{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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 ast import literal_eval\n",
    "from sklearn.preprocessing import Imputer, StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', delimiter=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 85 columns</p>\n",
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      ],
      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
       "2        -1                     3          2            3.0   \n",
       "3         2                     4          2            2.0   \n",
       "4        -1                     3          1            5.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3    ...            NaN         NaN   \n",
       "1                      4                 5    ...            2.0         3.0   \n",
       "2                      3                 5    ...            3.0         3.0   \n",
       "3                      1                 2    ...            2.0         2.0   \n",
       "4                      3                 2    ...            2.0         4.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
       "1         2.0         1.0          1.0       5.0       4.0     3.0   \n",
       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN       NaN  \n",
       "1          5.0       4.0  \n",
       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the structure of the data after it's loaded (e.g. print the number of\n",
    "# rows and columns, print the first few rows).\n",
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has 891221 rows\n",
      "The dataset has 85 columns\n"
     ]
    }
   ],
   "source": [
    "print('The dataset has',len(azdias) ,'rows')\n",
    "print('The dataset has',len(azdias.columns) ,'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EANicdtky1d1P7e6Du/vQJPdJ8n+7+/5J3p3kHvNmD07ypr1WJQDAJjUyz9STkzyhqv4l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\nAAAG/D9AhdULdFgUowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fafc299630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(feat_info.groupby('type').count()['attribute']\n",
    "                        .sort_values(ascending = False).plot(kind = 'bar', figsize = (10, 7)))\n",
    "plt.title('Feature Relations by Variable Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j5zI/x/kD6Qad7wBM7t8v6aZAWbbGHqa2XAhSAFX130nm4aq2VNUlwMOSHAR8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\nBxbrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fafc2d2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(feat_info.groupby('information_level').count()['type']\n",
    "                        .sort_values(ascending = False).plot(kind = 'bar', figsize = (10, 7)))\n",
    "plt.title('Feature Relations by Information Level')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 different types of missing data encodings. These types will be analyzed separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feat_info['missing_or_unknown'].replace({'[-1,X]': '[-1,\"X\"]',\n",
    "                                         '[-1,XX]': '[-1,\"XX\"]',\n",
    "                                          '[XX]': '[\"XX\"]'}, inplace = True)\n",
    "encodings = feat_info.set_index('attribute')['missing_or_unknown'].to_dict()\n",
    "encodings = {key: {k: np.nan for k in literal_eval(value)} for key, value in encodings.items()}\n",
    "for column,mapping in encodings.items():\n",
    "    azdias[column].replace(mapping, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\n",
    "\n",
    "For the remaining features, are there any patterns in which columns have, or share, missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Missing Data %')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1fCtz1cDO4+x7FFJViRZ4dkckiRpthloCKuqe6pqKbAY2BsY7cZVo160pKpOqaplVbVs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\nmpn2EFZVVwFPne7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1faf8670ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the dataset.\n",
    "azdias_nullcnt_col = pd.DataFrame(azdias.isnull().sum())\n",
    "azdias_nullcnt_col.columns =['num_nulls']\n",
    "(azdias_nullcnt_col/len(azdias) * 100).plot(kind = 'hist', figsize = (10, 7), bins = 60,\n",
    "                                            title = 'Number of Missing Values Distribution')       \n",
    "plt.xlabel('Missing Data %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using heatmap to investigate the pattern of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1faa3d29160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2LjAXYGhoiH0PmDvRw4qIiGiMujQK5KZDNEHexxERvdVNz4uXArtI2gmYCawi6cu23wRg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8YCEyNiPDACGBkRMyUdmLa/GBEXAlPT8gKyQTqfB56PiLnAO4Ft+4qTpkTdESAi9iBr6AA4DDhN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XcNQnn7ZNsfLPXZL7XULul4NEEtMi/S73NfogyksEJ9m+2ja/pdh9BmWEZYyZ5E3ECvqbQr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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1faa3fcfe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,15))\n",
    "sns.heatmap(azdias.isnull(), cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigate missing data categories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Missing Data %')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2SZKkibLOsB64qk5h5rFnALvMsP9S4DUD1w8CDhpOdZIkSWsGVzCQJEnqMcOaJElSjxnW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\nMzMzswJzsmZmZmZWYL8ANKcjsM+NTnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1faa40d4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_info_joined = feat_info.set_index('attribute').join(azdias_nullcnt_col)\n",
    "\n",
    "missing_std = (feat_info_joined.groupby('information_level').sum()/(feat_info_joined.groupby('information_level').count()\n",
    "                                                                   *len(azdias))*100) \n",
    "missing_std['num_nulls'].plot(kind = 'bar', figsize = (10, 7),title = 'Number of Missing Values by Informarion Level')\n",
    "plt.ylabel('Missing Data %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outlier columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outlier_columns = list(azdias_nullcnt_col[azdias_nullcnt_col['num_nulls'] > 200000].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GEBURTSJAHR</td>\n",
       "      <td>person</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TITEL_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ALTER_HH</td>\n",
       "      <td>household</td>\n",
       "      <td>interval</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>KK_KUNDENTYP</td>\n",
       "      <td>household</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>KBA05_BAUMAX</td>\n",
       "      <td>microcell_rr3</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       attribute information_level         type missing_or_unknown\n",
       "0       AGER_TYP            person  categorical             [-1,0]\n",
       "11   GEBURTSJAHR            person      numeric                [0]\n",
       "40      TITEL_KZ            person  categorical             [-1,0]\n",
       "43      ALTER_HH         household     interval                [0]\n",
       "47  KK_KUNDENTYP         household  categorical               [-1]\n",
