Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "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\n",
    "import ast\n",
    "from collections import Counter\n",
    "from pprint import pprint\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\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "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": [],
   "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations is 891221\n",
      "Number of features is 85\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of observations is {}\".format(azdias.shape[0]))\n",
    "print(\"Number of features is {}\".format(azdias.shape[1]))#azdias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
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       "3         2                     4          2            2.0   \n",
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       "\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",
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       "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",
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       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
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       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "              attribute information_level         type missing_or_unknown\n",
       "0              AGER_TYP            person  categorical             [-1,0]\n",
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   "source": [
    "feat_info.head()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>CJT_GESAMTTYP</td>\n",
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       "              attribute information_level         type missing_or_unknown\n",
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "feat_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = feat_info\n",
    "\n",
    "for i,V in enumerate(azdias.iteritems()):\n",
    "    m_u = a['missing_or_unknown'][i]\n",
    "    column_name = V[0]\n",
    "    m_u = m_u[1:-1].split(',')\n",
    "    if m_u != ['']:\n",
    "        hold = []\n",
    "        for c in m_u:\n",
    "            if c in ['X','XX']:\n",
    "                hold.append(c)\n",
    "            else:\n",
    "                hold.append(int(c))\n",
    "        azdias[column_name] = azdias[column_name].replace(hold, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0       NaN                   2.0          1            2.0   \n",
       "1       NaN                   1.0          2            5.0   \n",
       "2       NaN                   3.0          2            3.0   \n",
       "3       2.0                   4.0          2            2.0   \n",
       "4       NaN                   3.0          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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
     ]
    }
   ],
   "source": [
    "azdias.info()"
   ]
  },
  {
   "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": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "df = azdias.loc[:, azdias.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns which have more than 10% null values: 46\n",
      "Number of columns which have more than 20% null values: 6\n",
      "Number of columns which have more than 30% null values: 6\n"
     ]
    }
   ],
   "source": [
    "for i in [0.1,0.2,0.3]:\n",
    "    print(\"Number of columns which have more than {}% null values: {}\".format(int(i*100), len(df.columns[df.isnull().mean() > i])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will keep the columns that have less than 20% as null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP',\n",
       "       'KBA05_BAUMAX'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns[df.isnull().mean() > 0.2]\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "\n",
    "azdias.drop(labels = columns, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "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",
       "SEMIO_ERL                     0\n",
       "SEMIO_KULT                    0\n",
       "                          ...  \n",
       "MIN_GEBAEUDEJAHR          93148\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_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: 79, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "We see the number of columns which have certain percentage of null values.\n",
    "\n",
    "When we go from 20 % to 10 %, we see that number of features increased from 6 to 46. So, I think it is ok to drop 6 features from our data.\n",
    "\n",
    "The features which I dropped are ( definition from *Data_Dictionary.md* ):\n",
    "\n",
    "1. **AGER_TYP**: Best-age typology\n",
    "2. **GEBURTSJAHR**: Year of birth\n",
    "3. **ANZ_TITEL**: Number of professional academic title holders in household\n",
    "4. **KK_KUNDENTYP**: Consumer patterns over last 12 months\n",
    "5. **ANZ_HH_TITEL**: Number of professional academic title holders in building\n",
    "6. **ORTSGR_KLS9**: Size of community\n",
    "\n",
    "For other columns, there is a pattern in missing number of rows. Like take these columns:\n",
    "1. KBA05_ANTG1, KBA05_ANTG2, KBA05_ANTG3, KBA05_ANTG4\n",
    "2. PLZ8_ANTG1, PLZ8_ANTG2, PLZ8_ANTG3, PLZ8_ANTG4, PLZ8_BAUMAX, PLZ8_HHZ, PLZ8_GBZ\n",
    "3. CAMEO_DEUG_2015, CAMEO_DEU_2015, CAMEO_INTL_2015\n",
    "Like the first set of columns, all have same number of missing values. So, if a features is present with small modification then all its forms have same number of missing values. Some of which are mentioned above."
   ]
  },
  {
   "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. Make sure you report your observations in the discussion section. **Either way, you should continue your analysis below using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "row_index = azdias.isnull().sum(axis=1).index\n",
    "nan_count = azdias.isnull().sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_df = pd.DataFrame()\n",
    "nan_df['row_index'] = row_index\n",
    "nan_df['nan_count'] = nan_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_index</th>\n",
       "      <th>nan_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_index  nan_count\n",
       "0          0         43\n",
       "1          1          0\n",
       "2          2          0\n",
       "3          3          7\n",
       "4          4          0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let us see see a histogram that will help in deciding the threshold value for creating two subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 396
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nan_df['nan_count'], bins = 100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to me, 10 looks like a good threshold. There are many rows which have missing values count in range from 0 to 10 (both inclusive). Above than 10, we will include in other subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aboveThres_subset = azdias[azdias.isnull().sum(axis=1) > 10]\n",
    "belowThres_subset = azdias[azdias.isnull().sum(axis=1) <=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
    "nan_df = belowThres_subset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "FINANZTYP                0\n",
       "GREEN_AVANTGARDE         0\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",
       "ZABEOTYP                 0\n",
       "ANZ_PERSONEN             0\n",
       "ANZ_TITEL                0\n",
       "HH_EINKOMMEN_SCORE       0\n",
       "WOHNDAUER_2008           0\n",
       "GEBAEUDETYP              0\n",
       "MIN_GEBAEUDEJAHR         0\n",
       "OST_WEST_KZ              0\n",
       "WOHNLAGE                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df[nan_df == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the five columns whose distribution we will see in `lower` and `upper`.\n",
    "1. FINANZ_MINIMALIST: Financial typology\n",
    "2. ANREDE_KZ: Gender\n",
    "3. ZABEOTYP: Energy consumption typology \n",
    "4. OST_WEST_KZ: Building location via former East / West Germany (German Democratic Republic(GDR), Federal Republic of Germany(FRG))\n",
    "5. MIN_GEBAEUDEJAHR: First year building was mentioned in the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 633
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 6))\n",
    "f.suptitle('Distribution of Financial Typology', fontsize=16)\n",
    "sns.countplot(x='FINANZ_MINIMALIST', data=belowThres_subset, ax = ax1)\n",
    "ax1.set_title(\"Data with less misssing values\");\n",
    "sns.countplot(x='FINANZ_MINIMALIST', data=aboveThres_subset, ax = ax2)\n",
    "ax2.set_title(\"Data with more misssing values\")\n",
    "plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 633
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 6))\n",
    "f.suptitle('Distribution of Gender', fontsize=16)\n",
    "sns.countplot(x='ANREDE_KZ', data=belowThres_subset, ax = ax1)\n",
    "ax1.set_title(\"Data with less misssing values\");\n",
    "sns.countplot(x='ANREDE_KZ', data=aboveThres_subset, ax = ax2)\n",
    "ax2.set_title(\"Data with more misssing values\")\n",
    "plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 633
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 6))\n",
    "f.suptitle('Distribution of Energy Consumption Typology', fontsize=16)\n",
    "sns.countplot(x='ZABEOTYP', data=belowThres_subset, ax = ax1)\n",
    "ax1.set_title(\"Data with less misssing values\");\n",
    "sns.countplot(x='ZABEOTYP', data=aboveThres_subset, ax = ax2)\n",
    "ax2.set_title(\"Data with more misssing values\")\n",
    "plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 633
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize = (10, 6))\n",
    "f.suptitle('Distribution of Building location via former East / West Germany', fontsize=16)\n",
    "sns.countplot(x='OST_WEST_KZ', data=belowThres_subset, ax = ax1)\n",
    "ax1.set_title(\"Data with less misssing values\");\n",
    "sns.countplot(x='OST_WEST_KZ', data=aboveThres_subset, ax = ax2)\n",
    "ax2.set_title(\"Data with more misssing values\")\n",
    "plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pLbbqkyRJkiRJkjQsETFA0UrxbCAo2hruHNGiJEmSJEmS2mSrPkmSJEmSJPVMGaw5qo2h6zJzZW+rUbdFxL3A+4CHgEWZuWhkK5IkSZIkSWqfwSlJkiRJkiT1TBmsOamNoUsyc6C31UiSJEmSJElDDE5JkiRJkiRJkiRJkiRJ6jtjRroASZIkSZIkSZIkSZIkSdrTDE5JkiRJkiRJkiRJkiRJ6jsGpyRJkiRJkiRJkiRJkiT1HYNTkiRJkiRJkiRJkiRJkvqOwSlJkiRJkiRJkiRJkiRJfcfglCRJkiRJkiRJkiRJkqS+Y3BKkiRJkiRJkiRJkiRJUt8xOCVJkiRJkiRJkiRJkiSp7xickiRJkiRJkiRJkiRJktR3DE5JkiRJkiRJkiRJkiRJ6jsGpyRJkiRJkiRJkiRJkiT1HYNTkiRJkiRJkiRJkiRJkvrO/wCa+WcQ1Tco/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 659,
       "width": 1191
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ((ax1), (ax2)) = plt.subplots(2, 1, figsize = (20, 10))\n",
    "f.suptitle('Distribution of First year building was mentioned in the database', fontsize=16)\n",
    "sns.countplot(x='MIN_GEBAEUDEJAHR', data=belowThres_subset, ax = ax1)\n",
    "ax1.set_title(\"Data with less misssing values\");\n",
    "sns.countplot(x='MIN_GEBAEUDEJAHR', data=aboveThres_subset, ax = ax2)\n",
    "ax2.set_title(\"Data with more misssing values\");\n",
    "# plt.subplots_adjust(wspace=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I am dropping the two features that have same distribution in two subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['ANREDE_KZ', 'ZABEOTYP']\n",
    "belowThres_subset.drop(labels=columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "For the five columns that we have seen. Two of them have same distribution in both subsets. Those features are:\n",
    "1. ANDREDE_KZ: Gender\n",
    "2. ZABEOTYP: Energy consumption typology\n",
    "\n",
    "So, I am thinking that dropping these features will not be a major issue.\n",
    "\n",
    "On the other hand, the remaining three features have different distribution in two subsets of data. We will not drop them, because they are special columns. Those features are:\n",
    "\n",
    "1. FINANZ_MINIMALIST: Financial typology\n",
    "2. MIN_GEBAEUDEJAHR: First year building was mentioned in the database\n",
    "3. OST_WEST_KZ: Building location via former East / West Germany"
   ]
  },
  {
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ordinal        49\n",
       "categorical    21\n",
       "numeric         7\n",
       "mixed           7\n",
       "interval        1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "feat_info['type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, 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": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "cat_atts = feat_info.query(\" type == 'categorical' \")['attribute'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_cat_atts = []\n",
    "for att in cat_atts:\n",
    "    if att in belowThres_subset:\n",
    "        lower_cat_atts.append(att)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binary columns\n",
    "\n",
    "Let us see the binary columns, i.e., the columns that have two categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_list, multi_list = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_variable = belowThres_subset[lower_cat_atts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in cat_variable:\n",
    "    if len(cat_variable[t].value_counts()) == 2:\n",
    "        bin_list.append(t)\n",
    "    else:\n",
    "        multi_list.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bin_df = cat_variable[bin_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 780153 entries, 1 to 891220\n",
      "Data columns (total 4 columns):\n",
      "GREEN_AVANTGARDE    780153 non-null int64\n",
      "SOHO_KZ             780153 non-null float64\n",
      "VERS_TYP            745629 non-null float64\n",
      "OST_WEST_KZ         780153 non-null object\n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 49.8+ MB\n"
     ]
    }
   ],
   "source": [
    "bin_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP OST_WEST_KZ\n",
       "1                 0      1.0       2.0           W\n",
       "2                 1      0.0       1.0           W\n",
       "3                 0      0.0       1.0           W\n",
       "4                 0      0.0       2.0           W\n",
       "5                 0      0.0       2.0           W"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GREEN_AVANTGARDE        0\n",
       "SOHO_KZ                 0\n",
       "VERS_TYP            34524\n",
       "OST_WEST_KZ             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    2.0\n",
       "5    2.0\n",
       "Name: VERS_TYP, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df['VERS_TYP'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_df['VERS_TYP'].fillna(bin_df['VERS_TYP'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GREEN_AVANTGARDE    0\n",
       "SOHO_KZ             0\n",
       "VERS_TYP            0\n",
       "OST_WEST_KZ         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'O'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df['OST_WEST_KZ'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_df['OST_WEST_KZ'].replace(['W', 'O'], [1,0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_df['OST_WEST_KZ'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us see multi-level categorical features.\n",
    "\n",
    "#### Multi-level categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "multi_df = cat_variable[multi_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us see the number of categories for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({6: 2, 12: 1, 11: 1, 5: 2, 10: 1, 3: 1, 4: 1, 7: 1, 9: 1, 44: 1})"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = []\n",
    "for col in multi_df:\n",
    "    ls.append(multi_df[col].nunique())\n",
    "Counter(ls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that some categorical features have many category levels, let us see what those columns are and drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cols = [col for col in multi_df if multi_df[col].nunique() > 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df.drop(labels=cols, inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({6: 2, 5: 2, 3: 1, 4: 1, 7: 1})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = []\n",
    "for col in multi_df:\n",
    "    ls.append(multi_df[col].nunique())\n",
    "Counter(ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>LP_FAMILIE_GROB</th>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CJT_GESAMTTYP  FINANZTYP  LP_FAMILIE_GROB  LP_STATUS_GROB  \\\n",
       "1            5.0          1              3.0             1.0   \n",
       "2            3.0          1              1.0             2.0   \n",
       "3            2.0          6              NaN             4.0   \n",
       "4            5.0          5              5.0             2.0   \n",
       "5            2.0          2              1.0             2.0   \n",
       "\n",
       "   NATIONALITAET_KZ  SHOPPER_TYP  GEBAEUDETYP  \n",
       "1               1.0          3.0          8.0  \n",
       "2               1.0          2.0          1.0  \n",
       "3               1.0          1.0          1.0  \n",
       "4               1.0          2.0          1.0  \n",
       "5               1.0          0.0          1.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(780153, 7)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will replace the missing values and create dummy variable at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5.0\n",
       "2    3.0\n",
       "3    2.0\n",
       "4    5.0\n",
       "5    2.0\n",
       "Name: CJT_GESAMTTYP, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['CJT_GESAMTTYP'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3834"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['CJT_GESAMTTYP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on categorical data type, we should null values with mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df['CJT_GESAMTTYP'].fillna(multi_df['CJT_GESAMTTYP'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['CJT_GESAMTTYP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>LP_FAMILIE_GROB</th>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FINANZTYP  LP_FAMILIE_GROB  LP_STATUS_GROB  NATIONALITAET_KZ  SHOPPER_TYP  \\\n",
       "1          1              3.0             1.0               1.0          3.0   \n",
       "2          1              1.0             2.0               1.0          2.0   \n",
       "3          6              NaN             4.0               1.0          1.0   \n",
       "4          5              5.0             2.0               1.0          2.0   \n",
       "5          2              1.0             2.0               1.0          0.0   \n",
       "\n",
       "   GEBAEUDETYP  CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1          8.0                                                  0    \n",
       "2          1.0                                                  0    \n",
       "3          1.0                                                  0    \n",
       "4          1.0                                                  0    \n",
       "5          1.0                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \n",
       "1                                                  0                        \n",
       "2                                                  0                        \n",
       "3                                                  0                        \n",
       "4                                                  0                        \n",
       "5                                                  0                        "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"CJT_GESAMTTYP_advertising_&_consumption_minimalist\", \"CJT_GESAMTTYP_advertising_&_consumption_traditionalist\", \"CJT_GESAMTTYP_advertising_interested_store_shopper\", \"CJT_GESAMTTYP_advertising_interested_online_shopper\", \"CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast\", \"CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour\"]] = pd.get_dummies(multi_df['CJT_GESAMTTYP'])\n",
    "multi_df.drop('CJT_GESAMTTYP', axis = 1, inplace=True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1\n",
       "2    1\n",
       "3    6\n",
       "4    5\n",
       "5    2\n",
       "Name: FINANZTYP, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['FINANZTYP'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['FINANZTYP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LP_FAMILIE_GROB</th>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <th>FINANZTYP_hausbauer</th>\n",
       "      <th>FINANZTYP_vorsorger</th>\n",
       "      <th>FINANZTYP_anleger</th>\n",
       "      <th>FINANZTYP_unauffaelliger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LP_FAMILIE_GROB  LP_STATUS_GROB  NATIONALITAET_KZ  SHOPPER_TYP  \\\n",
       "1              3.0             1.0               1.0          3.0   \n",
       "2              1.0             2.0               1.0          2.0   \n",
       "3              NaN             4.0               1.0          1.0   \n",
       "4              5.0             2.0               1.0          2.0   \n",
       "5              1.0             2.0               1.0          0.0   \n",
       "\n",
       "   GEBAEUDETYP  CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1          8.0                                                  0    \n",
       "2          1.0                                                  0    \n",
       "3          1.0                                                  0    \n",
       "4          1.0                                                  0    \n",
