Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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\n",
    "use has been provided by Bertelsmann Arvato Analytics, and represents a real-life data science task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "from lib.workspace_utils import active_session\n",
    "import time \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\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",
    "All of the `.csv` data files in this project: they're semicolon (`;`) delimited, so I need an additional argument in  [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', delimiter=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
       "2        -1                     3          2            3.0   \n",
       "3         2                     4          2            2.0   \n",
       "4        -1                     3          1            5.0   \n",
       "\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",
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       "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",
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       "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/plain": [
       "(891221, 85)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "azdias.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <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",
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       "      <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",
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       "      <th>std</th>\n",
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       "      <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",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>3.000000</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>3.00000</td>\n",
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       "      <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",
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       "      <td>5.000000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINANZ_MINIMALIST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "  </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[-1,0]', '[-1,0,9]', '[0]', '[-1]', '[]', '[-1,9]', '[-1,X]',\n",
       "       '[XX]', '[-1,XX]'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique types of how missing values were encoded\n",
    "feat_info['missing_or_unknown'].unique()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Summary: we see that there are missing values that have been encoded with \"X\" or \"XX\". Let's have a closer look as to what columns have these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57    CAMEO_DEUG_2015\n",
      "Name: attribute, dtype: object\n",
      "58    CAMEO_DEU_2015\n",
      "Name: attribute, dtype: object\n",
      "59    CAMEO_INTL_2015\n",
      "Name: attribute, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#which columns have strange encodings with 'X' values?\n",
    "print(feat_info['attribute'][feat_info['missing_or_unknown']=='[-1,X]'])\n",
    "print(feat_info['attribute'][feat_info['missing_or_unknown']=='[XX]'])\n",
    "print(feat_info['attribute'][feat_info['missing_or_unknown']=='[-1,XX]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(373, 85)\n",
      "(373, 85)\n",
      "(373, 85)\n"
     ]
    }
   ],
   "source": [
    "# how many values were encoded with X or XX?\n",
    "print(azdias[azdias['CAMEO_DEUG_2015'] =='X'].shape)\n",
    "print(azdias[azdias['CAMEO_DEU_2015'] =='XX'].shape)\n",
    "print(azdias[azdias['CAMEO_INTL_2015'] =='XX'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "373\n",
      "373\n",
      "373\n"
     ]
    }
   ],
   "source": [
    "#are these the same rows that miss data for the three columns with 'X' encodings?\n",
    "print((azdias[azdias['CAMEO_DEUG_2015'] =='X'].index == azdias[azdias['CAMEO_DEU_2015'] =='XX'].index).sum())\n",
    "print((azdias[azdias['CAMEO_DEU_2015'] =='XX'].index == azdias[azdias['CAMEO_INTL_2015'] =='XX'].index).sum())\n",
    "print((azdias[azdias['CAMEO_DEUG_2015'] =='X'].index == azdias[azdias['CAMEO_INTL_2015'] =='XX'].index).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary: it seems like there are 373 rows that have missing values encoded with either 'X' or 'XX' and all these values come from just three columns 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015' and 'CAMEO_INTL_2015'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4896838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NaNs in the original dataset\n",
    "nans_origin = azdias.isna().sum().sum()\n",
    "nans_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    838727\n",
       "True      52494\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.duplicated().value_counts()"
   ]
  },
  {
   "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. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From initial look at the data, we know that the missing/unknown values were encoded either with digits -1, 0, 9 or with letters 'X' or 'XX'. In the following, I first convert X/XX values to NaNs. Then I convert digits to NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_clean = azdias.replace({'X': np.nan, 'XX': np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_missing_in_featinfo(col):\n",
    "    col = col.apply(lambda x: '[-1]' if x in ['[-1,X]', '[XX]', '[-1,XX]'] else x)\n",
    "    col = [eval(col[i]) for i in col.index]\n",
    "    return col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_info['missing_transform'] = transform_missing_in_featinfo(feat_info['missing_or_unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [-1, 0]\n",
       "1    [-1, 0, 9]\n",
       "2       [-1, 0]\n",
       "3           [0]\n",
       "4          [-1]\n",
       "Name: missing_transform, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info['missing_transform'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: we cannot just replace all -1, 0 or 9 values with NaNs. What if they are meaningful in another column? So we need to go through each column and replace values that were encoded as missing specifically for this column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs\n",
    "for i, col in enumerate(feat_info['attribute']):\n",
    "    azdias_clean.loc[azdias_clean[col].isin(feat_info['missing_transform'][i]), col] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternative way:\n",
    "#finding all digits in a string object '[-1, 0, 9]' and converting it to a list \n",
    "\n",
    "#import re\n",
    "#for i in feat_info['missing_or_unknown'].index:\n",
    "#    feat_info['missing_or_unknown'][i] = [int(s) for s in re.findall(r'-?\\d+', feat_info['missing_or_unknown'][i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8373929"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of NaNs after conversion:\n",
    "azdias_clean.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: In the original dataset we had 4896838 naturally missing values. After conversion we have now 8373929 NaNs. So, there are 3477091 values that were encoded as 'missing' or 'unknown'. <br>\n",
    "Is it a lot? Well, given that the original dataset has 891221 x 85 = 75753785 datapoints, the final prop of missing values after conversion is about 11%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>missing_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>0.769554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>0.003233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>0.005446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINANZ_MINIMALIST</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FINANZ_SPARER</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FINANZ_VORSORGER</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FINANZ_ANLEGER</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FINANZ_UNAUFFAELLIGER</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANZ_HAUSBAUER</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>0.440203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>0.005446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>0.124768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 features  missing_prop\n",
       "0                AGER_TYP      0.769554\n",
       "1    ALTERSKATEGORIE_GROB      0.003233\n",
       "2               ANREDE_KZ      0.000000\n",
       "3           CJT_GESAMTTYP      0.005446\n",
       "4       FINANZ_MINIMALIST      0.000000\n",
       "5           FINANZ_SPARER      0.000000\n",
       "6        FINANZ_VORSORGER      0.000000\n",
       "7          FINANZ_ANLEGER      0.000000\n",
       "8   FINANZ_UNAUFFAELLIGER      0.000000\n",
       "9        FINANZ_HAUSBAUER      0.000000\n",
       "10              FINANZTYP      0.000000\n",
       "11            GEBURTSJAHR      0.440203\n",
       "12        GFK_URLAUBERTYP      0.005446\n",
       "13       GREEN_AVANTGARDE      0.000000\n",
       "14             HEALTH_TYP      0.124768"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the NaNs in the data frame\n",
    "col_missing_prop = pd.DataFrame(azdias_clean.isna().mean(axis=0)).reset_index()\n",
    "col_missing_prop.columns = ['features', 'missing_prop']\n",
    "col_missing_prop.head(15)"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>missing_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TITEL_KZ</td>\n",
       "      <td>0.997576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>0.769554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>KK_KUNDENTYP</td>\n",
       "      <td>0.655967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>KBA05_BAUMAX</td>\n",
       "      <td>0.534687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>0.440203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ALTER_HH</td>\n",
       "      <td>0.348137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>KKK</td>\n",
       "      <td>0.177357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>REGIOTYP</td>\n",
       "      <td>0.177357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>W_KEIT_KIND_HH</td>\n",
       "      <td>0.166051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>KBA05_ANTG1</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>KBA05_ANTG2</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>KBA05_ANTG3</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>KBA05_ANTG4</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>KBA05_GBZ</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MOBI_REGIO</td>\n",
       "      <td>0.149597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          features  missing_prop\n",
       "40        TITEL_KZ      0.997576\n",
       "0         AGER_TYP      0.769554\n",
       "47    KK_KUNDENTYP      0.655967\n",
       "64    KBA05_BAUMAX      0.534687\n",
       "11     GEBURTSJAHR      0.440203\n",
       "43        ALTER_HH      0.348137\n",
       "70             KKK      0.177357\n",
       "73        REGIOTYP      0.177357\n",
       "48  W_KEIT_KIND_HH      0.166051\n",
       "60     KBA05_ANTG1      0.149597\n",
       "61     KBA05_ANTG2      0.149597\n",
       "62     KBA05_ANTG3      0.149597\n",
       "63     KBA05_ANTG4      0.149597\n",
       "65       KBA05_GBZ      0.149597\n",
       "71      MOBI_REGIO      0.149597"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the dataset.\n",
    "col_missing_prop_sorted = col_missing_prop.sort_values(by=['missing_prop'], ascending=False)\n",
    "col_missing_prop_sorted.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7383984048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "col_missing_values = azdias_clean.isna().sum(axis=0)\n",
    "plt.hist(col_missing_values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary: the majority of columns seem to have less than 200000 missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40        TITEL_KZ\n",
       "0         AGER_TYP\n",
       "47    KK_KUNDENTYP\n",
       "64    KBA05_BAUMAX\n",
       "11     GEBURTSJAHR\n",
       "43        ALTER_HH\n",
       "Name: features, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_del = col_missing_prop_sorted['features'][:6]\n",
    "col_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data engineering tasks such as re-encoding and imputation later.)\n",
    "azdias_fin = azdias_clean.drop(columns=col_del)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891221, 79)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "The analysis of missing values revealed that we have 6 columns with missing values for more than 200000 rows or in other words more than 30% of data in these columns is missing. These columns are: \n",
    "- TITEL_KZ\n",
    "- AGER_TYP\n",
    "- KK_KUNDENTYP\n",
    "- KBA05_BAUMAX\n",
    "- GEBURTSJAHR\n",
    "- ALTER_HH \n",
    "<br><br>\n",
    "Because of large amount of missing values, we cannot use imputation methods (or it will eventually skew the results) for these columns and hence it was decided to remove them from further analysis completely. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: I decided to look at missing values in each row on the dataset resulting from the previous step (not the original one) - i.e. the one with deleted 6 columns. I find that in this way I could retain more rows - e.g. when dividing into a subset with below 5 missing values in a row I get 719624 rows on the dataset with 79 columns vs. 599243 rows in the original dataset with 85 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>582109</td>\n",
       "      <td>0.594937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439981</th>\n",
       "      <td>439981</td>\n",
       "      <td>0.594937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rows_num  missing_prop\n",
       "732775    732775      0.620253\n",
       "643174    643174      0.620253\n",
       "472919    472919      0.607595\n",
       "345274    345274      0.594937\n",
       "299868    299868      0.594937\n",
       "582092    582092      0.594937\n",
       "681255    681255      0.594937\n",
       "818343    818343      0.594937\n",
       "582109    582109      0.594937\n",
       "439981    439981      0.594937"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "row_missing_prop = pd.DataFrame(azdias_fin.isna().mean(axis=1)).reset_index()\n",
    "row_missing_prop.columns = ['rows_num', 'missing_prop']\n",
    "row_missing_prop_sorted = row_missing_prop.sort_values(by=['missing_prop'], ascending=False)\n",
    "row_missing_prop_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73c4fd12b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_missing_values = azdias_fin.isna().sum(axis=1)\n",
    "plt.hist(row_missing_values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary**: based on the histogram, it seems like there are two different patterns for row missing values. About 800000 or something like 90% of rows have missing values in less than 20 columns, while the rest has much more  missing values - in more than 20 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subset_less_missing shape: (797426, 79)\n",
      "subset_more_missing shape: (93795, 79)\n",
      "prop of missing values in subset_less_missing relative to all datapoints: 1.59%\n",
      "prop of missing values in subset_less_missing relative to all datapoints: 6.41%\n"
     ]
    }
   ],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "subset_less_missing = azdias_fin[row_missing_values <= 20]\n",
    "subset_more_missing = azdias_fin[row_missing_values > 20]\n",
    "\n",
    "print(\"subset_less_missing shape:\", subset_less_missing.shape)\n",
    "print(\"subset_more_missing shape:\", subset_more_missing.shape)\n",
    "print(\"prop of missing values in subset_less_missing relative to all datapoints: {:.2f}%\".format(100*subset_less_missing.isna().sum().sum()/subset_less_missing.size))\n",
    "print(\"prop of missing values in subset_less_missing relative to all datapoints: {:.2f}%\".format(100*subset_more_missing.isna().sum().sum()/subset_less_missing.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ZABEOTYP', 'SEMIO_TRADV', 'SEMIO_PFLICHT', 'SEMIO_KAEM', 'SEMIO_DOM',\n",
       "       'SEMIO_KRIT', 'SEMIO_RAT', 'SEMIO_KULT', 'SEMIO_ERL', 'SEMIO_LUST',\n",
       "       'SEMIO_VERT', 'SEMIO_MAT', 'SEMIO_REL', 'SEMIO_SOZ', 'SEMIO_FAM',\n",
       "       'FINANZ_VORSORGER', 'ANREDE_KZ', 'FINANZ_MINIMALIST', 'FINANZ_SPARER',\n",
       "       'FINANZ_ANLEGER'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's create a list of top 20 columns that has the least of missing values (aka non-missing features)\n",
    "col_nomissing = azdias_clean.isna().sum(axis=0).sort_values(ascending=True).head(20).index\n",
    "col_nomissing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
    "def plot_subset_countplot_by_column(col):\n",
    "    base_color = sns.color_palette()[0]\n",
    "    plt.figure(figsize=(6, 2))\n",
    "    plt.subplots_adjust(wspace=0.5)\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.hist(data = subset_more_missing, x = col, color = base_color);\n",
    "    #plt.hist(subset_more_missing[col])\n",
    "    plt.xlabel(col)\n",
    "    plt.title('subset_more_missing')\n",
    "    \n",
    "    plt.subplot(1,2,2)\n",
    "    plt.hist(data = subset_less_missing, x = col, color = base_color);\n",
    "    #plt.hist(subset_less_missing[col])\n",
    "    plt.xlabel(col)\n",
