXxWk71WVX1NGAP4PAkz+kZt4jmVKpjquqpwH/SHDruJc254C8BPjNBzNY0f0XZBXg08OAkvzsurqpFttiSAAAKqUlEQVS+Q3Oq1Dk0b/DfBu7oDLoPSfIWmvl+um9MVb2lqnZsY47oOc4WwFuYoCgbcAzNh8xSmgL4vRPELgK2pjl95o+BU9u/XPZ1ABMU663DgDe2z9EbaY/I9vAqmt+Ti2lOAfv5qI5r8x6wLnFdseNyaFhc3xwajG3H6JVDQ8bsnUNDYnvlUMdzOzaHhsT2yqEhcb1yaP7nAc0R+Xt1GxfX53OkZ+yHga9X1Tf6xlXVK2neq78DvLznmM+hKT6HFmVjxjyKZmf814CHA2+eIPYBwH9X1TLgY8BxEz4/nTk0IvaNwJ5VtQPwCZpT4jrjgCfR/IHyfUkuBH7KvM+zEfsFvfaH1nafokfcyPzpiu3KoWFxab6v05k/HeONzZ+O2M786fH8jMyfjtjO/BkW1x5R7MyfAWu7f7gusUPjen4eDYtd232aRk1wbuGmdgN+HTh74P5RwFETxO/MhN/JauPuT3P+5x+uw9yPpt93XP6S5i9S19L8ted24FNrOeZb+4zZ9n0UcO3A/WcDX5pgrL2Ar0w4v/2AYwfuHwh8eC228y+A107y2gPfA7Zrl7cDvjdJzjDmO1mjYoGDgG8CW6xNngL/oyuHB2OBX6H5a+m17e0OmqOGj5pwzM7fmyHP7ZeB3Qbufx9Y3PP5WQTcCOww4et5G/ziX2AE+MlaPLePAy4c8di93gP65NCwuL45NCp2XA51jTkuh+bH9s2hHmN2Pe/DntuxOdTx/IzNoRFjjs2hHts5Mofm9Tuapni8hbu/Y3ePz7eOuDcN3L+Wnt9JGIxtlz9P+32TScZs255Lj+8Lt7FH03yWzeXQXTSn/U865m4TjPkm4LvAzgOv520TPD+PAH4IPHCC5/aPaU6Xn2vbCbhyLbbzhcCp89qG7Rd8uk/+jIj91MDjQ3OoK25c/owbc1QOjYj70bj86Tne0PwZFTsuf8Y8P535MyL2S+Pyp+d23it/RszhrTS/J732hYbFDtw/jzH7Q8Pi2rbOfZp5sX9Cz32akevp23FTvNF8eF5Dc+Rj7sIXT5ogfuc+L+a8mNB8+fFvJoxbTHvhCJpzj78B/NaE6xj6ptDR/8HAQweW/y+wfIL4bwCPb5ffCrx7gtiTgVdOuH3PAK6g+S5WaM4Hfl3P2G3bnzvRvBluPclrT3N+9+CXPf9qkpzp86YyZMzlwJUMKTjGxC0ZWH4dcFrf2HmPXcvoL8nPH3O7geU3AidPMN9Dac5lh2an8zranddxc22fo6/1eP3nj/kd2p1ymu8GXtwzbi6P7kfze/6qITFD3wPG5dCouD451DFmZw51xI3NoXHzHZVDHWOOzaGO2M4c6prruBzqGLMzhzri+uTQ0M8DmiP/gxcueG2fuK7Xo8eYr6b5bHjQBHG/DfzSwPPwHuA9fcec12fYhS9GzXW7gTH/huZ7en1j3zn3WtB8ll7Ud65t/p3QkUOjxryFuy9AcDBwes+4uRx6AHAu8LyOsXfj7gsldOZPV2yfHBoxZmf+jIptX8OxOdQ111H50zHXsfnTEduZP11zHZc/I56fRePyp2OuY/OHEfuH9NgXGhU78Ph5DPks6xizz+fR2P3ZPrl7r/VO0nlTvNF8/+Yqmr9svmWCuJNoTln5fzR/CRh69ZUhcb9Bcwj+MuDS9rZnj7inAJe0casYcaW0MesY+ibT0f8xNIXnt2mKl97PTxu/FFjZzvnzjClcBuK2oPmrzcPWYhv/jKZIWkVz5aAH9Iz7Bs3O5reB3Sd97Wn+0nQucHX78+E94/Zpl39G89fyoX95HhG7mmZncS6Phl3hbVjc6e3zcxnwBZoLGUyc46PekEaM+Ung8nbMFQzsMPeI3ZzmL4GrgG8x/A1/6FxprgZ16Fq8nr8BXNzmwwXA03vG/QHN+8lVNB+qw4rBoe8B43KoI25sDnXEduZQR9zYHBoVOy6HOsYcm0MdsZ051DXXcTnUMWZnDnXE9cmhoZ8HNO/ZF7av62eY9/7XEff6NofuoPly/McnGPMOms/PuW3403FxNAXkP7ev5yqaoyhb9h1zXp9hRdaouf7jwJifor0yX8/YrWiODlxOc+T3V/vOlWanceQfKDvG3Kcd79vtOh7TM+7dNEX+92hOQ+16/9uNu3euO/NnTOzYHBoR15k/o2L75tCwMcflT8dcx+ZPR2xn/nTNdVz+dIzZmT8dcWPzhxH7h/TbFxoV2/lZ1hHX5/No7P4sa1FkzZ2qIEmSJEmaAi98IUmSJElTZJElSZIkSVNkkSVJkiRJU2SRJUmSJElTZJElSZIkSVNkkSVJWjBJ/jLJbkn2TnLkOq7rzCRbTRhzaJID12XctZXkvCTLFmJsSdJsWWRJkhbSM2j+T9Rzaf4f3Vqrqj2r6scTxnykqk5cl3ElSZrPIkuStN4leXeSy4Bfo/nnm68Gjknyp0P6Hp/kmCRfTXJNkucmOS7Jd5IcP9Dv2iTbJHlwki8l+XaSVUle3j7+ziRXJrksyXvatrcmeVO7fF6SdyW5MMlVSZ7dtm+R5NQ27pQkF8w/ApVkjySnDtzfLckX2uVjkqxMckWSPxvxfPzHwPK+c9uVZHGS05Nc1N6e1bY/N8ml7e2SJA+d/FWQJM3KooWegCRp01NVf5zkM8ArgD8EzquqZ3WEbA08D3gJ8AXgWTSF2UVJllbVpQN9lwPXV9WLAZI8LMnDgX2AX66q6jitcFFV7ZpkT+Bo4PnAa4EfVdVTkjwZuHRI3DnA3yV5cFX9J/By4JT2sbdU1a1JNgPOTfKUqrqs+xn6hfcD76uqf0qyE3A28ATgTcDhVfXPSR4C/HfP9UmS1gOPZEmSFspTaQqWXwauHNP3C1VVwOXAjVV1eVXdBVwB7Dyv7+XA89ujUs+uqtuAn9AUIh9P8lLg9hHjfLb9efHAen8DOBmgqlYB9yqQquoO4MvAbydZBLwYOKN9+GVJvgVcAjwJeOKYbR30fOBvk1wKrAC2bI9a/TPw10leD2zVji9Juo/wSJYkab1KshQ4HtgBuAXYomnOpcCvV9V/DQn7WfvzroHlufv3+CyrqquSPB3YE/jLJF+pqrcl2RXYHdgfOILmyNioce4cWG96btopwOHArcBFVfXTJLvQHHX6tar6UXsa4AOHxNbA8uDj92P4c/LOJF9qt/H8JM+vqu/2nKckacY8kiVJWq+q6tKqWgpcRXNU5x+BF1XV0hEF1kSSPBq4vao+BbwHeFp7St3DqupM4A3A0glW+U/Ay9p1PxH4lRH9zgOeBvw+d58quCXwn8BtSR4J7DEi9sYkT0hyP5rTGud8haYgnNu2pe3Px7ZH894FrKQ5GihJuo/wSJYkab1Lspjme053Jfnlqhp3uuAkfgV4d5K7gP8HHAY8FDgjyQNpjky9cYL1fRg4ob1QxyU0pwveNr9TVd2Z5IvA7wEHtW3fTnIJzWmN19Cc5jfMkcAXgeuAVcBD2vbXAx9qx14EfB04FHhDkt+kOeJ2JXDWBNsjSZqxNKe4S5KkYdoLVty/qv47yWOBc4HHVdXPF3hqkqT7KI9kSZLUbQvgq0nuT3MU7DALLElSF49kSZIkSdIUeeELSZIkSZoiiyxJkiRJmiKLLEmSJEmaIossSZIkSZoiiyxJkiRJmiKLLEmSJEmaov8PeoP0ADrg8oUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61841aac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4)) # this creates a figure 8 inch wide, 4 inch high\n",
    "ax = sns.countplot(azdias.isnull().sum(axis=1))\n",
    "ax.set_xlabel('# missing values')\n",
    "plt.show()\n",
    "##number of rows/data points that has x missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "azdias_cleaned['num_null'] = azdias_cleaned.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(797426, 79)\n",
      "(93795, 79)\n"
     ]
    }
   ],
   "source": [
    "#split into datum that have less than and more than 20 missing values\n",
    "azdias_low = azdias_cleaned.query('num_null < 21').drop('num_null', axis=1)\n",
    "azdias_high = azdias_cleaned.query('num_null > 20').drop('num_null', axis=1)\n",
    "print(azdias_low.shape)\n",
    "print(azdias_high.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f618407e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i, col in enumerate(azdias_low.columns[:10]):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    sns.distplot(azdias_low[col][azdias_low[col].notnull()], label='low_NaN')\n",
    "    sns.distplot(azdias_high[col][azdias_high[col].notnull()], label='high_NaN')\n",
    "    plt.title('Distribution for column: {}'.format(col))\n",
    "    plt.tight_layout()\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
    "#subplot params: num_rows, num_cols, which_subplot\n",
    "def graph_value_dist(column):\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2, sharey=True)\n",
    "    ax1.set_title('low NaN data')\n",
    "    ax2.set_title('high NaN data')\n",
    "    sns.countplot(azdias_low[column], ax=ax1)\n",
    "    sns.countplot(azdias_high[column], ax=ax2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ANREDE_KZ', 'FINANZ_MINIMALIST', 'FINANZ_SPARER', 'FINANZ_VORSORGER', 'FINANZ_ANLEGER', 'GREEN_AVANTGARDE']\n"
     ]
    }
   ],
   "source": [
    "#get column names for distribution analysis\n",
    "cols=[1,3,4,5,6,11]\n",
    "col_names = []\n",
    "for i in cols:\n",
    "    null1 = azdias_low.iloc[:,i].isnull().sum()\n",
    "    null2 = azdias_high.iloc[:,i].isnull().sum()\n",
    "    col_name = azdias_low.iloc[:,i].name\n",
    "    if(null1+null2 == 0):\n",
    "        col_names.append(col_name)\n",
    "        \n",
    "print(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6181a637b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6180f1ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617f5d8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617f57a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617f4ad358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f617f4ced30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in col_names:\n",
    "    graph_value_dist(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "I looked at 6 features where the data had no missing values, and quite interestingly some of these features had even distributions of values between the two datasets, and some were quite uneven. \n",
    "\n",
    "The highest difference in distributions was seen in the columns:\n",
    "* FINANZ_ANLEGER\n",
    "* FINANZ_SPARER\n",
    "* FINANZ_VORSORGER\n",
    "\n",
    "This means that 3 out of the 6 features I looked at have a very different distribution of values between the two splits of data- NaN heavy data and NaN light data.\n",
    "This means that it might not be the best idea to drop a lot of the NaN datum, as it could disort the data. We should revist the high NaN rows later as some seem to be qualitatively different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "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'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    21\n",
       "numeric         7\n",
       "mixed           7\n",
       "interval        1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "#feat_info.groupby('type')['attribute'].nunique()\n",
    "feat_info['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, can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this, need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    205378.000000\n",
      "mean          1.743410\n",
      "std           0.674312\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           2.000000\n",
      "max           3.000000\n",
      "Name: AGER_TYP, dtype: float64\n",
      "685843\n",
      "count    891221.000000\n",
      "mean          1.522098\n",
      "std           0.499512\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           2.000000\n",
      "max           2.000000\n",
      "Name: ANREDE_KZ, dtype: float64\n",
      "0\n",
      "count    886367.000000\n",
      "mean          3.632838\n",
      "std           1.595021\n",
      "min           1.000000\n",
      "25%           2.000000\n",
      "50%           4.000000\n",
      "75%           5.000000\n",
      "max           6.000000\n",
      "Name: CJT_GESAMTTYP, dtype: float64\n",
      "4854\n",
      "count    891221.000000\n",
      "mean          3.790586\n",
      "std           1.987876\n",
      "min           1.000000\n",
      "25%           2.000000\n",
      "50%           4.000000\n",
      "75%           6.000000\n",
      "max           6.000000\n",
      "Name: FINANZTYP, dtype: float64\n",
      "0\n",
      "count    886367.000000\n",
      "mean          7.350304\n",
      "std           3.525723\n",
      "min           1.000000\n",
      "25%           5.000000\n",
      "50%           8.000000\n",
      "75%          10.000000\n",
      "max          12.000000\n",
      "Name: GFK_URLAUBERTYP, dtype: float64\n",
      "4854\n",
      "count    891221.000000\n",
      "mean          0.196612\n",
      "std           0.397437\n",
      "min           0.000000\n",
      "25%           0.000000\n",
      "50%           0.000000\n",
      "75%           0.000000\n",
      "max           1.000000\n",
      "Name: GREEN_AVANTGARDE, dtype: float64\n",
      "0\n",
      "count    813429.000000\n",
      "mean          3.922339\n",
