Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv(\"Udacity_AZDIAS_Subset.csv\", sep = ';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv(\"AZDIAS_Feature_Summary.csv\", sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891221, 85)\n",
      "(85, 4)\n"
     ]
    }
   ],
   "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",
    "print(azdias.shape)\n",
    "print(feat_info.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>2.0</td>\n",
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      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
       "2        -1                     3          2            3.0   \n",
       "3         2                     4          2            2.0   \n",
       "4        -1                     3          1            5.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3    ...            NaN         NaN   \n",
       "1                      4                 5    ...            2.0         3.0   \n",
       "2                      3                 5    ...            3.0         3.0   \n",
       "3                      1                 2    ...            2.0         2.0   \n",
       "4                      3                 2    ...            2.0         4.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
       "1         2.0         1.0          1.0       5.0       4.0     3.0   \n",
       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN       NaN  \n",
       "1          5.0       4.0  \n",
       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              attribute information_level         type missing_or_unknown\n",
      "0              AGER_TYP            person  categorical             [-1,0]\n",
      "1  ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]\n",
      "2             ANREDE_KZ            person  categorical             [-1,0]\n",
      "3         CJT_GESAMTTYP            person  categorical                [0]\n",
      "4     FINANZ_MINIMALIST            person      ordinal               [-1]\n"
     ]
    }
   ],
   "source": [
    "print(feat_info.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891221 entries, 0 to 891220\n",
      "Data columns (total 85 columns):\n",
      "AGER_TYP                 891221 non-null int64\n",
      "ALTERSKATEGORIE_GROB     891221 non-null int64\n",
      "ANREDE_KZ                891221 non-null int64\n",
      "CJT_GESAMTTYP            886367 non-null float64\n",
      "FINANZ_MINIMALIST        891221 non-null int64\n",
      "FINANZ_SPARER            891221 non-null int64\n",
      "FINANZ_VORSORGER         891221 non-null int64\n",
      "FINANZ_ANLEGER           891221 non-null int64\n",
      "FINANZ_UNAUFFAELLIGER    891221 non-null int64\n",
      "FINANZ_HAUSBAUER         891221 non-null int64\n",
      "FINANZTYP                891221 non-null int64\n",
      "GEBURTSJAHR              891221 non-null int64\n",
      "GFK_URLAUBERTYP          886367 non-null float64\n",
      "GREEN_AVANTGARDE         891221 non-null int64\n",
      "HEALTH_TYP               891221 non-null int64\n",
      "LP_LEBENSPHASE_FEIN      886367 non-null float64\n",
      "LP_LEBENSPHASE_GROB      886367 non-null float64\n",
      "LP_FAMILIE_FEIN          886367 non-null float64\n",
      "LP_FAMILIE_GROB          886367 non-null float64\n",
      "LP_STATUS_FEIN           886367 non-null float64\n",
      "LP_STATUS_GROB           886367 non-null float64\n",
      "NATIONALITAET_KZ         891221 non-null int64\n",
      "PRAEGENDE_JUGENDJAHRE    891221 non-null int64\n",
      "RETOURTYP_BK_S           886367 non-null float64\n",
      "SEMIO_SOZ                891221 non-null int64\n",
      "SEMIO_FAM                891221 non-null int64\n",
      "SEMIO_REL                891221 non-null int64\n",
      "SEMIO_MAT                891221 non-null int64\n",
      "SEMIO_VERT               891221 non-null int64\n",
      "SEMIO_LUST               891221 non-null int64\n",
      "SEMIO_ERL                891221 non-null int64\n",
      "SEMIO_KULT               891221 non-null int64\n",
      "SEMIO_RAT                891221 non-null int64\n",
      "SEMIO_KRIT               891221 non-null int64\n",
      "SEMIO_DOM                891221 non-null int64\n",
      "SEMIO_KAEM               891221 non-null int64\n",
      "SEMIO_PFLICHT            891221 non-null int64\n",
      "SEMIO_TRADV              891221 non-null int64\n",
      "SHOPPER_TYP              891221 non-null int64\n",
      "SOHO_KZ                  817722 non-null float64\n",
      "TITEL_KZ                 817722 non-null float64\n",
      "VERS_TYP                 891221 non-null int64\n",
      "ZABEOTYP                 891221 non-null int64\n",
      "ALTER_HH                 817722 non-null float64\n",
      "ANZ_PERSONEN             817722 non-null float64\n",
      "ANZ_TITEL                817722 non-null float64\n",
      "HH_EINKOMMEN_SCORE       872873 non-null float64\n",
      "KK_KUNDENTYP             306609 non-null float64\n",
      "W_KEIT_KIND_HH           783619 non-null float64\n",
      "WOHNDAUER_2008           817722 non-null float64\n",
      "ANZ_HAUSHALTE_AKTIV      798073 non-null float64\n",
      "ANZ_HH_TITEL             794213 non-null float64\n",
      "GEBAEUDETYP              798073 non-null float64\n",
      "KONSUMNAEHE              817252 non-null float64\n",
      "MIN_GEBAEUDEJAHR         798073 non-null float64\n",
      "OST_WEST_KZ              798073 non-null object\n",
      "WOHNLAGE                 798073 non-null float64\n",
      "CAMEO_DEUG_2015          792242 non-null object\n",
      "CAMEO_DEU_2015           792242 non-null object\n",
      "CAMEO_INTL_2015          792242 non-null object\n",
      "KBA05_ANTG1              757897 non-null float64\n",
      "KBA05_ANTG2              757897 non-null float64\n",
      "KBA05_ANTG3              757897 non-null float64\n",
      "KBA05_ANTG4              757897 non-null float64\n",
      "KBA05_BAUMAX             757897 non-null float64\n",
      "KBA05_GBZ                757897 non-null float64\n",
      "BALLRAUM                 797481 non-null float64\n",
      "EWDICHTE                 797481 non-null float64\n",
      "INNENSTADT               797481 non-null float64\n",
      "GEBAEUDETYP_RASTER       798066 non-null float64\n",
      "KKK                      770025 non-null float64\n",
      "MOBI_REGIO               757897 non-null float64\n",
      "ONLINE_AFFINITAET        886367 non-null float64\n",
      "REGIOTYP                 770025 non-null float64\n",
      "KBA13_ANZAHL_PKW         785421 non-null float64\n",
      "PLZ8_ANTG1               774706 non-null float64\n",
      "PLZ8_ANTG2               774706 non-null float64\n",
      "PLZ8_ANTG3               774706 non-null float64\n",
      "PLZ8_ANTG4               774706 non-null float64\n",
      "PLZ8_BAUMAX              774706 non-null float64\n",
      "PLZ8_HHZ                 774706 non-null float64\n",
      "PLZ8_GBZ                 774706 non-null float64\n",
      "ARBEIT                   794005 non-null float64\n",
      "ORTSGR_KLS9              794005 non-null float64\n",
      "RELAT_AB                 794005 non-null float64\n",
      "dtypes: float64(49), int64(32), object(4)\n",
      "memory usage: 578.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# to check the data type and if null(missing) values are present or not(naturally).\n",
    "azdias.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGER_TYP                 False\n",
      "ALTERSKATEGORIE_GROB     False\n",
      "ANREDE_KZ                False\n",
      "CJT_GESAMTTYP             True\n",
      "FINANZ_MINIMALIST        False\n",
      "FINANZ_SPARER            False\n",
      "FINANZ_VORSORGER         False\n",
      "FINANZ_ANLEGER           False\n",
      "FINANZ_UNAUFFAELLIGER    False\n",
      "FINANZ_HAUSBAUER         False\n",
      "FINANZTYP                False\n",
      "GEBURTSJAHR              False\n",
      "GFK_URLAUBERTYP           True\n",
      "GREEN_AVANTGARDE         False\n",
      "HEALTH_TYP               False\n",
      "LP_LEBENSPHASE_FEIN       True\n",
      "LP_LEBENSPHASE_GROB       True\n",
      "LP_FAMILIE_FEIN           True\n",
      "LP_FAMILIE_GROB           True\n",
      "LP_STATUS_FEIN            True\n",
      "LP_STATUS_GROB            True\n",
      "NATIONALITAET_KZ         False\n",
      "PRAEGENDE_JUGENDJAHRE    False\n",
      "RETOURTYP_BK_S            True\n",
      "SEMIO_SOZ                False\n",
      "SEMIO_FAM                False\n",
      "SEMIO_REL                False\n",
      "SEMIO_MAT                False\n",
      "SEMIO_VERT               False\n",
      "SEMIO_LUST               False\n",
      "                         ...  \n",
      "OST_WEST_KZ               True\n",
      "WOHNLAGE                  True\n",
      "CAMEO_DEUG_2015           True\n",
      "CAMEO_DEU_2015            True\n",
      "CAMEO_INTL_2015           True\n",
      "KBA05_ANTG1               True\n",
      "KBA05_ANTG2               True\n",
      "KBA05_ANTG3               True\n",
      "KBA05_ANTG4               True\n",
      "KBA05_BAUMAX              True\n",
      "KBA05_GBZ                 True\n",
      "BALLRAUM                  True\n",
      "EWDICHTE                  True\n",
      "INNENSTADT                True\n",
      "GEBAEUDETYP_RASTER        True\n",
      "KKK                       True\n",
      "MOBI_REGIO                True\n",
      "ONLINE_AFFINITAET         True\n",
      "REGIOTYP                  True\n",
      "KBA13_ANZAHL_PKW          True\n",
      "PLZ8_ANTG1                True\n",
      "PLZ8_ANTG2                True\n",
      "PLZ8_ANTG3                True\n",
      "PLZ8_ANTG4                True\n",
      "PLZ8_BAUMAX               True\n",
      "PLZ8_HHZ                  True\n",
      "PLZ8_GBZ                  True\n",
      "ARBEIT                    True\n",
      "ORTSGR_KLS9               True\n",
      "RELAT_AB                  True\n",
      "Length: 85, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "null_list = azdias.isnull().any()\n",
    "print(null_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85 entries, 0 to 84\n",
      "Data columns (total 4 columns):\n",
      "attribute             85 non-null object\n",
      "information_level     85 non-null object\n",
      "type                  85 non-null object\n",
      "missing_or_unknown    85 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "feat_info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'AGER_TYP'), (1, 'ALTERSKATEGORIE_GROB'), (2, 'ANREDE_KZ'), (3, 'CJT_GESAMTTYP'), (4, 'FINANZ_MINIMALIST'), (5, 'FINANZ_SPARER'), (6, 'FINANZ_VORSORGER'), (7, 'FINANZ_ANLEGER'), (8, 'FINANZ_UNAUFFAELLIGER'), (9, 'FINANZ_HAUSBAUER'), (10, 'FINANZTYP'), (11, 'GEBURTSJAHR'), (12, 'GFK_URLAUBERTYP'), (13, 'GREEN_AVANTGARDE'), (14, 'HEALTH_TYP'), (15, 'LP_LEBENSPHASE_FEIN'), (16, 'LP_LEBENSPHASE_GROB'), (17, 'LP_FAMILIE_FEIN'), (18, 'LP_FAMILIE_GROB'), (19, 'LP_STATUS_FEIN'), (20, 'LP_STATUS_GROB'), (21, 'NATIONALITAET_KZ'), (22, 'PRAEGENDE_JUGENDJAHRE'), (23, 'RETOURTYP_BK_S'), (24, 'SEMIO_SOZ'), (25, 'SEMIO_FAM'), (26, 'SEMIO_REL'), (27, 'SEMIO_MAT'), (28, 'SEMIO_VERT'), (29, 'SEMIO_LUST'), (30, 'SEMIO_ERL'), (31, 'SEMIO_KULT'), (32, 'SEMIO_RAT'), (33, 'SEMIO_KRIT'), (34, 'SEMIO_DOM'), (35, 'SEMIO_KAEM'), (36, 'SEMIO_PFLICHT'), (37, 'SEMIO_TRADV'), (38, 'SHOPPER_TYP'), (39, 'SOHO_KZ'), (40, 'TITEL_KZ'), (41, 'VERS_TYP'), (42, 'ZABEOTYP'), (43, 'ALTER_HH'), (44, 'ANZ_PERSONEN'), (45, 'ANZ_TITEL'), (46, 'HH_EINKOMMEN_SCORE'), (47, 'KK_KUNDENTYP'), (48, 'W_KEIT_KIND_HH'), (49, 'WOHNDAUER_2008'), (50, 'ANZ_HAUSHALTE_AKTIV'), (51, 'ANZ_HH_TITEL'), (52, 'GEBAEUDETYP'), (53, 'KONSUMNAEHE'), (54, 'MIN_GEBAEUDEJAHR'), (55, 'OST_WEST_KZ'), (56, 'WOHNLAGE'), (57, 'CAMEO_DEUG_2015'), (58, 'CAMEO_DEU_2015'), (59, 'CAMEO_INTL_2015'), (60, 'KBA05_ANTG1'), (61, 'KBA05_ANTG2'), (62, 'KBA05_ANTG3'), (63, 'KBA05_ANTG4'), (64, 'KBA05_BAUMAX'), (65, 'KBA05_GBZ'), (66, 'BALLRAUM'), (67, 'EWDICHTE'), (68, 'INNENSTADT'), (69, 'GEBAEUDETYP_RASTER'), (70, 'KKK'), (71, 'MOBI_REGIO'), (72, 'ONLINE_AFFINITAET'), (73, 'REGIOTYP'), (74, 'KBA13_ANZAHL_PKW'), (75, 'PLZ8_ANTG1'), (76, 'PLZ8_ANTG2'), (77, 'PLZ8_ANTG3'), (78, 'PLZ8_ANTG4'), (79, 'PLZ8_BAUMAX'), (80, 'PLZ8_HHZ'), (81, 'PLZ8_GBZ'), (82, 'ARBEIT'), (83, 'ORTSGR_KLS9'), (84, 'RELAT_AB')]\n"
     ]
    }
   ],
   "source": [
    "counter_list = list(enumerate(azdias.columns))\n",
    "print(counter_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_copy = azdias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98979"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to compare further after making the missing/unknown values as np.NaNs\n",
    "azdias_copy['CAMEO_INTL_2015'].isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['AGER_TYP'].isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'AGER_TYP'), (1, 'ALTERSKATEGORIE_GROB'), (2, 'ANREDE_KZ'), (3, 'CJT_GESAMTTYP'), (4, 'FINANZ_MINIMALIST'), (5, 'FINANZ_SPARER'), (6, 'FINANZ_VORSORGER'), (7, 'FINANZ_ANLEGER'), (8, 'FINANZ_UNAUFFAELLIGER'), (9, 'FINANZ_HAUSBAUER'), (10, 'FINANZTYP'), (11, 'GEBURTSJAHR'), (12, 'GFK_URLAUBERTYP'), (13, 'GREEN_AVANTGARDE'), (14, 'HEALTH_TYP'), (15, 'LP_LEBENSPHASE_FEIN'), (16, 'LP_LEBENSPHASE_GROB'), (17, 'LP_FAMILIE_FEIN'), (18, 'LP_FAMILIE_GROB'), (19, 'LP_STATUS_FEIN'), (20, 'LP_STATUS_GROB'), (21, 'NATIONALITAET_KZ'), (22, 'PRAEGENDE_JUGENDJAHRE'), (23, 'RETOURTYP_BK_S'), (24, 'SEMIO_SOZ'), (25, 'SEMIO_FAM'), (26, 'SEMIO_REL'), (27, 'SEMIO_MAT'), (28, 'SEMIO_VERT'), (29, 'SEMIO_LUST'), (30, 'SEMIO_ERL'), (31, 'SEMIO_KULT'), (32, 'SEMIO_RAT'), (33, 'SEMIO_KRIT'), (34, 'SEMIO_DOM'), (35, 'SEMIO_KAEM'), (36, 'SEMIO_PFLICHT'), (37, 'SEMIO_TRADV'), (38, 'SHOPPER_TYP'), (39, 'SOHO_KZ'), (40, 'TITEL_KZ'), (41, 'VERS_TYP'), (42, 'ZABEOTYP'), (43, 'ALTER_HH'), (44, 'ANZ_PERSONEN'), (45, 'ANZ_TITEL'), (46, 'HH_EINKOMMEN_SCORE'), (47, 'KK_KUNDENTYP'), (48, 'W_KEIT_KIND_HH'), (49, 'WOHNDAUER_2008'), (50, 'ANZ_HAUSHALTE_AKTIV'), (51, 'ANZ_HH_TITEL'), (52, 'GEBAEUDETYP'), (53, 'KONSUMNAEHE'), (54, 'MIN_GEBAEUDEJAHR'), (55, 'OST_WEST_KZ'), (56, 'WOHNLAGE'), (57, 'CAMEO_DEUG_2015'), (58, 'CAMEO_DEU_2015'), (59, 'CAMEO_INTL_2015'), (60, 'KBA05_ANTG1'), (61, 'KBA05_ANTG2'), (62, 'KBA05_ANTG3'), (63, 'KBA05_ANTG4'), (64, 'KBA05_BAUMAX'), (65, 'KBA05_GBZ'), (66, 'BALLRAUM'), (67, 'EWDICHTE'), (68, 'INNENSTADT'), (69, 'GEBAEUDETYP_RASTER'), (70, 'KKK'), (71, 'MOBI_REGIO'), (72, 'ONLINE_AFFINITAET'), (73, 'REGIOTYP'), (74, 'KBA13_ANZAHL_PKW'), (75, 'PLZ8_ANTG1'), (76, 'PLZ8_ANTG2'), (77, 'PLZ8_ANTG3'), (78, 'PLZ8_ANTG4'), (79, 'PLZ8_BAUMAX'), (80, 'PLZ8_HHZ'), (81, 'PLZ8_GBZ'), (82, 'ARBEIT'), (83, 'ORTSGR_KLS9'), (84, 'RELAT_AB')]\n"
     ]
    }
   ],
   "source": [
    "counter_list = list(enumerate(azdias_copy.columns))\n",
    "print(counter_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINANZ_MINIMALIST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FINANZ_SPARER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FINANZ_VORSORGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FINANZ_ANLEGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FINANZ_UNAUFFAELLIGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANZ_HAUSBAUER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>person</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RETOURTYP_BK_S</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SEMIO_SOZ</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SEMIO_FAM</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SEMIO_REL</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SEMIO_MAT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEMIO_VERT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SEMIO_LUST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>KBA05_ANTG1</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>KBA05_ANTG2</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>KBA05_ANTG3</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>KBA05_ANTG4</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>KBA05_BAUMAX</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>KBA05_GBZ</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>BALLRAUM</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>EWDICHTE</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>INNENSTADT</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>GEBAEUDETYP_RASTER</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>KKK</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MOBI_REGIO</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ONLINE_AFFINITAET</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>REGIOTYP</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>KBA13_ANZAHL_PKW</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>PLZ8_ANTG1</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PLZ8_ANTG2</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>PLZ8_ANTG3</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>PLZ8_ANTG4</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>PLZ8_HHZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>PLZ8_GBZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ARBEIT</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>RELAT_AB</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                attribute information_level         type missing_or_unknown\n",
       "0                AGER_TYP            person  categorical             [-1,0]\n",
       "1    ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]\n",
       "2               ANREDE_KZ            person  categorical             [-1,0]\n",
       "3           CJT_GESAMTTYP            person  categorical                [0]\n",
       "4       FINANZ_MINIMALIST            person      ordinal               [-1]\n",
       "5           FINANZ_SPARER            person      ordinal               [-1]\n",
       "6        FINANZ_VORSORGER            person      ordinal               [-1]\n",
       "7          FINANZ_ANLEGER            person      ordinal               [-1]\n",
       "8   FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]\n",
       "9        FINANZ_HAUSBAUER            person      ordinal               [-1]\n",
       "10              FINANZTYP            person  categorical               [-1]\n",
       "11            GEBURTSJAHR            person      numeric                [0]\n",
       "12        GFK_URLAUBERTYP            person  categorical                 []\n",
       "13       GREEN_AVANTGARDE            person  categorical                 []\n",
       "14             HEALTH_TYP            person      ordinal             [-1,0]\n",
       "15    LP_LEBENSPHASE_FEIN            person        mixed                [0]\n",
       "16    LP_LEBENSPHASE_GROB            person        mixed                [0]\n",
       "17        LP_FAMILIE_FEIN            person  categorical                [0]\n",
       "18        LP_FAMILIE_GROB            person  categorical                [0]\n",
       "19         LP_STATUS_FEIN            person  categorical                [0]\n",
       "20         LP_STATUS_GROB            person  categorical                [0]\n",
       "21       NATIONALITAET_KZ            person  categorical             [-1,0]\n",
       "22  PRAEGENDE_JUGENDJAHRE            person        mixed             [-1,0]\n",
       "23         RETOURTYP_BK_S            person      ordinal                [0]\n",
       "24              SEMIO_SOZ            person      ordinal             [-1,9]\n",
       "25              SEMIO_FAM            person      ordinal             [-1,9]\n",
       "26              SEMIO_REL            person      ordinal             [-1,9]\n",
       "27              SEMIO_MAT            person      ordinal             [-1,9]\n",
       "28             SEMIO_VERT            person      ordinal             [-1,9]\n",
       "29             SEMIO_LUST            person      ordinal             [-1,9]\n",
       "..                    ...               ...          ...                ...\n",
       "55            OST_WEST_KZ          building  categorical               [-1]\n",
       "56               WOHNLAGE          building        mixed               [-1]\n",
       "57        CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]\n",
       "58         CAMEO_DEU_2015     microcell_rr4  categorical               [XX]\n",
       "59        CAMEO_INTL_2015     microcell_rr4        mixed            [-1,XX]\n",
       "60            KBA05_ANTG1     microcell_rr3      ordinal               [-1]\n",
       "61            KBA05_ANTG2     microcell_rr3      ordinal               [-1]\n",
       "62            KBA05_ANTG3     microcell_rr3      ordinal               [-1]\n",
       "63            KBA05_ANTG4     microcell_rr3      ordinal               [-1]\n",
       "64           KBA05_BAUMAX     microcell_rr3        mixed             [-1,0]\n",
       "65              KBA05_GBZ     microcell_rr3      ordinal             [-1,0]\n",
       "66               BALLRAUM          postcode      ordinal               [-1]\n",
       "67               EWDICHTE          postcode      ordinal               [-1]\n",
       "68             INNENSTADT          postcode      ordinal               [-1]\n",
       "69     GEBAEUDETYP_RASTER        region_rr1      ordinal                 []\n",
       "70                    KKK        region_rr1      ordinal             [-1,0]\n",
       "71             MOBI_REGIO        region_rr1      ordinal                 []\n",
       "72      ONLINE_AFFINITAET        region_rr1      ordinal                 []\n",
       "73               REGIOTYP        region_rr1      ordinal             [-1,0]\n",
       "74       KBA13_ANZAHL_PKW    macrocell_plz8      numeric                 []\n",
       "75             PLZ8_ANTG1    macrocell_plz8      ordinal               [-1]\n",
       "76             PLZ8_ANTG2    macrocell_plz8      ordinal               [-1]\n",
       "77             PLZ8_ANTG3    macrocell_plz8      ordinal               [-1]\n",
       "78             PLZ8_ANTG4    macrocell_plz8      ordinal               [-1]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8        mixed             [-1,0]\n",
       "80               PLZ8_HHZ    macrocell_plz8      ordinal               [-1]\n",
       "81               PLZ8_GBZ    macrocell_plz8      ordinal               [-1]\n",
       "82                 ARBEIT         community      ordinal             [-1,9]\n",
       "83            ORTSGR_KLS9         community      ordinal             [-1,0]\n",
       "84               RELAT_AB         community      ordinal             [-1,9]\n",
       "\n",
       "[85 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "#for i, column in counter_list:\n",
    "#    vals = feat_info['missing_or_unknown'].iloc[i]\n",
    " #   if i in [57, 58, 59]: # since only these are in object type, manual detection of col-number from counter_list\n",
    "  #      a = vals.strip(\"[]\").split(\",\")\n",
    "        #print(a)\n",
    "   # else:\n",