    "    plt.title('subset_less_missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738399ec88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f738399ef60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a304e358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a2e22e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a3011080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change the values of col_nomissing[i] between 0 and 19  \n",
    "# to see the quality of distributions for these columns for the two subsets \n",
    "plot_subset_countplot_by_column(col_nomissing[0]);\n",
    "plot_subset_countplot_by_column(col_nomissing[4]);\n",
    "plot_subset_countplot_by_column(col_nomissing[8]);\n",
    "plot_subset_countplot_by_column(col_nomissing[10]);\n",
    "plot_subset_countplot_by_column(col_nomissing[12]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of columns: 79\n",
      "number of rows in the original dataset: 891221\n",
      "number of rows in the final dataset: 797426\n",
      "prop of final dataset with no missing data: 89.48%\n"
     ]
    }
   ],
   "source": [
    "azdias_fin = subset_less_missing\n",
    "\n",
    "#checking the size of the new dataset without 6 columns\n",
    "print(\"number of columns:\", azdias_fin.shape[1])\n",
    "print(\"number of rows in the original dataset:\", azdias.shape[0])\n",
    "print(\"number of rows in the final dataset:\", azdias_fin.shape[0])\n",
    "print(\"prop of final dataset with no missing data: {0:.2f}%\".format(100*azdias_fin.shape[0]/azdias.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, I need to make a few encoding changes. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    21\n",
       "mixed           7\n",
       "numeric         7\n",
       "interval        1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "feat_info['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, I need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AGER_TYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ANREDE_KZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>FINANZTYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>38</td>\n",
       "      <td>SHOPPER_TYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "      <td>SOHO_KZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40</td>\n",
       "      <td>TITEL_KZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41</td>\n",
       "      <td>VERS_TYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>ZABEOTYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>KK_KUNDENTYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>52</td>\n",
       "      <td>GEBAEUDETYP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55</td>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>58</td>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index         attribute\n",
       "0       0          AGER_TYP\n",
       "1       2         ANREDE_KZ\n",
       "2       3     CJT_GESAMTTYP\n",
       "3      10         FINANZTYP\n",
       "4      12   GFK_URLAUBERTYP\n",
       "5      13  GREEN_AVANTGARDE\n",
       "6      17   LP_FAMILIE_FEIN\n",
       "7      18   LP_FAMILIE_GROB\n",
       "8      19    LP_STATUS_FEIN\n",
       "9      20    LP_STATUS_GROB\n",
       "10     21  NATIONALITAET_KZ\n",
       "11     38       SHOPPER_TYP\n",
       "12     39           SOHO_KZ\n",
       "13     40          TITEL_KZ\n",
       "14     41          VERS_TYP\n",
       "15     42          ZABEOTYP\n",
       "16     47      KK_KUNDENTYP\n",
       "17     52       GEBAEUDETYP\n",
       "18     55       OST_WEST_KZ\n",
       "19     57   CAMEO_DEUG_2015\n",
       "20     58    CAMEO_DEU_2015"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "categorical_col = feat_info[feat_info['type'] == 'categorical'].attribute.reset_index()\n",
    "categorical_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_col['unique'] = [azdias_clean[categorical_col['attribute'][i]].unique() for i in range(categorical_col.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_col['num_categories'] = [len(azdias_clean[categorical_col['attribute'][i]].unique()) for i in range(categorical_col.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>attribute</th>\n",
       "      <th>unique</th>\n",
       "      <th>num_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>[nan, 2.0, 3.0, 1.0]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>[1.0, 2.0]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>[2.0, 5.0, 3.0, 4.0, 1.0, 6.0, nan]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>[4.0, 1.0, 6.0, 5.0, 2.0, 3.0]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>[10.0, 1.0, 5.0, 12.0, 9.0, 3.0, 8.0, 11.0, 4....</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>[0.0, 1.0]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>[2.0, 5.0, 1.0, nan, 10.0, 7.0, 11.0, 3.0, 8.0...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>[2.0, 3.0, 1.0, nan, 5.0, 4.0]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "      <td>[1.0, 2.0, 3.0, 9.0, 4.0, 10.0, 5.0, 8.0, 6.0,...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>[1.0, 2.0, 4.0, 5.0, 3.0, nan]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "      <td>[nan, 1.0, 3.0, 2.0]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>38</td>\n",
       "      <td>SHOPPER_TYP</td>\n",
       "      <td>[nan, 3.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "      <td>SOHO_KZ</td>\n",
       "      <td>[nan, 1.0, 0.0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40</td>\n",
       "      <td>TITEL_KZ</td>\n",
       "      <td>[nan, 4.0, 1.0, 3.0, 5.0, 2.0]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41</td>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>[nan, 2.0, 1.0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>ZABEOTYP</td>\n",
       "      <td>[3.0, 5.0, 4.0, 1.0, 6.0, 2.0]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>KK_KUNDENTYP</td>\n",
       "      <td>[nan, 1.0, 3.0, 6.0, 4.0, 2.0, 5.0]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>52</td>\n",
       "      <td>GEBAEUDETYP</td>\n",
       "      <td>[nan, 8.0, 1.0, 3.0, 2.0, 6.0, 4.0, 5.0]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55</td>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>[nan, W, O]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>[nan, 8, 4, 2, 6, 1, 9, 5, 7, 3]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>58</td>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>[nan, 8A, 4C, 2A, 6B, 8C, 4A, 2D, 1A, 1E, 9D, ...</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index         attribute  \\\n",
       "0       0          AGER_TYP   \n",
       "1       2         ANREDE_KZ   \n",
       "2       3     CJT_GESAMTTYP   \n",
       "3      10         FINANZTYP   \n",
       "4      12   GFK_URLAUBERTYP   \n",
       "5      13  GREEN_AVANTGARDE   \n",
       "6      17   LP_FAMILIE_FEIN   \n",
       "7      18   LP_FAMILIE_GROB   \n",
       "8      19    LP_STATUS_FEIN   \n",
       "9      20    LP_STATUS_GROB   \n",
       "10     21  NATIONALITAET_KZ   \n",
       "11     38       SHOPPER_TYP   \n",
       "12     39           SOHO_KZ   \n",
       "13     40          TITEL_KZ   \n",
       "14     41          VERS_TYP   \n",
       "15     42          ZABEOTYP   \n",
       "16     47      KK_KUNDENTYP   \n",
       "17     52       GEBAEUDETYP   \n",
       "18     55       OST_WEST_KZ   \n",
       "19     57   CAMEO_DEUG_2015   \n",
       "20     58    CAMEO_DEU_2015   \n",
       "\n",
       "                                               unique  num_categories  \n",
       "0                                [nan, 2.0, 3.0, 1.0]               4  \n",
       "1                                          [1.0, 2.0]               2  \n",
       "2                 [2.0, 5.0, 3.0, 4.0, 1.0, 6.0, nan]               7  \n",
       "3                      [4.0, 1.0, 6.0, 5.0, 2.0, 3.0]               6  \n",
       "4   [10.0, 1.0, 5.0, 12.0, 9.0, 3.0, 8.0, 11.0, 4....              13  \n",
       "5                                          [0.0, 1.0]               2  \n",
       "6   [2.0, 5.0, 1.0, nan, 10.0, 7.0, 11.0, 3.0, 8.0...              12  \n",
       "7                      [2.0, 3.0, 1.0, nan, 5.0, 4.0]               6  \n",
       "8   [1.0, 2.0, 3.0, 9.0, 4.0, 10.0, 5.0, 8.0, 6.0,...              11  \n",
       "9                      [1.0, 2.0, 4.0, 5.0, 3.0, nan]               6  \n",
       "10                               [nan, 1.0, 3.0, 2.0]               4  \n",
       "11                          [nan, 3.0, 2.0, 1.0, 0.0]               5  \n",
       "12                                    [nan, 1.0, 0.0]               3  \n",
       "13                     [nan, 4.0, 1.0, 3.0, 5.0, 2.0]               6  \n",
       "14                                    [nan, 2.0, 1.0]               3  \n",
       "15                     [3.0, 5.0, 4.0, 1.0, 6.0, 2.0]               6  \n",
       "16                [nan, 1.0, 3.0, 6.0, 4.0, 2.0, 5.0]               7  \n",
       "17           [nan, 8.0, 1.0, 3.0, 2.0, 6.0, 4.0, 5.0]               8  \n",
       "18                                        [nan, W, O]               3  \n",
       "19                   [nan, 8, 4, 2, 6, 1, 9, 5, 7, 3]              10  \n",
       "20  [nan, 8A, 4C, 2A, 6B, 8C, 4A, 2D, 1A, 1E, 9D, ...              45  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>attribute</th>\n",
       "      <th>unique</th>\n",
       "      <th>num_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "      <td>SOHO_KZ</td>\n",
       "      <td>[nan, 1.0, 0.0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41</td>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>[nan, 2.0, 1.0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55</td>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>[nan, W, O]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index    attribute           unique  num_categories\n",
       "12     39      SOHO_KZ  [nan, 1.0, 0.0]               3\n",
       "14     41     VERS_TYP  [nan, 2.0, 1.0]               3\n",
       "18     55  OST_WEST_KZ      [nan, W, O]               3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# because we have NaNs, those binary categorical values don't need to be reencoded that have 3 unique values\n",
    "categorical_col[categorical_col['num_categories'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's deal first with 'OST_WEST_KZ'\n",
    "azdias_fin = pd.get_dummies(data = azdias_fin, columns = ['OST_WEST_KZ'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             AGER_TYP\n",
       "2        CJT_GESAMTTYP\n",
       "3            FINANZTYP\n",
       "4      GFK_URLAUBERTYP\n",
       "6      LP_FAMILIE_FEIN\n",
       "7      LP_FAMILIE_GROB\n",
       "8       LP_STATUS_FEIN\n",
       "9       LP_STATUS_GROB\n",
       "10    NATIONALITAET_KZ\n",
       "11         SHOPPER_TYP\n",
       "13            TITEL_KZ\n",
       "15            ZABEOTYP\n",
       "16        KK_KUNDENTYP\n",
       "17         GEBAEUDETYP\n",
       "19     CAMEO_DEUG_2015\n",
       "20      CAMEO_DEU_2015\n",
       "Name: attribute, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choosing multi-categorical features and dropping OST_WEST_KZ column because it was reencoded separately\n",
    "cat_var_reencode = categorical_col[(categorical_col['num_categories'] > 3)].attribute\n",
    "cat_var_reencode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             AGER_TYP\n",
       "2        CJT_GESAMTTYP\n",
       "3            FINANZTYP\n",
       "4      GFK_URLAUBERTYP\n",
       "6      LP_FAMILIE_FEIN\n",
       "7      LP_FAMILIE_GROB\n",
       "8       LP_STATUS_FEIN\n",
       "9       LP_STATUS_GROB\n",
       "10    NATIONALITAET_KZ\n",
       "11         SHOPPER_TYP\n",
       "13            TITEL_KZ\n",
       "15            ZABEOTYP\n",
       "16        KK_KUNDENTYP\n",
       "17         GEBAEUDETYP\n",
       "19     CAMEO_DEUG_2015\n",
       "Name: attribute, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the last feature 'CAMEO_DEU_2015' has 45 values to reencode, will be dropped here:\n",
    "cat_var_reencode = cat_var_reencode[:-1]\n",
    "cat_var_reencode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             AGER_TYP\n",
       "2        CJT_GESAMTTYP\n",
       "3            FINANZTYP\n",
       "4      GFK_URLAUBERTYP\n",
       "7      LP_FAMILIE_GROB\n",
       "9       LP_STATUS_GROB\n",
       "10    NATIONALITAET_KZ\n",
       "11         SHOPPER_TYP\n",
       "13            TITEL_KZ\n",
       "15            ZABEOTYP\n",
       "16        KK_KUNDENTYP\n",
       "17         GEBAEUDETYP\n",
       "19     CAMEO_DEUG_2015\n",
       "Name: attribute, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since all features with 'GROB' are aggregations of the equivalent 'FEIN' features, we can drop 'FEIN'\n",
    "cat_var_reencode = cat_var_reencode.drop(labels=[6, 8])\n",
    "cat_var_reencode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#updating azdias_fin\n",
    "azdias_fin = azdias_fin.drop('CAMEO_DEU_2015', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# updating azdias_fin\n",
    "col_fein = ['LP_FAMILIE_FEIN', 'LP_STATUS_FEIN']\n",
    "azdias_fin = azdias_fin.drop(col_fein, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we need to filter columns that are in the final azdias dataset (because we removed some columns from the analysis when handling missing data)\n",
    "cat_var_reencode_fin = azdias_fin.columns[azdias_fin.columns.isin(cat_var_reencode)].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CJT_GESAMTTYP', 'FINANZTYP', 'GFK_URLAUBERTYP', 'LP_FAMILIE_GROB', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015']\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "print(cat_var_reencode_fin)\n",
    "print(len(cat_var_reencode_fin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_fin = pd.get_dummies(data = azdias_fin, columns = cat_var_reencode_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking new number of columns after reencoding \n",
    "len(azdias_fin.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OST_WEST_KZ_W', 'CJT_GESAMTTYP_1.0', 'CJT_GESAMTTYP_2.0',\n",
       "       'CJT_GESAMTTYP_3.0', 'CJT_GESAMTTYP_4.0', 'CJT_GESAMTTYP_5.0',\n",
       "       'CJT_GESAMTTYP_6.0', 'FINANZTYP_1.0', 'FINANZTYP_2.0', 'FINANZTYP_3.0',\n",
       "       'FINANZTYP_4.0', 'FINANZTYP_5.0', 'FINANZTYP_6.0',\n",
       "       'GFK_URLAUBERTYP_1.0', 'GFK_URLAUBERTYP_2.0', 'GFK_URLAUBERTYP_3.0',\n",
       "       'GFK_URLAUBERTYP_4.0', 'GFK_URLAUBERTYP_5.0', 'GFK_URLAUBERTYP_6.0',\n",
       "       'GFK_URLAUBERTYP_7.0', 'GFK_URLAUBERTYP_8.0', 'GFK_URLAUBERTYP_9.0',\n",
       "       'GFK_URLAUBERTYP_10.0', 'GFK_URLAUBERTYP_11.0', 'GFK_URLAUBERTYP_12.0',\n",
       "       'LP_FAMILIE_GROB_1.0', 'LP_FAMILIE_GROB_2.0', 'LP_FAMILIE_GROB_3.0',\n",
       "       'LP_FAMILIE_GROB_4.0', 'LP_FAMILIE_GROB_5.0', 'LP_STATUS_GROB_1.0',\n",
       "       'LP_STATUS_GROB_2.0', 'LP_STATUS_GROB_3.0', 'LP_STATUS_GROB_4.0',\n",
       "       'LP_STATUS_GROB_5.0', 'NATIONALITAET_KZ_1.0', 'NATIONALITAET_KZ_2.0',\n",
       "       'NATIONALITAET_KZ_3.0', 'SHOPPER_TYP_0.0', 'SHOPPER_TYP_1.0',\n",
       "       'SHOPPER_TYP_2.0', 'SHOPPER_TYP_3.0', 'ZABEOTYP_1.0', 'ZABEOTYP_2.0',\n",
       "       'ZABEOTYP_3.0', 'ZABEOTYP_4.0', 'ZABEOTYP_5.0', 'ZABEOTYP_6.0',\n",
       "       'GEBAEUDETYP_1.0', 'GEBAEUDETYP_2.0', 'GEBAEUDETYP_3.0',\n",
       "       'GEBAEUDETYP_4.0', 'GEBAEUDETYP_5.0', 'GEBAEUDETYP_6.0',\n",
       "       'GEBAEUDETYP_8.0', 'CAMEO_DEUG_2015_1', 'CAMEO_DEUG_2015_2',\n",
       "       'CAMEO_DEUG_2015_3', 'CAMEO_DEUG_2015_4', 'CAMEO_DEUG_2015_5',\n",
       "       'CAMEO_DEUG_2015_6', 'CAMEO_DEUG_2015_7', 'CAMEO_DEUG_2015_8',\n",
       "       'CAMEO_DEUG_2015_9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking new features that were added after reencoding. Should start from new version of 'OST_WEST_KZ'\n",
    "azdias_fin.columns[65:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: we reencoded 11 columns and dropped 3 new columns (in addition to 6 done previously). The resulting dataset has 129 columns. So this means that we have (85-11-3-6)=65 old columns. The rest 64 are our new binary reencoded features starting from OST_WEST_KZ_W."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "There are 21 categorical features in the dataset.\n",
    "Among these, we have 4 features with binary categories - ANREDE_KZ, GREEN_AVANTGARDE, SOHO_KZ and VERS_TYP. These can be left unchanged.\n",
    "One binary variable that takes on non-numeric values - OST_WEST_KZ - needs to be reencoded with dummy variable.\n",
    "All other categorical features need to be reencoded/dropped. So here's my approach:\n",
    "\n",
    "I first reencoded 'OST_WEST_KZ' with get_dummies, dropping the first column.\n",
    "\n",
    "I dropped 'CAMEO_DEU_2015' because it has too many values. \n",
    "\n",
    "I also excluded from the list of categorical features (ANREDE_KZ, GREEN_AVANTGARDE, SOHO_KZ and VERS_TYP) because they are binary and don't need to be reencoded. \n",
    "\n",
    "Then I dropped features LP_FAMILIE_FEIN and LP_STATUS_FEIN because they are highly correlated with LP_FAMILIE_GROB and LP_STATUS_GROB (which are basically aggregates of 'FEIN' features). <br>\n",
    "\n",
    "Finally, I checked what other categorical variables were dropped in the previous step when looking for missing values. It turned out that 3 columns were already removed (AGER_TYP, TITEL_KZ, KK_KUNDENTYP)\n",
    "\n",
    "After all, from 21 categorical features only 10 were left.\n",
    "\n",
    "*Reencoding*: I applied onehotencoding on 10 features that were left from the previous step. This created 64 binary vectors. Together with reencoded earlier OST_WEST_KZ, this means we added 64 new features that are dummies for their earlier versions."