       "64  KBA05_BAUMAX     microcell_rr3        mixed             [-1,0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_info[feat_info['attribute'].isin(outlier_columns)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the outlier columns will be treated as categorical features and transformed using one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The columns that are outliers: ['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX']\n"
     ]
    }
   ],
   "source": [
    "print('The columns that are outliers:', outlier_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set has 6 columns that has over 200,000 missing values. These columns (**AGER_TYP**, **GEBURTSJAHR**, **TITEL_KZ**, **ALTER_HH**, **KK_KUNDENTYP**, **KBA05_BAUMAX**) will be removed from the dataset. There are another 55 columns in the dataset that are missing around 150,000 values. From the heatmap visualization above, the missing values has strong correlations with each other - most missing values among different columns happens over the same row. From the barplot \"Missing Value by Information Level\", most of the missing values belongs to the feature group **household** and **micro_cell_rr3** (more than 20%)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Number of Missing Values')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zgRnAMVV1ZdvX4cBJSf4OuAT48sBPSpIkaYoZStCrqjvpPTTRX3vzGO0/Anxk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fab1ccfac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azdias_nullcnt_row = pd.DataFrame(azdias.isnull().sum(axis = 1))\n",
    "azdias_nullcnt_row.columns = ['num_nulls']\n",
    "azdias_nullcnt_row.plot(kind = 'hist', bins = 100, figsize = (10, 7), title ='Missing Data by Rows')\n",
    "plt.xlabel('Number of Missing Values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several rows that has more than 30 missing values across the column. Now build function that separates the data into two parts, and visualize the distributions separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualizeDistAzdias(azdias_nullcnt_row, column, threshold = 30):\n",
    "    # separate dataset based on number of null values in rows \n",
    "    lessNull_rows = list(azdias_nullcnt_row[azdias_nullcnt_row['num_nulls']  < threshold].index)\n",
    "    moreNull_rows = list(azdias_nullcnt_row[azdias_nullcnt_row['num_nulls']  > threshold].index)\n",
    "    # Flag the high missing value rows\n",
    "    azdias_moreNull = azdias.loc[moreNull_rows, :] \n",
    "    azdias_lessNull = azdias.loc[lessNull_rows, :] \n",
    "    # create subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, sharey = True, figsize = (20, 7))\n",
    "    ax1.set_title('Less Missing Values')\n",
    "    ax2.set_title('More Missing Values')\n",
    "    sns.barplot(x = column, y = column, ax = ax1,\n",
    "            data = azdias_lessNull, estimator= lambda x: len(x)/len(azdias_lessNull)* 100)\n",
    "    sns.barplot(x = column, y = column, ax = ax2, \n",
    "            data = azdias_moreNull, estimator= lambda x: len(x)/len(azdias_moreNull)* 100)\n",
    "    plt.ylabel('% values among total')\n",
    "    plt.suptitle(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now visualize 5 different columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HB5xUJlPXHjGMLH/WQisDALZr6z6FbXo4c1XdZtW+x86jKAAANl53f7K7b5Rk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\niHFmRFwl6Sjg08CBE2TeCTgeeARYBNw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fab17a28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tq+p5SY7P5aewfWq6kgCARbFsgFRVG5L8RZLfTHJ6Zgtv71dVr03yrO7+UZJ0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fab17a2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+VfV/kzwyyZfzk2/S7SSHTVUTALA81hQgbVZVV03yW0kelGSfJK9fRFEAAKy7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\nejmBJEmSJEmSpF7/AXXlS0B9tvkqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac1f11dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PV72YDQCY8owH3mvRJWxXnvTqNyy6BAAAttIsAdK7q+rYTBZfvGKSdydJVe2X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\nSZIk9bKAJEmSJEmSpF7/B2r1U6x3SpzsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac2b33e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AYFs3S4D0le6++dwrAQBg3v44yaurardMvgw8J8kfV9UVkzx7oZUBANs0U9gA\nAHYQ3f25JDepqisnqe4+e2r3MQsqCwBYA2YJkN4x9yoAAJi7qrpckvsm2S/JzlWVJOnuZy6wLABg\nDdhphmPuM/cqAADYGt6V5N6ZrHd0/tQLAGCUKWwAADuOa3b3XRddBACw9ngKGwDAjuPTVXWT7v7K\nogsBANYWT2EDANhx3D7JoVX1nSQX5JdfCN50sWUBANs6U9gAAHYcd1t0AQDA2jTLItpvnXsVAADM\nXXefkmSPJPccXnsMbQAAo2YZgXT1qnrJpnZ29xNWsR4AAOakqv4kySOTvGNoen1VHdndL11gWQDA\nGjBLgPSYJF9NckyS0zKZKw8AwNpzWJJbd/f5SVJVz03ymSQCJABg1CwB0t5J7p/kgUkuTPKWJG/v\n7rPmWRgAAKuuklw0tX1RfDkIAMxgxTWQuvuH3f2K7r5TkkMzmTd/YlU9fN7FAQCwql6T5PiqOqKq\njkjy2SSvWmxJAMBaMPNT2KrqFkkenOR3k7w/yQnzKgoAgNXX3S+oqo8luX0mI4/+sLu/sNiqAIC1\nYMUAqaqekeQeSb6W5M1JntrdF867MAAA5uI7mSxLsHOSqqpbdPfnF1wTALCNm2UE0l8l+XaSmw2v\nv6uqZPKtVXf3TedXHgAAq6Wq/iaTJQm+laSH5k5y0KJqAgDWhlkCpGvPvQoAALaGByS5bnf/bNGF\nAABry4oBUnefslx7VR2Y5CFJHrvaRQEAMBdfzeSBKGcuuhAAYG2ZeRHtJKmq/TMJjR6Qyfz5d8yj\nKAAA5uLZSb5QVV9NcsHGxu6+1+JKAgDWglkW0f61JA/K5AlsP0zyliTV3Xeac20AAKyuo5I8N8lX\nkvxiwbUAAGvILCOQvp7kk0nu2d3fTJKq+tO5VgUAwDz8oLtfsugiAIC1Z5YA6b6ZjED6aFV9IMmb\nM3kCGwAAa8sJVfXsJO/OxaewfX5xJQEAa8Esi2gfm+TYqrpikoOT/GmSvarq5UmO7e4PzrlGAABW\nx82Hn7eZauskBy2gFgBgDZl5Ee3uPj/JG5K8oaqumuR+SZ6SRIAEALAGWMMSANhSsyyifYUkP+/u\nnw/bN0hy9ySndLdvqwAA1pCq+v0kN0qy68a27n7m4ioCANaCnWY45gNJ9kuSqrpeks8kuU6Sxw5z\n6Depqn61qj5aVV+rqhOr6k+G9qtW1Yeq6qTh51Uu3ccAAGAlVfWKJA9M8vhM1rS8f5JrLbQoAGBN\nmCVAukp3nzS8PyTJm7r78UnuluQeK5x7YZIndfdvZDLX/rFVdcNMpr59uLuvn+TDwzYAAPN1u+5+\nRJKzuvsZSW6b5FcXXBMAsAZ9kjQ/AAAXnUlEQVTMEiD11PuDknwoSbr7Z0l+MXpi9+kbn+rR3ecm\n+VqSfZLcO8lRw2FHZbI4NwAA8/U/w8+fVNU1kvw8ybUXWA8AsEbMsoj2l6vqeUm+l+R6GRbNrqo9\nNudGVbVfJk/+OD7JXt19ejIJmarqaps451FJHpUk++677+bcDgCAS3rv0If7hySfz+SLwlcuPUgf\nDABYapYRSI9M8oNM1kH6ve7+ydB+wyTPm+UmVbVbkrcneWJ3nzNrcd19ZHcf0N0HrFu3btbTAABY\nRnf/TXef3d1vz2Tto1/v7qcvc5w+GABwMbOMQNqlu5+ztLG7P11Vp650clXtkkl49IbufsfQ/P2q\n2nsYfbR3kjM3q2oAAC6V7r4gyQWLrgMAWBtmGYH0sY1vqurDS/a9c+zEqqokr0ryte5+wdSud2ey\nIHeGn++aoQ4AAAAAFmCWEUg19f6qI/uWc2CShyf5SlV9cWh7WpLnJDmmqg5L8t+ZPEIWAAAAgG3Q\nLAFSb+L9ctsX39l9XDYdMt15hnsDALBKqurAJF/s7vOr6mFJbpHkxd19yoJLAwC2cbMESFerqsMz\nCYI2vs+wbVVFAIC14+VJblZVN0vy55ksNXB0kjsutCoAYJs3yxpIr0yye5Ldpt5v3P6X+ZUGAMAq\nu7C7O8m9Mxl59OJM+nUAAKNWHIHU3c/YGoUAADB351bVUzNZo/IOVXWZJLssuCYAYA1YcQRSVd2o\nqu41tf3Cqnr18LrFfMsDAGAVPTDJBUn+qLvPSLJPkn9YbEkAwFowyxS25yT5wdT2XZL8a5KPJnn6\nPIoCAGD1DaHR25Ncbmj6QZJjF1cRALBWzLKI9t7d/emp7XO6++1JUlWPnk9ZsP058KUHLrqEbcqn\nHv+pRZcAsMOpqkcmeVSSqya5biYjkF4RT8cFAFYwywikiy2s2N23mdq82uqWAwDAHD02yYFJzkmS\n7j4p+nMAwAxmCZBOq6pbL22sqtskOW31SwIAYE4u6O6fbdyoqp2T9ALrAQDWiFmmsD05yVuq6rVJ\nPj+03TLJIZksxAgAwNrw8ap6WpLLV9XvJvm/Sd6z4JoAgDVgxRFI3f0fSW6d5DJJDh1eOyW5zbAP\nAIC14SlJNiT5SpJHJ3lfkr9caEUAwJowywikdPeZWeGJa1X19u6+76pUxTbhv595k0WXsM3Y9+lf\nWXQJAHCpdfcvkrxyeAEAzGymAGlG11nFawEAsMqq6jtZZs2j7taPAwBGrWaAZAFGAIBt2wFT73dN\ncv8kV11QLQDAGjLLU9gAANgOdPcPp17f6+4XJTlo0XUBANu+1RyBVKt4LQAAVllV3WJqc6dMRiTt\nvqByAIA1ZMUAqape292HznCtJ1/6cgAAmKPnT72/MMnJSR6wmFIAgLVklhFIN53lQt39wUtZCwAA\nc9Tdd1p0DQDA2jRLgHSFqrp5NjFFrbs/v7olAQCwmqrq8LH93f2CrVULALA2zRIg7ZPJcOflAqSO\nhRcBALZ11jkCAC6VWQKkb3a3kAgAYI3q7mcsugYAYG1bzaewAQCwDauqXZMcluRGSXbd2N7df7Sw\nogCANWGnGY7587lXAQDA1vC6JFdPcpckH09yzSTnLrQiAGBNmGUE0guqqjex74Ik30ry7O7+0uqV\nBQDAHFyvu+9fVffu7qOq6o1J/m3RRQEA275ZAqQ/SLJXku8uab9WktMyGQL92iQ3X9XKAABYbT8f\nfp5dVTdOckaS/RZXDgCwVswSIL0wydO6+5Tpxqpal+SF3X3PqrrFXKoDAGA1HVlVV0nyV0nenWS3\n4T0AwKhZAqT9uvvLSxu7e31V7Te8/+tVrgsAgNX3mu6+KJP1j66z6GIAgLVjlkW0dx3Zd/nVKgQA\ngLn7TlUdWVV3rqpadDEAwNoxS4D0uap65NLGqjosyQmrXxIAAHNygyT/nuSxSU6uqpdV1e0XXBMA\nsAbMMoXtiUmOraqH5peB0QFJLpvkPvMqDACA1dXd/5PkmCTHDGshvTiT6WyXWWhhAMA2b8UAqbu/\nn+R2VXWnJDcemv+1uz8y18oAAFh1VXXHJA9Mcrckn0vygMVWBACsBbOMQEqSdPdHk3x0jrUAADBH\nVfWdJF/MZBTSn3X3+QsuCQBYI2YOkAAAWPNu1t3nLLoIYG37+G/dcdElbDPu+ImPL7oE2GpmWUQb\nAIDtgPAIANhSAiQAAAAARgmQAAAAABglQAIA2MFU1W2q6iNV9amqOnjR9QAA2z6LaAMAbOeq6urd\nfcZU0+FJ7pWkknw6yTsXUhgAsGbMdQRSVb26qs6sqq9OtR1RVd+rqi8Or7vPswYAAPKKqvqrqtp1\n2D47yUOSPDCJhbUBgBXNewrba5PcdZn2F3b3/sPrfXOuAQBgh9bdByf5YpL3VtXDkzwxyS+SXCGJ\nKWwAwIrmGiB19yeS/Gie9wAAYGXd/Z4kd0myR5J3JPmv7n5Jd29YbGUAwFqwqEW0H1dVXx6muF1l\nQTUAAOwQqupeVXVcko8k+WqSByW5T1W9qaquu9jqAIC1YBEB0suTXDfJ/klOT/L8TR1YVY+qqvVV\ntX7DBl+OAQBsob/NZPTRfZM8t7vP7u7Dkzw9ybOWHqwPBgAstdUDpO7+fndf1N2/SPLKJLcaOfbI\n7j6guw9Yt27d1isSAGD78uNMRh09KMmZGxu7+6TuftDSg/XBAICltnqAVFV7T23eJ5Nh1AAAzM99\nMlkw+8JMnr4GALBZdp7nxavqTUl+O8meVXVqkr9O8ttVtX+STnJykkfPswYAgB1dd/8gyUsXXQcA\nsHbNNUDq7gcv0/yqed4TAAAAgNW1qKewAQAAALBGCJAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAA\nAEYJkAAAAAAYJUACAAAAYNTOiy4AAAAAdlQve9J7Fl3CNuVxz7/noktgE4xAAgAAAGCUAAkAAACA\nUQIkAAAAAEYJkAAAAAAYJUACAAAAYJSnsAGQxBNAlvIEEAAA+CUjkAAAAAAYJUACAAAAYJQACQAA\nAIBRAiQAAAAARllEGwDm5FkPu9+iS9im/MXr37boEgAA2EJGIAEAAAAwSoAEAAAAwCgBEgAAAACj\nBEgAAAAAjNpuFtG+5Z8dvegStikn/MMjFl0CAAAAsJ0wAgkAAACAUdvNCCQAAIClDnzpgYsuYZvy\nqcd/atElAGuUEUgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAA\nAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwKi5BkhV9eqqOrOqvjrVdtWq+lBVnTT8vMo8\nawAAAADg0pn3CKTXJrnrkranJPlwd18/yYeHbQAAAAC2UXMNkLr7E0l+tKT53kmOGt4fleTgedYA\nAAAAwKWziDWQ9uru05Nk+Hm1BdQAAAAAwIy26UW0q+pRVbW+qtZv2LBh0eUAAOwQ9MEAgKUWESB9\nv6r2TpLh55mbOrC7j+zuA7r7gHXr1m21AgEAdmT6YADAUosIkN6d5JDh/SFJ3rWAGgAAAACY0VwD\npKp6U5LPJLlBVZ1aVYcleU6S362qk5L87rANAAAAwDZq53levLsfvIldd57nfQEAAABYPdv0ItoA\nAAAALJ4ACQAAAIBRAiQAAAAARgmQAAAAABg110W0AQCAzfPfz7zJokvYZuz79K8sugQABkYgAQAA\nADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEA\nAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKN2XnQBAACsXbf8\ns6MXXcI25YR/eMSiSwCAuTACCQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAA\nAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQA\nAAAARgmQAAAAABglQAIAAABg1M6LunFVnZzk3CQXJbmwuw9YVC0AAAAAbNrCAqTBnbr7BwuuAQAA\nAIARprABAAAAMGqRAVIn+WBVnVBVj1pgHQAAAACMWOQUtgO7+7SqulqSD1XV17v7E9MHDMHSo5Jk\n3333XUSNAAA7HH0wAGCphY1A6u7Thp9nJjk2ya2WOebI7j6guw9Yt27d1i4RAGCHpA8GACy1kACp\nqq5YVbtvfJ/k95J8dRG1AAAAADBuUVPY9kpybFVtrOGN3f2BBdUCAAAAwIiFBEjd/e0kN1vEvQEA\nAADYPIt8ChsAAAAAa4AACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABG\nCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAA\nRgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAA\nAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAA\nAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYNTCAqSqumtV/VdV\nfbOqnrKoOgCA/9/encfKVZZxHP/+0oq7ohYVKVJRZBFjabSiNUZwQdwqCQq4GxU14oIaFf3DuiTG\nuIBGgjGIgKKVsGiDBHdUAqIFq1ArWhVDBVMVt6oBoY9/zKkZT+8dmOHcO8yd7ye56cx73jnn6cMM\n/eWdc86VJEmSBhvLAlKSRcDJwOHAAcAxSQ4YRy2SJEmSJEkabFxnIK0ENlfVb6rqZmAtsHpMtUiS\nJEmSJGmAcS0g7QFc1/d8SzMmSZIkSZKkO5lU1fwfNHkBcFhVvbp5/lJgZVW9sTXvWODY5um+wDXz\nWuholgB/GncRC4w97Zb97J497Zb97N6k9HSvqtpt3EXIDCbAfs4Fe9ot+9k9e9q9Senp7cpg41pA\negKwpqoOa56fAFBVH5r3YjqWZH1VPXbcdSwk9rRb9rN79rRb9rN79lTTwPd5t+xn9+xpt+xn9+xp\n9xZaT8d1CduPgX2SPCzJLsDRwLox1SJJkiRJkqQBFo/joFV1S5LjgK8Di4DTqmrjOGqRJEmSJEnS\nYGNZQAKoqguBC8d1/Dn0mXEXsADZ027Zz+7Z027Zz+7ZU00D3+fdsp/ds6fdsp/ds6fdW1A9Hcs9\nkCRJkiRJkjQ5xnUPJEmSJEmSJE0IF5BGkOS0JFuTXD3L9iT5ZJLNSX6WZMV81zhJkuyZ5LtJNiXZ\nmOTNM8yxp0NIcrckP0ry06an75thzl2TfLnp6eVJls1/pZMlyaIkP0lywQzb7OeQklyb5KokG5Ks\nn2G7n/shJdk1yTlJftH8P/UJre32VBPNDNYtM1j3zGBzwwzWLTNYt6Ypf7mANJrTgWcO2H44sE/z\ncyxwyjzUNMluAd5WVfsDBwNvSHJAa449Hc5NwKFV9RhgOfDMJAe35rwK+EtVPQI4EfjwPNc4id4M\nbJplm/0czSFVtXyWX2/q5354nwAuqqr9gMew8/vVnmrSnY4ZrEtmsO6ZweaGGax7ZrDuTE3+cgFp\nBFX1feDGAVNWA2dWzw+BXZPsPj/VTZ6quqGqrmwe/4PeB26P1jR7OoSmT9uap3dpfto3PFsNnNE8\nPgd4apLMU4kTJ8lS4NnAqbNMsZ/d83M/hCT3AZ4MfBagqm6uqr+2ptlTTTQzWLfMYN0zg3XPDDYW\nfu5vp2nLXy4gzY09gOv6nm9h53+MNYPmlNODgMtbm+zpkJpTfTcAW4FvVtWsPa2qW4C/AQ+Y3yon\nyknAO4Dts2y3n8Mr4BtJrkhy7Azb/dwPZ2/gj8DnmtP8T01yz9Yce6qFzvf4iMxg3TGDdc4M1j0z\nWHemKn+5gDQ3Zlrx9tfd3YYk9wLOBd5SVX9vb57hJfZ0gKq6taqWA0uBlUkObE2xp7dTkucAW6vq\nikHTZhizn4OtqqoV9E7rfUOSJ7e229PhLAZWAKdU1UHAP4F3tebYUy10vsdHYAbrlhmsO2awOWMG\n685U5S8XkObGFmDPvudLgevHVMtESHIXesHlrKo6b4Yp9nREzSmUF7PzPSP+19Mki4H7MviygGm2\nCnhekmuBtcChSb7QmmM/h1RV1zd/bgXOB1a2pvi5H84WYEvfN93n0As07Tn2VAuZ7/EhmcHmjhms\nE2awOWAG69RU5S8XkObGOuBlzd3WDwb+VlU3jLuoO6vmGuXPApuq6uOzTLOnQ0iyW5Jdm8d3B54G\n/KI1bR3w8ubxkcB3qmoiV8LnWlWdUFVLq2oZcDS9Xr2kNc1+DiHJPZPce8dj4BlA+7cq+bkfQlX9\nAbguyb7N0FOBn7em2VMtdL7Hh2AG654ZrFtmsO6Zwbo1bflr8bgLmERJvgQ8BViSZAvwXno3yKOq\nPg1cCDwL2Az8C3jleCqdGKuAlwJXNdeLA7wbeCjY0xHtDpyRZBG9heKzq+qCJO8H1lfVOnqB8fNJ\nNtP7lubo8ZU7meznHfIg4PzmHpeLgS9W1UVJXgd+7u+ANwJnJdkF+A3wSnuqhcQM1jkzWPfMYPPA\nft4hZrDuTU3+iouzkiRJkiRJGsRL2CRJkiRJkjSQC0iSJEmSJEkayAUkSZIkSZIkDeQCkiRJkiRJ\nkgZyAUmSJEmSJEkDuYAkSZIkSZKkgVxAkqZckgcnWZvk10l+nuTCJI9McnWSw5JsaH62JbmmeXzm\ngP2tTHJxkl8luTLJ15I8utm2Jsnv+/a5IcmuSe6R5KwkVzXHvSTJvfr2eUSSSrJf39iyZuwDfWNL\nkvwnyaeSvKfvGLf2PX7vLONvSnJZkjT7WtSMP7FV99VJnjc3/zUkSdI0MH+Zv6RJlKoadw2SxqT5\nx/pS4Iyq+nQzthy4N3BKVR3YN/di4O1VtX7A/h4EXA68qKoubcaeBCypqq8kWQNsq6qPtl53ArBb\nVb21eb4vcG1V3dQ8PxvYHfh2Va1pxpYB3wb+XlUHNWOvB14LXFJVx/Xtf1tV/S8QzTaeZC3wrao6\nNclbgEdV1Wv6606yP/AD4IFVtX327kqSJO3M/GX+kibV4nEXIGmsDgH+syO8AFTVhiYcjOI4emHo\n0r79XXI7Xrc78Lu+11yz43HzTdiqptZ1wJq+1/0b2JTksU2wOgo4G3jIiPUfD1yS5LLm77KyPaGq\nNiW5BVgCbB3xOJIkaXqZv/6f+UuaEF7CJk23A4ErOtzfo4Arb2PO8X2nLX+3GTsNeGdzCvMHk+zT\nN//5wEVV9UvgxiQrWvtbCxydZClwK3D9qMVX1Q3AScBlwAer6sb2nCSPB7YDfxz1OJIkaaqZv/qY\nv6TJ4QKSpDmT5PIkm5J8om/4xKpa3vwcAr1v3YC9gY8A9wd+3JyqDHAMvZBC8+cxrcNcBDy9Gf9y\nB2WfDCyqqtNb48cn2QB8FDiqvP5XkiTdCZm/JM0VL2GTpttG4MiO97cC+CpAVT0+yZHAc27rhVW1\nDTgPOC/JduBZSbYChwIHJilgEVBJ3tH3upuTXAG8jd43cM+9I3+BqtreHKvtxPa9AyRJkkZg/tq5\nDvOXNAE8A0mabt8B7prkNTsGkjwO2GvE/Z0MvCLJE/vG7nFbL0qyKsn9mse7AAfQuyb/SODMqtqr\nqpZV1Z7Ab4EntXbxMeCdVfXnEeuWJEmaL+YvSRPJBSRpijWnAR8BPD29XyO7kd5NEq8Hbhphf3+g\ndyPFDyXZnORSeiHkU33T+q/B33HDyIcD30tyFfATYD1wLr3Tos9vHeZc4EWt426sqjOGrVeSJGm+\nmb8kTap4GamktiSrgRdX1QvHXYskSdI0MH9JurPzHkiS/k+S9wOrgVeMuRRJkqSpYP6SNAk8A0nS\n0JIcBny4NfzbqjpiHPVIkiQtdOYvSePmApIkSZIkSZIG8ibakiRJkiRJGsgFJEmSJEmSJA3kApIk\nSZIkSZIGcgFJkiRJkiRJA7mAJEmSJEmSpIH+C7U/IIfklw/sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac3775cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ['GFK_URLAUBERTYP', 'SEMIO_REL','SEMIO_VERT', 'HH_EINKOMMEN_SCORE', 'CJT_GESAMTTYP']:\n",
    "    visualizeDistAzdias(azdias_nullcnt_row, i, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visualizations above, there are clearly differences between the rows that have a large number of missing values and small number of missing values. These columns needs to be treated separately rather than simply dropping them. From the histogram of missing value distributions, the rows with more than 30 missing values will be dropped, while the rest of the missing values will be imputed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MEKYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4Qp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1faa417bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "(feat_info.groupby('type').count()['attribute']\n",