    "    #    try:\n",
    "            \n",
    "     #       if (type(azdias_copy[column][0]) == np.float64):\n",
    "      #          b = vals.strip(\"[]\").split(\",\")\n",
    "       #         a = [float(e) for e in b]\n",
    "        #    else:\n",
    "         #       b = vals.strip(\"[]\").split(\",\")\n",
    "          #      a = [int(e) for e in b]\n",
    "        #except ValueError: # ValueError will rise if the field is blank\n",
    "         #   pass\n",
    "    #for re in a:\n",
    "     #   azdias_copy[column].replace(re, np.NaN, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in feat_info.iterrows():\n",
    "    column = azdias_copy[row['attribute']]\n",
    "\n",
    "    missing_values = row['missing_or_unknown'][1:-1].split(',')\n",
    "    if missing_values != ['']:\n",
    "        for mv in missing_values:\n",
    "            if mv != 'X' and mv != 'XX':\n",
    "                column.replace(int(mv), np.nan, inplace = True)\n",
    "            else:\n",
    "                column.replace(mv, np.nan, inplace = True)\n",
    "\n",
    "    azdias_copy[row['attribute']] = column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51    133694\n",
       "41     92336\n",
       "24     91158\n",
       "14     62884\n",
       "43     56672\n",
       "54     45391\n",
       "25     39628\n",
       "22     33155\n",
       "23     26750\n",
       "13     26336\n",
       "45     26132\n",
       "55     23955\n",
       "52     20542\n",
       "31     19024\n",
       "34     18524\n",
       "15     16974\n",
       "44     14820\n",
       "12     13249\n",
       "35     10356\n",
       "32     10354\n",
       "33      9935\n",
       "Name: CAMEO_INTL_2015, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to see that the data values have been converted to NaNs correctly\n",
    "azdias_copy['CAMEO_INTL_2015'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "685843"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['AGER_TYP'].isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         2.0\n",
       "4         NaN\n",
       "5         3.0\n",
       "6         NaN\n",
       "7         NaN\n",
       "8         NaN\n",
       "9         NaN\n",
       "10        NaN\n",
       "11        NaN\n",
       "12        NaN\n",
       "13        NaN\n",
       "14        NaN\n",
       "15        1.0\n",
       "16        NaN\n",
       "17        NaN\n",
       "18        NaN\n",
       "19        NaN\n",
       "20        NaN\n",
       "21        NaN\n",
       "22        NaN\n",
       "23        NaN\n",
       "24        NaN\n",
       "25        NaN\n",
       "26        NaN\n",
       "27        NaN\n",
       "28        NaN\n",
       "29        2.0\n",
       "         ... \n",
       "891191    1.0\n",
       "891192    NaN\n",
       "891193    2.0\n",
       "891194    NaN\n",
       "891195    2.0\n",
       "891196    NaN\n",
       "891197    NaN\n",
       "891198    NaN\n",
       "891199    NaN\n",
       "891200    NaN\n",
       "891201    NaN\n",
       "891202    NaN\n",
       "891203    NaN\n",
       "891204    NaN\n",
       "891205    1.0\n",
       "891206    NaN\n",
       "891207    1.0\n",
       "891208    NaN\n",
       "891209    NaN\n",
       "891210    NaN\n",
       "891211    NaN\n",
       "891212    NaN\n",
       "891213    NaN\n",
       "891214    NaN\n",
       "891215    NaN\n",
       "891216    NaN\n",
       "891217    NaN\n",
       "891218    NaN\n",
       "891219    NaN\n",
       "891220    NaN\n",
       "Name: AGER_TYP, Length: 891221, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['AGER_TYP']"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891221 entries, 0 to 891220\n",
      "Data columns (total 85 columns):\n",
      "AGER_TYP                 205378 non-null float64\n",
      "ALTERSKATEGORIE_GROB     888340 non-null float64\n",
      "ANREDE_KZ                891221 non-null int64\n",
      "CJT_GESAMTTYP            886367 non-null float64\n",
      "FINANZ_MINIMALIST        891221 non-null int64\n",
      "FINANZ_SPARER            891221 non-null int64\n",
      "FINANZ_VORSORGER         891221 non-null int64\n",
      "FINANZ_ANLEGER           891221 non-null int64\n",
      "FINANZ_UNAUFFAELLIGER    891221 non-null int64\n",
      "FINANZ_HAUSBAUER         891221 non-null int64\n",
      "FINANZTYP                891221 non-null int64\n",
      "GEBURTSJAHR              498903 non-null float64\n",
      "GFK_URLAUBERTYP          886367 non-null float64\n",
      "GREEN_AVANTGARDE         891221 non-null int64\n",
      "HEALTH_TYP               780025 non-null float64\n",
      "LP_LEBENSPHASE_FEIN      793589 non-null float64\n",
      "LP_LEBENSPHASE_GROB      796649 non-null float64\n",
      "LP_FAMILIE_FEIN          813429 non-null float64\n",
      "LP_FAMILIE_GROB          813429 non-null float64\n",
      "LP_STATUS_FEIN           886367 non-null float64\n",
      "LP_STATUS_GROB           886367 non-null float64\n",
      "NATIONALITAET_KZ         782906 non-null float64\n",
      "PRAEGENDE_JUGENDJAHRE    783057 non-null float64\n",
      "RETOURTYP_BK_S           886367 non-null float64\n",
      "SEMIO_SOZ                891221 non-null int64\n",
      "SEMIO_FAM                891221 non-null int64\n",
      "SEMIO_REL                891221 non-null int64\n",
      "SEMIO_MAT                891221 non-null int64\n",
      "SEMIO_VERT               891221 non-null int64\n",
      "SEMIO_LUST               891221 non-null int64\n",
      "SEMIO_ERL                891221 non-null int64\n",
      "SEMIO_KULT               891221 non-null int64\n",
      "SEMIO_RAT                891221 non-null int64\n",
      "SEMIO_KRIT               891221 non-null int64\n",
      "SEMIO_DOM                891221 non-null int64\n",
      "SEMIO_KAEM               891221 non-null int64\n",
      "SEMIO_PFLICHT            891221 non-null int64\n",
      "SEMIO_TRADV              891221 non-null int64\n",
      "SHOPPER_TYP              780025 non-null float64\n",
      "SOHO_KZ                  817722 non-null float64\n",
      "TITEL_KZ                 2160 non-null float64\n",
      "VERS_TYP                 780025 non-null float64\n",
      "ZABEOTYP                 891221 non-null int64\n",
      "ALTER_HH                 580954 non-null float64\n",
      "ANZ_PERSONEN             817722 non-null float64\n",
      "ANZ_TITEL                817722 non-null float64\n",
      "HH_EINKOMMEN_SCORE       872873 non-null float64\n",
      "KK_KUNDENTYP             306609 non-null float64\n",
      "W_KEIT_KIND_HH           743233 non-null float64\n",
      "WOHNDAUER_2008           817722 non-null float64\n",
      "ANZ_HAUSHALTE_AKTIV      791610 non-null float64\n",
      "ANZ_HH_TITEL             794213 non-null float64\n",
      "GEBAEUDETYP              798073 non-null float64\n",
      "KONSUMNAEHE              817252 non-null float64\n",
      "MIN_GEBAEUDEJAHR         798073 non-null float64\n",
      "OST_WEST_KZ              798073 non-null object\n",
      "WOHNLAGE                 798073 non-null float64\n",
      "CAMEO_DEUG_2015          791869 non-null object\n",
      "CAMEO_DEU_2015           791869 non-null object\n",
      "CAMEO_INTL_2015          791869 non-null object\n",
      "KBA05_ANTG1              757897 non-null float64\n",
      "KBA05_ANTG2              757897 non-null float64\n",
      "KBA05_ANTG3              757897 non-null float64\n",
      "KBA05_ANTG4              757897 non-null float64\n",
      "KBA05_BAUMAX             414697 non-null float64\n",
      "KBA05_GBZ                757897 non-null float64\n",
      "BALLRAUM                 797481 non-null float64\n",
      "EWDICHTE                 797481 non-null float64\n",
      "INNENSTADT               797481 non-null float64\n",
      "GEBAEUDETYP_RASTER       798066 non-null float64\n",
      "KKK                      733157 non-null float64\n",
      "MOBI_REGIO               757897 non-null float64\n",
      "ONLINE_AFFINITAET        886367 non-null float64\n",
      "REGIOTYP                 733157 non-null float64\n",
      "KBA13_ANZAHL_PKW         785421 non-null float64\n",
      "PLZ8_ANTG1               774706 non-null float64\n",
      "PLZ8_ANTG2               774706 non-null float64\n",
      "PLZ8_ANTG3               774706 non-null float64\n",
      "PLZ8_ANTG4               774706 non-null float64\n",
      "PLZ8_BAUMAX              774706 non-null float64\n",
      "PLZ8_HHZ                 774706 non-null float64\n",
      "PLZ8_GBZ                 774706 non-null float64\n",
      "ARBEIT                   793846 non-null float64\n",
      "ORTSGR_KLS9              793947 non-null float64\n",
      "RELAT_AB                 793846 non-null float64\n",
      "dtypes: float64(57), int64(24), object(4)\n",
      "memory usage: 578.0+ MB\n",
      "None\n",
      "AGER_TYP                 685843\n",
      "ALTERSKATEGORIE_GROB       2881\n",
      "ANREDE_KZ                     0\n",
      "CJT_GESAMTTYP              4854\n",
      "FINANZ_MINIMALIST             0\n",
      "FINANZ_SPARER                 0\n",
      "FINANZ_VORSORGER              0\n",
      "FINANZ_ANLEGER                0\n",
      "FINANZ_UNAUFFAELLIGER         0\n",
      "FINANZ_HAUSBAUER              0\n",
      "FINANZTYP                     0\n",
      "GEBURTSJAHR              392318\n",
      "GFK_URLAUBERTYP            4854\n",
      "GREEN_AVANTGARDE              0\n",
      "HEALTH_TYP               111196\n",
      "LP_LEBENSPHASE_FEIN       97632\n",
      "LP_LEBENSPHASE_GROB       94572\n",
      "LP_FAMILIE_FEIN           77792\n",
      "LP_FAMILIE_GROB           77792\n",
      "LP_STATUS_FEIN             4854\n",
      "LP_STATUS_GROB             4854\n",
      "NATIONALITAET_KZ         108315\n",
      "PRAEGENDE_JUGENDJAHRE    108164\n",
      "RETOURTYP_BK_S             4854\n",
      "SEMIO_SOZ                     0\n",
      "SEMIO_FAM                     0\n",
      "SEMIO_REL                     0\n",
      "SEMIO_MAT                     0\n",
      "SEMIO_VERT                    0\n",
      "SEMIO_LUST                    0\n",
      "                          ...  \n",
      "OST_WEST_KZ               93148\n",
      "WOHNLAGE                  93148\n",
      "CAMEO_DEUG_2015           99352\n",
      "CAMEO_DEU_2015            99352\n",
      "CAMEO_INTL_2015           99352\n",
      "KBA05_ANTG1              133324\n",
      "KBA05_ANTG2              133324\n",
      "KBA05_ANTG3              133324\n",
      "KBA05_ANTG4              133324\n",
      "KBA05_BAUMAX             476524\n",
      "KBA05_GBZ                133324\n",
      "BALLRAUM                  93740\n",
      "EWDICHTE                  93740\n",
      "INNENSTADT                93740\n",
      "GEBAEUDETYP_RASTER        93155\n",
      "KKK                      158064\n",
      "MOBI_REGIO               133324\n",
      "ONLINE_AFFINITAET          4854\n",
      "REGIOTYP                 158064\n",
      "KBA13_ANZAHL_PKW         105800\n",
      "PLZ8_ANTG1               116515\n",
      "PLZ8_ANTG2               116515\n",
      "PLZ8_ANTG3               116515\n",
      "PLZ8_ANTG4               116515\n",
      "PLZ8_BAUMAX              116515\n",
      "PLZ8_HHZ                 116515\n",
      "PLZ8_GBZ                 116515\n",
      "ARBEIT                    97375\n",
      "ORTSGR_KLS9               97274\n",
      "RELAT_AB                  97375\n",
      "Length: 85, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#print(azdias_copy.columns)\n",
    "print(azdias_copy.info())\n",
    "print(azdias_copy.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZABEOTYP                      0\n",
      "SEMIO_TRADV                   0\n",
      "SEMIO_PFLICHT                 0\n",
      "SEMIO_KAEM                    0\n",
      "SEMIO_DOM                     0\n",
      "SEMIO_KRIT                    0\n",
      "SEMIO_RAT                     0\n",
      "SEMIO_KULT                    0\n",
      "SEMIO_ERL                     0\n",
      "SEMIO_LUST                    0\n",
      "SEMIO_VERT                    0\n",
      "SEMIO_MAT                     0\n",
      "SEMIO_REL                     0\n",
      "SEMIO_SOZ                     0\n",
      "SEMIO_FAM                     0\n",
      "FINANZ_VORSORGER              0\n",
      "ANREDE_KZ                     0\n",
      "FINANZ_MINIMALIST             0\n",
      "FINANZ_SPARER                 0\n",
      "FINANZ_ANLEGER                0\n",
      "FINANZ_UNAUFFAELLIGER         0\n",
      "FINANZ_HAUSBAUER              0\n",
      "GREEN_AVANTGARDE              0\n",
      "FINANZTYP                     0\n",
      "ALTERSKATEGORIE_GROB       2881\n",
      "GFK_URLAUBERTYP            4854\n",
      "LP_STATUS_GROB             4854\n",
      "LP_STATUS_FEIN             4854\n",
      "ONLINE_AFFINITAET          4854\n",
      "RETOURTYP_BK_S             4854\n",
      "                          ...  \n",
      "CAMEO_INTL_2015           99352\n",
      "ANZ_HAUSHALTE_AKTIV       99611\n",
      "KBA13_ANZAHL_PKW         105800\n",
      "PRAEGENDE_JUGENDJAHRE    108164\n",
      "NATIONALITAET_KZ         108315\n",
      "HEALTH_TYP               111196\n",
      "VERS_TYP                 111196\n",
      "SHOPPER_TYP              111196\n",
      "PLZ8_ANTG2               116515\n",
      "PLZ8_ANTG3               116515\n",
      "PLZ8_ANTG1               116515\n",
      "PLZ8_ANTG4               116515\n",
      "PLZ8_BAUMAX              116515\n",
      "PLZ8_HHZ                 116515\n",
      "PLZ8_GBZ                 116515\n",
      "KBA05_ANTG3              133324\n",
      "KBA05_ANTG2              133324\n",
      "KBA05_ANTG1              133324\n",
      "MOBI_REGIO               133324\n",
      "KBA05_GBZ                133324\n",
      "KBA05_ANTG4              133324\n",
      "W_KEIT_KIND_HH           147988\n",
      "KKK                      158064\n",
      "REGIOTYP                 158064\n",
      "ALTER_HH                 310267\n",
      "GEBURTSJAHR              392318\n",
      "KBA05_BAUMAX             476524\n",
      "KK_KUNDENTYP             584612\n",
      "AGER_TYP                 685843\n",
      "TITEL_KZ                 889061\n",
      "Length: 85, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "#nan_count = []\n",
    "#print(list(azdias_copy))\n",
    "#for cols in (list(azdias_copy)):\n",
    " #   print(cols)\n",
    "  #  nan_count = azdiascols.isna().sum()\n",
    "#print(nan_count)\n",
    "count_nan = len(azdias_copy) - azdias_copy.count()\n",
    "print(count_nan.sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 32.,   0.,  26.,  18.,   3.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
       "          1.,   0.,   1.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,   0.,\n",
       "          0.,   0.,   1.]),\n",
       " array([      0.  ,   35562.44,   71124.88,  106687.32,  142249.76,\n",
       "         177812.2 ,  213374.64,  248937.08,  284499.52,  320061.96,\n",
       "         355624.4 ,  391186.84,  426749.28,  462311.72,  497874.16,\n",
       "         533436.6 ,  568999.04,  604561.48,  640123.92,  675686.36,\n",
       "         711248.8 ,  746811.24,  782373.68,  817936.12,  853498.56,  889061.  ]),\n",
       " <a list of 25 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22fc39ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#x = azdias_copy.count\n",
    "#print(x)\n",
    "missing_cols = azdias_copy.isnull().sum()\n",
    "plt.hist(missing_cols, bins = 25, alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22dae42b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "missing_cols.plot.bar(figsize=(15,10))\n",
    "plt.show()\n",
    "#plt.hist(azdias_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missing_20 = [col for col in azdias_copy.columns if (azdias_copy[col].isnull().sum()/azdias_copy.shape[0]) * 100 > 20]\n",
    "print(missing_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGER_TYP\n",
      "GEBURTSJAHR\n",
      "TITEL_KZ\n",
      "ALTER_HH\n",
      "KK_KUNDENTYP\n",
      "KBA05_BAUMAX\n"
     ]
    }
   ],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "outlier_cols = missing_20\n",
    "for c in outlier_cols:\n",
    "    print(c)\n",
    "    del azdias_copy[c]\n",
    "#azdias_copy = azdias_copy.drop(outlier_cols, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#azdias_copy.plot.bar(figsize=(15,10))\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'TITEL_KZ'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'TITEL_KZ'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-fac19b4745e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mazdias_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TITEL_KZ'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# to show the column has been dropped.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2442\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'TITEL_KZ'"
     ]
    }
   ],
   "source": [
    "azdias_copy['TITEL_KZ'] # to show the column has been dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "- Yes, some of the columns have large amount of missing data, hence the columns having more than 20% of the mssing data have been removed. I have chosen 20% as a benchmark just by observing the bar-chart plotted above of data columns containing missing values.  \n",
    "- There was not a regular pattern in the missing values though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         43\n",
       "1          0\n",
       "2          0\n",
       "3          7\n",
       "4          0\n",
       "5          0\n",
       "6          0\n",
       "7          0\n",
       "8          0\n",
       "9          0\n",
       "10         0\n",
       "11        47\n",
       "12         6\n",
       "13         8\n",
       "14        47\n",
       "15         8\n",
       "16         6\n",
       "17        47\n",
       "18         3\n",
       "19         0\n",
       "20        10\n",
       "21         0\n",
       "22         0\n",
       "23         8\n",
       "24        47\n",
       "25         5\n",
       "26        19\n",
       "27         0\n",
       "28         0\n",
       "29         2\n",
       "          ..\n",
       "891191     0\n",
       "891192     0\n",
       "891193     0\n",
       "891194     0\n",
       "891195     0\n",
       "891196     0\n",
       "891197     0\n",
       "891198     0\n",
       "891199     0\n",
       "891200     0\n",
       "891201     0\n",
       "891202     0\n",
       "891203    14\n",
       "891204     0\n",
       "891205     0\n",
       "891206     0\n",
       "891207     0\n",
       "891208     3\n",
       "891209     0\n",
       "891210     0\n",
       "891211     0\n",
       "891212     0\n",
       "891213     0\n",
       "891214     0\n",
       "891215     0\n",
       "891216     3\n",
       "891217     4\n",
       "891218     5\n",
       "891219     0\n",
       "891220     0\n",
       "Length: 891221, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "missing_rows = azdias_copy.isnull().sum(axis=1)\n",
    "missing_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48         1\n",
       "49         2\n",
       "31         3\n",
       "28         5\n",
       "30         6\n",
       "29        12\n",
       "42        21\n",
       "27        24\n",
       "25        55\n",
       "26        59\n",
       "24        69\n",
       "39        77\n",
       "36        84\n",
       "22       129\n",
       "23       132\n",
       "40       137\n",
       "21       150\n",
       "44       155\n",
       "32       206\n",
       "20       349\n",
       "41       356\n",
       "38       421\n",
       "18       428\n",
       "45       494\n",
       "37       538\n",
       "17       677\n",
       "12       766\n",
       "11      1127\n",
       "19      1180\n",
       "14      2243\n",
       "16      2505\n",
       "33      2985\n",
       "9       3042\n",
       "13      3255\n",
       "35      3911\n",
       "15      4743\n",
       "10      5410\n",
       "34     10816\n",
       "4      12607\n",
       "7      13714\n",
       "6      13771\n",
       "1      15738\n",
       "3      17629\n",
       "5      22515\n",
       "8      24592\n",
       "43     27369\n",
       "2      27926\n",
       "47     45578\n",
       "0     623209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_rows.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     623209\n",
      "1      15738\n",
      "2      27926\n",
      "3      17629\n",
      "4      12607\n",
      "5      22515\n",
      "6      13771\n",
      "7      13714\n",
      "8      24592\n",
      "9       3042\n",
      "10      5410\n",
      "11      1127\n",
      "12       766\n",
      "13      3255\n",
      "14      2243\n",
      "15      4743\n",
      "16      2505\n",
      "17       677\n",
      "18       428\n",
      "19      1180\n",
      "20       349\n",
      "21       150\n",
      "22       129\n",
      "23       132\n",
      "24        69\n",
      "25        55\n",
      "26        59\n",
      "27        24\n",
      "28         5\n",
      "29        12\n",
      "30         6\n",
      "31         3\n",
      "32       206\n",
      "33      2985\n",
      "34     10816\n",
      "35      3911\n",
      "36        84\n",
      "37       538\n",
      "38       421\n",
      "39        77\n",
      "40       137\n",
      "41       356\n",
      "42        21\n",
      "43     27369\n",
      "44       155\n",
      "45       494\n",
      "47     45578\n",
      "48         1\n",
      "49         2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print((missing_rows.value_counts().sort_index()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell represents the number of missing values in the first column and the actual number of rows with those many entries missing in the second column.\n",
    "- For example, there are 5,73,234 rows with 0 missing values i.e. no missing values present.\n",
    "\n",
    "So a threshold of 24 missing values per row can be considered and the data can be divided into two different subsets.\n",
    "- A threshold of 24 missing values was chosen as it is half of the maximum number of entries missing i.e. 48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n",
      "891221\n"
     ]
    }
   ],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "rows_with_missing_few = missing_rows[missing_rows <= 24]\n",
    "rows_with_missing_large = missing_rows[missing_rows > 24]\n",
    "#print(rows_with_missing_large.value_counts().sort_index())\n",
    "print(len(rows_with_missing_large))\n",
    "print(len(rows_with_missing_few))\n",
    "print(len(rows_with_missing_large) + len(rows_with_missing_few))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare the distribution of values for at least five columns where there are\n",
    "# no or few missing values, between the two subsets.\n",
    "\n",