       "5          1.0                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "   FINANZTYP_minimalist  FINANZTYP_sparer  FINANZTYP_hausbauer  \\\n",
       "1                     1                 0                    0   \n",
       "2                     1                 0                    0   \n",
       "3                     0                 0                    0   \n",
       "4                     0                 0                    0   \n",
       "5                     0                 1                    0   \n",
       "\n",
       "   FINANZTYP_vorsorger  FINANZTYP_anleger  FINANZTYP_unauffaelliger  \n",
       "1                    0                  0                         0  \n",
       "2                    0                  0                         0  \n",
       "3                    0                  0                         1  \n",
       "4                    0                  1                         0  \n",
       "5                    0                  0                         0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"FINANZTYP_minimalist\", \"FINANZTYP_sparer\", \"FINANZTYP_hausbauer\", \"FINANZTYP_vorsorger\", \"FINANZTYP_anleger\", \"FINANZTYP_unauffaelliger\"]] = pd.get_dummies(multi_df['FINANZTYP'])\n",
    "multi_df.drop('FINANZTYP', axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3.0\n",
       "2    1.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "5    1.0\n",
       "Name: LP_FAMILIE_GROB, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_FAMILIE_GROB'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28850"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_FAMILIE_GROB'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df['LP_FAMILIE_GROB'].fillna(multi_df['LP_FAMILIE_GROB'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_FAMILIE_GROB'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LP_STATUS_GROB</th>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <th>FINANZTYP_hausbauer</th>\n",
       "      <th>FINANZTYP_vorsorger</th>\n",
       "      <th>FINANZTYP_anleger</th>\n",
       "      <th>FINANZTYP_unauffaelliger</th>\n",
       "      <th>lp_familie_grob_single</th>\n",
       "      <th>lp_familie_grob_couple</th>\n",
       "      <th>lp_familie_grob_single_parent</th>\n",
       "      <th>lp_familie_grob_family</th>\n",
       "      <th>lp_familie_grob_multiperson_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LP_STATUS_GROB  NATIONALITAET_KZ  SHOPPER_TYP  GEBAEUDETYP  \\\n",
       "1             1.0               1.0          3.0          8.0   \n",
       "2             2.0               1.0          2.0          1.0   \n",
       "3             4.0               1.0          1.0          1.0   \n",
       "4             2.0               1.0          2.0          1.0   \n",
       "5             2.0               1.0          0.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "                   ...                    FINANZTYP_sparer  \\\n",
       "1                  ...                                   0   \n",
       "2                  ...                                   0   \n",
       "3                  ...                                   0   \n",
       "4                  ...                                   0   \n",
       "5                  ...                                   1   \n",
       "\n",
       "   FINANZTYP_hausbauer  FINANZTYP_vorsorger  FINANZTYP_anleger  \\\n",
       "1                    0                    0                  0   \n",
       "2                    0                    0                  0   \n",
       "3                    0                    0                  0   \n",
       "4                    0                    0                  1   \n",
       "5                    0                    0                  0   \n",
       "\n",
       "   FINANZTYP_unauffaelliger  lp_familie_grob_single  lp_familie_grob_couple  \\\n",
       "1                         0                       0                       0   \n",
       "2                         0                       1                       0   \n",
       "3                         1                       1                       0   \n",
       "4                         0                       0                       0   \n",
       "5                         0                       1                       0   \n",
       "\n",
       "   lp_familie_grob_single_parent  lp_familie_grob_family  \\\n",
       "1                              1                       0   \n",
       "2                              0                       0   \n",
       "3                              0                       0   \n",
       "4                              0                       0   \n",
       "5                              0                       0   \n",
       "\n",
       "   lp_familie_grob_multiperson_household  \n",
       "1                                      0  \n",
       "2                                      0  \n",
       "3                                      0  \n",
       "4                                      1  \n",
       "5                                      0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"lp_familie_grob_single\", \"lp_familie_grob_couple\", \"lp_familie_grob_single_parent\", \"lp_familie_grob_family\", \"lp_familie_grob_multiperson_household\"]] = pd.get_dummies(multi_df[\"LP_FAMILIE_GROB\"])\n",
    "multi_df.drop(\"LP_FAMILIE_GROB\", axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.0\n",
       "2    2.0\n",
       "3    4.0\n",
       "4    2.0\n",
       "5    2.0\n",
       "Name: LP_STATUS_GROB, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_STATUS_GROB'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3834"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_STATUS_GROB'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df['LP_STATUS_GROB'].fillna(multi_df['LP_STATUS_GROB'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['LP_STATUS_GROB'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NATIONALITAET_KZ</th>\n",
       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <th>...</th>\n",
       "      <th>lp_familie_grob_single</th>\n",
       "      <th>lp_familie_grob_couple</th>\n",
       "      <th>lp_familie_grob_single_parent</th>\n",
       "      <th>lp_familie_grob_family</th>\n",
       "      <th>lp_familie_grob_multiperson_household</th>\n",
       "      <th>lp_status_grob_low_income_earners</th>\n",
       "      <th>lp_status_grob_average_earners</th>\n",
       "      <th>lp_status_grob_independents</th>\n",
       "      <th>lp_status_grob_houseowners</th>\n",
       "      <th>lp_status_grob_top_earners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NATIONALITAET_KZ  SHOPPER_TYP  GEBAEUDETYP  \\\n",
       "1               1.0          3.0          8.0   \n",
       "2               1.0          2.0          1.0   \n",
       "3               1.0          1.0          1.0   \n",
       "4               1.0          2.0          1.0   \n",
       "5               1.0          0.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "   FINANZTYP_minimalist             ...              lp_familie_grob_single  \\\n",
       "1                     1             ...                                   0   \n",
       "2                     1             ...                                   1   \n",
       "3                     0             ...                                   1   \n",
       "4                     0             ...                                   0   \n",
       "5                     0             ...                                   1   \n",
       "\n",
       "   lp_familie_grob_couple  lp_familie_grob_single_parent  \\\n",
       "1                       0                              1   \n",
       "2                       0                              0   \n",
       "3                       0                              0   \n",
       "4                       0                              0   \n",
       "5                       0                              0   \n",
       "\n",
       "   lp_familie_grob_family  lp_familie_grob_multiperson_household  \\\n",
       "1                       0                                      0   \n",
       "2                       0                                      0   \n",
       "3                       0                                      0   \n",
       "4                       0                                      1   \n",
       "5                       0                                      0   \n",
       "\n",
       "   lp_status_grob_low_income_earners  lp_status_grob_average_earners  \\\n",
       "1                                  1                               0   \n",
       "2                                  0                               1   \n",
       "3                                  0                               0   \n",
       "4                                  0                               1   \n",
       "5                                  0                               1   \n",
       "\n",
       "   lp_status_grob_independents  lp_status_grob_houseowners  \\\n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           1   \n",
       "4                            0                           0   \n",
       "5                            0                           0   \n",
       "\n",
       "   lp_status_grob_top_earners  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "5                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"lp_status_grob_low_income_earners\", \"lp_status_grob_average_earners\", \"lp_status_grob_independents\", \"lp_status_grob_houseowners\", \"lp_status_grob_top_earners\"]] = pd.get_dummies(multi_df[\"LP_STATUS_GROB\"])\n",
    "multi_df.drop(\"LP_STATUS_GROB\", axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    1.0\n",
       "5    1.0\n",
       "Name: NATIONALITAET_KZ, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['NATIONALITAET_KZ'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31899"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['NATIONALITAET_KZ'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df['NATIONALITAET_KZ'].fillna(multi_df['NATIONALITAET_KZ'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['NATIONALITAET_KZ'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>SHOPPER_TYP</th>\n",
       "      <th>GEBAEUDETYP</th>\n",
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       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <th>...</th>\n",
       "      <th>lp_familie_grob_family</th>\n",
       "      <th>lp_familie_grob_multiperson_household</th>\n",
       "      <th>lp_status_grob_low_income_earners</th>\n",
       "      <th>lp_status_grob_average_earners</th>\n",
       "      <th>lp_status_grob_independents</th>\n",
       "      <th>lp_status_grob_houseowners</th>\n",
       "      <th>lp_status_grob_top_earners</th>\n",
       "      <th>nationalitaet_kz_german_sounding</th>\n",
       "      <th>nationalitaet_kz_foreign_sounding</th>\n",
       "      <th>nationalitaet_kz_assimilated_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SHOPPER_TYP  GEBAEUDETYP  \\\n",
       "1          3.0          8.0   \n",
       "2          2.0          1.0   \n",
       "3          1.0          1.0   \n",
       "4          2.0          1.0   \n",
       "5          0.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "   FINANZTYP_minimalist  FINANZTYP_sparer                 ...                  \\\n",
       "1                     1                 0                 ...                   \n",
       "2                     1                 0                 ...                   \n",
       "3                     0                 0                 ...                   \n",
       "4                     0                 0                 ...                   \n",
       "5                     0                 1                 ...                   \n",
       "\n",
       "   lp_familie_grob_family  lp_familie_grob_multiperson_household  \\\n",
       "1                       0                                      0   \n",
       "2                       0                                      0   \n",
       "3                       0                                      0   \n",
       "4                       0                                      1   \n",
       "5                       0                                      0   \n",
       "\n",
       "   lp_status_grob_low_income_earners  lp_status_grob_average_earners  \\\n",
       "1                                  1                               0   \n",
       "2                                  0                               1   \n",
       "3                                  0                               0   \n",
       "4                                  0                               1   \n",
       "5                                  0                               1   \n",
       "\n",
       "   lp_status_grob_independents  lp_status_grob_houseowners  \\\n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           1   \n",
       "4                            0                           0   \n",
       "5                            0                           0   \n",
       "\n",
       "   lp_status_grob_top_earners  nationalitaet_kz_german_sounding  \\\n",
       "1                           0                                 1   \n",
       "2                           0                                 1   \n",
       "3                           0                                 1   \n",
       "4                           0                                 1   \n",
       "5                           0                                 1   \n",
       "\n",
       "   nationalitaet_kz_foreign_sounding  nationalitaet_kz_assimilated_names  \n",
       "1                                  0                                   0  \n",
       "2                                  0                                   0  \n",
       "3                                  0                                   0  \n",
       "4                                  0                                   0  \n",
       "5                                  0                                   0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"nationalitaet_kz_german_sounding\", \"nationalitaet_kz_foreign_sounding\", \"nationalitaet_kz_assimilated_names\"]] = pd.get_dummies(multi_df['NATIONALITAET_KZ'])\n",
    "multi_df.drop(\"NATIONALITAET_KZ\", axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34524"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['SHOPPER_TYP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_df['SHOPPER_TYP'].fillna(multi_df['SHOPPER_TYP'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df['SHOPPER_TYP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
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       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <th>FINANZTYP_hausbauer</th>\n",
       "      <th>...</th>\n",
       "      <th>lp_status_grob_independents</th>\n",
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       "      <th>lp_status_grob_top_earners</th>\n",
       "      <th>nationalitaet_kz_german_sounding</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GEBAEUDETYP  CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1          8.0                                                  0    \n",
       "2          1.0                                                  0    \n",
       "3          1.0                                                  0    \n",
       "4          1.0                                                  0    \n",
       "5          1.0                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "   FINANZTYP_minimalist  FINANZTYP_sparer  FINANZTYP_hausbauer  \\\n",
       "1                     1                 0                    0   \n",
       "2                     1                 0                    0   \n",
       "3                     0                 0                    0   \n",
       "4                     0                 0                    0   \n",
       "5                     0                 1                    0   \n",
       "\n",
       "               ...                lp_status_grob_independents  \\\n",
       "1              ...                                          0   \n",
       "2              ...                                          0   \n",
       "3              ...                                          0   \n",
       "4              ...                                          0   \n",
       "5              ...                                          0   \n",
       "\n",
       "   lp_status_grob_houseowners  lp_status_grob_top_earners  \\\n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           1                           0   \n",
       "4                           0                           0   \n",
       "5                           0                           0   \n",
       "\n",
       "   nationalitaet_kz_german_sounding  nationalitaet_kz_foreign_sounding  \\\n",
       "1                                 1                                  0   \n",
       "2                                 1                                  0   \n",
       "3                                 1                                  0   \n",
       "4                                 1                                  0   \n",
       "5                                 1                                  0   \n",
       "\n",
       "   nationalitaet_kz_assimilated_names  \\\n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "\n",
       "   shopper_typ_external_supplied_hedonists  shopper_typ_shopping_stressed  \\\n",
       "1                                        0                              0   \n",
       "2                                        0                              0   \n",
       "3                                        0                              1   \n",
       "4                                        0                              0   \n",
       "5                                        1                              0   \n",
       "\n",
       "   shopper_typ_family_shopper  shopper_typ_demanding_shopper  \n",
       "1                           0                              1  \n",
       "2                           1                              0  \n",
       "3                           0                              0  \n",
       "4                           1                              0  \n",
       "5                           0                              0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"shopper_typ_external_supplied_hedonists\", \"shopper_typ_shopping_stressed\", \"shopper_typ_family_shopper\", \"shopper_typ_demanding_shopper\"]]=pd.get_dummies(multi_df['SHOPPER_TYP'])\n",
    "multi_df.drop(\"SHOPPER_TYP\", axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GEBAEUDETYP    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[['GEBAEUDETYP']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <th>FINANZTYP_hausbauer</th>\n",
       "      <th>FINANZTYP_vorsorger</th>\n",
       "      <th>...</th>\n",
       "      <th>nationalitaet_kz_assimilated_names</th>\n",
       "      <th>shopper_typ_external_supplied_hedonists</th>\n",
       "      <th>shopper_typ_shopping_stressed</th>\n",
       "      <th>shopper_typ_family_shopper</th>\n",
       "      <th>shopper_typ_demanding_shopper</th>\n",
       "      <th>gebaeudetyp_residential_building</th>\n",
       "      <th>gebaeudetyp_residential_buildings_without_actually_known_household</th>\n",
       "      <th>gebaeudetyp_mixed_building</th>\n",
       "      <th>gebaeudetyp_mixed_building_without_actually_known_household</th>\n",
       "      <th>gebaeudetyp_mixed_building_without_actually_known_company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1                                                  0    \n",
       "2                                                  0    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                                  0        \n",
       "2                                                  0        \n",
       "3                                                  1        \n",
       "4                                                  0        \n",
       "5                                                  1        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                                  0    \n",
       "2                                                  1    \n",
       "3                                                  0    \n",
       "4                                                  0    \n",
       "5                                                  0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                                  0     \n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "4                                                  0     \n",
       "5                                                  0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                                  1      \n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "4                                                  1      \n",
       "5                                                  0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "5                                                  0                         \n",
       "\n",
       "   FINANZTYP_minimalist  FINANZTYP_sparer  FINANZTYP_hausbauer  \\\n",
       "1                     1                 0                    0   \n",
       "2                     1                 0                    0   \n",