   ]
  },
  {
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>attribute</th>\n",
       "      <th>num_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>KBA05_BAUMAX</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index              attribute  num_categories\n",
       "0     15    LP_LEBENSPHASE_FEIN              42\n",
       "1     16    LP_LEBENSPHASE_GROB              14\n",
       "2     22  PRAEGENDE_JUGENDJAHRE              16\n",
       "3     56               WOHNLAGE               9\n",
       "4     59        CAMEO_INTL_2015              23\n",
       "5     64           KBA05_BAUMAX               7\n",
       "6     79            PLZ8_BAUMAX               6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking which columns where defined as 'mixed'\n",
    "mixed_col = feat_info[feat_info['type'] == 'mixed'].attribute.reset_index()\n",
    "mixed_col['num_categories'] = [len(azdias[mixed_col['attribute'][i]].unique()) for i in range(mixed_col.shape[0])]\n",
    "mixed_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary**: there are 7 features marked as 'mixed' in the original dataset. 'KBA05_BAUMAX' was already removed from the dataset due to the missing values.LP_LEBENSPHASE_FEIN is basically the same as LP_LEBENSPHASE_GROB and will be dropped. Let's check if LP_LEBENSPHASE_GROB is highly correlated with categorical features 'LP_FAMILIE_FEIN'/'LP_STATUS_FEIN' that we already reencoded:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1c13860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lp_col = ['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'LP_FAMILIE_FEIN',\n",
    "       'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB']\n",
    "sns.heatmap(azdias[lp_col].corr(), cmap = 'rocket_r', annot = True, fmt = '.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary: LP_LEBENSPHASE_GROB is highly correlated with categorical feature 'LP_FAMILIE_FEIN' and so can be also dropped from further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_fin = azdias_fin.drop(['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, in the following we can concentrate on 4 'mixed' features - PRAEGENDE_JUGENDJAHRE, WOHNLAGE,  CAMEO_INTL_2015, PLZ8_BAUMAX. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing with PRAEGENDE_JUGENDJAHRE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from readme file we know that \n",
    "# values in the list [1,3,5,8,10,12,14] correspond to 'mainstream', will be encoded with 1\n",
    "# values in the list [2,4,6,7,9,11,13,15] correspond to 'avantgarde', will be encoded with 0\n",
    "\n",
    "azdias_fin['movement'] = azdias_fin['PRAEGENDE_JUGENDJAHRE'].replace([1,3,5,8,10,12,14], 1).replace([2,4,6,7,9,11,13,15], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1.0\n",
       "2     0.0\n",
       "3     1.0\n",
       "4     1.0\n",
       "5     1.0\n",
       "6     1.0\n",
       "7     1.0\n",
       "8     0.0\n",
       "9     0.0\n",
       "10    1.0\n",
       "Name: movement, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin['movement'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9999999999999999"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if the newly engineered feature 'mainstream' is correlated with feature 'GREEN_AVANTGARDE'\n",
    "(azdias_fin['GREEN_AVANTGARDE'].astype(int)).corr(azdias_fin['movement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_fin = azdias_fin.drop('movement', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Commentary: since we have already the feature 'GREEN_AVANTGARDE' in our dataset, there is no need to create a new feature 'mainstream' that is the opposite of 'GREEN_AVANTGARDE. Hence, it will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from readme file we can construct codes as dict \n",
    "codes = {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",
    "azdias_fin['decade'] = azdias_fin['PRAEGENDE_JUGENDJAHRE'].replace(codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    90.0\n",
       "2    90.0\n",
       "3    70.0\n",
       "4    70.0\n",
       "5    50.0\n",
       "Name: decade, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin['decade'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are done reengineering 'PRAEGENDE_JUGENDJAHRE' and can now drop it from further analysis\n",
    "azdias_fin = azdias_fin.drop('PRAEGENDE_JUGENDJAHRE', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing with CAMEO_INTL_2015**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.0\n",
       "2    4.0\n",
       "3    2.0\n",
       "4    3.0\n",
       "5    4.0\n",
       "Name: life_stage, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.    \n",
    "azdias_fin['life_stage'] = azdias_fin['CAMEO_INTL_2015'].astype(float)%10\n",
    "azdias_fin['life_stage'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5.0\n",
       "2    2.0\n",
       "3    1.0\n",
       "4    4.0\n",
       "5    5.0\n",
       "Name: wealth, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin['wealth'] = (azdias_fin['CAMEO_INTL_2015'].astype(float)/10)-azdias_fin['CAMEO_INTL_2015'].astype(float)%10/10\n",
    "azdias_fin['wealth'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are done reengineering 'CAMEO_INTL_2015' and can now drop it from further analysis\n",
    "azdias_fin = azdias_fin.drop('CAMEO_INTL_2015', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing with WOHNLAGE**:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature 'WOHNLAGE' is constructed from two different variables - one based on quality of the neighborhood and another one is flag for rural neighborhood (values 7 and 8). We cannot disaggregate info on quality of neigborhood. So this info will be gone (alternative - create dummy var on WOHNLAGE, but we have some information quality of neighborhood at the region level, so I decided not to create a dummy). <br>\n",
    "Let's create a new feature solely as flag for rural neighborhood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.0\n",
       "2    0.0\n",
       "3    1.0\n",
       "4    0.0\n",
       "5    1.0\n",
       "Name: rural, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin['rural'] = azdias_fin['WOHNLAGE'].replace([1,2,3,4,5], 0).replace([7,8], 1)\n",
    "azdias_fin['rural'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are done reengineering 'WOHNLAGE' and can now drop it from further analysis\n",
    "azdias_fin = azdias_fin.drop('WOHNLAGE', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing with PLZ8_BAUMAX**:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature PLZ8_BAUMAX is constructed from variables that refer to either family homes or business buildings. Since we have the information on family homes in other features (PLZ8_ANTG1 through PLZ8_ANTG4) we can ignore this information in PLZ8_BAUMAX and create a new feature that would only flag business buildings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "5    0.0\n",
       "Name: business_building, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_fin['business_building'] = azdias_fin['PLZ8_BAUMAX'].replace([1,2,3,4], 0).replace(5, 1)\n",
    "azdias_fin['business_building'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are done reengineering 'PLZ8_BAUMAX' and can now drop it from further analysis\n",
    "azdias_fin = azdias_fin.drop('PLZ8_BAUMAX', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'FINANZ_MINIMALIST',\n",
       "       'FINANZ_SPARER', 'FINANZ_VORSORGER', 'FINANZ_ANLEGER',\n",
       "       'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER', 'GREEN_AVANTGARDE',\n",
       "       'HEALTH_TYP',\n",
       "       ...\n",
       "       'CAMEO_DEUG_2015_5', 'CAMEO_DEUG_2015_6', 'CAMEO_DEUG_2015_7',\n",
       "       'CAMEO_DEUG_2015_8', 'CAMEO_DEUG_2015_9', 'decade', 'life_stage',\n",
       "       'wealth', 'rural', 'business_building'],\n",
       "      dtype='object', length=128)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking how many features left after this step\n",
    "azdias_fin.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: from the previous step, I have 129 features. In this step, I dropped 2 features ('LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB') and engineered 5 new features from 4 features that were then dropped (PRAEGENDE_JUGENDJAHRE, CAMEO_INTL_2015, WOHNLAGE, PLZ8_BAUMAX). So at the end we have 129-2+5-4=128 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are 7 features marked as 'mixed' in the dataset. \n",
    "\n",
    "1. 'LP_LEBENSPHASE_FEIN': I decided to drop it because it has to many categories (41) and we haves similar feature 'LP_LEBENSPHASE_GROB' which presents aggregated data. \n",
    "\n",
    "\n",
    "2. 'LP_LEBENSPHASE_GROB':  it turned out that 'LP_LEBENSPHASE_GROB' is highly correlated with the categorical variable 'LP_FAMILIE_FEIN' so that I decided to drop it too.\n",
    "\n",
    "\n",
    "3. 'PRAEGENDE_JUGENDJAHRE': Based on 'PRAEGENDE_JUGENDJAHRE' we created two new features - 'mainstream' which is a binary variable for movement and 'decade' which is interval-type variable for a decade.  However, the newly engineered feature 'mainstream' is fully negatively correlated with another feature in our dataset 'GREEN_AVANTGARDE'. So there is no need to duplicate this information. The feature 'mainstream' will be excluded. \n",
    "\n",
    "\n",
    "4. 'CAMEO_INTL_2015': Based on 'CAMEO_INTL_2015' we created two new ordinal features 'wealth' and 'life_stage' which do not need being reencoded. \n",
    "\n",
    "\n",
    "5. 'KBA05_BAUMAX': this feature was already dropped in earlier stages when discovered that it has lots of missing values\n",
    "\n",
    "\n",
    "6. 'WOHNLAGE': I created a new feature 'rural' that is dummy for rural neighborhood\n",
    "\n",
    "\n",
    "7. 'PLZ8_BAUMAX': I created new feature 'business_building' that is dummy for business building "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "The data frame should only have the columns that I want to keep. To summarize, it should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'FINANZ_MINIMALIST',\n",
       "       'FINANZ_SPARER', 'FINANZ_VORSORGER', 'FINANZ_ANLEGER',\n",
       "       'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER', 'GREEN_AVANTGARDE',\n",
       "       'HEALTH_TYP',\n",
       "       ...\n",
       "       'CAMEO_DEUG_2015_5', 'CAMEO_DEUG_2015_6', 'CAMEO_DEUG_2015_7',\n",
       "       'CAMEO_DEUG_2015_8', 'CAMEO_DEUG_2015_9', 'decade', 'life_stage',\n",
       "       'wealth', 'rural', 'business_building'],\n",
       "      dtype='object', length=128)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n",
    "azdias_fin.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(797426, 128)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do whatever you need to in order to ensure that the dataframe only contains\n",
    "# the columns that should be passed to the algorithm functions.\n",
    "azdias_fin.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTE:\n",
    "#### I've integrated this step in steps 1.2.1. and 1.2.2. I found it easier to follow transformations while they occur. My final data frame azdias_fin contains only the features I want to keep for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs, ...\n",
    "    print(\"Start step 1: dealing with missing values\")\n",
    "    df = df.replace({'X': np.nan, 'XX': np.nan})\n",
    "    feat_info['missing_transform'] = feat_info['missing_or_unknown'].apply(lambda x: '[-1]' if x in ['[-1,X]', '[XX]', '[-1,XX]'] else x)\n",
    "    feat_info['missing_transform'] = [eval(feat_info['missing_transform'][i]) for i in feat_info['missing_transform'].index]\n",
    "\n",
    "    for i, col in enumerate(feat_info['attribute']):\n",