    "def compare_distribution(column_name):\n",
    "    graph = plt.figure(figsize=(14,4))\n",
    "    ax1 = graph.add_subplot(121)\n",
    "    ax1.title.set_text('Rows with missing many entries')\n",
    "    print(len(azdias_copy.loc[rows_with_missing_large.index,column_name]))\n",
    "    sns.countplot(azdias_copy.loc[rows_with_missing_large.index,column_name])\n",
    "\n",
    "    ax2 = graph.add_subplot(122)\n",
    "    ax2.title.set_text('Rows with missing few entries')\n",
    "    print(len(azdias_copy.loc[~azdias_copy.index.isin(rows_with_missing_large.index),column_name]))\n",
    "    sns.countplot(azdias_copy.loc[~azdias_copy.index.isin(rows_with_missing_large.index),column_name]);\n",
    "\n",
    "    #graph.suptitle(column_name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_distribution_original(column_name):\n",
    "    graph = plt.figure(figsize=(14,4))\n",
    "    ax1 = graph.add_subplot(121)\n",
    "    ax1.title.set_text('Rows with missing few entries')\n",
    "    print(len(azdias_copy.loc[rows_with_missing_few.index, column_name]))\n",
    "    sns.countplot(azdias_copy.loc[rows_with_missing_few.index, column_name])\n",
    "\n",
    "    ax2 = graph.add_subplot(122)\n",
    "    ax2.title.set_text('Original Rows')\n",
    "    print(len(azdias_copy.loc[missing_rows.index, column_name]))\n",
    "    sns.countplot(azdias_copy.loc[missing_rows.index, column_name]);\n",
    "\n",
    "    graph.suptitle(column_name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22d938748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22d9a79e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bdc60b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bec6dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93315\n",
      "797906\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22be06fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#col_names_with_few_missing_vals = ['ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'CJT_GESAMTTYP', 'FINANZ_MINIMALIST', 'FINANZ_SPARER']\n",
    "col_names_with_few_or_no_missing_vals = ['ALTERSKATEGORIE_GROB',     \n",
    "'RETOURTYP_BK_S',             \n",
    "'LP_STATUS_GROB',             \n",
    "'LP_STATUS_FEIN',          \n",
    "'GFK_URLAUBERTYP']\n",
    "for coln in col_names_with_few_or_no_missing_vals:\n",
    "    compare_distribution(coln)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797906\n",
      "891221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bbf98d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797906\n",
      "891221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bb05e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797906\n",
      "891221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22d972748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797906\n",
      "891221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bb4db38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797906\n",
      "891221\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22bbcf5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_names_with_few_or_no_missing_vals = ['ALTERSKATEGORIE_GROB',     \n",
    "'RETOURTYP_BK_S',             \n",
    "'LP_STATUS_GROB',             \n",
    "'LP_STATUS_FEIN',          \n",
    "'GFK_URLAUBERTYP']\n",
    "for coln in col_names_with_few_or_no_missing_vals:\n",
    "    compare_distribution_original(coln)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the rows with missing large number of entries have been dropped rather stored separately\n",
    "azdias_many_missing_rows = azdias_copy.iloc[rows_with_missing_large.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows before dropping rows with missing vals: 891221\n",
      "Rows after dropping rows with missing vals: 797906\n"
     ]
    }
   ],
   "source": [
    "# Drop rows with many missing values\n",
    "\n",
    "print('Rows before dropping rows with missing vals:', azdias_copy.shape[0])\n",
    "\n",
    "azdias_copy = azdias_copy[~azdias_copy.index.isin(rows_with_missing_large.index)]\n",
    "\n",
    "print('Rows after dropping rows with missing vals:', azdias_copy.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "- The data with lots of missing values are qualitatively different from data with few or no missing values.\n",
    "- But when the data with few or no missing values was compared with the original data, there was much less difference between the charts.\n",
    "- Hence, the rows with missing large number of entries have been dropped rather stored separately for further use if needed.\n",
    "- A threshold of 24 missing values was chosen as it is half of the maximum number of entries missing i.e. 48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'GEBURTSJAHR',\n",
       " 'TITEL_KZ',\n",
       " 'ALTER_HH',\n",
       " 'KK_KUNDENTYP',\n",
       " 'KBA05_BAUMAX']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# firstly, remove the outlier columns which have been removed from the azdias_copy dataset\n",
    "for x in outlier_cols:\n",
    "    feat_info = feat_info[feat_info.attribute != x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    18\n",
       "numeric         6\n",
       "mixed           6\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "feat_info['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CJT_GESAMTTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FINANZ_MINIMALIST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FINANZ_SPARER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FINANZ_VORSORGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FINANZ_ANLEGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FINANZ_UNAUFFAELLIGER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANZ_HAUSBAUER</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FINANZTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GFK_URLAUBERTYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LP_STATUS_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NATIONALITAET_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RETOURTYP_BK_S</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SEMIO_SOZ</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SEMIO_FAM</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SEMIO_REL</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SEMIO_MAT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEMIO_VERT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SEMIO_LUST</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SEMIO_ERL</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SEMIO_KULT</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>MIN_GEBAEUDEJAHR</td>\n",
       "      <td>building</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>building</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>KBA05_ANTG1</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>KBA05_ANTG2</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>KBA05_ANTG3</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>KBA05_ANTG4</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>KBA05_GBZ</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>BALLRAUM</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>EWDICHTE</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>INNENSTADT</td>\n",
       "      <td>postcode</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>GEBAEUDETYP_RASTER</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>KKK</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MOBI_REGIO</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ONLINE_AFFINITAET</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>REGIOTYP</td>\n",
       "      <td>region_rr1</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>KBA13_ANZAHL_PKW</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>PLZ8_ANTG1</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PLZ8_ANTG2</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>PLZ8_ANTG3</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>PLZ8_ANTG4</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>PLZ8_HHZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>PLZ8_GBZ</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ARBEIT</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>RELAT_AB</td>\n",
       "      <td>community</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,9]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                attribute information_level         type missing_or_unknown\n",
       "1    ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]\n",
       "2               ANREDE_KZ            person  categorical             [-1,0]\n",
       "3           CJT_GESAMTTYP            person  categorical                [0]\n",
       "4       FINANZ_MINIMALIST            person      ordinal               [-1]\n",
       "5           FINANZ_SPARER            person      ordinal               [-1]\n",
       "6        FINANZ_VORSORGER            person      ordinal               [-1]\n",
       "7          FINANZ_ANLEGER            person      ordinal               [-1]\n",
       "8   FINANZ_UNAUFFAELLIGER            person      ordinal               [-1]\n",
       "9        FINANZ_HAUSBAUER            person      ordinal               [-1]\n",
       "10              FINANZTYP            person  categorical               [-1]\n",
       "12        GFK_URLAUBERTYP            person  categorical                 []\n",
       "13       GREEN_AVANTGARDE            person  categorical                 []\n",
       "14             HEALTH_TYP            person      ordinal             [-1,0]\n",
       "15    LP_LEBENSPHASE_FEIN            person        mixed                [0]\n",
       "16    LP_LEBENSPHASE_GROB            person        mixed                [0]\n",
       "17        LP_FAMILIE_FEIN            person  categorical                [0]\n",
       "18        LP_FAMILIE_GROB            person  categorical                [0]\n",
       "19         LP_STATUS_FEIN            person  categorical                [0]\n",
       "20         LP_STATUS_GROB            person  categorical                [0]\n",
       "21       NATIONALITAET_KZ            person  categorical             [-1,0]\n",
       "22  PRAEGENDE_JUGENDJAHRE            person        mixed             [-1,0]\n",
       "23         RETOURTYP_BK_S            person      ordinal                [0]\n",
       "24              SEMIO_SOZ            person      ordinal             [-1,9]\n",
       "25              SEMIO_FAM            person      ordinal             [-1,9]\n",
       "26              SEMIO_REL            person      ordinal             [-1,9]\n",
       "27              SEMIO_MAT            person      ordinal             [-1,9]\n",
       "28             SEMIO_VERT            person      ordinal             [-1,9]\n",
       "29             SEMIO_LUST            person      ordinal             [-1,9]\n",
       "30              SEMIO_ERL            person      ordinal             [-1,9]\n",
       "31             SEMIO_KULT            person      ordinal             [-1,9]\n",
       "..                    ...               ...          ...                ...\n",
       "54       MIN_GEBAEUDEJAHR          building      numeric                [0]\n",
       "55            OST_WEST_KZ          building  categorical               [-1]\n",
       "56               WOHNLAGE          building        mixed               [-1]\n",
       "57        CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]\n",
       "58         CAMEO_DEU_2015     microcell_rr4  categorical               [XX]\n",
       "59        CAMEO_INTL_2015     microcell_rr4        mixed            [-1,XX]\n",
       "60            KBA05_ANTG1     microcell_rr3      ordinal               [-1]\n",
       "61            KBA05_ANTG2     microcell_rr3      ordinal               [-1]\n",
       "62            KBA05_ANTG3     microcell_rr3      ordinal               [-1]\n",
       "63            KBA05_ANTG4     microcell_rr3      ordinal               [-1]\n",
       "65              KBA05_GBZ     microcell_rr3      ordinal             [-1,0]\n",
       "66               BALLRAUM          postcode      ordinal               [-1]\n",
       "67               EWDICHTE          postcode      ordinal               [-1]\n",
       "68             INNENSTADT          postcode      ordinal               [-1]\n",
       "69     GEBAEUDETYP_RASTER        region_rr1      ordinal                 []\n",
       "70                    KKK        region_rr1      ordinal             [-1,0]\n",
       "71             MOBI_REGIO        region_rr1      ordinal                 []\n",
       "72      ONLINE_AFFINITAET        region_rr1      ordinal                 []\n",
       "73               REGIOTYP        region_rr1      ordinal             [-1,0]\n",
       "74       KBA13_ANZAHL_PKW    macrocell_plz8      numeric                 []\n",
       "75             PLZ8_ANTG1    macrocell_plz8      ordinal               [-1]\n",
       "76             PLZ8_ANTG2    macrocell_plz8      ordinal               [-1]\n",
       "77             PLZ8_ANTG3    macrocell_plz8      ordinal               [-1]\n",
       "78             PLZ8_ANTG4    macrocell_plz8      ordinal               [-1]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8        mixed             [-1,0]\n",
       "80               PLZ8_HHZ    macrocell_plz8      ordinal               [-1]\n",
       "81               PLZ8_GBZ    macrocell_plz8      ordinal               [-1]\n",
       "82                 ARBEIT         community      ordinal             [-1,9]\n",
       "83            ORTSGR_KLS9         community      ordinal             [-1,0]\n",
       "84               RELAT_AB         community      ordinal             [-1,9]\n",
       "\n",
       "[79 rows x 4 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#azdias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#azdias_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
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    "scrolled": true
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   "outputs": [
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       "      <td>ZABEOTYP</td>\n",
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       "      <td>GEBAEUDETYP</td>\n",
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       "      <td>CAMEO_DEU_2015</td>\n",
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       "      <td>[XX]</td>\n",
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      "text/plain": [
       "           attribute information_level         type missing_or_unknown\n",
       "2          ANREDE_KZ            person  categorical             [-1,0]\n",
       "3      CJT_GESAMTTYP            person  categorical                [0]\n",
       "10         FINANZTYP            person  categorical               [-1]\n",
       "12   GFK_URLAUBERTYP            person  categorical                 []\n",
       "13  GREEN_AVANTGARDE            person  categorical                 []\n",
       "17   LP_FAMILIE_FEIN            person  categorical                [0]\n",
       "18   LP_FAMILIE_GROB            person  categorical                [0]\n",
       "19    LP_STATUS_FEIN            person  categorical                [0]\n",
       "20    LP_STATUS_GROB            person  categorical                [0]\n",
       "21  NATIONALITAET_KZ            person  categorical             [-1,0]\n",
       "38       SHOPPER_TYP            person  categorical               [-1]\n",
       "39           SOHO_KZ            person  categorical               [-1]\n",
       "41          VERS_TYP            person  categorical               [-1]\n",
       "42          ZABEOTYP            person  categorical             [-1,9]\n",
       "52       GEBAEUDETYP          building  categorical             [-1,0]\n",
       "55       OST_WEST_KZ          building  categorical               [-1]\n",
       "57   CAMEO_DEUG_2015     microcell_rr4  categorical             [-1,X]\n",
       "58    CAMEO_DEU_2015     microcell_rr4  categorical               [XX]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info[feat_info['type'] == 'categorical']"
   ]
  },
  {
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>6B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>1A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891191</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>6C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891192</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891193</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>6B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891194</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891195</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891196</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891197</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891198</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891199</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891200</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891201</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891202</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891203</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891204</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891205</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891206</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891207</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891208</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>2C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891209</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891210</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>8B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891211</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891212</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891213</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891214</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891215</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>5A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891216</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>7A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891217</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891218</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891219</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>9D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891220</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>6B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>797906 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ANREDE_KZ  CJT_GESAMTTYP  FINANZTYP  GFK_URLAUBERTYP  \\\n",
       "1               2            5.0          1             10.0   \n",
       "2               2            3.0          1             10.0   \n",
       "3               2            2.0          6              1.0   \n",
       "4               1            5.0          5              5.0   \n",
       "5               2            2.0          2              1.0   \n",
       "6               2            5.0          4             12.0   \n",
       "7               1            3.0          5              9.0   \n",
       "8               1            3.0          6              3.0   \n",
       "9               2            4.0          1             12.0   \n",
       "10              2            1.0          6              8.0   \n",
       "12              1            6.0          3             10.0   \n",
       "13              2            5.0          1             12.0   \n",
       "15              2            4.0          2             12.0   \n",
       "16              2            1.0          3             10.0   \n",
       "18              2            6.0          1             11.0   \n",
       "19              1            3.0          5              8.0   \n",
       "20              2            4.0          3             11.0   \n",
       "21              1            3.0          3             11.0   \n",
       "22              1            4.0          1              4.0   \n",
       "23              1            3.0          6             12.0   \n",
       "25              1            3.0          4              1.0   \n",
       "26              1            3.0          3              2.0   \n",
       "27              1            4.0          5              8.0   \n",
       "28              1            2.0          3              1.0   \n",
       "29              2            1.0          6              5.0   \n",
       "31              2            6.0          1              1.0   \n",
       "32              1            4.0          3             10.0   \n",
       "33              2            4.0          5              5.0   \n",
       "34              2            2.0          2              5.0   \n",
       "36              2            2.0          6              5.0   \n",
       "...           ...            ...        ...              ...   \n",
       "891191          2            1.0          2              6.0   \n",
       "891192          2            3.0          1             10.0   \n",
       "891193          1            3.0          5              4.0   \n",
       "891194          1            4.0          3              9.0   \n",
       "891195          2            6.0          2              5.0   \n",
       "891196          2            6.0          1             12.0   \n",
       "891197          2            1.0          5              5.0   \n",
       "891198          1            5.0          1             10.0   \n",
       "891199          1            3.0          1             11.0   \n",
       "891200          2            3.0          1             12.0   \n",
       "891201          1            3.0          6              7.0   \n",
       "891202          2            5.0          1             10.0   \n",
       "891203          2            1.0          5              8.0   \n",
       "891204          1            5.0          6             11.0   \n",
       "891205          1            2.0          2              5.0   \n",
       "891206          2            4.0          4             12.0   \n",
       "891207          2            1.0          2              8.0   \n",
       "891208          1            2.0          6             10.0   \n",
       "891209          2            5.0          1              9.0   \n",
       "891210          1            5.0          6              4.0   \n",
       "891211          1            2.0          2              1.0   \n",
       "891212          1            1.0          5              4.0   \n",
       "891213          2            5.0          6              8.0   \n",
       "891214          2            4.0          1             10.0   \n",
       "891215          2            6.0          1             12.0   \n",
       "891216          2            5.0          1             12.0   \n",