       "3                     0                 0                    0   \n",
       "4                     0                 0                    0   \n",
       "5                     0                 1                    0   \n",
       "\n",
       "   FINANZTYP_vorsorger  \\\n",
       "1                    0   \n",
       "2                    0   \n",
       "3                    0   \n",
       "4                    0   \n",
       "5                    0   \n",
       "\n",
       "                             ...                              \\\n",
       "1                            ...                               \n",
       "2                            ...                               \n",
       "3                            ...                               \n",
       "4                            ...                               \n",
       "5                            ...                               \n",
       "\n",
       "   nationalitaet_kz_assimilated_names  \\\n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "5                                   0   \n",
       "\n",
       "   shopper_typ_external_supplied_hedonists  shopper_typ_shopping_stressed  \\\n",
       "1                                        0                              0   \n",
       "2                                        0                              0   \n",
       "3                                        0                              1   \n",
       "4                                        0                              0   \n",
       "5                                        1                              0   \n",
       "\n",
       "   shopper_typ_family_shopper  shopper_typ_demanding_shopper  \\\n",
       "1                           0                              1   \n",
       "2                           1                              0   \n",
       "3                           0                              0   \n",
       "4                           1                              0   \n",
       "5                           0                              0   \n",
       "\n",
       "   gebaeudetyp_residential_building  \\\n",
       "1                                 0   \n",
       "2                                 1   \n",
       "3                                 1   \n",
       "4                                 1   \n",
       "5                                 1   \n",
       "\n",
       "   gebaeudetyp_residential_buildings_without_actually_known_household  \\\n",
       "1                                                  0                    \n",
       "2                                                  0                    \n",
       "3                                                  0                    \n",
       "4                                                  0                    \n",
       "5                                                  0                    \n",
       "\n",
       "   gebaeudetyp_mixed_building  \\\n",
       "1                           0   \n",
       "2                           0   \n",
       "3                           0   \n",
       "4                           0   \n",
       "5                           0   \n",
       "\n",
       "   gebaeudetyp_mixed_building_without_actually_known_household  \\\n",
       "1                                                  0             \n",
       "2                                                  0             \n",
       "3                                                  0             \n",
       "4                                                  0             \n",
       "5                                                  0             \n",
       "\n",
       "   gebaeudetyp_mixed_building_without_actually_known_company  \n",
       "1                                                  1          \n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "5                                                  0          \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_df[[\"gebaeudetyp_residential_building\", \"gebaeudetyp_residential_buildings_without_actually_known_household\", \"gebaeudetyp_mixed_building\", \"gebaeudetyp_mixed_building_without_actually_known_household\", \"gebaeudetyp_company_building_without_known_company\", \"gebaeudetyp_mixed_building_without_actually_known_household\", \"gebaeudetyp_mixed_building_without_actually_known_company\"]] = pd.get_dummies(multi_df['GEBAEUDETYP'])\n",
    "multi_df.drop(['GEBAEUDETYP','gebaeudetyp_company_building_without_known_company'], axis = 1, inplace = True)\n",
    "multi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "We can see that all two binary columns have numerical data type. So, we do not need to modify them. We can keep them as it is.\n",
    "\n",
    "It was mentioned above that one of the binary columns has a non-numerical data type. We cannot see that here because it can be the case that we have dropped that column in earlier processes of data cleaning.\n",
    "\n",
    "For multi-level categorical features, we have:\n",
    "- first filled the null values with mode\n",
    "- created dummy variables and named them accordingly\n",
    "- removed the original columns"
   ]
  },
  {
   "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>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>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",
       "64           KBA05_BAUMAX     microcell_rr3  mixed             [-1,0]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info[feat_info['type'] == 'mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_atts = list(feat_info[feat_info['type'] == 'mixed']['attribute'].values)\n",
    "del mixed_atts[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LP_LEBENSPHASE_FEIN',\n",
       " 'LP_LEBENSPHASE_GROB',\n",
       " 'PRAEGENDE_JUGENDJAHRE',\n",
       " 'WOHNLAGE',\n",
       " 'CAMEO_INTL_2015',\n",
       " 'PLZ8_BAUMAX']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_atts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df = belowThres_subset[mixed_atts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(mixed_atts) < set(belowThres_subset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(780153, 77)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "belowThres_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "\n",
    "PRAEGENDE_DATA = mixed_df[['PRAEGENDE_JUGENDJAHRE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRAEGENDE_JUGENDJAHRE    26474\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRAEGENDE_DATA.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRAEGENDE_JUGENDJAHRE\n",
       "1                   14.0\n",
       "2                   15.0\n",
       "3                    8.0\n",
       "4                    8.0\n",
       "5                    3.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRAEGENDE_DATA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRAEGENDE_DATA['PRAEGENDE_JUGENDJAHRE'].fillna(PRAEGENDE_DATA['PRAEGENDE_JUGENDJAHRE'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRAEGENDE_JUGENDJAHRE    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRAEGENDE_DATA.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "decade = {1: '40s', 2: '40s', 3: '50s', 4: '60s',\n",
    " 5: '60s', 6: '60s', 7: '60s', 8: '70s',\n",
    " 9: '70s', 10: '80s', 11: '80s', 12: '80s',\n",
    " 13: '80s', 14: '90s', 15: '90s'}\n",
    "\n",
    "\n",
    "movement = {1: '[1, 4]', 2: '[2, 4]', 3: '[1, 4]', 4: '[2, 4]',\n",
    " 5: '[1, 4]', 6: '[2, 2]', 7: '[2, 2]', 8: '[1, 4]',\n",
    " 9: '[2, 2]', 10: '[1, 2]', 11: '[2, 2]', 12: '[1, E]',\n",
    " 13: '[2, 2]', 14: '[1, 4]', 15: '[2, 4]'}\n",
    "\n",
    "\n",
    "PRAEGENDE_DATA[\"decade\"] = PRAEGENDE_DATA[\"PRAEGENDE_JUGENDJAHRE\"].map(decade)\n",
    "\n",
    "\n",
    "PRAEGENDE_DATA[\"movement\"] = PRAEGENDE_DATA[\"PRAEGENDE_JUGENDJAHRE\"].map(movement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decade</th>\n",
       "      <th>movement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90s</td>\n",
       "      <td>[1, 4]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90s</td>\n",
       "      <td>[2, 4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70s</td>\n",
       "      <td>[1, 4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70s</td>\n",
       "      <td>[1, 4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50s</td>\n",
       "      <td>[1, 4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  decade movement\n",
       "1    90s   [1, 4]\n",
       "2    90s   [2, 4]\n",
       "3    70s   [1, 4]\n",
       "4    70s   [1, 4]\n",
       "5    50s   [1, 4]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRAEGENDE_DATA.drop('PRAEGENDE_JUGENDJAHRE', axis = 1, inplace = True)\n",
    "PRAEGENDE_DATA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df[pd.get_dummies(PRAEGENDE_DATA).columns] = pd.get_dummies(PRAEGENDE_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df.drop('PRAEGENDE_JUGENDJAHRE', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "CAMEO_DATA = mixed_df[['CAMEO_INTL_2015']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMEO_INTL_2015    3656\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CAMEO_DATA.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAMEO_DATA['CAMEO_INTL_2015'].fillna(CAMEO_DATA['CAMEO_INTL_2015'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CAMEO_DATA['CAMEO_INTL_2015'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAMEO_DATA['life_stage'] = CAMEO_DATA['CAMEO_INTL_2015'].str[1:2]\n",
    "CAMEO_DATA['wealth_stage'] = CAMEO_DATA['CAMEO_INTL_2015'].str[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
       "      <td>15</td>\n",
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       "      <th>10</th>\n",
       "      <td>51</td>\n",
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       "      <th>12</th>\n",
       "      <td>43</td>\n",
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       "      <th>13</th>\n",
       "      <td>33</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>41</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>891190</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891191</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891192</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891193</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891194</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891195</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891196</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891197</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891198</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891199</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891200</th>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891201</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891202</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891204</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891205</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891206</th>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891207</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891208</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891209</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891210</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891211</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891212</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891213</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891214</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891215</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891216</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891217</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891218</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891219</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891220</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>780153 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CAMEO_INTL_2015 life_stage wealth_stage\n",
       "1                   51          1            5\n",
       "2                   24          4            2\n",
       "3                   12          2            1\n",
       "4                   43          3            4\n",
       "5                   54          4            5\n",
       "6                   22          2            2\n",
       "7                   14          4            1\n",
       "8                   13          3            1\n",
       "9                   15          5            1\n",
       "10                  51          1            5\n",
       "12                  43          3            4\n",
       "13                  33          3            3\n",
       "15                  41          1            4\n",
       "16                  41          1            4\n",
       "18                  24          4            2\n",
       "19                  34          4            3\n",
       "20                  24          4            2\n",
       "21                  55          5            5\n",
       "22                  51          1            5\n",
       "23                  43          3            4\n",
       "25                  33          3            3\n",
       "27                  51          1            5\n",
       "28                  13          3            1\n",
       "29                  12          2            1\n",
       "31                  51          1            5\n",
       "32                  14          4            1\n",
       "33                  41          1            4\n",
       "34                  25          5            2\n",
       "36                  41          1            4\n",
       "37                  24          4            2\n",
       "...                ...        ...          ...\n",
       "891190              15          5            1\n",
       "891191              44          4            4\n",
       "891192              32          2            3\n",
       "891193              43          3            4\n",
       "891194              24          4            2\n",
       "891195              55          5            5\n",
       "891196              23          3            2\n",
       "891197              24          4            2\n",
       "891198              14          4            1\n",
       "891199              51          1            5\n",
       "891200              52          2            5\n",
       "891201              41          1            4\n",
       "891202              41          1            4\n",
       "891204              24          4            2\n",
       "891205              34          4            3\n",
       "891206              25          5            2\n",
       "891207              41          1            4\n",
       "891208              14          4            1\n",
       "891209              51          1            5\n",
       "891210              41          1            4\n",
       "891211              51          1            5\n",
       "891212              51          1            5\n",
       "891213              34          4            3\n",
       "891214              23          3            2\n",
       "891215              31          1            3\n",
       "891216              41          1            4\n",
       "891217              51          1            5\n",
       "891218              24          4            2\n",
       "891219              51          1            5\n",
       "891220              43          3            4\n",
       "\n",
       "[780153 rows x 3 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CAMEO_DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAMEO_DATA.drop('CAMEO_INTL_2015', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df[pd.get_dummies(CAMEO_DATA).columns] = pd.get_dummies(CAMEO_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df.drop('CAMEO_INTL_2015', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>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>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",
       "64           KBA05_BAUMAX     microcell_rr3  mixed             [-1,0]\n",
       "79            PLZ8_BAUMAX    macrocell_plz8  mixed             [-1,0]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info[feat_info['type'] == 'mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['LP_LEBENSPHASE_FEIN'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df.drop('LP_LEBENSPHASE_FEIN', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['LP_LEBENSPHASE_GROB'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df.drop('LP_LEBENSPHASE_GROB', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['WOHNLAGE'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df.drop('WOHNLAGE', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['PLZ8_BAUMAX'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8115"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['PLZ8_BAUMAX'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed_df['PLZ8_BAUMAX'].fillna(mixed_df['PLZ8_BAUMAX'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_df['PLZ8_BAUMAX'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = [\"plz8_baumax_1-2_fam_homes\", \"plz8_baumax_3-5_fam_homes\", \"plz8_baumax_6-10_fam_homes\", \"plz8_baumax_10+_fam_homes\", \"plz8_baumax_business_buildings\"]\n",
    "mixed_df[new] = pd.get_dummies(mixed_df['PLZ8_BAUMAX'])\n",
    "mixed_df.drop('PLZ8_BAUMAX', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "For the two features mentioned specially, I have created two different variables for each.\n",
    "\n",
    "For `PRAEGENDE_JUGENDJAHRE`, the two new variables created were categorical columns(one for decade and one for movement). So, we have to create dummy variable for them.\n",
    "\n",
    "For `CAMEO_INTL_2015`, we created two variables covering the two dimensions of wealth stage and life stage.\n",
    "\n",
    "For other columns(`LP_LEBENSPHASE_GROB`, `LP_LEBENSPHASE_FEIN`, `WOHNLAGE`, `KBA05_BAUMAX`) which have mixed type, `KBA05_BAUMAX` was removed in earlier section of notebook because it had many `nan` values. The other three are dropped becauase they have too many categories.\n",
    "\n",
    "For `PLZ8_BAUMAX` we first replaced the null values with mode of `PLZ8_BAUMAX`. Then created dummy variables amd removed the original column."
   ]
  },
  {
   "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": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GEBURTSJAHR',\n",
       " 'ANZ_PERSONEN',\n",
       " 'ANZ_TITEL',\n",
       " 'ANZ_HAUSHALTE_AKTIV',\n",
       " 'ANZ_HH_TITEL',\n",
       " 'MIN_GEBAEUDEJAHR',\n",
       " 'KBA13_ANZAHL_PKW']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n",
    "# feat_info['type'].unique()\n",
    "numeric = list(feat_info[feat_info['type'] == 'numeric']['attribute'].values)\n",
    "numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEBURTSJAHR\n"
     ]
    }
   ],
   "source": [
    "for i in numeric:\n",
    "    if i not in belowThres_subset.columns:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "del numeric[4]\n",
    "del numeric[2]\n",
    "del numeric[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_df = belowThres_subset[numeric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal = list(feat_info[feat_info['type'] == 'ordinal']['attribute'].values)\n",
    "len(ordinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ordinal:\n",
    "    if i not in belowThres_subset.columns:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALTERSKATEGORIE_GROB',\n",
       " 'FINANZ_MINIMALIST',\n",
       " 'FINANZ_SPARER',\n",
       " 'FINANZ_VORSORGER',\n",
       " 'FINANZ_ANLEGER',\n",
       " 'FINANZ_UNAUFFAELLIGER',\n",
       " 'FINANZ_HAUSBAUER',\n",
       " 'HEALTH_TYP',\n",
       " 'RETOURTYP_BK_S',\n",
       " 'SEMIO_SOZ',\n",
       " 'SEMIO_FAM',\n",
       " 'SEMIO_REL',\n",
       " 'SEMIO_MAT',\n",
       " 'SEMIO_VERT',\n",
       " 'SEMIO_LUST',\n",
       " 'SEMIO_ERL',\n",
       " 'SEMIO_KULT',\n",
       " 'SEMIO_RAT',\n",
       " 'SEMIO_KRIT',\n",
       " 'SEMIO_DOM',\n",
       " 'SEMIO_KAEM',\n",
       " 'SEMIO_PFLICHT',\n",
       " 'SEMIO_TRADV',\n",
       " 'HH_EINKOMMEN_SCORE',\n",
       " 'W_KEIT_KIND_HH',\n",
       " 'WOHNDAUER_2008',\n",
       " 'KONSUMNAEHE',\n",
       " 'KBA05_ANTG1',\n",
       " 'KBA05_ANTG2',\n",
       " 'KBA05_ANTG3',\n",
       " 'KBA05_ANTG4',\n",
       " 'KBA05_GBZ',\n",
       " 'BALLRAUM',\n",
       " 'EWDICHTE',\n",
       " 'INNENSTADT',\n",
       " 'GEBAEUDETYP_RASTER',\n",
       " 'KKK',\n",
       " 'MOBI_REGIO',\n",
       " 'ONLINE_AFFINITAET',\n",
       " 'REGIOTYP',\n",
       " 'PLZ8_ANTG1',\n",
       " 'PLZ8_ANTG2',\n",
       " 'PLZ8_ANTG3',\n",
       " 'PLZ8_ANTG4',\n",
       " 'PLZ8_HHZ',\n",
       " 'PLZ8_GBZ',\n",
       " 'ARBEIT',\n",
       " 'ORTSGR_KLS9',\n",
       " 'RELAT_AB']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ORTSGR_KLS9'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "del ordinal[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_df = belowThres_subset[ordinal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(780153, 48)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat([bin_df, multi_df, numeric_df, mixed_df, ordinal_df], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "427170"
      ]
     },
     "execution_count": 132,
     "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",
    "final_df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have created different dataframes for different data types. Then performed encoding for different data types. We have done nothing for ordinal data types. Then combined all the dataframes into a single dataframe."