    "        df.loc[df[col].isin(feat_info['missing_transform'][i]), col] = np.nan \n",
    "    print(\"Checking number of missing values after step 1:\", df.isna().sum().sum())\n",
    "    print(\"\")\n",
    "        \n",
    "    # remove selected columns and rows, ...\n",
    "    print(\"Start step 2: removing selected columns and rows\")\n",
    "    col_del = ['TITEL_KZ', 'AGER_TYP', 'KK_KUNDENTYP', 'KBA05_BAUMAX', 'GEBURTSJAHR', 'ALTER_HH',\n",
    "               'LP_LEBENSPHASE_FEIN','LP_FAMILIE_FEIN', 'LP_STATUS_FEIN', 'CAMEO_DEU_2015', 'LP_LEBENSPHASE_GROB']\n",
    "\n",
    "    df = df.drop(df[col_del], axis=1)\n",
    "    row_missing_values = df.isna().sum(axis=1)\n",
    "    df = df[row_missing_values <= 20]   \n",
    "    print(\"Checking the new shape of the data frame after step 2:\", df.shape)\n",
    "    print(\"\")\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    print(\"Start step 3.1: reencoding with dummies\") \n",
    "    cat_var_reencode = ['CJT_GESAMTTYP', 'FINANZTYP', 'GFK_URLAUBERTYP', 'LP_FAMILIE_GROB',\n",
    "                        'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP',\n",
    "                        'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015']\n",
    "    \n",
    "    df = pd.get_dummies(data = df, columns = cat_var_reencode)    \n",
    "    df = pd.get_dummies(data = df, columns = ['OST_WEST_KZ'], drop_first=True)   \n",
    "    \n",
    "    print(\"Start step 3.2: reengineering features\")\n",
    "    codes = {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",
    "    df['decade'] = df['PRAEGENDE_JUGENDJAHRE'].replace(codes)\n",
    "    df['life_stage'] = df['CAMEO_INTL_2015'].astype(float)%10\n",
    "    df['wealth'] = (df['CAMEO_INTL_2015'].astype(float)/10)-df['CAMEO_INTL_2015'].astype(float)%10/10\n",
    "    df['rural'] = df['WOHNLAGE'].replace([1,2,3,4,5], 0).replace([7,8], 1)\n",
    "    df['business_building'] = df['PLZ8_BAUMAX'].replace([1,2,3,4], 0).replace(5, 1)\n",
    "    \n",
    "    col_reengineered = ['PRAEGENDE_JUGENDJAHRE', 'CAMEO_INTL_2015', 'WOHNLAGE', 'PLZ8_BAUMAX']\n",
    "    df = df.drop(df[col_reengineered], axis=1)\n",
    "    print(\"Checking the new shape of the data frame after step 3:\", df.shape)\n",
    "    print(\"Checking the number of missing values in the resulting data frame:\", df.isna().sum().sum())\n",
    "        \n",
    "    # Return the cleaned dataframe.\n",
    "    return df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start step 1: dealing with missing values\n",
      "Checking number of missing values after step 1: 8373929\n",
      "\n",
      "Start step 2: removing selected columns and rows\n",
      "Checking the new shape of the data frame after step 2: (797754, 74)\n",
      "\n",
      "Start step 3.1: reencoding with dummies\n",
      "Start step 3.2: reengineering features\n",
      "Checking the new shape of the data frame after step 3: (797754, 128)\n",
      "Checking the number of missing values in the resulting data frame: 751844\n"
     ]
    }
   ],
   "source": [
    "azdias_features = clean_data(azdias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: The number of missing values after step 1 is the same as when the step was performed without function call. The number of features after transformation via function is also the same. However, we retained more rows in the cleaned via function dataset compared to what we did earlier step by step (797754 vs 797426). The reason is  because in the clean_data function I have clumped together all 11 columns I decided to delete before removing rows with missing values in step 2. While earlier, I removed only 6 of them at first and the decision to remove the remaining 5 came later. <br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>CAMEO_DEUG_2015_6</th>\n",
       "      <th>CAMEO_DEUG_2015_7</th>\n",
       "      <th>CAMEO_DEUG_2015_8</th>\n",
       "      <th>CAMEO_DEUG_2015_9</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>decade</th>\n",
       "      <th>life_stage</th>\n",
       "      <th>wealth</th>\n",
       "      <th>rural</th>\n",
       "      <th>business_building</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "0                   1.0        2.0                1.0            5.0   \n",
       "1                   3.0        2.0                1.0            4.0   \n",
       "2                   4.0        2.0                4.0            2.0   \n",
       "3                   3.0        1.0                4.0            3.0   \n",
       "4                   1.0        2.0                3.0            1.0   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "0               2.0             5.0                    4.0               5.0   \n",
       "1               1.0             2.0                    3.0               5.0   \n",
       "2               5.0             2.0                    1.0               2.0   \n",
       "3               4.0             1.0                    3.0               2.0   \n",
       "4               5.0             2.0                    2.0               5.0   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP        ...          CAMEO_DEUG_2015_6  \\\n",
       "0               0.0         3.0        ...                        0.0   \n",
       "1               1.0         3.0        ...                        0.0   \n",
       "2               0.0         2.0        ...                        0.0   \n",
       "3               0.0         3.0        ...                        1.0   \n",
       "4               0.0         3.0        ...                        0.0   \n",
       "\n",
       "   CAMEO_DEUG_2015_7  CAMEO_DEUG_2015_8  CAMEO_DEUG_2015_9  OST_WEST_KZ_W  \\\n",
       "0                0.0                1.0                0.0            1.0   \n",
       "1                0.0                0.0                0.0            1.0   \n",
       "2                0.0                0.0                0.0            1.0   \n",
       "3                0.0                0.0                0.0            1.0   \n",
       "4                0.0                1.0                0.0            1.0   \n",
       "\n",
       "   decade  life_stage  wealth  rural  business_building  \n",
       "0    90.0         1.0     5.0    0.0                0.0  \n",
       "1    90.0         4.0     2.0    0.0                0.0  \n",
       "2    70.0         2.0     1.0    1.0                0.0  \n",
       "3    70.0         3.0     4.0    0.0                0.0  \n",
       "4    50.0         4.0     5.0    1.0                0.0  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 72,
     "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",
    "imputer = Imputer(strategy='most_frequent')\n",
    "azdias_features_imputed = imputer.fit_transform(azdias_features)\n",
    "\n",
    "azdias_features_imputed = pd.DataFrame(azdias_features_imputed, columns=azdias_features.columns)\n",
    "azdias_features_imputed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: since many columns in this dataset are ordinal, I decided to impute the most frequent values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if the result of imputation\n",
    "azdias_features_imputed.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "scaler = StandardScaler().fit(azdias_features_imputed) \n",
    "azdias_features_scaled = scaler.transform(azdias_features_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.03392459e-18, -6.37013622e-17,  4.62618673e-17,  1.12047418e-16,\n",
       "       -7.59036645e-17, -3.71591279e-17, -1.53953865e-16, -1.20669191e-16,\n",
       "        1.39373450e-16, -2.91799801e-16])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if scaler performed well on 10 first features\n",
    "azdias_features_scaled.mean(axis=0)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if scaler performed well on 10 first features\n",
    "azdias_features_scaled.std(axis=0)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: scaler works as expected - all features were transformed correctly and have mean 0 and std 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "Since I have decided to remove data with missing points earlier in this project, I didn't have to clean the dataset any further. \n",
    "The decision to remove these points was driven by another decision - namely to reencode 12 categorical featuers with their dummies. If I kept missing values at that stage, I could have ended up with additional 12 columns for NaNs. <br>\n",
    "Alternatively, if I decided to delete the 12 categorical features all together, I could have kept the certain level of missing data in rows (say up to 5) and later impute these values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(azdias_features_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "def scree_plot(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT:\n",
    "            None\n",
    "    '''\n",
    "    num_components=len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(6, 6))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a31241d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scree_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many components together explain about 80% of variance?\n",
    "cum_expl_var_ratio = np.cumsum(pca.explained_variance_ratio_)\n",
    "len(cum_expl_var_ratio[cum_expl_var_ratio <= 0.805])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca = PCA(55).fit(azdias_features_scaled)\n",
    "azdias_features_pca = pca.transform(azdias_features_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1068349 , 0.18904279, 0.24116579, 0.27267736, 0.29625962,\n",
       "       0.31635506, 0.33476651, 0.35233056, 0.36839587, 0.38360877,\n",
       "       0.39790406, 0.41175416, 0.42452159, 0.43675519, 0.44847467,\n",
       "       0.45984398, 0.47116332, 0.48177354, 0.49211878, 0.50240449,\n",
       "       0.51260587, 0.52251093, 0.53222582, 0.54189901, 0.55148549,\n",
       "       0.56084479, 0.5701031 , 0.57918112, 0.58823775, 0.59722533,\n",
       "       0.60604118, 0.61484042, 0.62356428, 0.63221521, 0.64078305,\n",
       "       0.6493133 , 0.65777359, 0.66607647, 0.67434415, 0.68247155,\n",
       "       0.69058794, 0.69861201, 0.70659242, 0.71456863, 0.72244326,\n",
       "       0.73029756, 0.73810932, 0.74590137, 0.75366262, 0.76133828,\n",
       "       0.76897082, 0.77650509, 0.78401029, 0.79134442, 0.79849783])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "As a rule of thumb, one should keep as many components that together they would explain about 80% of variance. From the scree plot we can conclude that about 55 components explain 80% of variance. So I decided to keep 55 PCA components for the clustering part of the project. <br>\n",
    "Moreover, the first three components add the most to the explanation of the variance (together explain about 25%), while the rest of the components' weights are rather small (~1-2%). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Let's check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- 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": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "\n",
    "def map_pca(i, df_scaled):\n",
    "    weights = pd.DataFrame(pca.components_[i-1], columns=[\"PCA_\"+str(i)], index = df_scaled.columns)\n",
    "    weights = weights.sort_values(by=[\"PCA_\"+str(i)])\n",
    "    print(\"positive top5 weights:\\n\", weights.tail(5))\n",
    "    print(\"\\n-----------------------------\\n\")\n",
    "    print(\"negative top5 weights:\\n\", weights.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive top5 weights:\n",
      "                        PCA_1\n",