      "std           3.941285\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           1.000000\n",
      "75%           8.000000\n",
      "max          11.000000\n",
      "Name: LP_FAMILIE_FEIN, dtype: float64\n",
      "77792\n",
      "count    813429.000000\n",
      "mean          2.381976\n",
      "std           1.701527\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           1.000000\n",
      "75%           4.000000\n",
      "max           5.000000\n",
      "Name: LP_FAMILIE_GROB, dtype: float64\n",
      "77792\n",
      "count    886367.000000\n",
      "mean          4.791151\n",
      "std           3.425305\n",
      "min           1.000000\n",
      "25%           2.000000\n",
      "50%           4.000000\n",
      "75%           9.000000\n",
      "max          10.000000\n",
      "Name: LP_STATUS_FEIN, dtype: float64\n",
      "4854\n",
      "count    886367.000000\n",
      "mean          2.432575\n",
      "std           1.474315\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           4.000000\n",
      "max           5.000000\n",
      "Name: LP_STATUS_GROB, dtype: float64\n",
      "4854\n",
      "count    782906.000000\n",
      "mean          1.168889\n",
      "std           0.475075\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           1.000000\n",
      "75%           1.000000\n",
      "max           3.000000\n",
      "Name: NATIONALITAET_KZ, dtype: float64\n",
      "108315\n",
      "count    780025.000000\n",
      "mean          1.590134\n",
      "std           1.027972\n",
      "min           0.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           2.000000\n",
      "max           3.000000\n",
      "Name: SHOPPER_TYP, dtype: float64\n",
      "111196\n",
      "count    817722.000000\n",
      "mean          0.008423\n",
      "std           0.091392\n",
      "min           0.000000\n",
      "25%           0.000000\n",
      "50%           0.000000\n",
      "75%           0.000000\n",
      "max           1.000000\n",
      "Name: SOHO_KZ, dtype: float64\n",
      "73499\n",
      "count    2160.000000\n",
      "mean        1.318519\n",
      "std         0.999504\n",
      "min         1.000000\n",
      "25%         1.000000\n",
      "50%         1.000000\n",
      "75%         1.000000\n",
      "max         5.000000\n",
      "Name: TITEL_KZ, dtype: float64\n",
      "889061\n",
      "count    780025.000000\n",
      "mean          1.511166\n",
      "std           0.499876\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           2.000000\n",
      "75%           2.000000\n",
      "max           2.000000\n",
      "Name: VERS_TYP, dtype: float64\n",
      "111196\n",
      "count    891221.000000\n",
      "mean          3.362438\n",
      "std           1.352704\n",
      "min           1.000000\n",
      "25%           3.000000\n",
      "50%           3.000000\n",
      "75%           4.000000\n",
      "max           6.000000\n",
      "Name: ZABEOTYP, dtype: float64\n",
      "0\n",
      "count    306609.000000\n",
      "mean          3.410640\n",
      "std           1.628844\n",
      "min           1.000000\n",
      "25%           2.000000\n",
      "50%           3.000000\n",
      "75%           5.000000\n",
      "max           6.000000\n",
      "Name: KK_KUNDENTYP, dtype: float64\n",
      "584612\n",
      "count    798073.000000\n",
      "mean          2.798641\n",
      "std           2.656713\n",
      "min           1.000000\n",
      "25%           1.000000\n",
      "50%           1.000000\n",
      "75%           3.000000\n",
      "max           8.000000\n",
      "Name: GEBAEUDETYP, dtype: float64\n",
      "93148\n",
      "count     798073\n",
      "unique         2\n",
      "top            W\n",
      "freq      629528\n",
      "Name: OST_WEST_KZ, dtype: object\n",
      "93148\n",
      "count     791869\n",
      "unique         9\n",
      "top            8\n",
      "freq      134441\n",
      "Name: CAMEO_DEUG_2015, dtype: object\n",
      "99352\n",
      "count     791869\n",
      "unique        44\n",
      "top           6B\n",
      "freq       56672\n",
      "Name: CAMEO_DEU_2015, dtype: object\n",
      "99352\n"
     ]
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "cat_vars = feat_info[feat_info['type'] == 'categorical']['attribute'].tolist()\n",
    "\n",
    "for var in cat_vars:\n",
    "    print(azdias[var].describe())\n",
    "    print(azdias[var].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    629528\n",
       "O    168545\n",
       "Name: OST_WEST_KZ, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['OST_WEST_KZ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    628987\n",
       "0    168439\n",
       "Name: WEST_KZ, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "azdias_low['WEST_KZ'] = azdias_low['OST_WEST_KZ'].replace({'W':1, 'O':0})\n",
    "azdias_low['WEST_KZ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imputer_freq(df, var): #fill in NaN with most common value in column\n",
    "    most_freq = df[var].value_counts().idxmax()\n",
    "    return df[var].fillna(most_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(797426, 80)\n",
      "(797426, 81)\n",
      "(797426, 82)\n",
      "(797426, 83)\n",
      "(797426, 84)\n",
      "(797426, 85)\n",
      "(797426, 85)\n",
      "(797426, 86)\n",
      "(797426, 87)\n",
      "(797426, 88)\n",
      "(797426, 89)\n",
      "(797426, 90)\n",
      "(797426, 90)\n",
      "(797426, 91)\n",
      "(797426, 92)\n",
      "(797426, 93)\n",
      "(797426, 94)\n",
      "(797426, 95)\n",
      "(797426, 96)\n",
      "(797426, 97)\n",
      "(797426, 98)\n",
      "(797426, 99)\n",
      "(797426, 100)\n",
      "(797426, 101)\n",
      "(797426, 101)\n",
      "(797426, 102)\n",
      "(797426, 103)\n",
      "(797426, 104)\n",
      "(797426, 105)\n",
      "(797426, 106)\n",
      "(797426, 107)\n",
      "(797426, 108)\n",
      "(797426, 109)\n",
      "(797426, 110)\n",
      "(797426, 111)\n",
      "(797426, 111)\n",
      "(797426, 112)\n",
      "(797426, 113)\n",
      "(797426, 114)\n",
      "(797426, 115)\n",
      "(797426, 115)\n",
      "(797426, 116)\n",
      "(797426, 117)\n",
      "(797426, 118)\n",
      "(797426, 119)\n",
      "(797426, 120)\n",
      "(797426, 121)\n",
      "(797426, 122)\n",
      "(797426, 123)\n",
      "(797426, 124)\n",
      "(797426, 124)\n",
      "(797426, 125)\n",
      "(797426, 126)\n",
      "(797426, 127)\n",
      "(797426, 128)\n",
      "(797426, 128)\n",
      "(797426, 129)\n",
      "(797426, 130)\n",
      "(797426, 130)\n",
      "(797426, 131)\n",
      "(797426, 132)\n",
      "(797426, 133)\n",
      "(797426, 133)\n",
      "(797426, 134)\n",
      "(797426, 135)\n",
      "(797426, 136)\n",
      "(797426, 137)\n",
      "(797426, 138)\n",
      "(797426, 138)\n",
      "(797426, 139)\n",
      "(797426, 140)\n",
      "(797426, 141)\n",
      "(797426, 142)\n",
      "(797426, 143)\n",
      "(797426, 144)\n",
      "(797426, 144)\n",
      "(797426, 145)\n",
      "(797426, 146)\n",
      "(797426, 147)\n",
      "(797426, 148)\n",
      "(797426, 149)\n",
      "(797426, 150)\n",
      "(797426, 151)\n",
      "(797426, 152)\n",
      "(797426, 152)\n",
      "(797426, 153)\n",
      "(797426, 154)\n",
      "(797426, 155)\n",
      "(797426, 156)\n",
      "(797426, 157)\n",
      "(797426, 158)\n",
      "(797426, 159)\n",
      "(797426, 160)\n",
      "(797426, 161)\n",
      "(797426, 162)\n",
      "(797426, 163)\n",
      "(797426, 164)\n",
      "(797426, 165)\n",
      "(797426, 166)\n",
      "(797426, 167)\n",
      "(797426, 168)\n",
      "(797426, 169)\n",
      "(797426, 170)\n",
      "(797426, 171)\n",
      "(797426, 172)\n",
      "(797426, 173)\n",
      "(797426, 174)\n",
      "(797426, 175)\n",
      "(797426, 176)\n",
      "(797426, 177)\n",
      "(797426, 178)\n",
      "(797426, 179)\n",
      "(797426, 180)\n",
      "(797426, 181)\n",
      "(797426, 182)\n",
      "(797426, 183)\n",
      "(797426, 184)\n",
      "(797426, 185)\n",
      "(797426, 186)\n",
      "(797426, 187)\n",
      "(797426, 188)\n",
      "(797426, 189)\n",
      "(797426, 190)\n",
      "(797426, 191)\n",
      "(797426, 192)\n",
      "(797426, 193)\n",
      "(797426, 194)\n",
      "(797426, 195)\n"
     ]
    }
   ],
   "source": [
    "one_hot_vars = ['CJT_GESAMTTYP', 'FINANZTYP','GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', \n",
    "                'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', \n",
    "                'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']\n",
    "for var in one_hot_vars:\n",
    "    imputed_data = imputer_freq(azdias_low, var) #fill na-- imputation\n",
    "    encode = pd.get_dummies(imputed_data, prefix=var) #one hot encode\n",
    "    azdias_low = azdias_low.drop(var, axis=1) # remove column we onehot\n",
    "    \n",
    "    for col in encode.columns: #insert new encoded columns into the dataset\n",
    "        azdias_low[col] = encode[col] \n",
    "        print(azdias_low.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KK_KUNDENTYP already cleaned from dataset\n",
      "TITEL_KZ already cleaned from dataset\n",
      "AGER_TYP already cleaned from dataset\n"
     ]
    }
   ],
   "source": [
    "drop_categorical = ['KK_KUNDENTYP', 'TITEL_KZ', 'AGER_TYP']\n",
    "for category in drop_categorical:\n",
    "    try: azdias_low = azdias_low.drop(category, axis=1)\n",
    "    except ValueError: print('{0} already cleaned from dataset'.format(category))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "* The non-numerical binary variable is: `OST_WEST_KZ`\n",
    "* `CAMEO_DEU_2015` and `CAMEO_DEUG_2015` is a multi-level categorical: alphanumeric\n",
    "* `SOHO_KZ` and `GREEN_AVANTGARDE` is a regular binary variable\n",