   ]
  },
  {
   "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": 133,
   "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",
    "    \n",
    "    # convert missing value codes into NaNs, ...\n",
    "    \n",
    "    a = feat_info\n",
    "\n",
    "    for i,V in enumerate(df.iteritems()):\n",
    "        m_u = a['missing_or_unknown'][i]\n",
    "        column_name = V[0]\n",
    "        m_u = m_u[1:-1].split(',')\n",
    "        if m_u != ['']:\n",
    "            hold = []\n",
    "            for c in m_u:\n",
    "                if c in ['X','XX']:\n",
    "                    hold.append(c)\n",
    "                else:\n",
    "                    hold.append(int(c))\n",
    "            df[column_name] = df[column_name].replace(hold, np.nan)\n",
    "\n",
    "    # remove selected columns and rows, ...\n",
    "    ## Assess missing data in each column\n",
    "    columns = ['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX']\n",
    "    df.drop(labels = columns, axis = 1, inplace = True)\n",
    "    \n",
    "    ## Assess missing data in each row\n",
    "    belowThres_subset = df[df.isnull().sum(axis=1) <=10]\n",
    "    columns2 = ['ANREDE_KZ', 'ZABEOTYP']\n",
    "    belowThres_subset.drop(labels=columns2, axis=1, inplace=True)\n",
    "    \n",
    "\n",
    "    # select, re-encode, and engineer column values.\n",
    "    cat_atts = feat_info.query(\" type == 'categorical' \")['attribute'].values\n",
    "    lower_cat_atts = []\n",
    "    for att in cat_atts:\n",
    "        if att in belowThres_subset:\n",
    "            lower_cat_atts.append(att)\n",
    "    \n",
    "    bin_list, multi_list = [], []\n",
    "    cat_variable = belowThres_subset[lower_cat_atts]\n",
    "    for t in cat_variable:\n",
    "        if len(cat_variable[t].value_counts()) == 2:\n",
    "            bin_list.append(t)\n",
    "        else:\n",
    "            multi_list.append(t)\n",
    "    \n",
    "    bin_df = cat_variable[bin_list]\n",
    "    \n",
    "    bin_df['VERS_TYP'].fillna(bin_df['VERS_TYP'].mode()[0], inplace = True)\n",
    "    \n",
    "    bin_df['OST_WEST_KZ'].replace(['W', 'O'], [1,0], inplace = True)\n",
    "    \n",
    "    multi_df = cat_variable[multi_list]\n",
    "    cols = [col for col in multi_df if multi_df[col].nunique() > 7]\n",
    "    multi_df.drop(labels=cols, inplace = True, axis = 1)\n",
    "    \n",
    "    multi_df['CJT_GESAMTTYP'].fillna(multi_df['CJT_GESAMTTYP'].mode()[0], inplace = True)\n",
    "    multi_df[[\"CJT_GESAMTTYP_advertising_&_consumption_minimalist\", \"CJT_GESAMTTYP_advertising_&_consumption_traditionalist\", \"CJT_GESAMTTYP_advertising_interested_store_shopper\", \"CJT_GESAMTTYP_advertising_interested_online_shopper\", \"CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast\", \"CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour\"]] = pd.get_dummies(multi_df['CJT_GESAMTTYP'])\n",
    "    multi_df.drop('CJT_GESAMTTYP', axis = 1, inplace=True)\n",
    "\n",
    "    multi_df[[\"FINANZTYP_minimalist\", \"FINANZTYP_sparer\", \"FINANZTYP_hausbauer\", \"FINANZTYP_vorsorger\", \"FINANZTYP_anleger\", \"FINANZTYP_unauffaelliger\"]] = pd.get_dummies(multi_df['FINANZTYP'])\n",
    "    multi_df.drop('FINANZTYP', axis = 1, inplace = True)\n",
    "    \n",
    "    multi_df['LP_FAMILIE_GROB'].fillna(multi_df['LP_FAMILIE_GROB'].mode()[0], inplace = True)\n",
    "    multi_df[[\"lp_familie_grob_single\", \"lp_familie_grob_couple\", \"lp_familie_grob_single_parent\", \"lp_familie_grob_family\", \"lp_familie_grob_multiperson_household\"]] = pd.get_dummies(multi_df[\"LP_FAMILIE_GROB\"])\n",
    "    multi_df.drop(\"LP_FAMILIE_GROB\", axis = 1, inplace = True)\n",
    "\n",
    "    multi_df['LP_STATUS_GROB'].fillna(multi_df['LP_STATUS_GROB'].mode()[0], inplace = True)\n",
    "    multi_df[[\"lp_status_grob_low_income_earners\", \"lp_status_grob_average_earners\", \"lp_status_grob_independents\", \"lp_status_grob_houseowners\", \"lp_status_grob_top_earners\"]] = pd.get_dummies(multi_df[\"LP_STATUS_GROB\"])\n",
    "    multi_df.drop(\"LP_STATUS_GROB\", axis = 1, inplace = True)\n",
    "    \n",
    "    multi_df['NATIONALITAET_KZ'].fillna(multi_df['NATIONALITAET_KZ'].mode()[0], inplace = True)\n",
    "    multi_df[[\"nationalitaet_kz_german_sounding\", \"nationalitaet_kz_foreign_sounding\", \"nationalitaet_kz_assimilated_names\"]] = pd.get_dummies(multi_df['NATIONALITAET_KZ'])\n",
    "    multi_df.drop(\"NATIONALITAET_KZ\", axis = 1, inplace = True)\n",
    "    \n",
    "    multi_df['SHOPPER_TYP'].fillna(multi_df['SHOPPER_TYP'].mode()[0], inplace = True)\n",
    "    multi_df[[\"shopper_typ_external_supplied_hedonists\", \"shopper_typ_shopping_stressed\", \"shopper_typ_family_shopper\", \"shopper_typ_demanding_shopper\"]]=pd.get_dummies(multi_df['SHOPPER_TYP'])\n",
    "    multi_df.drop(\"SHOPPER_TYP\", axis = 1, inplace = True)\n",
    "\n",
    "    multi_df[[\"gebaeudetyp_residential_building\", \"gebaeudetyp_residential_buildings_without_actually_known_household\", \"gebaeudetyp_mixed_building\", \"gebaeudetyp_mixed_building_without_actually_known_household\", \"gebaeudetyp_mixed_building_without_actually_known_household\", \"gebaeudetyp_mixed_building_without_actually_known_company\"]] = pd.get_dummies(multi_df['GEBAEUDETYP'])\n",
    "    multi_df.drop('GEBAEUDETYP', axis = 1, inplace = True)\n",
    "    \n",
    "    \n",
    "    mixed_atts = list(feat_info[feat_info['type'] == 'mixed']['attribute'].values)\n",
    "    del mixed_atts[5]\n",
    "    mixed_df = belowThres_subset[mixed_atts]\n",
    "    # Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "\n",
    "    PRAEGENDE_DATA = mixed_df[['PRAEGENDE_JUGENDJAHRE']]\n",
    "    PRAEGENDE_DATA['PRAEGENDE_JUGENDJAHRE'].fillna(PRAEGENDE_DATA['PRAEGENDE_JUGENDJAHRE'].mode()[0], inplace = True)\n",
    "    decade = {1: '40s', 2: '40s', 3: '50s', 4: '60s',\n",
    "     5: '60s', 6: '60s', 7: '60s', 8: '70s',\n",
    "     9: '70s', 10: '80s', 11: '80s', 12: '80s',\n",
    "     13: '80s', 14: '90s', 15: '90s'}\n",
    "\n",
    "\n",
    "    movement = {1: '[1, 4]', 2: '[2, 4]', 3: '[1, 4]', 4: '[2, 4]',\n",
    "     5: '[1, 4]', 6: '[2, 2]', 7: '[2, 2]', 8: '[1, 4]',\n",
    "     9: '[2, 2]', 10: '[1, 2]', 11: '[2, 2]', 12: '[1, E]',\n",
    "     13: '[2, 2]', 14: '[1, 4]', 15: '[2, 4]'}\n",
    "\n",
    "\n",
    "    PRAEGENDE_DATA[\"decade\"] = PRAEGENDE_DATA[\"PRAEGENDE_JUGENDJAHRE\"].map(decade)\n",
    "\n",
    "\n",
    "    PRAEGENDE_DATA[\"movement\"] = PRAEGENDE_DATA[\"PRAEGENDE_JUGENDJAHRE\"].map(movement)\n",
    "    PRAEGENDE_DATA.drop('PRAEGENDE_JUGENDJAHRE', axis = 1, inplace = True)\n",
    "    mixed_df[pd.get_dummies(PRAEGENDE_DATA).columns] = pd.get_dummies(PRAEGENDE_DATA)\n",
    "    mixed_df.drop('PRAEGENDE_JUGENDJAHRE', axis = 1, inplace = True)\n",
    "\n",
    "    # Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "    CAMEO_DATA = mixed_df[['CAMEO_INTL_2015']]\n",
    "    CAMEO_DATA['CAMEO_INTL_2015'].fillna(CAMEO_DATA['CAMEO_INTL_2015'].mode()[0], inplace = True)\n",
    "    CAMEO_DATA['life_stage'] = CAMEO_DATA['CAMEO_INTL_2015'].str[1:2]\n",
    "    CAMEO_DATA['wealth_stage'] = CAMEO_DATA['CAMEO_INTL_2015'].str[0:1]\n",
    "    CAMEO_DATA.drop('CAMEO_INTL_2015', axis = 1, inplace = True)\n",
    "    mixed_df[pd.get_dummies(CAMEO_DATA).columns] = pd.get_dummies(CAMEO_DATA)\n",
    "    mixed_df.drop('CAMEO_INTL_2015', axis = 1, inplace = True)\n",
    "\n",
    "    mixed_df.drop('LP_LEBENSPHASE_FEIN', axis = 1, inplace = True)\n",
    "\n",
    "    mixed_df.drop('LP_LEBENSPHASE_GROB', axis = 1, inplace = True)\n",
    "\n",
    "    mixed_df.drop('WOHNLAGE', axis = 1, inplace = True)\n",
    "\n",
    "    mixed_df['PLZ8_BAUMAX'].fillna(mixed_df['PLZ8_BAUMAX'].mode()[0], inplace = True)\n",
    "    new = [\"plz8_baumax_1-2_fam_homes\", \"plz8_baumax_3-5_fam_homes\", \"plz8_baumax_6-10_fam_homes\", \"plz8_baumax_10+_fam_homes\", \"plz8_baumax_business_buildings\"]\n",
    "    mixed_df[new] = pd.get_dummies(mixed_df['PLZ8_BAUMAX'])\n",
    "    mixed_df.drop('PLZ8_BAUMAX', axis = 1, inplace = True)\n",
    "\n",
    "    numeric = list(feat_info[feat_info['type'] == 'numeric']['attribute'].values)\n",
    "    del numeric[4]\n",
    "    del numeric[2]\n",
    "    del numeric[0]\n",
    "    numeric_df = belowThres_subset[numeric]\n",
    "\n",
    "    ordinal = list(feat_info[feat_info['type'] == 'ordinal']['attribute'].values)\n",
    "    del ordinal[-2]\n",
    "    values = []\n",
    "    for i in ordinal:\n",
    "        if i in belowThres_subset.columns:\n",
    "            values.append(i)\n",
    "    ordinal_df = belowThres_subset[values]\n",
    "    \n",
    "    final_df = pd.concat([bin_df, multi_df, numeric_df, mixed_df, ordinal_df], axis = 1)\n",
    "    \n",
    "\n",
    "    # Return the cleaned dataframe.\n",
    "    cleaned_df = final_df\n",
    "\n",
    "    return cleaned_df"
   ]
  },
  {
   "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 before applying your scaler. 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": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "nan_df = final_df.loc[:, final_df.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(780153, 28)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>KBA13_ANZAHL_PKW</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <th>KBA05_ANTG2</th>\n",
       "      <th>KBA05_ANTG3</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>3.0</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>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</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>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANZ_HAUSHALTE_AKTIV  KBA13_ANZAHL_PKW  ALTERSKATEGORIE_GROB  HEALTH_TYP  \\\n",
       "1                 11.0             963.0                   1.0         3.0   \n",
       "2                 10.0             712.0                   3.0         3.0   \n",
       "3                  1.0             596.0                   4.0         2.0   \n",
       "4                  3.0             435.0                   3.0         3.0   \n",
       "5                  5.0            1300.0                   1.0         3.0   \n",
       "\n",
       "   RETOURTYP_BK_S  W_KEIT_KIND_HH  KONSUMNAEHE  KBA05_ANTG1  KBA05_ANTG2  \\\n",
       "1             1.0             3.0          1.0          0.0          0.0   \n",
       "2             3.0             3.0          5.0          1.0          3.0   \n",
       "3             2.0             NaN          4.0          4.0          1.0   \n",
       "4             5.0             2.0          4.0          1.0          4.0   \n",
       "5             3.0             6.0          5.0          2.0          2.0   \n",
       "\n",
       "   KBA05_ANTG3    ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "1          0.0    ...                   3.0       3.0         2.0         3.0   \n",
       "2          1.0    ...                   2.0       2.0         3.0         3.0   \n",
       "3          0.0    ...                   1.0       NaN         2.0         2.0   \n",
       "4          1.0    ...                   5.0       5.0         2.0         4.0   \n",
       "5          0.0    ...                   1.0       5.0         2.0         3.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  RELAT_AB  \n",
       "1         2.0         1.0       5.0       4.0     3.0       4.0  \n",
       "2         1.0         0.0       4.0       4.0     3.0       2.0  \n",
       "3         2.0         0.0       3.0       4.0     2.0       3.0  \n",
       "4         2.0         1.0       3.0       3.0     4.0       5.0  \n",
       "5         1.0         1.0       5.0       5.0     2.0       3.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_cols = ['ANZ_PERSONEN', 'ANZ_HAUSHALTE_AKTIV', 'KBA13_ANZAHL_PKW']\n",
    "mode_cols = ['ALTERSKATEGORIE_GROB', 'HEALTH_TYP', 'RETOURTYP_BK_S', 'W_KEIT_KIND_HH', 'KONSUMNAEHE', 'KBA05_ANTG1']\n",
    "mode_cols.extend(['KBA05_ANTG2', 'KBA05_ANTG3', 'KBA05_ANTG4','KBA05_GBZ', 'BALLRAUM', 'EWDICHTE', 'INNENSTADT'])\n",
    "mode_cols.extend(['GEBAEUDETYP_RASTER', 'KKK', 'MOBI_REGIO', 'ONLINE_AFFINITAET', 'REGIOTYP', 'PLZ8_ANTG1', 'PLZ8_ANTG2'])\n",
    "mode_cols.extend(['PLZ8_ANTG3', 'PLZ8_ANTG4', 'PLZ8_HHZ', 'PLZ8_GBZ', 'ARBEIT', 'RELAT_AB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imp_mode = Imputer(strategy='most_frequent')\n",
    "imp_median = Imputer(strategy='median')\n",
    "for col in list(nan_df.columns):\n",
    "    if col in median_cols:\n",
    "        nan_df[col] = imp_median.fit_transform(nan_df[[col]])#.fillna(nan_df[col].median(), inplace = True)\n",
    "    else:\n",
    "        nan_df[col] = imp_mode.fit_transform(nan_df[[col]])#.fillna(nan_df[col].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[list(nan_df.columns)] = nan_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df[final_df.columns] = scaler.fit_transform(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.531199</td>\n",
       "      <td>10.871068</td>\n",
       "      <td>0.916487</td>\n",
       "      <td>0.518769</td>\n",
       "      <td>-0.364118</td>\n",
       "      <td>-0.466914</td>\n",
       "      <td>-0.475014</td>\n",
       "      <td>-0.581863</td>\n",
       "      <td>2.477561</td>\n",
       "      <td>-0.381123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177073</td>\n",
       "      <td>-0.873328</td>\n",
       "      <td>-0.258308</td>\n",
       "      <td>0.213255</td>\n",
       "      <td>0.406412</td>\n",
       "      <td>0.422909</td>\n",
       "      <td>1.435790</td>\n",
       "      <td>0.563767</td>\n",
       "      <td>-0.179002</td>\n",
       "      <td>0.679557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.882535</td>\n",
       "      <td>-0.091987</td>\n",
       "      <td>-1.091123</td>\n",
       "      <td>0.518769</td>\n",
       "      <td>-0.364118</td>\n",
       "      <td>-0.466914</td>\n",
       "      <td>2.105202</td>\n",
       "      <td>-0.581863</td>\n",
       "      <td>-0.403623</td>\n",
       "      <td>-0.381123</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.466455</td>\n",
       "      <td>-1.425174</td>\n",
       "      <td>0.775271</td>\n",
       "      <td>0.213255</td>\n",
       "      <td>-0.611406</td>\n",
       "      <td>-0.952555</td>\n",
       "      <td>0.405449</td>\n",
       "      <td>0.563767</td>\n",
       "      <td>-0.179002</td>\n",
       "      <td>-0.795517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.531199</td>\n",
       "      <td>-0.091987</td>\n",
       "      <td>-1.091123</td>\n",
       "      <td>0.518769</td>\n",
       "      <td>-0.364118</td>\n",
       "      <td>2.141721</td>\n",
       "      <td>-0.475014</td>\n",
       "      <td>-0.581863</td>\n",
       "      <td>-0.403623</td>\n",
       "      <td>-0.381123</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.109984</td>\n",
       "      <td>0.782208</td>\n",
       "      <td>-0.258308</td>\n",
       "      <td>-0.878709</td>\n",
       "      <td>0.406412</td>\n",
       "      <td>-0.952555</td>\n",
       "      <td>-0.624893</td>\n",
       "      <td>0.563767</td>\n",
       "      <td>-1.182990</td>\n",
       "      <td>-0.057980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.531199</td>\n",
       "      <td>-0.091987</td>\n",
       "      <td>0.916487</td>\n",
       "      <td>0.518769</td>\n",
       "      <td>-0.364118</td>\n",
       "      <td>-0.466914</td>\n",
       "      <td>-0.475014</td>\n",
       "      <td>-0.581863</td>\n",
       "      <td>2.477561</td>\n",
       "      <td>-0.381123</td>\n",
       "      <td>...</td>\n",
       "      <td>1.464131</td>\n",
       "      <td>0.230362</td>\n",
       "      <td>-0.258308</td>\n",
       "      <td>1.305220</td>\n",
       "      <td>0.406412</td>\n",
       "      <td>0.422909</td>\n",
       "      <td>-0.624893</td>\n",
       "      <td>-0.339941</td>\n",
       "      <td>0.824987</td>\n",
       "      <td>1.417095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.531199</td>\n",
       "      <td>-0.091987</td>\n",
       "      <td>0.916487</td>\n",
       "      <td>0.518769</td>\n",
       "      <td>-0.364118</td>\n",
       "      <td>2.141721</td>\n",
       "      <td>-0.475014</td>\n",
       "      <td>-0.581863</td>\n",
       "      <td>-0.403623</td>\n",
       "      <td>-0.381123</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.109984</td>\n",
       "      <td>0.230362</td>\n",
       "      <td>-0.258308</td>\n",
       "      <td>0.213255</td>\n",
       "      <td>-0.611406</td>\n",
       "      <td>0.422909</td>\n",
       "      <td>1.435790</td>\n",
       "      <td>1.467476</td>\n",
       "      <td>-1.182990</td>\n",
       "      <td>-0.057980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE    SOHO_KZ  VERS_TYP  OST_WEST_KZ  \\\n",
       "1         -0.531199  10.871068  0.916487     0.518769   \n",
       "2          1.882535  -0.091987 -1.091123     0.518769   \n",
       "3         -0.531199  -0.091987 -1.091123     0.518769   \n",
       "4         -0.531199  -0.091987  0.916487     0.518769   \n",
       "5         -0.531199  -0.091987  0.916487     0.518769   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "1                                          -0.364118    \n",
       "2                                          -0.364118    \n",
       "3                                          -0.364118    \n",
       "4                                          -0.364118    \n",
       "5                                          -0.364118    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "1                                          -0.466914        \n",
       "2                                          -0.466914        \n",
       "3                                           2.141721        \n",
       "4                                          -0.466914        \n",
       "5                                           2.141721        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "1                                          -0.475014    \n",
       "2                                           2.105202    \n",
       "3                                          -0.475014    \n",
       "4                                          -0.475014    \n",
       "5                                          -0.475014    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "1                                          -0.581863     \n",
       "2                                          -0.581863     \n",
       "3                                          -0.581863     \n",
       "4                                          -0.581863     \n",
       "5                                          -0.581863     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "1                                           2.477561      \n",
       "2                                          -0.403623      \n",
       "3                                          -0.403623      \n",
       "4                                           2.477561      \n",
       "5                                          -0.403623      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "1                                          -0.381123                         \n",
       "2                                          -0.381123                         \n",
       "3                                          -0.381123                         \n",
       "4                                          -0.381123                         \n",
       "5                                          -0.381123                         \n",
       "\n",
       "     ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "1    ...              0.177073 -0.873328   -0.258308    0.213255    0.406412   \n",
       "2    ...             -0.466455 -1.425174    0.775271    0.213255   -0.611406   \n",
       "3    ...             -1.109984  0.782208   -0.258308   -0.878709    0.406412   \n",
       "4    ...              1.464131  0.230362   -0.258308    1.305220    0.406412   \n",
       "5    ...             -1.109984  0.230362   -0.258308    0.213255   -0.611406   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ    ARBEIT  RELAT_AB  \n",
       "1    0.422909  1.435790  0.563767 -0.179002  0.679557  \n",
       "2   -0.952555  0.405449  0.563767 -0.179002 -0.795517  \n",
       "3   -0.952555 -0.624893  0.563767 -1.182990 -0.057980  \n",
       "4    0.422909 -0.624893 -0.339941  0.824987  1.417095  \n",
       "5    0.422909  1.435790  1.467476 -1.182990 -0.057980  \n",
       "\n",
       "[5 rows x 116 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "We have used `StandardScaler` for feature scaling. Imputation was performed before feature scaling. There is no specific reason. Just wanted to apply scaling to a data where mean and median can be calculated correctly. So, first performed and then performed the feature scaling."