       "891217          1            4.0          6              1.0   \n",
       "891218          2            4.0          1             10.0   \n",
       "891219          1            3.0          1              9.0   \n",
       "891220          1            1.0          6             12.0   \n",
       "\n",
       "        GREEN_AVANTGARDE  LP_FAMILIE_FEIN  LP_FAMILIE_GROB  LP_STATUS_FEIN  \\\n",
       "1                      0              5.0              3.0             2.0   \n",
       "2                      1              1.0              1.0             3.0   \n",
       "3                      0              NaN              NaN             9.0   \n",
       "4                      0             10.0              5.0             3.0   \n",
       "5                      0              1.0              1.0             4.0   \n",
       "6                      0              1.0              1.0             2.0   \n",
       "7                      0              1.0              1.0             1.0   \n",
       "8                      1              1.0              1.0            10.0   \n",
       "9                      1              1.0              1.0             3.0   \n",
       "10                     0              1.0              1.0             1.0   \n",
       "12                     0              5.0              3.0             9.0   \n",
       "13                     1              1.0              1.0             5.0   \n",
       "15                     0              1.0              1.0             8.0   \n",
       "16                     0              NaN              NaN             8.0   \n",
       "18                     0              1.0              1.0             8.0   \n",
       "19                     1              2.0              2.0            10.0   \n",
       "20                     1              1.0              1.0            10.0   \n",
       "21                     0              1.0              1.0            10.0   \n",
       "22                     0              1.0              1.0             2.0   \n",
       "23                     1              2.0              2.0            10.0   \n",
       "25                     0              NaN              NaN             8.0   \n",
       "26                     0              1.0              1.0             8.0   \n",
       "27                     1              7.0              4.0             3.0   \n",
       "28                     0              1.0              1.0             1.0   \n",
       "29                     1              1.0              1.0            10.0   \n",
       "31                     0              1.0              1.0             1.0   \n",
       "32                     1              2.0              2.0            10.0   \n",
       "33                     0             11.0              5.0             1.0   \n",
       "34                     1              1.0              1.0            10.0   \n",
       "36                     0              1.0              1.0             1.0   \n",
       "...                  ...              ...              ...             ...   \n",
       "891191                 0              1.0              1.0             4.0   \n",
       "891192                 0             10.0              5.0             2.0   \n",
       "891193                 0              2.0              2.0             3.0   \n",
       "891194                 1              7.0              4.0            10.0   \n",
       "891195                 0             10.0              5.0             3.0   \n",
       "891196                 0              7.0              4.0             4.0   \n",
       "891197                 1              1.0              1.0            10.0   \n",
       "891198                 1              1.0              1.0             5.0   \n",
       "891199                 0              1.0              1.0             1.0   \n",
       "891200                 0             10.0              5.0             2.0   \n",
       "891201                 0              1.0              1.0             3.0   \n",
       "891202                 0              1.0              1.0             1.0   \n",
       "891203                 0              NaN              NaN             3.0   \n",
       "891204                 0             11.0              5.0             9.0   \n",
       "891205                 0             10.0              5.0             1.0   \n",
       "891206                 0              1.0              1.0             9.0   \n",
       "891207                 1              2.0              2.0            10.0   \n",
       "891208                 0              2.0              2.0             9.0   \n",
       "891209                 0             10.0              5.0             1.0   \n",
       "891210                 0              1.0              1.0             1.0   \n",
       "891211                 0              1.0              1.0             1.0   \n",
       "891212                 0              1.0              1.0             1.0   \n",
       "891213                 1             11.0              5.0             6.0   \n",
       "891214                 0              1.0              1.0             1.0   \n",
       "891215                 0              1.0              1.0             2.0   \n",
       "891216                 0              1.0              1.0             2.0   \n",
       "891217                 0              1.0              1.0             1.0   \n",
       "891218                 0              NaN              NaN             4.0   \n",
       "891219                 0              9.0              5.0             2.0   \n",
       "891220                 0              1.0              1.0             1.0   \n",
       "\n",
       "        LP_STATUS_GROB  NATIONALITAET_KZ  SHOPPER_TYP  SOHO_KZ  VERS_TYP  \\\n",
       "1                  1.0               1.0          3.0      1.0       2.0   \n",
       "2                  2.0               1.0          2.0      0.0       1.0   \n",
       "3                  4.0               1.0          1.0      0.0       1.0   \n",
       "4                  2.0               1.0          2.0      0.0       2.0   \n",
       "5                  2.0               1.0          0.0      0.0       2.0   \n",
       "6                  1.0               1.0          1.0      0.0       1.0   \n",
       "7                  1.0               1.0          0.0      0.0       1.0   \n",
       "8                  5.0               1.0          3.0      0.0       2.0   \n",
       "9                  2.0               1.0          3.0      0.0       2.0   \n",
       "10                 1.0               1.0          2.0      0.0       2.0   \n",
       "12                 4.0               1.0          1.0      0.0       1.0   \n",
       "13                 2.0               1.0          3.0      0.0       2.0   \n",
       "15                 4.0               1.0          1.0      0.0       2.0   \n",
       "16                 4.0               3.0          2.0      0.0       2.0   \n",
       "18                 4.0               2.0          1.0      0.0       1.0   \n",
       "19                 5.0               1.0          2.0      0.0       2.0   \n",
       "20                 5.0               1.0          1.0      0.0       1.0   \n",
       "21                 5.0               1.0          1.0      0.0       2.0   \n",
       "22                 1.0               1.0          1.0      0.0       2.0   \n",
       "23                 5.0               2.0          0.0      0.0       2.0   \n",
       "25                 4.0               1.0          0.0      0.0       2.0   \n",
       "26                 4.0               1.0          1.0      0.0       1.0   \n",
       "27                 2.0               1.0          0.0      0.0       2.0   \n",
       "28                 1.0               1.0          1.0      0.0       2.0   \n",
       "29                 5.0               1.0          1.0      0.0       2.0   \n",
       "31                 1.0               1.0          1.0      0.0       1.0   \n",
       "32                 5.0               1.0          0.0      0.0       1.0   \n",
       "33                 1.0               2.0          1.0      0.0       2.0   \n",
       "34                 5.0               NaN          NaN      0.0       NaN   \n",
       "36                 1.0               1.0          2.0      0.0       2.0   \n",
       "...                ...               ...          ...      ...       ...   \n",
       "891191             2.0               1.0          3.0      0.0       2.0   \n",
       "891192             1.0               1.0          0.0      0.0       1.0   \n",
       "891193             2.0               1.0          2.0      0.0       1.0   \n",
       "891194             5.0               1.0          0.0      0.0       2.0   \n",
       "891195             2.0               1.0          3.0      0.0       2.0   \n",
       "891196             2.0               2.0          1.0      0.0       1.0   \n",
       "891197             5.0               1.0          2.0      0.0       2.0   \n",
       "891198             2.0               1.0          1.0      0.0       2.0   \n",
       "891199             1.0               2.0          2.0      0.0       1.0   \n",
       "891200             1.0               2.0          3.0      0.0       2.0   \n",
       "891201             2.0               1.0          2.0      0.0       2.0   \n",
       "891202             1.0               1.0          3.0      0.0       1.0   \n",
       "891203             2.0               1.0          1.0      0.0       1.0   \n",
       "891204             4.0               1.0          0.0      0.0       2.0   \n",
       "891205             1.0               1.0          2.0      0.0       1.0   \n",
       "891206             4.0               1.0          0.0      0.0       2.0   \n",
       "891207             5.0               1.0          3.0      0.0       1.0   \n",
       "891208             4.0               1.0          3.0      0.0       2.0   \n",
       "891209             1.0               3.0          2.0      0.0       2.0   \n",
       "891210             1.0               1.0          2.0      0.0       2.0   \n",
       "891211             1.0               1.0          1.0      0.0       1.0   \n",
       "891212             1.0               1.0          2.0      0.0       1.0   \n",
       "891213             3.0               1.0          1.0      0.0       1.0   \n",
       "891214             1.0               1.0          3.0      0.0       2.0   \n",
       "891215             1.0               2.0          1.0      0.0       1.0   \n",
       "891216             1.0               1.0          3.0      0.0       1.0   \n",
       "891217             1.0               NaN          NaN      0.0       NaN   \n",
       "891218             2.0               1.0          2.0      0.0       1.0   \n",
       "891219             1.0               1.0          0.0      0.0       2.0   \n",
       "891220             1.0               1.0          2.0      0.0       1.0   \n",
       "\n",
       "        ZABEOTYP  GEBAEUDETYP OST_WEST_KZ CAMEO_DEUG_2015 CAMEO_DEU_2015  \n",
       "1              5          8.0           W               8             8A  \n",
       "2              5          1.0           W               4             4C  \n",
       "3              3          1.0           W               2             2A  \n",
       "4              4          1.0           W               6             6B  \n",
       "5              4          1.0           W               8             8C  \n",
       "6              4          1.0           W               4             4A  \n",
       "7              1          8.0           W               2             2D  \n",
       "8              6          3.0           W               1             1A  \n",
       "9              4          3.0           W               1             1E  \n",
       "10             3          1.0           W               9             9D  \n",
       "12             1          1.0           W               6             6B  \n",
       "13             5          1.0           W               5             5C  \n",
       "15             3          8.0           W               8             8B  \n",
       "16             5          2.0           W               7             7A  \n",
       "18             4          1.0           W               4             4C  \n",
       "19             2          8.0           W               5             5D  \n",
       "20             3          3.0           W               4             4C  \n",
       "21             4          1.0           W               9             9E  \n",
       "22             4          3.0           W               9             9D  \n",
       "23             4          1.0           W               6             6B  \n",
       "25             5          1.0           W               5             5C  \n",
       "26             1          1.0           W             NaN            NaN  \n",
       "27             4          1.0           W               9             9B  \n",
       "28             6          1.0           W               1             1A  \n",
       "29             1          1.0           W               2             2A  \n",
       "31             4          8.0           W               9             9D  \n",
       "32             1          1.0           W               1             1B  \n",
       "33             3          8.0           W               8             8B  \n",
       "34             1          1.0           W               3             3D  \n",
       "36             3          1.0           W               7             7A  \n",
       "...          ...          ...         ...             ...            ...  \n",
       "891191         3          1.0           W               6             6C  \n",
       "891192         4          3.0           W               5             5B  \n",
       "891193         4          1.0           W               6             6B  \n",
       "891194         4          1.0           W               4             4C  \n",
       "891195         3          1.0           W               8             8D  \n",
       "891196         4          8.0           W               4             4B  \n",
       "891197         4          3.0           W               3             3C  \n",
       "891198         4          1.0           W               2             2C  \n",
       "891199         4          8.0           W               8             8A  \n",
       "891200         4          1.0           W               9             9A  \n",
       "891201         4          3.0           W               7             7A  \n",
       "891202         4          3.0           W               8             8B  \n",
       "891203         3          3.0           W               3             3D  \n",
       "891204         1          3.0           W               3             3C  \n",
       "891205         3          1.0           W               5             5E  \n",
       "891206         5          1.0           W               3             3D  \n",
       "891207         3          1.0           W               7             7A  \n",
       "891208         1          1.0           W               2             2C  \n",
       "891209         5          1.0           W               8             8A  \n",
       "891210         3          8.0           W               8             8B  \n",
       "891211         6          1.0           W               9             9D  \n",
       "891212         3          3.0           W               9             9D  \n",
       "891213         4          3.0           W               5             5E  \n",
       "891214         5          1.0           W               3             3A  \n",
       "891215         4          1.0           W               5             5A  \n",
       "891216         4          8.0           W               7             7A  \n",
       "891217         6          8.0           W               9             9D  \n",
       "891218         4          8.0           W               4             4C  \n",
       "891219         5          8.0           W               9             9D  \n",
       "891220         3          8.0           W               6             6B  \n",
       "\n",
       "[797906 rows x 18 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars = azdias_copy[feat_info[feat_info['type'] == 'categorical']['attribute']]\n",
    "cat_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    416089\n",
       "1    381817\n",
       "Name: ANREDE_KZ, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_vars['ANREDE_KZ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    381817\n",
      "2    416089\n",
      "Name: ANREDE_KZ, dtype: int64\n",
      "1.0     93306\n",
      "2.0    141260\n",
      "3.0    147203\n",
      "4.0    198280\n",
      "5.0    111104\n",
      "6.0    102075\n",
      "Name: CJT_GESAMTTYP, dtype: int64\n",
      "1    197117\n",
      "2    104748\n",
      "3     44757\n",
      "4     55916\n",
      "5    106321\n",
      "6    289047\n",
      "Name: FINANZTYP, dtype: int64\n",
      "1.0      50656\n",
      "2.0      43826\n",
      "3.0      53101\n",
      "4.0      60422\n",
      "5.0      70498\n",
      "6.0      25738\n",
      "7.0      40678\n",
      "8.0      83028\n",
      "9.0      57124\n",
      "10.0    102823\n",
      "11.0     75099\n",
      "12.0    130235\n",
      "Name: GFK_URLAUBERTYP, dtype: int64\n",
      "0    622698\n",
      "1    175208\n",
      "Name: GREEN_AVANTGARDE, dtype: int64\n",
      "1.0     402538\n",
      "2.0      98548\n",
      "3.0       4688\n",
      "4.0      11574\n",
      "5.0      11167\n",
      "6.0       8526\n",
      "7.0      19575\n",
      "8.0      21780\n",
      "9.0      10452\n",
      "10.0    128966\n",
      "11.0     48744\n",
      "Name: LP_FAMILIE_FEIN, dtype: int64\n",
      "1.0    402538\n",
      "2.0     98548\n",
      "3.0     27429\n",
      "4.0     49881\n",
      "5.0    188162\n",
      "Name: LP_FAMILIE_GROB, dtype: int64\n",
      "1.0     206852\n",
      "2.0     111100\n",
      "3.0      68917\n",
      "4.0      73969\n",
      "5.0      27582\n",
      "6.0      28894\n",
      "7.0       9203\n",
      "8.0      18794\n",
      "9.0     136344\n",
      "10.0    111573\n",
      "Name: LP_STATUS_FEIN, dtype: int64\n",
      "1.0    317952\n",
      "2.0    170468\n",
      "3.0     38097\n",
      "4.0    155138\n",
      "5.0    111573\n",
      "Name: LP_STATUS_GROB, dtype: int64\n",
      "1.0    667832\n",
      "2.0     63643\n",
      "3.0     32560\n",
      "Name: NATIONALITAET_KZ, dtype: int64\n",
      "0.0    127123\n",
      "1.0    247373\n",
      "2.0    205926\n",
      "3.0    180813\n",
      "Name: SHOPPER_TYP, dtype: int64\n",
      "0.0    791190\n",
      "1.0      6716\n",
      "Name: SOHO_KZ, dtype: int64\n",
      "1.0    367012\n",
      "2.0    394223\n",
      "Name: VERS_TYP, dtype: int64\n",
      "1    123414\n",
      "2     32988\n",
      "3    282178\n",
      "4    207510\n",
      "5     80956\n",
      "6     70860\n",
      "Name: ZABEOTYP, dtype: int64\n",
      "1.0    460370\n",
      "2.0      4899\n",
      "3.0    178646\n",
      "4.0       897\n",
      "5.0         1\n",
      "6.0       624\n",
      "8.0    152469\n",
      "Name: GEBAEUDETYP, dtype: int64\n",
      "O    168518\n",
      "W    629388\n",
      "Name: OST_WEST_KZ, dtype: int64\n",
      "1     36211\n",
      "2     83220\n",
      "3     86754\n",
      "4    103898\n",
      "5     55299\n",
      "6    105872\n",
      "7     77933\n",
      "8    134437\n",
      "9    108176\n",
      "Name: CAMEO_DEUG_2015, dtype: int64\n",
      "1A    10850\n",
      "1B     4071\n",
      "1C     4316\n",
      "1D    11909\n",
      "1E     5065\n",
      "2A    13245\n",
      "2B    15482\n",
      "2C    19420\n",
      "2D    35073\n",
      "3A    10532\n",
      "3B     7155\n",
      "3C    34763\n",
      "3D    34304\n",
      "4A    33149\n",
      "4B     9045\n",
      "4C    47814\n",
      "4D     8570\n",
      "4E     5320\n",
      "5A    12203\n",
      "5B    10354\n",
      "5C     9935\n",
      "5D    14943\n",
      "5E     3581\n",
      "5F     4283\n",
      "6A     6809\n",
      "6B    56671\n",
      "6C    14820\n",
      "6D     6073\n",
      "6E    16107\n",
      "6F     5392\n",
      "7A    34399\n",
      "7B    24503\n",
      "7C     9065\n",
      "7D     5333\n",
      "7E     4633\n",
      "8A    52435\n",
      "8B    33434\n",
      "8C    30992\n",
      "8D    17576\n",
      "9A    20542\n",
      "9B    27676\n",
      "9C    24987\n",
      "9D    28593\n",
      "9E     6378\n",
      "Name: CAMEO_DEU_2015, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "\n",
    "for x in cat_vars:\n",
    "    print(cat_vars[x].value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OST_WEST_KZ is the attribute that takes on non-numeric values.\n",
    "#cat_vars = pd.get_dummies(cat_vars, columns = ['OST_WEST_KZ'])\n",
    "azdias_copy = pd.get_dummies(azdias_copy, columns = ['OST_WEST_KZ'])\n",
    "del azdias_copy['OST_WEST_KZ_W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#azdias_copy['OST_WEST_KZ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    629388\n",
       "1    168518\n",
       "Name: OST_WEST_KZ_O, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['OST_WEST_KZ_O'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CJT_GESAMTTYP', 'FINANZTYP', 'GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']\n"
     ]
    }
   ],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "multi_var = []\n",
    "for x in cat_vars:\n",
    "    if (len(cat_vars[x].value_counts()) > 2):\n",
    "        multi_var.append(x)\n",
    "print(multi_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CJT_GESAMTTYP\n",
      "FINANZTYP\n",
      "GFK_URLAUBERTYP\n",
      "LP_FAMILIE_FEIN\n",
      "LP_FAMILIE_GROB\n",
      "LP_STATUS_FEIN\n",
      "LP_STATUS_GROB\n",
      "NATIONALITAET_KZ\n",
      "SHOPPER_TYP\n",
      "ZABEOTYP\n",
      "GEBAEUDETYP\n",
      "CAMEO_DEUG_2015\n",
      "CAMEO_DEU_2015\n"
     ]
    }
   ],
   "source": [
    "for mul in multi_var:\n",
    "    print(mul)\n",
    "    del azdias_copy[mul]\n",
    "    #azdias_copy.drop(mul, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#azdias_copy['CAMEO_DEU_2015']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "\n",
    "- Before starting, I dropped the rows in feat_info table which were the columns dropped in the azdias_copy dataset with many missing values.\n",
    "- Then I took the count of categorical features. And took only these categorical features in the separate cat_vars dataframe.\n",