    "    * `ANREDE_KZ` is binary, but takes values `[1,2]`\n",
    "\n",
    "The variables that need to be re-encoded are:\n",
    "* non-numerical binary var: `OST_WEST_KZ` will be converted to regular binary\n",
    "* all multi-level categoricals will be one hot encoded\n",
    "\n",
    "The variables I dropped are:\n",
    "* `KK_KUNDENTYP`\n",
    "* `TITEL_KZ`\n",
    "* `AGER_TYP`\n",
    "\n",
    "I dropped them because they had very few (relative to length of dataset) non-missing values. Which wouldn't be useful for analysis anyway.\n",
    "\n",
    "##### Engineering Steps\n",
    "For alphanumeric binary var `OST_WEST_KZ`:\n",
    "\n",
    " * converted `OST_WEST_KZ` to binary variable: `WEST_KZ`\n",
    "     * `W` maps to `1` (signifies True/1 for \"west\")\n",
    "     * `O` maps to `0`\n",
    "\n",
    "-------\n",
    "For categorical variables: `['CJT_GESAMTTYP', 'FINANZTYP','GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']`\n",
    "1. First, I imputed each variable column to prevent getting a one hot encoded column for NaN - missing values.\n",
    "    * to do this I find the highest frequency value in each common (most likely category), and then fill the missing values with that instead of a mean for example. The mean would be a bad choice to impute categorical variables as it makes the variable continuous rather than discrete.\n",
    "    \n",
    "2. Secondly, I used pandas.get_dummies to one hot encode each column, dropped the original column from the dataset, and then added the new encoded data columns to the dataset."
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.0    188697\n",
      "8.0     145988\n",
      "5.0      86416\n",
      "10.0     85808\n",
      "3.0      55195\n",
      "15.0     42547\n",
      "11.0     35752\n",
      "9.0      33570\n",
      "6.0      25652\n",
      "12.0     24446\n",
      "1.0      21282\n",
      "4.0      20451\n",
      "2.0       7479\n",
      "13.0      5764\n",
      "7.0       4010\n",
      "Name: PRAEGENDE_JUGENDJAHRE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "print(azdias['PRAEGENDE_JUGENDJAHRE'].value_counts())\n",
    "decade_mapping = {1:40, 2:40,3:50,4:50,5:60,6:60,7:60,8:70,9:70,10:80,11:80,12:80,13:80,14:90,15:90}\n",
    "avantgarde_mapping ={1:0,2:1,3:0,4:1,5:0,6:1,7:1,8:0,9:1,10:0,11:1,12:0,13:1,14:0,15:1}\n",
    "\n",
    "azdias_low['AVANTGARDE'] = azdias_low['PRAEGENDE_JUGENDJAHRE'].map(avantgarde_mapping)\n",
    "azdias_low['YOUTH_DECADE'] = azdias_low['PRAEGENDE_JUGENDJAHRE'].map(decade_mapping)\n",
    "\n",
    "#impute null values\n",
    "azdias_low['AVANTGARDE'] = imputer_freq(azdias_low, 'AVANTGARDE')\n",
    "azdias_low['YOUTH_DECADE'] = imputer_freq(azdias_low, 'YOUTH_DECADE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nulls: 6003\n",
      "value counts: 51    133673\n",
      "41     92309\n",
      "24     91099\n",
      "14     62851\n",
      "43     56653\n",
      "54     45376\n",
      "25     39605\n",
      "22     33137\n",
      "23     26667\n",
      "13     26311\n",
      "45     26126\n",
      "55     23939\n",
      "52     20538\n",
      "31     18979\n",
      "34     18517\n",
      "15     16966\n",
      "44     14817\n",
      "12     13234\n",
      "35     10352\n",
      "32     10346\n",
      "33      9928\n",
      "Name: CAMEO_INTL_2015, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "print('number of nulls: %s'%azdias_low['CAMEO_INTL_2015'].isnull().sum())\n",
    "print('value counts: %s' % azdias_low['CAMEO_INTL_2015'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_wealth(key):\n",
    "    if(len(str(key))==2):\n",
    "        tens = int(str(key)[0])\n",
    "        return tens\n",
    "    else: return np.nan\n",
    "    \n",
    "def decode_life(key):\n",
    "    if(len(str(key))==2):\n",
    "        ones = int(str(key)[1])\n",
    "        return ones\n",
    "    else: return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''ENGINEERING OF `CAMEO_INTL_2015` '''\n",
    "azdias_low['CAMEO_INTL_2015_WEALTH'] = azdias_low['CAMEO_INTL_2015'].apply(decode_wealth)\n",
    "azdias_low['CAMEO_INTL_2015_LIFE'] = azdias_low['CAMEO_INTL_2015'].apply(decode_life)\n",
    "\n",
    "#impute nan values\n",
    "azdias_low['CAMEO_INTL_2015_WEALTH'] = imputer_freq(azdias_low, 'CAMEO_INTL_2015_WEALTH')\n",
    "azdias_low['CAMEO_INTL_2015_LIFE'] = imputer_freq(azdias_low, 'CAMEO_INTL_2015_LIFE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE', 'KBA05_BAUMAX', 'PLZ8_BAUMAX']\n"
     ]
    }
   ],
   "source": [
    "mixed_feat = feat_info[feat_info['type'] == 'mixed']['attribute'].tolist()\n",
    "mixed_feat.remove('PRAEGENDE_JUGENDJAHRE')\n",
    "mixed_feat.remove('CAMEO_INTL_2015')\n",
    "print(mixed_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LP_LEBENSPHASE_FEIN has 49651 null values\n",
      "LP_LEBENSPHASE_GROB has 46830 null values\n",
      "WOHNLAGE has 0 null values\n"
     ]
    }
   ],
   "source": [
    "#INVESTIGATE REMAINING MIXED-VARS\n",
    "for feat in mixed_feat:\n",
    "    try: print('{0} has {1} null values'.format(feat, azdias_low[feat].isnull().sum()))\n",
    "    except KeyError: mixed_feat.remove(feat);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LP_LEBENSPHASE_FEIN has 1.0     58722\n",
      "5.0     52400\n",
      "6.0     43334\n",
      "2.0     37148\n",
      "8.0     28837\n",
      "11.0    25369\n",
      "7.0     24950\n",
      "29.0    24880\n",
      "13.0    24811\n",
      "10.0    24469\n",
      "12.0    22275\n",
      "31.0    22261\n",
      "30.0    21147\n",
      "15.0    18966\n",
      "3.0     18756\n",
      "19.0    18586\n",
      "37.0    17500\n",
      "4.0     16478\n",
      "14.0    16354\n",
      "20.0    16287\n",
      "32.0    15763\n",
      "39.0    15132\n",
      "40.0    14108\n",
      "27.0    13800\n",
      "16.0    13633\n",
      "38.0    13099\n",
      "35.0    13011\n",
      "34.0    12422\n",
      "9.0     12111\n",
      "21.0    11966\n",
      "28.0    11543\n",
      "24.0    11309\n",
      "25.0     9849\n",
      "36.0     9656\n",
      "23.0     8628\n",
      "22.0     6831\n",
      "18.0     6805\n",
      "33.0     5724\n",
      "17.0     5487\n",
      "26.0     3368\n",
      "Name: LP_LEBENSPHASE_FEIN, dtype: int64 null values\n",
      "LP_LEBENSPHASE_GROB has 2.0     149521\n",
      "1.0     131104\n",
      "3.0     109623\n",
      "12.0     69495\n",
      "4.0      51186\n",
      "5.0      47165\n",
      "9.0      46027\n",
      "10.0     38024\n",
      "11.0     31157\n",
      "8.0      28711\n",
      "6.0      27425\n",
      "7.0      21158\n",
      "Name: LP_LEBENSPHASE_GROB, dtype: int64 null values\n",
      "WOHNLAGE has 3.0    249592\n",
      "7.0    169269\n",
      "4.0    135906\n",
      "2.0    100328\n",
      "5.0     74305\n",
      "1.0     43891\n",
      "8.0     17394\n",
      "0.0      6741\n",
      "Name: WOHNLAGE, dtype: int64 null values\n",
      "PLZ8_BAUMAX has 1.0    499542\n",
      "5.0     97333\n",
      "2.0     70406\n",
      "4.0     56684\n",
      "3.0     50732\n",
      "Name: PLZ8_BAUMAX, dtype: int64 null values\n"
     ]
    }
   ],
   "source": [
    "for feat in mixed_feat:\n",
    "    print('{0} has {1} null values'.format(feat, azdias_low[feat].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural_map={1:0,2:0,3:0,4:0,5:0,7:1,8:1}\n",
    "azdias_low['RURAL'] = azdias_low['WOHNLAGE'].map(rural_map)\n",
    "azdias_high['RURAL'] = azdias_high['WOHNLAGE'].map(rural_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "-------\n",
    "###### Engineering  `PRAEGENDE_JUGENDJAHRE`\n",
    "1. created mapping from the current variable to a categorical `YOUTH_DECADE` variable\n",
    "    * denotes which decade the persons childhood was in. for example 60s became `60`\n",
    "2. created mapping from the current variable to a binary `AVANTGARDE` variable\n",
    "    * if the predominant movement during this persons childhood was avantgarde, the binary var denotes `1.0` otherwise it denotes `0.0`\n",
    "\n",
    "-------\n",
    "###### Engineering  `CAMEO_INTL_2015`\n",
    "1. first defined two mapping functions: `decode_wealth` and `decode_life`\n",
    "    * `decode_wealth` returns the first digit of the orginial variable which shows wealth status\n",
    "    * `decode_life` returns the second digit of the original variable showing life stage\n",
    "    \n",
    "2. I applied this function to the data set and created a new ordinal variable for each wealth and life stage\n",
    "   * `CAMEO_INTL_2015_WEALTH`\n",
    "   * `CAMEO_INTL_2015_LIFE`\n",
    "    \n",
    "###### Other mixed variables\n",
    "the remaining mixed variables that were still in data set after engineering step were:\n",
    "`['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE','PLZ8_BAUMAX']`\n",
    "\n",
    "* `LP_LEBENSPHASE_FEIN`\n",
    "    * this variable essentially encodes the same things as `CAMEO_INTL_2015`, so I decided to drop it\n",
    "\n",
    "* `LP_LEBENSPHASE_GROB`\n",
    "    * this variable is the same as the variable above but on rougher scale, dropping as well\n",
    "    \n",
    "* `WOHNLAGE`\n",
    "    * `CAMEO_DEU_2015` contains similar but more defined information. `WOHNLAGE` is encoded as 1: rural, 0: not-rural\n",
    "\n",
    "* `PLZ8_BAUMAX`\n",
    "    * this variable is encapsulated by the information in other `PLZ8` variables, is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "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",
    "*NB* data should only be from **the subset with few or no missing values**.\n",
    "\n",
    "--------\n",