   ]
  },
  {
   "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": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA()\n",
    "final_df_pca = pca.fit_transform(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.16762795e-01, 8.58979823e-02, 4.94806511e-02, 3.74845668e-02,\n",
       "       2.86195359e-02, 2.45293167e-02, 2.21360978e-02, 2.06564959e-02,\n",
       "       1.82189144e-02, 1.76124899e-02, 1.66309353e-02, 1.64626517e-02,\n",
       "       1.48156180e-02, 1.42318929e-02, 1.39642183e-02, 1.36822462e-02,\n",
       "       1.33066502e-02, 1.22271723e-02, 1.17465217e-02, 1.16347613e-02,\n",
       "       1.15010331e-02, 1.13435810e-02, 1.11041826e-02, 1.09261678e-02,\n",
       "       1.07417052e-02, 1.05278587e-02, 1.01631584e-02, 1.00880518e-02,\n",
       "       1.00163918e-02, 9.90671689e-03, 9.79038623e-03, 9.67894891e-03,\n",
       "       9.47562799e-03, 9.39195140e-03, 9.13486830e-03, 9.09336200e-03,\n",
       "       8.96124425e-03, 8.94031720e-03, 8.77825042e-03, 8.68196397e-03,\n",
       "       8.62453319e-03, 8.61515399e-03, 8.53756311e-03, 8.42902093e-03,\n",
       "       8.40240119e-03, 8.10023717e-03, 7.84554229e-03, 7.74352854e-03,\n",
       "       7.60781330e-03, 7.17359176e-03, 7.06615222e-03, 7.05399722e-03,\n",
       "       6.79177180e-03, 6.71974177e-03, 6.30062297e-03, 6.12045876e-03,\n",
       "       5.84498231e-03, 5.49125387e-03, 5.22055362e-03, 5.08957261e-03,\n",
       "       4.89241822e-03, 4.55336286e-03, 4.47323733e-03, 4.07217787e-03,\n",
       "       3.88416630e-03, 3.82660171e-03, 3.70992092e-03, 3.48294767e-03,\n",
       "       3.36170948e-03, 3.31493297e-03, 3.23462130e-03, 2.99707887e-03,\n",
       "       2.91173396e-03, 2.76705227e-03, 2.62266904e-03, 2.48166219e-03,\n",
       "       2.39098814e-03, 2.32735978e-03, 2.30383146e-03, 2.27437046e-03,\n",
       "       2.20447591e-03, 2.11920024e-03, 2.07382916e-03, 1.97456398e-03,\n",
       "       1.92861692e-03, 1.80028923e-03, 1.73215920e-03, 1.63039918e-03,\n",
       "       1.55381303e-03, 1.51992170e-03, 1.49070383e-03, 1.39868034e-03,\n",
       "       1.38058735e-03, 1.33004815e-03, 1.28948509e-03, 1.14928150e-03,\n",
       "       1.05976343e-03, 1.02816616e-03, 1.01818228e-03, 9.96949695e-04,\n",
       "       8.50330995e-04, 7.77671702e-04, 6.68796706e-04, 1.54673430e-05,\n",
       "       5.80556738e-29, 2.22151388e-29, 7.55218854e-31, 7.40304334e-32,\n",
       "       6.47197313e-32, 5.95496864e-32, 4.12962419e-32, 3.52615516e-32,\n",
       "       2.25917818e-32, 2.20169224e-32, 2.01718010e-32, 1.33370005e-32])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11676279, 0.20266078, 0.25214143, 0.289626  , 0.31824553,\n",
       "       0.34277485, 0.36491095, 0.38556744, 0.40378636, 0.42139885,\n",
       "       0.43802978, 0.45449243, 0.46930805, 0.48353994, 0.49750416,\n",
       "       0.51118641, 0.52449306, 0.53672023, 0.54846675, 0.56010151,\n",
       "       0.57160255, 0.58294613, 0.59405031, 0.60497648, 0.61571818,\n",
       "       0.62624604, 0.6364092 , 0.64649725, 0.65651364, 0.66642036,\n",
       "       0.67621075, 0.6858897 , 0.69536532, 0.70475728, 0.71389214,\n",
       "       0.72298551, 0.73194675, 0.74088707, 0.74966532, 0.75834728,\n",
       "       0.76697181, 0.77558697, 0.78412453, 0.79255355, 0.80095595,\n",
       "       0.80905619, 0.81690173, 0.82464526, 0.83225308, 0.83942667,\n",
       "       0.84649282, 0.85354682, 0.86033859, 0.86705833, 0.87335895,\n",
       "       0.87947941, 0.88532439, 0.89081565, 0.8960362 , 0.90112577,\n",
       "       0.90601819, 0.91057156, 0.91504479, 0.91911697, 0.92300114,\n",
       "       0.92682774, 0.93053766, 0.93402061, 0.93738232, 0.94069725,\n",
       "       0.94393187, 0.94692895, 0.94984068, 0.95260774, 0.9552304 ,\n",
       "       0.95771207, 0.96010306, 0.96243041, 0.96473425, 0.96700862,\n",
       "       0.96921309, 0.97133229, 0.97340612, 0.97538069, 0.9773093 ,\n",
       "       0.97910959, 0.98084175, 0.98247215, 0.98402596, 0.98554589,\n",
       "       0.98703659, 0.98843527, 0.98981586, 0.99114591, 0.99243539,\n",
       "       0.99358467, 0.99464444, 0.9956726 , 0.99669078, 0.99768773,\n",
       "       0.99853806, 0.99931574, 0.99998453, 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the variation of cumulative sum of variance with the number of features to see the optimum value for number of components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bq3848PmIeH5mZreOI2JHSsBLMyDiFsqX8ndSAg72ZvX0dVAyymwA/MUA+7E28GVKthSAP1CO8S1VX7sN0FdXEXEcJatF3SpKxqsbKMFke1Iyf0zYHfhORByUmUuHNZYhewrw76z+287NlPN0N+Uc7sfqIIR5wN9FxH2Z+eEBthHAZyhBF1Cy2nwPuIlyDjsF8EzVlpSsIttUy8so01DeQZm67Em03kMvAK6kZGPpKiJeSNmXZmDgnZTAq2XVZ1sBj6f1PhqFj1PuTygZVS6nZNxaSLkvFtXqbgmcExEHZvcp0YLW4MWkBEZdT5mSLClBG3tW/054fNX/E3IaU/ENywjP1b8Br26sW0l5Tv+aMoXpPrQem6dQAj4Pzsw7B9iNA4FP1vbhWso0ag8Au7L6/QIlwPCzwCERsSvlGT9xnS+lBKTcA+xIa4DqJsAXImKPzFzZ78CqAMq/r636OSVQdCVl2su9ap89DDgtIu7NzF6BZgso2a8ObXx0D+U9tYxybA+g9T5+IfCIiDg0M+8fYD8WA//B6mfg9ZTjfA9lOs39af3b98kR8f3MvLCPvt9PCW6qux/4IeX59wDlObtPbRvbUIJOn5yZP54N+zEVEbE25Rpc1PjoRspUlHdQfvfZiPIOeBSzZIpYSZIkSfqjcaekslgsFovFYrFYLBbL7C90mBqv+mxt4Fe1z1YA23fpa2mt7mycGu/O6t/llC/N5zfqTwQj1ducCby1tv9vAdZutFsf+FSj3WU99mFxh7GtBN5JY0orynR+JzJ5GqbX9NjOQkoQTb3NhcCBHeo/r3HOE3jpAMd4Ykq/FdV+rNemTcdraIBr4BAmT+/3OWCrRr0A/pQSDFGve1Gb87+g2p+Jckmt/q8an02UbYawLyd0uBZuBY4A5jXqb00JzKu3WQnsPsB9M3GeHqRkYNq03bltLB/V6GNxl201695e/XsD8Gdt9mld4CONNvfRY+o4SmBRc0rEK4FnNc9vVX8+JaDxNOC2Lv0urfW3pMcY6nXvrP18DrBjm/qHNtokJVtbx6naqmvzfuCLwIuAjTrUm1f1f1Wj/4/3cR0uqdVfOt3regbP1SsbfT4IfBjYuM0xfDWTp2X8jynenxcDe7apv7h2vU+U51b7mpSAk0n3DiXwqzlF5TE9xnZ6h7H9DHhqm/qPYfKUeXcAW/TYzvGNNisowUTrNuqtQ5m+cUWj/kkD7sfE8bsAeEKb+tsC5zbaXNrHNfiSRpt7KZkLJ91PlODoUxv1f0Tj/T9T+0EJmlwEPLlR/8O0fzctovF+ogSy19teBuzbZX82ro7ZN4APDvuZYLFYLBaLxWKxWCxTKWMfgMVisVgsFovFYrFYZn+hSyBU9flfNj4/tUtfS+tfAPbY7pJa3aUDjrk+ntMH2LekZGKY9IVkrc06jf1YScnEswo4tEu7eZTMR/Vt7dql/uI2Y3sQeFGPfX9Vo81yYMMu9U9q1D+NNkEHjTZb0xoMdTOw1gDHeBVw2Aiv2XnATxvb/EiPNjsBv2m0OXpU1+iA+3NCm2N4O7BLj3anNdqcN8B9M1G6BtI12h/VaLt4gLpJCc7Yqsc2Ptdo0zEQhBLk9qNG/f8EFva5Px2DP5h6INRE+Wq3+4wSAHFTo80re+zrdgOcq/UogToTfd9Lm2C3mbreR3WuKEGoyxv9Htujr/0p74F6m6d3qd/u/jyP7gExz2rUnwhQuoouwX2UYKhVtXZdg3uYHHiTlCxQ3a7th1GC9FreC13q70AJwqs/34/oMa7DaQ3aXUWX51mH/fgyHd47VZt1qn2tt9mtS/3NaA2CW06X3wW6nP/Xjnk/FjXqnjDAfVgP9L6dRrBgj7Ydr3eLxWKxWCwWi8VimcnSaZ52SZIkSZIGcSplCqYJr4iIYU+bNZPenZmXd/owM+8D/rm2ai1KUMFHM/NbXdo9yOppBCcsHnBsn8rML3SrkJmnUbIBTdgIeFm7uhGxIXBMbdVVlOCfVT22cTPwmtqqR1KmOOrXxzPzmwPUH9SzaJ267cfAm7o1yMyfMXlax78Z8riG6c2Z+ZMedV5LmdJowuKIGGTqwf/KzH8dfGhT9vLMvKVHnRMby0/pUvd5wB615WuBIzNzRT+Dycxb+6k3BXcAr+p2n2XmTcBfNVZ3nHY0ixs7fd6m/j203sPrUI7XuIzqXL2S8gyc8N+Z2XwON/u6FHhPY/Xr+xlH5T7gZZn5hy7bOJsyXd6EjSnBJy/PLtPwZeaVlCClCftExHoDjA3g1d2u7SxT7b2CkhVuwksiYtMOTY6hdfq2U7LHVHqZ+VVa36PzgNd1HXWr24Cjsst0etW7+v81Vnd7XryOMgXshP/b7XeBmhMp04ZOGORaGcV+TMd2tZ/Py8zl/Tbsdr1LkiRJ0kwyEEqSJEmSNG3VF3j14IQFTA5WmCvuBk7po965jeVkcpBTP+0e38+gat7XZ733NpaP7FDvZbR+8XtiZj7QzwYy8xuUaZomPKfPsSVlqrVRau7v+/rZr+rL+ytqq3aPiD2HOrLhuBE4o1elzLwXOLmxutO10E6z7ShdmJkX96qUmddRsqNM6HYPHd1YfnsVVDBun8jMO3pVysyvAz+srdp3mEGmmfkjWoNY9x9W31MwqnPVvN6P77PdR4FlteXDugQCNX2+Chbtpfk+OC8zr2hbs3O7ebQGkPVyUVehb8IAABJmSURBVGYu6VWpCkj899qqtSnTcLZTP8armPz+6eRESiapP/YTEdFn23/NzLv6qNcMuG37vKi2Ww8MvKpX0PGEzEzgY7VVj46Inftpy5D3Y8g2m4FtSJIkSdLQGQglSZIkSRqWz9Ca3eLFEbH7uAYzDd/NzLv7qPfzxvJP+8nGkpm30ZplY5AvGi/PzOZ2O23nR7Sej70j4mFtqh5S+/kPwDcGGA/AhbWfD+yzzVWZuXTA7QzqSbWf7wW+NkDbz3Xpa7Y4q/ryvR+fbyw/sc92dwEX9D+kafvvAerWr+2291BEzAcOqq36DfD1KYxrFPoKsKhM9fz9UUSsHRGbR8T2EbGoXoDf1qruMmjfwzCqcxURC4G9a6t+kZmX9dO2CvD9Yr074IA+N/3tPus1n+fntK012c8ay4O8R4Z67VXX0Fa1Vef3kdUNgMy8ndZ93hjYtc+x9fW8yMxfUaY5nNDpWO0GbFFb7prRqo0LG8v9vg+HvR/TdV3t5ydHxDizxEmSJEnSlCzoXUWSJEmSpN4yc1VEHM/qL07nUbJCPH98o5qSXlONTfhdY/m6trU6t92g+nnDbhUbvte7yqT6E4ENC4HH0ppdBlq/rL0B2LL/hBxAmQJqwrYRMa+aArCbfjKeTFlEPBzYvrbqygGn7Lmksbx321rj1fe1kJm3RsRNwLbVqn7358oBgq2G4doB6tYzqHS6h3ZtfPbdPq7NmXAvcPUA9Zvnem/gU90aVFmjXkoJdNwD6DeT0SYDjGuYRnWudgfqAaCXDtj+ElqnDt2byZl52hn1e6TZblTvkSuAB1j9N+R2z47muqkc48Ma/V3TR7tBnxcT0wd2OlbNwKVbqyCvfi1sLPfbdtj7MV1nAn9W/Twf+EpEfBn4NPA/fQaKS5IkSdJYGQglSZIkSRqmLwLvZPWULX8WEXtn5g/GOKZB9TNFDZn5QCNgqK92lfoUbWsN0K6vbFA1zawhm9cXqiwsW9ZWPYbWabIGFZRAit/2qLesx+fT1cyUcf2A7ZvBCLNxeqCpXAsTgVAbRsTCzFzRo82oz1PTIPdQfTqtTn/f2rKxPEjAwSgtHTDIp+t9XBcRGwMfAP6ccj8OalTBFb2M6lyN61nQ77XcnK5zqu1G8h7JzBVVEOUO1ap2195sP8bQ+rzodKy2aSz/8wD9t9Nv8OGw92O6zqJkY5uY6jYoAe3PBx6IiB8A36FkCzw/M5ePaBySJEmSNGVOjSdJkiRJGpoqe8x7GqvfP46xTMNUs5DMRKaZZhaQXppfsG7cWN6EqQVLdLN+H3VGnVGiuZ/TPW7jypLTzbCvhXZmOvPHsO+hhzeWZ8sX9iM5dxGxCXAu8Gqmfl+P62+FozpX43oWrKnvkXbX3liO8Qiyu/UbuNSvft6Fo9iPaanGcwTwYSYH3C0A9gfeCHwFuC0i/isinjazo5QkSZKk7gyEkiRJkiQNVWZ+DbistuqZEdGcckazwygySgw7sEoalpmc5m8cPgjsVVv+A2UKvZdX67egTKs1PzNjogDnz/hIe1vTz5Vmn2G/D+fsuzAzV2bmscDOwPsoUyS2C9hai5I56pyI+FJEbNCmjiRJkiTNOKfGkyRJkiSNwnHAt2vL7wcOmamNR8Sa+h9/Bp22aqPGcjPLyh2N5csyc/8BtzEbNfdzusftzmmMZVSGfS2siZrXdz9ZsGbC0M9dRGwLvLK26tfAUzOzOe1YO7MheGFU5+qh8CwY1IYMNu1l/Ri0e26sKce4eQ0elplnj2Uks0Rm/pKS5fM9Vca5A4CDgT8B9qE12Ov5lEDLZ870OCVJkiSpaU39w7AkSZIkaYwy8xzggtqqxVOcOqU+Lcsg/5lntgQ8DNuOA9bfqbF8W30hM1fQOo3RI6YyqFmo+SV/8zj0snOP/maD6VwLv6vO/ZruN43lXccyiskWDRis2fU+rhxGa1DCW/sMggLYcoCxjMqoztVD4VkwqL6fHRGxENi2tqrdtbemHOPmvq0p78OhyMw7M/PszHxHZu4HbA+cDKyqVTs0Ip41nhFKkiRJ0moGQkmSJEmSRuW4xvL7ptBHPUinmTWim92msK25YN9p1F8BXN2mziW1n3eIiM0GHtUsk5m/BW6orXp89YV+vw5oLP9g+qMaur6vhYjYktZghtm4P6NwDa3PkANnSba4dYHHDlC/ea7bnb9m8Mm3+um4yiT1yAHGMiqjOlc/BlbWlvcbsP1ceBYMapD3yJ60BiG32//mukGzCs6WY3xJY3kuZ0cc+dSSmXlTZr4ZOL7x0XNHvW1JkiRJ6mU2/PFHkiRJkrQGyswLaf0y/oCIeM6A3dQzQ6wfEdv02e7QAbczVzwhIvrKthERjwN2qa36QWaubFP1f+rNgBdOY3yzyXdrP68HDHLtvbSxfPH0hzN0L4iI6F0NgBc1lmfj/gxdZq6iNTPdlsCzxzScpuY56ebFjeV2568ZKPq7NnXaOXKAcYzMqM5VlfmsHlizU0Ts3U/biFhA6/MwgUunO6ZZYKjXXmYuBW6prVocEVv003lEPBx4Rm3VcuDaAcY3TN+j9b45PCIeNqaxTFcz498o9+OMxvKiEW5LkiRJkvpiIJQkSZIkaZTaZYXqN3gD4MrG8jN7NYiITYDXDLCNuaZ5TDt5T2P5sx3qfQq4r7b8jojYdOBRzT6fayy/q58MMxHxp8BetVVXZ+YPhzqy4dgOeGWvShGxDvCmxupO18Ka6JTG8kkRsfZYRtLqmH7usyp4tH49fi8zr29TdXljuTndWLu+NwOO7VVvBo3qXDWfBc1nYyevAzavLZ9dZZub654cEYt7VYqIrYC/qK36A/ClDtXrx3g+8M4+x/JuYK16P5k58mxG7WTmA8CptVXbAK8fx1iG4K7G8iinv2wGXbYLuJYkSZKkGWUglCRJkiRpZDLz+8BXa6v2pARw9Ou8xvJbu30xXmVvOAOY89O7dfGKiOiatSkijgKOqK26C/hMu7qZeSvwL7VV2wBfHjQYKiIOjoiewRcz6JvAz2vLewF/361BRDwK+OfG6o8NeVzD9IGIeEyPOp+g9Z5bkpnXjHBMs83XgR/Vlh8LfLrfTC/9ZraZgk2B0yJifpdtb8Pk6/HjHapf1Vh+Y7eNR8S6wJm0BvqM26jO1Rm0BoY8LyKO6dHXvkyezvWj/Yxjjji127UdEWtRjtsGtdVnZuYdHZp8AlhVW35tRHSdIq36/HW1VQ8C/9R11KP3D8C9teWTIqKZFauriNg4Io7oXXN0MvM+4KbaqoO7PWvqIuKNEbFB75p/1Mwqd90AbSVJkiRpJAyEkiRJkiSN2rspUwoNLDOvBS6prXo08F8RsXWzbkQ8ATgXeC6Ts6OsKZZTMmp9NiLeWWX7+aOIWC8iTgD+vdHubZnZbaqs44ArassHA1dExNFVwERbEbFzRLwlIn4AnA88aoB9GanMfJCSGax+7b0lIs5oBgBEcThwIa2ZMy5m8rGcLZYDDwfOj4gjmtmuImLriDgLOKq2+n7gr2duiONXZZd5Ga1Zz14AXBIRh7bLEhYR8yPikIg4jdbAnGGZeD49D/hmROzYZgzPAC6iBCZO+C4lg1s7Z9MawPGqiPhgu4CGiDgI+A7wVMr9MSuyHI3qXGXm75kcGPaxiPjHiGiZUjAiFkTEq4BzgPVrH30hM7/FmmE55Vl9UUQc0vywCq48B3h6bfWdwDs6dZiZvwD+rrZqPvDFiHhbm/fU2hHxFuCLVb0JH6je+WOTmb8Bjq6tmg+cGRGfqaabbat69z4nIj4J/Ap4y4iH2o/6VJM7AmdFxGER8ZiIWFQrzSmHTwZuiohTIuIZzfM3ISLWiYg3AB+qrU4mZ2CTJEmSpBm3YNwDkCRJkiSt2TLzqoj4PPCSKXbxFsoXehNT6j0N+GVEXEL5wnFdYDdKkBSUrBJHAV+Z6phnsZOANwBbAX8LvD0iLqYEMmwGPBFYr9HmK0yecqpFZt5bTQn3P8BO1eptq3Yfj4grgJuBuykZQjajZGvZqE13s0Zm/m9EvBc4vrb6FcCREXEpcCPleO1Fa8AJwK+Bl2XmKmanNwL/BmwBnAXcHBE/pJyj7YH9mfwf4N6amT+e0VHOAtUz6Cjg08BEdqG9gP8G7oiIy4Fl1WdbUTLXTQQANqeYGoYrKdnK/hx4BnB9FUz4C2Ah8Dhgh0abZcBRnaYNy8zbI+JkSuDphGOBo6tn5a3AhsDjac0QdjKwL/CU6e7UMIzqXGXmJyPiYFZPJzkPeDPw19Uz9BZgY8qxeESj+TXAX01vz2aVv6EE1O0EnBsRP6dkFLufEjDzhEb9VcDRVZBQN++lvIOeVi0vpGThO646xrdTgjefSGumKSjv+HczC2Tm5yJiB1qn8j2S8t64hRJwdwclSGojyr26E7PvPxx/HHgpq/fhT6vSdAOwqLFuI0pA2NHAAxFxLSXD1J2U/d4G2BtoBkl9JDObUxpLkiRJ0owzEEqSJEmSNBOOB15Ia/aHvmTmRVXWgQ+z+gu9tYCD2lS/H/iLzPxqRLT5eM67DTgM+DYlGGkDSiBFJ18DXtwpeKIuM2+opoM6g5KpZsJalOCAfXt08QAlCGdWycwTIuL3lC/kJ/4OMh94UlXauQZ4dmYuHf0Ip+w84NXAqZT92boq7STwrsz88AyNbdbJzC9ExK3A5ynBYxM2ZXXgxkw6hjIt3XMoz7V9qtLOrcChmXl9jz5PBHalZFGasD6d9+8U4G2UTHqzxgjP1asoASxvYPW7ZCGwuEubi4DDu0wJN+dk5qcjYjvg/dWqHavSzkpKENSX+uj3gYh4DuUdUp9Obn1as0s1/SdwZGau7Dn4GZKZfxsRP6FkBNy49tFWVenlzpEMbACZeXFEvJky3d/Av3vVLAD2qErHzQEfAd40je1IkiRJ0tDMtv+pIkmSJElaA2XmT+k8pVM/7T8KPBu4ukOVB4FvAvtn5pS3Mxdk5hWULCin0ToVVt0NwKsz8/BBvlzOzOWZeThlaryv0zpFVTsrgSWUrF3bZuZF/W5rJmXmyZQvcc8C/tCl6i8pQRJ7zvIgKAAy8wxKMNe3KfdAO5cBB2XmSTM2sFkqM8+nBHwcR7lHulkOfJYy1eYoxrICOBx4PSU7VDv3UQIxdusny0qVvexFVZ/dsvdcDByRma+pppCcdUZxrrJ4I3AA5Z55oEv1qynZ4w5ek4KgJmTm3wLPolwL7TxIyRD4hEHeqZm5IjNfUuu7UxBuUp5Nz8nMIzKz23N5LKrgr+0pmap+0UeTGymBqc+g/L4ydpn5QUqGuX+kTK25DFjRo9mTq/pXULKBdbOSEnB9YGYeO1ufJ5IkSZIeeqKP/xQqSZIkSdKsERGPBfajZFNZQZmu5ZLMvHmsAxuDiNiAErS0LbAJJWPU1cCl/WSB6qP/hZSggUWU6aLWpmR9Wgb8BLg2M3sFS80qEbEuJZvYdpR9uo9y3H6YmdeOc2zdRMQJtE7xt0M9WCsitqFMh7cNJcvNLZT7olcWoYesiNiFElS4GWUqqHspAUTXAFcNc1rEiFhKCaoAOD8zF9c+C8ozbWfgkdU4bgDOzcwpZVmLiLWqPh9HeTb8jnJNXJ6Zv5zaXozPKM5VRGxEeX5uTck2dTcl+9alcyEQsl8RcTqrpwQkM6Px+aNZPT1oUKYFvWAY79SI2JwSWLMlJbPSXZTz9p0+ptqbVSJie0pmxM0o99T9lPtqKXBNZt40vtGNRvU7xu6UoMTNKdNQrqAEH15HeW/+fnwjlCRJkqT2DISSJEmSJEma5XoFQml26xYIJY1Sr0AoSZIkSZLWNE6NJ0mSJEmSJEmSJEmSJGnOMxBKkiRJkiRJkiRJkiRJ0pxnIJQkSZIkSZIkSZIkSZKkOc9AKEmSJEmSJEmSJEmSJElznoFQkiRJkiRJkiRJkiRJkuY8A6EkSZIkSZIkSZIkSZIkzXkGQkmSJEmSJEmSJEmSJEma8yIzxz0GSZIkSZIkSZIkSZIkSZoWM0JJkiRJkiRJkiRJkiRJmvMMhJIkSZIkSZIkSZIkSZI05xkIJUmSJEmSJEmSJEmSJGnOMxBKkiRJkiRJkiRJkiRJ0pxnIJQkSZIkSZIkSZIkSZKkOc9AKEmSJEmSJEmSJEmSJElznoFQkiRJkiRJkiRJkiRJkuY8A6EkSZIkSZIkSZIkSZIkzXkGQkmSJEmSJEmSJEmSJEma8wyEkiRJkiRJkiRJkiRJkjTnGQglSZIkSZIkSZIkSZIkac4zEEqSJEmSJEmSJEmSJEnSnGcglCRJkiRJkiRJkiRJkqQ57/8DyfhslXFiNTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 604,
       "width": 1185
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,117)\n",
    "y = pca.explained_variance_ratio_.cumsum()\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(x = x, y = y)\n",
    "plt.xlabel(\"Number of principal components\", size = 20)\n",
    "plt.ylabel(\"Variance explained\", size = 20)\n",
    "plt.xticks(np.arange(0, max(x)+5, 5), fontsize=14)\n",
    "plt.yticks(np.arange(0,1.1,0.1), fontsize=14)\n",
    "plt.axvline(60, color = 'red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the appropriate number of principal components is around 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca60 = PCA(n_components = 60)\n",
    "final_df_pca = pca60.fit_transform(final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "The plot above showed us an optimal value to choose for principal componnents to choose so that we can explain 90% variance in our data. After number of components is 60, variance increases slowly."
   ]
  },
  {
   "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": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 150,
     "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",
    "pca3 = PCA(n_components=3)\n",
    "pca3.fit(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df3 = pca3.fit_transform(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_df3 = pd.DataFrame(pca3.components_, columns = final_df.columns, index = [\"PC-0\", \"PC-1\", \"PC-2\"]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def i_th_component(i):\n",
    "    \"\"\"\n",
    "    Args\n",
    "    1. i: It is the number of principal component. It can have values 1,2 and 3.\n",
    "    \n",
    "    Returns\n",
    "    A dataframe with index as feature name and value as the corresponding weight.\n",
    "    \"\"\"\n",
    "    pci = \"PC-{}\".format(i)\n",
    "    features = pc_df3[pci]\n",
    "    return pd.DataFrame(features.sort_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC-0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>-0.204868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>-0.201931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.198867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>-0.192600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_1-2_fam_homes</th>\n",