    "- Next step was to find which of these cat_vars are binary / multi-level. There was no need to encode the binary vars, one var viz. OST_WEST_KZ was textual which is now converted to numeric using One Hot Encoding method(pandas - get_dummies)\n",
    "- To keep things simple, I have dropped the multi-level categorical columns from the azdias_copy dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>building</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>PLZ8_BAUMAX</td>\n",
       "      <td>macrocell_plz8</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                attribute information_level   type missing_or_unknown\n",
       "15    LP_LEBENSPHASE_FEIN            person  mixed                [0]\n",
       "16    LP_LEBENSPHASE_GROB            person  mixed                [0]\n",
       "22  PRAEGENDE_JUGENDJAHRE            person  mixed             [-1,0]\n",
       "56               WOHNLAGE          building  mixed               [-1]\n",
       "59        CAMEO_INTL_2015     microcell_rr4  mixed            [-1,XX]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info[feat_info['type'] == 'mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0    182964\n",
       "8.0     141598\n",
       "10.0     85793\n",
       "5.0      84682\n",
       "3.0      53840\n",
       "15.0     42540\n",
       "11.0     35746\n",
       "9.0      33569\n",
       "6.0      25652\n",
       "12.0     24446\n",
       "1.0      20678\n",
       "4.0      20451\n",
       "2.0       7479\n",
       "13.0      5762\n",
       "7.0       4009\n",
       "Name: PRAEGENDE_JUGENDJAHRE, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['PRAEGENDE_JUGENDJAHRE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'] =  azdias_copy['PRAEGENDE_JUGENDJAHRE']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#azdias_copy = pd.get_dummies(azdias_copy, columns = ['PRAEGENDE_JUGENDJAHRE_MOVEMENT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         14.0\n",
       "2         15.0\n",
       "3          8.0\n",
       "4          8.0\n",
       "5          3.0\n",
       "6         10.0\n",
       "7          8.0\n",
       "8         11.0\n",
       "9         15.0\n",
       "10         3.0\n",
       "12         8.0\n",
       "13        15.0\n",
       "15         5.0\n",
       "16        14.0\n",
       "18        10.0\n",
       "19         9.0\n",
       "20        11.0\n",
       "21        10.0\n",
       "22        14.0\n",
       "23         9.0\n",
       "25        14.0\n",
       "26         8.0\n",
       "27        11.0\n",
       "28        10.0\n",
       "29         6.0\n",
       "31        10.0\n",
       "32        15.0\n",
       "33         8.0\n",
       "34         4.0\n",
       "36         8.0\n",
       "          ... \n",
       "891191     3.0\n",
       "891192    14.0\n",
       "891193     5.0\n",
       "891194    11.0\n",
       "891195     8.0\n",
       "891196    14.0\n",
       "891197    11.0\n",
       "891198    15.0\n",
       "891199    10.0\n",
       "891200    14.0\n",
       "891201    10.0\n",
       "891202    10.0\n",
       "891203     NaN\n",
       "891204    10.0\n",
       "891205     3.0\n",
       "891206    14.0\n",
       "891207     2.0\n",
       "891208     8.0\n",
       "891209    14.0\n",
       "891210     8.0\n",
       "891211     8.0\n",
       "891212     3.0\n",
       "891213    11.0\n",
       "891214    14.0\n",
       "891215    10.0\n",
       "891216    14.0\n",
       "891217    10.0\n",
       "891218    14.0\n",
       "891219    14.0\n",
       "891220     3.0\n",
       "Name: PRAEGENDE_JUGENDJAHRE_MOVEMENT, Length: 797906, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "797906"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_to_replace = {1:0, 2:1, 3:0, 4:1, 5:0, \n",
    "                   6:1, 7:1, 8:0, 9:1, 10:0,\n",
    "                  11:1, 12:0, 13:1, 14:0, 15:1}\n",
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'] = azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'].map(vals_to_replace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         0.0\n",
       "2         1.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "5         0.0\n",
       "6         0.0\n",
       "7         0.0\n",
       "8         1.0\n",
       "9         1.0\n",
       "10        0.0\n",
       "12        0.0\n",
       "13        1.0\n",
       "15        0.0\n",
       "16        0.0\n",
       "18        0.0\n",
       "19        1.0\n",
       "20        1.0\n",
       "21        0.0\n",
       "22        0.0\n",
       "23        1.0\n",
       "25        0.0\n",
       "26        0.0\n",
       "27        1.0\n",
       "28        0.0\n",
       "29        1.0\n",
       "31        0.0\n",
       "32        1.0\n",
       "33        0.0\n",
       "34        1.0\n",
       "36        0.0\n",
       "         ... \n",
       "891191    0.0\n",
       "891192    0.0\n",
       "891193    0.0\n",
       "891194    1.0\n",
       "891195    0.0\n",
       "891196    0.0\n",
       "891197    1.0\n",
       "891198    1.0\n",
       "891199    0.0\n",
       "891200    0.0\n",
       "891201    0.0\n",
       "891202    0.0\n",
       "891203    NaN\n",
       "891204    0.0\n",
       "891205    0.0\n",
       "891206    0.0\n",
       "891207    1.0\n",
       "891208    0.0\n",
       "891209    0.0\n",
       "891210    0.0\n",
       "891211    0.0\n",
       "891212    0.0\n",
       "891213    1.0\n",
       "891214    0.0\n",
       "891215    0.0\n",
       "891216    0.0\n",
       "891217    0.0\n",
       "891218    0.0\n",
       "891219    0.0\n",
       "891220    0.0\n",
       "Name: PRAEGENDE_JUGENDJAHRE_MOVEMENT, Length: 797906, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "797906"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(azdias_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_DECADE'] = azdias_copy['PRAEGENDE_JUGENDJAHRE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_to_replace = {1:1, 2:1, 3:2, 4:2, 5:3, \n",
    "                   6:3, 7:3, 8:4, 9:4, 10:5,\n",
    "                  11:5, 12:5, 13:5, 14:6, 15:6}\n",
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_DECADE'] = azdias_copy['PRAEGENDE_JUGENDJAHRE_DECADE'].map(vals_to_replace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         6.0\n",
       "2         6.0\n",
       "3         4.0\n",
       "4         4.0\n",
       "5         2.0\n",
       "6         5.0\n",
       "7         4.0\n",
       "8         5.0\n",
       "9         6.0\n",
       "10        2.0\n",
       "12        4.0\n",
       "13        6.0\n",
       "15        3.0\n",
       "16        6.0\n",
       "18        5.0\n",
       "19        4.0\n",
       "20        5.0\n",
       "21        5.0\n",
       "22        6.0\n",
       "23        4.0\n",
       "25        6.0\n",
       "26        4.0\n",
       "27        5.0\n",
       "28        5.0\n",
       "29        3.0\n",
       "31        5.0\n",
       "32        6.0\n",
       "33        4.0\n",
       "34        2.0\n",
       "36        4.0\n",
       "         ... \n",
       "891191    2.0\n",
       "891192    6.0\n",
       "891193    3.0\n",
       "891194    5.0\n",
       "891195    4.0\n",
       "891196    6.0\n",
       "891197    5.0\n",
       "891198    6.0\n",
       "891199    5.0\n",
       "891200    6.0\n",
       "891201    5.0\n",
       "891202    5.0\n",
       "891203    NaN\n",
       "891204    5.0\n",
       "891205    2.0\n",
       "891206    6.0\n",
       "891207    1.0\n",
       "891208    4.0\n",
       "891209    6.0\n",
       "891210    4.0\n",
       "891211    4.0\n",
       "891212    2.0\n",
       "891213    5.0\n",
       "891214    6.0\n",
       "891215    5.0\n",
       "891216    6.0\n",
       "891217    5.0\n",
       "891218    6.0\n",
       "891219    6.0\n",
       "891220    2.0\n",
       "Name: PRAEGENDE_JUGENDJAHRE_DECADE, Length: 797906, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['PRAEGENDE_JUGENDJAHRE_DECADE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del azdias_copy['PRAEGENDE_JUGENDJAHRE']\n",
    "#azdias_copy.drop('PRAEGENDE_JUGENDJAHRE', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6106"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['CAMEO_INTL_2015'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "azdias_copy['CAMEO_INTL_2015_HOUSEHOLDS'] = azdias_copy['CAMEO_INTL_2015'] \n",
    "azdias_copy['CAMEO_INTL_2015_LIFE_STG'] = azdias_copy['CAMEO_INTL_2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_to_replace = {'11':'1', '12':'1', '13':'1', '14':'1', '15':'1',\n",
    "                   '21':'2', '22':'2', '23':'2', '24':'2', '25':'2',\n",
    "                   '31':'3', '32':'3', '33':'3', '34':'3', '35':'3',\n",
    "                   '41':'4', '42':'4', '43':'4', '44':'4', '45':'4',\n",
    "                   '51':'5', '52':'5', '53':'5', '54':'5', '55':'5'}\n",
    "azdias_copy['CAMEO_INTL_2015_HOUSEHOLDS'] = azdias_copy['CAMEO_INTL_2015_HOUSEHOLDS'].map(vals_to_replace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals_to_replace = {'11':'1', '12':'2', '13':'3', '14':'4', '15':'5',\n",
    "                   '21':'1', '22':'2', '23':'3', '24':'4', '25':'5',\n",
    "                   '31':'1', '32':'2', '33':'3', '34':'4', '35':'5',\n",
    "                   '41':'1', '42':'2', '43':'3', '44':'4', '45':'5',\n",
    "                   '51':'1', '52':'2', '53':'3', '54':'4', '55':'5'}\n",
    "azdias_copy['CAMEO_INTL_2015_LIFE_STG'] = azdias_copy['CAMEO_INTL_2015_LIFE_STG'].map(vals_to_replace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    223577\n",
       "2    190652\n",
       "4    189959\n",
       "1    119431\n",
       "3     68181\n",
       "Name: CAMEO_INTL_2015_HOUSEHOLDS, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['CAMEO_INTL_2015_HOUSEHOLDS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    245039\n",
       "4    232761\n",
       "3    119670\n",
       "5    117040\n",
       "2     77290\n",
       "Name: CAMEO_INTL_2015_LIFE_STG, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['CAMEO_INTL_2015_LIFE_STG'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6106"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_copy['CAMEO_INTL_2015_LIFE_STG'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "del azdias_copy['CAMEO_INTL_2015']\n",
    "#azdias_copy.drop('CAMEO_INTL_2015', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "\n",
    "- Using the simple map() function I have re-engineered two new columns for each of the given columns viz. `PRAEGENDE_JUGENDJAHRE` and `CAMEO_INTL_2015`\n",
    "- Talking about the 'PRAEGENDE_JUGENDJAHRE' column, I have made two new columns and dropped the original one. \n",
    "The columns are `PRAEGENDE_JUGENDJAHRE_MOVEMENT`(binary) where 0 represents the movement is mainstream and 1 represents the movement is avantgarde and `PRAEGENDE_JUGENDJAHRE_DECADE`(multi-level) where each of the digits represent the decades starting from the 40s to 90s.\n",
    "- And the second `CAMEO_INT_2015` is dropped and two new columns are introduced as `CAMEO_INT_2015_HOUSEHOLD`(multi-level) where the levels represent different conditions from Wealthy to Poorer and the second `CAMEO_INT_2015_LIFE_STG`(multi-level) where the levels represent different stages of a family from Couples and Singles to Elders in Retirement\n",
    "- I will be dropping the rest mixed attributes in the next section for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "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",
    "\n",
    "mixed_cat = ['LP_LEBENSPHASE_FEIN',\n",
    " 'LP_LEBENSPHASE_GROB',\n",
    " 'WOHNLAGE',\n",
    " 'PLZ8_BAUMAX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for mul in mixed_cat:\n",
    "    del azdias_copy[mul]\n",
    "    #azdias_copy.drop(mul, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    18\n",
       "numeric         6\n",
       "mixed           6\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 79,
     "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",
    "len(azdias_copy.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
    "    df_copy = df\n",
    "    # Identify missing or unknown data values and convert them to NaNs.\n",
    "    feat_info = pd.read_csv(\"AZDIAS_Feature_Summary.csv\", sep = ';')\n",
    "    #for i, column in list(enumerate(df_copy.columns)):\n",
    "        \n",
    "     #   vals = feat_info['missing_or_unknown'].iloc[i]\n",
    "      #  if i in [57, 58, 59]: # since only these are in object type, manual detection of col-number from counter_list\n",
    "       #     a = vals.strip(\"[]\").split(\",\")\n",
    "        #print(a)\n",
    "        #else:\n",
    "         #   try:\n",
    "            \n",
    "          #      if (type(df_copy[column][0]) == np.float64):\n",
    "           #         b = vals.strip(\"[]\").split(\",\")\n",
    "            #        a = [float(e) for e in b]\n",
    "             #   else:\n",
    "              #      b = vals.strip(\"[]\").split(\",\")\n",
    "               #     a = [int(e) for e in b]\n",
    "            #except ValueError: # ValueError will rise if the field is blank\n",
    "             #   pass\n",
    "        #for re in a:\n",
    "         #   df_copy[column].replace(re, np.NaN, inplace = True)\n",
    "    for index, row in feat_info.iterrows():\n",
    "        column = df_copy[row['attribute']]\n",
    "\n",
    "        missing_values = row['missing_or_unknown'][1:-1].split(',')\n",
    "        if missing_values != ['']:\n",
    "            for mv in missing_values:\n",
    "                if mv != 'X' and mv != 'XX':\n",
    "                    column.replace(int(mv), np.nan, inplace = True)\n",
    "                else:\n",
    "                    column.replace(mv, np.nan, inplace = True)\n",
    "\n",
    "        df_copy[row['attribute']] = column\n",
    "    \n",
    "    # remove selected columns and rows, ...\n",
    "    # columns\n",
    "    # Before I had chosen 20%, but after thorough checking of the customers data,\n",
    "    # I have incread the same benchmark to 50%\n",
    "    missing_50 = [col for col in df_copy.columns if (df_copy[col].isnull().sum()/df_copy.shape[0]) * 100 > 50]\n",
    "    for m in missing_50:\n",
    "        del df_copy[m]\n",
    "    #df_copy = df_copy.drop(missing_20, axis = 1)\n",
    "    # rows\n",
    "    missing_rows = df_copy.isnull().sum(axis=1)\n",
    "    rows_with_missing_large = missing_rows[missing_rows > 24]\n",
    "    print('Rows before dropping rows with missing vals:', df_copy.shape[0])\n",
    "    df_copy = df_copy[~df_copy.index.isin(rows_with_missing_large.index)]\n",
    "    print('Rows after dropping rows with missing vals:', df_copy.shape[0])\n",
    "    # select, re-encode, and engineer column values.\n",
    "    for x in missing_20:\n",
    "        feat_info = feat_info[feat_info.attribute != x]\n",
    "    cat_vars = df_copy[feat_info[feat_info['type'] == 'categorical']['attribute']]\n",
    "    #print(df_copy.columns)\n",
    "    df_copy = pd.get_dummies(df_copy, columns = ['OST_WEST_KZ'])\n",
    "    del df_copy['OST_WEST_KZ_W']\n",
    "    multi_var = []\n",
    "    for x in cat_vars:\n",
    "        if (len(cat_vars[x].value_counts()) > 2):\n",
    "            multi_var.append(x)\n",
    "    for mul in multi_var:\n",
    "        del df_copy[mul]\n",
    "        #df_copy.drop(mul, axis = 1)\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'] =  df_copy['PRAEGENDE_JUGENDJAHRE']\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_DECADE'] = df_copy['PRAEGENDE_JUGENDJAHRE']\n",
    "    vals_to_replace = {1:0, 2:1, 3:0, 4:1, 5:0, \n",
    "                   6:1, 7:1, 8:0, 9:1, 10:0,\n",
    "                  11:1, 12:0, 13:1, 14:0, 15:1}\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'] = df_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT'].map(vals_to_replace)\n",
    "    vals_to_replace = {1:1, 2:1, 3:2, 4:2, 5:3, \n",
    "                   6:3, 7:3, 8:4, 9:4, 10:5,\n",
    "                  11:5, 12:5, 13:5, 14:6, 15:6}\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_DECADE'] = df_copy['PRAEGENDE_JUGENDJAHRE_DECADE'].map(vals_to_replace)\n",
    "    del df_copy['PRAEGENDE_JUGENDJAHRE']\n",
    "    #df_copy.drop('PRAEGENDE_JUGENDJAHRE', axis = 1)\n",
    "    df_copy['CAMEO_INTL_2015_HOUSEHOLDS'] = df_copy['CAMEO_INTL_2015'] \n",
    "    df_copy['CAMEO_INTL_2015_LIFE_STG'] = df_copy['CAMEO_INTL_2015']\n",
    "    vals_to_replace = {'11':'1', '12':'1', '13':'1', '14':'1', '15':'1',\n",
    "                   '21':'2', '22':'2', '23':'2', '24':'2', '25':'2',\n",
    "                   '31':'3', '32':'3', '33':'3', '34':'3', '35':'3',\n",
    "                   '41':'4', '42':'4', '43':'4', '44':'4', '45':'4',\n",
    "                   '51':'5', '52':'5', '53':'5', '54':'5', '55':'5'}\n",
    "    df_copy['CAMEO_INTL_2015_HOUSEHOLDS'] = df_copy['CAMEO_INTL_2015_HOUSEHOLDS'].map(vals_to_replace)\n",
    "    vals_to_replace = {'11':'1', '12':'2', '13':'3', '14':'4', '15':'5',\n",
    "                   '21':'1', '22':'2', '23':'3', '24':'4', '25':'5',\n",
    "                   '31':'1', '32':'2', '33':'3', '34':'4', '35':'5',\n",
    "                   '41':'1', '42':'2', '43':'3', '44':'4', '45':'5',\n",
    "                   '51':'1', '52':'2', '53':'3', '54':'4', '55':'5'}\n",
    "    df_copy['CAMEO_INTL_2015_LIFE_STG'] = df_copy['CAMEO_INTL_2015_LIFE_STG'].map(vals_to_replace)\n",
    "    del df_copy['CAMEO_INTL_2015']\n",
    "    #df_copy.drop('CAMEO_INTL_2015', axis = 1)\n",
    "    mixed_cat = ['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE', 'PLZ8_BAUMAX']\n",
    "    for mul in mixed_cat:\n",
    "        del df_copy[mul]\n",
    "        #df_copy.drop(mul, axis = 1)\n",
    "    # Return the cleaned dataframe.\n",
    "    return df_copy\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": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       64.000000\n",
       "mean     11879.843750\n",
       "std      18006.765171\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%        592.000000\n",
       "75%      23200.000000\n",
       "max      64764.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "azdias_copy.isnull().sum().describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#azdias_copy['CAMEO_DEU_2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values = 'NaN', strategy = 'median')\n",
    "azdias_imputed = pd.DataFrame(imputer.fit_transform(azdias_copy), index = azdias_copy.index, columns = azdias_copy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(797906, 64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_imputed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    64\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_imputed.isnull().sum().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "azdias_scaled = pd.DataFrame(scaler.fit_transform(azdias_imputed), index = azdias_imputed.index, columns = azdias_imputed.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "\n",
    "- Applied the describe() function to the `AZDIAS` dataset to check the distributions of missing values.\n",
    "- Checked if it has been applied correctly so that no missing values are present.\n",
    "- Made a decision to use the Imputer function to replace the missing values with the median of the column i.e. the middle value of the column since the dataset is repititive(i.e. there is repitition of values).\n",
    "- Further, used the StandardScaler and stored it into the `azdias_scaled` dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "azdias_scaled_pca = pca.fit_transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-inf\n"
     ]
    }
   ],
   "source": [
    "print(pca.score(azdias_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# took help from the code provided in the classroom for this function.\n",
    "def scree_plot(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT:\n",
    "            None\n",
    "    '''\n",
    "    num_components = len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "    \n",
    "    ax.xaxis.set_tick_params(width=0)\n",
    "    ax.yaxis.set_tick_params(width=2, length=12)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2198f64a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "scree_plot(pca)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca1 = PCA(n_components = 39)\n",
    "azdias_scaled_pca1 = pca1.fit_transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-67.4582296476\n"
     ]
    }
   ],
   "source": [
    "print(pca1.score(azdias_scaled)) # prints negative average log-likelihood score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca2 = PCA(n_components = 28)\n",
    "azdias_scaled_pca2 = pca2.fit_transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-69.0101002513\n"
     ]
    }
   ],
   "source": [
    "print(pca2.score(azdias_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca3 = PCA(n_components = 21)\n",
    "azdias_scaled_pca3 = pca3.fit_transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-70.8000071582\n"
     ]
    }
   ],
   "source": [
    "#scree_plot(pca)\n",
    "print(pca3.score(azdias_scaled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)\n",
    "- I am retaining 28 features for further analysis becuase I took the negative log-likelihood score of the Principal Component Analysis and came to the conculsion that I want to retain a decent amount of features hence cannot lose more features for my analysis. Although with 21 components a good score is received I have decided to go with 28 features.\n",
    "- The screeplot has been written with the help of the code taught in the classroom. And using this plot, we can see there is small deviation near 20, 30, and 40."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator # since I have to use itemgetter() function in the sorting step\n",
    "# https://stackoverflow.com/questions/18595686/how-does-operator-itemgetter-and-sort-work-in-python reference\n",
    "def pca_link_weights(pca, i):\n",
    "    map_wt = {}\n",
    "    for counter, feature in enumerate(azdias_copy.columns):\n",
    "        # mapping step\n",
    "        map_wt[feature] = pca.components_[i][counter]\n",
    "    \n",
    "    # sorting step\n",
    "    sorted_weights = sorted(map_wt.items(), key = operator.itemgetter(1), reverse=True)\n",
    "    \n",
    "    return sorted_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ALTERSKATEGORIE_GROB', 0.26110705676504575),\n",
       " ('FINANZ_VORSORGER', 0.23391564122329941),\n",
       " ('SEMIO_ERL', 0.23145407634017168),\n",
       " ('SEMIO_LUST', 0.18442301436726025),\n",
       " ('RETOURTYP_BK_S', 0.16395999136698036),\n",
       " ('W_KEIT_KIND_HH', 0.11935205917499146),\n",
       " ('FINANZ_HAUSBAUER', 0.11599590454454743),\n",
       " ('SEMIO_KRIT', 0.115487292200114),\n",
       " ('SEMIO_KAEM', 0.11206071722227066),\n",
       " ('ANREDE_KZ', 0.090463018984169863),\n",