    "Some variables have been dropped already. Below, I will go through all features that have been encoded or need to be dropped again to double check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning LP_LEBENSPHASE_FEIN:\n",
      "cleaning LP_LEBENSPHASE_GROB:\n",
      "cleaning WOHNLAGE:\n",
      "cleaning PLZ8_BAUMAX:\n",
      "cleaning PRAEGENDE_JUGENDJAHRE:\n",
      "cleaning CAMEO_INTL_2015:\n",
      "cleaning OST_WEST_KZ:\n"
     ]
    }
   ],
   "source": [
    "#double check that features have been dropped\n",
    "one_hot_vars = ['CJT_GESAMTTYP', 'FINANZTYP','GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', \n",
    "                'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', \n",
    "                'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']\n",
    "drop_categorical = ['KK_KUNDENTYP', 'TITEL_KZ', 'AGER_TYP',\n",
    "                       'LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE', 'KBA05_BAUMAX','PLZ8_BAUMAX'] #high error categoricals\n",
    "\n",
    "mixed_cat = ['PRAEGENDE_JUGENDJAHRE', 'CAMEO_INTL_2015']\n",
    "for feature in one_hot_vars+drop_categorical+mixed_cat + ['OST_WEST_KZ']:\n",
    "    if feature in azdias_low.columns:\n",
    "        print('cleaning {0}:'.format(feature))\n",
    "        azdias_low = azdias_low.drop(feature, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\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",
    "    \n",
    "    '''all main cleaning steps:'''\n",
    "    # convert missing value codes into NaNs, ...\n",
    "    for col in df:\n",
    "        missing_rep = define_missing(feat_info[feat_info['attribute']==col]['missing_or_unknown'].values[0])\n",
    "        if(missing_rep[0] == ''): \n",
    "            df[col] = df[col].replace('', np.nan)\n",
    "        else:\n",
    "            replace_with = [np.nan for i in missing_rep] #create list of None same length as missing_rep\n",
    "            df[col] = df[col].replace(missing_rep, replace_with)\n",
    "    # remove selected columns and rows, ...\n",
    "    features_to_clean = ['AGER_TYP','GEBURTSJAHR','TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP',\n",
    "                         'KBA05_BAUMAX']\n",
    "    \n",
    "    df = df.drop(features_to_clean, axis=1)\n",
    "    df['num_null'] = df.isnull().sum(axis=1) #count null for each row\n",
    "    \n",
    "    #keep only low missing value rows\n",
    "    df = df.query('num_null < 21').drop('num_null', axis=1)\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    cat_vars = feat_info[feat_info['type'] == 'categorical']['attribute'].tolist()\n",
    "    mixed_vars = feat_info[feat_info['type'] == 'mixed']['attribute'].tolist()\n",
    "    \n",
    "    #re-encode binary var\n",
    "    df['WEST_KZ'] = df['OST_WEST_KZ'].replace({'W':1, 'O':0})\n",
    "    \n",
    "    #one hot encode\n",
    "    one_hot_vars = ['CJT_GESAMTTYP', 'FINANZTYP','GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', \n",
    "                'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', \n",
    "                'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']\n",
    "    \n",
    "    for var in one_hot_vars:\n",
    "        imputed_data = imputer_freq(df, var) #fill na-- imputation\n",
    "        encode = pd.get_dummies(imputed_data, prefix=var) #one hot encode\n",
    "\n",
    "        for col in encode.columns: #insert new encoded columns into the dataset\n",
    "            df[col] = encode[col] \n",
    "            \n",
    "    #engineer mixed vars\n",
    "    decade_mapping = {1:40, 2:40,3:50,4:50,5:60,6:60,7:60,8:70,\n",
    "                      9:70,10:80,11:80,12:80,13:80,14:90,15:90}\n",
    "    avantgarde_mapping ={1:0,2:1,3:0,4:1,5:0,6:1,7:1,8:0,\n",
    "                         9:1,10:0,11:1,12:0,13:1,14:0,15:1}\n",
    "    \n",
    "    rural_mapping = {1:0,2:0,3:0,4:0,5:0,7:1,8:1}\n",
    "        #map variables\n",
    "    df['RURAL'] = df['WOHNLAGE'].map(rural_mapping)\n",
    "    df['AVANTGARDE'] = df['PRAEGENDE_JUGENDJAHRE'].map(avantgarde_mapping)\n",
    "    df['YOUTH_DECADE'] = df['PRAEGENDE_JUGENDJAHRE'].map(decade_mapping)\n",
    "    \n",
    "    df['CAMEO_INTL_2015_WEALTH'] = df['CAMEO_INTL_2015'].apply(decode_wealth)\n",
    "    df['CAMEO_INTL_2015_LIFE'] = df['CAMEO_INTL_2015'].apply(decode_life)\n",
    "    \n",
    "    #remove all features either re-encoded, or not needed for analysis\n",
    "    drop_categorical = ['KK_KUNDENTYP', 'TITEL_KZ', 'AGER_TYP',\n",
    "                       'LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB',\n",
    "                        'WOHNLAGE', 'KBA05_BAUMAX','PLZ8_BAUMAX'] #high error categoricals\n",
    "    \n",
    "    shape_before = df.shape[1]\n",
    "    mixed_cat = ['PRAEGENDE_JUGENDJAHRE', 'CAMEO_INTL_2015']\n",
    "    to_drop = one_hot_vars+drop_categorical+mixed_cat+['OST_WEST_KZ']\n",
    "\n",
    "    #already removed in high NaN column cleaning, make sure we dont try to drop again\n",
    "    for removed in ['AGER_TYP','GEBURTSJAHR','TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP',\n",
    "                         'KBA05_BAUMAX']:\n",
    "        if removed in to_drop:\n",
    "            to_drop.remove(removed)\n",
    "    \n",
    "    df = df.drop(to_drop, axis=1) \n",
    "\n",
    "    # Return the cleaned dataframe.\n",
    "    \n",
    "    return df\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. \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. (RobustScaler also ok)\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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      193.000000\n",
       "mean      3573.564767\n",
       "std      11363.386643\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max      64433.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "azdias_low.isnull().sum(axis=0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    21\n",
       "numeric         7\n",
       "mixed           7\n",
       "interval        1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#impute all variables using mode since many are categorical or binary, very few purely continuous vars\n",
    "#highest missing value is 38,000 which is still only about 5.5% of values\n",
    "imputer = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)\n",
    "azdias_imputed = pd.DataFrame(imputer.fit_transform(azdias_low),\n",
    "                              index=azdias_low.index, columns=azdias_low.columns)\n",
    "\n",
    "#end result is new imputed DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    797426.0\n",
       "mean          0.0\n",
       "std           0.0\n",
       "min           0.0\n",
       "25%           0.0\n",
       "50%           0.0\n",
       "75%           0.0\n",
       "max           0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_imputed.isnull().sum(axis=1).describe()\n",
    "#check that no more null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "scaler = StandardScaler()\n",
    "\n",
    "azdias_scaled = pd.DataFrame(scaler.fit_transform(azdias_imputed),\n",
    "                              index=azdias_imputed.index, columns=azdias_imputed.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>CAMEO_DEU_2015_9A</th>\n",
       "      <th>CAMEO_DEU_2015_9B</th>\n",
       "      <th>CAMEO_DEU_2015_9C</th>\n",
       "      <th>CAMEO_DEU_2015_9D</th>\n",
       "      <th>CAMEO_DEU_2015_9E</th>\n",
       "      <th>AVANTGARDE</th>\n",
       "      <th>YOUTH_DECADE</th>\n",
       "      <th>CAMEO_INTL_2015_WEALTH</th>\n",
       "      <th>CAMEO_INTL_2015_LIFE</th>\n",
       "      <th>RURAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "      <td>7.974260e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.509483e-17</td>\n",
       "      <td>8.277811e-17</td>\n",
       "      <td>1.346548e-16</td>\n",
       "      <td>-8.425725e-17</td>\n",
       "      <td>-3.776250e-17</td>\n",
       "      <td>-9.630418e-17</td>\n",
       "      <td>1.835286e-16</td>\n",
       "      <td>-8.126334e-17</td>\n",
       "      <td>-1.561111e-17</td>\n",
       "      <td>-8.625319e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.060988e-17</td>\n",
       "      <td>3.715659e-18</td>\n",
       "      <td>-1.407852e-18</td>\n",
       "      <td>1.045464e-16</td>\n",
       "      <td>5.556559e-17</td>\n",
       "      <td>-1.561111e-17</td>\n",
       "      <td>-3.827753e-16</td>\n",
       "      <td>4.856197e-17</td>\n",
       "      <td>4.130886e-17</td>\n",
       "      <td>3.727688e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "      <td>1.000001e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.766334e+00</td>\n",
       "      <td>-1.043765e+00</td>\n",
       "      <td>-1.494609e+00</td>\n",
       "      <td>-1.155304e+00</td>\n",
       "      <td>-1.766955e+00</td>\n",
       "      <td>-1.249881e+00</td>\n",
       "      <td>-1.184467e+00</td>\n",
       "      <td>-1.502317e+00</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>-1.633717e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.625923e-01</td>\n",
       "      <td>-1.895778e-01</td>\n",
       "      <td>-1.798523e-01</td>\n",
       "      <td>-1.928439e-01</td>\n",
       "      <td>-8.974298e-02</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>-2.316187e+00</td>\n",
       "      <td>-1.551476e+00</td>\n",
       "      <td>-1.249157e+00</td>\n",
       "      <td>-5.528315e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.828722e-01</td>\n",
       "      <td>-1.043765e+00</td>\n",
       "      <td>-7.686897e-01</td>\n",
       "      <td>-1.155304e+00</td>\n",
       "      <td>-1.040726e+00</td>\n",
       "      <td>-1.249881e+00</td>\n",
       "      <td>-1.184467e+00</td>\n",