    "                        .sort_values(ascending = False).plot(kind = 'bar', figsize = (10, 7)))\n",
    "plt.title('# Features by Variable Types')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assess categorical variables: which are binary, which are multi-level, and which one needs to be re-encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features unique values:\n",
      "AGER_TYP : [nan 2.0 3.0 1.0]\n",
      "ANREDE_KZ : [1 2]\n",
      "CJT_GESAMTTYP : [2.0 5.0 3.0 4.0 1.0 6.0 nan]\n",
      "FINANZTYP : [4 1 6 5 2 3]\n",
      "GFK_URLAUBERTYP : [10.0 1.0 5.0 12.0 9.0 3.0 8.0 11.0 4.0 2.0 7.0 6.0 nan]\n",
      "GREEN_AVANTGARDE : [0 1]\n",
      "LP_FAMILIE_FEIN : [2.0 5.0 1.0 nan 10.0 7.0 11.0 3.0 8.0 4.0 6.0 9.0]\n",
      "LP_FAMILIE_GROB : [2.0 3.0 1.0 nan 5.0 4.0]\n",
      "LP_STATUS_FEIN : [1.0 2.0 3.0 9.0 4.0 10.0 5.0 8.0 6.0 7.0 nan]\n",
      "LP_STATUS_GROB : [1.0 2.0 4.0 5.0 3.0 nan]\n",
      "NATIONALITAET_KZ : [nan 1.0 3.0 2.0]\n",
      "SHOPPER_TYP : [nan 3.0 2.0 1.0 0.0]\n",
      "SOHO_KZ : [nan 1.0 0.0]\n",
      "TITEL_KZ : [nan 4.0 1.0 3.0 5.0 2.0]\n",
      "VERS_TYP : [nan 2.0 1.0]\n",
      "ZABEOTYP : [3 5 4 1 6 2]\n",
      "KK_KUNDENTYP : [nan 1.0 3.0 6.0 4.0 2.0 5.0]\n",
      "GEBAEUDETYP : [nan 8.0 1.0 3.0 2.0 6.0 4.0 5.0]\n",
      "OST_WEST_KZ : [nan 'W' 'O']\n",
      "CAMEO_DEUG_2015 : [nan '8' '4' '2' '6' '1' '9' '5' '7' '3']\n",
      "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' '9C'\n",
      " '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F' '5F' '1C']\n"
     ]
    }
   ],
   "source": [
    "categoricals = list(feat_info[feat_info['type'] == 'categorical']['attribute'].values)\n",
    "print('Categorical features unique values:')\n",
    "_ = azdias[categoricals].apply(lambda x: print(x.name, ':', x.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The binary variables that are encoded by non-numerical features are **OST_WEST_KZ ** and **CAMEO_DEU_2015 **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the categorical variables (except for **GREEN_AVANTGARDE**, which is already encoded as [0,1] binary) are encoded by one-hot encoding. Since categorical features does not have a specific ordering, the relative numerical values does not have any meaning, therefore the ordering effect needs to be removed by encoding these features as one-hot columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LP_LEBENSPHASE_FEIN : [15.0 21.0 3.0 nan 32.0 8.0 2.0 5.0 10.0 4.0 6.0 23.0 12.0 20.0 1.0 11.0\n",
      " 25.0 13.0 7.0 18.0 31.0 19.0 38.0 35.0 30.0 22.0 14.0 33.0 29.0 24.0 28.0\n",
      " 37.0 26.0 39.0 27.0 36.0 9.0 34.0 40.0 16.0 17.0]\n",
      "LP_LEBENSPHASE_GROB : [4.0 6.0 1.0 nan 10.0 2.0 3.0 5.0 7.0 12.0 11.0 9.0 8.0]\n",
      "PRAEGENDE_JUGENDJAHRE : [nan 14.0 15.0 8.0 3.0 10.0 11.0 5.0 9.0 6.0 4.0 2.0 1.0 12.0 13.0 7.0]\n",
      "WOHNLAGE : [nan 4.0 2.0 7.0 3.0 5.0 1.0 8.0 0.0]\n",
      "CAMEO_INTL_2015 : [nan '51' '24' '12' '43' '54' '22' '14' '13' '15' '33' '41' '34' '55' '25'\n",
      " '23' '31' '52' '35' '45' '44' '32']\n",
      "KBA05_BAUMAX : [nan 5.0 1.0 2.0 3.0 4.0]\n",
      "PLZ8_BAUMAX : [nan 1.0 2.0 4.0 5.0 3.0]\n"
     ]
    }
   ],
   "source": [
    "mixed = list(feat_info[feat_info['type'] == 'mixed']['attribute'].values)\n",
    "_ = azdias[mixed].apply(lambda x: print(x.name, ':', x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "dropped = set.intersection(set(outlier_columns), set(categoricals), set(mixed))\n",
    "one_hot = set(outlier_columns) - dropped\n",
    "cate_encode = list(set.union(set(categoricals),set(mixed)) \n",
    "                 - dropped - set(['GREEN_AVANTGARDE', 'PRAEGENDE_JUGENDJAHRE', 'CAMEO_INTL_2015']))\n",
    "azdias = pd.get_dummies(azdias, columns=cate_encode, dummy_na = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PRAEGENDE_JUGENDJAHRE**\n",
    "\n",
    "Dominating movement of person's youth (avantgarde vs. mainstream; east vs. west)\n",
    "\n",
    "- -1: unknown\n",
    "- 0: unknown\n",
    "- 1: 40s - war years (Mainstream, E+W)\n",
    "- 2: 40s - reconstruction years (Avantgarde, E+W)\n",
    "- 3: 50s - economic miracle (Mainstream, E+W)\n",
    "- 4: 50s - milk bar / Individualisation (Avantgarde, E+W)\n",
    "- 5: 60s - economic miracle (Mainstream, E+W)\n",
    "- 6: 60s - generation 68 / student protestors (Avantgarde, W)\n",
    "- 7: 60s - opponents to the building of the Wall (Avantgarde, E)\n",
    "- 8: 70s - family orientation (Mainstream, E+W)\n",
    "- 9: 70s - peace movement (Avantgarde, E+W)\n",
    "- 10: 80s - Generation Golf (Mainstream, W)\n",
    "- 11: 80s - ecological awareness (Avantgarde, W)\n",
    "- 12: 80s - FDJ / communist party youth organisation (Mainstream, E)\n",
    "- 13: 80s - Swords into ploughshares (Avantgarde, E)\n",
    "- 14: 90s - digital media kids (Mainstream, E+W)\n",
    "- 15: 90s - ecological awareness (Avantgarde, E+W)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_map = pd.DataFrame({-1: [np.nan, np.nan],\n",
    "                          0: [np.nan, np.nan],\n",
    "                          1: [40, 1],\n",
    "                          2: [40, 0],\n",
    "                          3: [50, 1],\n",
    "                          4: [50, 0],\n",
    "                          5: [60, 1],\n",
    "                          6: [60, 0],\n",
    "                          7: [60, 0],\n",
    "                          8: [70, 1],\n",
    "                          9: [70, 0],\n",
    "                          10: [80, 1],\n",
    "                          11: [80, 0],\n",
    "                          12: [80, 1],\n",
    "                          13: [80, 0],\n",
    "                          14: [90, 1],\n",
    "                          15: [90, 0],\n",
    "                        np.nan: [np.nan, np.nan]}).T\n",
    "\n",
    "# Create column that encodes decades\n",
    "decades_map = full_map[0].to_dict()\n",
    "azdias['DECADES'] = azdias['PRAEGENDE_JUGENDJAHRE'].map(decades_map)\n",
    "\n",
    "# Create column that encodes movements \n",
    "movements_map = full_map[1].to_dict()\n",
    "azdias['MOVEMENTS'] = azdias['PRAEGENDE_JUGENDJAHRE'].map(movements_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CAMEO_INTL_2015**\n",
    "\n",
    "German CAMEO: Wealth / Life Stage Typology, mapped to international code\n",
    "\n",
    "- -1: unknown\n",
    "- 11: Wealthy Households - Pre-Family Couples & Singles\n",
    "- 12: Wealthy Households - Young Couples With Children\n",
    "- 13: Wealthy Households - Families With School Age Children\n",
    "- 14: Wealthy Households - Older Families &  Mature Couples\n",
    "- 15: Wealthy Households - Elders In Retirement\n",
    "- 21: Prosperous Households - Pre-Family Couples & Singles\n",
    "- 22: Prosperous Households - Young Couples With Children\n",
    "- 23: Prosperous Households - Families With School Age Children\n",
    "- 24: Prosperous Households - Older Families & Mature Couples\n",
    "- 25: Prosperous Households - Elders In Retirement\n",
    "- 31: Comfortable Households - Pre-Family Couples & Singles\n",
    "- 32: Comfortable Households - Young Couples With Children\n",
    "- 33: Comfortable Households - Families With School Age Children\n",
    "- 34: Comfortable Households - Older Families & Mature Couples\n",
    "- 35: Comfortable Households - Elders In Retirement\n",
    "- 41: Less Affluent Households - Pre-Family Couples & Singles\n",
