Arvato Project Workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project: Create a Customer Segmentation Report for Arvato Financial Services\n",
    "\n",
    "In this project, you will analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. You'll use unsupervised learning techniques to perform customer segmentation, identifying the parts of the population that best describe the core customer base of the company. Then, you'll apply what you've learned on a third dataset with demographics information for targets of a marketing campaign for the company, and use a model to predict which individuals are most likely to convert into becoming customers for the company. 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",
    "The versions of those two datasets used in this project will include many more features and has not been pre-cleaned. You are also free to choose whatever approach you'd like to analyzing the data rather than follow pre-determined steps. In your work on this project, make sure that you carefully document your steps and decisions, since your main deliverable for this project will be a blog post reporting your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from src.utils import *\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import types\n",
    "import pickle\n",
    "import dill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import Imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomState = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Get to Know the Data\n",
    "\n",
    "There are four data files associated with this project:\n",
    "\n",
    "- `Udacity_AZDIAS_052018.csv`: Demographics data for the general population of Germany; 891 211 persons (rows) x 366 features (columns).\n",
    "- `Udacity_CUSTOMERS_052018.csv`: Demographics data for customers of a mail-order company; 191 652 persons (rows) x 369 features (columns).\n",
    "- `Udacity_MAILOUT_052018_TRAIN.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 982 persons (rows) x 367 (columns).\n",
    "- `Udacity_MAILOUT_052018_TEST.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 833 persons (rows) x 366 (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. Use the information from the first two files to figure out how customers (\"CUSTOMERS\") are similar to or differ from the general population at large (\"AZDIAS\"), then use your analysis to make predictions on the other two files (\"MAILOUT\"), predicting which recipients are most likely to become a customer for the mail-order company.\n",
    "\n",
    "The \"CUSTOMERS\" file contains three extra columns ('CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'), which provide broad information about the customers depicted in the file. The original \"MAILOUT\" file included one additional column, \"RESPONSE\", which indicated whether or not each recipient became a customer of the company. For the \"TRAIN\" subset, this column has been retained, but in the \"TEST\" subset it has been removed; it is against that withheld column that your final predictions will be assessed in the Kaggle competition.\n",
    "\n",
    "Otherwise, all of the remaining columns are the same between the three data files. For more information about the columns depicted in the files, you can refer to two Excel spreadsheets provided in the workspace. [One of them](./DIAS Information Levels - Attributes 2017.xlsx) is a top-level list of attributes and descriptions, organized by informational category. [The other](./DIAS Attributes - Values 2017.xlsx) is a detailed mapping of data values for each feature in alphabetical order.\n",
    "\n",
    "In the below cell, we've provided some initial code to load in the first two datasets. Note for all of the `.csv` data files in this project that they're semicolon (`;`) delimited, so an additional argument in the [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call has been included to read in the data properly. Also, considering the size of the datasets, it may take some time for them to load completely.\n",
    "\n",
    "You'll notice when the data is loaded in that a warning message will immediately pop up. Before you really start digging into the modeling and analysis, you're going to need to perform some cleaning. Take some time to browse the structure of the data and look over the informational spreadsheets to understand the data values. Make some decisions on which features to keep, which features to drop, and if any revisions need to be made on data formats. It'll be a good idea to create a function with pre-processing steps, since you'll need to clean all of the datasets before you work with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Satya\\AppData\\Local\\Temp\\ipykernel_23664\\2758223681.py:2: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  azdias = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_AZDIAS_052018.csv', sep=';')\n",
      "C:\\Users\\Satya\\AppData\\Local\\Temp\\ipykernel_23664\\2758223681.py:3: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  customers = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_CUSTOMERS_052018.csv', sep=';')\n"
     ]
    }
   ],
   "source": [
    "# load in the data\n",
    "azdias = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_AZDIAS_052018.csv', sep=';')\n",
    "customers = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_CUSTOMERS_052018.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891221 entries, 0 to 891220\n",
      "Columns: 366 entries, LNR to ALTERSKATEGORIE_GROB\n",
      "dtypes: float64(267), int64(93), object(6)\n",
      "memory usage: 2.4+ GB\n"
     ]
    }
   ],
   "source": [
    "azdias.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 191652 entries, 0 to 191651\n",
      "Columns: 369 entries, LNR to ALTERSKATEGORIE_GROB\n",
      "dtypes: float64(267), int64(94), object(8)\n",
      "memory usage: 539.5+ MB\n"
     ]
    }
   ],
   "source": [
    "customers.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the values in the Columns that threw a warning when we loaded the csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = load_info('data/DIAS Attributes - Values 2017.xlsx', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>0</td>\n",
       "      <td>no classification possible</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>1</td>\n",
       "      <td>passive elderly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>2</td>\n",
       "      <td>cultural elderly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>3</td>\n",
       "      <td>experience-driven elderly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attribute         Description Value                     Meaning Missing\n",
       "0  AGER_TYP  best-ager typology    -1                     unknown    True\n",
       "1  AGER_TYP  best-ager typology     0  no classification possible   False\n",
       "2  AGER_TYP  best-ager typology     1             passive elderly   False\n",
       "3  AGER_TYP  best-ager typology     2            cultural elderly   False\n",
       "4  AGER_TYP  best-ager typology     3   experience-driven elderly   False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CAMEO_DEUG_2015', 'CAMEO_INTL_2015'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gettig the name of the columns that read_csv warned about having mixed data types\n",
    "azdias.columns[18:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_columns('CAMEO', azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CAMEO_DEUG_2015', 'CAMEO_DEU_2015', 'CAMEO_DEUINTL_2015']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attributes('CAMEO', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The value 'CAMEO_DEUINTL_2015' in attributes excel corresponds to the column 'CAMEO_INTL_2015'\n",
    "attributes.replace({'Attribute':{'CAMEO_DEUINTL_2015':'CAMEO_INTL_2015'}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cameo_cols = get_attributes('CAMEO', attributes)\n",
    "cols_cameo = get_attributes('CAMEO', attributes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the unqiue values in each of the above columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Column CAMEO_DEUG_2015:  [nan 8.0 4.0 2.0 6.0 1.0 9.0 5.0 7.0 3.0 '4' '3' '7' '2' '8' '9' '6' '5'\n",
      " '1' 'X']\n",
      "Unique Values in Column CAMEO_DEU_2015:  [nan '8A' '4C' '2A' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5C' '8B' '7A' '5D'\n",
      " '9E' '9B' '1B' '3D' '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C'\n",
      " '9C' '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F'\n",
      " '5F' '1C' 'XX']\n",
      "Unique Values in Column CAMEO_INTL_2015:  [nan 51.0 24.0 12.0 43.0 54.0 22.0 14.0 13.0 15.0 33.0 41.0 34.0 55.0 25.0\n",
      " 23.0 31.0 52.0 35.0 45.0 44.0 32.0 '22' '24' '41' '12' '54' '51' '44'\n",
      " '35' '23' '25' '14' '34' '52' '55' '31' '32' '15' '13' '43' '33' '45'\n",
      " 'XX']\n"
     ]
    }
   ],
   "source": [
    "for col in cameo_cols:\n",
    "    get_unique_vals(azdias, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Column CAMEO_DEUG_2015:  [1.0 nan 5.0 4.0 7.0 3.0 9.0 2.0 6.0 8.0 '6' '3' '8' '9' '2' '4' '1' '7'\n",
      " '5' 'X']\n",
      "Unique Values in Column CAMEO_DEU_2015:  ['1A' nan '5D' '4C' '7B' '3B' '1D' '9E' '2D' '4A' '6B' '9D' '8B' '5C' '9C'\n",
      " '4E' '6C' '8C' '8A' '5B' '9B' '3D' '2A' '3C' '5F' '7A' '1E' '2C' '7C'\n",
      " '5A' '2B' '6D' '7E' '5E' '6E' '3A' '9A' '4B' '1C' '1B' '6A' '8D' '7D'\n",
      " '6F' '4D' 'XX']\n",
      "Unique Values in Column CAMEO_INTL_2015:  [13.0 nan 34.0 24.0 41.0 23.0 15.0 55.0 14.0 22.0 43.0 51.0 33.0 25.0 44.0\n",
      " 54.0 32.0 12.0 35.0 31.0 45.0 52.0 '45' '25' '55' '51' '14' '54' '43'\n",
      " '22' '15' '24' '35' '23' '12' '44' '41' '52' '31' '13' '34' '32' '33'\n",
      " 'XX']\n"
     ]
    }
   ],
   "source": [
    "for col in cameo_cols:\n",
    "    get_unique_vals(customers, col)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a column to indicate missing values info; taken from attributes CSVs that have been provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([True, False, nan], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes['Missing'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical Features or columns can be identified using three approaches:\n",
    "- columns with dtype object in the dataframe\n",
    "- columns containing the string 'klasse' or 'typ' in their name\n",
    "- columns mentioned in the attribute file with the words classification or typology in their description\n",
    "\n",
    "Out of all the potential categorical columns obtained using the above methods, some of them already might be in an encoded format, while some might need further transformation or contain information that could be made into a different columns altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANZ_HAUSHALTE_AKTIV</td>\n",
       "      <td>number of households in the building</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value (typically coded from 1-10)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANZ_HH_TITEL</td>\n",
       "      <td>number of academic title holder in building</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value (typically coded from 1-10)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANZ_PERSONEN</td>\n",
       "      <td>number of adult persons in the household</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value (typically coded from 1-3)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANZ_TITEL</td>\n",
       "      <td>number of professional title holder in household</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value (typically coded from 1-10)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>1A</td>\n",
       "      <td>Work-Life-Balance</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>1B</td>\n",
       "      <td>Wealthy Best Ager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>1C</td>\n",
       "      <td>Successful Songwriter</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>1D</td>\n",
       "      <td>Old Nobility</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>1E</td>\n",
       "      <td>City Nobility</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>2A</td>\n",
       "      <td>Cottage Chic</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>2B</td>\n",
       "      <td>Noble Jogger</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>2C</td>\n",
       "      <td>Established gourmet</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>2D</td>\n",
       "      <td>Fine Management</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>3A</td>\n",
       "      <td>Career &amp; Family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>3B</td>\n",
       "      <td>Powershopping Families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>3C</td>\n",
       "      <td>Rural Neighborhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>3D</td>\n",
       "      <td>Secure Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>4A</td>\n",
       "      <td>Family Starter</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>4B</td>\n",
       "      <td>Family Life</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>4C</td>\n",
       "      <td>String Trimmer</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>4D</td>\n",
       "      <td>Empty Nest</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>4E</td>\n",
       "      <td>Golden Ager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5A</td>\n",
       "      <td>Younger Employees</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5B</td>\n",
       "      <td>Suddenly Family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5C</td>\n",
       "      <td>Family First</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5D</td>\n",
       "      <td>Stock Market Junkies</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5E</td>\n",
       "      <td>Coffee Rider</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>5F</td>\n",
       "      <td>Active Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6A</td>\n",
       "      <td>Jobstarter</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6B</td>\n",
       "      <td>Petty Bourgeois</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6C</td>\n",
       "      <td>Long-established</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6D</td>\n",
       "      <td>Sportgardener</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6E</td>\n",
       "      <td>Urban Parents</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>6F</td>\n",
       "      <td>Frugal Aging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>7A</td>\n",
       "      <td>Journeymen</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>7B</td>\n",
       "      <td>Mantaplatte</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>7C</td>\n",
       "      <td>Factory Worker</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>7D</td>\n",
       "      <td>Rear Window</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>7E</td>\n",
       "      <td>Interested Retirees</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>8A</td>\n",
       "      <td>Multi-culteral</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>8B</td>\n",
       "      <td>Young &amp; Mobile</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>8C</td>\n",
       "      <td>Prefab</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>8D</td>\n",
       "      <td>Town Seniors</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>9A</td>\n",
       "      <td>First Shared Apartment</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>9B</td>\n",
       "      <td>Temporary Workers</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>9C</td>\n",
       "      <td>Afternoon Talk Show</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>9D</td>\n",
       "      <td>Mini-Jobber</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CAMEO_DEU_2015</td>\n",
       "      <td>CAMEO classification 2015 - detailled classifi...</td>\n",
       "      <td>9E</td>\n",
       "      <td>Socking Away</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>year of birth</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>KBA13_ANZAHL_PKW</td>\n",
       "      <td>number of cars in the PLZ8</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>MIN_GEBAEUDEJAHR</td>\n",
       "      <td>year the building was first mentioned in our d...</td>\n",
       "      <td>…</td>\n",
       "      <td>numeric value</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>size of the community</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>O</td>\n",
       "      <td>East (GDR)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>W</td>\n",
       "      <td>West (FRG)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Attribute                                        Description  \\\n",
       "0   ANZ_HAUSHALTE_AKTIV               number of households in the building   \n",
       "1          ANZ_HH_TITEL        number of academic title holder in building   \n",
       "2          ANZ_PERSONEN           number of adult persons in the household   \n",
       "3             ANZ_TITEL  number of professional title holder in household    \n",
       "4        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "5        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "6        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "7        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "8        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "9        CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "10       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "11       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "12       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "13       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "14       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "15       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "16       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "17       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "18       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "19       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "20       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "21       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "22       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "23       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "24       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "25       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "26       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "27       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "28       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "29       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "30       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "31       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "32       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "33       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "34       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "35       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "36       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "37       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "38       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "39       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "40       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "41       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "42       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "43       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "44       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "45       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "46       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "47       CAMEO_DEU_2015  CAMEO classification 2015 - detailled classifi...   \n",
       "48          GEBURTSJAHR                                      year of birth   \n",
       "49     KBA13_ANZAHL_PKW                         number of cars in the PLZ8   \n",
       "50     MIN_GEBAEUDEJAHR  year the building was first mentioned in our d...   \n",
       "51          ORTSGR_KLS9                              size of the community   \n",
       "52          OST_WEST_KZ                 flag indicating the former GDR/FRG   \n",
       "53          OST_WEST_KZ                 flag indicating the former GDR/FRG   \n",
       "\n",
       "   Value                                    Meaning Missing  \n",
       "0      …  numeric value (typically coded from 1-10)   False  \n",
       "1      …  numeric value (typically coded from 1-10)   False  \n",
       "2      …   numeric value (typically coded from 1-3)   False  \n",
       "3      …  numeric value (typically coded from 1-10)   False  \n",
       "4     1A                          Work-Life-Balance   False  \n",
       "5     1B                          Wealthy Best Ager   False  \n",
       "6     1C                      Successful Songwriter   False  \n",
       "7     1D                               Old Nobility   False  \n",
       "8     1E                              City Nobility   False  \n",
       "9     2A                               Cottage Chic   False  \n",
       "10    2B                               Noble Jogger   False  \n",
       "11    2C                        Established gourmet   False  \n",
       "12    2D                            Fine Management   False  \n",
       "13    3A                            Career & Family   False  \n",
       "14    3B                     Powershopping Families   False  \n",
       "15    3C                         Rural Neighborhood   False  \n",
       "16    3D                          Secure Retirement   False  \n",
       "17    4A                             Family Starter   False  \n",
       "18    4B                                Family Life   False  \n",
       "19    4C                             String Trimmer   False  \n",
       "20    4D                                 Empty Nest   False  \n",
       "21    4E                                Golden Ager   False  \n",
       "22    5A                          Younger Employees   False  \n",
       "23    5B                            Suddenly Family   False  \n",
       "24    5C                               Family First   False  \n",
       "25    5D                       Stock Market Junkies   False  \n",
       "26    5E                               Coffee Rider   False  \n",
       "27    5F                          Active Retirement   False  \n",
       "28    6A                                 Jobstarter   False  \n",
       "29    6B                            Petty Bourgeois   False  \n",
       "30    6C                           Long-established   False  \n",
       "31    6D                              Sportgardener   False  \n",
       "32    6E                              Urban Parents   False  \n",
       "33    6F                               Frugal Aging   False  \n",
       "34    7A                                 Journeymen   False  \n",
       "35    7B                                Mantaplatte   False  \n",
       "36    7C                             Factory Worker   False  \n",
       "37    7D                                Rear Window   False  \n",
       "38    7E                        Interested Retirees   False  \n",
       "39    8A                             Multi-culteral   False  \n",
       "40    8B                             Young & Mobile   False  \n",
       "41    8C                                     Prefab   False  \n",
       "42    8D                               Town Seniors   False  \n",
       "43    9A                     First Shared Apartment   False  \n",