       "      <td>-7.920337e-01</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>-3.116745e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.625923e-01</td>\n",
       "      <td>-1.895778e-01</td>\n",
       "      <td>-1.798523e-01</td>\n",
       "      <td>-1.928439e-01</td>\n",
       "      <td>-8.974298e-02</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>-9.502996e-01</td>\n",
       "      <td>-8.696736e-01</td>\n",
       "      <td>-1.249157e+00</td>\n",
       "      <td>-5.528315e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.005893e-01</td>\n",
       "      <td>9.580705e-01</td>\n",
       "      <td>-4.276997e-02</td>\n",
       "      <td>1.914482e-01</td>\n",
       "      <td>4.117312e-01</td>\n",
       "      <td>1.082835e-01</td>\n",
       "      <td>-4.697522e-01</td>\n",
       "      <td>-8.175029e-02</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>-3.116745e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.625923e-01</td>\n",
       "      <td>-1.895778e-01</td>\n",
       "      <td>-1.798523e-01</td>\n",
       "      <td>-1.928439e-01</td>\n",
       "      <td>-8.974298e-02</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>4.155877e-01</td>\n",
       "      <td>4.939317e-01</td>\n",
       "      <td>9.484474e-02</td>\n",
       "      <td>-5.528315e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.184051e+00</td>\n",
       "      <td>9.580705e-01</td>\n",
       "      <td>6.831497e-01</td>\n",
       "      <td>8.648242e-01</td>\n",
       "      <td>1.137960e+00</td>\n",
       "      <td>7.873659e-01</td>\n",
       "      <td>9.596769e-01</td>\n",
       "      <td>6.285331e-01</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>1.010368e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.625923e-01</td>\n",
       "      <td>-1.895778e-01</td>\n",
       "      <td>-1.798523e-01</td>\n",
       "      <td>-1.928439e-01</td>\n",
       "      <td>-8.974298e-02</td>\n",
       "      <td>-5.305674e-01</td>\n",
       "      <td>1.098531e+00</td>\n",
       "      <td>1.175734e+00</td>\n",
       "      <td>7.668454e-01</td>\n",
       "      <td>-5.528315e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.184051e+00</td>\n",
       "      <td>9.580705e-01</td>\n",
       "      <td>1.409069e+00</td>\n",
       "      <td>1.538200e+00</td>\n",
       "      <td>1.137960e+00</td>\n",
       "      <td>1.466448e+00</td>\n",
       "      <td>1.674391e+00</td>\n",
       "      <td>1.338816e+00</td>\n",
       "      <td>1.884775e+00</td>\n",
       "      <td>1.010368e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.150354e+00</td>\n",
       "      <td>5.274880e+00</td>\n",
       "      <td>5.560118e+00</td>\n",
       "      <td>5.185540e+00</td>\n",
       "      <td>1.114293e+01</td>\n",
       "      <td>1.884775e+00</td>\n",
       "      <td>1.098531e+00</td>\n",
       "      <td>1.175734e+00</td>\n",
       "      <td>1.438846e+00</td>\n",
       "      <td>1.808870e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ALTERSKATEGORIE_GROB     ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "count          7.974260e+05  7.974260e+05       7.974260e+05   7.974260e+05   \n",
       "mean           8.509483e-17  8.277811e-17       1.346548e-16  -8.425725e-17   \n",
       "std            1.000001e+00  1.000001e+00       1.000001e+00   1.000001e+00   \n",
       "min           -1.766334e+00 -1.043765e+00      -1.494609e+00  -1.155304e+00   \n",
       "25%           -7.828722e-01 -1.043765e+00      -7.686897e-01  -1.155304e+00   \n",
       "50%            2.005893e-01  9.580705e-01      -4.276997e-02   1.914482e-01   \n",
       "75%            1.184051e+00  9.580705e-01       6.831497e-01   8.648242e-01   \n",
       "max            1.184051e+00  9.580705e-01       1.409069e+00   1.538200e+00   \n",
       "\n",
       "       FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "count      7.974260e+05    7.974260e+05           7.974260e+05   \n",
       "mean      -3.776250e-17   -9.630418e-17           1.835286e-16   \n",
       "std        1.000001e+00    1.000001e+00           1.000001e+00   \n",
       "min       -1.766955e+00   -1.249881e+00          -1.184467e+00   \n",
       "25%       -1.040726e+00   -1.249881e+00          -1.184467e+00   \n",
       "50%        4.117312e-01    1.082835e-01          -4.697522e-01   \n",
       "75%        1.137960e+00    7.873659e-01           9.596769e-01   \n",
       "max        1.137960e+00    1.466448e+00           1.674391e+00   \n",
       "\n",
       "       FINANZ_HAUSBAUER  GREEN_AVANTGARDE    HEALTH_TYP      ...       \\\n",
       "count      7.974260e+05      7.974260e+05  7.974260e+05      ...        \n",
       "mean      -8.126334e-17     -1.561111e-17 -8.625319e-17      ...        \n",
       "std        1.000001e+00      1.000001e+00  1.000001e+00      ...        \n",
       "min       -1.502317e+00     -5.305674e-01 -1.633717e+00      ...        \n",
       "25%       -7.920337e-01     -5.305674e-01 -3.116745e-01      ...        \n",
       "50%       -8.175029e-02     -5.305674e-01 -3.116745e-01      ...        \n",
       "75%        6.285331e-01     -5.305674e-01  1.010368e+00      ...        \n",
       "max        1.338816e+00      1.884775e+00  1.010368e+00      ...        \n",
       "\n",
       "       CAMEO_DEU_2015_9A  CAMEO_DEU_2015_9B  CAMEO_DEU_2015_9C  \\\n",
       "count       7.974260e+05       7.974260e+05       7.974260e+05   \n",
       "mean       -2.060988e-17       3.715659e-18      -1.407852e-18   \n",
       "std         1.000001e+00       1.000001e+00       1.000001e+00   \n",
       "min        -1.625923e-01      -1.895778e-01      -1.798523e-01   \n",
       "25%        -1.625923e-01      -1.895778e-01      -1.798523e-01   \n",
       "50%        -1.625923e-01      -1.895778e-01      -1.798523e-01   \n",
       "75%        -1.625923e-01      -1.895778e-01      -1.798523e-01   \n",
       "max         6.150354e+00       5.274880e+00       5.560118e+00   \n",
       "\n",
       "       CAMEO_DEU_2015_9D  CAMEO_DEU_2015_9E    AVANTGARDE  YOUTH_DECADE  \\\n",
       "count       7.974260e+05       7.974260e+05  7.974260e+05  7.974260e+05   \n",
       "mean        1.045464e-16       5.556559e-17 -1.561111e-17 -3.827753e-16   \n",
       "std         1.000001e+00       1.000001e+00  1.000001e+00  1.000001e+00   \n",
       "min        -1.928439e-01      -8.974298e-02 -5.305674e-01 -2.316187e+00   \n",
       "25%        -1.928439e-01      -8.974298e-02 -5.305674e-01 -9.502996e-01   \n",
       "50%        -1.928439e-01      -8.974298e-02 -5.305674e-01  4.155877e-01   \n",
       "75%        -1.928439e-01      -8.974298e-02 -5.305674e-01  1.098531e+00   \n",
       "max         5.185540e+00       1.114293e+01  1.884775e+00  1.098531e+00   \n",
       "\n",
       "       CAMEO_INTL_2015_WEALTH  CAMEO_INTL_2015_LIFE         RURAL  \n",
       "count            7.974260e+05          7.974260e+05  7.974260e+05  \n",
       "mean             4.856197e-17          4.130886e-17  3.727688e-17  \n",
       "std              1.000001e+00          1.000001e+00  1.000001e+00  \n",
       "min             -1.551476e+00         -1.249157e+00 -5.528315e-01  \n",
       "25%             -8.696736e-01         -1.249157e+00 -5.528315e-01  \n",
       "50%              4.939317e-01          9.484474e-02 -5.528315e-01  \n",
       "75%              1.175734e+00          7.668454e-01 -5.528315e-01  \n",
       "max              1.175734e+00          1.438846e+00  1.808870e+00  \n",
       "\n",
       "[8 rows x 193 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "1. I first had to make sure that there were no null values in the data set. Most of the data had very few nulls left. Only two features had more than 20,000 NaN, which is only about 5% of all the values.\n",
    "    * I decided that the best imputation method was mode instead of mean. Most of the values are categorical, and even some of the numeric values like birth year wouldnt make sense if they were continuous. Therefore, I imputed using most_frequent\n",
    "    \n",
    "2. I used the StandardScaler() provided by sklearn.preprocessing to scale all the values to mean 0 and stdDev = 1. This was suggested in the writeup."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "Apply PCA on scaled data\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": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "pca = PCA()\n",
    "pca_fit = pca.fit(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scree_plot(pca): ##code from helper_functions.py in PCA lesson\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",
    "    RETURN: None\n",
    "    '''\n",
    "    num_components= len(pca.explained_variance_ratio_)\n",
    "    idx = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(15, 10))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(idx, vals)\n",
    "    ax.plot(idx, cumvals)\n",
    "    for i in range(num_components):\n",
    "        if(i%20 == 0 or i<6):\n",
    "            ax.annotate(r\"%s%%\" % ((str(vals[i]*100)[:4])), (idx[i]+0.2, vals[i]), va=\"bottom\", ha=\"center\", fontsize=8)\n",
    " \n",
    "    ax.xaxis.set_tick_params(width=0, gridOn=True)\n",
    "    ax.yaxis.set_tick_params(width=2, length=12, gridOn=True)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6181aeafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "scree_plot(pca_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca = PCA(n_components=75)\n",
    "pca.fit(azdias_scaled)\n",
    "pca_data = pca.transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(797426, 75)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "The Principal Component Analysis with all of the Principal Components showed that the first 6 Principal Components each explain more than 3% of the total variance, with the highest explained variance for a single Principal Component being ~8%. The first 15 Principal Components explain 40% of all the variance. I am retaining 75 of the 185 Prinicpal Components for the analysis, as they explain about 75% of the total variance.\n",