      "HH_EINKOMMEN_SCORE  0.181879\n",
      "wealth              0.182751\n",
      "PLZ8_ANTG4          0.186437\n",
      "PLZ8_ANTG3          0.192128\n",
      "LP_STATUS_GROB_1.0  0.198213\n",
      "\n",
      "-----------------------------\n",
      "\n",
      "negative top5 weights:\n",
      "                       PCA_1\n",
      "FINANZ_MINIMALIST -0.204375\n",
      "MOBI_REGIO        -0.193761\n",
      "PLZ8_ANTG1        -0.192182\n",
      "KBA05_GBZ         -0.186407\n",
      "KBA05_ANTG1       -0.184530\n"
     ]
    }
   ],
   "source": [
    "map_pca(1, azdias_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive top5 weights:\n",
      "                          PCA_2\n",
      "SEMIO_LUST            0.161946\n",
      "SEMIO_ERL             0.187227\n",
      "ZABEOTYP_3.0          0.203869\n",
      "FINANZ_VORSORGER      0.214276\n",
      "ALTERSKATEGORIE_GROB  0.229072\n",
      "\n",
      "-----------------------------\n",
      "\n",
      "negative top5 weights:\n",
      "                           PCA_2\n",
      "decade                -0.224657\n",
      "FINANZ_SPARER         -0.216684\n",
      "SEMIO_REL             -0.216012\n",
      "FINANZ_UNAUFFAELLIGER -0.212395\n",
      "SEMIO_TRADV           -0.206713\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "map_pca(2, azdias_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive top5 weights:\n",
      "                   PCA_3\n",
      "FINANZTYP_5.0  0.136707\n",
      "SEMIO_KULT     0.248099\n",
      "SEMIO_SOZ      0.257945\n",
      "SEMIO_FAM      0.258574\n",
      "SEMIO_VERT     0.327127\n",
      "\n",
      "-----------------------------\n",
      "\n",
      "negative top5 weights:\n",
      "                PCA_3\n",
      "ANREDE_KZ  -0.353231\n",
      "SEMIO_KAEM -0.325678\n",
      "SEMIO_DOM  -0.297417\n",
      "SEMIO_KRIT -0.271625\n",
      "SEMIO_ERL  -0.199927\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "map_pca(3, azdias_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "1. **PCA_1 can be called \"low income earners segment\"**: <br>\n",
    "The first principal component PCA_1 has the following top5 features with the highest/lowest weights:\n",
    "\n",
    "*negative weights*:\n",
    "MOBI_REGIO, FINANZ_MINIMALIST, KBA05_ANTG1, PLZ8_ANTG1, KBA05_GBZ <br>\n",
    "\n",
    "*positive weights*: HH_EINKOMMEN_SCORE, wealth, PLZ8_ANTG4, PLZ8_ANTG3, LP_STATUS_GROB_1.0 <br>\n",
    "\n",
    "The PCA_1 is mainly about financial wealth and the number of buildings in the region/microcell where the person lives. Thus, positive weights here point out to 'low-income earners', 'poor households', 'very low income' and to region with 'high share of 6 or 10+ family homes' (which is logical given that poorer families tend to live in the densely populated areas which are usually cheaper). The negative weights are associated with such features as mobility, low financial interest, share of 1-2 family homes. From the way that these features are constructed, we can say that the relationship between the negatively weighted features and positively weighted features are as follows:\n",
    "the more low-income earners, the higher the share of 6 or 10+ family homes in the microcell, poorer households and more low income families is associated with higher mobility, higher 'low financial interest', lower share of 1-2 family homes. \n",
    "\n",
    "2. **PCA_2 can be called \"conservative seniors segment\"**: <br>\n",
    "The second principal component PCA_2 has the following top5 features with the highest/lowest weights:\n",
    "\n",
    "*negative weights*: decade, SEMIO_REL, FINANZ_SPARER, FINANZ_UNAUFFAELLIGER, SEMIO_TRADV <br>\n",
    "\n",
    "*positive weights*: SEMIO_LUST, SEMIO_ERL, ZABEOTYP_3.0, FINANZ_VORSORGER, ALTERSKATEGORIE_GROB\n",
    "\n",
    "The PCA_2 revolves around such topics as money saving, conservatism/religiosity, old age. Thus, positive weights point out to the fact that the persons in this segment are the least 'sensual-minded', 'low event-oriented', believe their energy is ‘fair supplied’, they also have low financial provisions/savings and are seniors (>50 years). When considering the negatively weighted features and their relationship with the positively weighted features we get fuller picture of the individuals in this segment. In addition to the said above, they are born in earlier decade (probably 1940s/50s ), they have the highest affinity to religion, they are financially modest, more active money-savers and are the most tradional-minded. \n",
    "\n",
    "3. **PCA_3 can be called \"practical male segment\"**: <br>\n",
    "The third principal component PCA_3 has the following top5 features with the highest/lowest weights:\n",
    "\n",
    "*negative weights*: ANREDE_KZ, SEMIO_KAEM, SEMIO_DOM, SEMIO_KRIT, SEMIO_ERL <br>\n",
    "\n",
    "*positive weights*: FINANZ_MINIMALIST, SEMIO_KULT, SEMIO_FAM, SEMIO_SOZ, SEMIO_VERT\n",
    "\n",
    "The PCA_3 creates a distinctive segment consiting of mostly male that are self-confident, less family oriented and mostly event-oriented. So, the positively weighted components are associated here with such characteristics as 'the least dreamful', 'the least socially-minded', 'the least family-minded', 'the least cultural-minded', the least 'low financially interested'. While negatively weighted components correspond to features such as 'male', highly ‘combative attitude’,  'very dominant-minded', 'the most critical-minded', 'the most event-oriented'. \n"
   ]
  },
  {
   "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, I 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": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating 5 clusters\n",
      "sum of squared distances: 85420965.92336746\n",
      "time for this cluster: 215.34582018852234\n",
      "calculating 10 clusters\n",
      "sum of squared distances: 80123262.11216384\n",
      "time for this cluster: 428.9132068157196\n",
      "calculating 15 clusters\n",
      "sum of squared distances: 77357465.88423884\n",
      "time for this cluster: 837.2431218624115\n",
      "calculating 20 clusters\n",
      "sum of squared distances: 75472896.78918467\n",
      "time for this cluster: 999.5252838134766\n",
      "calculating 25 clusters\n",
      "sum of squared distances: 72981948.56508066\n",
      "time for this cluster: 1267.9483151435852\n",
      "Clustering complete in 62m 30s\n"
     ]
    }
   ],
   "source": [
    "# Over a number of different cluster counts...\n",
    "range_n_clusters = [5, 10, 15, 20, 25]\n",
    "sum_of_squared_distances = []\n",
    "since = time.time()\n",
    "\n",
    "with active_session():\n",
    "    for n_clusters in range_n_clusters:\n",
    "        cluster_start = time.time()\n",
    "        print(\"calculating {} clusters\".format(n_clusters))\n",
    "        # run k-means clustering on the data and...\n",
    "        clusterer = KMeans(n_clusters=n_clusters, random_state=120).fit(azdias_features_scaled)\n",
    "\n",
    "        # compute the average within-cluster distances.\n",
    "        sum_of_squared_distances.append(clusterer.inertia_)\n",
    "        print(\"sum of squared distances:\", clusterer.inertia_)\n",
    "        print(\"time for this cluster:\", time.time() - cluster_start)\n",
    "\n",
    "time_elapsed = time.time() - since\n",
    "print('Clustering complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mFmO+XE2NmAiGdKob6JCMMSZfhXYfiUhXEankPr9SRMaISD3/h1Y+hIQIj1/WhuRmNfjH+8v44qftgQ7JGGPy5c2YwkvAIRFpC9wDbAQm+zWqcqZCaAgvXtGBM+Krctu0H0hfvyfQIRljTJ68SQrH3WJK/YDnVPU5nLpFxgdR4WFMGJZEnWoVuXbSQlZtPxDokIwx5i+8SQoHROQ+4CrgUxEJxal6anx0WqVwJg/vRGSFUK4en86WvYcDHZIxxvyJN0lhEJAFDFfV7UAd4Em/RlWOxVeLYtLwThw8epyhr3/P7wePBjokY4zJUWhScBPBdCDCnbQbeN+fQZV3LWpV5rWhiWz+/TDDJy3k0NHjgQ7JGGMA784+ug6n2ukr7qQ6ODfMMafgzIbV+d/gdvy4eS+3vPkDx06cDHRIxhjjVffRzUBXYD+Aqv4C1PRnUMHi3Na1eKRfa75euZP73ltmVz0bYwLOm4vXslT1qHOfHBCRMMC+vYrJlZ3rsetAFs/N+oUaMRHce27zQIdkjAli3iSFOSJyP1BRRM4BbgI+9m9YweWO3k3YeSCLl9LWUiM6guHdGgQ6JGNMkPImKfwdGIFz97Trgc+A1/wZVLAREf5zcWv2HMzikU9WEBsTwUVtawc6LGNMEPJmTKEiMF5VB6hqf2C8O80Uo9AQ4bnB7elU/zTuensJ3/yyO9AhGWOCkDdJYRZ/TgIVga/8E05wi6wQyqtXJ9KoRjTXv5HB8i37Ah2SMSbIeJMUIlU150707vMo/4UU3KpUrMCk4Z2oGhXOsAnpbPztYKBDMsYEEW+SwkER6ZD9QkQ6AlafwY/iKkcyaXgnTpxUrno9nV0HsgIdkjEmSHiTFO4A3hGReSIyD3gLuMW/YZnGNaMZPyyJXQeyGDYhnQNHjgU6JGNMEPCmzMVCoDlwI87pqC1UdZE3GxeRUSLyk4gsF5FpIhKZa/6dIrJCRJaKyCy7T8Ofta9bjRev7MDK7Qe4Ycoiso6fCHRIxphyztt7NCcBbYD2wBARGVrYCiJSB7gNSFTV1kAoMDjXYj+489vglNJ4wtvAg0VKs5o8cVkb5q/5jbve/pGTJ+26QWOM/xR6nYKIvAE0ApYA2T9VFe9utBOGc9HbMZzB6a2eM1V1tsfLBcCVXmwz6FzWMZ5dmVn89/OVxEZH8NCFLcm+wtwYY4qTNxevJQIt1cfCPKq6RUSeAjbhDEzPVNWZBawyAvjcl30Ek+t7NGTn/izGz19PzcoR3JTcONAhGWPKISnsu15E3gFuU9VtPm1YpBpOye1BwF7gHeBdVZ2Sx7JX4gxe91TVv5xqIyIjgZEAcXFxHVNTU30JJUdmZibR0dFFWrc0OKnKuKVZLNh2ghGtw+ke7/97HZX1Nitp1l6+sfbyzam0V0pKyiJVTSxsOW+OFGKBFSKSjnOzHQBU9aJC1usNrFfVXQAi8h7QBfhTUhCR3sA/yCchuPsaB4wDSExM1OTkZC/C/qu0tDSKum5p0a37SUZMWsjEFb/RpWMberWI8+v+ykOblSRrL99Ye/mmJNrLm6TwcBG3vQnoLCJRON1HvYAMzwVEpD3OfRrOVdWdRdxPUAkPC+GlKzsyZNwCbn5zMVOv7UzHetUCHZYxppzw5pTUOXk9vFjve5wzihbjFNMLAcaJyCMikn2U8SQQjXMdxBIR+ajobyV4REeEMeGaJE6vHMmISQtZs/NAoEMyxpQT3tx5rbOILBSRTBE5KiInRGS/NxtX1YdUtbmqtlbVq1Q1S1UfVNWP3Pm9VTVOVdu5j8K6pIwrNjqCycPPJCwkhKGvp7Ntn11kbow5dd5cpzAWGAL8glMM71p3mgmwutWjmHhNEvuPHOfq8ensO2RXPRtjTo1XF6+p6hogVFVPqOoEINmvURmvta5ThXFXdWTD7kNcO3khR47ZVc/GmKLzJikcEpFwYImIPCEio4BKfo7L+KBL41jGDGpLxsbfueXNHzh+4mSgQzLGlFHeJIWr3OVuAQ4CCcCl/gzK+O6CNrV56IKWfPXzDh74YDk+XmtojDGAd0nhYlU9oqr7VfVfqnoncIG/AzO+G9a1ATenNCJ14Wae+XJ1oMMxxpRB3iSFq/OYNqyY4zDFZHSfZgxMjOd/X6/hje82BDocY0wZk+/FayIyBLgcaJDr+oHKwG/+DswUjYjwf5ecwZ6DR3nwo5+oHh3BeWfUCnRYxpgyoqArmr8FtuGUuXjaY/oBYKk/gzKnJiw0hOeHdOCK1xZwR+oSqkWFc1aj6oEOyxhTBuTbfaSqG1U1DaeG0Tz3KuZtQDxgdZtLuYrhoYwflkTd6lGMnJzBiq1eXW9ojAly3owpzAUi3ZvmzAKuASb6MyhTPKpGhTN5eCeiI8O4ekI6m/ccCnRIxphSzpukIKp6COc01OdV9RKgpX/DMsWldtWKTBreiaxjJxg6Pp3fMvMsRGuMMYCXSUFEzgKuAD51p3lTXdWUEk3jYhg/LImtew8zfOJCDmYdD3RIxphSypukcAdwH/C+qv4kIg2B2YWsY0qZxPqnMfbyDizbso8bpy7m6HG76tkY81fels6+SFUfd1+vU9Xb/B+aKW7ntIzjsUvPYO7qXdw7fSknT9pVz8aYPyvoOoVnVfUOEfkY+Mu3h5W5LpsGJdVl14Esnpq5mtjocP5xvg0PGWP+UNDYwBvu36dKIhBTcm5OaczOA1m8Om89NWMiua5Hw0CHZIwpJfJNCqq6yP07R0RquM93lVRgxn9EhIcubMVvmUd59LOfiY0J55L28YEOyxhTCuQ7piCOh0VkN7ASWC0iu0TkwZILz/hLaIgwZlBbzmpYnbvfWUraKrtFtjGm4IHmO4CuQJKqVlfVasCZQFf3ngqmjIsIC+WVoR1pGhfDTVMXs2Tz3kCHZIwJsIKSwlBgiKquz56gquuAK915phyoHFmBicOTqB4dzvCJC1m3KzPQIRljAqigpFBBVXfnnuiOK1TwX0impNWMiWTy8DMRYOj4dHbuPxLokIwxAVJQUjhaxHmmDGoQW4kJ1ySx5+BRho5PZ/+RY4EOyRgTAAUlhbYisj+PxwHgjJIK0JScNvFVefnKjqzZmcl1kzI4cuxEoEMyxpSwgkpnh6pq5TweMarqVfeRiIwSkZ9EZLmITBORyFzzI0TkLRFZIyLfi0j9U3s75lT1aFqDpwa05fv1exj11hKO21XPxgQVb2ofFYlbavs2IFFVWwOhwOBci40AflfVxsAzwOP+isd47+L2dXjg/BZ8vnw7D3xzmNkr7XRVY4KF35KCKwyoKCJhQBSwNdf8fsAk9/m7QC8RsRv4lALXdm/I+GGJKHDNxIUMm5DOmp12ZpIx5Z2o+q97QERuBx4FDgMzVfWKXPOXA+eq6q/u67XAmbnPehKRkcBIgLi4uI6pqalFiiczM5Po6OgirRus9u7P5LvfIvho7VGOnoDedcO4qHE4lSpY7s6LfcZ8Y+3lm1Npr5SUlEWqmljYcn5LCiJSDZgODAL2Au8A76rqFI9lfgL+lispdFLV3/LbbmJiomZkZBQpprS0NJKTk4u0brDKbrPdmVk8PXMVqQs3Uy0qnNF9mjEoKYHQEEsOnuwz5htrL9+cSnuJiFdJoaAyFwfyOftov4h4c8Pf3sB6Vd2lqseA94AuuZb5FUhw9xcGVAH2eLFtU8JioyN47NI2fHxLNxrXiOb+95dxwfPfsGBdvvnbGFMGFXT2UYyqVgaeBf4O1AHigXuB/3ix7U1AZxGJcscJegE/51rmI+Bq93l/4Gv1Z3+WOWWt61Thres7M/by9uw7dJTB4xZw89TFdv9nY8oJbwaa/6aqL6rqAVXdr6ovAZcVtpKqfo8zeLwYWObua5yIPCIi2fdieB2oLiJrgDtxko8p5USEC9rUZtZdyYzq3ZRZK3fQa8wcnp65ikNH7VafxpRl3txr+YSIXAGk4txsZwjg1VVNqvoQ8FCuyQ96zD8CDPAuVFPaVAwP5fbeTRiQGM/jM1by/NdreCfjV/7etzn92tXGTiQzpuzx5kjhcmAgsMN9DHCnGQNA7aoVeW5we9694SxqxERwx1tLuOylb/nRqq4aU+Z4c4/mDaraT1VjVbWGql6sqhtKIDZTxiTWP40Pb+7KE/3bsGnPYfq9MJ/R7/xoBfaMKUMKTQoi0lREZrnXFCAibUTkAf+HZsqikBBhYGICs0f35PqeDflwyRZSnkrjxbQ1VkvJmDLAm+6jV4H7gGMAqrqUv5arMOZPYiIrcF/fFnw5qiddGsfyxIxV9HlmLl/8tB07wcyY0subpBClqum5ptkpJsYr9WMr8erQRN4Y0YmIsBCuf2MRV77+Pau2Hwh0aMaYPHiTFHaLSCOcM48Qkf7ANr9GZcqd7k1q8Pnt3fnXRa1YvmU/fZ+by4MfLuf3g3ZrDmNKE29OSb0ZGAc0F5EtwHrgioJXMeavwkJDuLpLfS5qW5tnvlrNlAUb+XDJVkb1bsIVnetRIdTf9RmNMYUp8H+hiITglL7uDdQAmqtqN1XdWCLRmXKpWqVwHunXms9u707rOpV5+OMVnPfcPOb9sivQoRkT9ApMCqp6ErjFfX5QVa0j2BSb5qdXZsqIMxl3VUeyjp/kqtfTuXZSBht2Hwx0aMYELW+O178UkdEikiAip2U//B6ZCQoiQp9Wp/PlnT2499zmfLd2N+c8M4fHPv+ZA3afaGNKnDdjCsPdvzd7TFOgYfGHY4JVRFgoNyY34rIOdXjii1W8Mmcd0xdt4Z5zm9G/QzwhVqLbmBLhzRXNDfJ4WEIwflGzciRPDWjLhzd3pe5pFbnn3aX0e2E+GRusoroxJcGr0z1EpLWIDBSRodkPfwdmglvbhKpMv7ELzw1ux64DWfR/+Ttum/YDW/ceDnRoxpRrhXYfichDQDLQEvgM6At8A0z2a2Qm6IkI/drV4ZyWcbyctpZX5q5j5ort3NizMSN7NKRieGigQzSm3PHmSKE/zg1ytqvqNUBbIMKvURnjISo8jDv7NOOrO3vSq3kcz3y1mt5j5vDJ0q1WMsOYYuZNUjjsnpp6XEQqAzuxQWYTAAmnRfHCFR1IHdmZyhUrcMubPzDolQUs37Iv0KEZU254kxQyRKQqTmG8RTh3UstdC8mYEtO5YXU+ubUbj116Bmt2ZXLh2G+4772l7M7MCnRoxpR5hY4pqOpN7tOXRWQGUNmtlGpMwISGCEM61eW8M2rx/KxfmPjtBj75cRu39WrC1V3qEx5mJTOMKQpv7qfQI/sB1AWqus+NCbgqFSvwwAUt+WJUDxLrV+PRz37mb8/O5euVO2y8wZgi8Obitbs9nkcCnXC6kc72S0TGFEGjGtFMuKYTs1fu5N+frmD4xAx6Nq3BPy9oQeOaMYEOz5gyw5vuows9X4tIAvCE3yIy5hSkNK9JtyaxTP5uI89+tZpzn53H0LPqc3uvJlSJqhDo8Iwp9YrS8for0Lq4AzGmuFQIDWFEtwakjU5mYFICE75dT8rTaUz9fiMnTlqXkjEF8ebitedxb7CDk0TaAT/6MyhjikP16Aj+75IzuOLMujzy8Qr+8f5y3vhuIw9d2IqzGlUPdHjGlEpenZKKM4awCPgOuFdVryxsJRFpJiJLPB77ReSOXMtUEZGPReRHEflJRK4p0rswpgCtalchdWRnXryiAweOHGfIqwu4ccoiNu85FOjQjCl1vBlTmFSUDavqKpyjCkQkFNgCvJ9rsZuBFap6oYjUAFaJyFRVtXs0mmIlIpx3Ri3Obl6T1+at44XZa5m1cicjuzfkxuRGVIrw5pwLY8o/b7qPlvFH99GfZgGqqm282E8vYG0ed2xTIEZEBIgG9gDHvdieMUUSWSGUW85uQv+OCTw+YyVjZ6/hnUWb+Xvf5vRrW8dKdJugJ4Wdyy0i2WcaveH+vQI4BEwC8ObWnCIyHlisqmNzTY8BPgKaAzHAIFX9NI/1RwIjAeLi4jqmpqYWtss8ZWZmEh0dXaR1g1V5b7M1v59g6sqjrN93kkZVQriiRTgNqxa90F55b6/iZu3lm1Npr5SUlEWqmljYct4khfmq2rWwaQWsHw5sBVqp6o5c8/oDXYE7gUbAl0BbVd2f3/YSExM1IyPDm13/RVpaGsnJyUWFLwqnAAAYj0lEQVRaN1gFQ5udPKm898MWHp+xkl0Hsri0Qx3uPbc5cZUjfd5WMLRXcbL28s2ptJeIeJUUvBloriQi3Tw23AWo5EMsfXGOEnbkMe8a4D11rAHW4xw1GFNiQkKE/h3jmT06mRuTG/HJj9tIeSqNF2av4cixE4EOz5gS5U1SGAG8ICIbRGQD8CJ/3KLTG0OAafnM24Qz3oCIxAHNgHU+bNuYYhMdEca95zbnqzt70r1JLE9+sYpznpnDjOXbrWSGCRre3I5zkaq2BdrgdO20U9XF3mxcRKKAc4D3PKbdICI3uC//DXRxB7Nn4ZzuutvXN2FMcapbPYpXrkpk6rVnElUhjBumLOKK175n5fZ8ezWNKTfyTQoicqGI1POYdAcwV0Q+EpEG3mxcVQ+panVV3ecx7WVVfdl9vlVV+6jqGaraWlWnFPWNGFPcujaO5dPbuvHvfq1YsW0/5z03jwc+WMaeg3bGtCm/CjpSeBTYBSAiFwBX4nQbfQS87P/QjAm8sNAQrjqrPmmjkxl6Vn2mpW8m+cnZTJi/nmMnTgY6PGOKXUFJQVU1+5LPS4HX3a6k14Aa/g/NmNKjalQ4D1/Uihm3d6dtQlX+9fEK+j43j7mrdwU6NGOKVUFJQUQkWkRCcAaDZ3nM8/1cPWPKgSZxMUwe3onXhiZy/MRJho5P59pJC1m/+2CgQzOmWBR0RfOzwBJgP/CzqmYAiEh7YFsJxGZMqSQi9G4ZR/emsUycv4Hnv15Dn2fmcE3XBrQKtbOUTNmWb1JQ1fEi8gVQkz9XRd2Oc32BMUEtIiyU63s24pIOdXjqi1W8Om8dIcDnOxcxuFMC3ZvUINTKZpgypsDaR6q6BaeQnec0O0owxkPNmEie6N+W63s24snp35K+YQ8zftpOnaoVGZAYz8DEBGpXrRjoMI3xit3d3Jhi0qhGNIObh7Pgvl68cHkHGtaoxLNf/UK3x7/mmgnpfPHTdjtjyZR6+R4piEgDVV1fksEYUx6Eh4VwfptanN+mFpv3HOLtjM28nbGZ699YRI2YCPp3jGdwUgL1qvtSLcaYklHQkcK7ACIyq4BljDEFSDgtirv6NGP+vWfz2tBE2sZX4ZU5a+n5ZBpDxi3gwyVbrL6SKVUKGlMIEZGHgKYicmfumao6xn9hGVO+hIWG0LtlHL1bxrF93xHeXbSZ1IWbuT11CVWjKnBp+3gGd0qgaVxMoEM1Qa6gpDAYuNhdxj6pxhST06tEcsvZTbgpuTHz1+4mdeFm3liwgfHz19OhblUGd6rLBW1qERVud4MzJa+gU1JXAY+LyFJV/bwEYzImKISECN2b1KB7kxr8lpnFe4u3MG3hJu55dyn//ngFF7WrzeCkupwRXyXQoZog4s1PkW9FZAzQw309B3jEs8idMebUVI+O4LoeDbm2ewMyNv7OtPRNvLvoV6Z+v4lWtSszuFNd+rWrTeXICoEO1ZRz3pySOh44AAx0H/uBCf4MyphgJSIk1T+NMQPbkf6P3jzSrxUnFf75wXI6PfoVd739Ixkb9tj9HYzfeHOk0EhVL/N4/S8RWeKvgIwxjioVKzD0rPpc1bkey7bsY1r6Zj5asoXpi3+lcc1oBiclcGmHeE6rFB7oUE054k1SOCwi3VT1GwAR6Qoc9m9YxphsIkKb+Kq0ia/KA+e34NOl25i2cBP/+fRnnpixij6t4hjSqS5nNaxOiJXVMKfIm6RwAzBZRLJHu34HrvZfSMaY/FSKCGNgUgIDkxJYuX0/qembef+HLXyydBt1T4tiUFICAzrGU7OyFTI2RVNoUlDVH4G2IlLZfW33JDSmFGh+emUevqgVf+/bnC9+2s609E08+cUqxny5mrOb12RwUgI9m9YgLNSq2RjveX0itCUDY0qnyAqh9GtXh37t6rB+90FSF25i+qJf+XLFDk6vHMnAxHgGJiUQXy0q0KGaMsCujjGmHGkQW4n7+rZgdJ9mzPp5B9PSN/P87DU8P3sN3ZvUYHBSAr1bxBEeZkcPJm+WFIwphyqEhnBu61qc27oWW/Ye5u2FTlG+m6YupnqlcPp3jGdQUgINa0QHOlRTyhSaFEQkFDgfqO+5vNU+MqZsqFO1IqPOacptvZowd/UuUhdu4rVv1vPK3HV0anAaQzol0Ld1LSIrhAY6VFMKeHOk8DFwBFgGWDF4Y8qo0BAhpXlNUprXZOeBI7y76FfeWriZUW/9yEMf/sQl7eswuFNdWtSqHOhQTQB5kxTiVbWNrxsWkWbAWx6TGgIPquqzuZZLxrkfdAVgt6r29HVfxhjf1IyJ5KbkxtzQoxEL1v3GtIWbmZa+mUnfbaRtQlWGJCVwQdvaREdYD3Ow8eZf/HMR6aOqM33ZsFtQrx3kdEFtAd73XEZEqgIvAueq6iYRqenLPowxpyYkROjSOJYujWP5/eBR3vthC6npm/j7e8v49ycruLBtbQZ3qkvb+CqI2IVxwcCbpLAAeF9EQoBjgACqqr4cY/YC1qrqxlzTLwfeU9VNOBvd6cM2jTHFqFqlcEZ0a8DwrvVZvGkvqemb+HDJVlIXbqb56TEMTkrgkvbxVImyonzlmRRWWEtE1uHcV2GZFrEKl4iMBxar6thc07O7jVrh3LPhOVWdnMf6I4GRAHFxcR1TU1OLEgaZmZlER9vZFr6wNvNNeWuvQ8eUBduOM/fX42zYf5IKIZB4eijJ8RVoWi3klI8eylt7+duptFdKSsoiVU0sbDlvksIXQF9VLdIgs4iEA1uBVqq6I9e8sUAizpFEReA74HxVXZ3f9hITEzUjI6MooZCWlkZycnKR1g1W1ma+Kc/ttXzLPlIXbuLDH7ZyIOs4DWMrMSgpgcs6xhMbHVGkbZbn9vKHU2kvEfEqKXjTfbQNSBORz4Gs7Ik+nJLaF+coYUce837FGVw+CBwUkblAWyDfpGCMCYzWdarwnzpncP95Lfhs2XZS0zfx2OcreWrmKs5pGcfgpLp0axxrRfnKOG+Swnr3Ee4+fDUEmJbPvA+BsSIS5m77TOCZIuzDGFNCosLD6N8xnv4d4/llxwFSF27mvcW/8tmy7dSpWtEpypcYT60qFQMdqikCbwri/auoGxeRKOAc4HqPaTe4231ZVX8WkRnAUpxrIF5T1eVF3Z8xpmQ1iYvhnxe05J5zmzHzpx2kLtzEmC9X8+xXq0lpVpNBSQmc3bymFeUrQ7y5onk28JeBB1U9u7B1VfUQUD3XtJdzvX4SeLLQSI0xpVZEWCgXtq3NhW1rs/G3g7ydsZl3Mn5l1sqd1IyJYEBiPIMS61K3uhXlK+286T4a7fE8ErgMOO6fcIwxZV296pW4+2/NGdW7KV+v3Enqws28lLaWF2avpWvj6gxOqkufVnFEhFlZjdLIm+6jRbkmzReROX6KxxhTToSFhtCn1en0aXU62/Yd5p0Mp6zGrdN+oFpUBS7tEE+LUKucU9p40310msfLEKAjcLrfIjLGlDu1qlTktl5NuDmlMd+s2U1q+iYmfbsBVFl2dDk3n92YmjF2t7jSwJvuo0U4YwqC0220Hhjhz6CMMeVTaIjQs2kNejatwda9h7l/ylymfL+JtzI2M6xLA27o2ZCqUUU5ydEUF2+6jxqURCDGmOBSu2pFhrWO4OHBSTz71WpembuWqQs2cm33hozo3sCK8QVIvueJiUiSiJzu8XqoiHwoIv/L1aVkjDFFVj+2Es8Obs+M23vQpXF1nvlqNd0f/5pX567jyLETgQ4v6BR08vArwFEAEekB/BeYDOwDxvk/NGNMMGl2egyvXJXIhzd3pXWdKjz62c/0fHI2byzYyNHjNiBdUgpKCqGqusd9PggYp6rTVfWfQGP/h2aMCUZtE6ryxogzeWtkZ+qeFsU/P1hOrzFpvLvoV06cLFJNTuODApOCW34CnIJ1X3vMs84+Y4xfndmwOm9ffxYTr0miSsUKjH7nR/o8M4dPl27jpCUHvynoy30aMEdEdgOHgXkAItIYpwvJGGP8SkRIblaTnk1rMGP5dp7+cjU3v7mYVrUrM7pPM5Kb1bCb/xSzfJOCqj4qIrOAWsBMj3sphAC3lkRwxhgDTnLoe0Yt+rQ6nQ+XbOHZr37hmokL6VivGqP7NOOsRtUL34jxSoHdQKq6II9pVtbaGBMQoSHCpR3iubBtbd7O2Mzzs9Yw5NUFdGscy+i/NaNdQtVAh1jmWelCY0yZUyE0hCvOrEfa3ck8cH4LVmzbz8UvzOe6yRms3L4/0OGVaZYUjDFlVmSFUK7t3pC596Rw1zlNWbDuN/o+N4/bpv3A+t0HAx1emWRJwRhT5kVHhHFrrybMuyeFG3s24ssVO+g9Zg5/n76ULXsPBzq8MsWSgjGm3KgaFc495zZn7j0pXNW5Hu8t3kLKk2k8/NFP7DqQVfgGjCUFY0z5UyMmgocvasXsu5O5tEMd3liwkR5PzObxGSvZe+hooMMr1SwpGGPKrTpVK/Lfy9rw1Z096dMqjpfnrKX747P536xfyMyye4XlxZKCMabcaxBbiecGt+fz27vTuVF1xny5mh5PzOa1eVZ0LzdLCsaYoNH89Mq8OjSRD27uSqvalfnPp07RvSlWdC+HJQVjTNBp5xbdm3ZdZ+KrRfGAW3RvuhXds6RgjAleZzWqzrs3nMWEYUlUjqzAXe/8yLnPzuXzZdv4o7JPcLGkYIwJaiJCSvOafHxLN168ogMnVblx6mIuHPsNs1ftDLrk4LekICLNRGSJx2O/iNyRz7JJInJCRPr7Kx5jjClISIhw3hm1mDmqJ08PaMu+w8e4ZsJCBr7yHd+v+y3Q4ZUYv90XQVVXAe0ARCQU2AK8n3s5d97jwBf+isUYY7wVGiJc1tGj6N7XvzBo3AK6N4lldJ9mtC3nRfdKqvuoF7BWVTfmMe9WYDqws4RiMcaYQoWHhXBl53rMuTuFf5zXguVb9tHvhfmMnJzBqu0HAh2e30hJ9JeJyHhgsaqOzTW9DvAmcDbwOvCJqr6bx/ojgZEAcXFxHVNTU4sUR2ZmJtHR0UVaN1hZm/nG2ss3Zam9Dh9XZm44xowNxzhyHM6sFcoljcOJq1RyQ7On0l4pKSmLVDWxsOX8nhREJBzYCrRS1R255r0DPK2qC0RkIvkkBU+JiYmakZFRpFjS0tJITk4u0rrBytrMN9ZevimL7bX30FFenrOOid+u59gJZWBiPLee3YTaVSv6fd+n0l4i4lVSKIl7LffFOUrYkce8RCDVvZ1eLHCeiBxX1Q9KIC5jjPFZ1ahw/t63OcO71efF2Wt58/tNTF+0hSs61+Wm5MbUiIkIdIinpCSOe4bg3O/5L1S1garWV9X6wLvATZYQjDFlQc2YSB6+qBVfj+7Jxe1rM/k7p+jeEzNWsu/QsUCHV2R+TQoiEgWcA7znMe0GEbnBn/s1xpiSEl8tiif6t+XLUT3o3TKOF9PW0u2Jrxn79S8cLINF9/zafaSqh4Dquaa9nM+yw/wZizHG+FPDGtE8P6Q9NyU34umZq3lq5momzN/AjcmNuLJzPSIrhAY6RK/YFc3GGFOMWtSqzGtXJ/LeTV1oXiuG/3z6M8lPpvHm95s4dqL0F92zpGCMMX7QoW41pl7bmTevO5PaVSO5//1l9B4zh/d/KN1F9ywpGGOMH3VpFMv0G7swflgiUeFhjHrLKbo3Y3npLLpnScEYY/xMRDi7eRyf3tqNsZe354QqN0xZzEVj55NWyoruWVIwxpgSEhIiXNCmNjPv6MGT/dvw+6GjDJuwkEGvLCB9/Z5AhwdYUjDGmBIXFhrCgMQEvr4rmX/3a8WG3w4y8JXvGDo+naW/7g1obJYUjDEmQMLDQrjqrPrMuTuF+89rztJf93LR2Plc/0YGq3cEpuieJQVjjAmwiuGhjOzRiHn3pHBH7ybMX/Mbf3t2LqPeWsLG3w6WaCwlUfvIGGOMF2IiK3BH76ZcfVZ9Xp67lknfbuDjH7cyIDGB23o1LpEYLCkYY0wpU61SOPf1bcGIrg0YO3sN09I3MX3xr1zWOIxkP+/buo+MMaaUqlk5kkf6tebru5Lp17Y2sRXF7/u0pGCMMaVcwmlRPDmgLR3j/N+5Y0nBGGNMDksKxhhjclhSMMYYk8OSgjHGmByWFIwxxuSwpGCMMSaHJQVjjDE5LCkYY4zJIaXp5g7eEJFdwMYirh4L7C7GcIKBtZlvrL18Y+3lm1Npr3qqWqOwhcpcUjgVIpKhqomBjqMssTbzjbWXb6y9fFMS7WXdR8YYY3JYUjDGGJMj2JLCuEAHUAZZm/nG2ss31l6+8Xt7BdWYgjHGmIIF25GCMcaYAlhSMMYYkyNokoKIbBCRZSKyREQyAh1PaSMi40Vkp4gs95h2moh8KSK/uH+rBTLG0iSf9npYRLa4n7ElInJeIGMsTUQkQURmi8jPIvKTiNzuTrfPWB4KaC+/f8aCZkxBRDYAiapqF8rkQUR6AJnAZFVt7U57Atijqv8Vkb8D1VT13kDGWVrk014PA5mq+lQgYyuNRKQWUEtVF4tIDLAIuBgYhn3G/qKA9hqInz9jQXOkYAqmqnOBPbkm9wMmuc8n4XwoDfm2l8mHqm5T1cXu8wPAz0Ad7DOWpwLay++CKSkoMFNEFonIyEAHU0bEqeo2cD6kQM0Ax1MW3CIiS93uJesKyYOI1AfaA99jn7FC5Wov8PNnLJiSQldV7QD0BW52D/+NKU4vAY2AdsA24OnAhlP6iEg0MB24Q1X3Bzqe0i6P9vL7ZyxokoKqbnX/7gTeBzoFNqIyYYfbt5ndx7kzwPGUaqq6Q1VPqOpJ4FXsM/YnIlIB5wtuqqq+5062z1g+8mqvkviMBUVSEJFK7mANIlIJ6AMsL3gtA3wEXO0+vxr4MICxlHrZX26uS7DPWA4REeB14GdVHeMxyz5jecivvUriMxYUZx+JSEOcowOAMOBNVX00gCGVOiIyDUjGKc27A3gI+AB4G6gLbAIGqKoNrpJveyXjHNYrsAG4Pru/PNiJSDdgHrAMOOlOvh+nn9w+Y7kU0F5D8PNnLCiSgjHGGO8ERfeRMcYY71hSMMYYk8OSgjHGmByWFIwxxuSwpGCMMSaHJQVTqoiIisjTHq9Hu4XmimPbE0Wkf3Fsq5D9DHCrW87OY15TEflMRNa4y7wtInEikiwinxRxf3eISNSpR26MJQVT+mQBl4pIbKAD8SQioT4sPgK4SVVTcm0jEvgUeElVG6tqC5yyBTVOMbw7AJ+Sgo/vxwQRSwqmtDmOcx/aUbln5P6lLyKZ7t9kEZnj/upeLSL/FZErRCTdvYdGI4/N9BaRee5yF7jrh4rIkyKy0C00dr3HdmeLyJs4FxHljmeIu/3lIvK4O+1BoBvwsog8mWuVy4HvVPXj7AmqOltV/3RVqlszf7TH6+UiUt+9Mv9TEfnRnTZIRG4DagOzs49MRKSPiHwnIotF5B23fk72PUUeFJFvgAEicpuIrHDfc2oh/y4mSIQFOgBj8vACsNS9n4O32gItcMpZrwNeU9VO7s1JbsX5NQ1QH+iJU1Rstog0BoYC+1Q1SUQigPkiMtNdvhPQWlXXe+5MRGoDjwMdgd9xKvBerKqPiMjZwGhVzX0zp9Y4dfGL6lxgq6qe78ZQRVX3icidQIqq7naPsB4AeqvqQRG5F7gTeMTdxhFV7eauvxVooKpZIlL1FOIy5YgdKZhSx60GORm4zYfVFro16LOAtUD2l/oynESQ7W1VPamqv+Akj+Y4tbCGisgSnLIL1YEm7vLpuROCKwlIU9VdqnocmAr4u/LuMpwjncdFpLuq7stjmc5AS5zEtgSnnlA9j/lveTxfCkwVkStxjtCMsaRgSq1ncfrmK3lMO477mXULhoV7zMvyeH7S4/VJ/nxEnLuuiwIC3Kqq7dxHA1XNTioH84lPvH0jHn7CObIoTM77dEUCqOpqd/1lwGNuV1VecX3p8V5aquoIj/me7+d8nKOyjsAiEbGeA2NJwZROblG0t3ESQ7YN/PGl2g+oUIRNDxCREHecoSGwCvgCuNEtVZx9hlClgjaCc0TRU0Ri3UHbIcCcQtZ5E+giIudnTxCRc0XkjFzLbQA6uPM7AA3c57WBQ6o6BXgqexngABDjPl8AdHW7xRCRKBFpmjsQEQkBElR1NnAPUBWILiR+EwTsl4EpzZ4GbvF4/SrwoYikA7PI/1d8QVbhfHnHATeo6hEReQ2ni2mxewSyi0JuC6mq20TkPmA2zq/zz1S1wLLPqnrYHdx+VkSeBY7hdOHcjtNllW06f3RnLQRWu9PPAJ4UkZPuuje608cBn4vINlVNEZFhwDR3fAScMYbV/FkoMEVEqrjxP6OqewuK3wQHq5JqjDEmh3UfGWOMyWFJwRhjTA5LCsYYY3JYUjDGGJPDkoIxxpgclhSMMcbksKRgjDEmx/8Dun+DIaUz5ZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a2ea8198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "plt.plot(range_n_clusters, sum_of_squared_distances)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.xticks(range_n_clusters, range_n_clusters)\n",
    "plt.ylabel('Sum of Squared Distances')\n",
    "plt.title('Elbow Method for Optimal k')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
    "clusterer = KMeans(n_clusters=10, random_state = 123).fit(azdias_features_pca)\n",
    "azdias_preds = clusterer.predict(azdias_features_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "797754"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(azdias_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    " We can see from the plot above that the line has a slight \"elbow\" at 10 clusters, meaning that each additional cluster reduces the distances between classes much less (marginal gain drops). I also tested some other combinations of cluster numbers earlier. But none gives a nice elbow. So after all I decided to cluster the data into 10 segments."
   ]
  },
  {
   "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. \n",
    "\n",
    "I first load the customers data and apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function I created earlier. \n",
    "\n",
    "Then I use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, I don't use a `.fit()` or `.fit_transform()` method to re-fit the old objects. \n",
    "\n",
    "Then I carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start step 1: dealing with missing values\n",
      "Checking number of missing values after step 1: 3112918\n",
      "\n",