       " ('EWDICHTE', 0.088565531716314164),\n",
       " ('PLZ8_ANTG3', 0.088122800902359452),\n",
       " ('ORTSGR_KLS9', 0.087034637970134623),\n",
       " ('PLZ8_ANTG4', 0.084083273811133341),\n",
       " ('CAMEO_INTL_2015_HOUSEHOLDS', 0.071540840788634832),\n",
       " ('SEMIO_DOM', 0.070877136637171601),\n",
       " ('KBA05_ANTG4', 0.069481965525319767),\n",
       " ('FINANZ_MINIMALIST', 0.068151772001478053),\n",
       " ('ARBEIT', 0.065230732094304564),\n",
       " ('RELAT_AB', 0.062807259840391316),\n",
       " ('WOHNDAUER_2008', 0.061253856730555258),\n",
       " ('ANZ_HAUSHALTE_AKTIV', 0.061201599335518657),\n",
       " ('PLZ8_ANTG2', 0.061029469256412297),\n",
       " ('HH_EINKOMMEN_SCORE', 0.054092535269399646),\n",
       " ('KBA05_ANTG3', 0.044961222933534772),\n",
       " ('VERS_TYP', 0.031703376492346391),\n",
       " ('ANZ_HH_TITEL', 0.030607203314465679),\n",
       " ('OST_WEST_KZ_O', 0.024745189040181997),\n",
       " ('REGIOTYP', 0.016685873474242743),\n",
       " ('PLZ8_HHZ', 0.011371558556823478),\n",
       " ('ANZ_TITEL', 0.0071113160195007121),\n",
       " ('CAMEO_INTL_2015_LIFE_STG', 0.0033432617666410368),\n",
       " ('KKK', -0.00039141212988597307),\n",
       " ('SOHO_KZ', -0.0018626561786666911),\n",
       " ('KBA05_ANTG2', -0.0080385889419905855),\n",
       " ('GREEN_AVANTGARDE', -0.014815599671280842),\n",
       " ('PRAEGENDE_JUGENDJAHRE_MOVEMENT', -0.014815599671280842),\n",
       " ('KBA13_ANZAHL_PKW', -0.034385569217303145),\n",
       " ('GEBAEUDETYP_RASTER', -0.042139999517608183),\n",
       " ('MIN_GEBAEUDEJAHR', -0.048081009595681998),\n",
       " ('HEALTH_TYP', -0.057816814557137809),\n",
       " ('BALLRAUM', -0.058349024606259819),\n",
       " ('ANZ_PERSONEN', -0.060190424788154812),\n",
       " ('KONSUMNAEHE', -0.067215746197274648),\n",
       " ('PLZ8_GBZ', -0.067904252386945155),\n",
       " ('SEMIO_VERT', -0.06979780842962513),\n",
       " ('INNENSTADT', -0.071612596775240442),\n",
       " ('KBA05_ANTG1', -0.072112158735151824),\n",
       " ('KBA05_GBZ', -0.083364962384768704),\n",
       " ('MOBI_REGIO', -0.084851817231991797),\n",
       " ('PLZ8_ANTG1', -0.08604483243234809),\n",
       " ('SEMIO_SOZ', -0.10333169819597424),\n",
       " ('SEMIO_MAT', -0.16378930262990665),\n",
       " ('ONLINE_AFFINITAET', -0.16500048243604087),\n",
       " ('SEMIO_RAT', -0.17119292838022493),\n",
       " ('SEMIO_FAM', -0.18472625220966069),\n",
       " ('FINANZ_ANLEGER', -0.20468727349241078),\n",
       " ('SEMIO_KULT', -0.22011833443604281),\n",
       " ('FINANZ_UNAUFFAELLIGER', -0.22787918377293914),\n",
       " ('SEMIO_PFLICHT', -0.22838686575150177),\n",
       " ('SEMIO_TRADV', -0.23221997141794726),\n",
       " ('FINANZ_SPARER', -0.23674526697552464),\n",
       " ('PRAEGENDE_JUGENDJAHRE_DECADE', -0.25287430552782669),\n",
       " ('SEMIO_REL', -0.25505293079504188)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "\n",
    "pca_link_weights(pca2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SEMIO_VERT', 0.34507872473537776),\n",
       " ('SEMIO_SOZ', 0.26214096596451425),\n",
       " ('SEMIO_FAM', 0.24973283536069854),\n",
       " ('SEMIO_KULT', 0.23493326454338476),\n",
       " ('FINANZ_MINIMALIST', 0.15593757999733626),\n",
       " ('RETOURTYP_BK_S', 0.10892197750817711),\n",
       " ('FINANZ_VORSORGER', 0.10144152898825924),\n",
       " ('W_KEIT_KIND_HH', 0.091332325443531759),\n",
       " ('ALTERSKATEGORIE_GROB', 0.079385998509173994),\n",
       " ('SEMIO_REL', 0.069154123803737186),\n",
       " ('SEMIO_LUST', 0.064832115038093108),\n",
       " ('SEMIO_MAT', 0.055671741821490747),\n",
       " ('GREEN_AVANTGARDE', 0.047427291437126387),\n",
       " ('PRAEGENDE_JUGENDJAHRE_MOVEMENT', 0.047427291437126387),\n",
       " ('ORTSGR_KLS9', 0.046736508904619521),\n",
       " ('EWDICHTE', 0.046114131120888475),\n",
       " ('PLZ8_ANTG4', 0.045214033107575463),\n",
       " ('PLZ8_ANTG3', 0.044892571458893242),\n",
       " ('ARBEIT', 0.035000018517157061),\n",
       " ('WOHNDAUER_2008', 0.032945022203138878),\n",
       " ('RELAT_AB', 0.031923674227328287),\n",
       " ('PLZ8_ANTG2', 0.030099683697357552),\n",
       " ('KBA05_ANTG4', 0.028456719278492311),\n",
       " ('CAMEO_INTL_2015_HOUSEHOLDS', 0.027774277116518076),\n",
       " ('ANZ_HAUSHALTE_AKTIV', 0.025413680133921066),\n",
       " ('OST_WEST_KZ_O', 0.015301825079503843),\n",
       " ('ANZ_HH_TITEL', 0.013375081163878992),\n",
       " ('KBA05_ANTG3', 0.010112628930331208),\n",
       " ('ANZ_TITEL', 0.0096849908247658399),\n",
       " ('PLZ8_HHZ', 0.0038886486040073226),\n",
       " ('VERS_TYP', 0.0024321967808402564),\n",
       " ('SOHO_KZ', 0.00021316008826998095),\n",
       " ('REGIOTYP', -0.0053618500946852818),\n",
       " ('CAMEO_INTL_2015_LIFE_STG', -0.0087549138758216336),\n",
       " ('ANZ_PERSONEN', -0.0091458921099217775),\n",
       " ('KBA05_ANTG2', -0.010035043126404651),\n",
       " ('KKK', -0.015439549054015414),\n",
       " ('MIN_GEBAEUDEJAHR', -0.017604533210384028),\n",
       " ('HH_EINKOMMEN_SCORE', -0.018500247272115185),\n",
       " ('HEALTH_TYP', -0.021633234879952157),\n",
       " ('KBA05_ANTG1', -0.022263403282276939),\n",
       " ('KBA13_ANZAHL_PKW', -0.023356725924958188),\n",
       " ('KBA05_GBZ', -0.026303379012918932),\n",
       " ('MOBI_REGIO', -0.029660748919962048),\n",
       " ('GEBAEUDETYP_RASTER', -0.030470635267401795),\n",
       " ('BALLRAUM', -0.034769556562656193),\n",
       " ('KONSUMNAEHE', -0.037814586827478278),\n",
       " ('PLZ8_GBZ', -0.038296235539113904),\n",
       " ('FINANZ_HAUSBAUER', -0.041658410486568494),\n",
       " ('INNENSTADT', -0.042774443247021017),\n",
       " ('PLZ8_ANTG1', -0.045911543443796492),\n",
       " ('ONLINE_AFFINITAET', -0.055344679416188279),\n",
       " ('SEMIO_PFLICHT', -0.079023092759588118),\n",
       " ('SEMIO_TRADV', -0.079452034552872419),\n",
       " ('FINANZ_UNAUFFAELLIGER', -0.10005907670791961),\n",
       " ('FINANZ_SPARER', -0.10668399101714643),\n",
       " ('PRAEGENDE_JUGENDJAHRE_DECADE', -0.10716305319507077),\n",
       " ('SEMIO_ERL', -0.17591006127154998),\n",
       " ('FINANZ_ANLEGER', -0.1896333831998499),\n",
       " ('SEMIO_RAT', -0.21773687977451997),\n",
       " ('SEMIO_KRIT', -0.27580299523054241),\n",
       " ('SEMIO_DOM', -0.31403072114469238),\n",
       " ('SEMIO_KAEM', -0.33665438200691289),\n",
       " ('ANREDE_KZ', -0.36835105234595134)]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "pca_link_weights(pca2, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('GREEN_AVANTGARDE', 0.40012032662057101),\n",
       " ('PRAEGENDE_JUGENDJAHRE_MOVEMENT', 0.40012032662057101),\n",
       " ('EWDICHTE', 0.2623517094151645),\n",
       " ('ORTSGR_KLS9', 0.24974069949625716),\n",
       " ('PLZ8_HHZ', 0.13865597261378909),\n",
       " ('ONLINE_AFFINITAET', 0.13656672725306829),\n",
       " ('SEMIO_DOM', 0.11241780570869039),\n",
       " ('PLZ8_ANTG2', 0.10729585835515491),\n",
       " ('PLZ8_ANTG3', 0.10225062298266271),\n",
       " ('ANZ_PERSONEN', 0.098698565613639239),\n",
       " ('RELAT_AB', 0.096919535862765291),\n",
       " ('KBA05_ANTG1', 0.094259588557912596),\n",
       " ('PLZ8_ANTG4', 0.08346175013188685),\n",
       " ('FINANZ_UNAUFFAELLIGER', 0.08026289665489228),\n",
       " ('SEMIO_KAEM', 0.077558215487971288),\n",
       " ('FINANZ_MINIMALIST', 0.066086771766083574),\n",
       " ('MOBI_REGIO', 0.062084202377312887),\n",
       " ('SEMIO_TRADV', 0.060573440732682157),\n",
       " ('CAMEO_INTL_2015_LIFE_STG', 0.057770291592102022),\n",
       " ('ARBEIT', 0.054949294115727965),\n",
       " ('KBA05_GBZ', 0.053238616596132493),\n",
       " ('SEMIO_RAT', 0.051075914419073873),\n",
       " ('KBA13_ANZAHL_PKW', 0.049377132606535548),\n",
       " ('ANZ_TITEL', 0.04898544331499758),\n",
       " ('PRAEGENDE_JUGENDJAHRE_DECADE', 0.040352411371313304),\n",
       " ('ANZ_HH_TITEL', 0.037088322369083902),\n",
       " ('ANREDE_KZ', 0.036265551539211391),\n",
       " ('PLZ8_GBZ', 0.033327484687043114),\n",
       " ('SEMIO_MAT', 0.018281194540172675),\n",
       " ('VERS_TYP', 0.015590638579003032),\n",
       " ('SEMIO_KRIT', 0.01534600724388213),\n",
       " ('SEMIO_PFLICHT', 0.013853219646661759),\n",
       " ('WOHNDAUER_2008', 0.0072511211985779528),\n",
       " ('HEALTH_TYP', 0.0072105212767295864),\n",
       " ('SEMIO_SOZ', 0.0036586217914272969),\n",
       " ('RETOURTYP_BK_S', 0.0035424027994548366),\n",
       " ('KBA05_ANTG2', 0.002843181453843048),\n",
       " ('SOHO_KZ', 0.0020139559500254372),\n",
       " ('SEMIO_LUST', -0.0010840810473007061),\n",
       " ('FINANZ_SPARER', -0.0045616831540269409),\n",
       " ('SEMIO_REL', -0.0068194722917415632),\n",
       " ('SEMIO_VERT', -0.013920623111026237),\n",
       " ('FINANZ_VORSORGER', -0.017152430229036453),\n",
       " ('MIN_GEBAEUDEJAHR', -0.019220486838710367),\n",
       " ('SEMIO_FAM', -0.02065218450408806),\n",
       " ('SEMIO_ERL', -0.024132443169645131),\n",
       " ('ANZ_HAUSHALTE_AKTIV', -0.029268542171909155),\n",
       " ('KBA05_ANTG4', -0.031690615956950045),\n",
       " ('SEMIO_KULT', -0.035866382858104846),\n",
       " ('ALTERSKATEGORIE_GROB', -0.043418596041662032),\n",
       " ('PLZ8_ANTG1', -0.05278947458175344),\n",
       " ('KBA05_ANTG3', -0.073773787385455994),\n",
       " ('W_KEIT_KIND_HH', -0.085861704987423501),\n",
       " ('GEBAEUDETYP_RASTER', -0.088308504935282833),\n",
       " ('OST_WEST_KZ_O', -0.10764396752281841),\n",
       " ('FINANZ_HAUSBAUER', -0.10779977220595412),\n",
       " ('CAMEO_INTL_2015_HOUSEHOLDS', -0.12297522662872752),\n",
       " ('FINANZ_ANLEGER', -0.12613598051250871),\n",
       " ('KONSUMNAEHE', -0.15070421604195072),\n",
       " ('REGIOTYP', -0.21260928876991683),\n",
       " ('INNENSTADT', -0.22181361475961786),\n",
       " ('BALLRAUM', -0.2362303070315985),\n",
       " ('HH_EINKOMMEN_SCORE', -0.24653754994547022),\n",
       " ('KKK', -0.27435177300425201)]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "pca_link_weights(pca2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)\n",
    "- Yes, we can interpret positive and negative values from the detailed investigation of the first few principal components generated in a meaningful way.\n",
    "- For example, in the first Principal Component generated, `SEMIO_ERL` & `FINANZ_VORSORGER` have almost equal weights. From the Data Dictionary, `SEMIO_ERL` is **Personality typology** for _**event oriented**_ people and `FINANZ_VORSORGER` is **Financial typology** for people in the _**be prepared**_ category, i.e. we can conclude the fact that people with event oriented personality are prepared for any event that might come across maybe financially or in any other way.\n",
    "- In the second Principal Component generated, `PLZ8_ANTG4`, `PLZ8_ANTG3`, `ORTSGR_KLS9` have almost common weights.\n",
    "Each of the columns stand for __'Number of 6-10 family houses in the PLZ8 region', 'Number of 10+ family houses in the PLZ8 region', 'Size of community'__ respectively. That means as the number of family houses increase in a particular region the size of community also increases. This statement is nothing but an obvious fact.\n",
    "- In the third Principal Component generated, `FINANZ_HAUSBAUER` & `CAMEO_INTL_2015_HOUSEHOLDS` have  weights almost near to each other. This tells us that 'German Wealth Typology'(`CAMEO_INTL_2015_HOUSEHOLDS`) and 'German Financial Typology for people who own the Homes'(`FINANZ_HAUSBAUER`) are related i.e. __as the person's wealth increases, his/hers chances of owning his/her own Home also increases.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Clusters =  2 , Score =  37767569.7305\n",
      "Number of Clusters =  5 , Score =  30927123.4274\n",
      "Number of Clusters =  8 , Score =  27689245.4083\n",
      "Number of Clusters =  12 , Score =  25632117.2863\n",
      "Number of Clusters =  15 , Score =  24452891.3835\n",
      "Number of Clusters =  18 , Score =  23282699.2353\n",
      "Number of Clusters =  20 , Score =  22670145.1102\n",
      "Number of Clusters =  23 , Score =  21926358.9486\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# Over a number of different cluster counts...\n",
    "model_score = []\n",
    "cluster_centers = [2, 5, 8, 12, 15, 18, 20, 23] # chosen arbitrarily\n",
    "for i in cluster_centers:\n",
    "\n",
    "    # run k-means clustering on the data and...\n",
    "    kmeans = KMeans(n_clusters=i, random_state=0).fit(azdias_scaled_pca2)\n",
    "    \n",
    "    # compute the average within-cluster distances.\n",
    "    score = np.abs(kmeans.score(azdias_scaled_pca2)) # taking absolute value since the score will be negative.\n",
    "    model_score.append(score)\n",
    "    print(\"Number of Clusters = \", i, \", Score = \", score)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2251d8b00>"
      ]
     },
     "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",
    "model_score = [36952034.3838, 30155792.2825, 26929714.6163, 25110874.5613, 24034416.6325,\n",
    "              23245432.0463, 22789939.0319, 22262675.4181]\n",
    "plt.plot(cluster_centers, model_score);\n",
    "plt.xlabel('K');\n",
    "plt.ylabel('Model Score');\n",
    "plt.title('Model Score vs. K');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "kmeans_8 =  KMeans(n_clusters=8, random_state=0).fit(azdias_scaled_pca2)\n",
    "kmeans_predict = kmeans_8.predict(azdias_scaled_pca2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "\n",
    "- I have decided to segment the population into 8 cluster since from the graph we can see the elbow figure at `k = 8`.\n",
    "- The scores have also been printed and there is a great decrease from k=2 to k=8 of ~10 units. Hence I have chosen k=8.\n",
    "- The absolute value of the score has been taken to eliminate the negative sign and also since we are interested in the magnitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv(\"Udacity_CUSTOMERS_Subset.csv\", sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 191652 entries, 0 to 191651\n",
      "Data columns (total 85 columns):\n",
      "AGER_TYP                 191652 non-null int64\n",
      "ALTERSKATEGORIE_GROB     191652 non-null int64\n",
      "ANREDE_KZ                191652 non-null int64\n",
      "CJT_GESAMTTYP            188439 non-null float64\n",
      "FINANZ_MINIMALIST        191652 non-null int64\n",
      "FINANZ_SPARER            191652 non-null int64\n",
      "FINANZ_VORSORGER         191652 non-null int64\n",
      "FINANZ_ANLEGER           191652 non-null int64\n",
      "FINANZ_UNAUFFAELLIGER    191652 non-null int64\n",
      "FINANZ_HAUSBAUER         191652 non-null int64\n",
      "FINANZTYP                191652 non-null int64\n",
      "GEBURTSJAHR              191652 non-null int64\n",
      "GFK_URLAUBERTYP          188439 non-null float64\n",
      "GREEN_AVANTGARDE         191652 non-null int64\n",
      "HEALTH_TYP               191652 non-null int64\n",
      "LP_LEBENSPHASE_FEIN      188439 non-null float64\n",
      "LP_LEBENSPHASE_GROB      188439 non-null float64\n",
      "LP_FAMILIE_FEIN          188439 non-null float64\n",
      "LP_FAMILIE_GROB          188439 non-null float64\n",
      "LP_STATUS_FEIN           188439 non-null float64\n",
      "LP_STATUS_GROB           188439 non-null float64\n",
      "NATIONALITAET_KZ         191652 non-null int64\n",
      "PRAEGENDE_JUGENDJAHRE    191652 non-null int64\n",
      "RETOURTYP_BK_S           188439 non-null float64\n",
      "SEMIO_SOZ                191652 non-null int64\n",
      "SEMIO_FAM                191652 non-null int64\n",
      "SEMIO_REL                191652 non-null int64\n",
      "SEMIO_MAT                191652 non-null int64\n",
      "SEMIO_VERT               191652 non-null int64\n",
      "SEMIO_LUST               191652 non-null int64\n",
      "SEMIO_ERL                191652 non-null int64\n",
      "SEMIO_KULT               191652 non-null int64\n",
      "SEMIO_RAT                191652 non-null int64\n",
      "SEMIO_KRIT               191652 non-null int64\n",
      "SEMIO_DOM                191652 non-null int64\n",
      "SEMIO_KAEM               191652 non-null int64\n",
      "SEMIO_PFLICHT            191652 non-null int64\n",
      "SEMIO_TRADV              191652 non-null int64\n",
      "SHOPPER_TYP              191652 non-null int64\n",
      "SOHO_KZ                  145056 non-null float64\n",
      "TITEL_KZ                 145056 non-null float64\n",
      "VERS_TYP                 191652 non-null int64\n",
      "ZABEOTYP                 191652 non-null int64\n",
      "ALTER_HH                 145056 non-null float64\n",
      "ANZ_PERSONEN             145056 non-null float64\n",
      "ANZ_TITEL                145056 non-null float64\n",
      "HH_EINKOMMEN_SCORE       188684 non-null float64\n",
      "KK_KUNDENTYP             79715 non-null float64\n",
      "W_KEIT_KIND_HH           137910 non-null float64\n",
      "WOHNDAUER_2008           145056 non-null float64\n",
      "ANZ_HAUSHALTE_AKTIV      141725 non-null float64\n",
      "ANZ_HH_TITEL             139542 non-null float64\n",
      "GEBAEUDETYP              141725 non-null float64\n",
      "KONSUMNAEHE              145001 non-null float64\n",
      "MIN_GEBAEUDEJAHR         141725 non-null float64\n",
      "OST_WEST_KZ              141725 non-null object\n",
      "WOHNLAGE                 141725 non-null float64\n",
      "CAMEO_DEUG_2015          141224 non-null object\n",
      "CAMEO_DEU_2015           141224 non-null object\n",
      "CAMEO_INTL_2015          141224 non-null object\n",
      "KBA05_ANTG1              135672 non-null float64\n",
      "KBA05_ANTG2              135672 non-null float64\n",
      "KBA05_ANTG3              135672 non-null float64\n",
      "KBA05_ANTG4              135672 non-null float64\n",
      "KBA05_BAUMAX             135672 non-null float64\n",
      "KBA05_GBZ                135672 non-null float64\n",
      "BALLRAUM                 141693 non-null float64\n",
      "EWDICHTE                 141693 non-null float64\n",
      "INNENSTADT               141693 non-null float64\n",
      "GEBAEUDETYP_RASTER       141725 non-null float64\n",
      "KKK                      137392 non-null float64\n",
      "MOBI_REGIO               135672 non-null float64\n",
      "ONLINE_AFFINITAET        188439 non-null float64\n",
      "REGIOTYP                 137392 non-null float64\n",
      "KBA13_ANZAHL_PKW         140371 non-null float64\n",
      "PLZ8_ANTG1               138888 non-null float64\n",
      "PLZ8_ANTG2               138888 non-null float64\n",
      "PLZ8_ANTG3               138888 non-null float64\n",
      "PLZ8_ANTG4               138888 non-null float64\n",
      "PLZ8_BAUMAX              138888 non-null float64\n",
      "PLZ8_HHZ                 138888 non-null float64\n",
      "PLZ8_GBZ                 138888 non-null float64\n",
      "ARBEIT                   141176 non-null float64\n",
      "ORTSGR_KLS9              141176 non-null float64\n",
      "RELAT_AB                 141176 non-null float64\n",
      "dtypes: float64(49), int64(32), object(4)\n",
      "memory usage: 124.3+ MB\n"
     ]
    }
   ],
   "source": [
    "customers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows before dropping rows with missing vals: 191652\n",
      "Rows after dropping rows with missing vals: 141693\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GEBURTSJAHR</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>...</th>\n",
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       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_MOVEMENT</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_DECADE</th>\n",
       "      <th>CAMEO_INTL_2015_HOUSEHOLDS</th>\n",
       "      <th>CAMEO_INTL_2015_LIFE_STG</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1942.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1942.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191622</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191623</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191624</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191625</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191626</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191627</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191628</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191629</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191630</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191631</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191632</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191633</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191634</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191635</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191636</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191637</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191638</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191639</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191640</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191641</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191642</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191643</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191644</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191645</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191646</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191647</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191648</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191649</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191650</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191651</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141693 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "0                        4.0          1                  5              1   \n",
       "2                        4.0          2                  5              1   \n",
       "3                        4.0          1                  5              1   \n",
       "4                        3.0          1                  3              1   \n",
       "5                        3.0          1                  5              1   \n",
       "6                        4.0          1                  5              1   \n",
       "7                        4.0          1                  5              1   \n",
       "8                        4.0          2                  2              2   \n",
       "9                        3.0          1                  5              2   \n",
       "11                       4.0          1                  5              1   \n",
       "12                       4.0          1                  5              2   \n",
       "13                       3.0          1                  5              2   \n",
       "14                       4.0          2                  3              1   \n",
       "15                       3.0          1                  5              1   \n",