       "      <td>-0.192402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>-0.189563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.147652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.145519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.136346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_top_earners</th>\n",
       "      <td>-0.120008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_houseowners</th>\n",
       "      <td>-0.113855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>-0.109164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_stage_2</th>\n",
       "      <td>-0.107671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>-0.106389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>-0.105797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>-0.105494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_stage_1</th>\n",
       "      <td>-0.102609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>-0.102476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gebaeudetyp_residential_building</th>\n",
       "      <td>-0.094694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement_[2, 2]</th>\n",
       "      <td>-0.092268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <td>-0.090366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>-0.077502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_60s</th>\n",
       "      <td>-0.073557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANZAHL_PKW</th>\n",
       "      <td>-0.067852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>-0.066469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>-0.065183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage_4</th>\n",
       "      <td>-0.064961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationalitaet_kz_german_sounding</th>\n",
       "      <td>-0.063480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_familie_grob_multiperson_household</th>\n",
       "      <td>-0.059968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <td>-0.058265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>0.068040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>0.070169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>0.071123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>0.071720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement_[1, 4]</th>\n",
       "      <td>0.072424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gebaeudetyp_mixed_building</th>\n",
       "      <td>0.072505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>0.078258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_familie_grob_single</th>\n",
       "      <td>0.080953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>0.084961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_10+_fam_homes</th>\n",
       "      <td>0.087886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_90s</th>\n",
       "      <td>0.095852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>0.100627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>0.102553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>0.112141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.118343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <td>0.119780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG3</th>\n",
       "      <td>0.121495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_business_buildings</th>\n",
       "      <td>0.123781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <td>0.126851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.135432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>0.135711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>0.137145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage_1</th>\n",
       "      <td>0.144333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>0.150106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_stage_5</th>\n",
       "      <td>0.152194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.161368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>0.187237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.190562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>0.196782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_low_income_earners</th>\n",
       "      <td>0.199984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           PC-0\n",
       "FINANZ_MINIMALIST                     -0.204868\n",
       "MOBI_REGIO                            -0.201931\n",
       "PLZ8_ANTG1                            -0.198867\n",
       "KBA05_ANTG1                           -0.192600\n",
       "plz8_baumax_1-2_fam_homes             -0.192402\n",
       "KBA05_GBZ                             -0.189563\n",
       "PLZ8_GBZ                              -0.147652\n",
       "KONSUMNAEHE                           -0.145519\n",
       "INNENSTADT                            -0.136346\n",
       "lp_status_grob_top_earners            -0.120008\n",
       "lp_status_grob_houseowners            -0.113855\n",
       "GREEN_AVANTGARDE                      -0.109164\n",
       "wealth_stage_2                        -0.107671\n",
       "ALTERSKATEGORIE_GROB                  -0.106389\n",
       "FINANZ_VORSORGER                      -0.105797\n",
       "GEBAEUDETYP_RASTER                    -0.105494\n",
       "wealth_stage_1                        -0.102609\n",
       "BALLRAUM                              -0.102476\n",
       "gebaeudetyp_residential_building      -0.094694\n",
       "movement_[2, 2]                       -0.092268\n",
       "FINANZTYP_sparer                      -0.090366\n",
       "ANZ_PERSONEN                          -0.077502\n",
       "decade_60s                            -0.073557\n",
       "KBA13_ANZAHL_PKW                      -0.067852\n",
       "SEMIO_ERL                             -0.066469\n",
       "SEMIO_LUST                            -0.065183\n",
       "life_stage_4                          -0.064961\n",
       "nationalitaet_kz_german_sounding      -0.063480\n",
       "lp_familie_grob_multiperson_household -0.059968\n",
       "WOHNDAUER_2008                        -0.058265\n",
       "...                                         ...\n",
       "SEMIO_FAM                              0.068040\n",
       "SEMIO_MAT                              0.070169\n",
       "FINANZ_ANLEGER                         0.071123\n",
       "FINANZ_UNAUFFAELLIGER                  0.071720\n",
       "movement_[1, 4]                        0.072424\n",
       "gebaeudetyp_mixed_building             0.072505\n",
       "SEMIO_TRADV                            0.078258\n",
       "lp_familie_grob_single                 0.080953\n",
       "SEMIO_RAT                              0.084961\n",
       "plz8_baumax_10+_fam_homes              0.087886\n",
       "decade_90s                             0.095852\n",
       "SEMIO_REL                              0.100627\n",
       "SEMIO_PFLICHT                          0.102553\n",
       "RELAT_AB                               0.112141\n",
       "ARBEIT                                 0.118343\n",
       "ANZ_HAUSHALTE_AKTIV                    0.119780\n",
       "KBA05_ANTG3                            0.121495\n",
       "plz8_baumax_business_buildings         0.123781\n",
       "FINANZTYP_minimalist                   0.126851\n",
       "PLZ8_ANTG2                             0.135432\n",
       "KBA05_ANTG4                            0.135711\n",
       "FINANZ_SPARER                          0.137145\n",
       "life_stage_1                           0.144333\n",
       "FINANZ_HAUSBAUER                       0.150106\n",
       "wealth_stage_5                         0.152194\n",
       "EWDICHTE                               0.161368\n",
       "HH_EINKOMMEN_SCORE                     0.187237\n",
       "PLZ8_ANTG4                             0.190562\n",
       "PLZ8_ANTG3                             0.196782\n",
       "lp_status_grob_low_income_earners      0.199984\n",
       "\n",
       "[116 rows x 1 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_th_component(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>-0.227938</td>\n",
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       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>-0.224238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>-0.223610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>-0.213705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_TRADV</th>\n",
       "      <td>-0.212726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>-0.205507</td>\n",
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       "    <tr>\n",
       "      <th>decade_90s</th>\n",
       "      <td>-0.190546</td>\n",
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       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>-0.174494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>-0.171613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <td>-0.164725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>-0.140787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>-0.137449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <td>-0.128013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_vorsorger</th>\n",
       "      <td>-0.097651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.093030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_1-2_fam_homes</th>\n",
       "      <td>-0.091441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_hausbauer</th>\n",
       "      <td>-0.085406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_GBZ</th>\n",
       "      <td>-0.083997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>-0.078028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.074819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>-0.072131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_PERSONEN</th>\n",
       "      <td>-0.068657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.067574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.067195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>-0.067063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_familie_grob_family</th>\n",
       "      <td>-0.066158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_80s</th>\n",
       "      <td>-0.060728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <td>-0.057687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_external_supplied_hedonists</th>\n",
       "      <td>-0.055848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <td>-0.054786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <td>0.059826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_70s</th>\n",
       "      <td>0.062215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_stage_5</th>\n",
       "      <td>0.063260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.063572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.064537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_familie_grob_single</th>\n",
       "      <td>0.064779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationalitaet_kz_german_sounding</th>\n",
       "      <td>0.066644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>0.067727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_40s</th>\n",
       "      <td>0.067736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG4</th>\n",
       "      <td>0.068568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_unauffaelliger</th>\n",
       "      <td>0.071184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <td>0.072892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>0.074989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>0.080031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_demanding_shopper</th>\n",
       "      <td>0.080437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.082166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_sparer</th>\n",
       "      <td>0.088365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.088633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>0.092852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_50s</th>\n",
       "      <td>0.100721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_anleger</th>\n",
       "      <td>0.101497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>0.106790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <td>0.109035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <td>0.122273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_60s</th>\n",
       "      <td>0.129220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>0.160166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>0.167645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>0.189631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>0.223419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <td>0.240264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        PC-1\n",
       "FINANZ_SPARER                                      -0.227938\n",
       "FINANZ_UNAUFFAELLIGER                              -0.224238\n",
       "SEMIO_REL                                          -0.223610\n",
       "SEMIO_PFLICHT                                      -0.213705\n",
       "SEMIO_TRADV                                        -0.212726\n",
       "FINANZ_ANLEGER                                     -0.205507\n",
       "decade_90s                                         -0.190546\n",
       "SEMIO_KULT                                         -0.174494\n",
       "SEMIO_RAT                                          -0.171613\n",
       "ONLINE_AFFINITAET                                  -0.164725\n",
       "SEMIO_FAM                                          -0.140787\n",
       "SEMIO_MAT                                          -0.137449\n",
       "FINANZTYP_minimalist                               -0.128013\n",
       "FINANZTYP_vorsorger                                -0.097651\n",
       "PLZ8_ANTG1                                         -0.093030\n",
       "plz8_baumax_1-2_fam_homes                          -0.091441\n",
       "FINANZTYP_hausbauer                                -0.085406\n",
       "KBA05_GBZ                                          -0.083997\n",
       "MOBI_REGIO                                         -0.078028\n",
       "PLZ8_GBZ                                           -0.074819\n",
       "KBA05_ANTG1                                        -0.072131\n",
       "ANZ_PERSONEN                                       -0.068657\n",
       "INNENSTADT                                         -0.067574\n",
       "KONSUMNAEHE                                        -0.067195\n",
       "SEMIO_SOZ                                          -0.067063\n",
       "lp_familie_grob_family                             -0.066158\n",
       "decade_80s                                         -0.060728\n",
       "CJT_GESAMTTYP_advertising_interested_online_sho... -0.057687\n",
       "shopper_typ_external_supplied_hedonists            -0.055848\n",
       "HEALTH_TYP                                         -0.054786\n",
       "...                                                      ...\n",
       "ANZ_HAUSHALTE_AKTIV                                 0.059826\n",
       "decade_70s                                          0.062215\n",
       "wealth_stage_5                                      0.063260\n",
       "PLZ8_ANTG2                                          0.063572\n",
       "ARBEIT                                              0.064537\n",