    "- 42: Less Affluent Households - Young Couples With Children\n",
    "- 43: Less Affluent Households - Families With School Age Children\n",
    "- 44: Less Affluent Households - Older Families & Mature Couples\n",
    "- 45: Less Affluent Households - Elders In Retirement\n",
    "- 51: Poorer Households - Pre-Family Couples & Singles\n",
    "- 52: Poorer Households - Young Couples With Children\n",
    "- 53: Poorer Households - Families With School Age Children\n",
    "- 54: Poorer Households - Older Families & Mature Couples\n",
    "- 55: Poorer Households - Elders In Retirement\n",
    "- XX: unknown\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "azdias['WEALTH'] = pd.to_numeric(azdias['CAMEO_INTL_2015'])//10\n",
    "azdias['LIFE_STAGE'] = pd.to_numeric(azdias['CAMEO_INTL_2015'])%10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two columns that contains more than 1 dimensions of data, **PRAEGENDE_JUGENDJAHRE** and **CAMEO_INTL_2015** are engineered in ways that could separate dimensions. The rest of the mixed values columns only contains 1 dimension, therefore are left as the same as their original states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outlier_columns = list(set(outlier_columns) - one_hot)\n",
    "azdias.drop((['CAMEO_INTL_2015', 'PRAEGENDE_JUGENDJAHRE'] + outlier_columns), axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_data(df, outlier_columns):\n",
    "    \"\"\"\n",
    "    Perform feature trimming, re-encoding, and engineering for demographics\n",
    "    data\n",
    "    \n",
    "    INPUT: Demographics DataFrame\n",
    "    OUTPUT: Trimmed and cleaned demographics DataFrame\n",
    "    \"\"\"\n",
    "    \n",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs\n",
    "    feat_info['missing_or_unknown'].replace({'[-1,X]': '[-1,\"X\"]',\n",
    "                                         '[-1,XX]': '[-1,\"XX\"]',\n",
    "                                          '[XX]': '[\"XX\"]'}, inplace = True)\n",
    "    encodings = feat_info.set_index('attribute')['missing_or_unknown'].to_dict()\n",
    "    encodings = {key: {k: np.nan for k in literal_eval(value)} for key, value in encodings.items()}\n",
    "    for columns, mapping in encodings.items():\n",
    "        df[columns].replace(mapping, inplace = True)\n",
    "    \n",
    "    # catgorical variables\n",
    "    categoricals = list(feat_info[feat_info['type'] == 'categorical']['attribute'].values)\n",
    "    mixed = list(feat_info[feat_info['type'] == 'categorical']['attribute'].values)\n",
    "    dropped = set.intersection(set(outlier_columns), set(categoricals), set(mixed))\n",
    "    one_hot = set(outlier_columns) - dropped\n",
    "    cate_encode = list(set.union(set(categoricals),set(mixed)) \n",
    "                 - dropped - set(['GREEN_AVANTGARDE', 'PRAEGENDE_JUGENDJAHRE', 'CAMEO_INTL_2015']))\n",
    "    df = pd.get_dummies(df, columns=cate_encode, dummy_na = True)\n",
    "    \n",
    "    # mixed variables - PRAEGENDE_JUGENDJAHRE\n",
    "    full_map = pd.DataFrame({-1: [np.nan, np.nan],\n",
    "                              0: [np.nan, np.nan],\n",
    "                              1: [40, 1],\n",
    "                              2: [40, 0],\n",
    "                              3: [50, 1],\n",
    "                              4: [50, 0],\n",
    "                              5: [60, 1],\n",
    "                              6: [60, 0],\n",
    "                              7: [60, 0],\n",
    "                              8: [70, 1],\n",
    "                              9: [70, 0],\n",
    "                              10: [80, 1],\n",
    "                              11: [80, 0],\n",
    "                              12: [80, 1],\n",
    "                              13: [80, 0],\n",
    "                              14: [90, 1],\n",
    "                              15: [90, 0],\n",
    "                              np.nan: [np.nan, np.nan]}).T\n",
    "\n",
    "    # Create column that encodes decades\n",
    "    decades_map = full_map[0].to_dict()\n",
    "    df['DECADES'] = df['PRAEGENDE_JUGENDJAHRE'].map(decades_map)\n",
    "\n",
    "    # Create column that encodes movements \n",
    "    movements_map = full_map[1].to_dict()\n",
    "    df['MOVEMENTS'] = df['PRAEGENDE_JUGENDJAHRE'].map(movements_map)\n",
    "    \n",
    "    # mixed variables - CAMEO_INTL_2015\n",
    "    df['WEALTH'] = pd.to_numeric(df['CAMEO_INTL_2015'])//10\n",
    "    df['LIFE_STAGE'] = pd.to_numeric(df['CAMEO_INTL_2015'])%10\n",
    "    \n",
    "    # drop extra columns\n",
    "    outlier_columns = list(set(outlier_columns) - one_hot)\n",
    "    df.drop(['CAMEO_INTL_2015', 'PRAEGENDE_JUGENDJAHRE'] + outlier_columns, axis = 1, inplace = True)\n",
    "    \n",
    "    # Return the cleaned dataframe.\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run azdias through clean data and make sure the columns are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "azdias = clean_data(azdias, outlier_columns)\n",
    "feature_names = list(azdias.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing data impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imputer = Imputer()\n",
    "imputer_model = imputer.fit(azdias)\n",
    "azdias_impute = pd.DataFrame(imputer_model.transform(azdias), columns = feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply a standard scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scalar = StandardScaler()\n",
    "scalar_model = scalar.fit(azdias_impute)\n",
    "azdias_scaled = pd.DataFrame(scalar_model.transform(azdias_impute), columns = feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing data are imputed first, then the data is scaled. Since the rows that have large number of missing data are dropped before, imputing missing values will not have a large impact on the overall distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply PCA to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA()\n",
    "pca_model= pca.fit(azdias_scaled)\n",
    "azdias_pca = pca_model.transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot % variance explained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def variance_plot(pca):\n",
    "    '''\n",
    "    Creates a variance plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "    \n",
    "    OUTPUT: number of components suggested\n",
    "    '''\n",
    "    num_components=len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(15, 8))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind[:150], vals[:150])\n",
    "    ax.plot(ind[:150], cumvals[:150])\n",
    "    try:\n",
    "        for i in np.arange(0, 150, 10):\n",
    "            ax.annotate(r\"%s%%\" % ((str(cumvals[i]*100)[:4])), (ind[i] , cumvals[i] + 0.02), \n",
    "                        va=\"bottom\", ha=\"center\", fontsize=12)\n",
    "\n",
    "        ax.xaxis.set_tick_params(width=0)\n",
    "        ax.yaxis.set_tick_params(width=2, length=12)\n",
    "        ax.set_xlim([-1, 150])\n",
    "        ax.set_xlabel(\"Principal Component\")\n",
    "        ax.set_ylabel(\"Variance Explained (%)\")\n",
    "        plt.title('Explained Variance Per Principal Component')\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    fit_components = np.argmax(cumvals > 0.85)\n",