       "44    9B                          Temporary Workers   False  \n",
       "45    9C                        Afternoon Talk Show   False  \n",
       "46    9D                                Mini-Jobber   False  \n",
       "47    9E                               Socking Away   False  \n",
       "48     …                              numeric value   False  \n",
       "49     …                              numeric value   False  \n",
       "50     …                              numeric value   False  \n",
       "51    -1                                    unknown    True  \n",
       "52     O                                 East (GDR)   False  \n",
       "53     W                                 West (FRG)   False  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_attributes_info = attributes.loc[attributes.Value.apply(lambda x: (type(x)==str) & (x!='-1, 0') & (x!='-1, 9')), :]\n",
    "categorical_attributes_info = categorical_attributes_info.reset_index(drop=True)\n",
    "categorical_attributes_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'…'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_attributes_info.loc[1, 'Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CAMEO_DEU_2015', 'ORTSGR_KLS9', 'OST_WEST_KZ'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing off all numeric value cols from the above\n",
    "categorical_attributes_info = categorical_attributes_info[categorical_attributes_info.Value!='…']\n",
    "categorical_attributes = categorical_attributes_info.Attribute.unique()\n",
    "categorical_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>gender</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>gender</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BIP_FLAG</td>\n",
       "      <td>business-flag indicating companies in the buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>no company in the building</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIP_FLAG</td>\n",
       "      <td>business-flag indicating companies in the buil...</td>\n",
       "      <td>1</td>\n",
       "      <td>company in the building</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>Green avantgarde</td>\n",
       "      <td>0</td>\n",
       "      <td>doesn't belong to the green avantgarde</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>the environmental sustainability is the domina...</td>\n",
       "      <td>1</td>\n",
       "      <td>belongs to the green avantgarde</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KBA05_SEG6</td>\n",
       "      <td>share of upper class cars (BMW 7er etc.) in th...</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KBA05_SEG6</td>\n",
       "      <td>share of upper class cars (BMW 7er etc.) in th...</td>\n",
       "      <td>1</td>\n",
       "      <td>some</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>O</td>\n",
       "      <td>East (GDR)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>W</td>\n",
       "      <td>West (FRG)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SOHO_FLAG</td>\n",
       "      <td>small office/home office flag</td>\n",
       "      <td>0</td>\n",
       "      <td>no small office/home office</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SOHO_FLAG</td>\n",
       "      <td>small office/home office flag</td>\n",
       "      <td>1</td>\n",
       "      <td>small office/home office</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>insurance typology</td>\n",
       "      <td>1</td>\n",
       "      <td>social-safety driven</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>insurance typology</td>\n",
       "      <td>2</td>\n",
       "      <td>individualistic-accepting risks</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attribute                                        Description Value  \\\n",
       "0          ANREDE_KZ                                             gender     1   \n",
       "1          ANREDE_KZ                                             gender     2   \n",
       "2           BIP_FLAG  business-flag indicating companies in the buil...     0   \n",
       "3           BIP_FLAG  business-flag indicating companies in the buil...     1   \n",
       "4   GREEN_AVANTGARDE                                   Green avantgarde     0   \n",
       "5   GREEN_AVANTGARDE  the environmental sustainability is the domina...     1   \n",
       "6         KBA05_SEG6  share of upper class cars (BMW 7er etc.) in th...     0   \n",
       "7         KBA05_SEG6  share of upper class cars (BMW 7er etc.) in th...     1   \n",
       "8        OST_WEST_KZ                 flag indicating the former GDR/FRG     O   \n",
       "9        OST_WEST_KZ                 flag indicating the former GDR/FRG     W   \n",
       "10         SOHO_FLAG                      small office/home office flag     0   \n",
       "11         SOHO_FLAG                      small office/home office flag     1   \n",
       "12          VERS_TYP                                insurance typology      1   \n",
       "13          VERS_TYP                                insurance typology      2   \n",
       "\n",
       "                                   Meaning Missing  \n",
       "0                                     male   False  \n",
       "1                                   female   False  \n",
       "2               no company in the building   False  \n",
       "3                  company in the building   False  \n",
       "4   doesn't belong to the green avantgarde   False  \n",
       "5          belongs to the green avantgarde   False  \n",
       "6                                     none   False  \n",
       "7                                     some   False  \n",
       "8                               East (GDR)   False  \n",
       "9                               West (FRG)   False  \n",
       "10             no small office/home office   False  \n",
       "11                small office/home office   False  \n",
       "12                    social-safety driven   False  \n",
       "13         individualistic-accepting risks   False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_attrib = attributes.query(\"Missing == False\").groupby('Attribute').size()\n",
    "binary_attrib_index = binary_attrib[binary_attrib  == 2].index\n",
    "binary_attributes_info = attributes.loc[attributes.Attribute.apply(lambda x: x in binary_attrib_index), :].query(\"Missing == False\")\n",
    "binary_attributes_info.reset_index(inplace=True, drop=True)\n",
    "binary_attributes_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = set(list(categorical_attributes_info.Attribute.unique()) + list(binary_attributes_info.Attribute.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "typ_attributes_slicer = attributes.Attribute.str.contains('TYP')\n",
    "klasse_attributes_slicer = attributes.Attribute.str.contains('KLASSE')\n",
    "typ_descr_slicer = attributes.Description.str.contains('typ')\n",
    "class_descr_slicer = attributes.Description.str.contains('class')\n",
    "klasse_type_slicer = (typ_attributes_slicer | klasse_attributes_slicer | typ_descr_slicer | class_descr_slicer)\n",
    "\n",
    "klasse_type_attributes = attributes.loc[klasse_type_slicer, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KBA13_SEG_MITTELKLASSE',\n",
       " 'ORTSGR_KLS9',\n",
       " 'VERS_TYP',\n",
       " 'ALTERSKATEGORIE_GROB',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'KBA13_SEG_OBERKLASSE',\n",
       " 'KBA05_SEG4',\n",
       " 'KBA13_SEG_KOMPAKTKLASSE',\n",
       " 'KBA13_SEG_OBEREMITTELKLASSE',\n",
       " 'REGIOTYP',\n",
       " 'CAMEO_INTL_2015',\n",
       " 'KBA05_BAUMAX',\n",
       " 'SHOPPER_TYP',\n",
       " 'PLZ8_BAUMAX',\n",
       " 'ZABEOTYP',\n",
       " 'CAMEO_DEU_2015',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'KBA05_SEG3',\n",
       " 'KBA13_KRSSEG_OBER',\n",
       " 'KBA05_SEG6',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'FINANZ_HAUSBAUER',\n",
       " 'GEBAEUDETYP_RASTER',\n",
       " 'KBA05_MOD2',\n",
       " 'RETOURTYP_BK_S',\n",
       " 'KBA05_KRSOBER',\n",
       " 'FINANZ_UNAUFFAELLIGER',\n",
       " 'FINANZ_VORSORGER',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'AGER_TYP',\n",
       " 'FINANZ_SPARER',\n",
       " 'FINANZ_MINIMALIST',\n",
       " 'KBA05_MOD3',\n",
       " 'KBA05_MOD1',\n",
       " 'GEBAEUDETYP',\n",
       " 'FINANZ_ANLEGER',\n",
       " 'HEALTH_TYP',\n",
       " 'FINANZTYP',\n",
       " 'KBA05_SEG5',\n",
       " 'D19_KONSUMTYP']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klasse_type_cols = klasse_type_attributes.Attribute.unique()\n",
    "klasse_type_cols = list(set(klasse_type_cols)&set(azdias.columns))\n",
    "klasse_type_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ORTSGR_KLS9',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'KBA13_SEG_OBERKLASSE',\n",
       " 'KBA05_SEG4',\n",
       " 'REGIOTYP',\n",
       " 'CAMEO_INTL_2015',\n",
       " 'KBA05_BAUMAX',\n",
       " 'ZABEOTYP',\n",
       " 'CAMEO_DEU_2015',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'KBA05_SEG3',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'KBA05_MOD2',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'KBA05_MOD3',\n",
       " 'KBA05_MOD1',\n",
       " 'GEBAEUDETYP',\n",
       " 'FINANZTYP',\n",
       " 'KBA05_SEG5',\n",
       " 'D19_KONSUMTYP']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_cardinality_klasse_type_cols = [col for col in klasse_type_cols if (azdias[col].nunique()>5)]\n",
    "high_cardinality_klasse_type_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015',\n",
       "       'D19_LETZTER_KAUF_BRANCHE', 'EINGEFUEGT_AM', 'OST_WEST_KZ'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_cat = azdias.select_dtypes(exclude=np.number).columns\n",
    "cols_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.pipelines import Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data = Clean(azdias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the warning by the read_csv call these columns seem to have mixed data types. Before wed deal with that problem we will replace the **'X'** or **'XX'** values with NaNsas these strings represent unknowns/missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Column CAMEO_DEUG_2015:  [nan 8.0 4.0 2.0 6.0 1.0 9.0 5.0 7.0 3.0 '4' '3' '7' '2' '8' '9' '6' '5'\n",
      " '1']\n",
      "Unique Values in Column CAMEO_DEU_2015:  [nan '8A' '4C' '2A' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5C' '8B' '7A' '5D'\n",
      " '9E' '9B' '1B' '3D' '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C'\n",
      " '9C' '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F'\n",
      " '5F' '1C']\n",
      "Unique Values in Column CAMEO_INTL_2015:  [nan 51.0 24.0 12.0 43.0 54.0 22.0 14.0 13.0 15.0 33.0 41.0 34.0 55.0 25.0\n",
      " 23.0 31.0 52.0 35.0 45.0 44.0 32.0 '22' '24' '41' '12' '54' '51' '44'\n",
      " '35' '23' '25' '14' '34' '52' '55' '31' '32' '15' '13' '43' '33' '45']\n"
     ]
    }
   ],
   "source": [
    "# Replacing 'X' & 'XX' Values\n",
    "clean_data.fit_transform('CAMEO_DEUG_2015', {'X': np.nan})\n",
    "clean_data.fit_transform('CAMEO_INTL_2015', {'XX': np.nan})\n",
    "clean_data.fit_transform('CAMEO_DEU_2015', {'XX': np.nan})\n",
    "\n",
    "# Checking the col unique values after replacement \n",
    "for col in cameo_cols:\n",
    "    get_unique_vals(azdias, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert string values to int so that the columns have right dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMEO_DEUG_2015    object\n",
       "CAMEO_INTL_2015    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.iloc[:, 18:20].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('CAMEO_DEUG_2015', lambda x: eval(x) if type(x)==str else x)\n",
    "clean_data.fit_transform('CAMEO_INTL_2015', lambda x: eval(x) if type(x)==str else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMEO_DEUG_2015    float64\n",
       "CAMEO_INTL_2015    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.iloc[:, 18:20].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick glance at the data we have (including metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNR</th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <th>ALTER_HH</th>\n",
       "      <th>ALTER_KIND1</th>\n",
       "      <th>ALTER_KIND2</th>\n",
       "      <th>ALTER_KIND3</th>\n",
       "      <th>ALTER_KIND4</th>\n",
       "      <th>ALTERSKATEGORIE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>...</th>\n",
       "      <th>VHN</th>\n",
       "      <th>VK_DHT4A</th>\n",
       "      <th>VK_DISTANZ</th>\n",
       "      <th>VK_ZG11</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>910215</td>\n",
       "      <td>-1</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>...</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>910220</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>910225</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>910226</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>910241</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 366 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LNR  AGER_TYP  AKT_DAT_KL  ALTER_HH  ALTER_KIND1  ALTER_KIND2  \\\n",
       "0  910215        -1         NaN       NaN          NaN          NaN   \n",
       "1  910220        -1         9.0       0.0          NaN          NaN   \n",
       "2  910225        -1         9.0      17.0          NaN          NaN   \n",
       "3  910226         2         1.0      13.0          NaN          NaN   \n",
       "4  910241        -1         1.0      20.0          NaN          NaN   \n",
       "\n",
       "   ALTER_KIND3  ALTER_KIND4  ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV  ...  \\\n",
       "0          NaN          NaN                   NaN                  NaN  ...   \n",
       "1          NaN          NaN                  21.0                 11.0  ...   \n",
       "2          NaN          NaN                  17.0                 10.0  ...   \n",
       "3          NaN          NaN                  13.0                  1.0  ...   \n",
       "4          NaN          NaN                  14.0                  3.0  ...   \n",
       "\n",
       "   VHN  VK_DHT4A  VK_DISTANZ  VK_ZG11  W_KEIT_KIND_HH  WOHNDAUER_2008  \\\n",
       "0  NaN       NaN         NaN      NaN             NaN             NaN   \n",
       "1  4.0       8.0        11.0     10.0             3.0             9.0   \n",
       "2  2.0       9.0         9.0      6.0             3.0             9.0   \n",
       "3  0.0       7.0        10.0     11.0             NaN             9.0   \n",
       "4  2.0       3.0         5.0      4.0             2.0             9.0   \n",
       "\n",
       "   WOHNLAGE ZABEOTYP  ANREDE_KZ  ALTERSKATEGORIE_GROB  \n",
       "0       NaN        3          1                     2  \n",
       "1       4.0        5          2                     1  \n",
       "2       2.0        5          2                     3  \n",
       "3       7.0        3          2                     4  \n",
       "4       3.0        4          1                     3  \n",
       "\n",
       "[5 rows x 366 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>LNR</th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <th>ALTER_HH</th>\n",
       "      <th>ALTER_KIND1</th>\n",
       "      <th>ALTER_KIND2</th>\n",
       "      <th>ALTER_KIND3</th>\n",
       "      <th>ALTER_KIND4</th>\n",
       "      <th>ALTERSKATEGORIE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>...</th>\n",
       "      <th>VK_ZG11</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <th>PRODUCT_GROUP</th>\n",
       "      <th>CUSTOMER_GROUP</th>\n",
       "      <th>ONLINE_PURCHASE</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9626</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9628</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</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>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>SINGLE_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143872</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143873</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>COSMETIC</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143874</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LNR  AGER_TYP  AKT_DAT_KL  ALTER_HH  ALTER_KIND1  ALTER_KIND2  \\\n",
       "0    9626         2         1.0      10.0          NaN          NaN   \n",
       "1    9628        -1         9.0      11.0          NaN          NaN   \n",
       "2  143872        -1         1.0       6.0          NaN          NaN   \n",
       "3  143873         1         1.0       8.0          NaN          NaN   \n",
       "4  143874        -1         1.0      20.0          NaN          NaN   \n",
       "\n",
       "   ALTER_KIND3  ALTER_KIND4  ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV  ...  \\\n",
       "0          NaN          NaN                  10.0                  1.0  ...   \n",
       "1          NaN          NaN                   NaN                  NaN  ...   \n",
       "2          NaN          NaN                   0.0                  1.0  ...   \n",
       "3          NaN          NaN                   8.0                  0.0  ...   \n",
       "4          NaN          NaN                  14.0                  7.0  ...   \n",
       "\n",
       "   VK_ZG11  W_KEIT_KIND_HH  WOHNDAUER_2008  WOHNLAGE  ZABEOTYP  \\\n",
       "0      2.0             6.0             9.0       7.0         3   \n",
       "1      3.0             0.0             9.0       NaN         3   \n",
       "2     11.0             6.0             9.0       2.0         3   \n",
       "3      2.0             NaN             9.0       7.0         1   \n",
       "4      4.0             2.0             9.0       3.0         1   \n",
       "\n",
       "       PRODUCT_GROUP  CUSTOMER_GROUP ONLINE_PURCHASE ANREDE_KZ  \\\n",
       "0  COSMETIC_AND_FOOD     MULTI_BUYER               0         1   \n",
       "1               FOOD    SINGLE_BUYER               0         1   \n",
       "2  COSMETIC_AND_FOOD     MULTI_BUYER               0         2   \n",
       "3           COSMETIC     MULTI_BUYER               0         1   \n",
       "4               FOOD     MULTI_BUYER               0         1   \n",
       "\n",
       "  ALTERSKATEGORIE_GROB  \n",
       "0                    4  \n",
       "1                    4  \n",
       "2                    4  \n",
       "3                    4  \n",
       "4                    3  \n",
       "\n",
       "[5 rows x 369 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "      <th>Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>age classification through prename analysis</td>\n",
       "      <td>-1, 0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "      <td>[-1, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALTER_HH</td>\n",
       "      <td>main age within the household</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown / no main age detectable</td>\n",
       "      <td>True</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>gender</td>\n",
       "      <td>-1, 0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "      <td>[-1, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BALLRAUM</td>\n",
       "      <td>distance to next urban centre</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Attribute                                   Description  Value  \\\n",
       "0              AGER_TYP                            best-ager typology     -1   \n",
       "1  ALTERSKATEGORIE_GROB  age classification through prename analysis   -1, 0   \n",
       "2              ALTER_HH                 main age within the household      0   \n",
       "3             ANREDE_KZ                                        gender  -1, 0   \n",
       "4              BALLRAUM                distance to next urban centre      -1   \n",
       "\n",
       "                            Meaning Missing      Val  \n",
       "0                           unknown    True     [-1]  \n",
       "1                           unknown    True  [-1, 0]  \n",
       "2  unknown / no main age detectable    True      [0]  \n",
       "3                           unknown    True  [-1, 0]  \n",
       "4                           unknown    True     [-1]  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting attribute info for Values that mean unkown or missing info\n",
    "unknowns = attributes[(attributes.Missing == True)|(attributes.Meaning == 'unknown')].copy()\n",
    "unknowns.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Fix the values in the Value column so that they all have a uniform pattern\n",
    "unknowns.loc[:, 'Val'] = unknowns.Value.apply(create_missing_val_list)\n",
    "unknowns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dict mapping missing/unknown values to NaN for each column\n",
    "unknowns_dict = {v.Attribute: {val: np.nan for val in v.Val} for _, v in unknowns.iterrows()}\n",
    "\n",
    "# Number of Columns to perform the replacement operation on\n",
    "len(unknowns_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIP_FLAG\n",
      "D19_KK_KUNDENTYP\n",
      "GEOSCORE_KLS7\n",
      "HAUSHALTSSTRUKTUR\n",
      "KBA13_CCM_1400_2500\n",
      "SOHO_FLAG\n",
      "WACHSTUMSGEBIET_NB\n"
     ]
    }
   ],
   "source": [
    "# Replacing all values that indicate missing values with NaNs \n",
    "for col, map_dict in unknowns_dict.items():\n",
    "    if col in azdias.columns:\n",
    "        clean_data.fit_transform(col, map_dict)\n",
    "    else:\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# azdias.replace(unknowns_dict, inplace=True)\n",
    "# customers.replace(unknowns_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ORTSGR_KLS9',\n",
       " 'LP_LEBENSPHASE_FEIN',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'KBA05_MODTEMP',\n",
       " 'LP_LEBENSPHASE_GROB',\n",
       " 'LP_FAMILIE_FEIN']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_with_extra_encoding = []\n",
    "# Select columns that have been decribed in attributes excel\n",
    "attributes_without_numeric_vals = set(attributes[~(attributes.Meaning.apply(lambda x: 'numer' in str(x)))].Attribute)\n",
    "described_cols = list(set(azdias.columns)&attributes_without_numeric_vals)\n",
    "for col in described_cols:\n",
    "    # Extracts all unique non-null values of the column and stores it in data_col_unique_vals\n",
    "    data_col_unique_vals = azdias[col].dropna().unique()\n",
    "    # Fetch all unique values of the 'Value' column of the attribute info for the given column and store it in attr_encodings\n",
    "    attr_encodings = get_attribute_info(col, attributes)['Value'].unique()\n",
    "    # Take the set difference between data_col_unique_vals and attr_encodings, store it in diff_encodings\n",
    "    diff_encodings = set(data_col_unique_vals) - (set(attr_encodings)-{'…'})\n",
    "    # If there is at least one encoding difference, append the column name to the cols_with_extra_encoding list\n",
    "    if (len(diff_encodings)>=1):\n",
    "        cols_with_extra_encoding.append(col)\n",
    "\n",
    "# Remove columns that we have already processed        \n",
    "cols_with_extra_encoding = list(set(cols_with_extra_encoding) -  (set(binary_attributes_info.Attribute)))\n",
    "cols_with_extra_encoding"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dealing with binary columns"
   ]
  },
  {
   "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>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>gender</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>gender</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BIP_FLAG</td>\n",
       "      <td>business-flag indicating companies in the buil...</td>\n",
       "      <td>0</td>\n",
       "      <td>no company in the building</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIP_FLAG</td>\n",