       "lp_familie_grob_single                              0.064779\n",
       "nationalitaet_kz_german_sounding                    0.066644\n",
       "SEMIO_KAEM                                          0.067727\n",
       "decade_40s                                          0.067736\n",
       "KBA05_ANTG4                                         0.068568\n",
       "FINANZTYP_unauffaelliger                            0.071184\n",
       "CJT_GESAMTTYP_advertising_&_consumption_minimalist  0.072892\n",
       "FINANZ_MINIMALIST                                   0.074989\n",
       "SEMIO_KRIT                                          0.080031\n",
       "shopper_typ_demanding_shopper                       0.080437\n",
       "EWDICHTE                                            0.082166\n",
       "FINANZTYP_sparer                                    0.088365\n",
       "PLZ8_ANTG4                                          0.088633\n",
       "PLZ8_ANTG3                                          0.092852\n",
       "decade_50s                                          0.100721\n",
       "FINANZTYP_anleger                                   0.101497\n",
       "FINANZ_HAUSBAUER                                    0.106790\n",
       "CJT_GESAMTTYP_advertising_&_consumption_traditi...  0.109035\n",
       "W_KEIT_KIND_HH                                      0.122273\n",
       "decade_60s                                          0.129220\n",
       "RETOURTYP_BK_S                                      0.160166\n",
       "SEMIO_LUST                                          0.167645\n",
       "SEMIO_ERL                                           0.189631\n",
       "FINANZ_VORSORGER                                    0.223419\n",
       "ALTERSKATEGORIE_GROB                                0.240264\n",
       "\n",
       "[116 rows x 1 columns]"
      ]
     },
     "execution_count": 155,
     "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",
    "i_th_component(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SEMIO_KAEM</th>\n",
       "      <td>-0.343574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_DOM</th>\n",
       "      <td>-0.313807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KRIT</th>\n",
       "      <td>-0.291915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_ERL</th>\n",
       "      <td>-0.220140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_RAT</th>\n",
       "      <td>-0.173279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <td>-0.170730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_minimalist</th>\n",
       "      <td>-0.118119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_family_shopper</th>\n",
       "      <td>-0.113040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>-0.071875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement_[1, 4]</th>\n",
       "      <td>-0.068582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <td>-0.063718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_90s</th>\n",
       "      <td>-0.062872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <td>-0.059970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNENSTADT</th>\n",
       "      <td>-0.056651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_1-2_fam_homes</th>\n",
       "      <td>-0.053413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_familie_grob_single_parent</th>\n",
       "      <td>-0.050841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>-0.050582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KONSUMNAEHE</th>\n",
       "      <td>-0.049356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BALLRAUM</th>\n",
       "      <td>-0.048975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH_EINKOMMEN_SCORE</th>\n",
       "      <td>-0.046825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKK</th>\n",
       "      <td>-0.045730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_demanding_shopper</th>\n",
       "      <td>-0.045548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEBAEUDETYP_RASTER</th>\n",
       "      <td>-0.043424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <td>-0.039723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_PFLICHT</th>\n",
       "      <td>-0.033011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationalitaet_kz_assimilated_names</th>\n",
       "      <td>-0.032518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGIOTYP</th>\n",
       "      <td>-0.032033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <td>-0.031698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_stage_2</th>\n",
       "      <td>-0.031411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_low_income_earners</th>\n",
       "      <td>-0.031158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_stage_1</th>\n",
       "      <td>0.027538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationalitaet_kz_foreign_sounding</th>\n",
       "      <td>0.030664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gebaeudetyp_mixed_building</th>\n",
       "      <td>0.031779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_LUST</th>\n",
       "      <td>0.032558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_independents</th>\n",
       "      <td>0.032926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_shopping_stressed</th>\n",
       "      <td>0.033582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELAT_AB</th>\n",
       "      <td>0.034212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARBEIT</th>\n",
       "      <td>0.034365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <td>0.036945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plz8_baumax_business_buildings</th>\n",
       "      <td>0.048233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>0.051122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>0.051576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement_[2, 4]</th>\n",
       "      <td>0.057134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>0.060188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EWDICHTE</th>\n",
       "      <td>0.060431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade_60s</th>\n",
       "      <td>0.063423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <td>0.063522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lp_status_grob_top_earners</th>\n",
       "      <td>0.064688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement_[2, 2]</th>\n",
       "      <td>0.065248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETOURTYP_BK_S</th>\n",
       "      <td>0.083678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_MAT</th>\n",
       "      <td>0.091281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <td>0.092396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_REL</th>\n",
       "      <td>0.117940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopper_typ_external_supplied_hedonists</th>\n",
       "      <td>0.142841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <td>0.149353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZTYP_anleger</th>\n",
       "      <td>0.152439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_KULT</th>\n",
       "      <td>0.265245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_FAM</th>\n",
       "      <td>0.279278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_SOZ</th>\n",
       "      <td>0.281537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIO_VERT</th>\n",
       "      <td>0.348257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        PC-2\n",
       "SEMIO_KAEM                                         -0.343574\n",
       "SEMIO_DOM                                          -0.313807\n",
       "SEMIO_KRIT                                         -0.291915\n",
       "SEMIO_ERL                                          -0.220140\n",
       "SEMIO_RAT                                          -0.173279\n",
       "FINANZ_ANLEGER                                     -0.170730\n",
       "FINANZTYP_minimalist                               -0.118119\n",
       "shopper_typ_family_shopper                         -0.113040\n",
       "FINANZ_SPARER                                      -0.071875\n",
       "movement_[1, 4]                                    -0.068582\n",
       "FINANZ_HAUSBAUER                                   -0.063718\n",
       "decade_90s                                         -0.062872\n",
       "FINANZ_UNAUFFAELLIGER                              -0.059970\n",
       "INNENSTADT                                         -0.056651\n",
       "plz8_baumax_1-2_fam_homes                          -0.053413\n",
       "lp_familie_grob_single_parent                      -0.050841\n",
       "PLZ8_ANTG1                                         -0.050582\n",
       "KONSUMNAEHE                                        -0.049356\n",
       "BALLRAUM                                           -0.048975\n",
       "HH_EINKOMMEN_SCORE                                 -0.046825\n",
       "KKK                                                -0.045730\n",
       "shopper_typ_demanding_shopper                      -0.045548\n",
       "GEBAEUDETYP_RASTER                                 -0.043424\n",
       "PLZ8_GBZ                                           -0.039723\n",
       "SEMIO_PFLICHT                                      -0.033011\n",
       "nationalitaet_kz_assimilated_names                 -0.032518\n",
       "REGIOTYP                                           -0.032033\n",
       "CJT_GESAMTTYP_advertising_enthu_with_restricted... -0.031698\n",
       "wealth_stage_2                                     -0.031411\n",
       "lp_status_grob_low_income_earners                  -0.031158\n",
       "...                                                      ...\n",
       "life_stage_1                                        0.027538\n",
       "nationalitaet_kz_foreign_sounding                   0.030664\n",
       "gebaeudetyp_mixed_building                          0.031779\n",
       "SEMIO_LUST                                          0.032558\n",
       "lp_status_grob_independents                         0.032926\n",
       "shopper_typ_shopping_stressed                       0.033582\n",
       "RELAT_AB                                            0.034212\n",
       "ARBEIT                                              0.034365\n",
       "PLZ8_ANTG2                                          0.036945\n",
       "plz8_baumax_business_buildings                      0.048233\n",
       "PLZ8_ANTG3                                          0.051122\n",
       "PLZ8_ANTG4                                          0.051576\n",
       "movement_[2, 4]                                     0.057134\n",
       "FINANZ_VORSORGER                                    0.060188\n",
       "EWDICHTE                                            0.060431\n",
       "decade_60s                                          0.063423\n",
       "W_KEIT_KIND_HH                                      0.063522\n",
       "lp_status_grob_top_earners                          0.064688\n",
       "movement_[2, 2]                                     0.065248\n",
       "RETOURTYP_BK_S                                      0.083678\n",
       "SEMIO_MAT                                           0.091281\n",
       "GREEN_AVANTGARDE                                    0.092396\n",
       "SEMIO_REL                                           0.117940\n",
       "shopper_typ_external_supplied_hedonists             0.142841\n",
       "FINANZ_MINIMALIST                                   0.149353\n",
       "FINANZTYP_anleger                                   0.152439\n",
       "SEMIO_KULT                                          0.265245\n",
       "SEMIO_FAM                                           0.279278\n",
       "SEMIO_SOZ                                           0.281537\n",
       "SEMIO_VERT                                          0.348257\n",
       "\n",
       "[116 rows x 1 columns]"
      ]
     },
     "execution_count": 156,
     "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",
    "i_th_component(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "Well, visualising it with a graph is very difficult because of large number of features(116). So, here are some observations from only the numbers and features' information from data dictionary. I have studied the features which have highest and lowest value for weight.\n",
    "\n",
    "#### Component-1\n",
    "\n",
    "Some observation in this component are:\n",
    "\n",
    "- It consists mainly factors like size of building.\n",
    "- It takes into account the financial factor a little bit.\n",
    "\n",
    "#### Component-2\n",
    "\n",
    "Some observation in this component are:\n",
    "\n",
    "- It takes into account financial factors and age related factors.\n",
    "- It take small account for personality typology.\n",
    "\n",
    "#### Component-3\n",
    "\n",
    "Some observation in this component are:\n",
    "\n",
    "- It takes huge account for personality type.\n",
    "- Some financial and shopping related factors are also there in this component.\n",
    "- Few age related factors are also there."
   ]
  },
  {
   "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": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans\n",
    "# # Over a number of different cluster counts...\n",
    "# n_clusters = []\n",
    "# scores = []\n",
    "# for n_cluster in range(2,31,1):\n",
    "#     km = KMeans(n_clusters=n_cluster)\n",
    "\n",
    "#     # run k-means clustering on the data and...\n",
    "#     km.fit(transformed_df60)\n",
    "    \n",
    "#     # compute the average within-cluster distances.\n",
    "#     score = km.score(transformed_df60)\n",
    "    \n",
    "#     # make two arrays, one will contains the number of clusters and other will contain the scores\n",
    "#     n_clusters.append(n_cluster)\n",
    "#     scores.append(score)\n",
    "#     print(n_clusters)\n",
    "#     print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have saved the scores to a list so that I do not need to run the above cell every time I open the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 585,
       "width": 1163
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "n_clusters = np.arange(2,31,1)\n",
    "score = [-73867993.33116542, -69991658.22665577, -67318220.76468655, -65464239.171299845, -64323294.3339153, -63224188.97082328, -62404011.75989443, -61619662.20101176, -60871438.066457376, -60175792.18780826, -59714794.40880887, -58995779.35118965, -58381755.45821163, -58021185.37767402, -57252549.660566024, -56859463.47143221, -56387628.93776458, -56165196.46424212, -55626570.31012152, -55395892.11752362, -55007028.039218664, -54101652.511369325, -53975464.821662426, -53849171.13572646, -53549828.00786775, -52798755.82031626, -52869778.14796039, -52610270.085623205, -52503547.25643524]\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(x=n_clusters, y=score)\n",
    "plt.axvline(20, color = 'red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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",
    "km20 = KMeans(n_clusters=20)\n",
    "km_azdias = km20.fit(final_df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_preds = km_azdias.predict(final_df_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "Clustering was performed for values of cluster from 2 to 30. From the plot above, we can see that 20 is an optimum value for clusters. Score increases slowly after 20 clusters."