    "    return fit_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Afv75Zw4cOMDmzZvJz8/n/vvvr7aevn37cuTIEf75z39SVlZGfHw8+/bto6ioCIA33niD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\ncSJkHF6LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac400c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_components = variance_plot(pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the number of components that will explain at least 85% of the total variance, then reapply PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(fit_components)\n",
    "pca_model= pca.fit(azdias_scaled)\n",
    "azdias_pca = pca_model.transform(azdias_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the scree plot above, I selected the number of components that will represent at least 95% of the total vairances, which gives the number of components to be 120"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getComponentLinkage(pca, component_number, feature_names):\n",
    "    \"\"\"Prints the sorted relevancy of each feature given the component\n",
    "       \n",
    "       INPUT: pca object, the component number, feature names\n",
    "    \"\"\"\n",
    "    component_linkage = pd.DataFrame(pca.components_, columns = feature_names)\n",
    "    relevancy = pd.DataFrame(component_linkage.iloc[component_number + 1,:]\n",
    "                             .transpose().sort_values(ascending = False))\n",
    "    relevancy.columns = ['weights']\n",
    "    \n",
    "    return relevancy.head(5), relevancy.tail(5).sort_values('weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualizeRelevancy(head_df, tail_df, PC_number):\n",
    "    \"\"\"Plot the top and bottom weights of PCs  \n",
    "       \n",
    "       INPUT: head, tail dfs\n",
    "    \"\"\"\n",
    "    \n",
    "    # create subplots\n",
    "    ind = np.arange(len(head_df))\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, sharey = True, figsize = (16, 5))\n",
    "    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)\n",
    "    plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)\n",
    "    ax1.set_title('Highest Positive Relevancy')\n",
    "    ax2.set_title('Highest Negative Relevancy')\n",
    "    sns.barplot(x = head_df.index, y = head_df['weights'],  ax = ax1)\n",
    "    sns.barplot(x = tail_df.index, y = tail_df['weights'], ax = ax2, data = tail_df)\n",
    "    \n",
    "    for i in range(len(head_df)):\n",
    "        ax1.annotate(r\"%s%%\" % ((str(head_df['weights'][i]*100)[:4])), (ind[i] , -0.06), \n",
    "                    va=\"bottom\", ha=\"center\", fontsize=12)\n",
    "        ax2.annotate(r\"%s%%\" % ((str(tail_df['weights'][i]*100)[:5])), (ind[i] , 0.02), \n",
    "                    va=\"bottom\", ha=\"center\", fontsize=12)\n",
    "    plt.ylabel('Weights')\n",
    "    plt.suptitle('PC' + str(PC_number))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First component relevancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZjBw4EBatmzJxhtvzMknn8yTTz5Zpxi++uorLrvsMm666Sbef/99unTpQps2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\n8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac3c1c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1_head, c1_tail = getComponentLinkage(pca_model, 1, feature_names)\n",
    "visualizeRelevancy(c1_head, c1_tail, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second component relevancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JNg38ROB2C11VqO3xIVIetaJwV8HeFK5xvZ26H48gARARgyhcj/VXlr9WSpK0cvLaF7en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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fab187af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c2_head, c2_tail = getComponentLinkage(pca, 2, feature_names)\n",
    "visualizeRelevancy(c2_head, c2_tail, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third component relevancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R5s2bs84663D00UdXGTK22mqrSq+kW9H//vc/zjvvPK666iree+89unbtSuvW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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac20c64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c3_head, c3_tail = getComponentLinkage(pca, 3, feature_names)\n",
    "visualizeRelevancy(c3_head, c3_tail, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First Component**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first component is mix a person's personality topology, financial topology and age factors. Most of the weights are relatively uniform. Esitmated age has opposite weights with person's birth decades, which aligns with our intuition\n",
    "\n",
    "Top positively correlated features with PC1,\n",
    "\n",
    "* Energy comsuption topology (**ZABEOTYP - Fairly Supplied**) ~ 21.8 %\n",
    "* Finacial topology -be prepared (**FINANZ_VORSORGER**) ~17.7 %\n",
    "* Estimated age (**ALTERSKATEGORIE_GROB**) ~ 17.4 %\n",
    "\n",
    "Top negatively correlated features with PC1,\n",
    "\n",
    "* Person's birth decades (**DECADES**)  ~ -21.5 % \n",
    "* Traditional minded (**SEMIO_TRADEV**)  ~ -19.2 %\n",
    "* Cultural minded (**SEMIO_KULT**) ~ -18.1 %\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Second Component**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second component is mostly related to genders of customers and perosnality traits. The male and female represents the largest positive and negative weights.\n",
    "\n",
    "Top positively correlated features with PC2,\n",
    "\n",
    "\n",
    "* Gender - Male (**ANREDE_KZ_1**) ~33.3 %\n",
    "* Family minded (**SEMIO_FAM**) ~ 17.4 %\n",
    "* Dreamful personalities (**SEMIO_VERT**) ~ 17.7 %\n",
    "\n",
    "the negatively correlated features with PC2,\n",
    "\n",
    "* Gender - Male (**ANREDE_KZ_2**) ~ - 33.3%\n",
    "* Combative personalities (**SEMIO_KAEM**) ~ - 28.4%\n",
    "* Dominated personalities (**SEMIO_DOM**) ~ - 25.8%\n",
    "\n",
    "Opposite personalities traits are captured in PC2, as more peaceful personalities have large positive weights and more aggressive personalities have large negative weights. Personalities has significant effects on personal behaviors, the weights in second component valided the point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Third Component**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third components is describing the person's life stages, family traits and income levels. \n",
    "\n",
    "Top positively correlated feattures with PC3,\n",
    "\n",
    "* Life stages - rough scale (**LP_LEBENSPHASE_GROB**) ~ 25.2 %\n",
    "* Life stages - fine scale (**LP_LEBENSPHASE_FEIN**) ~ 25.0 %\n",
    "* Number of adults in house (**ANZ_PERSONEN**) ~ 21.9 %\n",
    "\n",
    "Top negatively correlated features with PC3,\n",
    "\n",
    "* Family type - fine scale (**LP_FAMILIE_FEIN**) ~ - 23.4 %\n",
    "* Family type - rough scale (**LP_FAMILIE_GROB**) ~ - 23.4 %\n",
    "* Movements (**MOVEMENTS**) ~ - 18.6 %\n",
    "\n",
    "Different types of families, life stages and the immigrating activities will also affects people's behaviors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_nums =  [1, 4, 8] + list(np.arange(10, 36, 5)) \n",
    "avg_d = []\n",
    "for i in cluster_nums:\n",
    "    # run k-means clustering on the data\n",
    "    kmeans = KMeans(i)\n",
    "    kmeans.fit(azdias_pca)\n",
    "    # compute the average within-cluster distances\n",