       "      <td>business-flag indicating companies in the buil...</td>\n",
       "      <td>1</td>\n",
       "      <td>company in the building</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>Green avantgarde</td>\n",
       "      <td>0</td>\n",
       "      <td>doesn't belong to the green avantgarde</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GREEN_AVANTGARDE</td>\n",
       "      <td>the environmental sustainability is the domina...</td>\n",
       "      <td>1</td>\n",
       "      <td>belongs to the green avantgarde</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KBA05_SEG6</td>\n",
       "      <td>share of upper class cars (BMW 7er etc.) in th...</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KBA05_SEG6</td>\n",
       "      <td>share of upper class cars (BMW 7er etc.) in th...</td>\n",
       "      <td>1</td>\n",
       "      <td>some</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>O</td>\n",
       "      <td>East (GDR)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OST_WEST_KZ</td>\n",
       "      <td>flag indicating the former GDR/FRG</td>\n",
       "      <td>W</td>\n",
       "      <td>West (FRG)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SOHO_FLAG</td>\n",
       "      <td>small office/home office flag</td>\n",
       "      <td>0</td>\n",
       "      <td>no small office/home office</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SOHO_FLAG</td>\n",
       "      <td>small office/home office flag</td>\n",
       "      <td>1</td>\n",
       "      <td>small office/home office</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>insurance typology</td>\n",
       "      <td>1</td>\n",
       "      <td>social-safety driven</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>VERS_TYP</td>\n",
       "      <td>insurance typology</td>\n",
       "      <td>2</td>\n",
       "      <td>individualistic-accepting risks</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attribute                                        Description Value  \\\n",
       "0          ANREDE_KZ                                             gender     1   \n",
       "1          ANREDE_KZ                                             gender     2   \n",
       "2           BIP_FLAG  business-flag indicating companies in the buil...     0   \n",
       "3           BIP_FLAG  business-flag indicating companies in the buil...     1   \n",
       "4   GREEN_AVANTGARDE                                   Green avantgarde     0   \n",
       "5   GREEN_AVANTGARDE  the environmental sustainability is the domina...     1   \n",
       "6         KBA05_SEG6  share of upper class cars (BMW 7er etc.) in th...     0   \n",
       "7         KBA05_SEG6  share of upper class cars (BMW 7er etc.) in th...     1   \n",
       "8        OST_WEST_KZ                 flag indicating the former GDR/FRG     O   \n",
       "9        OST_WEST_KZ                 flag indicating the former GDR/FRG     W   \n",
       "10         SOHO_FLAG                      small office/home office flag     0   \n",
       "11         SOHO_FLAG                      small office/home office flag     1   \n",
       "12          VERS_TYP                                insurance typology      1   \n",
       "13          VERS_TYP                                insurance typology      2   \n",
       "\n",
       "                                   Meaning Missing  \n",
       "0                                     male   False  \n",
       "1                                   female   False  \n",
       "2               no company in the building   False  \n",
       "3                  company in the building   False  \n",
       "4   doesn't belong to the green avantgarde   False  \n",
       "5          belongs to the green avantgarde   False  \n",
       "6                                     none   False  \n",
       "7                                     some   False  \n",
       "8                               East (GDR)   False  \n",
       "9                               West (FRG)   False  \n",
       "10             no small office/home office   False  \n",
       "11                small office/home office   False  \n",
       "12                    social-safety driven   False  \n",
       "13         individualistic-accepting risks   False  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_attributes_info"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few columns above with values other than 0,1 used to indicate a binary choice; We will re-encode these values even though a a pair like 1,2 equally serves the purpose in order to standardise interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('OST_WEST_KZ',{'O':0, 'W':1})\n",
    "clean_data.fit_transform('ANREDE_KZ', {1:0, 2:1})\n",
    "clean_data.fit_transform('VERS_TYP', {1:0, 2:1})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets deal with columns that have mismatched encodings; values whose meaning hasn't been described in the attributes excel files  that have been provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  3.,  1.,  0.,  5.,  4., nan])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_FAMILIE_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2., nan,  1.,  0.,  5.,  4.,  3.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['LP_FAMILIE_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>2</td>\n",
       "      <td>couple</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>3</td>\n",
       "      <td>single parent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>6</td>\n",
       "      <td>family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>9</td>\n",
       "      <td>multiperson household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attribute      Description Value                Meaning Missing\n",
       "1903  LP_FAMILIE_GROB  familytyp rough     1                single    False\n",
       "1904  LP_FAMILIE_GROB  familytyp rough     2                 couple   False\n",
       "1905  LP_FAMILIE_GROB  familytyp rough     3          single parent   False\n",
       "1906  LP_FAMILIE_GROB  familytyp rough     4                    NaN     NaN\n",
       "1907  LP_FAMILIE_GROB  familytyp rough     5                    NaN     NaN\n",
       "1908  LP_FAMILIE_GROB  familytyp rough     6                 family   False\n",
       "1909  LP_FAMILIE_GROB  familytyp rough     7                    NaN     NaN\n",
       "1910  LP_FAMILIE_GROB  familytyp rough     8                    NaN     NaN\n",
       "1911  LP_FAMILIE_GROB  familytyp rough     9  multiperson household   False\n",
       "1912  LP_FAMILIE_GROB  familytyp rough    10                    NaN     NaN\n",
       "1913  LP_FAMILIE_GROB  familytyp rough    11                    NaN     NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('LP_FAMILIE_GROB', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('LP_FAMILIE_GROB', {0:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  5.,  1.,  0., 10.,  7., 11.,  3.,  8.,  4.,  6., nan,  9.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_FAMILIE_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2., nan,  1.,  0., 10.,  8.,  6., 11.,  9.,  7.,  5.,  3.,  4.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['LP_FAMILIE_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('LP_FAMILIE_FEIN', {0:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  6.,  1.,  0., 10.,  2.,  3.,  5.,  7., 12., 11.,  9.,  8.,\n",
       "       nan])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_LEBENSPHASE_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5., nan,  3.,  0., 10.,  2.,  8., 12., 11.,  1.,  4.,  6.,  7.,\n",
       "        9.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['LP_LEBENSPHASE_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>1</td>\n",
       "      <td>single low-income- and average earners of youn...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>2</td>\n",
       "      <td>single low-income- and average earners of high...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>3</td>\n",
       "      <td>single high-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>4</td>\n",
       "      <td>single low-income- and average earner-couples</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>5</td>\n",
       "      <td>single high-income earner-couples</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>6</td>\n",
       "      <td>single parents</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>7</td>\n",
       "      <td>single low-income- and average earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>8</td>\n",
       "      <td>high-income earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>9</td>\n",
       "      <td>average earners of younger age from mulitperso...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>10</td>\n",
       "      <td>low-income- and average earners of higher age ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>11</td>\n",
       "      <td>high-income earners of younger age from multip...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>lifestage rough</td>\n",
       "      <td>12</td>\n",
       "      <td>high-income earners of higher age from multipe...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Attribute      Description Value  \\\n",
       "1954  LP_LEBENSPHASE_GROB  lifestage rough     1   \n",
       "1955  LP_LEBENSPHASE_GROB  lifestage rough     2   \n",
       "1956  LP_LEBENSPHASE_GROB  lifestage rough     3   \n",
       "1957  LP_LEBENSPHASE_GROB  lifestage rough     4   \n",
       "1958  LP_LEBENSPHASE_GROB  lifestage rough     5   \n",
       "1959  LP_LEBENSPHASE_GROB  lifestage rough     6   \n",
       "1960  LP_LEBENSPHASE_GROB  lifestage rough     7   \n",
       "1961  LP_LEBENSPHASE_GROB  lifestage rough     8   \n",
       "1962  LP_LEBENSPHASE_GROB  lifestage rough     9   \n",
       "1963  LP_LEBENSPHASE_GROB  lifestage rough    10   \n",
       "1964  LP_LEBENSPHASE_GROB  lifestage rough    11   \n",
       "1965  LP_LEBENSPHASE_GROB  lifestage rough    12   \n",
       "\n",
       "                                                Meaning Missing  \n",
       "1954  single low-income- and average earners of youn...   False  \n",
       "1955  single low-income- and average earners of high...   False  \n",
       "1956                         single high-income earners   False  \n",
       "1957     single low-income- and average earner-couples    False  \n",
       "1958                  single high-income earner-couples   False  \n",
       "1959                                     single parents   False  \n",
       "1960     single low-income- and average earner-families   False  \n",
       "1961                        high-income earner-families   False  \n",
       "1962  average earners of younger age from mulitperso...   False  \n",
       "1963  low-income- and average earners of higher age ...   False  \n",
       "1964  high-income earners of younger age from multip...   False  \n",
       "1965  high-income earners of higher age from multipe...   False  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('LP_LEBENSPHASE_GROB', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('LP_LEBENSPHASE_GROB', {0:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15., 21.,  3.,  0., 32.,  8.,  2.,  5., 10.,  4.,  6., 23., 12.,\n",
       "       20.,  1., 11., 25., 13.,  7., 18., 31., 19., 38., 35., 30., 22.,\n",
       "       14., 33., 29., 24., 28., 37., 26., 39., 27., 36.,  9., 34., nan,\n",
       "       40., 16., 17.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_LEBENSPHASE_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., nan, 13.,  0., 31., 17.,  6., 28.,  5., 27., 40., 35.,  2.,\n",
       "       19., 38., 36.,  8., 34., 10., 12., 26., 11.,  9., 37., 14., 39.,\n",
       "       32.,  7., 15., 23., 21., 25., 33., 16., 24., 30., 18.,  4., 22.,\n",
       "        1., 29.,  3.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['LP_LEBENSPHASE_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>1</td>\n",
       "      <td>single low-income earners of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>2</td>\n",
       "      <td>single low-income earners of middle age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>3</td>\n",
       "      <td>single average earners of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>4</td>\n",
       "      <td>single average earners of middle age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>5</td>\n",
       "      <td>single low-income earners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>6</td>\n",
       "      <td>single low-income earners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>7</td>\n",
       "      <td>single average earners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>8</td>\n",
       "      <td>single average earners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>9</td>\n",
       "      <td>single independant persons</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>10</td>\n",
       "      <td>wealthy single homeowners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>11</td>\n",
       "      <td>single homeowners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>12</td>\n",
       "      <td>single homeowners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>13</td>\n",
       "      <td>single top earners of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>14</td>\n",
       "      <td>low-income- and average earner-couples of youn...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>15</td>\n",
       "      <td>low-income earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>16</td>\n",
       "      <td>average earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>17</td>\n",
       "      <td>independant couples</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>18</td>\n",
       "      <td>wealthy homeowner-couples of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>19</td>\n",
       "      <td>homeowner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>20</td>\n",
       "      <td>top earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>21</td>\n",
       "      <td>single parent low-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>22</td>\n",
       "      <td>single parent average earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>23</td>\n",
       "      <td>single parent high-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>24</td>\n",
       "      <td>low-income earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>25</td>\n",
       "      <td>average earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>26</td>\n",
       "      <td>independant families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>27</td>\n",
       "      <td>homeowner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>28</td>\n",
       "      <td>top earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>29</td>\n",
       "      <td>low-income earners of younger age from mulitpe...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>30</td>\n",
       "      <td>average earners of younger age from mulitperso...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>31</td>\n",
       "      <td>low-income earners of higher age from mulitper...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>32</td>\n",
       "      <td>average earners of higher age from mulitperson...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>33</td>\n",
       "      <td>independant persons of younger age from mulitp...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>34</td>\n",
       "      <td>homeowners of younger age from mulitperson hou...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>35</td>\n",
       "      <td>top earners of younger age from mulitperson ho...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>36</td>\n",
       "      <td>independant persons of higher age from mulitpe...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>37</td>\n",
       "      <td>homeowners of advanced age from mulitperson ho...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>38</td>\n",
       "      <td>homeowners at retirement age from mulitperson ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>39</td>\n",
       "      <td>top earners of middle age from mulitperson hou...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>40</td>\n",
       "      <td>top earners at retirement age from mulitperson...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Attribute      Description Value  \\\n",
       "1914  LP_LEBENSPHASE_FEIN  lifestage fine      1   \n",
       "1915  LP_LEBENSPHASE_FEIN  lifestage fine      2   \n",
       "1916  LP_LEBENSPHASE_FEIN  lifestage fine      3   \n",
       "1917  LP_LEBENSPHASE_FEIN  lifestage fine      4   \n",
       "1918  LP_LEBENSPHASE_FEIN  lifestage fine      5   \n",
       "1919  LP_LEBENSPHASE_FEIN  lifestage fine      6   \n",
       "1920  LP_LEBENSPHASE_FEIN  lifestage fine      7   \n",
       "1921  LP_LEBENSPHASE_FEIN  lifestage fine      8   \n",
       "1922  LP_LEBENSPHASE_FEIN  lifestage fine      9   \n",
       "1923  LP_LEBENSPHASE_FEIN  lifestage fine     10   \n",
       "1924  LP_LEBENSPHASE_FEIN  lifestage fine     11   \n",
       "1925  LP_LEBENSPHASE_FEIN  lifestage fine     12   \n",
       "1926  LP_LEBENSPHASE_FEIN  lifestage fine     13   \n",
       "1927  LP_LEBENSPHASE_FEIN  lifestage fine     14   \n",
       "1928  LP_LEBENSPHASE_FEIN  lifestage fine     15   \n",
       "1929  LP_LEBENSPHASE_FEIN  lifestage fine     16   \n",
       "1930  LP_LEBENSPHASE_FEIN  lifestage fine     17   \n",
       "1931  LP_LEBENSPHASE_FEIN  lifestage fine     18   \n",
       "1932  LP_LEBENSPHASE_FEIN  lifestage fine     19   \n",
       "1933  LP_LEBENSPHASE_FEIN  lifestage fine     20   \n",
       "1934  LP_LEBENSPHASE_FEIN  lifestage fine     21   \n",
       "1935  LP_LEBENSPHASE_FEIN  lifestage fine     22   \n",
       "1936  LP_LEBENSPHASE_FEIN  lifestage fine     23   \n",
       "1937  LP_LEBENSPHASE_FEIN  lifestage fine     24   \n",
       "1938  LP_LEBENSPHASE_FEIN  lifestage fine     25   \n",
       "1939  LP_LEBENSPHASE_FEIN  lifestage fine     26   \n",
       "1940  LP_LEBENSPHASE_FEIN  lifestage fine     27   \n",
       "1941  LP_LEBENSPHASE_FEIN  lifestage fine     28   \n",
       "1942  LP_LEBENSPHASE_FEIN  lifestage fine     29   \n",
       "1943  LP_LEBENSPHASE_FEIN  lifestage fine     30   \n",
       "1944  LP_LEBENSPHASE_FEIN  lifestage fine     31   \n",
       "1945  LP_LEBENSPHASE_FEIN  lifestage fine     32   \n",
       "1946  LP_LEBENSPHASE_FEIN  lifestage fine     33   \n",
       "1947  LP_LEBENSPHASE_FEIN  lifestage fine     34   \n",
       "1948  LP_LEBENSPHASE_FEIN  lifestage fine     35   \n",
       "1949  LP_LEBENSPHASE_FEIN  lifestage fine     36   \n",
       "1950  LP_LEBENSPHASE_FEIN  lifestage fine     37   \n",
       "1951  LP_LEBENSPHASE_FEIN  lifestage fine     38   \n",
       "1952  LP_LEBENSPHASE_FEIN  lifestage fine     39   \n",
       "1953  LP_LEBENSPHASE_FEIN  lifestage fine     40   \n",
       "\n",
       "                                                Meaning Missing  \n",
       "1914           single low-income earners of younger age   False  \n",
       "1915            single low-income earners of middle age   False  \n",
       "1916             single average earners of younger age    False  \n",
       "1917               single average earners of middle age   False  \n",
       "1918          single low-income earners of advanced age   False  \n",
       "1919       single low-income earners at retirement age    False  \n",
       "1920             single average earners of advanced age   False  \n",
       "1921           single average earners at retirement age   False  \n",
       "1922                         single independant persons   False  \n",
       "1923                          wealthy single homeowners   False  \n",
       "1924                  single homeowners of advanced age   False  \n",
       "1925                single homeowners at retirement age   False  \n",
       "1926                  single top earners of higher age    False  \n",
       "1927  low-income- and average earner-couples of youn...   False  \n",
       "1928            low-income earner-couples of higher age   False  \n",
       "1929              average earner-couples of higher age    False  \n",
       "1930                                independant couples   False  \n",
       "1931          wealthy homeowner-couples of younger age    False  \n",
       "1932                    homeowner-couples of higher age   False  \n",
       "1933                   top earner-couples of higher age   False  \n",
       "1934                   single parent low-income earners   False  \n",
       "1935                      single parent average earners   False  \n",
       "1936                  single parent high-income earners   False  \n",
       "1937                         low-income earner-families   False  \n",
       "1938                           average earner-families    False  \n",
       "1939                               independant families   False  \n",
       "1940                                 homeowner-families   False  \n",
       "1941                                top earner-families   False  \n",
       "1942  low-income earners of younger age from mulitpe...   False  \n",
       "1943  average earners of younger age from mulitperso...   False  \n",
       "1944  low-income earners of higher age from mulitper...   False  \n",
       "1945  average earners of higher age from mulitperson...   False  \n",
       "1946  independant persons of younger age from mulitp...   False  \n",
       "1947  homeowners of younger age from mulitperson hou...   False  \n",
       "1948  top earners of younger age from mulitperson ho...   False  \n",
       "1949  independant persons of higher age from mulitpe...   False  \n",
       "1950  homeowners of advanced age from mulitperson ho...   False  \n",
       "1951  homeowners at retirement age from mulitperson ...   False  \n",
       "1952  top earners of middle age from mulitperson hou...   False  \n",
       "1953  top earners at retirement age from mulitperson...   False  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('LP_LEBENSPHASE_FEIN', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('LP_LEBENSPHASE_FEIN', {0:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>size of the community</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;= 2.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>2</td>\n",
       "      <td>2.001 to 5.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>3</td>\n",
       "      <td>5.001 to 10.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>4</td>\n",
       "      <td>10.001 to 20.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>5</td>\n",
       "      <td>20.001 to 50.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>6</td>\n",
       "      <td>50.001 to 100.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>7</td>\n",
       "      <td>100.001 to 300.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>8</td>\n",
       "      <td>300.001 to 700.000 inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>ORTSGR_KLS9</td>\n",
       "      <td>'- classified number of inhabitants</td>\n",
       "      <td>9</td>\n",
       "      <td>&gt; 700.000  inhabitants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Attribute                          Description Value  \\\n",
       "2003  ORTSGR_KLS9                size of the community    -1   \n",
       "2004  ORTSGR_KLS9  '- classified number of inhabitants     1   \n",
       "2005  ORTSGR_KLS9  '- classified number of inhabitants     2   \n",
       "2006  ORTSGR_KLS9  '- classified number of inhabitants     3   \n",