   ]
  },
  {
   "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": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv(\"Udacity_CUSTOMERS_Subset.csv\", sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "customers_final=clean_data(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "## filling null values\n",
    "nan_df = customers_final.loc[:, customers_final.isnull().any()]\n",
    "\n",
    "for col in list(nan_df.columns):\n",
    "    nan_df[col] = imp_mode.transform(nan_df[[col]])\n",
    "\n",
    "customers_final[list(nan_df.columns)] = nan_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "## feature scaling\n",
    "customers_final[customers_final.columns] = scaler.transform(customers_final)\n",
    "#transform the customers data using pca object\n",
    "customers_final_pca = pca60.transform(customers_final)\n",
    "#predict clustering using the kmeans object\n",
    "customers_preds = km_azdias.predict(customers_final_pca)"
   ]
  },
  {
   "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": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 375,
       "width": 561
      }
     },
     "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",
    "azdias_proportion = []\n",
    "customers_proportion = []\n",
    "x = [i+1 for i in range(20)]\n",
    "for i in range(20):\n",
    "    azdias_proportion.append((azdias_preds == i).sum()/len(azdias_preds))\n",
    "    customers_proportion.append((customers_preds == i).sum()/len(customers_preds))\n",
    "\n",
    "\n",
    "df_general = pd.DataFrame({'cluster' : x, 'azdias_proportion' : azdias_proportion, 'customers_proportion':customers_proportion})\n",
    "\n",
    "df_general.plot(x='cluster', y = ['azdias_proportion', 'customers_proportion'], kind='bar', figsize=(9,6))\n",
    "plt.ylabel('proportion of persons in each cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</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>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</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>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP  OST_WEST_KZ  \\\n",
       "0               1.0     -0.0       2.0          1.0   \n",
       "1              -0.0      0.0       1.0          0.0   \n",
       "2               0.0      0.0       2.0          1.0   \n",
       "3               0.0     -0.0       2.0          1.0   \n",
       "4              -0.0     -0.0       2.0          1.0   \n",
       "5              -0.0     -0.0       1.0          1.0   \n",
       "6              -0.0     -0.0       2.0          1.0   \n",
       "7              -0.0      0.0       2.0          1.0   \n",
       "8              -0.0      0.0       1.0          0.0   \n",
       "9               0.0     -0.0       2.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "0                                               -0.0    \n",
       "1                                                1.0    \n",
       "2                                               -0.0    \n",
       "3                                                1.0    \n",
       "4                                               -0.0    \n",
       "5                                               -0.0    \n",
       "6                                                0.0    \n",
       "7                                                0.0    \n",
       "8                                                0.0    \n",
       "9                                               -0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "0                                                1.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "3                                                0.0        \n",
       "4                                                0.0        \n",
       "5                                                1.0        \n",
       "6                                                0.0        \n",
       "7                                                0.0        \n",
       "8                                                1.0        \n",
       "9                                                1.0        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "0                                                0.0    \n",
       "1                                               -0.0    \n",
       "2                                                1.0    \n",
       "3                                               -0.0    \n",
       "4                                                1.0    \n",
       "5                                               -0.0    \n",
       "6                                                1.0    \n",
       "7                                               -0.0    \n",
       "8                                               -0.0    \n",
       "9                                               -0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "0                                                0.0     \n",
       "1                                               -0.0     \n",
       "2                                                0.0     \n",
       "3                                               -0.0     \n",
       "4                                               -0.0     \n",
       "5                                                0.0     \n",
       "6                                               -0.0     \n",
       "7                                                1.0     \n",
       "8                                               -0.0     \n",
       "9                                                0.0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "0                                                0.0      \n",
       "1                                               -0.0      \n",
       "2                                                0.0      \n",
       "3                                               -0.0      \n",
       "4                                                0.0      \n",
       "5                                                0.0      \n",
       "6                                               -0.0      \n",
       "7                                               -0.0      \n",
       "8                                               -0.0      \n",
       "9                                                0.0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "0                                               -0.0                         \n",
       "1                                                0.0                         \n",
       "2                                               -0.0                         \n",
       "3                                               -0.0                         \n",
       "4                                               -0.0                         \n",
       "5                                                0.0                         \n",
       "6                                               -0.0                         \n",
       "7                                               -0.0                         \n",
       "8                                               -0.0                         \n",
       "9                                                0.0                         \n",
       "\n",
       "     ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0    ...                   1.0       6.0         2.0         4.0         2.0   \n",
       "1    ...                   1.0       7.0         2.0         4.0         2.0   \n",
       "2    ...                   1.0       4.0         2.0         5.0         2.0   \n",
       "3    ...                   1.0       5.0         3.0         3.0         1.0   \n",
       "4    ...                   2.0       4.0         1.0         4.0         3.0   \n",
       "5    ...                   1.0       5.0         2.0         2.0         2.0   \n",
       "6    ...                   3.0       6.0         1.0         3.0         2.0   \n",
       "7    ...                   2.0       5.0         3.0         2.0         1.0   \n",
       "8    ...                   1.0       4.0         2.0         2.0         1.0   \n",
       "9    ...                   2.0       1.0         1.0         4.0         2.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  RELAT_AB  \n",
       "0         1.0       4.0       3.0     3.0       3.0  \n",
       "1         1.0       3.0       3.0     5.0       5.0  \n",
       "2         1.0       5.0       4.0     4.0       4.0  \n",
       "3         0.0       5.0       5.0     3.0       3.0  \n",
       "4         2.0       5.0       3.0     4.0       4.0  \n",
       "5         1.0       3.0       3.0     4.0       5.0  \n",
       "6         1.0       4.0       3.0     3.0       2.0  \n",
       "7         1.0       5.0       5.0     2.0       1.0  \n",
       "8         0.0       3.0       3.0     4.0       3.0  \n",
       "9         1.0       4.0       3.0     3.0       4.0  \n",
       "\n",
       "[10 rows x 116 columns]"
      ]
     },
     "execution_count": 167,
     "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",
    "data = scaler.inverse_transform(pca60.inverse_transform(customers_final_pca[np.where(customers_preds==7)])).round()\n",
    "df = pd.DataFrame(data=data,\n",
    "          index=np.array(range(0, data.shape[0])),\n",
    "          columns=final_df.columns)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
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       "      <td>4.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</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",
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       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>-0.0</td>\n",
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       "      <th>6</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.0</td>\n",
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       "      <td>-0.0</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP  OST_WEST_KZ  \\\n",
       "0              -0.0     -0.0       2.0          1.0   \n",
       "1              -0.0     -0.0       2.0          1.0   \n",
       "2               0.0     -0.0       1.0          1.0   \n",
       "3              -0.0      0.0       2.0          1.0   \n",
       "4               0.0     -0.0       2.0          1.0   \n",
       "5               0.0      0.0       2.0          1.0   \n",
       "6              -0.0     -0.0       2.0          0.0   \n",
       "7              -0.0      0.0       1.0          0.0   \n",
       "8               0.0      0.0       1.0          0.0   \n",
       "9               0.0     -0.0       2.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "0                                                1.0    \n",
       "1                                                0.0    \n",
       "2                                                1.0    \n",
       "3                                                1.0    \n",
       "4                                                1.0    \n",
       "5                                               -0.0    \n",
       "6                                               -0.0    \n",
       "7                                                0.0    \n",
       "8                                                0.0    \n",
       "9                                                0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "0                                               -0.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "3                                               -0.0        \n",
       "4                                                0.0        \n",
       "5                                                1.0        \n",
       "6                                               -0.0        \n",
       "7                                                1.0        \n",
       "8                                                1.0        \n",
       "9                                                1.0        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "0                                               -0.0    \n",
       "1                                               -0.0    \n",
       "2                                                0.0    \n",
       "3                                               -0.0    \n",
       "4                                               -0.0    \n",
       "5                                                0.0    \n",
       "6                                                1.0    \n",
       "7                                                0.0    \n",
       "8                                                0.0    \n",
       "9                                               -0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "0                                                0.0     \n",
       "1                                                1.0     \n",
       "2                                               -0.0     \n",
       "3                                                0.0     \n",
       "4                                               -0.0     \n",
       "5                                                0.0     \n",
       "6                                                0.0     \n",
       "7                                               -0.0     \n",
       "8                                               -0.0     \n",
       "9                                               -0.0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "0                                                0.0      \n",
       "1                                               -0.0      \n",
       "2                                               -0.0      \n",
       "3                                                0.0      \n",
       "4                                               -0.0      \n",
       "5                                                0.0      \n",
       "6                                                0.0      \n",
       "7                                               -0.0      \n",
       "8                                               -0.0      \n",
       "9                                                0.0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "0                                               -0.0                         \n",
       "1                                               -0.0                         \n",
       "2                                               -0.0                         \n",
       "3                                                0.0                         \n",
       "4                                               -0.0                         \n",
       "5                                                0.0                         \n",
       "6                                                0.0                         \n",
       "7                                                0.0                         \n",
       "8                                               -0.0                         \n",
       "9                                                0.0                         \n",
       "\n",
       "     ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0    ...                   1.0       7.0         2.0         4.0         2.0   \n",
       "1    ...                   3.0       6.0         2.0         3.0         2.0   \n",
       "2    ...                   1.0       6.0         3.0         2.0         1.0   \n",
       "3    ...                   0.0       1.0         3.0         2.0         1.0   \n",
       "4    ...                   1.0       6.0         2.0         4.0         2.0   \n",
       "5    ...                   2.0       3.0         2.0         3.0         2.0   \n",
       "6    ...                   2.0       6.0         1.0         3.0         3.0   \n",
       "7    ...                   1.0       6.0         1.0         3.0         3.0   \n",
       "8    ...                   1.0       5.0         2.0         3.0         2.0   \n",
       "9    ...                   0.0       3.0         2.0         3.0         2.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  RELAT_AB  \n",
       "0         0.0       3.0       3.0     4.0       4.0  \n",
       "1         0.0       3.0       3.0     4.0       5.0  \n",
       "2        -0.0       2.0       3.0     3.0       3.0  \n",
       "3         0.0       3.0       3.0     3.0       4.0  \n",
       "4         1.0       3.0       3.0     4.0       3.0  \n",
       "5         1.0       6.0       5.0     1.0       0.0  \n",
       "6         2.0       3.0       2.0     4.0       3.0  \n",
       "7         2.0       3.0       2.0     4.0       4.0  \n",
       "8         0.0       3.0       3.0     5.0       4.0  \n",
       "9         1.0       4.0       4.0     3.0       2.0  \n",
       "\n",
       "[10 rows x 116 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2 = scaler.inverse_transform(pca60.inverse_transform(final_df_pca[np.where(azdias_preds==7)])).round()\n",
    "df_2 = pd.DataFrame(data=data_2,\n",
    "          index=np.array(range(0, data_2.shape[0])),\n",
    "          columns=final_df.columns)\n",
    "df_2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this cluster belongs to senior citizen people with low to average financial topology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
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       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP  OST_WEST_KZ  \\\n",
       "0               0.0      0.0       2.0          1.0   \n",
       "1              -0.0      0.0       1.0          1.0   \n",
       "2               0.0      0.0       1.0          1.0   \n",
       "3               0.0      0.0       2.0          1.0   \n",
       "4              -0.0     -0.0       2.0          0.0   \n",
       "5               1.0      0.0       1.0          1.0   \n",
       "6               0.0      0.0       1.0          1.0   \n",
       "7               0.0      0.0       2.0          1.0   \n",
       "8              -0.0      0.0       1.0          0.0   \n",
       "9               0.0      0.0       2.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "0                                               -0.0    \n",
       "1                                                1.0    \n",
       "2                                                1.0    \n",
       "3                                                0.0    \n",
       "4                                                1.0    \n",
       "5                                                0.0    \n",
       "6                                               -0.0    \n",
       "7                                               -0.0    \n",
       "8                                               -0.0    \n",
       "9                                               -0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "0                                                1.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "3                                                0.0        \n",
       "4                                               -0.0        \n",
       "5                                                1.0        \n",
       "6                                                0.0        \n",
       "7                                               -0.0        \n",
       "8                                                1.0        \n",
       "9                                               -0.0        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "0                                               -0.0    \n",
       "1                                                0.0    \n",
       "2                                                0.0    \n",
       "3                                               -0.0    \n",
       "4                                                0.0    \n",
       "5                                               -0.0    \n",
       "6                                               -0.0    \n",
       "7                                                0.0    \n",
       "8                                                0.0    \n",
       "9                                               -0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "0                                                0.0     \n",
       "1                                               -0.0     \n",
       "2                                               -0.0     \n",
       "3                                               -0.0     \n",
       "4                                                0.0     \n",
       "5                                               -0.0     \n",
       "6                                                1.0     \n",
       "7                                                0.0     \n",
       "8                                                0.0     \n",
       "9                                                1.0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "0                                                0.0      \n",
       "1                                               -0.0      \n",
       "2                                               -0.0      \n",
       "3                                                1.0      \n",
       "4                                               -0.0      \n",
       "5                                               -0.0      \n",
       "6                                                0.0      \n",
       "7                                                1.0      \n",
       "8                                                0.0      \n",
       "9                                                0.0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "0                                                0.0                         \n",
       "1                                               -0.0                         \n",
       "2                                               -0.0                         \n",
       "3                                               -0.0                         \n",
       "4                                                0.0                         \n",
       "5                                               -0.0                         \n",
       "6                                               -0.0                         \n",
       "7                                               -0.0                         \n",
       "8                                                0.0                         \n",
       "9                                                0.0                         \n",
       "\n",
       "     ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0    ...                   1.0       5.0         1.0         4.0         3.0   \n",
       "1    ...                   2.0       5.0         1.0         4.0         3.0   \n",
       "2    ...                   2.0       7.0         1.0         4.0         3.0   \n",
       "3    ...                   2.0       5.0         1.0         4.0         3.0   \n",
       "4    ...                   1.0       5.0         1.0         4.0         3.0   \n",
       "5    ...                   1.0       5.0         1.0         4.0         4.0   \n",
       "6    ...                   3.0       7.0         1.0         4.0         3.0   \n",
       "7    ...                   3.0       1.0         1.0         4.0         4.0   \n",
       "8    ...                   1.0       3.0         1.0         4.0         3.0   \n",
       "9    ...                   2.0       6.0         1.0         4.0         3.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  RELAT_AB  \n",
       "0         1.0       4.0       3.0     4.0       4.0  \n",
       "1         1.0       4.0       3.0     1.0       0.0  \n",
       "2         1.0       4.0       3.0     3.0       3.0  \n",
       "3         1.0       5.0       4.0     3.0       4.0  \n",
       "4         1.0       3.0       2.0     4.0       3.0  \n",
       "5         1.0       3.0       2.0     4.0       4.0  \n",
       "6         1.0       3.0       2.0     4.0       3.0  \n",
       "7         1.0       4.0       3.0     4.0       5.0  \n",
       "8         1.0       5.0       4.0     4.0       4.0  \n",
       "9         1.0       2.0       2.0     4.0       4.0  \n",
       "\n",
       "[10 rows x 116 columns]"
      ]
     },
     "execution_count": 169,
     "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",
    "data_3 = scaler.inverse_transform(pca60.inverse_transform(customers_final_pca[np.where(customers_preds==17)])).round()\n",
    "df = pd.DataFrame(data=data_3,\n",
    "          index=np.array(range(0, data_3.shape[0])),\n",
    "          columns=final_df.columns)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>SOHO_KZ</th>\n",
       "      <th>VERS_TYP</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_minimalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_consumption_traditionalist</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_store_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_interested_online_shopper</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_&amp;_cross_channel_enthusiast</th>\n",
       "      <th>CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour</th>\n",
       "      <th>...</th>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <th>REGIOTYP</th>\n",
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       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
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       "    <tr>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP  OST_WEST_KZ  \\\n",
       "0              -0.0     -0.0       1.0          1.0   \n",
       "1              -0.0      0.0       2.0          1.0   \n",
       "2              -0.0     -0.0       2.0          1.0   \n",
       "3               0.0     -0.0       2.0          1.0   \n",
       "4              -0.0     -0.0       1.0          0.0   \n",
       "5               1.0      0.0       2.0          0.0   \n",
       "6               0.0      0.0       2.0          1.0   \n",
       "7              -0.0     -0.0       2.0          1.0   \n",
       "8               0.0     -0.0       2.0          1.0   \n",
       "9              -0.0      0.0       2.0          1.0   \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_minimalist  \\\n",
       "0                                                0.0    \n",
       "1                                                0.0    \n",
       "2                                               -0.0    \n",
       "3                                                0.0    \n",
       "4                                                1.0    \n",
       "5                                                0.0    \n",
       "6                                               -0.0    \n",
       "7                                               -0.0    \n",
       "8                                                0.0    \n",
       "9                                                0.0    \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_consumption_traditionalist  \\\n",
       "0                                                0.0        \n",
       "1                                                0.0        \n",
       "2                                                1.0        \n",
       "3                                                0.0        \n",
       "4                                                0.0        \n",
       "5                                                0.0        \n",
       "6                                                1.0        \n",
       "7                                               -0.0        \n",
       "8                                               -0.0        \n",
       "9                                               -0.0        \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_store_shopper  \\\n",
       "0                                               -0.0    \n",
       "1                                                0.0    \n",
       "2                                               -0.0    \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",
       "\n",
       "   CJT_GESAMTTYP_advertising_interested_online_shopper  \\\n",
       "0                                               -0.0     \n",
       "1                                               -0.0     \n",
       "2                                                0.0     \n",
       "3                                                1.0     \n",
       "4                                               -0.0     \n",
       "5                                                1.0     \n",
       "6                                                0.0     \n",
       "7                                                0.0     \n",
       "8                                               -0.0     \n",
       "9                                                0.0     \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_&_cross_channel_enthusiast  \\\n",
       "0                                                1.0      \n",
       "1                                               -0.0      \n",
       "2                                                0.0      \n",
       "3                                               -0.0      \n",
       "4                                               -0.0      \n",
       "5                                               -0.0      \n",
       "6                                                0.0      \n",
       "7                                                0.0      \n",
       "8                                               -0.0      \n",
       "9                                                0.0      \n",
       "\n",
       "   CJT_GESAMTTYP_advertising_enthu_with_restricted_cross_channel_behaviour  \\\n",
       "0                                               -0.0                         \n",
       "1                                                1.0                         \n",
       "2                                                0.0                         \n",
       "3                                               -0.0                         \n",
       "4                                               -0.0                         \n",
       "5                                                0.0                         \n",
       "6                                                0.0                         \n",
       "7                                                1.0                         \n",
       "8                                                0.0                         \n",
       "9                                                0.0                         \n",
       "\n",
       "     ...     ONLINE_AFFINITAET  REGIOTYP  PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0    ...                   1.0       5.0         1.0         4.0         3.0   \n",
       "1    ...                   2.0       7.0         1.0         4.0         3.0   \n",
       "2    ...                   0.0       7.0         1.0         4.0         3.0   \n",
       "3    ...                   1.0       6.0         1.0         4.0         3.0   \n",
       "4    ...                   2.0       5.0         1.0         4.0         3.0   \n",
       "5    ...                   4.0       3.0         1.0         4.0         3.0   \n",
       "6    ...                   1.0       6.0         1.0         4.0         3.0   \n",
       "7    ...                   4.0       5.0         1.0         4.0         3.0   \n",
       "8    ...                   5.0       5.0         1.0         4.0         3.0   \n",
       "9    ...                   4.0       5.0         1.0         4.0         3.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  RELAT_AB  \n",
       "0         1.0       5.0       4.0     4.0       4.0  \n",
       "1         1.0       4.0       3.0     3.0       4.0  \n",
       "2         1.0       4.0       3.0     3.0       4.0  \n",
       "3         1.0       3.0       2.0     4.0       4.0  \n",
       "4         1.0       3.0       2.0     5.0       5.0  \n",
       "5         1.0       3.0       2.0     4.0       3.0  \n",
       "6         1.0       3.0       2.0     4.0       4.0  \n",
       "7         1.0       3.0       2.0     4.0       5.0  \n",
       "8         1.0       3.0       2.0     3.0       3.0  \n",
       "9         1.0       5.0       3.0     3.0       4.0  \n",
       "\n",
       "[10 rows x 116 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_4 = scaler.inverse_transform(pca60.inverse_transform(final_df_pca[np.where(azdias_preds==17)])).round()\n",
    "df = pd.DataFrame(data=data_4,\n",
    "          index=np.array(range(0, data_4.shape[0])),\n",
    "          columns=final_df.columns)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this cluster belongs to younger people, with medium to high financial topology, with lower affinity for personality typology."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "- From the visualization above, we observe that for half of clusters there is a good match between the general population and the customers population. So, the company can use this info to focus on a part of the general population for their business growth.\n",
    "\n",
    "- We must consider the fact that we have removed many columns, which may contain very important information. If we had less null values in those features or somehow if we could keep those features in our analyis then would see much better match between the general population and the customers population."
   ]
  },
  {
   "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."
   ]
  }
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