    "\n",
    "It is interesting to note that the last 50 components (#125-175) explain very little of the variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "- To investigate the features, 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",
    "- 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": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def investigate_PC(pca, component, feature_names):\n",
    "    #code inspired by helper_functions.py in PCA lesson\n",
    "    '''\n",
    "    Prints association of a feature to the weights of its components\n",
    "    INPUT: pca: PCA object, compnent: which component is to be investigated (0 indexed),\n",
    "            feature_names: the corresponding feature names to PCA\n",
    "    OUTPUT: None\n",
    "    '''\n",
    "    \n",
    "    num_out = 10\n",
    "    \n",
    "    if(component <= len(pca.components_)):\n",
    "        pca_feature_map = pd.DataFrame({'weight': pca.components_[component],\n",
    "                                        'name': feature_names})\n",
    "        \n",
    "        pca_feature_map = pca_feature_map.sort_values(by='weight', ascending=False)\n",
    "        \n",
    "        print('Principal Component {}\\n---------------\\n'.format(component+1))\n",
    "        print('TOP {0} PRINCIPAL COMPONENTS \\n {1}'.format(num_out, pca_feature_map.iloc[:num_out,:]))\n",
    "        print('\\n BOTTOM {0} PRINCIPAL COMPONENTS \\n {1}'.format(num_out, pca_feature_map.iloc[-num_out:,:]))\n",
    "            \n",
    "    else:\n",
    "        print('Error in selecting component')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Principal Component 1\n",
      "---------------\n",
      "\n",
      "TOP 10 PRINCIPAL COMPONENTS \n",
      "                        name    weight\n",
      "110      LP_STATUS_GROB_1.0  0.196198\n",
      "29       HH_EINKOMMEN_SCORE  0.184257\n",
      "190  CAMEO_INTL_2015_WEALTH  0.184198\n",
      "52               PLZ8_ANTG3  0.182683\n",
      "53               PLZ8_ANTG4  0.175970\n",
      "57              ORTSGR_KLS9  0.158971\n",
      "42                 EWDICHTE  0.156777\n",
      "7          FINANZ_HAUSBAUER  0.147975\n",
      "39              KBA05_ANTG4  0.129277\n",
      "100      LP_STATUS_FEIN_1.0  0.126992\n",
      "\n",
      " BOTTOM 10 PRINCIPAL COMPONENTS \n",
      "                    name    weight\n",
      "113  LP_STATUS_GROB_4.0 -0.112923\n",
      "192               RURAL -0.115940\n",
      "43           INNENSTADT -0.129658\n",
      "55             PLZ8_GBZ -0.132682\n",
      "34          KONSUMNAEHE -0.137710\n",
      "36          KBA05_ANTG1 -0.180067\n",
      "40            KBA05_GBZ -0.181211\n",
      "50           PLZ8_ANTG1 -0.183000\n",
      "46           MOBI_REGIO -0.187957\n",
      "2     FINANZ_MINIMALIST -0.191273\n"
     ]
    }
   ],
   "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",
    "investigate_PC(pca, 0, azdias_scaled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Principal Component 2\n",
      "---------------\n",
      "\n",
      "TOP 10 PRINCIPAL COMPONENTS \n",
      "                      name    weight\n",
      "0    ALTERSKATEGORIE_GROB  0.232889\n",
      "4        FINANZ_VORSORGER  0.218994\n",
      "124            ZABEOTYP_3  0.199978\n",
      "17              SEMIO_ERL  0.180259\n",
      "16             SEMIO_LUST  0.163505\n",
      "10         RETOURTYP_BK_S  0.155426\n",
      "30         W_KEIT_KIND_HH  0.123088\n",
      "61      CJT_GESAMTTYP_2.0  0.108279\n",
      "70            FINANZTYP_5  0.098996\n",
      "100    LP_STATUS_FEIN_1.0  0.097016\n",
      "\n",
      " BOTTOM 10 PRINCIPAL COMPONENTS \n",
      "                       name    weight\n",
      "47       ONLINE_AFFINITAET -0.161964\n",
      "18              SEMIO_KULT -0.165087\n",
      "19               SEMIO_RAT -0.168245\n",
      "5           FINANZ_ANLEGER -0.204721\n",
      "23           SEMIO_PFLICHT -0.205375\n",
      "24             SEMIO_TRADV -0.208003\n",
      "13               SEMIO_REL -0.214557\n",
      "6    FINANZ_UNAUFFAELLIGER -0.214972\n",
      "3            FINANZ_SPARER -0.227059\n",
      "189           YOUTH_DECADE -0.230622\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "investigate_PC(pca, 1, azdias_scaled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Principal Component 3\n",
      "---------------\n",
      "\n",
      "TOP 10 PRINCIPAL COMPONENTS \n",
      "                   name    weight\n",
      "15          SEMIO_VERT  0.316899\n",
      "12           SEMIO_FAM  0.258536\n",
      "11           SEMIO_SOZ  0.255493\n",
      "18          SEMIO_KULT  0.249721\n",
      "70         FINANZTYP_5  0.137373\n",
      "2    FINANZ_MINIMALIST  0.130359\n",
      "118    SHOPPER_TYP_0.0  0.122717\n",
      "122         ZABEOTYP_1  0.119285\n",
      "13           SEMIO_REL  0.113028\n",
      "14           SEMIO_MAT  0.088319\n",
      "\n",
      " BOTTOM 10 PRINCIPAL COMPONENTS \n",
      "                    name    weight\n",
      "101  LP_STATUS_FEIN_2.0 -0.079073\n",
      "120     SHOPPER_TYP_2.0 -0.092057\n",
      "66          FINANZTYP_1 -0.099714\n",
      "19            SEMIO_RAT -0.152760\n",
      "5        FINANZ_ANLEGER -0.155240\n",
      "17            SEMIO_ERL -0.207281\n",
      "20           SEMIO_KRIT -0.266130\n",
      "21            SEMIO_DOM -0.279330\n",
      "22           SEMIO_KAEM -0.311478\n",
      "1             ANREDE_KZ -0.342268\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "investigate_PC(pca, 2, azdias_scaled.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "*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",
    "##### 1st Principal Component\n",
    "The first principal component is explained mostly by the `LP_STATUS_GROB_1.0` variable which shows the social status: *low-income earner* of a specific person. The next three features with highest weight: `HH_EINKOMMEN_SCORE`, `CAMEO_INTL_2015_WEALTH`, `PLZ8_ANTG3`, are all also a certain measure of wealth or income. We can reconcile this with the fact that features with large weights in the same sign are a sign of correlation between the variables. Realistically, we would expect that each feature portraying wealth would be correlated.\n",
    "The three most negatively correlated features are: `FINANZ_MINIMALIST`,`KBA05_ANTG1`, `MOBI_REGIO`. For example: `FINANZ_MINIMALIST`, shows the lack of interest of someone in finance, which has an obvious negative correlation to wealth.\n",
    "\n",
    "##### 2nd Principal Component\n",
    "The second principal component is explained mostly by: `ALTERSKATEGORIE_GROB`,  `FINANZ_VORSORGER`, `ZABEOTYP_3`, and the least by: `YOUTH_DECADE`, `FINANZ_SPARER`, `SEMIO_REL`. This is a mixture of age, financial awareness, and environmental/religious awareness respectively. \n",
    "\n",
    "##### 3rd Principal Component\n",
    "I find this third component most interesting, as many of the features that explain this principal component relate to personality traits. The most explanatory feature is `SEMIO_VERT` which is a measure of 'dreamfulness', followed by `SEMIO_FAM`: family-mindedness, `SEMIO_SOZ`: socially-mindedness and so forth. The least explanatory features, and those most negatively correlated are interestingly: `ANREDE_KZ` which shows gender, `SEMIO_KAEM` which is a combative-minded feature, `SEMIO_DOM` which is a dominant-minded feature and so forth.\n",
    "\n",
    "------\n",
    "What is interesting is that as we move through the principal components, at least the first three, the weights on features become larger. This means that those features more strongly explain the principal component, after accounting for the variance captured by earlier principal components."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "I'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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_score=[]\n",
    "clusters = [2,4,6,8,10,12,14,16,18,20]\n",
    "# Over a number of different cluster counts...\n",
    "for i in clusters:\n",
    "    # run k-means clustering on the data and...\n",
    "    kmeans = KMeans(i).fit(pca_data)\n",
    "    \n",
    "    # compute the average within-cluster distances.\n",
    "    model_score.append(abs(kmeans.score(pca_data)))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could also use `Silhouette Coefficient` to analyze number of clusters: http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0aac3f6438>"
      ]
     },
     "metadata": {},
     "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",
    "fig, ax = plt.subplots()\n",
    "ax.plot(clusters, model_score)\n",
    "ax.set(ylabel='avg distance', xlabel='# Clusters: k')\n",
    "ax.grid()\n",
    "plt.xticks(np.arange(2,21,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(797426, 75)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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(10).fit(pca_data)\n",
    "kmeans_pred = kmeans.predict(pca_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "When investigating clustering and drawing scree-plot, I did not find a specific kink in the graph, but rather the average distance kept decreasing. The largest decrease happened in the first 5 clusters, before the benefit of adding an extra cluster starts levelling out. This is why I chose to segment the population into 10 clusters, a nice round number where the average distance is starting to level out on the scree plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that I have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, I interpret how the general population fits apply to the customer data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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_cleaned = clean_data(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEBAEUDETYP_5.0\n"