       "2007  ORTSGR_KLS9  '- classified number of inhabitants     4   \n",
       "2008  ORTSGR_KLS9  '- classified number of inhabitants     5   \n",
       "2009  ORTSGR_KLS9  '- classified number of inhabitants     6   \n",
       "2010  ORTSGR_KLS9  '- classified number of inhabitants     7   \n",
       "2011  ORTSGR_KLS9  '- classified number of inhabitants     8   \n",
       "2012  ORTSGR_KLS9  '- classified number of inhabitants     9   \n",
       "\n",
       "                             Meaning Missing  \n",
       "2003                         unknown    True  \n",
       "2004            <= 2.000 inhabitants   False  \n",
       "2005      2.001 to 5.000 inhabitants   False  \n",
       "2006     5.001 to 10.000 inhabitants   False  \n",
       "2007    10.001 to 20.000 inhabitants   False  \n",
       "2008    20.001 to 50.000 inhabitants   False  \n",
       "2009   50.001 to 100.000 inhabitants   False  \n",
       "2010  100.001 to 300.000 inhabitants   False  \n",
       "2011  300.001 to 700.000 inhabitants   False  \n",
       "2012          > 700.000  inhabitants   False  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('ORTSGR_KLS9',attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>-1, 9</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>1</td>\n",
       "      <td>promoted</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>2</td>\n",
       "      <td>stayed upper level</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>3</td>\n",
       "      <td>stayed lower/average level</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>4</td>\n",
       "      <td>demoted</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>KBA05_MODTEMP</td>\n",
       "      <td>development of the most common car segment in ...</td>\n",
       "      <td>5</td>\n",
       "      <td>new building</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Attribute                                        Description  Value  \\\n",
       "1025  KBA05_MODTEMP  development of the most common car segment in ...  -1, 9   \n",
       "1026  KBA05_MODTEMP  development of the most common car segment in ...      1   \n",
       "1027  KBA05_MODTEMP  development of the most common car segment in ...      2   \n",
       "1028  KBA05_MODTEMP  development of the most common car segment in ...      3   \n",
       "1029  KBA05_MODTEMP  development of the most common car segment in ...      4   \n",
       "1030  KBA05_MODTEMP  development of the most common car segment in ...      5   \n",
       "\n",
       "                         Meaning Missing  \n",
       "1025                     unknown    True  \n",
       "1026                    promoted   False  \n",
       "1027          stayed upper level   False  \n",
       "1028  stayed lower/average level   False  \n",
       "1029                     demoted   False  \n",
       "1030                new building   False  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('KBA05_MODTEMP',attributes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets deal with attributes that have values with unexplained meaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no classification possible', 'no company in the building',\n",
       "       'no transactions known', 'no transaction known',\n",
       "       'no Online-transactions within the last 12 months',\n",
       "       \"doesn't belong to the green avantgarde\",\n",
       "       'classification not possible', 'none', 'no 1-2 family homes',\n",
       "       'no 3-5 family homes', 'no 6-10 family homes',\n",
       "       'no >10 family homes', 'external supplied hedonists ',\n",
       "       'no small office/home office', 'no score calculated'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.query(\"Value == 0 & Missing == False\").Meaning.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>0</td>\n",
       "      <td>classification not possible</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>0</td>\n",
       "      <td>no score calculated</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attribute        Description Value                      Meaning Missing\n",
       "745   HEALTH_TYP    health typology     0  classification not possible   False\n",
       "2230    WOHNLAGE  residential-area      0          no score calculated   False"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes[attributes.Meaning.isin(['classification not possible', 'no score calculated'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Attribute</th>\n",
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       "      <th>Missing</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>0</td>\n",
       "      <td>no score calculated</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>1</td>\n",
       "      <td>very good neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>2</td>\n",
       "      <td>good neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>3</td>\n",
       "      <td>average neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>4</td>\n",
       "      <td>poor neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>5</td>\n",
       "      <td>very poor neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>7</td>\n",
       "      <td>rural neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>8</td>\n",
       "      <td>new building in rural neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Attribute        Description Value                              Meaning  \\\n",
       "2229  WOHNLAGE  residential-area     -1                              unknown   \n",
       "2230  WOHNLAGE  residential-area      0                  no score calculated   \n",
       "2231  WOHNLAGE  residential-area      1              very good neighbourhood   \n",
       "2232  WOHNLAGE  residential-area      2                   good neighbourhood   \n",
       "2233  WOHNLAGE  residential-area      3                average neighbourhood   \n",
       "2234  WOHNLAGE  residential-area      4                   poor neighbourhood   \n",
       "2235  WOHNLAGE  residential-area      5              very poor neighbourhood   \n",
       "2236  WOHNLAGE  residential-area      7                  rural neighbourhood   \n",
       "2237  WOHNLAGE  residential-area      8  new building in rural neighbourhood   \n",
       "\n",
       "     Missing  \n",
       "2229    True  \n",
       "2230   False  \n",
       "2231   False  \n",
       "2232   False  \n",
       "2233   False  \n",
       "2234   False  \n",
       "2235   False  \n",
       "2236   False  \n",
       "2237   False  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('WOHNLAGE', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data.fit_transform('WOHNLAGE', {0:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>0</td>\n",
       "      <td>classification not possible</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>1</td>\n",
       "      <td>critical reserved</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>2</td>\n",
       "      <td>sanitary affine</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>HEALTH_TYP</td>\n",
       "      <td>health typology</td>\n",
       "      <td>3</td>\n",
       "      <td>jaunty hedonists</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attribute      Description Value                      Meaning Missing\n",
       "744  HEALTH_TYP  health typology    -1                      unknown    True\n",
       "745  HEALTH_TYP  health typology     0  classification not possible   False\n",
       "746  HEALTH_TYP  health typology     1           critical reserved    False\n",
       "747  HEALTH_TYP  health typology     2            sanitary affine     False\n",
       "748  HEALTH_TYP  health typology     3            jaunty hedonists    False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('HEALTH_TYP', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply all transformations to customers dataframe\n",
    "clean_data.transform(customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check columns that might be specific to a particular dataframe and refer the attributes file for more info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(azdias.columns) & set(attributes.Attribute) & set(customers.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(attributes.Attribute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting columns exclusive to a dataframe\n",
    "attributes_notin_df = set(attributes.Attribute) - set(customers.columns)\n",
    "cols_not_in_attributes = set(customers.columns) - set(attributes.Attribute)\n",
    "customers_only_cols = list(set(customers.columns) - set(azdias.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRODUCT_GROUP unique values:  ['COSMETIC_AND_FOOD' 'FOOD' 'COSMETIC']\n",
      "CUSTOMER_GROUP unique values:  ['MULTI_BUYER' 'SINGLE_BUYER']\n",
      "ONLINE_PURCHASE unique values:  [0 1]\n"
     ]
    }
   ],
   "source": [
    "# Printing Unique Values in customer only columns\n",
    "for col in customers_only_cols:\n",
    "    print(f'{col} unique values: ',customers[col].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now we will drop the customer specific columns. We can always perform a join post cleaning and add the required values back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PRODUCT_GROUP', 'CUSTOMER_GROUP', 'ONLINE_PURCHASE']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = customers_only_cols\n",
    "cols_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Customer only cols\n",
    "customers.drop(cols_to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure out the optimum percentage of NaN threshold such that both dataframes end up with same number of columns being dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KKK', 'REGIOTYP'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculates the number columns with NaNs greater than 0\n",
    "customers_missing_val = customers.isnull().sum() / len(customers)\n",
    "customers_missing_val = customers_missing_val[customers_missing_val>0]\n",
    "azdias_missing_val = azdias.isnull().sum() / len(azdias)\n",
    "azdias_missing_val = azdias_missing_val[azdias_missing_val>0]\n",
    "\n",
    "# gets the columns with NaNs greater than 30 % of df len (num of rows)\n",
    "customers_cols_to_drop = get_cols_to_drop(customers, 0.30)\n",
    "azdias_cols_to_drop = get_cols_to_drop(azdias, 0.30)\n",
    "set(customers_cols_to_drop) - set(azdias_cols_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KKK         0.313401\n",
       "REGIOTYP    0.313401\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_missing_val[['KKK', 'REGIOTYP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KKK         0.177357\n",
       "REGIOTYP    0.177357\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_missing_val[['KKK', 'REGIOTYP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the threshold such that equal number of cols are dropped\n",
    "customers_cols_to_drop = get_cols_to_drop(customers, 0.33)\n",
    "azdias_cols_to_drop = get_cols_to_drop(azdias, 0.33)\n",
    "set(customers_cols_to_drop) - set(azdias_cols_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns based on above threshold\n",
    "customers.dropna(thresh=int(0.66*len(customers)), axis=1, inplace=True)\n",
    "azdias.dropna(thresh=int(0.66*len(azdias)), axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify same num of columns are present \n",
    "set(azdias.columns) == set(customers.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the number of missing values per row and then construct a hist to check both dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculates the number rows with NaNs greater than 0\n",
    "azdias_missing_rows = azdias.isnull().sum(axis=1)\n",
    "azdias_missing_rows = azdias_missing_rows[azdias_missing_rows>0]\n",
    "customers_missing_rows = customers.isnull().sum(axis=1)\n",
    "customers_missing_rows = customers_missing_rows[customers_missing_rows>0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(azdias_missing_rows.values, label='azdias')\n",
    "plt.hist(customers_missing_rows.values, label='customers')\n",
    "plt.xlabel('Number of Missing Values')\n",
    "plt.ylabel('Number of Rows')\n",
    "plt.legend()\n",
    "plt.title('Distribution of Missing Values across Rows');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{20: 71.2, 25: 89.0, 50: 178.0, 70: 249.2, 75: 267.0, 80: 284.8}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the % of row size\n",
    "proportion_cols = {}\n",
    "for percent in [20, 25, 50, 70, 75, 80]:\n",
    "    proportion_cols[percent] = (percent/100)*azdias.shape[1]\n",
    "\n",
    "proportion_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26.75735186692547, 11.87225166372875)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get proportion of rows that would be dropped given a particular threshold (100) of missing values\n",
    "get_missing_rows_percent(customers, 100), get_missing_rows_percent(azdias, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26.768309227140858, 11.890316767670422)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get proportion of rows that would be dropped given a particular threshold (25 % of columns length) of missing values\n",
    "get_missing_rows_percent(customers, proportion_cols[25]), get_missing_rows_percent(azdias, proportion_cols[25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_threshold_rows = np.ceil(proportion_cols[75])\n",
    "na_threshold_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# value of thresh arg determines the minimum non-na values required beyond which the row is considered for dropping \n",
    "azdias.dropna(thresh=na_threshold_rows, axis=0, inplace=True)\n",
    "customers.dropna(thresh=na_threshold_rows, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(azdias.isnull().sum(axis=1).values, label='azdias')\n",
    "plt.hist(customers.isnull().sum(axis=1).values, label='customers')\n",
    "plt.xlabel('Number of Missing Values')\n",
    "plt.ylabel('Number of Rows')\n",
    "plt.legend()\n",
    "plt.title('Distribution of Missing Values across Rows');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/clean_data.pkl', 'wb') as file:\n",
    "    dill.dump(clean_data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias.to_pickle('data/azdias_cleaned.pkl')\n",
    "customers.to_pickle('data/customers_cleaned.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias=pd.read_pickle('data/azdias_cleaned.pkl')\n",
    "customers=pd.read_pickle('data/customers_cleaned.pkl')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering by re-encoding certain columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.pipelines import FeatureEngineer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_engine = FeatureEngineer(azdias)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dealing with cameo Cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>CAMEO classification 2015 - international typo...</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>11</td>\n",
       "      <td>Wealthy Households-Pre-Family Couples &amp; Singles</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>12</td>\n",
       "      <td>Wealthy Households-Young Couples With Children</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>13</td>\n",
       "      <td>Wealthy Households-Families With School Age Ch...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>14</td>\n",
       "      <td>Wealthy Households-Older Families &amp;  Mature Co...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>15</td>\n",
       "      <td>Wealthy Households-Elders In Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>21</td>\n",
       "      <td>Prosperous Households-Pre-Family Couples &amp; Sin...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>22</td>\n",
       "      <td>Prosperous Households-Young Couples With Children</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>23</td>\n",
       "      <td>Prosperous Households-Families With School Age...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>24</td>\n",
       "      <td>Prosperous Households-Older Families &amp; Mature ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>25</td>\n",
       "      <td>Prosperous Households-Elders In Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>31</td>\n",
       "      <td>Comfortable Households-Pre-Family Couples &amp; Si...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>32</td>\n",
       "      <td>Comfortable Households-Young Couples With Chil...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>33</td>\n",
       "      <td>Comfortable Households-Families With School Ag...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>34</td>\n",
       "      <td>Comfortable Households-Older Families &amp; Mature...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>35</td>\n",
       "      <td>Comfortable Households-Elders In Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>41</td>\n",
       "      <td>Less Affluent Households-Pre-Family Couples &amp; ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>42</td>\n",
       "      <td>Less Affluent Households-Young Couples With Ch...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>43</td>\n",
       "      <td>Less Affluent Households-Families With School ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>44</td>\n",
       "      <td>Less Affluent Households-Older Families &amp; Matu...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>45</td>\n",
       "      <td>Less Affluent Households-Elders In Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>51</td>\n",
       "      <td>Poorer Households-Pre-Family Couples &amp; Singles</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>52</td>\n",
       "      <td>Poorer Households-Young Couples With Children</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>53</td>\n",
       "      <td>Poorer Households-Families With School Age Chi...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>54</td>\n",
       "      <td>Poorer Households-Older Families &amp; Mature Couples</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>(each German CAMEO code belongs to one interna...</td>\n",
       "      <td>55</td>\n",
       "      <td>Poorer Households-Elders In Retirement</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attribute                                        Description Value  \\\n",
       "105  CAMEO_INTL_2015  CAMEO classification 2015 - international typo...    -1   \n",
       "106  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    11   \n",
       "107  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    12   \n",
       "108  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    13   \n",
       "109  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    14   \n",
       "110  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    15   \n",
       "111  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    21   \n",
       "112  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    22   \n",
       "113  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    23   \n",
       "114  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    24   \n",
       "115  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    25   \n",
       "116  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    31   \n",
       "117  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    32   \n",
       "118  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    33   \n",
       "119  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    34   \n",
       "120  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    35   \n",
       "121  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    41   \n",
       "122  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    42   \n",
       "123  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    43   \n",
       "124  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    44   \n",
       "125  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    45   \n",
       "126  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    51   \n",
       "127  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    52   \n",
       "128  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    53   \n",
       "129  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    54   \n",
       "130  CAMEO_INTL_2015  (each German CAMEO code belongs to one interna...    55   \n",
       "\n",
       "                                               Meaning Missing  \n",
       "105                                            unknown    True  \n",
       "106    Wealthy Households-Pre-Family Couples & Singles   False  \n",
       "107     Wealthy Households-Young Couples With Children   False  \n",
       "108  Wealthy Households-Families With School Age Ch...   False  \n",
       "109  Wealthy Households-Older Families &  Mature Co...   False  \n",
       "110            Wealthy Households-Elders In Retirement   False  \n",
       "111  Prosperous Households-Pre-Family Couples & Sin...   False  \n",
       "112  Prosperous Households-Young Couples With Children   False  \n",
       "113  Prosperous Households-Families With School Age...   False  \n",
       "114  Prosperous Households-Older Families & Mature ...   False  \n",
       "115         Prosperous Households-Elders In Retirement   False  \n",
       "116  Comfortable Households-Pre-Family Couples & Si...   False  \n",
       "117  Comfortable Households-Young Couples With Chil...   False  \n",
       "118  Comfortable Households-Families With School Ag...   False  \n",
       "119  Comfortable Households-Older Families & Mature...   False  \n",
       "120        Comfortable Households-Elders In Retirement   False  \n",
       "121  Less Affluent Households-Pre-Family Couples & ...   False  \n",
       "122  Less Affluent Households-Young Couples With Ch...   False  \n",
       "123  Less Affluent Households-Families With School ...   False  \n",
       "124  Less Affluent Households-Older Families & Matu...   False  \n",
       "125      Less Affluent Households-Elders In Retirement   False  \n",
       "126     Poorer Households-Pre-Family Couples & Singles   False  \n",
       "127      Poorer Households-Young Couples With Children   False  \n",
       "128  Poorer Households-Families With School Age Chi...   False  \n",
       "129  Poorer Households-Older Families & Mature Couples   False  \n",
       "130             Poorer Households-Elders In Retirement   False  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info(cols_cameo[2], attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new columns by applying function to cameo cols\n",
    "feature_engine.apply_transform('CAMEO_INTL_HH_ECON', cols_cameo[2], lambda x: x//10)\n",
    "feature_engine.apply_transform('CAMEO_INTL_FAM_INFO', cols_cameo[2], lambda x: x%10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_engine.apply_transform('CAMEO_DEU_LEBENSSTIL', cols_cameo[1], lambda x: x[1] if type(x)==str else x)\n",
    "# azdias['CAMEO_DEU_LEBENSSTIL'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(azdias['CAMEO_DEU_LEBENSSTIL'].unique())\n",
    "# feature_engine.apply_remap('CAMEO_DEU_LEBENSSTIL', 'CAMEO_DEU_LEBENSSTIL', {'A':1, 'C':3, 'B':2, 'D':4, 'E':5, 'F':6})\n",
    "# print(azdias['CAMEO_DEU_LEBENSSTIL'].unique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dealing with ther columns with extra encodings & other issues we noticed earlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>2</td>\n",
       "      <td>couple</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>3</td>\n",
       "      <td>single parent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>6</td>\n",
       "      <td>family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>9</td>\n",