       "16                       4.0          1                  5              1   \n",
       "17                       4.0          1                  4              3   \n",
       "18                       2.0          1                  2              4   \n",
       "19                       4.0          2                  3              2   \n",
       "20                       4.0          1                  5              3   \n",
       "21                       4.0          1                  3              1   \n",
       "22                       1.0          2                  5              1   \n",
       "23                       3.0          1                  3              3   \n",
       "24                       4.0          2                  4              1   \n",
       "25                       3.0          2                  5              2   \n",
       "26                       4.0          2                  5              1   \n",
       "27                       3.0          1                  5              2   \n",
       "28                       4.0          2                  3              1   \n",
       "29                       3.0          2                  3              1   \n",
       "30                       3.0          2                  4              1   \n",
       "31                       3.0          1                  5              2   \n",
       "...                      ...        ...                ...            ...   \n",
       "191622                   4.0          1                  5              1   \n",
       "191623                   4.0          1                  5              1   \n",
       "191624                   4.0          1                  5              1   \n",
       "191625                   4.0          2                  5              1   \n",
       "191626                   2.0          1                  3              3   \n",
       "191627                   3.0          2                  2              1   \n",
       "191628                   4.0          1                  5              1   \n",
       "191629                   4.0          2                  2              1   \n",
       "191630                   3.0          1                  5              1   \n",
       "191631                   4.0          2                  2              1   \n",
       "191632                   3.0          1                  4              1   \n",
       "191633                   4.0          1                  5              1   \n",
       "191634                   3.0          1                  5              1   \n",
       "191635                   4.0          2                  5              1   \n",
       "191636                   4.0          1                  5              1   \n",
       "191637                   3.0          2                  5              2   \n",
       "191638                   4.0          1                  3              1   \n",
       "191639                   3.0          1                  4              3   \n",
       "191640                   3.0          1                  4              1   \n",
       "191641                   4.0          1                  5              1   \n",
       "191642                   4.0          2                  2              1   \n",
       "191643                   4.0          1                  5              1   \n",
       "191644                   4.0          2                  2              1   \n",
       "191645                   4.0          1                  5              1   \n",
       "191646                   2.0          2                  2              1   \n",
       "191647                   3.0          1                  5              1   \n",
       "191648                   4.0          2                  5              1   \n",
       "191649                   4.0          1                  5              1   \n",
       "191650                   3.0          2                  2              1   \n",
       "191651                   2.0          1                  5              1   \n",
       "\n",
       "        FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0                      5               1                      2   \n",
       "2                      5               1                      4   \n",
       "3                      5               2                      1   \n",
       "4                      4               4                      5   \n",
       "5                      5               1                      2   \n",
       "6                      5               1                      1   \n",
       "7                      5               1                      2   \n",
       "8                      5               1                      1   \n",
       "9                      4               1                      3   \n",
       "11                     5               1                      3   \n",
       "12                     4               3                      2   \n",
       "13                     4               2                      4   \n",
       "14                     5               1                      2   \n",
       "15                     5               1                      1   \n",
       "16                     5               1                      3   \n",
       "17                     1               4                      5   \n",
       "18                     2               2                      3   \n",
       "19                     4               3                      3   \n",
       "20                     4               2                      3   \n",
       "21                     5               2                      1   \n",
       "22                     5               1                      1   \n",
       "23                     4               1                      2   \n",
       "24                     5               2                      2   \n",
       "25                     4               4                      2   \n",
       "26                     5               2                      1   \n",
       "27                     3               1                      2   \n",
       "28                     5               1                      1   \n",
       "29                     5               1                      2   \n",
       "30                     5               1                      2   \n",
       "31                     3               3                      2   \n",
       "...                  ...             ...                    ...   \n",
       "191622                 5               1                      1   \n",
       "191623                 5               1                      3   \n",
       "191624                 5               1                      4   \n",
       "191625                 5               2                      1   \n",
       "191626                 3               1                      3   \n",
       "191627                 5               1                      2   \n",
       "191628                 5               1                      1   \n",
       "191629                 5               1                      2   \n",
       "191630                 5               1                      1   \n",
       "191631                 5               1                      2   \n",
       "191632                 5               1                      1   \n",
       "191633                 5               1                      4   \n",
       "191634                 5               1                      2   \n",
       "191635                 5               2                      1   \n",
       "191636                 5               1                      1   \n",
       "191637                 4               2                      3   \n",
       "191638                 5               1                      1   \n",
       "191639                 3               2                      4   \n",
       "191640                 5               1                      2   \n",
       "191641                 5               1                      2   \n",
       "191642                 5               2                      1   \n",
       "191643                 5               1                      3   \n",
       "191644                 5               1                      2   \n",
       "191645                 5               1                      3   \n",
       "191646                 5               1                      2   \n",
       "191647                 5               1                      1   \n",
       "191648                 5               2                      2   \n",
       "191649                 5               1                      1   \n",
       "191650                 5               1                      2   \n",
       "191651                 5               1                      1   \n",
       "\n",
       "        FINANZ_HAUSBAUER  GEBURTSJAHR  GREEN_AVANTGARDE  \\\n",
       "0                      2          NaN                 1   \n",
       "2                      4          NaN                 1   \n",
       "3                      2          NaN                 0   \n",
       "4                      2       1960.0                 0   \n",
       "5                      3          NaN                 1   \n",
       "6                      2       1942.0                 1   \n",
       "7                      2       1938.0                 1   \n",
       "8                      5       1914.0                 0   \n",
       "9                      1       1959.0                 1   \n",
       "11                     2       1943.0                 1   \n",
       "12                     1       1964.0                 0   \n",
       "13                     1       1961.0                 0   \n",
       "14                     5       1923.0                 0   \n",
       "15                     2       1953.0                 1   \n",
       "16                     2       1943.0                 1   \n",
       "17                     1       1985.0                 1   \n",
       "18                     3          NaN                 0   \n",
       "19                     3       1963.0                 0   \n",
       "20                     1       1965.0                 1   \n",
       "21                     4       1947.0                 0   \n",
       "22                     2       1930.0                 0   \n",
       "23                     2       1962.0                 0   \n",
       "24                     3       1942.0                 0   \n",
       "25                     1       1967.0                 1   \n",
       "26                     3       1935.0                 0   \n",
       "27                     1          NaN                 1   \n",
       "28                     5       1930.0                 0   \n",
       "29                     5       1953.0                 0   \n",
       "30                     2       1948.0                 1   \n",
       "31                     1       1962.0                 1   \n",
       "...                  ...          ...               ...   \n",
       "191622                 2          NaN                 0   \n",
       "191623                 2       1948.0                 1   \n",
       "191624                 2       1955.0                 1   \n",
       "191625                 2       1943.0                 1   \n",
       "191626                 2       1964.0                 0   \n",
       "191627                 5       1939.0                 0   \n",
       "191628                 2          NaN                 1   \n",
       "191629                 5       1930.0                 0   \n",
       "191630                 2          NaN                 1   \n",
       "191631                 5       1938.0                 0   \n",
       "191632                 4       1955.0                 0   \n",
       "191633                 2       1952.0                 1   \n",
       "191634                 3       1951.0                 0   \n",
       "191635                 3       1934.0                 0   \n",
       "191636                 2          NaN                 1   \n",
       "191637                 1       1963.0                 1   \n",
       "191638                 4       1945.0                 0   \n",
       "191639                 1          NaN                 0   \n",
       "191640                 4       1956.0                 1   \n",
       "191641                 2       1935.0                 1   \n",
       "191642                 5       1937.0                 0   \n",
       "191643                 2       1947.0                 1   \n",
       "191644                 5       1956.0                 1   \n",
       "191645                 2       1955.0                 0   \n",
       "191646                 5       1957.0                 0   \n",
       "191647                 2          NaN                 1   \n",
       "191648                 3          NaN                 0   \n",
       "191649                 2       1944.0                 1   \n",
       "191650                 5          NaN                 0   \n",
       "191651                 2       1937.0                 0   \n",
       "\n",
       "                  ...             PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  \\\n",
       "0                 ...                  5.0       5.0     1.0          2.0   \n",
       "2                 ...                  3.0       2.0     3.0          5.0   \n",
       "3                 ...                  3.0       4.0     1.0          3.0   \n",
       "4                 ...                  3.0       3.0     3.0          5.0   \n",
       "5                 ...                  5.0       5.0     3.0          7.0   \n",
       "6                 ...                  5.0       5.0     2.0          3.0   \n",
       "7                 ...                  3.0       3.0     3.0          4.0   \n",
       "8                 ...                  5.0       4.0     3.0          8.0   \n",
       "9                 ...                  5.0       5.0     3.0          6.0   \n",
       "11                ...                  3.0       3.0     2.0          5.0   \n",
       "12                ...                  4.0       5.0     1.0          1.0   \n",
       "13                ...                  4.0       4.0     4.0          5.0   \n",
       "14                ...                  5.0       3.0     3.0          8.0   \n",
       "15                ...                  5.0       5.0     3.0          6.0   \n",
       "16                ...                  3.0       4.0     3.0          5.0   \n",
       "17                ...                  5.0       5.0     1.0          3.0   \n",
       "18                ...                  3.0       1.0     3.0          8.0   \n",
       "19                ...                  3.0       4.0     3.0          5.0   \n",
       "20                ...                  3.0       4.0     4.0          6.0   \n",
       "21                ...                  4.0       3.0     1.0          3.0   \n",
       "22                ...                  4.0       5.0     1.0          2.0   \n",
       "23                ...                  5.0       2.0     3.0          9.0   \n",
       "24                ...                  3.0       3.0     2.0          6.0   \n",
       "25                ...                  3.0       4.0     1.0          3.0   \n",
       "26                ...                  5.0       5.0     1.0          2.0   \n",
       "27                ...                  4.0       3.0     3.0          7.0   \n",
       "28                ...                  5.0       3.0     3.0          7.0   \n",
       "29                ...                  3.0       2.0     3.0          8.0   \n",
       "30                ...                  5.0       5.0     2.0          3.0   \n",
       "31                ...                  4.0       5.0     2.0          1.0   \n",
       "...               ...                  ...       ...     ...          ...   \n",
       "191622            ...                  3.0       3.0     2.0          2.0   \n",
       "191623            ...                  4.0       3.0     2.0          4.0   \n",
       "191624            ...                  4.0       4.0     2.0          5.0   \n",
       "191625            ...                  5.0       5.0     2.0          4.0   \n",
       "191626            ...                  4.0       4.0     4.0          8.0   \n",
       "191627            ...                  5.0       3.0     3.0          7.0   \n",
       "191628            ...                  5.0       5.0     2.0          4.0   \n",
       "191629            ...                  3.0       2.0     2.0          5.0   \n",
       "191630            ...                  5.0       5.0     4.0          7.0   \n",
       "191631            ...                  4.0       3.0     4.0          7.0   \n",
       "191632            ...                  3.0       4.0     3.0          1.0   \n",
       "191633            ...                  4.0       4.0     3.0          5.0   \n",
       "191634            ...                  3.0       4.0     1.0          2.0   \n",
       "191635            ...                  3.0       3.0     3.0          5.0   \n",
       "191636            ...                  3.0       3.0     4.0          5.0   \n",
       "191637            ...                  3.0       3.0     3.0          7.0   \n",
       "191638            ...                  4.0       1.0     3.0          7.0   \n",
       "191639            ...                  2.0       3.0     4.0          5.0   \n",
       "191640            ...                  5.0       5.0     3.0          7.0   \n",
       "191641            ...                  4.0       5.0     2.0          1.0   \n",
       "191642            ...                  3.0       1.0     4.0          9.0   \n",
       "191643            ...                  5.0       5.0     2.0          4.0   \n",
       "191644            ...                  4.0       3.0     3.0          5.0   \n",
       "191645            ...                  3.0       3.0     3.0          5.0   \n",
       "191646            ...                  1.0       1.0     3.0          9.0   \n",
       "191647            ...                  5.0       4.0     3.0          8.0   \n",
       "191648            ...                  NaN       NaN     1.0          4.0   \n",
       "191649            ...                  5.0       5.0     3.0          7.0   \n",
       "191650            ...                  2.0       3.0     3.0          4.0   \n",
       "191651            ...                  4.0       5.0     1.0          3.0   \n",
       "\n",
       "        RELAT_AB  OST_WEST_KZ_O  PRAEGENDE_JUGENDJAHRE_MOVEMENT  \\\n",
       "0            1.0              0                             1.0   \n",
       "2            3.0              0                             1.0   \n",
       "3            1.0              0                             0.0   \n",
       "4            1.0              0                             0.0   \n",
       "5            5.0              0                             1.0   \n",
       "6            2.0              0                             1.0   \n",
       "7            3.0              0                             1.0   \n",
       "8            3.0              0                             0.0   \n",
       "9            4.0              0                             1.0   \n",
       "11           1.0              0                             1.0   \n",
       "12           1.0              0                             0.0   \n",
       "13           5.0              0                             0.0   \n",
       "14           5.0              0                             0.0   \n",
       "15           1.0              0                             1.0   \n",
       "16           3.0              0                             1.0   \n",
       "17           1.0              0                             1.0   \n",
       "18           5.0              0                             0.0   \n",
       "19           5.0              0                             0.0   \n",
       "20           5.0              0                             1.0   \n",
       "21           1.0              0                             0.0   \n",
       "22           1.0              0                             0.0   \n",
       "23           5.0              0                             0.0   \n",
       "24           3.0              0                             0.0   \n",
       "25           1.0              0                             1.0   \n",
       "26           1.0              0                             0.0   \n",
       "27           4.0              0                             1.0   \n",
       "28           5.0              0                             0.0   \n",
       "29           5.0              0                             0.0   \n",
       "30           3.0              0                             1.0   \n",
       "31           2.0              0                             1.0   \n",
       "...          ...            ...                             ...   \n",
       "191622       2.0              0                             0.0   \n",
       "191623       3.0              0                             1.0   \n",
       "191624       1.0              0                             1.0   \n",
       "191625       3.0              0                             1.0   \n",
       "191626       5.0              0                             0.0   \n",
       "191627       3.0              0                             0.0   \n",
       "191628       3.0              0                             1.0   \n",
       "191629       1.0              0                             0.0   \n",
       "191630       5.0              0                             1.0   \n",
       "191631       5.0              0                             0.0   \n",
       "191632       3.0              0                             0.0   \n",
       "191633       2.0              0                             1.0   \n",
       "191634       1.0              0                             0.0   \n",
       "191635       3.0              0                             0.0   \n",
       "191636       4.0              0                             1.0   \n",
       "191637       5.0              0                             1.0   \n",
       "191638       3.0              0                             0.0   \n",
       "191639       4.0              0                             0.0   \n",
       "191640       4.0              0                             1.0   \n",
       "191641       2.0              0                             1.0   \n",
       "191642       3.0              1                             0.0   \n",
       "191643       1.0              0                             1.0   \n",