     ]
    }
   ],
   "source": [
    "for column in azdias_scaled.columns:\n",
    "    if column not in customers_cleaned.columns:\n",
    "        print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_cleaned['GEBAEUDETYP_5.0'] = 0\n",
    "#this column was missing from customer dataset after one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=10, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_reduced = pca.transform(scaler.transform(imputer.transform(customers_cleaned)))\n",
    "\n",
    "customer_clusters = kmeans.predict(customer_reduced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, I 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, I 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "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",
    "gen_pop = azdias_scaled.shape[0]\n",
    "gen_pop_all = azdias.shape[0] #including all dropped high NaN data\n",
    "\n",
    "customer_pop = customers_cleaned.shape[0]\n",
    "customer_pop_all = customers.shape[0] #including all customers with high NaN data\n",
    "\n",
    "gen_pop_cluster_count = np.bincount(kmeans_pred)\n",
    "customer_cluster_count = np.bincount(customer_clusters)\n",
    "\n",
    "gen_pop_cluster_prop = [x/gen_pop for x in gen_pop_cluster_count]\n",
    "customer_cluster_prop = [x/customer_pop for x in customer_cluster_count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_data = pd.DataFrame({'gen_pop': gen_pop_cluster_prop, 'customers': customer_cluster_prop}, index=np.arange(1,11))\n",
    "prop_data.index.names=['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create data frame where each entry in `gen_pop` is % of gen_pop in a cluster\n",
    "# each entry in `customers` is% of customer population in a cluster\n",
    "prop_data_all = pd.DataFrame({'gen_pop': [x/gen_pop_all for x in gen_pop_cluster_count], \n",
    "                              'customers': [x/customer_pop_all for x in customer_cluster_count]},\n",
    "                             index=np.arange(1,11))\n",
    "prop_data_all.index.names=['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0aac8cee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0aac8ceb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''CLUSTER COUNT - LOW NAN DATA'''\n",
    "\n",
    "plt.figure(figsize=(10,5)) # this creates a figure 14 inch wide, 4 inch high\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, sharey=False)\n",
    "fig.suptitle('Cluster COUNT - Low NaN Data')\n",
    "ax1.set_title('General Population')\n",
    "ax2.set_title('Customer Population')\n",
    "sns.countplot(kmeans_pred+1, ax=ax1)\n",
    "sns.countplot(customer_clusters+1, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0aacce49b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0aacce4860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''CLUSTER PROPORTION - LOW NAN DATA'''\n",
    "\n",
    "plt.figure(figsize=(14,4)) # this creates a figure 14 inch wide, 4 inch high\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, sharey=True)\n",
    "fig.suptitle('Cluster PROPORTION - Low NaN Data')\n",
    "ax1.set_title('General Population')\n",
    "ax2.set_title('Customer Population')\n",
    "sns.barplot(x=prop_data.index, y=prop_data.gen_pop, ax=ax1)\n",
    "sns.barplot(x=prop_data.index, y=prop_data.customers, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ab14acf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ab14c31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''CLUSTER PROPORTION - ALL DATA'''\n",
    "\n",
    "plt.figure(figsize=(14,4)) # this creates a figure 14 inch wide, 4 inch high\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, sharey=True)\n",
    "fig.suptitle('Cluster Proportion - All Data')\n",
    "ax1.set_title('General Population')\n",
    "ax2.set_title('Customer Population')\n",
    "sns.barplot(x=prop_data_all.index, y=prop_data_all.gen_pop, ax=ax1)\n",
    "sns.barplot(x=prop_data_all.index, y=prop_data_all.customers, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Cluster Representation - LOW NaN Data\n",
      "----------\n",
      "\n",
      "Cluster 1: UNDER represented by -8.32%\n",
      "Cluster 2: OVER represented by 6.42%\n",
      "Cluster 3: UNDER represented by -11.93%\n",
      "Cluster 4: OVER represented by 7.32%\n",
      "Cluster 5: UNDER represented by -2.24%\n",
      "Cluster 6: UNDER represented by -2.78%\n",
      "Cluster 7: UNDER represented by -7.32%\n",
      "Cluster 8: OVER represented by 24.98%\n",
      "Cluster 9: UNDER represented by -3.78%\n",
      "Cluster 10: UNDER represented by -2.34%\n"
     ]
    }
   ],
   "source": [
    "''' What kinds of people are part of a cluster that is overrepresented \n",
    "in the customer data compared to the general population?'''\n",
    "\n",
    "rep = {}\n",
    "\n",
    "print('\\n Cluster Representation - LOW NaN Data\\n----------\\n')\n",
    "for i in np.arange(0,10):\n",
    "    a = customer_cluster_prop[i]\n",
    "    b = gen_pop_cluster_prop[i]\n",
    "    rep[i+1] = (a-b)*100\n",
    "\n",
    "for x in rep:\n",
    "    if(rep[x]>0):\n",
    "        print('Cluster %s: OVER represented by %.2f%%'% (x, rep[x]))\n",
    "    elif(rep[x]<0):\n",
    "        print('Cluster %s: UNDER represented by %.2f%%'% (x, rep[x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Cluster Over Representation - FULL Population\n",
      "----------\n",
      "\n",
      "Cluster 1: UNDER represented by -7.57%\n",
      "Cluster 2: OVER represented by 4.06%\n",
      "Cluster 3: UNDER represented by -10.84%\n",
      "Cluster 4: OVER represented by 3.51%\n",
      "Cluster 5: UNDER represented by -3.65%\n",
      "Cluster 6: UNDER represented by -3.76%\n",
      "Cluster 7: UNDER represented by -6.74%\n",
      "Cluster 8: OVER represented by 16.42%\n",
      "Cluster 9: UNDER represented by -3.91%\n",
      "Cluster 10: UNDER represented by -3.09%\n"
     ]
    }
   ],
   "source": [
    "full_rep = {}\n",
    "\n",
    "print('\\n Cluster Over Representation - FULL Population\\n----------\\n')\n",
    "for i in np.arange(0,10):\n",
    "    a = prop_data_all.customers.iloc[i]\n",
    "    b = prop_data_all.gen_pop.iloc[i]\n",
    "    full_rep[i+1] = (a-b)*100\n",
    "\n",
    "for x in full_rep:\n",
    "    if(full_rep[x]>0):\n",
    "        print('Cluster %s: OVER represented by %.2f%%'% (x, full_rep[x]))\n",
    "    elif(full_rep[x]<0):\n",
    "        print('Cluster %s: UNDER represented by %.2f%%'% (x, full_rep[x]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_centers(cluster):\n",
    "    cluster_center = np.around(kmeans.cluster_centers_[cluster-1], decimals=2)\n",
    "    clusters_sorted = cluster_center[np.argsort(cluster_center)[:]]\n",
    "    pca_undone=np.around(pca.inverse_transform(kmeans.cluster_centers_[cluster-1]), \n",
    "                                                 decimals=2)\n",
    "    pca_undone_sorted = pca_undone[np.argsort(pca_undone)[:]]\n",
    "    print('Cluster %s' % cluster)\n",
    "    print('sorted array\\n-----\\n Top 3: %s \\n Bottom 3: %s '% (np.flip(clusters_sorted[-3:],0), \n",
    "                                                               clusters_sorted[:3]))\n",
    "    print('\\n original array\\n-----\\n %s'% cluster_center)\n",
    "    \n",
    "    print('PCA sorted\\n-----\\n Top 3: %s \\n Bottom 3: %s '% (np.flip(pca_undone_sorted[-3:],0), \n",
    "                                                               pca_undone_sorted[:3]))\n",
    "    print('\\n PCA Undone\\n-----\\n %s'% pca_undone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 3\n",
      "sorted array\n",
      "-----\n",
      " Top 3: [ 4.62  0.72  0.38] \n",
      " Bottom 3: [-2.43 -2.14 -0.53] \n",
      "\n",
      " original array\n",
      "-----\n",
      " [ 4.62 -2.14 -2.43  0.72 -0.26 -0.21 -0.29 -0.4   0.1   0.38 -0.47 -0.15\n",
      "  0.14 -0.05 -0.05 -0.04 -0.33  0.22 -0.05  0.02  0.07 -0.27  0.3   0.01\n",
      " -0.53  0.03 -0.06  0.2  -0.04  0.11  0.37 -0.06  0.02  0.14  0.1  -0.01\n",
      "  0.05 -0.22 -0.25 -0.01 -0.08 -0.04  0.05 -0.04 -0.1  -0.03 -0.02 -0.01\n",
      "  0.02 -0.01 -0.07 -0.03 -0.02  0.02  0.04 -0.04  0.    0.01  0.    0.02\n",
      "  0.    0.01  0.01  0.04  0.01 -0.    0.01 -0.   -0.    0.04 -0.03 -0.01\n",
      "  0.01  0.05  0.02]\n",
      "PCA sorted\n",
      "-----\n",
      " Top 3: [ 1.46  1.31  1.24] \n",
      " Bottom 3: [-1.4  -1.02 -0.97] \n",
      "\n",
      " PCA Undone\n",
      "-----\n",
      " [-0.97  0.93 -1.4   1.15 -0.88  1.03  0.84  0.82 -0.43  0.13 -0.63 -0.47\n",
      " -0.13  0.66  0.42 -1.02 -0.55 -0.11 -0.05  1.24  0.53  0.84  0.93  0.97\n",
      "  0.91 -0.02 -0.02 -0.26 -0.06  0.73 -0.04 -0.38  0.35  0.04 -0.48 -0.25\n",
      " -0.6   0.14  0.47  0.4  -0.62 -0.31  0.56 -0.43 -0.29  0.11 -0.63  0.17\n",
      "  0.14 -0.18 -0.6   0.45  0.63  0.6   0.18 -0.4   0.38  0.57  0.36 -0.07\n",
      " -0.26 -0.29  0.13 -0.07  0.24  0.27  1.31 -0.35 -0.17 -0.11 -0.39 -0.52\n",
      " -0.03 -0.12 -0.12 -0.1  -0.15 -0.1  -0.17 -0.01  0.15 -0.04  0.07  0.4\n",
      "  0.21 -0.14  0.12  0.16  0.06  0.01  0.   -0.09  0.04 -0.12 -0.16  0.21\n",
      " -0.14  0.2  -0.06 -0.19  0.02  1.46 -0.24 -0.25 -0.16 -0.15 -0.11 -0.15\n",
      " -0.4  -0.38  1.05 -0.42 -0.19 -0.44 -0.38 -0.33  0.15  0.33 -0.27  0.02\n",
      "  0.4  -0.22 -0.44 -0.17 -0.51  0.57  0.66 -0.05 -0.27 -0.06  0.16 -0.04\n",
      " -0.    0.    0.19 -0.15 -0.29 -0.24 -0.26 -0.03 -0.    0.13  0.29  0.4\n",