       "      <td>multiperson household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>LP_FAMILIE_GROB</td>\n",
       "      <td>familytyp rough</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attribute      Description Value                Meaning Missing\n",
       "1903  LP_FAMILIE_GROB  familytyp rough     1                single    False\n",
       "1904  LP_FAMILIE_GROB  familytyp rough     2                 couple   False\n",
       "1905  LP_FAMILIE_GROB  familytyp rough     3          single parent   False\n",
       "1906  LP_FAMILIE_GROB  familytyp rough     4                    NaN     NaN\n",
       "1907  LP_FAMILIE_GROB  familytyp rough     5                    NaN     NaN\n",
       "1908  LP_FAMILIE_GROB  familytyp rough     6                 family   False\n",
       "1909  LP_FAMILIE_GROB  familytyp rough     7                    NaN     NaN\n",
       "1910  LP_FAMILIE_GROB  familytyp rough     8                    NaN     NaN\n",
       "1911  LP_FAMILIE_GROB  familytyp rough     9  multiperson household   False\n",
       "1912  LP_FAMILIE_GROB  familytyp rough    10                    NaN     NaN\n",
       "1913  LP_FAMILIE_GROB  familytyp rough    11                    NaN     NaN"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP_FAMILIE_GROB_attribute_values = get_attribute_info('LP_FAMILIE_GROB', attributes).copy()\n",
    "LP_FAMILIE_GROB_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  1., nan,  5.,  2.,  4.])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_FAMILIE_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a dict using attributes df\n",
    "LP_FAMILIE_GROB_attribute_values.Meaning = LP_FAMILIE_GROB_attribute_values.Meaning.str.strip()\n",
    "# https://stackoverflow.com/questions/18695605/how-to-convert-a-dataframe-to-a-dictionary\n",
    "LP_FAMILIE_GROB_attribute_values.ffill(inplace=True)\n",
    "LP_FAMILIE_GROB_attribute_values['Meaning'].replace(\n",
    "    {'single':1, 'couple':2, 'single parent':3, 'family':4, 'multiperson household':5},\n",
    "    inplace=True)\n",
    "LP_FAMILIE_GROB_dict = LP_FAMILIE_GROB_attribute_values.set_index('Value')['Meaning'].to_dict()\n",
    "\n",
    "# Create new column from the applied encoding\n",
    "feature_engine.apply_remap('LP_FAMILIE_GROB_HH', 'LP_FAMILIE_GROB', \n",
    "                           LP_FAMILIE_GROB_dict\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Missing</th>\n",
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       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>2</td>\n",
       "      <td>couple</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>3</td>\n",
       "      <td>young single parent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>4</td>\n",
       "      <td>single parent with teenager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>5</td>\n",
       "      <td>single parent with child of full age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>6</td>\n",
       "      <td>young family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>7</td>\n",
       "      <td>family with teenager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>8</td>\n",
       "      <td>family with child of full age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>9</td>\n",
       "      <td>shared flat</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>10</td>\n",
       "      <td>two-generational household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>11</td>\n",
       "      <td>multi-generational household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attribute     Description Value  \\\n",
       "1892  LP_FAMILIE_FEIN  familytyp fine     1   \n",
       "1893  LP_FAMILIE_FEIN  familytyp fine     2   \n",
       "1894  LP_FAMILIE_FEIN  familytyp fine     3   \n",
       "1895  LP_FAMILIE_FEIN  familytyp fine     4   \n",
       "1896  LP_FAMILIE_FEIN  familytyp fine     5   \n",
       "1897  LP_FAMILIE_FEIN  familytyp fine     6   \n",
       "1898  LP_FAMILIE_FEIN  familytyp fine     7   \n",
       "1899  LP_FAMILIE_FEIN  familytyp fine     8   \n",
       "1900  LP_FAMILIE_FEIN  familytyp fine     9   \n",
       "1901  LP_FAMILIE_FEIN  familytyp fine    10   \n",
       "1902  LP_FAMILIE_FEIN  familytyp fine    11   \n",
       "\n",
       "                                   Meaning Missing  \n",
       "1892                               single    False  \n",
       "1893                                couple   False  \n",
       "1894                  young single parent    False  \n",
       "1895           single parent with teenager   False  \n",
       "1896  single parent with child of full age   False  \n",
       "1897                         young family    False  \n",
       "1898                 family with teenager    False  \n",
       "1899         family with child of full age   False  \n",
       "1900                           shared flat   False  \n",
       "1901            two-generational household   False  \n",
       "1902          multi-generational household   False  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP_FAMILIE_FEIN_attribute_values = get_attribute_info('LP_FAMILIE_FEIN', attributes).copy()\n",
    "LP_FAMILIE_FEIN_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.,  1., nan, 10.,  2.,  7., 11.,  8.,  4.,  6.,  9.,  3.])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_FAMILIE_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column from the applied encoding\n",
    "# We will use the dict above as the values are similar\n",
    "feature_engine.apply_remap('LP_FAMILIE_FEIN_HH', 'LP_FAMILIE_FEIN',\n",
    "                            LP_FAMILIE_GROB_dict\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Column LP_FAMILIE_GROB:  [ 3.  1. nan  5.  2.  4.]\n",
      "Unique Values in Column LP_FAMILIE_FEIN:  [ 5.  1. nan 10.  2.  7. 11.  8.  4.  6.  9.  3.]\n"
     ]
    }
   ],
   "source": [
    "# Check unique values in above columns after transformations/replacement\n",
    "for col in ['LP_FAMILIE_GROB', 'LP_FAMILIE_FEIN']:\n",
    "    get_unique_vals(azdias, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>2</td>\n",
       "      <td>couple</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>3</td>\n",
       "      <td>young single parent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>4</td>\n",
       "      <td>single parent with teenager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>5</td>\n",
       "      <td>single parent with child of full age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>6</td>\n",
       "      <td>young family</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>7</td>\n",
       "      <td>family with teenager</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>8</td>\n",
       "      <td>family with child of full age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>9</td>\n",
       "      <td>shared flat</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>10</td>\n",
       "      <td>two-generational household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>LP_FAMILIE_FEIN</td>\n",
       "      <td>familytyp fine</td>\n",
       "      <td>11</td>\n",
       "      <td>multi-generational household</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Attribute     Description Value  \\\n",
       "1892  LP_FAMILIE_FEIN  familytyp fine     1   \n",
       "1893  LP_FAMILIE_FEIN  familytyp fine     2   \n",
       "1894  LP_FAMILIE_FEIN  familytyp fine     3   \n",
       "1895  LP_FAMILIE_FEIN  familytyp fine     4   \n",
       "1896  LP_FAMILIE_FEIN  familytyp fine     5   \n",
       "1897  LP_FAMILIE_FEIN  familytyp fine     6   \n",
       "1898  LP_FAMILIE_FEIN  familytyp fine     7   \n",
       "1899  LP_FAMILIE_FEIN  familytyp fine     8   \n",
       "1900  LP_FAMILIE_FEIN  familytyp fine     9   \n",
       "1901  LP_FAMILIE_FEIN  familytyp fine    10   \n",
       "1902  LP_FAMILIE_FEIN  familytyp fine    11   \n",
       "\n",
       "                                   Meaning Missing  \n",
       "1892                               single    False  \n",
       "1893                                couple   False  \n",
       "1894                  young single parent    False  \n",
       "1895           single parent with teenager   False  \n",
       "1896  single parent with child of full age   False  \n",
       "1897                         young family    False  \n",
       "1898                 family with teenager    False  \n",
       "1899         family with child of full age   False  \n",
       "1900                           shared flat   False  \n",
       "1901            two-generational household   False  \n",
       "1902          multi-generational household   False  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP_FAMILIE_FEIN_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a dict using attributes df\n",
    "LP_FAMILIE_FEIN_attribute_values.Meaning = ['no_child', 'no_child', 'young', \n",
    "                                            'with_teenager', 'with_full_age_child', \n",
    "                                            'young', 'with_teenager', 'with_full_age_child',\n",
    "                                            'shared', 'two_generational', 'multi_generational'\n",
    "                                            ]\n",
    "LP_FAMILIE_FEIN_attribute_values.Meaning.replace({'no_child':1, 'young':2, 'with_teenager':3, 'with_full_age_child':4, \n",
    "                                                  'shared':5, 'two_generational':6, 'multi_generational':7},\n",
    "                                                   inplace=True\n",
    "                                                   )\n",
    "\n",
    "LP_FAMILIE_FEIN_dict = LP_FAMILIE_FEIN_attribute_values.set_index(\"Value\")['Meaning'].to_dict()\n",
    "\n",
    "# Create new column from the applied encoding\n",
    "feature_engine.apply_remap('LP_FAMILIE_FEIN_INFO', 'LP_FAMILIE_FEIN', LP_FAMILIE_FEIN_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  1., nan,  6.,  3.,  7.,  2.,  5.])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_FAMILIE_FEIN_INFO'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>1</td>\n",
       "      <td>low-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>3</td>\n",
       "      <td>average earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>6</td>\n",
       "      <td>independants</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>8</td>\n",
       "      <td>houseowners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>LP_STATUS_GROB</td>\n",
       "      <td>social status rough</td>\n",
       "      <td>10</td>\n",
       "      <td>top earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attribute          Description Value             Meaning Missing\n",
       "1976  LP_STATUS_GROB  social status rough     1  low-income earners   False\n",
       "1977  LP_STATUS_GROB  social status rough     2                 NaN     NaN\n",
       "1978  LP_STATUS_GROB  social status rough     3     average earners   False\n",
       "1979  LP_STATUS_GROB  social status rough     4                 NaN     NaN\n",
       "1980  LP_STATUS_GROB  social status rough     5                 NaN     NaN\n",
       "1981  LP_STATUS_GROB  social status rough     6        independants   False\n",
       "1982  LP_STATUS_GROB  social status rough     7                 NaN     NaN\n",
       "1983  LP_STATUS_GROB  social status rough     8         houseowners   False\n",
       "1984  LP_STATUS_GROB  social status rough     9                 NaN     NaN\n",
       "1985  LP_STATUS_GROB  social status rough    10        top earners    False"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP_STATUS_GROB_attribute_values = get_attribute_info('LP_STATUS_GROB', attributes).copy()\n",
    "LP_STATUS_GROB_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  4.,  5.,  3., nan])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_STATUS_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a dict using attributes df\n",
    "LP_STATUS_GROB_attribute_values.Meaning.ffill(inplace=True)\n",
    "LP_STATUS_GROB_attribute_values.Meaning.replace({'low-income earners':1, 'average earners':2, \n",
    "                                                 'independants':3, 'houseowners':4, 'top earners ':5\n",
    "                                                 },\n",
    "                                                 inplace=True\n",
    "                                                )\n",
    "LP_STATUS_GROB_dict = LP_STATUS_GROB_attribute_values.set_index('Value')['Meaning'].to_dict()\n",
    "\n",
    "# Replace column values using the applied encoding\n",
    "feature_engine.apply_remap('LP_STATUS_GROB', 'LP_STATUS_GROB', LP_STATUS_GROB_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_STATUS_GROB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>1</td>\n",
       "      <td>single low-income earners of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>2</td>\n",
       "      <td>single low-income earners of middle age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>3</td>\n",
       "      <td>single average earners of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>4</td>\n",
       "      <td>single average earners of middle age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>5</td>\n",
       "      <td>single low-income earners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>6</td>\n",
       "      <td>single low-income earners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>7</td>\n",
       "      <td>single average earners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>8</td>\n",
       "      <td>single average earners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>9</td>\n",
       "      <td>single independant persons</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>10</td>\n",
       "      <td>wealthy single homeowners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>11</td>\n",
       "      <td>single homeowners of advanced age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>12</td>\n",
       "      <td>single homeowners at retirement age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>13</td>\n",
       "      <td>single top earners of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>14</td>\n",
       "      <td>low-income- and average earner-couples of youn...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>15</td>\n",
       "      <td>low-income earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>16</td>\n",
       "      <td>average earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>17</td>\n",
       "      <td>independant couples</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>18</td>\n",
       "      <td>wealthy homeowner-couples of younger age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>19</td>\n",
       "      <td>homeowner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>20</td>\n",
       "      <td>top earner-couples of higher age</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>21</td>\n",
       "      <td>single parent low-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>22</td>\n",
       "      <td>single parent average earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>23</td>\n",
       "      <td>single parent high-income earners</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>24</td>\n",
       "      <td>low-income earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>25</td>\n",
       "      <td>average earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>26</td>\n",
       "      <td>independant families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>27</td>\n",
       "      <td>homeowner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>28</td>\n",
       "      <td>top earner-families</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>29</td>\n",
       "      <td>low-income earners of younger age from mulitpe...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>30</td>\n",
       "      <td>average earners of younger age from mulitperso...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>31</td>\n",
       "      <td>low-income earners of higher age from mulitper...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>32</td>\n",
       "      <td>average earners of higher age from mulitperson...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>33</td>\n",
       "      <td>independant persons of younger age from mulitp...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>34</td>\n",
       "      <td>homeowners of younger age from mulitperson hou...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>35</td>\n",
       "      <td>top earners of younger age from mulitperson ho...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>36</td>\n",
       "      <td>independant persons of higher age from mulitpe...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>37</td>\n",
       "      <td>homeowners of advanced age from mulitperson ho...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>38</td>\n",
       "      <td>homeowners at retirement age from mulitperson ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>39</td>\n",
       "      <td>top earners of middle age from mulitperson hou...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>lifestage fine</td>\n",
       "      <td>40</td>\n",
       "      <td>top earners at retirement age from mulitperson...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Attribute      Description Value  \\\n",
       "1914  LP_LEBENSPHASE_FEIN  lifestage fine      1   \n",
       "1915  LP_LEBENSPHASE_FEIN  lifestage fine      2   \n",
       "1916  LP_LEBENSPHASE_FEIN  lifestage fine      3   \n",
       "1917  LP_LEBENSPHASE_FEIN  lifestage fine      4   \n",
       "1918  LP_LEBENSPHASE_FEIN  lifestage fine      5   \n",
       "1919  LP_LEBENSPHASE_FEIN  lifestage fine      6   \n",
       "1920  LP_LEBENSPHASE_FEIN  lifestage fine      7   \n",
       "1921  LP_LEBENSPHASE_FEIN  lifestage fine      8   \n",
       "1922  LP_LEBENSPHASE_FEIN  lifestage fine      9   \n",
       "1923  LP_LEBENSPHASE_FEIN  lifestage fine     10   \n",
       "1924  LP_LEBENSPHASE_FEIN  lifestage fine     11   \n",
       "1925  LP_LEBENSPHASE_FEIN  lifestage fine     12   \n",
       "1926  LP_LEBENSPHASE_FEIN  lifestage fine     13   \n",
       "1927  LP_LEBENSPHASE_FEIN  lifestage fine     14   \n",
       "1928  LP_LEBENSPHASE_FEIN  lifestage fine     15   \n",
       "1929  LP_LEBENSPHASE_FEIN  lifestage fine     16   \n",
       "1930  LP_LEBENSPHASE_FEIN  lifestage fine     17   \n",
       "1931  LP_LEBENSPHASE_FEIN  lifestage fine     18   \n",
       "1932  LP_LEBENSPHASE_FEIN  lifestage fine     19   \n",
       "1933  LP_LEBENSPHASE_FEIN  lifestage fine     20   \n",
       "1934  LP_LEBENSPHASE_FEIN  lifestage fine     21   \n",
       "1935  LP_LEBENSPHASE_FEIN  lifestage fine     22   \n",
       "1936  LP_LEBENSPHASE_FEIN  lifestage fine     23   \n",
       "1937  LP_LEBENSPHASE_FEIN  lifestage fine     24   \n",
       "1938  LP_LEBENSPHASE_FEIN  lifestage fine     25   \n",
       "1939  LP_LEBENSPHASE_FEIN  lifestage fine     26   \n",
       "1940  LP_LEBENSPHASE_FEIN  lifestage fine     27   \n",
       "1941  LP_LEBENSPHASE_FEIN  lifestage fine     28   \n",
       "1942  LP_LEBENSPHASE_FEIN  lifestage fine     29   \n",
       "1943  LP_LEBENSPHASE_FEIN  lifestage fine     30   \n",
       "1944  LP_LEBENSPHASE_FEIN  lifestage fine     31   \n",
       "1945  LP_LEBENSPHASE_FEIN  lifestage fine     32   \n",
       "1946  LP_LEBENSPHASE_FEIN  lifestage fine     33   \n",
       "1947  LP_LEBENSPHASE_FEIN  lifestage fine     34   \n",
       "1948  LP_LEBENSPHASE_FEIN  lifestage fine     35   \n",
       "1949  LP_LEBENSPHASE_FEIN  lifestage fine     36   \n",
       "1950  LP_LEBENSPHASE_FEIN  lifestage fine     37   \n",
       "1951  LP_LEBENSPHASE_FEIN  lifestage fine     38   \n",
       "1952  LP_LEBENSPHASE_FEIN  lifestage fine     39   \n",
       "1953  LP_LEBENSPHASE_FEIN  lifestage fine     40   \n",
       "\n",
       "                                                Meaning Missing  \n",
       "1914           single low-income earners of younger age   False  \n",
       "1915            single low-income earners of middle age   False  \n",
       "1916             single average earners of younger age    False  \n",
       "1917               single average earners of middle age   False  \n",
       "1918          single low-income earners of advanced age   False  \n",
       "1919       single low-income earners at retirement age    False  \n",
       "1920             single average earners of advanced age   False  \n",
       "1921           single average earners at retirement age   False  \n",
       "1922                         single independant persons   False  \n",
       "1923                          wealthy single homeowners   False  \n",
       "1924                  single homeowners of advanced age   False  \n",
       "1925                single homeowners at retirement age   False  \n",
       "1926                  single top earners of higher age    False  \n",
       "1927  low-income- and average earner-couples of youn...   False  \n",
       "1928            low-income earner-couples of higher age   False  \n",
       "1929              average earner-couples of higher age    False  \n",
       "1930                                independant couples   False  \n",
       "1931          wealthy homeowner-couples of younger age    False  \n",
       "1932                    homeowner-couples of higher age   False  \n",
       "1933                   top earner-couples of higher age   False  \n",
       "1934                   single parent low-income earners   False  \n",
       "1935                      single parent average earners   False  \n",
       "1936                  single parent high-income earners   False  \n",
       "1937                         low-income earner-families   False  \n",
       "1938                           average earner-families    False  \n",
       "1939                               independant families   False  \n",
       "1940                                 homeowner-families   False  \n",
       "1941                                top earner-families   False  \n",
       "1942  low-income earners of younger age from mulitpe...   False  \n",
       "1943  average earners of younger age from mulitperso...   False  \n",
       "1944  low-income earners of higher age from mulitper...   False  \n",
       "1945  average earners of higher age from mulitperson...   False  \n",
       "1946  independant persons of younger age from mulitp...   False  \n",
       "1947  homeowners of younger age from mulitperson hou...   False  \n",
       "1948  top earners of younger age from mulitperson ho...   False  \n",
       "1949  independant persons of higher age from mulitpe...   False  \n",
       "1950  homeowners of advanced age from mulitperson ho...   False  \n",
       "1951  homeowners at retirement age from mulitperson ...   False  \n",
       "1952  top earners of middle age from mulitperson hou...   False  \n",
       "1953  top earners at retirement age from mulitperson...   False  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LP_LEBENSPHASE_FEIN_attribute_values = get_attribute_info('LP_LEBENSPHASE_FEIN', attributes).copy()\n",
    "LP_LEBENSPHASE_FEIN_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dict of encodings using attributes df\n",
    "LP_LEBENSPHASE_FEIN_attribute_values.Meaning = ['younger', 'middle', 'younger', 'middle', 'advanced', \n",
    "                                                'retirement', 'advanced', 'retirement', 'middle', 'middle',\n",
    "                                                'advanced', 'retirement', 'higher', 'younger', 'higher', \n",
    "                                                'higher', 'middle', 'younger', 'higher', 'higher',\n",