      "Start step 2: removing selected columns and rows\n",
      "Checking the new shape of the data frame after step 2: (141689, 74)\n",
      "\n",
      "Start step 3.1: reencoding with dummies\n",
      "Start step 3.2: reengineering features\n",
      "Checking the new shape of the data frame after step 3: (141689, 127)\n",
      "Checking the number of missing values in the resulting data frame: 105205\n"
     ]
    }
   ],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "customers_clean = clean_data(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GEBAEUDETYP_5.0'}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(azdias_features.columns)-set(customers_clean.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_col = azdias_features.columns.get_loc('GEBAEUDETYP_5.0')\n",
    "new_col = np.zeros(shape=customers_clean['GEBAEUDETYP_4.0'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Commentary**: It turned out that the customers dataset doesn't have values for 'GEBAEUDETYP'=5. So after getting dummies for the categorial variables, we have 127 features in the customers dataset instead of 128 features as in the azdias dataset. That's why I added a new column to customers_clean dataset called 'GEBAEUDETYP_5.0' filled with 0s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141689, 128)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_clean.insert(loc=idx_col, column='GEBAEUDETYP_5.0', value=new_col)\n",
    "customers_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if GEBAEUDETYP_5.0 was correctly inserted\n",
    "idx_col == customers_clean.columns.get_loc('GEBAEUDETYP_5.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_clean_imputed = imputer.transform(customers_clean)\n",
    "\n",
    "customers_scaled = scaler.transform(customers_clean_imputed)  \n",
    "\n",
    "customers_pca = pca.transform(customers_scaled)\n",
    "\n",
    "customer_preds = clusterer.predict(customers_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141689"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(customer_preds)"
   ]
  },
  {
   "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": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>customers_count</th>\n",
       "      <th>population_count</th>\n",
       "      <th>customers_prop</th>\n",
       "      <th>population_prop</th>\n",
       "      <th>diff</th>\n",
       "      <th>overrepresented</th>\n",
       "      <th>underrepresented</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4695</td>\n",
       "      <td>39139</td>\n",
       "      <td>3.31</td>\n",
       "      <td>4.91</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1162</td>\n",
       "      <td>65789</td>\n",
       "      <td>0.82</td>\n",
       "      <td>8.25</td>\n",
       "      <td>-7.43</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14335</td>\n",
       "      <td>85160</td>\n",
       "      <td>10.12</td>\n",
       "      <td>10.67</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2038</td>\n",
       "      <td>83417</td>\n",
       "      <td>1.44</td>\n",
       "      <td>10.46</td>\n",
       "      <td>-9.02</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1486</td>\n",
       "      <td>89844</td>\n",
       "      <td>1.05</td>\n",
       "      <td>11.26</td>\n",
       "      <td>-10.21</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>55745</td>\n",
       "      <td>84540</td>\n",
       "      <td>39.34</td>\n",
       "      <td>10.60</td>\n",
       "      <td>28.74</td>\n",
       "      <td>target</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>14447</td>\n",
       "      <td>100340</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12.58</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>17705</td>\n",
       "      <td>107107</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.43</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>27395</td>\n",
       "      <td>84016</td>\n",
       "      <td>19.33</td>\n",
       "      <td>10.53</td>\n",
       "      <td>8.80</td>\n",
       "      <td>target</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2681</td>\n",
       "      <td>58402</td>\n",
       "      <td>1.89</td>\n",
       "      <td>7.32</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-</td>\n",
       "      <td>not a target</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  customers_count  population_count  customers_prop  \\\n",
       "0        0             4695             39139            3.31   \n",
       "1        1             1162             65789            0.82   \n",
       "2        2            14335             85160           10.12   \n",
       "3        3             2038             83417            1.44   \n",
       "4        4             1486             89844            1.05   \n",
       "5        5            55745             84540           39.34   \n",
       "6        6            14447            100340           10.20   \n",
       "7        7            17705            107107           12.50   \n",
       "8        8            27395             84016           19.33   \n",
       "9        9             2681             58402            1.89   \n",
       "\n",
       "   population_prop   diff overrepresented underrepresented  \n",
       "0             4.91  -1.60               -     not a target  \n",
       "1             8.25  -7.43               -     not a target  \n",
       "2            10.67  -0.55               -                -  \n",
       "3            10.46  -9.02               -     not a target  \n",
       "4            11.26 -10.21               -     not a target  \n",
       "5            10.60  28.74          target                -  \n",
       "6            12.58  -2.38               -     not a target  \n",
       "7            13.43  -0.93               -                -  \n",
       "8            10.53   8.80          target                -  \n",
       "9             7.32  -5.43               -     not a target  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "customers_preds_prop = round(100*pd.Series(customer_preds).value_counts()/customer_preds.shape[0], 2)\n",
    "azdias_preds_prop = round(100*pd.Series(azdias_preds).value_counts()/azdias_preds.shape[0], 2)\n",
    "preds = pd.DataFrame({'customers_count': pd.Series(customer_preds).value_counts(), \n",
    "                      'population_count': pd.Series(azdias_preds).value_counts(), \n",
    "                      'customers_prop': customers_preds_prop, 'population_prop': azdias_preds_prop})\n",
    "preds['diff'] = [preds.customers_prop[x]-preds.population_prop[x] for x in preds.index]\n",
    "preds['overrepresented'] = ['target' if preds['diff'][x] > 1 else '-' for x in preds.index]\n",
    "preds['underrepresented'] = ['not a target' if preds['diff'][x] < -1 else '-' for x in preds.index]\n",
    "preds.insert(loc=0, column='cluster', value=preds.index)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a4d8ec88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10, 5))\n",
    "tidy = pd.melt(preds, id_vars=['cluster'], value_vars=['customers_prop', 'population_prop'], var_name='dataset', value_name='%')\n",
    "sns.barplot(x='cluster', y='%', hue='dataset', data=tidy);\n",
    "plt.title('Cluster ratios for customers and population datasets', fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpret_clusters(cluster_num):\n",
    "    pca_inversed = pca.inverse_transform(clusterer.cluster_centers_[cluster_num, :])\n",
    "    scaler_inversed = np.around(scaler.inverse_transform(pca_inversed), decimals=2)\n",
    "    results_df = pd.DataFrame(scaler_inversed, azdias_features.columns)\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target cluster number: 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERS_TYP</th>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_TITEL</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_2.0</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_3.0</th>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_2.0</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_3.0</th>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_6.0</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_1.0</th>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_2.0</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_3.0</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_4.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_5.0</th>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_6.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_8.0</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_1</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_2</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_3</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_4</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_5</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_6</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_7</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_8</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_9</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <td>64.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage</th>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth</th>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_building</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0\n",
       "ALTERSKATEGORIE_GROB    3.37\n",
       "ANREDE_KZ               1.45\n",
       "FINANZ_MINIMALIST       4.60\n",
       "FINANZ_SPARER           1.54\n",
       "FINANZ_VORSORGER        4.33\n",
       "FINANZ_ANLEGER          1.65\n",
       "FINANZ_UNAUFFAELLIGER   2.04\n",
       "FINANZ_HAUSBAUER        2.13\n",
       "GREEN_AVANTGARDE        0.97\n",
       "HEALTH_TYP              2.08\n",
       "RETOURTYP_BK_S          3.70\n",
       "SEMIO_SOZ               4.25\n",
       "SEMIO_FAM               3.55\n",
       "SEMIO_REL               2.96\n",
       "SEMIO_MAT               3.15\n",
       "SEMIO_VERT              4.88\n",
       "SEMIO_LUST              5.32\n",
       "SEMIO_ERL               5.11\n",
       "SEMIO_KULT              3.61\n",
       "SEMIO_RAT               3.06\n",
       "SEMIO_KRIT              4.20\n",
       "SEMIO_DOM               4.66\n",
       "SEMIO_KAEM              4.12\n",
       "SEMIO_PFLICHT           3.12\n",
       "SEMIO_TRADV             3.08\n",
       "SOHO_KZ                 0.01\n",
       "VERS_TYP                1.52\n",
       "ANZ_PERSONEN            2.31\n",
       "ANZ_TITEL               0.01\n",
       "HH_EINKOMMEN_SCORE      2.23\n",
       "...                      ...\n",
       "SHOPPER_TYP_2.0         0.23\n",
       "SHOPPER_TYP_3.0         0.35\n",
       "ZABEOTYP_1.0            0.47\n",
       "ZABEOTYP_2.0            0.13\n",
       "ZABEOTYP_3.0            0.33\n",
       "ZABEOTYP_4.0            0.08\n",
       "ZABEOTYP_5.0           -0.03\n",
       "ZABEOTYP_6.0            0.03\n",
       "GEBAEUDETYP_1.0         0.77\n",
       "GEBAEUDETYP_2.0         0.01\n",
       "GEBAEUDETYP_3.0         0.09\n",
       "GEBAEUDETYP_4.0         0.00\n",
       "GEBAEUDETYP_5.0        -0.00\n",
       "GEBAEUDETYP_6.0         0.00\n",
       "GEBAEUDETYP_8.0         0.12\n",
       "CAMEO_DEUG_2015_1       0.18\n",
       "CAMEO_DEUG_2015_2       0.29\n",
       "CAMEO_DEUG_2015_3       0.14\n",
       "CAMEO_DEUG_2015_4       0.16\n",
       "CAMEO_DEUG_2015_5       0.07\n",
       "CAMEO_DEUG_2015_6       0.09\n",
       "CAMEO_DEUG_2015_7       0.04\n",
       "CAMEO_DEUG_2015_8       0.01\n",
       "CAMEO_DEUG_2015_9       0.02\n",
       "OST_WEST_KZ_W           0.91\n",
       "decade                 64.91\n",
       "life_stage              3.69\n",