       "191644       5.0              0                             1.0   \n",
       "191645       3.0              0                             0.0   \n",
       "191646       5.0              0                             0.0   \n",
       "191647       5.0              0                             1.0   \n",
       "191648       1.0              0                             0.0   \n",
       "191649       5.0              0                             1.0   \n",
       "191650       4.0              0                             0.0   \n",
       "191651       1.0              0                             0.0   \n",
       "\n",
       "        PRAEGENDE_JUGENDJAHRE_DECADE  CAMEO_INTL_2015_HOUSEHOLDS  \\\n",
       "0                                2.0                           1   \n",
       "2                                2.0                           3   \n",
       "3                                1.0                           2   \n",
       "4                                4.0                           4   \n",
       "5                                2.0                           3   \n",
       "6                                2.0                           2   \n",
       "7                                2.0                           1   \n",
       "8                                1.0                           5   \n",
       "9                                4.0                           1   \n",
       "11                               2.0                           1   \n",
       "12                               4.0                           2   \n",
       "13                               4.0                           4   \n",
       "14                               1.0                           5   \n",
       "15                               3.0                           1   \n",
       "16                               2.0                           1   \n",
       "17                               6.0                           1   \n",
       "18                               6.0                           4   \n",
       "19                               4.0                           2   \n",
       "20                               5.0                           3   \n",
       "21                               3.0                           4   \n",
       "22                               1.0                           2   \n",
       "23                               4.0                           5   \n",
       "24                               2.0                           3   \n",
       "25                               5.0                           2   \n",
       "26                               2.0                           2   \n",
       "27                               4.0                           4   \n",
       "28                               1.0                           5   \n",
       "29                               3.0                           5   \n",
       "30                               3.0                           3   \n",
       "31                               4.0                           2   \n",
       "...                              ...                         ...   \n",
       "191622                           1.0                           4   \n",
       "191623                           3.0                           1   \n",
       "191624                           4.0                           5   \n",
       "191625                           2.0                           2   \n",
       "191626                           4.0                           2   \n",
       "191627                           2.0                           5   \n",
       "191628                           3.0                           2   \n",
       "191629                           1.0                           5   \n",
       "191630                           2.0                           5   \n",
       "191631                           2.0                           4   \n",
       "191632                           4.0                           1   \n",
       "191633                           3.0                           1   \n",
       "191634                           3.0                           3   \n",
       "191635                           1.0                           4   \n",
       "191636                           1.0                           3   \n",
       "191637                           4.0                           2   \n",
       "191638                           3.0                           5   \n",
       "191639                           5.0                           2   \n",
       "191640                           4.0                           1   \n",
       "191641                           2.0                           1   \n",
       "191642                           2.0                           5   \n",
       "191643                           3.0                           1   \n",
       "191644                           4.0                           4   \n",
       "191645                           4.0                           4   \n",
       "191646                           4.0                           1   \n",
       "191647                           2.0                           1   \n",
       "191648                           3.0                           3   \n",
       "191649                           2.0                           2   \n",
       "191650                           4.0                           2   \n",
       "191651                           2.0                           3   \n",
       "\n",
       "        CAMEO_INTL_2015_LIFE_STG  \n",
       "0                              3  \n",
       "2                              4  \n",
       "3                              4  \n",
       "4                              1  \n",
       "5                              4  \n",
       "6                              3  \n",
       "7                              5  \n",
       "8                              5  \n",
       "9                              5  \n",
       "11                             4  \n",
       "12                             2  \n",
       "13                             3  \n",
       "14                             1  \n",
       "15                             5  \n",
       "16                             5  \n",
       "17                             4  \n",
       "18                             1  \n",
       "19                             4  \n",
       "20                             3  \n",
       "21                             3  \n",
       "22                             4  \n",
       "23                             1  \n",
       "24                             3  \n",
       "25                             2  \n",
       "26                             5  \n",
       "27                             4  \n",
       "28                             4  \n",
       "29                             1  \n",
       "30                             2  \n",
       "31                             4  \n",
       "...                          ...  \n",
       "191622                         3  \n",
       "191623                         4  \n",
       "191624                         4  \n",
       "191625                         5  \n",
       "191626                         4  \n",
       "191627                         5  \n",
       "191628                         2  \n",
       "191629                         5  \n",
       "191630                         1  \n",
       "191631                         4  \n",
       "191632                         4  \n",
       "191633                         4  \n",
       "191634                         3  \n",
       "191635                         5  \n",
       "191636                         4  \n",
       "191637                         5  \n",
       "191638                         2  \n",
       "191639                         2  \n",
       "191640                         5  \n",
       "191641                         4  \n",
       "191642                         5  \n",
       "191643                         5  \n",
       "191644                         1  \n",
       "191645                         3  \n",
       "191646                         4  \n",
       "191647                         4  \n",
       "191648                         2  \n",
       "191649                         4  \n",
       "191650                         4  \n",
       "191651                         3  \n",
       "\n",
       "[141693 rows x 66 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "customers_clean = clean_data(customers)\n",
    "customers_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ALTERSKATEGORIE_GROB                233\n",
       "ANREDE_KZ                             0\n",
       "FINANZ_MINIMALIST                     0\n",
       "FINANZ_SPARER                         0\n",
       "FINANZ_VORSORGER                      0\n",
       "FINANZ_ANLEGER                        0\n",
       "FINANZ_UNAUFFAELLIGER                 0\n",
       "FINANZ_HAUSBAUER                      0\n",
       "GEBURTSJAHR                       45421\n",
       "GREEN_AVANTGARDE                      0\n",
       "HEALTH_TYP                         2332\n",
       "RETOURTYP_BK_S                     3108\n",
       "SEMIO_SOZ                             0\n",
       "SEMIO_FAM                             0\n",
       "SEMIO_REL                             0\n",
       "SEMIO_MAT                             0\n",
       "SEMIO_VERT                            0\n",
       "SEMIO_LUST                            0\n",
       "SEMIO_ERL                             0\n",
       "SEMIO_KULT                            0\n",
       "SEMIO_RAT                             0\n",
       "SEMIO_KRIT                            0\n",
       "SEMIO_DOM                             0\n",
       "SEMIO_KAEM                            0\n",
       "SEMIO_PFLICHT                         0\n",
       "SEMIO_TRADV                           0\n",
       "SOHO_KZ                               0\n",
       "VERS_TYP                           2332\n",
       "ALTER_HH                          21592\n",
       "ANZ_PERSONEN                          0\n",
       "                                  ...  \n",
       "KONSUMNAEHE                           6\n",
       "MIN_GEBAEUDEJAHR                      0\n",
       "KBA05_ANTG1                        6021\n",
       "KBA05_ANTG2                        6021\n",
       "KBA05_ANTG3                        6021\n",
       "KBA05_ANTG4                        6021\n",
       "KBA05_GBZ                          6023\n",
       "BALLRAUM                             32\n",
       "EWDICHTE                             32\n",
       "INNENSTADT                           32\n",
       "GEBAEUDETYP_RASTER                    0\n",
       "KKK                               10111\n",
       "MOBI_REGIO                         6021\n",
       "ONLINE_AFFINITAET                  3108\n",
       "REGIOTYP                          10111\n",
       "KBA13_ANZAHL_PKW                   1333\n",
       "PLZ8_ANTG1                         2805\n",
       "PLZ8_ANTG2                         2805\n",
       "PLZ8_ANTG3                         2805\n",
       "PLZ8_ANTG4                         2805\n",
       "PLZ8_HHZ                           2805\n",
       "PLZ8_GBZ                           2805\n",
       "ARBEIT                              572\n",
       "ORTSGR_KLS9                         549\n",
       "RELAT_AB                            572\n",
       "OST_WEST_KZ_O                         0\n",
       "PRAEGENDE_JUGENDJAHRE_MOVEMENT     1298\n",
       "PRAEGENDE_JUGENDJAHRE_DECADE       1298\n",
       "CAMEO_INTL_2015_HOUSEHOLDS          616\n",
       "CAMEO_INTL_2015_LIFE_STG            616\n",
       "Length: 66, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imputer = Imputer(missing_values = 'NaN', strategy = 'median')\n",
    "imputer_fit = imputer.fit(customers_clean)\n",
    "customers_clean_imputed = pd.DataFrame(imputer_fit.transform(customers_clean), \n",
    "                                       index = customers_clean.index, columns = customers_clean.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler = StandardScaler()\n",
    "scaler_fit = scaler.fit(customers_clean_imputed)\n",
    "customers_cleaned_scaled = pd.DataFrame(scaler_fit.transform(customers_clean_imputed), \n",
    "                             index = customers_clean_imputed.index, columns = customers_clean_imputed.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pca2 = PCA(n_components = 28)\n",
    "pca_fit = pca2.fit(customers_cleaned_scaled)\n",
    "customers_cleaned_pca_final = pca_fit.transform(customers_cleaned_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kmeans_customers =  KMeans(n_clusters=8, random_state=0).fit(customers_cleaned_pca_final)\n",
    "kmeans_customers_predict = kmeans_8.predict(customers_cleaned_pca_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5755961.8181899758"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(kmeans_8.score(customers_cleaned_pca_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(kmeans_customers_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd22521dfd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "\n",
    "general_prop = []\n",
    "customers_prop = []\n",
    "x = [i+1 for i in range(8)] # for 8 clusters\n",
    "for i in range(8):\n",
    "    general_prop.append((kmeans_predict == i).sum()/len(kmeans_predict))\n",
    "    customers_prop.append((kmeans_customers_predict == i).sum()/len(kmeans_customers_predict))\n",
    "\n",
    "\n",
    "df_general = pd.DataFrame({'cluster' : x, 'prop_general' : general_prop, 'prop_customers':customers_prop})\n",
    "\n",
    "#ax = sns.countplot(x='index', y = df_general['prop_1', 'prop_2'], data=df_general )\n",
    "df_general.plot(x='cluster', y = ['prop_general', 'prop_customers'], kind='bar', figsize=(9,6))\n",
    "plt.ylabel('proportion of persons in each cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ANZ_HAUSHALTE_AKTIV', 0.28645223183534663),\n",
       " ('FINANZ_UNAUFFAELLIGER', 0.21671715098234218),\n",
       " ('PLZ8_ANTG1', 0.18577894285506139),\n",
       " ('FINANZ_SPARER', 0.16960176407858951),\n",
       " ('ONLINE_AFFINITAET', 0.16353963555764739),\n",
       " ('RELAT_AB', 0.16249561021647035),\n",
       " ('ORTSGR_KLS9', 0.15875281129769281),\n",
       " ('PLZ8_ANTG3', 0.14632494208029664),\n",
       " ('OST_WEST_KZ_O', 0.13453374956548381),\n",
       " ('KONSUMNAEHE', 0.13194204510662136),\n",
       " ('KBA05_ANTG2', 0.13079645955122027),\n",
       " ('ARBEIT', 0.1106792960435177),\n",
       " ('FINANZ_ANLEGER', 0.11058366969739805),\n",
       " ('KBA05_ANTG3', 0.10846330334453988),\n",
       " ('MIN_GEBAEUDEJAHR', 0.10635141451605343),\n",
       " ('PLZ8_ANTG2', 0.10100049097322336),\n",
       " ('GEBAEUDETYP_RASTER', 0.097951296113786016),\n",
       " ('BALLRAUM', 0.091709963467780209),\n",
       " ('PRAEGENDE_JUGENDJAHRE_MOVEMENT', 0.090059111038201731),\n",
       " ('GREEN_AVANTGARDE', 0.082977022838834041),\n",
       " ('MOBI_REGIO', 0.08012291934612191),\n",
       " ('SEMIO_SOZ', 0.065553694020176967),\n",
       " ('SEMIO_LUST', 0.065467003249199601),\n",
       " ('CAMEO_INTL_2015_LIFE_STG', 0.057993650831943612),\n",
       " ('KBA05_ANTG1', 0.057303959767879768),\n",
       " ('REGIOTYP', 0.051261910712407932),\n",
       " ('SEMIO_FAM', 0.034238133416610611),\n",
       " ('SEMIO_DOM', 0.033407435276262672),\n",
       " ('SEMIO_RAT', 0.031460268119141002),\n",
       " ('CAMEO_INTL_2015_HOUSEHOLDS', 0.026470112024952669),\n",
       " ('ALTERSKATEGORIE_GROB', 0.026190578367122158),\n",
       " ('EWDICHTE', 0.025510344560400366),\n",
       " ('PRAEGENDE_JUGENDJAHRE_DECADE', 0.020411680204483425),\n",
       " ('HEALTH_TYP', 0.015391216333799881),\n",
       " ('SEMIO_REL', 0.015078273745923191),\n",
       " ('SEMIO_KULT', 0.0045490732497179276),\n",
       " ('SEMIO_ERL', -0.0025349700789205551),\n",
       " ('RETOURTYP_BK_S', -0.004514905612707204),\n",
       " ('VERS_TYP', -0.0063467635204547124),\n",
       " ('SEMIO_MAT', -0.012654945404620969),\n",
       " ('SEMIO_PFLICHT', -0.015128414402801965),\n",
       " ('W_KEIT_KIND_HH', -0.016083322563842331),\n",
       " ('SEMIO_TRADV', -0.01668565811333891),\n",
       " ('WOHNDAUER_2008', -0.022105333242427441),\n",
       " ('FINANZ_MINIMALIST', -0.035938778666066207),\n",
       " ('ANREDE_KZ', -0.051568788907844068),\n",
       " ('ANZ_PERSONEN', -0.052614163725672139),\n",
       " ('PLZ8_GBZ', -0.071606662580296077),\n",
       " ('SEMIO_KAEM', -0.08653061201305981),\n",
       " ('PLZ8_HHZ', -0.10302513776886138),\n",
       " ('FINANZ_HAUSBAUER', -0.10547537667912549),\n",
       " ('KBA05_GBZ', -0.10601342599504274),\n",
       " ('SOHO_KZ', -0.10701134395714967),\n",
       " ('SEMIO_VERT', -0.11369309168583155),\n",
       " ('SEMIO_KRIT', -0.11587146835377221),\n",
       " ('INNENSTADT', -0.13545880756527764),\n",
       " ('KKK', -0.13908232338915172),\n",
       " ('PLZ8_ANTG4', -0.1502269332262649),\n",
       " ('KBA13_ANZAHL_PKW', -0.15053565755797177),\n",
       " ('FINANZ_VORSORGER', -0.19150089497590478),\n",
       " ('ANZ_HH_TITEL', -0.20227607453313684),\n",
       " ('KBA05_ANTG4', -0.21413497013924609),\n",
       " ('ANZ_TITEL', -0.27193013576216685),\n",
       " ('HH_EINKOMMEN_SCORE', -0.38137317907120133)]"
      ]
     },
     "execution_count": 122,
     "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",
    "pca_link_weights(pca2, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('KONSUMNAEHE', 0.22928742875663896),\n",
       " ('MIN_GEBAEUDEJAHR', 0.22574056908926876),\n",
       " ('SEMIO_VERT', 0.20390861288530157),\n",
       " ('BALLRAUM', 0.16641400456210714),\n",
       " ('KBA05_ANTG2', 0.089584752453466254),\n",
       " ('PRAEGENDE_JUGENDJAHRE_DECADE', 0.085611386029215156),\n",
       " ('SEMIO_ERL', 0.077075897242549832),\n",
       " ('W_KEIT_KIND_HH', 0.0749565727328593),\n",
       " ('FINANZ_UNAUFFAELLIGER', 0.071151687678353087),\n",
       " ('FINANZ_SPARER', 0.067538992664353537),\n",
       " ('ALTERSKATEGORIE_GROB', 0.061501325540945104),\n",
       " ('ANREDE_KZ', 0.060405591359010524),\n",
       " ('PLZ8_ANTG3', 0.055705332188038068),\n",
       " ('FINANZ_ANLEGER', 0.048096133896656734),\n",
       " ('KKK', 0.043877610007135553),\n",
       " ('SEMIO_DOM', 0.039423081177375703),\n",
       " ('GREEN_AVANTGARDE', 0.039421519063120525),\n",
       " ('CAMEO_INTL_2015_HOUSEHOLDS', 0.038529830411702368),\n",
       " ('ANZ_HAUSHALTE_AKTIV', 0.033531854143968469),\n",
       " ('FINANZ_HAUSBAUER', 0.033531108401612331),\n",
       " ('HEALTH_TYP', 0.030352270816241811),\n",
       " ('CAMEO_INTL_2015_LIFE_STG', 0.026462167210676627),\n",
       " ('KBA05_ANTG1', 0.022744337092250724),\n",
       " ('SEMIO_KRIT', 0.021145238209998155),\n",
       " ('INNENSTADT', 0.017149617310920337),\n",
       " ('SEMIO_LUST', -0.0033860514379555132),\n",
       " ('SEMIO_MAT', -0.0051040702654185461),\n",
       " ('VERS_TYP', -0.0057827558337804202),\n",
       " ('SOHO_KZ', -0.0070403658936511598),\n",
       " ('KBA13_ANZAHL_PKW', -0.014092727805546545),\n",
       " ('SEMIO_KAEM', -0.02345778249071527),\n",
       " ('PLZ8_GBZ', -0.02556018835113984),\n",
       " ('KBA05_ANTG3', -0.039505224219493318),\n",
       " ('SEMIO_REL', -0.040618538929663293),\n",
       " ('SEMIO_SOZ', -0.045055774158066088),\n",
       " ('EWDICHTE', -0.046242112755785561),\n",
       " ('REGIOTYP', -0.04827191501939692),\n",
       " ('SEMIO_FAM', -0.049403278410991999),\n",
       " ('ANZ_TITEL', -0.049699168973094843),\n",
       " ('WOHNDAUER_2008', -0.052584488954525263),\n",
       " ('KBA05_GBZ', -0.057425724521918034),\n",
       " ('SEMIO_TRADV', -0.058742789817021995),\n",
       " ('SEMIO_PFLICHT', -0.060044124332194457),\n",
       " ('SEMIO_KULT', -0.067612155569187926),\n",
       " ('GEBAEUDETYP_RASTER', -0.067860642875695487),\n",
       " ('HH_EINKOMMEN_SCORE', -0.068329829526219063),\n",
       " ('OST_WEST_KZ_O', -0.08651468414018082),\n",
       " ('PRAEGENDE_JUGENDJAHRE_MOVEMENT', -0.086772459836794227),\n",
       " ('FINANZ_MINIMALIST', -0.088753496693161682),\n",
       " ('SEMIO_RAT', -0.08889618620059539),\n",
       " ('FINANZ_VORSORGER', -0.091384803288416472),\n",
       " ('PLZ8_HHZ', -0.099795559826849775),\n",
       " ('MOBI_REGIO', -0.10417204216086373),\n",
       " ('RELAT_AB', -0.12005364826501067),\n",
       " ('ANZ_HH_TITEL', -0.12028978138054547),\n",
       " ('PLZ8_ANTG4', -0.12884803382657994),\n",
       " ('KBA05_ANTG4', -0.14801558686193819),\n",
       " ('ANZ_PERSONEN', -0.19200733191004785),\n",
       " ('RETOURTYP_BK_S', -0.24033840073720225),\n",
       " ('ORTSGR_KLS9', -0.25210276812638865),\n",
       " ('PLZ8_ANTG2', -0.2978142635796156),\n",
       " ('ONLINE_AFFINITAET', -0.32461522257407832),\n",
       " ('PLZ8_ANTG1', -0.33321445359585583),\n",
       " ('ARBEIT', -0.33479987388096066)]"
      ]
     },
     "execution_count": 123,
     "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",
    "pca_link_weights(pca2, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\n",
    "- By looking at the bar graph above, I am interested in **`cluster 8`** that is **overrepresented** in the customer data compared to the general population. And **`cluster 6`** that is **underrepresented** in the customer data compared to the general population.\n",
    "- The Problem Statement is : To conduct Direct Marketing Campaigns towards audiences that will have the highest expected rate of returns. \n",
    "- **FOR CLUSTER 8:**\n",
    "    - By looking at the above features and using Data Dictionary for reference, `Estimated household net income`('HH_EINKOMMEN_SCORE'), `Number of professional academic title holders in household`('ANZ_TITEL') are the two major features in the customer data that are relatively unpopular with the mail-order company.\n",
    "    - `Online affinity`('ONLINE_AFFINITAET'), `Size of community`('ORTSGR_KLS9') are two of the features which are popular rather they do matter for some analytical use to the mail-order company since these features will help the company to target a large amount of audience.\n",
    "- **FOR CLUSTER 6:**\n",
    "    - By looking at the above features and using Data Dictionary for reference, `People with event-oriented Personality typology`('SEMIO_ERL') and `People with Dreamful personality typology`('SEMIO_VERT') are the relatively popular features with the company since these are the people who would engage more in the reading and reverting back to the mails / other campaigns and remain active with the company.\n",
    "    - Whereas, `Dominating movement of person's youth (avantgarde vs. mainstream` ('PRAEGENDE_JUGENDJAHRE_MOVEMENT') and `People with Rational Personality typology`('SEMIO_RAT') are the unpopular features with the mail_order company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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