    "                                                'middle', 'middle', 'middle', 'middle', 'middle', \n",
    "                                                'middle', 'middle', 'middle', 'younger', 'younger',\n",
    "                                                'higher', 'higher', 'younger', 'younger', 'younger', \n",
    "                                                'higher', 'advanced', 'retirement', 'middle', 'retirement']\n",
    "LP_LEBENSPHASE_FEIN_attribute_values.Meaning.replace({'younger':1, 'middle':2, 'higher':3, 'advanced':4, 'retirement':5}, \n",
    "                                                     inplace=True\n",
    "                                                     )\n",
    "LP_LEBENSPHASE_FEIN_dict = LP_LEBENSPHASE_FEIN_attribute_values.set_index('Value')['Meaning'].to_dict()\n",
    "\n",
    "# Create a new column using above dict\n",
    "feature_engine.apply_remap('LP_LEBENSPHASE_FEIN_ALTER', 'LP_LEBENSPHASE_FEIN', LP_LEBENSPHASE_FEIN_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1., nan,  3.,  5.,  4.])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_LEBENSPHASE_FEIN_ALTER'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dict of encodings using attributes df\n",
    "lebensphase_einkomnen = ['low', 'low', 'average', 'average', 'low', \n",
    "                         'low', 'average', 'average', 'average', 'wealthy',\n",
    "                         'average', 'average', 'top', 'average', 'low', \n",
    "                         'average', 'average', 'wealthy', 'wealthy', 'top', \n",
    "                         'low', 'average', 'wealthy', 'low', 'average', \n",
    "                         'average', 'average', 'top', 'low', 'average', \n",
    "                         'low', 'average', 'average', 'average', 'top',\n",
    "                         'average', 'average', 'average', 'top', 'top']\n",
    "LP_LEBENSPHASE_FEIN_attribute_values.Meaning = lebensphase_einkomnen\n",
    "LP_LEBENSPHASE_FEIN_attribute_values.Meaning.replace({'low':1, 'average':2, 'wealthy':3, 'top':4}, inplace=True)\n",
    "LP_LEBENSPHASE_FEIN_einkomnen_dict = LP_LEBENSPHASE_FEIN_attribute_values.set_index('Value')['Meaning'].to_dict()\n",
    "\n",
    "# Create a new column using above encoding dict\n",
    "feature_engine.apply_remap('LP_LEBENSPHASE_FEIN_EINK', 'LP_LEBENSPHASE_FEIN', LP_LEBENSPHASE_FEIN_einkomnen_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., nan,  3.,  4.])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_LEBENSPHASE_FEIN_EINK'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  3.,  9.,  4.,  1., 10.,  5.,  8.,  6.,  7., nan])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['LP_STATUS_FEIN'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>0</td>\n",
       "      <td>no score calculated</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>1</td>\n",
       "      <td>very good neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>2</td>\n",
       "      <td>good neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>3</td>\n",
       "      <td>average neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>4</td>\n",
       "      <td>poor neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>5</td>\n",
       "      <td>very poor neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>7</td>\n",
       "      <td>rural neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>WOHNLAGE</td>\n",
       "      <td>residential-area</td>\n",
       "      <td>8</td>\n",
       "      <td>new building in rural neighbourhood</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Attribute        Description Value                              Meaning  \\\n",
       "2229  WOHNLAGE  residential-area     -1                              unknown   \n",
       "2230  WOHNLAGE  residential-area      0                  no score calculated   \n",
       "2231  WOHNLAGE  residential-area      1              very good neighbourhood   \n",
       "2232  WOHNLAGE  residential-area      2                   good neighbourhood   \n",
       "2233  WOHNLAGE  residential-area      3                average neighbourhood   \n",
       "2234  WOHNLAGE  residential-area      4                   poor neighbourhood   \n",
       "2235  WOHNLAGE  residential-area      5              very poor neighbourhood   \n",
       "2236  WOHNLAGE  residential-area      7                  rural neighbourhood   \n",
       "2237  WOHNLAGE  residential-area      8  new building in rural neighbourhood   \n",
       "\n",
       "     Missing  \n",
       "2229    True  \n",
       "2230   False  \n",
       "2231   False  \n",
       "2232   False  \n",
       "2233   False  \n",
       "2234   False  \n",
       "2235   False  \n",
       "2236   False  \n",
       "2237   False  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_attribute_info('WOHNLAGE', attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  2.,  7.,  3.,  5.,  1.,  8., nan])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['WOHNLAGE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column indicating rural neighbourhood\n",
    "feature_engine.apply_transform('WOHNLAGE_LANDLICH', 'WOHNLAGE', lambda x: 1 if ((x==7) or (x==8)) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['WOHNLAGE_LANDLICH'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new columns based on class of neighbourhood w.r.t. ordinal scale\n",
    "feature_engine.apply_remap('WOHNLAGE_KLASSE', 'WOHNLAGE', {5:1, 4:2, 3:3, 2:4, 1:5, 7:1, 8:2})\n",
    "# Create new columns based on type of neighbourhood w.r.t. ordinal scale \n",
    "feature_engine.apply_remap('WOHNLAGE_TYP', 'WOHNLAGE', {5:3, 4:4, 3:5, 2:6, 1:7, 7:1, 8:2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMEO_DEU_2015              object\n",
       "D19_LETZTER_KAUF_BRANCHE    object\n",
       "EINGEFUEGT_AM               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.dtypes[azdias.dtypes == object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create numerical encodings for D19_LETZTER_KAUF_BRANCHE column\n",
    "# sklearn's column transformers and categorical encoder would have also worked\n",
    "LETZTER_KAUF_BRANCHE_dict = {v:k for k, v in enumerate([category for category in list(azdias['D19_LETZTER_KAUF_BRANCHE'].unique()) if type(category)==str])}\n",
    "\n",
    "# Apply encodings\n",
    "feature_engine.apply_remap('D19_LETZTER_KAUF_BRANCHE', 'D19_LETZTER_KAUF_BRANCHE', LETZTER_KAUF_BRANCHE_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.,\n",
       "       12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.,\n",
       "       25., 26., 27., 28., 29., 30., 31., 32., 33., 34.])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['D19_LETZTER_KAUF_BRANCHE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create numerical encodings using values in attributes df \n",
    "cameo_deu_2015_map = {v:k+1 for k,v in enumerate(attributes[attributes.Attribute=='CAMEO_DEU_2015'].Value)}\n",
    "\n",
    "# Apply encodings\n",
    "feature_engine.apply_remap('CAMEO_DEU_2015', cols_cameo[1], cameo_deu_2015_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>1</td>\n",
       "      <td>upper class</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>2</td>\n",
       "      <td>upper middleclass</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>3</td>\n",
       "      <td>established middleclasse</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>4</td>\n",
       "      <td>consumption-oriented middleclass</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>5</td>\n",
       "      <td>active middleclass</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>6</td>\n",
       "      <td>low-consumption middleclass</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>7</td>\n",
       "      <td>lower middleclass</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>8</td>\n",
       "      <td>working class</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>CAMEO_DEUG_2015</td>\n",
       "      <td>CAMEO classification 2015 - Uppergroup</td>\n",
       "      <td>9</td>\n",
       "      <td>urban working class</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Attribute                             Description Value  \\\n",
       "51  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup    -1   \n",
       "52  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     1   \n",
       "53  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     2   \n",
       "54  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     3   \n",
       "55  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     4   \n",
       "56  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     5   \n",
       "57  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     6   \n",
       "58  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     7   \n",
       "59  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     8   \n",
       "60  CAMEO_DEUG_2015  CAMEO classification 2015 - Uppergroup     9   \n",
       "\n",
       "                              Meaning Missing  \n",
       "51                            unknown    True  \n",
       "52                        upper class   False  \n",
       "53                  upper middleclass   False  \n",
       "54           established middleclasse   False  \n",
       "55  consumption-oriented middleclass    False  \n",
       "56                active middleclass    False  \n",
       "57        low-consumption middleclass   False  \n",
       "58                  lower middleclass   False  \n",
       "59                      working class   False  \n",
       "60                urban working class   False  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CAMEO_DEUG_attribute_values = get_attribute_info(cols_cameo[0], attributes).copy()\n",
    "CAMEO_DEUG_attribute_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.,  4.,  2.,  6.,  1.,  9.,  5.,  7.,  3., nan])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias[cols_cameo[0]].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new columns based on income class w.r.t. ordinal scale\n",
    "feature_engine.apply_remap(f'{cols_cameo[0]}_KLASSE', cols_cameo[0], {8:1,9:1,7:2,6:2,5:2,4:2,3:2,2:2,1:3})\n",
    "feature_engine.apply_remap(f'{cols_cameo[0]}_TYP', cols_cameo[0], {8:1,9:2,7:3,6:4,5:5,4:6,3:7,2:8,1:9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply all transformations to customers dataframe\n",
    "feature_engine.transform(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking if columns are same\n",
    "assert(len(set(azdias.columns)-set(customers.columns)) == 0)\n",
    "assert(len(set(customers.columns)-set(azdias.columns)) == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clearing up memory usage\n",
    "for i in [LP_FAMILIE_GROB_attribute_values, LP_FAMILIE_FEIN_attribute_values, LP_STATUS_GROB_attribute_values, LP_LEBENSPHASE_FEIN_attribute_values]:\n",
    "    i = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns that have become redundant after above processing\n",
    "# LNR represents unique code for each observation & 'EINGEFUEGT_AM' literally translates to inserted on\n",
    "azdias.drop(['LNR', 'EINGEFUEGT_AM', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN', 'CAMEO_INTL_2015'], axis=1, inplace=True)\n",
    "customers.drop(['LNR', 'EINGEFUEGT_AM', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN', 'CAMEO_INTL_2015'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    '''Reduce Memory usage of df'''\n",
    "    int_64_cols = df.select_dtypes(include='int64').columns\n",
    "    df[int_64_cols] = df[int_64_cols].astype('int32')\n",
    "\n",
    "    float_64_cols = df.select_dtypes(include='float64').columns\n",
    "    df[float_64_cols] = df[float_64_cols].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 785252 entries, 1 to 891220\n",
      "Columns: 363 entries, AKT_DAT_KL to CAMEO_DEUG_2015_TYP\n",
      "dtypes: float64(276), int64(87)\n",
      "memory usage: 2.1 GB\n"
     ]
    }
   ],
   "source": [
    "azdias.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_mem_usage(azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 785252 entries, 1 to 891220\n",
      "Columns: 363 entries, AKT_DAT_KL to CAMEO_DEUG_2015_TYP\n",
      "dtypes: float32(276), int32(87)\n",
      "memory usage: 1.1 GB\n"
     ]
    }
   ],
   "source": [
    "azdias.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 140350 entries, 0 to 191651\n",
      "Columns: 363 entries, AKT_DAT_KL to CAMEO_DEUG_2015_TYP\n",
      "dtypes: float64(276), int64(87)\n",
      "memory usage: 389.8 MB\n"
     ]
    }
   ],
   "source": [
    "customers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_mem_usage(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 140350 entries, 0 to 191651\n",
      "Columns: 363 entries, AKT_DAT_KL to CAMEO_DEUG_2015_TYP\n",
      "dtypes: float32(276), int32(87)\n",
      "memory usage: 195.4 MB\n"
     ]
    }
   ],
   "source": [
    "customers.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/feat_eng.pkl', 'wb') as handle:\n",
    "    dill.dump(feature_engine, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias.to_pickle('data/azdias_feat_engineered.pkl')\n",
    "customers.to_pickle('data/customers_feat_engineered.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = pd.read_pickle('data/azdias_feat_engineered.pkl')\n",
    "customers = pd.read_pickle('data/customers_feat_engineered.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Customer Segmentation Report\n",
    "\n",
    "The main bulk of your analysis will come in this part of the project. Here, you should use unsupervised learning techniques to describe the relationship between the demographics of the company's existing customers and the general population of Germany. By the end of this part, you should be able to describe parts of the general population that are more likely to be part of the mail-order company's main customer base, and which parts of the general population are less so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impute missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute = Imputer(strategy='most_frequent')\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "imputer.set_output(transform='pandas')\n",
    "\n",
    "imputer.fit(azdias)\n",
    "azdias = imputer.transform(azdias)\n",
    "customers = imputer.transform(customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias.to_pickle('data/azdias_imputed.pkl')\n",
    "customers.to_pickle('data/customers_imputed.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = pd.read_pickle('data/azdias_imputed.pkl')\n",
    "customers = pd.read_pickle('data/customers_imputed.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale Values before performing dimensional reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(azdias)\n",
    "scaler.set_output(transform='pandas')\n",
    "\n",
    "azdias = pd.DataFrame(scaler.transform(azdias), columns=azdias.columns)\n",
    "\n",
    "customers = pd.DataFrame(scaler.transform(customers), columns=customers.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias.to_pickle('data/azdias_scaled.pkl')\n",
    "customers.to_pickle('data/customers_scaled.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = pd.read_pickle('data/azdias_scaled.pkl')\n",
    "customers = pd.read_pickle('data/customers_scaled.pkl')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(random_state=42)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(random_state=randomState)\n",
    "pca.fit(azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "variance = pca.explained_variance_ratio_\n",
    "cumulative_variance = np.cumsum(variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Variance explained by Components\n",
    "fig, ax1 = plt.subplots(figsize=(10, 5))\n",
    "ax1.bar(range(1, len(variance)+1), variance, label='Individual variance')\n",
    "ax1.set_xlabel('Principal component')\n",
    "ax1.set_ylabel('Proportion of variance explained')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(range(1, len(variance)+1), cumulative_variance, label='Cumulative variance')\n",
    "ax2.set_ylabel('Proportion of variance explained')\n",
    "fig.legend()\n",
    "fig.tight_layout(pad=4)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(176, 177)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check thresholds\n",
    "indices_evr_lt_90 = np.where(cumulative_variance<=0.90)\n",
    "indices_evr_gt_90 = np.where(cumulative_variance>0.90)\n",
    "indices_evr_lt_90[0][-1], indices_evr_gt_90[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot lines indicating thresholds\n",
    "plt.ylim(min(cumulative_variance))\n",
    "plt.axhline(y=0.9, color='r')\n",
    "plt.axvline(x=176, color='r')\n",
    "\n",
    "plt.axhline(y=0.86, color='r', linestyle='--')\n",
    "plt.axvline(x=150, color='r', linestyle='--')\n",
    "\n",
    "plt.plot(cumulative_variance)\n",
    "plt.xlabel('Principal component')\n",
    "plt.ylabel('Proportion of variance explained')\n",
    "plt.title(\"Explained Variance (Cumulative) per N Principal Components\")\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will perform PCA considering both ~90% & ~85% of variance explained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA with 176 Components ~ 90% Variance\n",
    "pca_176 = PCA(random_state=randomState, n_components=176)\n",
    "pca_176.fit(azdias)\n",
    "azdias_pca_176 = pca_176.transform(azdias)\n",
    "customers_pca_176 = pca_176.transform(customers)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Explained Variance of Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_evr(pca_176)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Feature Weights of top 4 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_feature_weights_list = [get_feature_weights(pca_176, azdias, i) for i in range(4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q8dlnn0GhUCAmJkZnm4+PDxYsWAAhBEJDQx/Lue3s7DBx4kRER0ejbt26eOONN3D58mXMnDkTjRo10pk6VRUvvfQSNm7ciGXLlqFjx44wMTGBt7c3Ro0aBQsLC/To0QONGjXC1atXER0dDY1GU+70rYpMmDABq1evRlBQEGbNmgVnZ2f8+OOPWLp0Kd5//320bNmyWvUDD4NxYGAg+vbti/DwcPTt2xd169ZFTk4OfvjhByQnJ+PIkSNwcnLCjBkzsHXrVmi1WkRERMDOzg7ffPMNfvzxR8TGxkKj0VS7DkPq16+P3r174x//+AcsLS2xdOlS/Prrrzqv5nzcNT2ua/xRL730EtLS0vDDDz+gUaNGsLa2Lnca1datW7FixQq89tprcHZ2RklJCQ4fPoyFCxeiefPmGDlyZJXPT8+JWn5omIiIqqH0bTGHDh2qsN2mTZuEVqsVNjY2Qq1WC2dnZzFgwACdVwBevnxZBAcHi7p16wpra2sRGBgoTpw4YfDNOwsXLhTNmjUTpqamAoCIj48XQgjx4MEDERsbK1xdXYW5ubnw9vYWe/bsKfctPWVff1hWenq6CAoKEnZ2dsLMzEw0adJEBAUFldv+Udu2bRMAdF65WerWrVvCxMREABA///xztc5t6LWcDx48EHPmzBFNmzYVKpVKtG3bVmzdulW0a9dOvPHGG5X2vfQNOKVjWVrrgAEDhK2trVAoFKL0r+vExESh1WpFw4YNhUqlEo0bNxaDBg0Sx44dq3RsDL2lRwghLly4IIYMGSLq1asnzMzMhLu7u/jss8/E/fv39Wr87LPPKj1PWYWFheKLL74Q3bp1EzY2NkKpVIrGjRuLN998U/z44486bY8fPy5effVVodFohEqlEu3atdMZEyHKH8Py/jyUvu2m9PWyQjx8S88HH3wgli5dKtzc3ISZmZnw8PAQ33zzjV79NanJ0M9VCOOuM0N1l+1n2esvKytL9OjRQ9SpU0fvzVCPOnXqlBgwYIBwdnYW5ubmwtzcXHh4eIhPPvlE3Lx5s9z96PmnEEKIp/wdg4iISNbOnTsHDw8PzJgxo8J/FIuePoVCgQ8++ABLliyp7VKInhpO6SEiIqqBo0ePIjk5Gd27d4eNjQ1Onz6N2NhY2NjY4J133qnt8oiIGPiJiIhqwtLSEocPH0ZcXBxu374NjUYDX19fzJ07t9beukJEVBan9BARERERyRj/pV0iIiIiIhlj4CciIiIikjEGfiIiIiIiGeNDu0QvuAcPHuDKlSuwtraGQqGo7XKIiIjICEII3LlzB40bN670H/lj4Cd6wV25cgWOjo61XQYRERFVw6VLl9C0adMK2zDwE73grK2tATz8H4aNjU0tV0NERETGyMvLg6Ojo/T3eEUY+IlecKXTeGxsbBj4iYiInjPGTMflQ7tERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDKmrO0CiEjmIjW1XQHR8yEyt7YrICKZ4h1+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+CnKgsLC0P//v111m3YsAHm5uaIjY1FZGQkFAqFtGg0GvTs2RPp6ekGjxcVFQVTU1PMmzdPb1tOTg6GDBkCd3d3mJiYYPz48XptNm7cCG9vb9ja2sLS0hJeXl5Ys2aN0f25du0a3nvvPTg5OUGtVsPBwQEBAQHIyMiQ2ri4uGDhwoVIS0vT6ZuhJSEhAS4uLhW2uXDhAgDA19fX4PbRo0cbVbuh80ydOtXovhMREZH8KWu7AHr+rVy5Eh988AG+/PJLjBw5EpGRkWjdujV27doFALh16xbmz5+Pfv364fLly9BoNDr7x8fHY/LkyVi1apVeWC0qKkKDBg0wffp0LFiwwOD57ezsMH36dHh4eEClUmHr1q0YMWIE7O3tERAQUGn9wcHBKCkpQWJiIlxdXfHHH39g9+7duHXrll7b7t27IycnR/o8btw45OXlIT4+Xlqn0WgQFBSE+/fv6+x7+/ZtvPzyy+jYsSOcnJyk9aNGjcKsWbN02tapU6fSukvNmjULo0aNkj5bWVkZvS8RERHJHwM/1UhsbCwiIiKQlJSE4OBgab1SqYSDgwMAwMHBATNnzkR8fDzOnDmDTp06Se3S09NRWFiIWbNmYfXq1di7dy969eolbXdxccGiRYsAAKtWrTJYg6+vr87ncePGITExEfv27as08N++fRv79u1DWloafHx8AADOzs7o3LmzwfYqlUrqFwBYWFigqKhIZ13p+rIePHiAsLAwaDQarF27FgqFQtpWp04dvf2rwtraukb7ExERkbxxSg9V29SpUzF79mxs3bpVJ+w/qqioCAkJCbC1tYW7u7vOtri4OISEhMDMzAwhISGIi4urUU1CCOzevRunT5/W+eJQHisrK1hZWWHTpk0oKiqq0bkrMnXqVBw8eBCbN2+GjY3NYz12TEwM6tWrBy8vL8ydOxfFxcUVti8qKkJeXp7OQkRERPLFwE/V8tNPPyEmJgabN29Gnz599LYfP35cCtMWFhaYP38+kpOTdcJuXl4eUlJSMHToUADA0KFDsWHDhmoF0NzcXFhZWUGlUiEoKAiLFy+Gn59fpfsplUokJCQgMTERtra26NGjB6ZNm4Zjx45VuYbyJCcn4/PPP8e6devQokULve1Lly6Vxqp0SUxMNOrY48aNw7p165CamoqxY8di4cKFGDNmTIX7REdHQ6PRSIujo2O1+kVERETPBwZ+qpa2bdvCxcUFERERuHPnjt52d3d3ZGVlISsrC0eOHMH777+PgQMH4vDhw1KbpKQkuLq6ol27dgAALy8vuLq6Yt26dVWux9raGllZWTh06BDmzp2LiRMnIi0tzah9g4ODceXKFWzZsgUBAQFIS0tDhw4dkJCQUOU6HpWZmYl33nkH8+bNK3d6UWhoqDRWpcsbb7xh1PEnTJgAHx8ftG3bFiNHjsTy5csRFxeHmzdvlrvPp59+itzcXGm5dOlStfpGREREzwcGfqqWJk2aID09HTk5OQgMDNQL/SqVCs2bN0fz5s3Rvn17zJs3D02aNMHChQulNqtWrUJ2djaUSqW0ZGdnV2taj4mJCZo3bw4vLy9MmjQJAwYMQHR0tNH7m5ubw8/PDxEREdi/fz/CwsIwY8aMKtdR1vXr19G/f3+8+eab+Pjjj8ttp9FopLEqXao77adr164AgLNnz5bbRq1Ww8bGRmchIiIi+WLgp2pzcnJCeno6rl27Bn9//0qn4piamqKwsBDAwyk/hw8fRlpams6d7b179+LQoUM4ceJEjWoTQtRoTr6npycKCgqqvX9JSQkGDBgAe3t7rFy5strHqarMzEwAQKNGjZ7aOYmIiOjZxrf0UI00bdoUaWlp0Gq18Pf3x44dOwAA9+7dw9WrVwEAd+7cwfr163Hy5ElMmTIFwMOHdTt37mzwwdpu3bohLi5Oeg1nVlYWACA/Px/Xr19HVlYWVCoVPD09ATyck+7t7Q03NzcUFxdj27ZtWL16NZYtW1Zp/Tdv3sTAgQMRHh6Otm3bwtraGocPH0ZsbCxef/31ao/L+PHjcfToUezatQu3b9/W225nZweVSgUAuHv3rjRWpdRqNerWrVvhOTIyMnDgwAFotVpoNBocOnQIEyZMwGuvvabz2k8iIiJ6sTHwU42VTu/RarXw8/ND9+7dkZ2dLd1lrlOnDtzc3LBs2TIMHz4cxcXFWLt2rRT+HxUcHIzo6GjExMRApVKhffv20rYjR44gKSkJzs7OOH/+PACgoKAAY8aMweXLl2FhYQEPDw+sXbsWgwcPrrR2KysrdOnSBQsWLMBvv/2GkpISODo6YtSoUZg2bVq1x2Tp0qUAoPMK0rJSU1Ol14muWLECK1as0NkeEBCA7du3V3gOtVqN9evXY+bMmSgqKoKzszNGjRqFyZMnV7tuIiIikh+FEELUdhFEVHvy8vKg0WiQm5v7ZObzR2oqb0NEQGRubVdARM+Rqvz9zTn8REREREQyxsBPsnbx4kW9d9yXXS5evFjbJZYrKiqq3Lr79u1b2+URERHRc4Jz+EnWGjduLD30W972Z9Xo0aMxaNAgg9ssLCyecjVERET0vGLgJ1lTKpVo3rx5bZdRLXZ2drCzs6vtMoiIiOg5xyk9REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkY8raLoCIZC4yt7YrICIieqHxDj8RERERkYwx8BMRERERyRgDPxERERGRjDHwExERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwx8BMRERERyRgDPxERERGRjDHwExERERHJmLK2CyAieiIiNbVdAVHVRObWdgVEJFO8w09EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/OUICwtD//79ddZt2LAB5ubmiI2NRWRkJBQKhbRoNBr07NkT6enpBo8XFRUFU1NTzJs3T29bTk4OhgwZAnd3d5iYmGD8+PF6bTZu3Ahvb2/Y2trC0tISXl5eWLNmjdH9uXbtGt577z04OTlBrVbDwcEBAQEByMjIkNq4uLjo9Kl0ebTmlJQU9O7dG3Xr1kWdOnXg7u6O8PBwZGZmGlVLQkICbG1tddadOnUKTZs2xZtvvomioiKkpaVBoVDg9u3bBo/x6PiXLh4eHlIbX19fnbH09fWFQqHAunXrdI61cOFCuLi4AAB+/fVXKBQKHDx4UKdNly5doFarcffuXWldcXEx6tSpg6+//lpaV1hYiLp168LOzg6FhYUGazdm/BISEgz2z9zcXGoTFhZm8OezadMmKBQKg+cmIiKiFw8Dv5FWrlyJ0NBQLFmyBJMnTwYAtG7dGjk5OcjJyUFGRgZatGiBfv36ITc3V2//+Ph4TJ48GatWrdLbVlRUhAYNGmD69Olo166dwfPb2dlh+vTpyMjIwLFjxzBixAiMGDECO3bsMKr+4OBgHD16FImJiThz5gy2bNkCX19f3Lp1S6fdrFmzpD6VLh9++KG0fcqUKRg8eDC8vLywZcsWZGdn4+uvv4abmxumTZtmVC2POnToEHr27ImAgAB89913UKvVRu1XdvxLl3379lW4j7m5Of7+97+jpKTE4HYPDw80atQIqamp0rr8/HxkZmbC3t4e+/fvl9YfPHgQhYWF0Gq10rqUlBS0adMGnp6e2Lhxo97xqzJ+NjY2ev27cOGCXn9iYmLw559/VthvIiIienEpa7uA50FsbCwiIiKQlJSE4OBgab1SqYSDgwMAwMHBATNnzkR8fDzOnDmDTp06Se3S09NRWFiIWbNmYfXq1di7dy969eolbXdxccGiRYsAwOAXAuDh3emyxo0bh8TEROzbtw8BAQEV1n/79m3s27cPaWlp8PHxAQA4Ozujc+fOem2tra2lPj3qwIEDiI2NxaJFi/DRRx9J65s1awYfHx8IISqsw5A9e/bg9ddfx+jRo/HZZ59Vad+y42+skJAQ/PDDD1ixYgXGjBljsI2vry/S0tIwdepUAMAvv/yCli1bwsfHB2lpaejTpw8AIC0tDU2aNEGLFi2kfePi4jB06FAIIRAXF4fQ0FBpW1XHT6FQVNq/Pn364OzZs4iOjkZsbGyVxoKIiIheDLzDX4mpU6di9uzZ2Lp1q07Yf1RRUZE0VcXd3V1nW1xcHEJCQmBmZoaQkBDExcXVqCYhBHbv3o3Tp0/rfHEoj5WVFaysrLBp0yYUFRVV+7zJycmwsrIqNyhXdRrJ999/j6CgIEyfPr3KYb+6bGxsMG3aNMyaNQsFBQUG22i1Wuzbtw/37t0DAKSmpsLX1xc+Pj46d/5TU1N17u7/9ttvyMjIwKBBgzBo0CDs378fv//+u7T9cY8fAJiamiIqKgqLFy/G5cuXjdqnqKgIeXl5OgsRERHJFwN/BX766SfExMRg8+bN0l3dso4fPy6FaQsLC8yfPx/JycmwsbGR2uTl5SElJQVDhw4FAAwdOhQbNmyoVsjKzc2FlZUVVCoVgoKCsHjxYvj5+VW6n1KpREJCAhITE2Fra4sePXpg2rRpOHbsmF7bKVOmSH0qXdLS0gAAZ86cgaurK5TK//1i6PPPP9dpa2g6kyH5+fkYOHAgPvnkE+lOelWVHf/SZeTIkZXuN2bMGJibm+Pzzz83uN3X1xcFBQU4dOgQAEi/GfHx8cHhw4dx9+5dFBcX48CBAzqBf9WqVejbt680hz8wMFDnNzZVHb/Sn3fZxd/fX6/eN954A15eXpgxY0blgwYgOjoaGo1GWhwdHY3aj4iIiJ5PDPwVaNu2LVxcXBAREYE7d+7obXd3d0dWVhaysrJw5MgRvP/++xg4cCAOHz4stUlKSoKrq6s0N9/Lywuurq56D44aw9raGllZWTh06BDmzp2LiRMnSmG8MsHBwbhy5Qq2bNmCgIAApKWloUOHDkhISNBp98knn0h9Kl26dOkibX/0LnR4eDiysrLw1VdfoaCgwOhpPRYWFvDz88OKFStw6tQpo/Z5VNnxL13mzp1b6X5qtRqzZs3CZ599hhs3buhtb9GiBZo2bYq0tDTk5eUhMzMTPj4+aNiwIZo1a4Z//vOfOHDgAAoLC9G7d28AwP3795GYmCh9sQMefrlLTEzE/fv3pXVVGb/Sn3fZJT4+3mCfYmJikJiYiJMnT1ba/08//RS5ubnScunSpUr3ISIioucX5/BXoEmTJkhJSYFWq0VgYCC2b98Oa2trabtKpULz5s2lz+3bt8emTZuwcOFCrF27FsDDu77Z2dk6d3UfPHiAuLg4vPvuu1Wqx8TERDqfl5cXTp06hejoaL35/eUxNzeHn58f/Pz8EBERgZEjR2LGjBkICwuT2tSvX1+nT2W1aNEC+/btQ0lJCczMzAAAtra2sLW1NXo6SSlTU1Ns2rQJwcHB0Gq12LNnDzw9Pat0jEfHvyqGDh2K+fPnY86cOdIbesry9fVFamoq2rZtixYtWsDe3h4ApGk9arUazs7O0r47duzAf//7XwwePFjnOPfv38fOnTvRt2/fKo9f2Z93ZXr16oWAgABMmzZN5+dpiFqtNvrBaCIiInr+8Q5/JZycnJCeno5r167B39+/0qk4pqam0usYjx8/jsOHDyMtLU3nLu3evXtx6NAhnDhxoka1CSFqNCff09Oz3HnshoSEhCA/Px9Lly6t9jnLUqvV2LhxIzp37gytVlvj8agKExMTREdHY9myZTh//rzedq1Wi/379+Pnn3/W+UJV+uBuWlqadHcfePicxltvvaV3Rz40NFR6ZuNxj9+j5s2bhx9++EHnTUJEREREvMNvhNLpHVqtFv7+/tKrMO/du4erV68CAO7cuYP169fj5MmTmDJlCoCHIbBz584GH6zt1q0b4uLisGDBAgBAVlYWgIdz269fv46srCyoVCrprnd0dDS8vb3h5uaG4uJibNu2DatXr8ayZcsqrf/mzZsYOHAgwsPD0bZtW1hbW+Pw4cOIjY3F66+/rtP2zp07Up9K1alTBzY2NujWrRsmTZqESZMm4cKFC3jzzTfh6OiInJwcxMXFQaFQwMSkat8hVSoVUlJSMGjQIPTu3Ru7d+/GSy+9JG0/fvy4zm9VgIe/3QB0x7+UQqFAw4YNjTp3UFAQunTpgq+++kpvH61Wi4KCAqxatQorVqyQ1vv4+CAsLAympqYIDw8HAFy/fh0//PADtmzZgjZt2ugc5+2330ZQUBCuX79e5fETQuj1DwDs7e0NjvNLL72E0NBQLF682Kj+ExER0YuBgd9ITZo0QXp6OrRaLfz8/NC9e3dkZ2ejUaNGAB6GYjc3NyxbtgzDhw9HcXEx1q5dK4X/RwUHByM6OhoxMTFQqVRo3769tO3IkSNISkqCs7OzdPe5oKAAY8aMweXLl2FhYQEPDw+sXbtWbwqJIVZWVujSpQsWLFiA3377DSUlJXB0dMSoUaP03v0eERGBiIgInXXvvfceli9fDgCYP38+OnfujGXLlmHVqlW4e/cuGjZsiF69eiEjI0PngWVjmZmZ4dtvv0VISIgU+ksZ+rJUOs+97PiXUqvV+Ouvv4w+d0xMDLp37663vlmzZnB2dsaFCxekV5kCD68DJycn/Pbbb9IDu6tXr4alpSVefvllveNotVpYW1tjzZo1mDhxYpXGLy8vT69/wMN/qK2813XOnj0b3377rdH9JyIiIvlTiOq8PJ2IZCMvLw8ajQa5ubnV+sL2zIrU1HYFRFUTadxbzoiIgKr9/c05/EREREREMsbALwMXL17Ue1972eXixYtPtZ7WrVuXW8s333zzVGshIiIietFxDr8MNG7cWHrot7ztT9O2bdtQUlJicJuxD9QSERER0ePBwC8DSqWy2u+jfxKcnZ1ruwQiIiIi+v84pYeIiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGRMWdsFEBE9EZG5tV0BERHRM4F3+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGlLVdABHRExGpqe0KiKomMre2KyAimeIdfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgf0aEhYWhf//+Ous2bNgAc3NzxMbGIjIyEgqFQlo0Gg169uyJ9PR0g8eLioqCqakp5s2bp7ctJycHQ4YMgbu7O0xMTDB+/Hi9Nhs3boS3tzdsbW1haWkJLy8vrFmz5rH1B4DUp8DAQL39Y2NjoVAo4OvrK62LjIyEl5eXXtvz589DoVAgKyvLqNrGjRuHjh07Qq1WGzxeWWfPnoW1tTVsbW31tqWnp6Njx44wNzeHq6srli9fbtT5AWDFihXo2bMn6tati7p166JPnz7417/+pdNm7969ePXVV9G4cWMoFAps2rRJ7zhCCERGRqJx48awsLCAr68vsrOzja6DiIiI5I+B/xm1cuVKhIaGYsmSJZg8eTIAoHXr1sjJyUFOTg4yMjLQokUL9OvXD7m5uXr7x8fHY/LkyVi1apXetqKiIjRo0ADTp09Hu3btDJ7fzs4O06dPR0ZGBo4dO4YRI0ZgxIgR2LFjx2PrDwA0atQIqampuHz5sl79Tk5O1TpXZYQQCA8Px+DBgytsV1JSgpCQEPTs2VNv27lz5/DKK6+gZ8+eyMzMxLRp0/DRRx8hJSXFqBrS0tIQEhKC1NRUZGRkwMnJCf7+/vjvf/8rtSkoKEC7du2wZMmSco8TGxuLzz//HEuWLMGhQ4fg4OAAPz8/3Llzx6g6iIiISP4Y+J9BsbGxGDt2LJKSkjBy5EhpvVKphIODAxwcHODp6YmZM2ciPz8fZ86c0dk/PT0dhYWFmDVrFgoKCrB3716d7S4uLli0aBGGDx8OjUZjsAZfX1+88cYbaNWqFdzc3DBu3Di0bdsW+/bte2z9AQB7e3v4+/sjMTFRWrd//37cuHEDQUFBVT6XMb744gt88MEHcHV1rbDd3//+d3h4eGDQoEF625YvXw4nJycsXLgQrVq1wsiRIxEeHo758+cbVcM333yDMWPGwMvLCx4eHlixYgUePHiA3bt3S2369u2LOXPm4M033zR4DCEEFi5ciOnTp+PNN99EmzZtkJiYiLt37yIpKcmoOoiIiEj+GPifMVOnTsXs2bOxdetWBAcHl9uuqKgICQkJsLW1hbu7u862uLg4hISEwMzMDCEhIYiLi6tRTUII7N69G6dPn0avXr2qtK8x/QkPD0dCQoL0edWqVQgNDYVKpapJ2TWyZ88efPfdd/jyyy8Nbs/IyIC/v7/OuoCAABw+fBglJSVVPt/du3dRUlICOzs7o/c5d+4crl69qlOHWq2Gj48P9u/fX+5+RUVFyMvL01mIiIhIvpS1XQD9z08//YTNmzdj9+7d6N27t97248ePw8rKCsDDgGhtbY3169fDxsZGapOXl4eUlBQp8A0dOhQ9evTA4sWLddoZIzc3F02aNEFRURFMTU2xdOlS+Pn5Pbb+lOrXrx9Gjx6NvXv3omPHjvj222+xb98+g9ORyo5BKSGE8Z0yws2bNxEWFoa1a9eWO2ZXr15Fw4YNddY1bNgQ9+7dw40bN9CoUaMqnXPq1Klo0qQJ+vTpY/Q+V69elc77aB0XLlwod7/o6GjMnDmzSvURERHR84t3+J8hbdu2hYuLCyIiIgzOwXZ3d0dWVhaysrJw5MgRvP/++xg4cCAOHz4stUlKSoKrq6s0N9/Lywuurq5Yt25dleuxtrZGVlYWDh06hLlz52LixIlIS0t7bP0pZWZmhqFDhyI+Ph7fffcdWrZsibZt2xpsW3YMSpdt27ZVtWsVGjVqFIYMGVLpbzMUCoXO59IvHo+ur0xsbCySk5OxceNGmJubV63YcuqoqIZPP/0Uubm50nLp0qUqn5OIiIieHwz8z5AmTZogPT0dOTk5CAwM1AvJKpUKzZs3R/PmzdG+fXvMmzcPTZo0wcKFC6U2q1atQnZ2NpRKpbRkZ2dXa1qPiYkJmjdvDi8vL0yaNAkDBgxAdHT0Y+tPWeHh4dIUmvDw8HLblR2D0sXZ2blK/arMnj17MH/+fGn83nnnHeTm5kKpVEq/dXBwcJDusJe6du0alEol6tWrZ/S55s+fj6ioKOzcubPcLznlcXBwAACDdTx6178stVoNGxsbnYWIiIjki4H/GePk5IT09HRcu3YN/v7+lc6vNjU1RWFhIYCH010OHz6MtLQ0nTvge/fuxaFDh3DixIka1SaEQFFRUZX2MbY/rVu3RuvWrXHixAkMGTKkRnXWVEZGhs74zZo1S/ptxxtvvAEA6NatG37++Wed/Xbu3Alvb2+YmZkZdZ7PPvsMs2fPxvbt2+Ht7V3lOps1awYHBwedOoqLi5Geno7u3btX+XhEREQkT5zD/wxq2rQp0tLSoNVq4e/vL70K8969e9Ld3Dt37mD9+vU4efIkpkyZAuDhw7qdO3c2OBWlW7duiIuLw4IFCwBAemd9fn4+rl+/jqysLKhUKnh6egJ4OM/b29sbbm5uKC4uxrZt27B69WosW7bssfTH0NuB9uzZg5KSEoPvvH+czp49i/z8fFy9ehWFhYXSWHh6ekKlUqFVq1Y67Q8fPgwTExO0adNGWjd69GgsWbIEEydOxKhRo5CRkYG4uDgkJycbVUNsbCz+8Y9/ICkpCS4uLtLP1crKSnpGIT8/H2fPnpX2OXfuHLKysmBnZwcnJycoFAqMHz8eUVFRaNGiBVq0aIGoqCjUqVOn1r80ERER0bODgf8ZVTodRqvVws/PD927d0d2drb0MGidOnXg5uaGZcuWYfjw4SguLsbatWul8P+o4OBgREdHIyYmBiqVCu3bt5e2HTlyBElJSXB2dsb58+cBPHwH/JgxY3D58mVYWFjAw8MDa9eurfTd9cb2Z+fOnXptLC0tq3Xsqho5cqTOP1hWOhbnzp2Di4uLUcdo1qwZtm3bhgkTJuDLL79E48aN8cUXX1T4ZqWyli5diuLiYgwYMEBn/YwZMxAZGQng4RcNrVYrbZs4cSIA4O2335beajR58mQUFhZizJgx+PPPP9GlSxfs3LkT1tbWRtVBRERE8qcQj/sVJ0T0XMnLy4NGo0Fubq685vNHGv43JoieWZH6/4giEVF5qvL3N+fwExERERHJGAM/VdnFixelueaGlosXL9ZabaNHjy63rtGjRz+1Oioan19++eWp1UFERETEKT1UZffu3ZPm+hvi4uICpbJ2Hg+5du1auW8CsrGxgb29/VOpo+zDto9q0qQJLCwsnkodxuCUHqJnBKf0EFEVVOXvbz60S1WmVCrRvHnz2i7DIHt7+6cW6ivyrI4PERERvXg4pYeIiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGRMWdsFEBE9EZG5tV0BERHRM4F3+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGlLVdABEREQEuU3+s7RKIZOP8vKDaLuGZwjv8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsAvc2FhYejfv7/Oug0bNsDc3ByxsbGIjIyEQqGQFo1Gg549eyI9Pd3g8aKiomBqaop58+bpbcvJycGQIUPg7u4OExMTjB8/Xq/Nxo0b4e3tDVtbW1haWsLLywtr1qypUn8UCgVGjx6tt23MmDFQKBQICwvTa//oEhgYKLVxcXGR1ltYWMDDwwOfffYZhBA6x8/MzMTAgQPRsGFDmJubo2XLlhg1ahTOnDkDADh//rzBcykUChw4cAAAkJCQIK0zNTVF3bp10aVLF8yaNQu5ubkG+1pR7ZmZmejXrx/s7e1hbm4OFxcXDB48GDdu3DB6TImIiEjeGPhfMCtXrkRoaCiWLFmCyZMnAwBat26NnJwc5OTkICMjAy1atEC/fv30AigAxMfHY/LkyVi1apXetqKiIjRo0ADTp09Hu3btDJ7fzs4O06dPR0ZGBo4dO4YRI0ZgxIgR2LFjh9F9cHR0xLp161BYWCit++uvv5CcnAwnJye99oGBgVL/Spfk5GSdNrNmzUJOTg5OnTqFjz/+GNOmTcPXX38tbd+6dSu6du2KoqIifPPNNzh16hTWrFkDjUaDf/zjHzrH2rVrl975OnbsKG23sbFBTk4OLl++jP379+Pdd9/F6tWr4eXlhStXrhhd+7Vr19CnTx/Ur18fO3bswKlTp7Bq1So0atQId+/eNXo8iYiISN6UtV0APT2xsbGIiIhAUlISgoODpfVKpRIODg4AAAcHB8ycORPx8fE4c+YMOnXqJLVLT09HYWEhZs2ahdWrV2Pv3r3o1auXtN3FxQWLFi0CAINfCADA19dX5/O4ceOQmJiIffv2ISAgwKh+dOjQAb///js2btyI0NBQAA9/c+Do6AhXV1e99mq1WupfeaytraU2I0eOxLJly7Bz50689957uHv3LkaMGIFXXnkF33//vbRPs2bN0KVLF9y+fVvnWPXq1avwfAqFQtreqFEjtGrVCq+++ipat26NyZMnY+3atUbVvn//fuTl5WHlypVQKpVSTb17966wr0VFRSgqKpI+5+XlVdieiIiInm+8w/+CmDp1KmbPno2tW7fqhP1HFRUVISEhAba2tnB3d9fZFhcXh5CQEJiZmSEkJARxcXE1qkkIgd27d+P06dM6XxyMMWLECMTHx0ufV61ahfDw8BrVU1pTWloaTp06BTMzMwDAjh07cOPGDek3Io+ytbWt8Xnt7e0RGhqKLVu24P79+0bt4+DggHv37uH777/Xm35UkejoaGg0GmlxdHSsbtlERET0HGDgfwH89NNPiImJwebNm9GnTx+97cePH4eVlRWsrKxgYWGB+fPnIzk5GTY2NlKbvLw8pKSkYOjQoQCAoUOHYsOGDdW6O5ybmwsrKyuoVCoEBQVh8eLF8PPzq9Ixhg0bhn379uH8+fO4cOEC/vnPf0q1PWrr1q1S/0qX2bNn67SZMmUKrKysoFarodVqIYTARx99BAD4z3/+AwDw8PAwqrbu3bvrnc+YEO/h4YE7d+7g5s2bRtXetWtXTJs2DUOGDEH9+vXRt29ffPbZZ/jjjz8qPM+nn36K3Nxcabl06ZJR/SIiIqLnE6f0vADatm2LGzduICIiAp06dYK1tbXOdnd3d2zZsgUAcOfOHaxfvx4DBw5EamoqvL29AQBJSUlwdXWV5uZ7eXnB1dUV69atw7vvvluleqytrZGVlYX8/Hzs3r0bEydOhKurq950n4rUr18fQUFBSExMhBACQUFBqF+/vsG2Wq0Wy5Yt01lnZ2en8/mTTz5BWFgYrl+/junTp6N3797o3r07AFTp7jkArF+/Hq1atdJZZ2pqWul+pedRKBRG1z537lxMnDgRe/bswYEDB7B8+XJERUVh7969eOmllwyeR61WQ61WG90fIiIier4x8L8AmjRpgpSUFGi1WgQGBmL79u06oV+lUqF58+bS5/bt22PTpk1YuHChNJ981apVyM7OluaKA8CDBw8QFxdX5cBvYmIinc/LywunTp1CdHR0lQI/AISHh2Ps2LEAgC+//LLcdpaWljr9M6R+/fpo3rw5mjdvjpSUFDRv3hxdu3ZFnz590LJlSwDAr7/+im7dulVal6OjY6XnM+TUqVOwsbFBvXr1qlR7vXr1MHDgQAwcOBDR0dFo37495s+fj8TExCrXQERERPLDKT0vCCcnJ6Snp+PatWvw9/evdCqOqamp9Bac48eP4/Dhw0hLS0NWVpa07N27F4cOHcKJEydqVJsQQuchUmMFBgaiuLgYxcXFRj/wa4y6deviww8/xMcffwwhBPz9/VG/fn3ExsYabP/oQ7vVce3aNSQlJaF///4wMan+H0uVSgU3NzcUFBTUuCYiIiKSB97hf4E0bdoUaWlp0Gq18Pf3l16Fee/ePVy9ehXA/6b0nDx5ElOmTAHw8GHdzp07G3ywtlu3boiLi8OCBQsAAFlZWQCA/Px8XL9+HVlZWVCpVPD09ATw8IFRb29vuLm5obi4GNu2bcPq1av1pq0Yw9TUFKdOnZL+uzxFRUVS/0oplcpypwABwAcffICYmBikpKRgwIABWLlyJQYOHIjXXnsNH330EZo3b44bN27g22+/xcWLF7Fu3Tpp35s3b+qdz9bWFubm5gAefsG5evUqhBC4ffs2MjIyEBUVBY1Go/fvG1RU+9atW7Fu3Tq89dZbaNmyJYQQ+OGHH7Bt2zadB5qJiIjoxcbA/4Jp0qQJ0tPTodVq4efnh+7duyM7OxuNGjUCANSpUwdubm5YtmwZhg8fjuLiYqxdu1YK/48KDg5GdHQ0YmJioFKp0L59e2nbkSNHkJSUBGdnZ5w/fx4AUFBQgDFjxuDy5cvSP3K1du1aDB48uFr9KftgcXm2b98u9a+Uu7s7fv3113L3adCgAYYNG4bIyEi8+eabeP3117F//35ER0djyJAhyMvLg6OjI3r37o05c+bo7Gvowejk5GS89dZbAB4+AN2oUSMoFArY2NjA3d0db7/9NsaNG6fXn4pq9/T0RJ06dTBp0iRcunQJarUaLVq0wMqVKzFs2LBKx4WIiIheDApR1ScSiUhW8vLyoNFokJuba9QXKCJ6Mlym/ljbJRDJxvl5QbVdwhNXlb+/OYefiIiIiEjGGPjpmXHx4kW9d86XXS5evFjbJRIRERE9dziHn54ZjRs3lh76LW87EREREVUNAz89M5RKZbXeX09ERERE5eOUHiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjFlbRdAREREwPl5QbVdAhHJFO/wExERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwx8BMRERERyRgDPxERERGRjDHwExERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwpa7sAIiIiAlym/ljbJRDJyvl5QbVdwjODd/iJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSsSoE/LCwM/fv311m3YcMGmJubIzY2FpGRkVAoFNKi0WjQs2dPpKenGzxeVFQUTE1NMW/ePL1tOTk5GDJkCNzd3WFiYoLx48frtdm4cSO8vb1ha2sLS0tLeHl5Yc2aNUb359q1a3jvvffg5OQEtVoNBwcHBAQEICMjQ2rj4uKi06fS5dGaU1JS0Lt3b9StWxd16tSBu7s7wsPDkZmZaXQ9hYWFmDFjBtzd3aFWq1G/fn0MGDAA2dnZOu2MHefKaj9//rzecbp27YoffvhB5zgJCQkGj2Nubi61CQsLg0KhwOjRo/X6NWbMGCgUCoSFhem0L72WDB277FJ67IqWsjU8ugQGBuqMycKFCw2Of+l4ZGVlSevu3LkDX19feHh44NKlS1K9mzZtMniMsiq6vu/fv4/o6Gh4eHjAwsICdnZ26Nq1K+Lj46U2j/P6JCIiohdXje7wr1y5EqGhoViyZAkmT54MAGjdujVycnKQk5ODjIwMtGjRAv369UNubq7e/vHx8Zg8eTJWrVqlt62oqAgNGjTA9OnT0a5dO4Pnt7Ozw/Tp05GRkYFjx45hxIgRGDFiBHbs2GFU/cHBwTh69CgSExNx5swZbNmyBb6+vrh165ZOu1mzZkl9Kl0+/PBDafuUKVMwePBgeHl5YcuWLcjOzsbXX38NNzc3TJs2zahaioqK0KdPH6xatQqzZ8/GmTNnsG3bNty/fx9dunTBgQMHdNobO86V1Q4Au3btQk5ODg4ePIjOnTsjODgYJ06c0GljY2Ojd5wLFy7otHF0dMS6detQWFgorfvrr7+QnJwMJyencvte9pgLFy7UO9eiRYt0PgMPr51H1wFAYGCgXp3JyclG/AT0Xb9+HVqtFvn5+di3bx8cHR2rtH9F13dkZCQWLlyI2bNn4+TJk0hNTcWoUaPw559/Sm0e1/VJRERELzZldXeMjY1FREQEkpKSEBwc/L8DKpVwcHAAADg4OGDmzJmIj4/HmTNn0KlTJ6ldeno6CgsLMWvWLKxevRp79+5Fr169pO0uLi5YtGgRABgMTADg6+ur83ncuHFITEzEvn37EBAQUGH9t2/fxr59+5CWlgYfHx8AgLOzMzp37qzX1traWurTow4cOIDY2FgsWrQIH330kbS+WbNm8PHxgRCiwjpKLVy4EBkZGcjMzJS+4Dg7OyMlJQVdunTBO++8gxMnTkh3s40d54pqL1WvXj04ODjAwcEBc+fOxeLFi5Gamoo2bdpIbRQKRaXH6dChA37//Xds3LgRoaGhAB7+FsbR0RGurq7l7lf2uBqNxuC5NBqNzmdbW1uD9ZTeCa+pS5cuwc/PD40aNcKWLVtgbW1dpf0ru75/+OEHjBkzBgMHDpTWlf1i+7iuT0OKiopQVFQkfc7Ly6tS34iIiOj5Uq07/FOnTsXs2bOxdetWnbD/qKKiIiQkJMDW1hbu7u462+Li4hASEgIzMzOEhIQgLi6uOqVIhBDYvXs3Tp8+rROsymNlZQUrKyts2rRJJ/xUVXJyMqysrDBmzBiD20sDemWSkpLg5+en99sMExMTTJgwASdPnsTRo0cN7lvROFdFSUkJVqxYAQAwMzOr1jFGjBihMy1l1apVCA8Pr3ZNteH06dPo0aMHPDw8sH379iqHfaDy69vBwQF79uzB9evXDe7/uK5PQ6Kjo6HRaKSlqr+5ICIioudLlQP/Tz/9hJiYGGzevBl9+vTR2378+HEprFhYWGD+/PlITk6GjY2N1CYvLw8pKSkYOnQoAGDo0KHYsGFDte405ubmwsrKCiqVCkFBQVi8eDH8/Pwq3U+pVCIhIQGJiYmwtbVFjx49MG3aNBw7dkyv7ZQpU6Q+lS5paWkAgDNnzsDV1RVK5f9+WfL555/rtDU0nelRZ86cQatWrQxuK11/5swZaZ0x41xZ7aW6d+8OKysrmJubY9KkSXBxccGgQYN02pSOc9nF399fr9Zhw4Zh3759OH/+PC5cuIB//vOf0s/5adi6datenbNnz67SMYYPHw43NzekpKRArVZXuQZjru/PP/8c169fh4ODA9q2bYvRo0fjp59+krY/ruvTkE8//RS5ubnSUvpsAhEREclTlaf0tG3bFjdu3EBERAQ6deqkd/fT3d0dW7ZsAfDwgcf169dj4MCBSE1Nhbe3N4CHd7NdXV2lu9leXl5wdXXFunXr8O6771apHmtra2RlZSE/Px+7d+/GxIkT4erqqjfdx5Dg4GAEBQXhl19+QUZGBrZv347Y2FisXLlS5wHTTz75ROczADRp0kT670fv4oeHh+O1117DwYMHMXToUKOn9ZSndP+y5zFmnI2pHQDWr18PDw8PnDlzBuPHj8fy5cthZ2en08ba2hr//ve/ddZZWFjo1Vq/fn0EBQUhMTERQggEBQWhfv36Ve90NWm1Wixbtkxn3aN9qczrr7+O77//HikpKXpffIxhzPXt6emJEydO4MiRI9i3bx/27t2LV199FWFhYVi5ciWAx3d9PkqtVlfriwwRERE9n6oc+Js0aYKUlBRotVoEBgbqTXlQqVRo3ry59Ll9+/bYtGkTFi5ciLVr1wJ4OM0jOztb5674gwcPEBcXV+XAb2JiIp3Py8sLp06dQnR0tFGBHwDMzc3h5+cHPz8/REREYOTIkZgxY4ZOgKpfv75On8pq0aIF9u3bh5KSEmkajK2tLWxtbXH58mWj+9GyZUucPHnS4LZff/1VOlcpY8a5stpLOTo6okWLFmjRogWsrKwQHByMkydPwt7eXmpTdpwrEx4ejrFjxwIAvvzyS6P2eVwsLS2NrrM806ZNQ9u2bREaGgohBAYPHlyl/Y29vk1MTNCpUyd06tQJEyZMwNq1azFs2DBMnz4dzZo1A1Dz65OIiIioWnP4nZyckJ6ejmvXrsHf37/SqTimpqbSm1uOHz+Ow4cPIy0tDVlZWdKyd+9eHDp0SO/tMFUlhKjRnGdPT08UFBQY3T4kJAT5+flYunRptc8JAG+99RZ27dqlN0//wYMHWLBgATw9Pct9W1GpsuNcXT4+PmjTpg3mzp1b7WMEBgaiuLgYxcXFlT48/az6+9//jtmzZyM0NLRKb/mpyfXt6ekJABVef1W9PomIiIiq/Zaepk2bIi0tDVqtFv7+/tKrMO/du4erV68C+N9Uk5MnT2LKlCkAHj7M2LlzZ4MP1nbr1g1xcXFYsGABAEjvQ8/Pz8f169eRlZUFlUolBaPo6Gh4e3vDzc0NxcXF2LZtG1avXq03pcOQmzdvYuDAgQgPD0fbtm1hbW2Nw4cPIzY2Fq+//rpO2zt37kh9KlWnTh3Y2NigW7dumDRpEiZNmoQLFy7gzTffhKOjI3JychAXFweFQgETk8q/V02YMAGbN2/Gq6++iv/7v/9Dly5d8McffyAqKgqnTp3Crl27dKb0VDbOxtRenkmTJmHgwIGYPHmyNDVECKF3HACwt7fX65+pqSlOnTol/ffTVFRUpFenUqnUmVb03//+V+dd+wAMvjZ06tSpMDU1xbBhw/DgwQPpzUMAcO7cOb1jNG/e3Ojre8CAAejRowe6d+8OBwcHnDt3Dp9++ilatmwJDw+Px3Z9EhEREVU78AMPp/ekp6dDq9XCz88P3bt3R3Z2Nho1agTgYehwc3PDsmXLMHz4cBQXF2Pt2rV6obRUcHAwoqOjERMTA5VKhfbt20vbjhw5gqSkJDg7O+P8+fMAHt4JHTNmDC5fvgwLCwt4eHhg7dq1Rk3BsLKyQpcuXbBgwQL89ttvKCkpgaOjI0aNGqX37vyIiAhERETorHvvvfewfPlyAMD8+fPRuXNnLFu2DKtWrcLdu3fRsGFD9OrVCxkZGUYFL3Nzc+zZswfR0dGYNm0aLly4AGtra2i1Whw4cEDnFZkAKhznqtRuSL9+/eDi4oK5c+dKv7nIy8uTzldWTk6OwVdC1lbY3L59u16d7u7u0rQo4OHPa/78+Tpt4uPjDU4D++STT2Bqaoq3334bDx48wLBhwwAAEydO1Gu7Y8cOo6/vgIAAJCcnIzo6Grm5uXBwcEDv3r0RGRkJpVL5WK9PIiIierEpRE2fKCWi51peXh40Gg1yc3P5WwGiWuQy9cfaLoFIVs7PC6rtEp6oqvz9XaN/aZeIiIiIiJ5tsg38Fy9e1Hs3ednl4sWLT7We1q1bl1vLN99881RrISIiIqIXR43m8D/LGjdurPdQ5aPbn6Zt27ahpKTE4LaGDRs+1VqIiIiI6MUh28CvVCqfqXeTOzs713YJRERERPQCku2UHiIiIiIiYuAnIiIiIpI1Bn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMQZ+IiIiIiIZY+AnIiIiIpIxBn4iIiIiIhlj4CciIiIikjEGfiIiIiIiGWPgJyIiIiKSMWVtF0BERETA+XlBtV0CEckU7/ATEREREckYAz8RERERkYwx8BMRERERyRgDPxERERGRjDHwExERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwx8BMRERERyRgDPxERERGRjClruwAiIiICXKb+WNslENETcn5eUK2en3f4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhk7IUO/GFhYejfv7/Oug0bNsDc3ByxsbGIjIyEQqGQFo1Gg549eyI9Pd3g8aKiomBqaop58+bpbcvJycGQIUPg7u4OExMTjB8/Xq/Nxo0b4e3tDVtbW1haWsLLywtr1qypUX8AIC0tDQqFArdv39b5bGi5evUqAOj03cTEBI0bN0ZoaCguXbqkc+zff/8dISEhaNy4MczNzdG0aVO8/vrrOHPmDBISEso9T+mSlpZWYZ8ePYaVlRU6duyIjRs36rTz9fWFQqEwOPavvPIKFAoFIiMjAQBTp05Fq1atdNqcOnUKCoUCw4YN01m/Zs0amJmZIT8/v8I6AejUqVQq4eTkhIkTJ6KoqKjc/pQu5ubmUpuwsDCDfdm0aRMUCoX02cXFpcKxvXDhQqU1ExERkfy90IH/UStXrkRoaCiWLFmCyZMnAwBat26NnJwc5OTkICMjAy1atEC/fv2Qm5urt398fDwmT56MVatW6W0rKipCgwYNMH36dLRr187g+e3s7DB9+nRkZGTg2LFjGDFiBEaMGIEdO3Y83o7+f6dPn5b6VrrY29tL20v7fvnyZaxfvx7Hjx/HoEGDpO3FxcXw8/NDXl4eNm7ciNOnT2P9+vVo06YNcnNzMXjwYJ1jd+vWDaNGjdJZ171790rrtLGxkdpnZmYiICAAgwYNwunTp3XaOTo6Ij4+XmfdlStXsGfPHjRq1Ehap9Vq8euvv0pfboCHX4IcHR2Rmpqqs39aWho6d+4MKysro8Y0Pj4eOTk5OHfuHJYuXYo1a9Zgzpw55fandHk0nJubmyMmJgZ//vlnuec6dOiQ3nFOnTqFxo0b49VXX4WTk5NRNRMREZG8MfD/f7GxsRg7diySkpIwcuRIab1SqYSDgwMcHBzg6emJmTNnIj8/H2fOnNHZPz09HYWFhZg1axYKCgqwd+9ene0uLi5YtGgRhg8fDo1GY7AGX19fvPHGG2jVqhXc3Nwwbtw4tG3bFvv27Xv8HQZgb28v9a10MTH53yVR2vfGjRujZ8+eGDVqFA4cOIC8vDwAwMmTJ/H7779j6dKl6Nq1K5ydndGjRw/MnTsXnTp1goWFhc6xVSoV6tSpo7euMgqFQmrfokULzJkzByYmJjh27JhOu379+uHmzZv45z//Ka1LSEiAv7+/zheZv/3tbzAzM9P57UJaWho++OAD3LlzB2fPntVZr9VqjR5TW1tbODg4wNHREf369cNrr72Gf//73+X2p3Rp2LChTps+ffrAwcEB0dHR5Z6rQYMGOsewt7fH+PHjodFosHbtWp3fBpRVVFSEvLw8nYWIiIjki4EfD6d4zJ49G1u3bkVwcHC57YqKipCQkABbW1u4u7vrbIuLi0NISAjMzMwQEhKCuLi4GtUkhMDu3btx+vRp9OrVq0bHehyuXr2KjRs3wtTUFKampgAeBk4TExNs2LAB9+/ffyp13L9/H4mJiQCADh066GxTqVQIDQ3VucufkJCA8PBwnXaWlpbo1KmTzt389PR0vPzyy+jRo4e0/tKlS/j999+rFPjLOnPmDFJTU9GlS5cq72tqaoqoqCgsXrwYly9fNmqfqVOn4uDBg9i8eTNsbGzKbRcdHQ2NRiMtjo6OVa6PiIiInh8vfOD/6aefEBMTg82bN6NPnz56248fPw4rKytYWVnBwsIC8+fPR3Jysk6gysvLQ0pKCoYOHQoAGDp0KDZs2FCtO6e5ubmwsrKCSqVCUFAQFi9eDD8/P6P337p1q1Rv6dK3b1+DbZs2barT7tEvMaV9r1OnDho1aiTdBbe0tAQANGnSBF988QUiIiJQt25d9O7dG7Nnz8bvv/9e5X5XpHRMSsfl/fffx9dffw03Nze9tu+88w6+/fZb6bcsubm5CAoK0mvn6+sr3eE/efIkCgsL0b59e/j4+EjrU1NToVarjZp2VCokJARWVlYwNzeHu7s7WrdujU8//bTc/pQu/v7+esd644034OXlhRkzZlR63uTkZHz++edYt24dWrRoUWHbTz/9FLm5udLy6HMZREREJC/K2i6gtrVt2xY3btxAREQEOnXqBGtra53t7u7u2LJlCwDgzp07WL9+PQYOHIjU1FR4e3sDAJKSkuDq6irNzffy8oKrqyvWrVuHd999t0r1WFtbIysrC/n5+di9ezcmTpwIV1dX+Pr6GrW/VqvFsmXLdNYdPHhQ+jJS1i+//KLTX6VS93Io7XtRURE2b96M7777DnPnztVp88EHH2D48OFITU3FwYMH8d133yEqKgpbtmyp0heVilhbW0vTYu7evYtdu3bhvffeQ7169fDqq6/qtG3bti1atGiBDRs2IDU1FcOGDYOZmZneMbVaLaKionDlyhWkpaXhb3/7G0xNTeHj44MvvvgCwMPpPF27doWFhYXRtS5YsAB9+vTB/fv3cfbsWUycOBHDhg3DunXrDPanVHnniImJQe/evTFp0qRyz5mZmYl33nkH8+bNQ0BAQKU1qtVqqNVqI3tEREREz7sXPvA3adIEKSkp0Gq1CAwMxPbt23VCsEqlQvPmzaXP7du3x6ZNm7Bw4UKsXbsWALBq1SpkZ2frBOYHDx4gLi6uyoHfxMREOp+XlxdOnTqF6OhoowO/paWlTr0Ayp0S0qxZM9ja2pZ7rLJ9b926Nf7zn//g/fff13tzkLW1NV577TW89tprmDNnDgICAjBnzpzHFvjLjgnwMNTv3LkTMTExeoEfAMLDw/Hll1/i5MmT+Ne//mXwmD169IBKpUJaWhpSU1Ph4+MDAPD29kZubq40HScsLKxKtTo4OEi1uru7486dOwgJCcGcOXOk9Y/2pyK9evVCQEAApk2bZrCW69evo3///njzzTfx8ccfV6lWIiIiejG88FN6AMDJyQnp6em4du0a/P39K52KY2pqisLCQgAPp70cPnwYaWlpyMrKkpa9e/fi0KFDOHHiRI1qE0LovNaxNv3jH/9AcnKy3t3pshQKBTw8PFBQUPBEayn7M3jUkCFDcPz4cbRp0waenp4G21hYWKBLly5IS0vD3r17pS9USqUS3bt3x+rVq3H+/Plqz98vWyeAcms1xrx58/DDDz9g//79OutLSkowYMAA2NvbY+XKlTWqk4iIiOTrhb/DX6pp06bSG1n8/f2lV2Heu3dPen1j6ZSekydPYsqUKQAePqzbuXNngw/WduvWDXFxcViwYAEAICsrCwCQn5+P69evIysrCyqVSgql0dHR8Pb2hpubG4qLi7Ft2zasXr1ab4rO43Lt2jX89ddfOuvq1atncAoMALi6uuL1119HREQEtm7diqysLMyYMQPDhg2Dp6cnVCoV0tPTsWrVKml8HgchhPQzKCwsxM8//4wdO3YgIiLCYPu6desiJyen3H6U0mq10s+m7APAPj4+iImJkb4UVMXt27dx9epVPHjwAP/5z38wa9YstGzZUue9/2X7U5a9vb3OW5JKvfTSSwgNDcXixYt11o8fPx5Hjx7Frl27pH9joSw7Ozuj3oJERERE8sbAX0aTJk2Qnp4OrVYLPz8/dO/eHdnZ2dI73OvUqQM3NzcsW7YMw4cPR3FxMdauXVtuuA0ODkZ0dDRiYmKgUqnQvn17aduRI0eQlJQEZ2dnnD9/HgBQUFCAMWPG4PLly7CwsICHhwfWrl2LwYMHP5H+PvqQLgBkZGSga9eu5e4zadIk9OjRAwcPHoSbmxtcXFwwc+ZMnD9/HgqFQvo8YcKEx1ZnXl6e9DNQq9VwdnbGrFmzKvxSUdFUpVJarRazZs1CYGCgznQsHx8f/P3vf8fLL79c5bnuI0aMAPC/V2/26tULUVFROscv25+ycnJy4ODgYPC4s2fPxrfffquzbunSpQCATp06GdwnNTXV6KlgREREJF8KIYSo7SKIqPbk5eVBo9EgNze3wtd5EtGT5TL1x9ougYiekPPz9N8YWFNV+fubc/iJiIiIiGSMgf85cfHiRb13t5ddLl68WNslVkvr1q3L7dM333xT2+VJoqKiyq2zvH/ngIiIiOhZwDn8z4nGjRtLD/2Wt/15tG3bNpSUlBjc1rBhw6dcTflGjx6NQYMGGdxWlff0ExERET1tDPzPCaVSafS7258nzs7OtV2CUezs7GBnZ1fbZRARERFVGaf0EBERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwx8BMRERERyRgDPxERERGRjDHwExERERHJGAM/EREREZGMMfATEREREckYAz8RERERkYwx8BMRERERyZiytgsgIiIi4Py8oNougYhkinf4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiIiIiEjGGPiJiIiIiGRMWdsFEFHtEkIAAPLy8mq5EiIiIjJW6d/bpX+PV4SBn+gFd+fOHQCAo6NjLVdCREREVXXnzh1oNJoK2yiEMV8LiEi2Hjx4gCtXrsDa2hoKhaJax8jLy4OjoyMuXboEGxubx1zh84vjoo9jYhjHxTCOiz6OiWEv4rgIIXDnzh00btwYJiYVz9LnHX6iF5yJiQmaNm36WI5lY2PzwvyPtio4Lvo4JoZxXAzjuOjjmBj2oo1LZXf2S/GhXSIiIiIiGWPgJyIiIiKSMQZ+IqoxtVqNGTNmQK1W13YpzxSOiz6OiWEcF8M4Lvo4JoZxXCrGh3aJiIiIiGSMd/iJiIiIiGSMgZ+IiIiISMYY+ImIiIiIZIyBn4iIiIhIxhj4iYiIiIhkjIGfiCr1559/YtiwYdBoNNBoNBg2bBhu375d4T4bN25EQEAA6tevD4VCgaysLL02vr6+UCgUOstbb731ZDrxBDypcSkqKsKHH36I+vXrw9LSEq+99houX778ZDrxmFVnTIQQiIyMROPGjWFhYQFfX19kZ2frtHnerpWlS5eiWbNmMDc3R8eOHfHLL79U2D49PR0dO3aEubk5XF1dsXz5cr02KSkp8PT0hFqthqenJ77//vsnVf4T87jHJSEhQe+6UCgU+Ouvv55kNx67qoxLTk4OhgwZAnd3d5iYmGD8+PEG271o14sx4yKX66U6GPiJqFJDhgxBVlYWtm/fju3btyMrKwvDhg2rcJ+CggL06NED8+bNq7DdqFGjkJOTIy1fffXV4yz9iXpS4zJ+/Hh8//33WLduHfbt24f8/Hz069cP9+/ff9xdeOyqMyaxsbH4/PPPsWTJEhw6dAgODg7w8/PDnTt3dNo9L9fK+vXrMX78eEyfPh2ZmZno2bMn+vbti4sXLxpsf+7cObzyyivo2bMnMjMzMW3aNHz00UdISUmR2mRkZGDw4MEYNmwYjh49imHDhmHQoEE4ePDg0+pWjT2JcQEAGxsbnesiJycH5ubmT6NLj0VVx6WoqAgNGjTA9OnT0a5dO4NtXsTrxZhxAZ7/66XaBBFRBU6ePCkAiAMHDkjrMjIyBADx66+/Vrr/uXPnBACRmZmpt83Hx0eMGzfuMVb79Dypcbl9+7YwMzMT69atk9b997//FSYmJmL79u2Prf4noTpj8uDBA+Hg4CDmzZsnrfvrr7+ERqMRy5cvl9Y9T9dK586dxejRo3XWeXh4iKlTpxpsP3nyZOHh4aGz7r333hNdu3aVPg8aNEgEBgbqtAkICBBvvfXWY6r6yXsS4xIfHy80Gs1jr/Vpquq4lFXen4sX8Xopq7xxkcP1Ul28w09EFcrIyIBGo0GXLl2kdV27doVGo8H+/ftrfPxvvvkG9evXR+vWrfHxxx/r3dV9Vj2pcTly5AhKSkrg7+8vrWvcuDHatGnzWMb7SarOmJw7dw5Xr17V6a9arYaPj4/ePs/DtVJcXIwjR47o9AcA/P39yx2DjIwMvfYBAQE4fPgwSkpKKmzzrF8TpZ7UuABAfn4+nJ2d0bRpU/Tr1w+ZmZmPvwNPSHXGxRgv4vVirOf5eqkJZW0XQETPtqtXr8Le3l5vvb29Pa5evVqjY4eGhqJZs2ZwcHDAiRMn8Omnn+Lo0aP4+eefa3Tcp+FJjcvVq1ehUqlQt25dnfUNGzas8Xg/adUZk9L1DRs21FnfsGFDXLhwQfr8vFwrN27cwP379w32p6IxMNT+3r17uHHjBho1alRum2f9mij1pMbFw8MDCQkJeOmll5CXl4dFixahR48eOHr0KFq0aPHE+vO4VGdcjPEiXi/GeN6vl5pg4Cd6QUVGRmLmzJkVtjl06BAAQKFQ6G0TQhhcXxWjRo2S/rtNmzZo0aIFvL298e9//xsdOnSo0bGr61kYF0Oe1HGN8TTG5NHtj+7zLF4rFamsP8a0f3R9VY/5LHrc49K1a1d07dpV2t6jRw906NABixcvxhdffPG4yn7insTP9kW8Xiojl+ulOhj4iV5QY8eOrfQtJy4uLjh27Bj++OMPvW3Xr1/Xu/tSUx06dICZmRn+85//1FqIq+1xcXBwQHFxMf7880+du/zXrl1D9+7dq33cmniSY+Lg4ADg4R3JRo0aSeuvXbtW4Tg+C9eKIfXr14epqaneXciK+uPg4GCwvVKpRL169Sps87j/DD4pT2pcHmViYoJOnTrhP//5z+Mp/AmrzrgY40W8XqrjebteaoJz+IleUPXr14eHh0eFi7m5Obp164bc3Fz861//kvY9ePAgcnNzH3sAzc7ORklJiU7we9pqe1w6duwIMzMznakqOTk5OHHiRK0F/ic5JqXTdMr2t7i4GOnp6RX291m4VgxRqVTo2LGj3lSjn3/+udz+dOvWTa/9zp074e3tDTMzswrb1NY1UVVPalweJYRAVlbWM3ddlKc642KMF/F6qY7n7XqpkVp4UJiInjOBgYGibdu2IiMjQ2RkZIiXXnpJ9OvXT6eNu7u72Lhxo/T55s2bIjMzU/z4448CgFi3bp3IzMwUOTk5Qgghzp49K2bOnCkOHTokzp07J3788Ufh4eEh2rdvL+7du/dU+1ddT2JchBBi9OjRomnTpmLXrl3i3//+t+jdu7do167dczEu1RmTefPmCY1GIzZu3CiOHz8uQkJCRKNGjUReXp4Q4vm7VtatWyfMzMxEXFycOHnypBg/frywtLQU58+fF0IIMXXqVDFs2DCp/e+//y7q1KkjJkyYIE6ePCni4uKEmZmZ2LBhg9Tmn//8pzA1NRXz5s0Tp06dEvPmzRNKpVLnjUjPuicxLpGRkWL79u3it99+E5mZmWLEiBFCqVSKgwcPPvX+VVdVx0UIITIzM0VmZqbo2LGjGDJkiMjMzBTZ2dnS9hfxehGi8nGRw/VSXQz8RFSpmzdvitDQUGFtbS2sra1FaGio+PPPP3XaABDx8fHS5/j4eAFAb5kxY4YQQoiLFy+KXr16CTs7O6FSqYSbm5v46KOPxM2bN59ex2roSYyLEEIUFhaKsWPHCjs7O2FhYSH69esnLl68+HQ6VUPVGZMHDx6IGTNmCAcHB6FWq0WvXr3E8ePHpe3P47Xy5ZdfCmdnZ6FSqUSHDh1Eenq6tO3tt98WPj4+Ou3T0tJE+/bthUqlEi4uLmLZsmV6x/zuu++Eu7u7MDMzEx4eHiIlJeVJd+Oxe9zjMn78eOHk5CRUKpVo0KCB8Pf3F/v3738aXXmsqjouhv4f4uzsrNPmRbxeKhsXuVwv1aEQ4v8/AUNERERERLLDOfxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQyxsBPRERERCRjDPxERERERDLGwE9EREREJGMM/EREREREMsbAT0REREQkYwz8REREREQy9v8A73/y88meHm8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    feature_weights_i = top_feature_weights_list[i]\n",
    "    plt.title(f'Feature Weights for Component {i+1}')\n",
    "    plot_feature_weights(feature_weights_i)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KMeans Clusering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elbow method to choose optimum number of clusters\n",
    "\n",
    "We will use yellowbrick's KElbowVisualizer to visualize the optimum value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.modelling import plot_elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_elbow(azdias_pca_176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KMeans Clustering for data with 176 dims\n",
    "kmeans = KMeans(n_clusters=10, random_state=randomState, n_init='auto')\n",
    "kmeans.fit(azdias_pca_176)\n",
    "azdias_clusters = kmeans.predict(azdias_pca_176)\n",
    "customers_clusters = kmeans.predict(customers_pca_176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.utils import plot_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Num of Observations in each Cluster for data with 176 dims\n",
    "plot_clusters(azdias_clusters, customers_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('models/imputer.pkl', 'wb') as imputer_pkl:\n",
    "#     pickle.dump(imputer, imputer_pkl)\n",
    "\n",
    "# with open('models/scaler.pkl', 'wb') as scaler_pkl:\n",
    "#     pickle.dump(scaler, scaler_pkl)\n",
    "\n",
    "# with open('models/pca_176.pkl', 'wb') as pca_pkl:\n",
    "#     pickle.dump(pca_176, pca_pkl)\n",
    "\n",
    "# with open('models/kmeans.pkl', 'wb') as kmeans_pkl:\n",
    "#     pickle.dump(kmeans, kmeans_pkl)\n",
    "\n",
    "# with open('metadata/high_cardinality_klasse_type_cols.pkl', 'wb') as cols_pkl:\n",
    "#     pickle.dump(high_cardinality_klasse_type_cols, cols_pkl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Supervised Learning Model\n",
    "\n",