       "wealth                  1.92\n",
       "rural                   0.09\n",
       "business_building       0.01\n",
       "\n",
       "[128 rows x 1 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is overrepresented in the\n",
    "# customer data compared to the general population?\n",
    "print(\"target cluster number:\", preds['diff'].values.argmax())\n",
    "target_cluster = interpret_clusters(preds['diff'].values.argmax())\n",
    "target_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underrepresented cluster number: 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>6.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERS_TYP</th>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_TITEL</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_2.0</th>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_3.0</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_2.0</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_3.0</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_6.0</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_1.0</th>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_2.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_3.0</th>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_4.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_5.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_6.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_8.0</th>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_1</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_2</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_3</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_4</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_5</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_6</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_7</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_8</th>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_9</th>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <td>86.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage</th>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth</th>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural</th>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_building</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0\n",
       "ALTERSKATEGORIE_GROB    1.79\n",
       "ANREDE_KZ               1.98\n",
       "FINANZ_MINIMALIST       1.12\n",
       "FINANZ_SPARER           4.35\n",
       "FINANZ_VORSORGER        2.26\n",
       "FINANZ_ANLEGER          4.18\n",
       "FINANZ_UNAUFFAELLIGER   3.75\n",
       "FINANZ_HAUSBAUER        4.21\n",
       "GREEN_AVANTGARDE        0.02\n",
       "HEALTH_TYP              2.36\n",
       "RETOURTYP_BK_S          2.70\n",
       "SEMIO_SOZ               3.32\n",
       "SEMIO_FAM               3.92\n",
       "SEMIO_REL               5.27\n",
       "SEMIO_MAT               4.71\n",
       "SEMIO_VERT              2.29\n",
       "SEMIO_LUST              3.15\n",
       "SEMIO_ERL               4.39\n",
       "SEMIO_KULT              4.06\n",
       "SEMIO_RAT               5.94\n",
       "SEMIO_KRIT              5.48\n",
       "SEMIO_DOM               6.11\n",
       "SEMIO_KAEM              6.09\n",
       "SEMIO_PFLICHT           6.05\n",
       "SEMIO_TRADV             5.34\n",
       "SOHO_KZ                 0.01\n",
       "VERS_TYP                1.53\n",
       "ANZ_PERSONEN            1.46\n",
       "ANZ_TITEL               0.00\n",
       "HH_EINKOMMEN_SCORE      5.62\n",
       "...                      ...\n",
       "SHOPPER_TYP_2.0         0.46\n",
       "SHOPPER_TYP_3.0         0.16\n",
       "ZABEOTYP_1.0            0.00\n",
       "ZABEOTYP_2.0            0.01\n",
       "ZABEOTYP_3.0            0.13\n",
       "ZABEOTYP_4.0            0.47\n",
       "ZABEOTYP_5.0            0.29\n",
       "ZABEOTYP_6.0            0.10\n",
       "GEBAEUDETYP_1.0         0.41\n",
       "GEBAEUDETYP_2.0         0.00\n",
       "GEBAEUDETYP_3.0         0.32\n",
       "GEBAEUDETYP_4.0         0.00\n",
       "GEBAEUDETYP_5.0         0.00\n",
       "GEBAEUDETYP_6.0         0.00\n",
       "GEBAEUDETYP_8.0         0.26\n",
       "CAMEO_DEUG_2015_1       0.01\n",
       "CAMEO_DEUG_2015_2       0.01\n",
       "CAMEO_DEUG_2015_3       0.02\n",
       "CAMEO_DEUG_2015_4       0.01\n",
       "CAMEO_DEUG_2015_5       0.06\n",
       "CAMEO_DEUG_2015_6       0.11\n",
       "CAMEO_DEUG_2015_7       0.14\n",
       "CAMEO_DEUG_2015_8       0.32\n",
       "CAMEO_DEUG_2015_9       0.32\n",
       "OST_WEST_KZ_W           0.76\n",
       "decade                 86.77\n",
       "life_stage              2.02\n",
       "wealth                  4.39\n",
       "rural                  -0.01\n",
       "business_building       0.29\n",
       "\n",
       "[128 rows x 1 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is underrepresented in the\n",
    "# customer data compared to the general population?\n",
    "print(\"underrepresented cluster number:\", preds['diff'].values.argmin())\n",
    "underrepres_cluster = interpret_clusters(preds['diff'].values.argmin())\n",
    "underrepres_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exploring the second target cluster: 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VERS_TYP</th>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_TITEL</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_2.0</th>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPER_TYP_3.0</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_1.0</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_2.0</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_3.0</th>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_4.0</th>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_5.0</th>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZABEOTYP_6.0</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_1.0</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_2.0</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_3.0</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_4.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_5.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_6.0</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_8.0</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_1</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_2</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_3</th>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_4</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_5</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_6</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_7</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_8</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMEO_DEUG_2015_9</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <td>65.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage</th>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth</th>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rural</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_building</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0\n",
       "ALTERSKATEGORIE_GROB    3.28\n",
       "ANREDE_KZ               1.11\n",
       "FINANZ_MINIMALIST       4.50\n",
       "FINANZ_SPARER           1.67\n",
       "FINANZ_VORSORGER        4.30\n",
       "FINANZ_ANLEGER          2.20\n",
       "FINANZ_UNAUFFAELLIGER   1.74\n",
       "FINANZ_HAUSBAUER        2.29\n",
       "GREEN_AVANTGARDE        0.05\n",
       "HEALTH_TYP              2.31\n",
       "RETOURTYP_BK_S          3.85\n",
       "SEMIO_SOZ               4.82\n",
       "SEMIO_FAM               4.66\n",
       "SEMIO_REL               3.72\n",
       "SEMIO_MAT               3.35\n",
       "SEMIO_VERT              5.76\n",
       "SEMIO_LUST              4.79\n",
       "SEMIO_ERL               4.30\n",
       "SEMIO_KULT              4.82\n",
       "SEMIO_RAT               2.67\n",
       "SEMIO_KRIT              3.59\n",
       "SEMIO_DOM               3.14\n",
       "SEMIO_KAEM              2.65\n",
       "SEMIO_PFLICHT           3.38\n",
       "SEMIO_TRADV             2.94\n",
       "SOHO_KZ                 0.01\n",
       "VERS_TYP                1.56\n",
       "ANZ_PERSONEN            1.83\n",
       "ANZ_TITEL               0.00\n",
       "HH_EINKOMMEN_SCORE      3.89\n",
       "...                      ...\n",
       "SHOPPER_TYP_2.0         0.21\n",
       "SHOPPER_TYP_3.0         0.18\n",
       "ZABEOTYP_1.0            0.27\n",
       "ZABEOTYP_2.0            0.01\n",
       "ZABEOTYP_3.0            0.43\n",
       "ZABEOTYP_4.0            0.21\n",
       "ZABEOTYP_5.0           -0.02\n",
       "ZABEOTYP_6.0            0.09\n",
       "GEBAEUDETYP_1.0         0.71\n",
       "GEBAEUDETYP_2.0         0.02\n",
       "GEBAEUDETYP_3.0         0.14\n",
       "GEBAEUDETYP_4.0         0.00\n",
       "GEBAEUDETYP_5.0         0.00\n",
       "GEBAEUDETYP_6.0         0.00\n",
       "GEBAEUDETYP_8.0         0.12\n",
       "CAMEO_DEUG_2015_1       0.04\n",
       "CAMEO_DEUG_2015_2       0.15\n",
       "CAMEO_DEUG_2015_3       0.19\n",
       "CAMEO_DEUG_2015_4       0.23\n",
       "CAMEO_DEUG_2015_5       0.08\n",
       "CAMEO_DEUG_2015_6       0.14\n",
       "CAMEO_DEUG_2015_7       0.07\n",
       "CAMEO_DEUG_2015_8       0.05\n",
       "CAMEO_DEUG_2015_9       0.03\n",
       "OST_WEST_KZ_W           0.78\n",
       "decade                 65.18\n",
       "life_stage              3.26\n",
       "wealth                  2.57\n",
       "rural                   0.58\n",
       "business_building       0.01\n",
       "\n",
       "[128 rows x 1 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"exploring the second target cluster:\", preds['diff'].sort_values().index[-2])\n",
    "interpret_clusters(preds['diff'].sort_values().index[-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "The cluster analysis of customer data and democraphics data shows that there are primarily 2 segments of the population that are interested in the company's products. The target audiences for the company are thus clusters 5 and 8, because these segments are overrepresented in the customer data. \n",
    "\n",
    "Almost 40% of customers are in Cluster 5, followed by almost 20% in cluster 8. \n",
    "\n",
    "TARGET GROUPS: <br>\n",
    "\n",
    "**Cluster 5 - \"conservative prosperous middle-aged green avangarde\"**. This segment is comprised of predominantly  prosperous middle-aged Germans (from middle class to upper class) located in West Germany born in the 1960s. They prefer 'green energy' and can be classified as \"green avantgarde\". These are older families with kids probably already grown-up (but also single/couples). Conservative, likely religious, rational, materialistic and dutiful.\n",
    "\n",
    "**Cluster 8 - \"dominant-minded middle-aged men with average income\"**: the second target group consists of mostly dominant-minded and combative men of the same age as the first target group. However, they are less affluent than the first group and belong to established/consumption oriented middleclass with average income. They are unlikely to have kids and live a bit further away from the city center than the first target group.\n",
    "\n",
    "LEAST LIKELY CUSTOMER GROUP:\n",
    "\n",
    "**Cluster 4 - \"single less affluent women in their 20s-30s\"**: The least likely customers are women born in the 1980s that come from working class and are less affluent. They are probably single or are young couples with children. Not religious, dreamful, irrational and not conservative. Price-driven with regards to energy consumption. \n"
   ]
  }
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