      " -0.06 -0.   -0.03 -0.13 -0.08 -0.12 -0.11 -0.15 -0.17 -0.13 -0.08 -0.14\n",
      " -0.11 -0.17 -0.07 -0.17 -0.03 -0.03  0.03 -0.05 -0.03  0.02 -0.03 -0.04\n",
      "  0.05 -0.03  0.04 -0.04 -0.02 -0.02  0.08  0.12  0.01  0.02 -0.02  0.24\n",
      "  0.14  0.12  0.04  0.24  0.2   0.17  0.2  -0.04 -0.43  0.93  0.62 -0.45\n",
      " -0.49]\n"
     ]
    }
   ],
   "source": [
    "cluster_centers(cluster=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 8\n",
      "sorted array\n",
      "-----\n",
      " Top 3: [ 3.26  1.06  0.69] \n",
      " Bottom 3: [-4.43 -1.01 -0.98] \n",
      "\n",
      " original array\n",
      "-----\n",
      " [-4.43  0.5   1.06  3.26 -0.9  -1.01 -0.98 -0.7  -0.01  0.1   0.45  0.44\n",
      "  0.25  0.    0.39 -0.23  0.09  0.21 -0.1   0.69 -0.66 -0.55  0.14 -0.03\n",
      "  0.19  0.2   0.07  0.18 -0.03 -0.13  0.16 -0.04 -0.07  0.26  0.08  0.06\n",
      "  0.05  0.05 -0.08 -0.01  0.02 -0.02 -0.05 -0.06  0.01  0.03  0.01 -0.02\n",
      " -0.01  0.07  0.01  0.    0.06  0.   -0.02  0.01 -0.03  0.05 -0.04  0.05\n",
      "  0.02  0.01 -0.02 -0.01  0.01 -0.02  0.01  0.04 -0.01 -0.03  0.02  0.05\n",
      " -0.03  0.04 -0.03]\n",
      "PCA sorted\n",
      "-----\n",
      " Top 3: [ 2.28  2.28  1.9 ] \n",
      " Bottom 3: [-1.34 -0.84 -0.81] \n",
      "\n",
      " PCA Undone\n",
      "-----\n",
      " [ 0.4  -0.08  1.06 -0.58  0.4  -0.58 -0.24 -0.81  1.9  -0.17  0.09  0.02\n",
      " -0.24 -0.39 -0.28  0.24  0.33  0.19 -0.19 -0.34 -0.14  0.05 -0.07 -0.4\n",
      " -0.25  0.02 -0.1   0.45 -0.1  -1.34 -0.2   0.33 -0.43 -0.12  0.29 -0.06\n",
      "  0.99 -0.15 -0.61 -0.45  0.77  0.01 -0.05  0.11  0.35 -0.51  0.91  0.35\n",
      " -0.52  0.2   0.62 -0.32 -0.47 -0.45  0.06  0.48 -0.22 -0.04 -0.19  0.28\n",
      "  0.02  0.18 -0.02 -0.11 -0.07  0.02 -0.58  0.37  0.35 -0.12  0.13  0.07\n",
      "  0.01 -0.04  0.14 -0.07  0.19  0.07  0.24  0.15 -0.1   0.04 -0.13 -0.33\n",
      " -0.46  0.17 -0.05 -0.04 -0.01 -0.03  0.06  0.13  0.01  0.26  0.25 -0.46\n",
      "  0.17 -0.05  0.11  0.37 -0.52 -0.36 -0.23 -0.3  -0.23 -0.2  -0.11 -0.16\n",
      " -0.45  2.28 -0.72 -0.47 -0.23 -0.49  2.28  0.24 -0.18 -0.14  0.06 -0.14\n",
      " -0.13  0.24  0.73  0.42 -0.1  -0.3  -0.34 -0.22  0.45  0.07 -0.41  0.03\n",
      "  0.03 -0.   -0.15  0.49  0.57  0.13  0.15 -0.03 -0.11 -0.19 -0.37 -0.36\n",
      "  0.14  0.09  0.12  0.45  0.19  0.23  0.12  0.25  0.43  0.06  0.04  0.06\n",
      "  0.08  0.03  0.01  0.13  0.09  0.06 -0.07  0.   -0.01 -0.01  0.02  0.03\n",
      " -0.03 -0.06 -0.02  0.04 -0.14 -0.01 -0.12 -0.12 -0.05 -0.06 -0.04 -0.21\n",
      " -0.18 -0.17 -0.11 -0.14 -0.19 -0.16 -0.18 -0.06  1.9  -0.41 -0.84  0.49\n",
      " -0.17]\n"
     ]
    }
   ],
   "source": [
    "cluster_centers(cluster=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 2\n",
      "sorted array\n",
      "-----\n",
      " Top 3: [ 2.17  1.94  0.72] \n",
      " Bottom 3: [-1.49 -1.42 -1.41] \n",
      "\n",
      " original array\n",
      "-----\n",
      " [-1.49  2.17 -1.42 -1.41 -1.    1.94 -0.64  0.65 -0.12 -0.21  0.7   0.41\n",
      " -0.5   0.09  0.04  0.06  0.72 -0.39  0.04 -0.26 -0.06  0.42  0.22  0.07\n",
      " -0.3  -0.09 -0.   -0.17 -0.03 -0.13 -0.39 -0.14 -0.1   0.19  0.1   0.2\n",
      " -0.07  0.17 -0.01  0.05  0.07 -0.12  0.12 -0.13  0.11  0.07  0.05  0.\n",
      "  0.09  0.07 -0.05  0.08  0.07  0.02  0.01 -0.07  0.04  0.01  0.05 -0.01\n",
      " -0.03  0.03  0.01  0.04 -0.06 -0.01 -0.01 -0.03  0.02 -0.06  0.01 -0.02\n",
      " -0.    0.11 -0.04]\n",
      "PCA sorted\n",
      "-----\n",
      " Top 3: [ 1.61  1.4   0.9 ] \n",
      " Bottom 3: [-0.73 -0.68 -0.68] \n",
      "\n",
      " PCA Undone\n",
      "-----\n",
      " [ 0.63  0.39  0.01 -0.61  0.61 -0.21 -0.61  0.54 -0.15 -0.14  0.35 -0.44\n",
      " -0.58 -0.68 -0.38 -0.29  0.41  0.67 -0.68 -0.37  0.41  0.14  0.21 -0.56\n",
      " -0.55 -0.04 -0.02 -0.27 -0.09  0.3   0.31  0.16 -0.17 -0.08  0.28 -0.15\n",
      "  0.02  0.36  0.17 -0.28  0.11  0.39 -0.61  0.47  0.18 -0.05  0.14 -0.56\n",
      " -0.14  0.27  0.34 -0.19 -0.37 -0.43 -0.    0.38 -0.32 -0.67 -0.37 -0.11\n",
      "  0.16  0.4  -0.15 -0.04 -0.2  -0.18 -0.28  0.37 -0.42 -0.2   0.05  0.27\n",
      "  0.04 -0.18 -0.04  0.04  0.14  0.05 -0.06  0.05 -0.2   0.04 -0.04  0.08\n",
      "  0.25  0.04 -0.07 -0.03 -0.05 -0.08 -0.11 -0.14 -0.03 -0.08 -0.19  0.25\n",
      "  0.04 -0.08 -0.2  -0.18 -0.45 -0.45  0.9   1.4  -0.01 -0.15 -0.11 -0.11\n",
      " -0.36 -0.32 -0.73  1.61 -0.19 -0.38 -0.32  0.21 -0.17 -0.11 -0.18 -0.04\n",
      "  0.1   0.09 -0.44 -0.24  0.66 -0.18 -0.26  0.16  0.06 -0.17 -0.11 -0.09\n",
      "  0.02 -0.04  0.09 -0.02 -0.05  0.15  0.11  0.03  0.2   0.02 -0.14 -0.26\n",
      "  0.06  0.04  0.   -0.11 -0.01 -0.09 -0.06 -0.06  0.07 -0.05 -0.07  0.15\n",
      "  0.13  0.03 -0.01  0.12  0.07 -0.04  0.05  0.01 -0.02  0.    0.03  0.02\n",
      "  0.02  0.17  0.03  0.04  0.07  0.    0.05 -0.04  0.01 -0.02  0.07 -0.12\n",
      " -0.13  0.    0.01 -0.1  -0.1  -0.17 -0.16  0.04 -0.15 -0.6  -0.16  0.28\n",
      "  0.64]\n"
     ]
    }
   ],
   "source": [
    "cluster_centers(cluster=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "When including the population count for all including those we dropped for analysis as they had missing data, the culsters: `8`,`2`,`4` were the most over represented in the customer data compared to the general population data (`16.42%`, `4.06%%`, `3.51%`. These percentages were higher for low-NaN data. Based on the full general population data, it seems that clusters `4`, `5`, `6`, `9`, `10` were closely represented in both general population and customer data (difference of < 4%).\n",
    "\n",
    "The must under-represented compared to full population data were clusters: `3`, `1`, and `7` by a substantial `10.84%`, `7.57%`, and `6.74%` respectively.\n",
    "\n",
    "To see which features define each cluster the most, I took the inverse transformation of the PCA, which gives us back the original features but on a standard scale. This is much easier to interpret than the principal components themselves.\n",
    "\n",
    "#### Over-Represented Cluster: #8\n",
    "* `LP_STATUS_FEIN_10.0` is the highest impact feature in the 8th cluster. It represents the highest income housholds and highest social status.\n",
    "    * `'LP_STATUS_GROB_5.0` represents the same aspect but on a less refined scale\n",
    "* `GREEN_AVANTGARDE` is the feature with the third highest impact in the 8th cluster. It represents participation and interest in environmental issues during a person's youth.\n",
    "* the least impactful feature in the 8th cluster is `HH_EINKOMMEN_SCORE`, which corroberates our first point as it is correlated with low income, but high income and status is the biggest definer of our cluster.\n",
    "\n",
    "Therefore, people with a high income and high social status who are also environmentally conscious are significantly over represented in the customer data in comparison to the general population. This is a good target audience for any marketing campaing.\n",
    "\n",
    "----\n",
    "\n",
    "#### Over-Represented Cluster: #2\n",
    "Two of the features cluster two is characterized by are:\n",
    "* `LP_STATUS_GROB_2.0`: average income and social status\n",
    "* `LP_STATUS_FEIN_4.0`: villagers\n",
    "\n",
    "While average earning villagers are over-represented in the customer data, they are more closely represented than the people in cluster #8 above. Nevertheless, it could still probably be worth to target these people in a campaign\n",
    "\n",
    "----\n",
    "\n",
    "#### Under-Represented Cluster: #3\n",
    "* `LP_STATUS_FEIN_2.0` is the biggest factor in the third cluster, and that represents orientation-seeking low-income earners. \n",
    "* `FINANZTYP_1` is the second most important factor, and it represents people who are not interested in finance.\n",
    "* `SEMIO_RAT` is a variable that portrays rationality (higher values means less rational).\n",
    "\n",
    "This means that low income people who are both irrational and do not care for finance much are under-represented in the customer population (which makes sense since this is a financial services company).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### Could Also Visualize Clustering in 2 Dimensions:\n",
    "\n",
    "```python\n",
    "reduced_data = PCA(n_components=2).fit_transform(df)\n",
    "clusters = KMeans(n_clusters=best_k, random_state=100).fit(reduced_data)\n",
    "\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "hx = (x_max-x_min)/1000.\n",
    "hy = (y_max-y_min)/1000.\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, hx), np.arange(y_min, y_max, hy))\n",
    "\n",
    "Z = clusters.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "centroids = clusters.cluster_centers_\n",
    "\n",
    "def PCA_plot(Z, centroids):\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure(1, figsize=(20, 10))\n",
    "    plt.clf()\n",
    "    plt.imshow(Z, interpolation='nearest',\n",
    "               extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "               cmap=plt.cm.Paired,\n",
    "               aspect='auto', origin='lower')\n",
    "\n",
    "    plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.',\n",
    "             markersize=2, alpha=0.1)\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='x', s=169, linewidths=3,\n",
    "                color='w', zorder=10)\n",
    "    plt.title('Clustering on the dataset (PCA-reduced data)\\n'\n",
    "              'Centroids are marked with white cross')\n",
    "    plt.xlim(x_min, x_max)\n",
    "    plt.ylim(y_min, y_max)\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.show()\n",
    "\n",
    "PCA_plot(Z, centroids)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}