    "Now that you've found which parts of the population are more likely to be customers of the mail-order company, it's time to build a prediction model. Each of the rows in the \"MAILOUT\" data files represents an individual that was targeted for a mailout campaign. Ideally, we should be able to use the demographic information from each individual to decide whether or not it will be worth it to include that person in the campaign.\n",
    "\n",
    "The \"MAILOUT\" data has been split into two approximately equal parts, each with almost 43 000 data rows. In this part, you can verify your model with the \"TRAIN\" partition, which includes a column, \"RESPONSE\", that states whether or not a person became a customer of the company following the campaign. In the next part, you'll need to create predictions on the \"TEST\" partition, where the \"RESPONSE\" column has been withheld."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('models/imputer.pkl', 'rb') as imputer_pkl:\n",
    "#     pickle.load(imputer, imputer_pkl)\n",
    "\n",
    "# with open('models/scaler.pkl', 'rb') as scaler_pkl:\n",
    "#     pickle.dump(scaler, scaler_pkl)\n",
    "\n",
    "# with open('models/pca_176.pkl', 'rb') as pca_pkl:\n",
    "#     pickle.dump(pca_176, pca_pkl)\n",
    "\n",
    "# with open('models/kmeans.pkl', 'rb') as kmeans_pkl:\n",
    "#     pickle.dump(kmeans, kmeans_pkl)\n",
    "\n",
    "# with open('metadata/high_cardinality_klasse_type_cols.pkl', 'rb') as cols_pkl:\n",
    "#     pickle.dump(high_cardinality_klasse_type_cols, cols_pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('models/clean_data.pkl', 'rb') as clean:\n",
    "#     clean_data = dill.load(clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('models/feat_eng.pkl', 'rb') as fe:\n",
    "#     feature_engine = dill.load(fe)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Satya\\AppData\\Local\\Temp\\ipykernel_23664\\2290286062.py:1: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  mailout_train = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_MAILOUT_052018_TRAIN.csv', sep=';')\n"
     ]
    }
   ],
   "source": [
    "mailout_train = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_MAILOUT_052018_TRAIN.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_counts = mailout_train['RESPONSE'].value_counts()/len(mailout_train['RESPONSE'])\n",
    "response_counts.plot.bar(color=['b', 'r'])\n",
    "plt.xticks(ticks=[0,1], labels=['No', 'Yes'])\n",
    "plt.xlabel('RESPONSE')\n",
    "plt.ylabel('Percent')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying All Transformations\\Processing applied to Gen Pop & Customers Data\n",
    "clean_data.transform(mailout_train)\n",
    "mailout_train_clean = mailout_train.copy()\n",
    "feature_engine.transform(mailout_train_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Columns\n",
    "mailout_train_clean.drop(list(get_cols_to_drop(mailout_train_clean, 0.30)), axis=1, inplace=True)\n",
    "mailout_train_clean.drop(['EINGEFUEGT_AM', 'LNR', \n",
    "                          'CAMEO_INTL_2015', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN'\n",
    "                          ], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating X, y for modelling\n",
    "X_train, y_train = mailout_train_clean.drop('RESPONSE', axis=1), mailout_train_clean['RESPONSE']\n",
    "\n",
    "# Verifying Integrity\n",
    "assert(X_train.shape[1] == azdias.shape[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some more Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding New Features to capture information learn during the Unsupervided learning part i.e. Customer Segmentation\n",
    "# We use the dimensional reduction and KMeans clustering performed on azdias dataset to get two new features\n",
    "X_train_imputed = imputer.transform(X_train)\n",
    "X_train_imputed_scaled = scaler.transform(X_train_imputed)\n",
    "X_train_pca_176 = pca_176.transform(X_train_imputed_scaled)\n",
    "cluster_p176_c10 = kmeans.predict(X_train_pca_176)\n",
    "X_train_imputed['cluster_p176_c10'] = cluster_p176_c10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating One Hot Encoding for Certain Columns\n",
    "ohe_cols = list((set(klasse_type_cols)-set(high_cardinality_klasse_type_cols))&set(mailout_train_clean.columns))\n",
    "X_train_ohe = pd.get_dummies(X_train_imputed, columns=ohe_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports for modelling\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from src.modelling import GridSearch_ClassifierCV"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Creating train dataset & labels that will taken as inputs by classification algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train = X_train_ohe\n",
    "train = pd.concat([X_train_ohe, X_train_imputed[ohe_cols]], axis=1)\n",
    "\n",
    "labels = y_train"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce mem usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = None\n",
    "customers = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.75563909774436"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for hyper parameter for imbalanced dataset\n",
    "scale_pos_weight = sum(y_train==0)/sum(y_train==1)\n",
    "scale_pos_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.764680952419457,\n",
       " {'learning_rate': 0.1, 'max_depth': 3, 'scale_pos_weight': None})"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_clf = XGBClassifier(objective='binary:logistic', \n",
    "                        random_state=randomState\n",
    "                        )\n",
    "xgb_param_grid = {\n",
    "                  'scale_pos_weight': [None, 10, scale_pos_weight],\n",
    "                  'learning_rate':[0.1,0.01],\n",
    "                  'max_depth': [3,12,24]\n",
    "                 }\n",
    "                 \n",
    "xgb_grid = GridSearch_ClassifierCV(xgb_clf, train, labels,\n",
    "                                   params=xgb_param_grid,\n",
    "                                   cv=3\n",
    "                                  )\n",
    "xgb_grid.best_score_, xgb_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7586573733567302,\n",
       " {'boosting_type': 'dart', 'is_unbalance': None, 'max_depth': 50})"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_clf = LGBMClassifier(objective='binary',\n",
    "                        random_state=randomState,\n",
    "                        )\n",
    "\n",
    "lgb_param_grid = {# 'feature_fraction':[1, 0.9],\n",
    "                  'boosting_type' : ['gbdt', 'dart'],\n",
    "                  'max_depth': [50, 100],\n",
    "                  'is_unbalance' : [True, None]\n",
    "                 # 'num_leaves':[100, 500]\n",
    "                 }\n",
    "                 \n",
    "lgb_grid = GridSearch_ClassifierCV(lgb_clf, train, labels,\n",
    "                                   params=lgb_param_grid,\n",
    "                                   cv=3\n",
    "                                  )\n",
    "lgb_grid.best_score_, lgb_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6668600760932467, {'max_depth': 10, 'n_estimators': 900})"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(random_state=randomState\n",
    "                                )\n",
    "rf_param_grid = {\n",
    "                 'max_depth': [10, 50, 90],\n",
    "                 'n_estimators':[100, 500, 900]\n",
    "                 }\n",
    "                 \n",
    "rf_grid = GridSearch_ClassifierCV(rf_clf, train, labels,\n",
    "                                  params=rf_param_grid,\n",
    "                                  cv=3\n",
    "                                  )\n",
    "rf_grid.best_score_, rf_grid.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Algorithms/Techniques to deal with Imbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7444815808662346,\n",
       " {'classifier__learning_rate': 0.01, 'classifier__max_depth': 3})"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_imb_clf = XGBClassifier(objective='binary:logistic', \n",
    "                            random_state=randomState\n",
    "                            )\n",
    "\n",
    "xgb_imb_param_grid = {\n",
    "                      'learning_rate':[0.1,0.01],\n",
    "                      'max_depth': [3,12,24]\n",
    "                      }\n",
    "\n",
    "xgb_imb_clf_pipeline = Pipeline([('smote', SMOTE(random_state=randomState)),\n",
    "                                 ('classifier', xgb_imb_clf) \n",
    "                                 ])\n",
    "\n",
    "xgb_imb_grid = GridSearch_ClassifierCV(xgb_imb_clf_pipeline, train, labels,\n",
    "                                       params={'classifier__'+k:v for k,v in xgb_imb_param_grid.items()},\n",
    "                                       cv=3\n",
    "                                       )\n",
    "\n",
    "xgb_imb_grid.best_score_, xgb_imb_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.689834570512831,\n",
       " {'classifier__boosting_type': 'gbdt', 'classifier__max_depth': 50})"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_imb_clf = LGBMClassifier(objective='binary',\n",
    "                        random_state=randomState,\n",
    "                        )\n",
    "\n",
    "lgb_imb_param_grid = {\n",
    "                      'boosting_type' : ['gbdt', 'dart'],\n",
    "                      'max_depth': [50, 100],\n",
    "                      }\n",
    "\n",
    "lgb_imb_clf_pipeline = Pipeline([('smote', SMOTE(random_state=randomState)),\n",
    "                                 ('classifier', lgb_imb_clf) \n",
    "                                 ])\n",
    "\n",
    "lgb_imb_grid = GridSearch_ClassifierCV(lgb_imb_clf_pipeline, train, labels,\n",
    "                                       params={'classifier__'+k:v for k,v in lgb_imb_param_grid.items()},\n",
    "                                       cv=3\n",
    "                                      )\n",
    "\n",
    "lgb_imb_grid.best_score_, lgb_imb_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6371978247509427,\n",
       " {'classifier__max_depth': 90, 'classifier__n_estimators': 900})"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_imb_clf = RandomForestClassifier(random_state=randomState\n",
    "                                    )\n",
    "rf_imb_param_grid = {\n",
    "                     'max_depth': [50, 90],\n",
    "                     'n_estimators':[500, 900]\n",
    "                     }\n",
    "                 \n",
    "rf_imb_clf_pipeline = Pipeline([('smote', SMOTE(random_state=randomState)),\n",
    "                                ('classifier', rf_imb_clf) \n",
    "                                ])\n",
    "\n",
    "rf_imb_grid = GridSearch_ClassifierCV(rf_imb_clf_pipeline, train, labels,\n",
    "                                      params={'classifier__'+k:v for k,v in rf_imb_param_grid.items()},\n",
    "                                      cv=3\n",
    "                                      )\n",
    "\n",
    "rf_imb_grid.best_score_, rf_imb_grid.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summarise Grid Search Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>model</th>\n",
       "      <th>best_params</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': 3, 'scale_pos_weight': None}</td>\n",
       "      <td>0.764681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lightgbm</td>\n",
       "      <td>{'boosting_type': 'dart', 'is_unbalance': None, 'max_depth': 50}</td>\n",
       "      <td>0.758657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>randomforest</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 900}</td>\n",
       "      <td>0.666860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xgboost with smote</td>\n",
       "      <td>{'classifier__learning_rate': 0.01, 'classifier__max_depth': 3}</td>\n",
       "      <td>0.744482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lightgbm with smote</td>\n",
       "      <td>{'classifier__boosting_type': 'gbdt', 'classifier__max_depth': 50}</td>\n",
       "      <td>0.689835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>randomforest with smote</td>\n",
       "      <td>{'classifier__max_depth': 90, 'classifier__n_estimators': 900}</td>\n",
       "      <td>0.637198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     model  \\\n",
       "0                  xgboost   \n",
       "1                 lightgbm   \n",
       "2             randomforest   \n",
       "3       xgboost with smote   \n",
       "4      lightgbm with smote   \n",
       "5  randomforest with smote   \n",
       "\n",
       "                                                          best_params  \\\n",
       "0    {'learning_rate': 0.1, 'max_depth': 3, 'scale_pos_weight': None}   \n",
       "1    {'boosting_type': 'dart', 'is_unbalance': None, 'max_depth': 50}   \n",
       "2                              {'max_depth': 10, 'n_estimators': 900}   \n",
       "3     {'classifier__learning_rate': 0.01, 'classifier__max_depth': 3}   \n",
       "4  {'classifier__boosting_type': 'gbdt', 'classifier__max_depth': 50}   \n",
       "5      {'classifier__max_depth': 90, 'classifier__n_estimators': 900}   \n",
       "\n",
       "      score  \n",
       "0  0.764681  \n",
       "1  0.758657  \n",
       "2  0.666860  \n",
       "3  0.744482  \n",
       "4  0.689835  \n",
       "5  0.637198  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_df = pd.DataFrame({'model': ['xgboost', 'lightgbm', 'randomforest', \n",
    "                                     'xgboost with smote', 'lightgbm with smote', \n",
    "                                     'randomforest with smote'\n",
    "                                     ],\n",
    "                            'best_params': [xgb_grid.best_params_, lgb_grid.best_params_, rf_grid.best_params_, \n",
    "                                            xgb_imb_grid.best_params_, lgb_imb_grid.best_params_, \n",
    "                                            rf_imb_grid.best_params_\n",
    "                                            ],\n",
    "                            'score': [xgb_grid.best_score_, lgb_grid.best_score_, rf_grid.best_score_, \n",
    "                                      xgb_imb_grid.best_score_, lgb_imb_grid.best_score_, \n",
    "                                      rf_imb_grid.best_score_\n",
    "                                      ]\n",
    "                           })\n",
    "\n",
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(summary_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retraining models using best params obtained above on entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.878149592694912"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(objective='binary:logistic', random_state=randomState,\n",
    "                      **xgb_grid.best_params_\n",
    "                      )\n",
    "model.fit(train, labels)\n",
    "\n",
    "y_pred_probab = model.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_probab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9301486393334267"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light_model = LGBMClassifier(random_state=randomState,\n",
    "                             objective='binary',\n",
    "                             **lgb_grid.best_params_\n",
    "                             )\n",
    "light_model.fit(train, labels)\n",
    "\n",
    "y_pred_light_probab = light_model.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_light_probab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.955797009315653"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_model = RandomForestClassifier(random_state=randomState,\n",
    "                                      **rf_grid.best_params_\n",
    "                                      )\n",
    "forest_model.fit(train, labels)\n",
    "\n",
    "y_pred_forest_probab = forest_model.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_forest_probab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Synthetic Minority Oversampling Technique\n",
    "sm = SMOTE(random_state=randomState)\n",
    "train_resampled, labels_resampled = sm.fit_resample(train, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7695094662770525"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_imb = XGBClassifier(objective='binary:logistic', \n",
    "                          random_state=randomState,\n",
    "                         **{k.split('__')[1]:v for k,v in xgb_imb_grid.best_params_.items()}\n",
    "                      )\n",
    "model_imb.fit(train_resampled, labels_resampled)\n",
    "\n",
    "y_pred_imb_probab = model_imb.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_imb_probab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8973981249966774"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light_imb_model = LGBMClassifier(random_state=randomState,\n",
    "                                 objective='binary',\n",
    "                                 **{k.split('__')[1]: v for k, v in lgb_imb_grid.best_params_.items()}\n",
    "                                 )\n",
    "light_imb_model.fit(train_resampled, labels_resampled)\n",
    "\n",
    "y_pred_imb_light_probab = light_imb_model.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_imb_light_probab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9919829697387472"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_imb_model = RandomForestClassifier(random_state=randomState,\n",
    "                                          **{k.split('__')[1]: v for k, v in rf_imb_grid.best_params_.items()}\n",
    "                                          )\n",
    "forest_imb_model.fit(train_resampled, labels_resampled)\n",
    "\n",
    "y_pred_imb_forest_probab = forest_imb_model.predict_proba(train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred_imb_forest_probab)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summarise Evaluation Scores on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>model</th>\n",
       "      <th>training score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>0.878150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lightgbm</td>\n",
       "      <td>0.930149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>randomforest</td>\n",
       "      <td>0.955797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xgboost with smote</td>\n",
       "      <td>0.769509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lightgbm with smote</td>\n",
       "      <td>0.897398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>randomforest with smote</td>\n",
       "      <td>0.991983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     model  training score\n",
       "0                  xgboost        0.878150\n",
       "1                 lightgbm        0.930149\n",
       "2             randomforest        0.955797\n",
       "3       xgboost with smote        0.769509\n",
       "4      lightgbm with smote        0.897398\n",
       "5  randomforest with smote        0.991983"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame({'model': ['xgboost', 'lightgbm', 'randomforest', \n",
    "                                     'xgboost with smote', 'lightgbm with smote', \n",
    "                                     'randomforest with smote'\n",
    "                                     ],\n",
    "                            'training score': [roc_auc_score(y_train, y_pred_probab), \n",
    "                                               roc_auc_score(y_train, y_pred_light_probab),\n",
    "                                               roc_auc_score(y_train, y_pred_forest_probab),\n",
    "                                               roc_auc_score(y_train, y_pred_imb_probab),\n",
    "                                               roc_auc_score(y_train, y_pred_imb_light_probab),\n",
    "                                               roc_auc_score(y_train, y_pred_imb_forest_probab)                                               \n",
    "                                               ]\n",
    "                           })\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save models\n",
    "with open('models/xgb.pkl', 'wb') as model_file:\n",
    "    pickle.dump(model, model_file)\n",
    "with open('models/lgb.pkl', 'wb') as model_file:\n",
    "    pickle.dump(light_model, model_file)\n",
    "with open('models/rf.pkl', 'wb') as model_file:\n",
    "    pickle.dump(forest_model, model_file)\n",
    "with open('models/xgb_smote.pkl', 'wb') as model_file:\n",
    "    pickle.dump(model_imb, model_file)\n",
    "with open('models/lgb_smote.pkl', 'wb') as model_file:\n",
    "    pickle.dump(light_imb_model, model_file)\n",
    "with open('models/rf_smote.pkl', 'wb') as model_file:\n",
    "    pickle.dump(forest_imb_model, model_file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Satya\\AppData\\Local\\Temp\\ipykernel_23664\\440238055.py:1: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  mailout_test = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_MAILOUT_052018_TEST.csv', sep=';')\n"
     ]
    }
   ],
   "source": [
    "mailout_test = pd.read_csv('../../data/Term2/capstone/arvato_data/Udacity_MAILOUT_052018_TEST.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying All Transformations\\Processing applied to Gen Pop & Customers Data\n",
    "clean_data.transform(mailout_test)\n",
    "mailout_test_clean = mailout_test.copy()\n",
    "feature_engine.transform(mailout_test_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Columns\n",
    "mailout_test_clean.drop(get_cols_to_drop(mailout_test_clean, 0.3), axis=1, inplace=True)\n",
    "mailout_test_clean.drop(['EINGEFUEGT_AM', \n",
    "                         'CAMEO_INTL_2015', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN'\n",
    "                         ], \n",
    "                         axis=1, inplace=True\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping ID column\n",
    "X_test = mailout_test_clean.drop('LNR', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Features created from information extracted by KMeans model \n",
    "X_test_imputed = imputer.transform(X_test)\n",
    "X_test_imputed_scaled = scaler.transform(X_test_imputed)\n",
    "X_test_pca_176 = pca_176.transform(X_test_imputed_scaled)\n",
    "test_cluster_p176_c10 = kmeans.predict(X_test_pca_176)\n",
    "X_test_imputed['cluster_p176_c10'] = test_cluster_p176_c10\n",
    "\n",
    "# Creating OHE cols\n",
    "X_test_ohe = pd.get_dummies(X_test_imputed, columns=ohe_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating test dataset for prediction\n",
    "\n",
    "# test = X_test_ohe\n",
    "test = pd.concat([X_test_ohe, X_test_imputed[ohe_cols]], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = model.predict_proba(test)\n",
    "y_test_light = light_model.predict_proba(test)\n",
    "y_test_forest = forest_model.predict_proba(test)\n",
    "\n",
    "y_test_imb = model_imb.predict_proba(test)\n",
    "y_test_imb_light = light_imb_model.predict_proba(test)\n",
    "y_test_imb_forest = forest_imb_model.predict_proba(test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_submission(response_vals, submission_file='', index_vals=mailout_test_clean.LNR):\n",
    "    '''Creat Kaggle Submission CSV'''\n",
    "    submission = pd.DataFrame({'LNR': index_vals, 'RESPONSE': response_vals})\n",
    "    assert len(submission) == 42833\n",
    "    submission.to_csv(f'results/arvato_kaggle_submission{submission_file}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_submission(y_test[:, 1], '_xgb')\n",
    "create_submission(y_test_light[:, 1], '_light')\n",
    "create_submission(y_test_forest[:, 1], '_forest')\n",
    "\n",
    "create_submission(y_test_imb[:, 1], '_xgb_imb')\n",
    "create_submission(y_test_imb_light[:, 1], '_light_imb')\n",
    "create_submission(y_test_imb_forest[:, 1], '_forest_imb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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