    "    avg_d.append(-kmeans.score(azdias_pca))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scores and find the right number of clusters using the elbow method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9MXFcm9ef2jn+hdk+vPaf1Z0TGp1GkZCZWTO8kpbnzWw+XrF5ZXHopcBE51wHvPr8pyyY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac212b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(cluster_nums, avg_d,  linestyle='--', \n",
    "         marker='o')\n",
    "\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Average Distance')\n",
    "plt.title('Average Distance Between Points and Cluster Center VS. Number of Clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, though there is no obvious elbow in the dataset, the number of clusters could be chosen as 25. The below selection also shows 25 is the number of clusters that should be implemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_change = np.diff(avg_d)\n",
    "n_clusters = cluster_nums[np.argmin(abs(d_change)) - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refit the K means cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters)\n",
    "kmeans_best = kmeans.fit(azdias_pca)\n",
    "azdias_impute['clusters'] = kmeans_best.predict(azdias_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the elbow method, the average distance does not decrease a lot after number of clusters reaches 20. The appropriate number of clusters is therefore chosen to be 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply preprocessing, feature transformation, and clustering from the general demographics onto the customer data, \n",
    "obtaining cluster predictions for the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "customers = clean_data(customers, outlier_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit PCA using the same sklearn object used to fit the demographics data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform_data(df, feature_names, imputer, scalar, pca):\n",
    "    \"\"\"Impute, Scale and find PCA of the input data\"\"\"\n",
    "    if set(feature_names) != set(df.columns):\n",
    "        missing_cols = list(set(feature_names) - set(df.columns))\n",
    "        df = pd.concat([df, pd.DataFrame(columns = missing_cols)])\n",
    "        df = df.reindex(columns = feature_names)\n",
    "        df[missing_cols] = 0\n",
    "    df_imputed = imputer.transform(df)\n",
    "    df_scaled = scalar.transform(df_imputed)\n",
    "    df_pca = pca.transform(df_scaled)\n",
    "    \n",
    "    return df_pca, pd.DataFrame(df_imputed, columns=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "customers_pca, customers_impute = transform_data(customers, feature_names, imputer_model, scalar_model, pca_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the clustering object on customer level data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "customers_impute['clusters'] = kmeans_best.predict(customers_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group count on all the data in different clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# general demographics\n",
    "azdias_clusters_cnt = pd.DataFrame(azdias_impute.groupby(['clusters'], as_index= False).count().iloc[:, :2])\n",
    "azdias_clusters_cnt.iloc[:, 1] = azdias_clusters_cnt.iloc[:, 1]/sum(azdias_clusters_cnt.iloc[:, 1]) *100\n",
    "azdias_clusters_cnt['dataset'] = 'General'\n",
    "\n",
    "# customers demographics\n",
    "customers_clusters_cnt = pd.DataFrame(customers_impute.groupby(['clusters'], as_index= False).count().iloc[:, :2])\n",
    "customers_clusters_cnt.iloc[:, 1] = customers_clusters_cnt.iloc[:, 1]/sum(customers_clusters_cnt.iloc[:, 1]) *100\n",
    "customers_clusters_cnt['dataset'] = 'Customers'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate clusters together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters_cnt = pd.concat([azdias_clusters_cnt, customers_clusters_cnt])\n",
    "clusters_cnt.columns = ['clusters','percentage %','dataset'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the % of datapoints in each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AnsD0AcxPkiSpMlpWrEXEZcAtwGYRMTsiPlxOOpROh0AjYt2I+F05+Abgpoi4\nE/gL8NvM/N9W5SlJklRlrbwa9L1djD+izrjHgH3K5w8Do1qVlyRJ0tLEXzCQJEmqMIs1SZKkCrNY\nkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaow\nizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqMIs1SZKk\nCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMk\nSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqrCWFWsRcWFEPBER02vGnRQRf4+IaeVjny7m3Tsi\n7o+Iv0bE8a3KUZIkqepa2bN2EbB3nfHfz8zR5eN3nSdGxArAWcC7gC2A90bEFi3MU5IkqbJaVqxl\n5g3A032YdXvgr5n5cGb+C/g5sF9Tk5MkSVpKtOOctaMj4q7yMOkadaavB8yqGZ5djqsrIo6MiCkR\nMWXu3LnNzlWSJKmtVhzg5Z0NfB3I8u93gQ91ahN15suuAmbmecB5AGPHju2ynSRJUis9evLWPbbZ\n4MS7ex13QHvWMnNOZi7KzFeA8ykOeXY2G1i/Zng48NhA5CdJklQ1A1qsRcQ6NYP7A9PrNJsMbBoR\nG0XEYOBQ4MqByE+SJKlqWnYYNCIuA3YD1o6I2cBXgd0iYjTFYc2ZwMfKtusCP8rMfTJzYUQcDVwN\nrABcmJn3tCpPSZKkKmtZsZaZ760z+oIu2j4G7FMz/Dtgidt6SJIkLW/8BQNJkqQKs1iTJEmqMIs1\nSZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqz\nWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmq\nMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqsIaLtYj4r4i4LSKmRcQnWpmU\nJEmSCl0WaxExqtOoDwA7AmOAj7cyKUmSJBVW7GbaJyIigBMz8x/ALOAbwCvAYwORnCRJ0vKuy2It\nMz9W9q6dGxFTgK8AbwNWBr4+QPlJkiQt17o9Zy0z78zM/YBpwJXAOpl5ZWa+PCDZSZIkLee6O2ft\nqIi4IyJuB1YB9gbWiIirI2LnActQkiRpOdZdz9onMnMbiosKxmfmwsw8HTgU2H9AspMkSVrOdXeB\nwd8j4uvAa4H7OkZm5jPAZ1qANgENAAAauElEQVSdmCRJkrov1vYD9gIWANf2NnBEXAjsCzyRmVuV\n404F/gv4F/AQ8MHMfLbOvDOB+cAiYGFmju3t8iVJkpYFXR4Gzcx/ZeZVmfm/mbmoD7EvojjPrda1\nwFaZORJ4APhiN/O/PTNHW6hJkqTlWct+biozbwCe7jTumsxcWA7eCgxv1fIlSZKWBe38bdAPAb/v\nYloC10TE1Ig4srsgEXFkREyJiClz585tepKSJEnt1FCxFhH/EREfLJ8Pi4iN+rPQiPgysBC4tIsm\nO2XmGOBdwCcjYpeuYmXmeZk5NjPHDhs2rD9pSZIkVU6PxVpEfBX4Av8+v2wQ8NO+LjAiDqe48OB9\nmZn12mTmY+XfJ4CJwPZ9XZ4kSdLSrJGetf2BccALsLiQGtqXhUXE3hSF37jMfLGLNqtExNCO58Ce\nwPS+LE+SJGlp10ix9q+yByxhcQHVo4i4DLgF2CwiZkfEh4EzKQq9ayNiWkScU7ZdNyJ+V876BuCm\niLgT+Avw28z83169KkmSpGVEd/dZ6/CLiDgXWD0iPkpxYcD5Pc2Ume+tM/qCLto+BuxTPn8YGNVA\nXpIkScu8Hou1zPxORLwTeA7YDDgxM3t9k1xJkiT1XiM9a5TFmQWaJEnSAOuxWIuI+ZTnq9WYB0wB\nPlsetpQkSVILNNKz9j3gMeBnQACHAm8E7gcuBHZrVXKSJEnLu0auBt07M8/NzPmZ+Vxmngfsk5mX\nA2u0OD9JkqTlWiPF2isRcXBEvKZ8HFwzre5NbSVJktQcjRRr7wM+ADwBzCmfvz8iXgsc3cLcJEmS\nlnuN3LrjYeC/uph8U3PTkSRJUq1GrgYdAnwY2BIY0jE+Mz/UwrwkSZJEY4dBL6G4+nMv4E/AcGB+\nK5OSJElSoZFi7U2Z+RXghcy8GPhPYOvWpiVJkiRorFhbUP59NiK2AlYDRrQsI0mSJC3WyE1xz4uI\nNYATgCuBVYGvtDQrSZIkAY0Va9dl5jPADcDGABGxUUuzkiRJEtDYYdBf1Rk3odmJSJIkaUld9qxF\nxFsobtexWkQcUDPpddTcwkOSJEmt091h0M2AfYHVefVNcecDH21lUpIkSSp0Waxl5q+BX0fEWzPz\nlgHMSZIkSaVGLjD4a0R8ieJ2HYvb+wsGkiRJrddIsfZr4Ebg/4BFrU1HkiRJtRop1lbOzC+0PBNJ\nkiQtoZFbd/wmIvZpeSaSJElaQiPF2qcpCraXIuK5iJgfEc+1OjFJkiQ1cBg0M4cORCKSJElaUo89\na1F4f0R8pRxePyK2b31qkiRJauQw6A+BtwL/rxx+HjirZRlJkiRpsUauBt0hM8dExB0AmflMRAxu\ncV6SJEmisZ61BRGxApAAETEMeKWlWUmSJAlorFg7HZgIvD4ivgHcBHyzpVlJkiQJaOxq0EsjYiqw\nBxDAuzNzRsszkyRJUs/FWkTsCNyTmWeVw0MjYofMvK3l2UmSJC3nGjkMejbFFaAdXijHSZIkqcUa\nKdYiM7NjIDNfobGrSCVJktRPjRRrD0fEpyJiUPn4NPBwI8Ej4sKIeCIipteMWzMiro2IB8u/a3Qx\n7+Flmwcj4vDGXo4kSdKypZFi7SjgbcDfgdnADsCRDca/CNi707jjgesyc1PgunL4VSJiTeCr5bK2\nB77aVVEnSZK0LOv2cGZ5f7X3ZeahfQmemTdExIhOo/cDdiufXwxcD3yhU5u9gGsz8+kyj2spir7L\n+pKHJEnS0qrbnrXMXERRXDXTGzLz8TL+48Dr67RZD5hVMzy7HLeEiDgyIqZExJS5c+c2OVVJkqT2\nauQw6M0RcWZE7BwRYzoeLc4r6ozLOuPIzPMyc2xmjh02bFiL05IkSRpYjVzV+bby78k14xLYvY/L\nnBMR62Tm4xGxDvBEnTaz+fehUoDhFIdLJUmSliuN/ILB25u8zCuBw4FTyr+/rtPmauCbNRcV7Al8\nscl5SJIkVV6Ph0Ej4g0RcUFE/L4c3iIiPtxI8Ii4DLgF2CwiZpfznQK8MyIeBN5ZDhMRYyPiRwDl\nhQVfByaXj5M7LjaQJElanjRyGPQi4MfAl8vhB4DLgQt6mjEz39vFpD3qtJ0CfKRm+ELgwgbykyRJ\nWmY1coHB2pn5C+AVgMxcCCxqaVaSJEkCGivWXoiItSivxix/2H1eS7OSJEkS0Nhh0M9QXBSwSUTc\nDAwDDmxpVpIkSQIauxr09ojYFdiM4v5n92fmgpZnJkmSpJ6LtYgYAnwC+A+KQ6E3RsQ5mflSq5OT\nJEla3jVyGPQnwHzgjHL4vcAlwEGtSkqSJEmFRoq1zTJzVM3wHyPizlYlJEmSpH9r5GrQO8orQAGI\niB2Am1uXkiRJkjo00rO2A3BYRDxaDm8AzIiIu4HMzJEty06SJGk510ixtnfLs5AkSVJdjdy6428D\nkYgkSZKW1Mg5a5IkSWoTizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJ\nkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNY\nkyRJqjCLNUmSpApbsd0JSJKkf3v05K17bLPBiXcPQCaqCnvWJEmSKsyeNUlS29mbJHXNnjVJkqQK\ns2dNkiR1advxP+l2+tRTDxugTJZfA96zFhGbRcS0msdzEXFspza7RcS8mjYnDnSekiRJVTDgPWuZ\neT8wGiAiVgD+Dkys0/TGzNx3IHOTJEmqmnafs7YH8FBm/q3NeUiSJFVSu4u1Q4HLupj21oi4MyJ+\nHxFbdhUgIo6MiCkRMWXu3LmtyVKSJKlN2lasRcRgYBzwyzqTbwc2zMxRwBnApK7iZOZ5mTk2M8cO\nGzasNclKkiS1STuvBn0XcHtmzuk8ITOfq3n+u4j4YUSsnZlPDmiGA6SnK23Aq20kqS+8f5uWBe08\nDPpeujgEGhFvjIgon29PkedTA5ibJElSJbSlZy0iVgbeCXysZtxRAJl5DnAg8PGIWAj8Ezg0M7Md\nuUqSJLVTW4q1zHwRWKvTuHNqnp8JnDnQeUmSJFVNu68GlSRJUjcs1iRJkirMYk2SJKnC/CF3aSnm\nbQkkadlnz5okSVKF2bMmVVQjN0ueOHQAEpEktZU9a5IkSRVmsSZJklRhFmuSJEkVZrEmSZJUYRZr\nkiRJFWaxJkmSVGHeukN90shtJaaeetgAZCJJ0rLNnjVJkqQKs2dN6id7GaVlT0/7tfu0BpI9a5Ik\nSRVmsSZJklRhFmuSJEkVZrEmSZJUYRZrkiRJFWaxJkmSVGHeukNS03gbE0lqPnvWJEmSKsyetV6w\n10CSJA00e9YkSZIqzJ41SdJSqZGjHROHDkAiUovZsyZJklRh9qxpqfPoyVv32GaDE+8egEwkSWo9\ne9YkSZIqzGJNkiSpwizWJEmSKsxiTZIkqcIs1iRJkiqsbcVaRMyMiLsjYlpETKkzPSLi9Ij4a0Tc\nFRFj2pGnJElSO7X71h1vz8wnu5j2LmDT8rEDcHb5V1I/eTNRSVp6VPkw6H7AT7JwK7B6RKzT7qQk\nSZIGUjuLtQSuiYipEXFknenrAbNqhmeX414lIo6MiCkRMWXu3LktSlWSJKk92lms7ZSZYygOd34y\nInbpND3qzJNLjMg8LzPHZubYYcOGtSJPSZKktmlbsZaZj5V/nwAmAtt3ajIbWL9meDjw2MBkJ0mS\nVA1tKdYiYpWIGNrxHNgTmN6p2ZXAYeVVoTsC8zLz8QFOVZIkqa3adTXoG4CJEdGRw88y838j4iiA\nzDwH+B2wD/BX4EXgg23KVZIkqW3aUqxl5sPAqDrjz6l5nsAnBzIvSZKkqmn3fdYkqa0auefc1FMP\nG4BMJKk+izVJkgaIN6RWX1T5priSJEnLPXvWJElSnz168tY9ttngxLsHIJNllz1rkiRJFWaxJkmS\nVGEeBpVUeV6xKWl5Zs+aJElShVmsSZIkVZiHQVUp3oNIklRF7fx8slhTy3g5tyRJ/edhUEmSpAqz\nWJMkSaowizVJkqQKs1iTJEmqMIs1SZKkCrNYkyRJqjCLNUmSpAqzWJMkSaowizVJkqQKs1iTJEmq\nMIs1SZKkCrNYkyRJqjB/yF2S1FLbjv9Jj20mDh2ARKSllMXaMq6Rf5JTTz1sADKRJEl94WFQSZKk\nCrNYkyRJqjCLNUmSpArznDVJy4RHT966xzYbnHj3AGQiSc1lsSY/5CRJqjCLNUlainiFt7T88Zw1\nSZKkCrNYkyRJqrABL9YiYv2I+GNEzIiIeyLi03Xa7BYR8yJiWvk4caDzlCRJqoJ2nLO2EPhsZt4e\nEUOBqRFxbWbe26ndjZm5bxvykyRJqowBL9Yy83Hg8fL5/IiYAawHdC7WWsorICVJ0tKgreesRcQI\nYBvgtjqT3xoRd0bE7yNiy25iHBkRUyJiyty5c1uUqSRJUnu0rViLiFWBXwHHZuZznSbfDmyYmaOA\nM4BJXcXJzPMyc2xmjh02bFjrEpYkSWqDthRrETGIolC7NDOv6Dw9M5/LzOfL578DBkXE2gOcpiRJ\nUtu142rQAC4AZmTm97po88ayHRGxPUWeTw1clpIkSdXQjqtBdwI+ANwdEdPKcV8CNgDIzHOAA4GP\nR8RC4J/AoZmZbchVkiSprdpxNehNQPTQ5kzgzIHJSJIkqbr8BQNJkqQKs1iTJEmqsHacs9Zy247/\nSY9tJg4dgEQkSZL6yZ41SZKkClsme9YkSZK6srT95KQ9a5IkSRVmsSZJklRhFmuSJEkVZrEmSZJU\nYV5gIElqWE8nZlfppOxWWtpOUNfSzZ41SZKkCrNnTZIkLTOWxRvjW6xJUg885CWpnTwMKkmSVGEW\na5IkSRVmsSZJklRhFmuSJEkV5gUGktQmXrggqRH2rEmSJFWYxZokSVKFWaxJkiRVmMWaJElShXmB\nQZN5wrAkSWome9YkSZIqzGJNkiSpwizWJEmSKsxiTZIkqcK8wEDLjW3H/6THNlNPPWwAMpGk5vL/\n27LNYk2SBDT2gT9x6AAkIulVLNaWEt4SZOnm+ydJ6iuLNamGRVXruY4lqXcs1iRpGWNBLC1bvBpU\nkiSpwuxZk6QW8GR9Sc3Slp61iNg7Iu6PiL9GxPF1pq8UEZeX02+LiBEDn6UkSVL7DXjPWkSsAJwF\nvBOYDUyOiCsz896aZh8GnsnMN0XEocC3gEMGOldJkpYVnsu49GrHYdDtgb9m5sMAEfFzYD+gtljb\nDzipfD4BODMiIjNzIBOVJEntY4FZiIGufyLiQGDvzPxIOfwBYIfMPLqmzfSyzexy+KGyzZN14h0J\nHFkObgbc32AqawNLxGuCpS1uK2MvbXFbGXtpi9vK2Etb3FbGXtritjK2cVsfe2mL28rYVYm7YWYO\n66lRO3rWos64zhVjI22KkZnnAef1OomIKZk5trfzLWtxWxl7aYvbythLW9xWxl7a4rYy9tIWt5Wx\njdv62Etb3FbGXtrituMCg9nA+jXDw4HHumoTESsCqwFPD0h2kiRJFdKOYm0ysGlEbBQRg4FDgSs7\ntbkSOLx8fiDwB89XkyRJy6MBPwyamQsj4mjgamAF4MLMvCciTgamZOaVwAXAJRHxV4oetUNbkEqv\nD50uo3FbGXtpi9vK2Etb3FbGXtritjL20ha3lbGN2/rYS1vcVsZequIO+AUGkiRJapw/NyVJklRh\nFmuSJEkVttwVaz391FU/4l4YEU+U94hrmohYPyL+GBEzIuKeiPh0k+IOiYi/RMSdZdyvNSNuTfwV\nIuKOiPhNk+POjIi7I2JaRExpYtzVI2JCRNxXruu3NinuZmWuHY/nIuLYJsU+rnzvpkfEZRExpElx\nP13GvKe/udbbLyJizYi4NiIeLP+u0aS4B5U5vxIRfbp0vou4p5bbxV0RMTEiVm9i7K+XcadFxDUR\nsW4z4tZM+1xEZESs3aR8T4qIv9dsz/v0Nm53OUfEMeX/53si4ttNyvnymnxnRsS0JsUdHRG3dvwv\niojtexu3m9ijIuKW8n/dVRHxuj7ErfvZ0d/9r5u4/dr/uonb7/2vm9j92v+6ilszvc/73xIyc7l5\nUFzQ8BCwMTAYuBPYokmxdwHGANObnPM6wJjy+VDggWbkTHEvu1XL54OA24Adm5j3Z4CfAb9p8vqY\nCazdgm3jYuAj5fPBwOotWMYKwD8oboLY31jrAY8Ary2HfwEc0YS4WwHTgZUpLkD6P2DTfsRbYr8A\nvg0cXz4/HvhWk+JuTnFj7OuBsU3Md09gxfL5t/qSbzexX1fz/FPAOc2IW45fn+JCrr/1ZZ/pIt+T\ngM81YTurF/vt5fa2Ujn8+mati5rp3wVObFK+1wDvKp/vA1zfxHUxGdi1fP4h4Ot9iFv3s6O/+183\ncfu1/3UTt9/7Xzex+7X/dRW3HO7X/tf5sbz1rC3+qavM/BfQ8VNX/ZaZN9CCe8Fl5uOZeXv5fD4w\ng+KDur9xMzOfLwcHlY+mXG0SEcOB/wR+1Ix4rVZ+a92F4ipkMvNfmflsCxa1B/BQZv6tSfFWBF4b\nxb0IV2bJ+xX2xebArZn5YmYuBP4E7N/XYF3sF/tRFMeUf9/djLiZOSMzG/0Fk97EvaZcFwC3Utwb\nslmxn6sZXIU+7IPd/O/5PvD5vsTsIW6/dRH748Apmfly2eaJJsUFICICOBi4rElxE+jo8VqNPu5/\nXcTeDLihfH4t8J4+xO3qs6Nf+19Xcfu7/3UTt9/7Xzex+7X/9fD53K/9r7PlrVhbD5hVMzybJhQ+\nAyUiRgDbUPSCNSPeCuUhgSeAazOzKXGB0yg20leaFK9WAtdExNQofmqsGTYG5gI/juLQ7Y8iYpUm\nxa51KH34oKgnM/8OfAd4FHgcmJeZ1zQh9HRgl4hYKyJWpugxWL+HeXrrDZn5OBT/7IDXNzl+K30I\n+H0zA0bENyJiFvA+4MQmxRwH/D0z72xGvE6OLg8dXdjbQ2g9eDOwc0TcFhF/iojtmhgbYGdgTmY+\n2KR4xwKnlu/dd4AvNikuFPvhuPL5QfRzH+z02dG0/a/Zn0kNxO33/tc5drP2v9q4rdj/lrdireGf\nsaqaiFgV+BVwbKdvA32WmYsyczTFN5XtI2Kr/saMiH2BJzJzar8TrG+nzBwDvAv4ZETs0oSYK1Ic\nhjg7M7cBXqA4PNA0UdwAehzwyybFW4PiG/JGwLrAKhHx/v7GzcwZFIcargX+l+JUgYXdzrSciIgv\nU6yLS5sZNzO/nJnrl3GP7ql9T8oi+8s0qfDr5GxgE2A0xZeE7zYx9orAGsCOwHjgF2VvWLO8lyZ9\nWSp9HDiufO+Oo+yZb5IPUfx/m0pxeO1ffQ3Uis+OdsRtxv5XL3Yz9r/auGWOTd//lrdirZGfuqqc\niBhEsSFcmplXNDt+ecjvemDvJoTbCRgXETMpDjPvHhE/bUJcADLzsfLvE8BEikPb/TUbmF3TsziB\nonhrpncBt2fmnCbFewfwSGbOzcwFwBXA25oRODMvyMwxmbkLxeGZZvVEdJgTEesAlH97fbhroEXE\n4cC+wPuyPCGlBX5GHw531bEJRRF/Z7kfDgduj4g39jdwZs4pv+S9ApxPc/a/DrOBK8pTNP5C0TPf\n/xOzWfyzhQcAlzcjXulwiv0Oii9hTVsXmXlfZu6ZmdtSFJgP9SVOF58d/d7/WvWZ1FXcZux/DeTc\np/2vTtyW7H/LW7HWyE9dVUr5zfICYEZmfq+JcYd1XFUTEa+l+PC/r79xM/OLmTk8M0dQrN8/ZGa/\ne3wAImKViBja8ZzixNN+X32bmf8AZkXEZuWoPYB7+xu3k2Z/q38U2DEiVi63kT0ozpfot4h4ffl3\nA4oPuGbmDa/+ObnDgV83OX5TRcTewBeAcZn5YpNjb1ozOI7m7IN3Z+brM3NEuR/OpjgJ+h/9jd3x\nIV/anybsfzUmAbuXy3kzxYU+TzYp9juA+zJzdpPiQfFFf9fy+e408UtNzT74GuAE4Jw+xOjqs6Nf\n+18LP5Pqxm3G/tdN7H7tf/Xitmz/y35eobC0PSjOwXmA4pvKl5sY9zKKwwILyjfnw02K+x8Uh2rv\nAqaVj32aEHckcEcZdzp9uEKqgWXsRhOvBqU4t+zO8nFPk9+/0cCUcn1MAtZoYuyVgaeA1Zq8fr9G\n8c9lOnAJ5VV0TYh7I0WxeiewRz9jLbFfAGsB11F8uF0HrNmkuPuXz18G5gBXNynuXynOde3Y/3p9\nxWY3sX9Vvn93AVdRnPTc77idps+kb1eD1sv3EuDuMt8rgXWauC4GAz8t18ftwO7NWhfARcBRTd6O\n/wOYWu4ntwHbNjH2pyk+px4ATqH8taFexq372dHf/a+buP3a/7qJ2+/9r5vY/dr/uorbjP2v88Of\nm5IkSaqw5e0wqCRJ0lLFYk2SJKnCLNYkSZIqzGJNkiSpwizWJEmSKsxiTdJyISJOiojP9WG+1SPi\nE63ISZIaYbEmSd1bHehVsRYF/79Kagr/mUhaJkXEYeUPjt8ZEZd0mnZ9RIwtn69d/iwMEbFlRPwl\nIqaV825KcVPSTcpxp5btxkfE5LLN18pxIyJiRkT8kOKmrutHxEURMT0i7o6I4wbw5UtahqzY7gQk\nqdkiYkuKH1PeKTOfjIg1gU81MOtRwA8y89LyJ+lWAI4HtsrM0WXsPYFNKX4LMoArI2IXip8A2wz4\nYGZ+IiK2pbgj+lblfKs391VKWl7YsyZpWbQ7MCEznwTIzKcbnO8W4EsR8QVgw8z8Z502e5aPOyh6\n0N5CUbwB/C0zby2fPwxsHBFnlL9v+FzfXoqk5Z3FmqRlUVD8Zl9XFvLv/39DOkZm5s8oftD5n8DV\nEbF7F7H/JzNHl483ZeYF5bQXamI9A4wCrgc+Cfyoj69F0nLOYk3Ssug64OCIWAugPAxaayawbfn8\nwI6REbEx8HBmnk7xQ+UjgfnA0Jp5rwY+FBGrlvOsFxGv75xARKwNvCYzfwV8BRjThNclaTnkOWuS\nljmZeU9EfAP4U0QsojhkObOmyXeAX0TEB4A/1Iw/BHh/RCwA/gGcnJlPR8TNETEd+H1mjo+IzYFb\nIgLgeeD9wKJOaawH/LjmqtAvNvdVSlpeRGZ3RwokSZLUTh4GlSRJqjCLNUmSpAqzWJMkSaowizVJ\nkqQKs1iTJEmqMIs1SZKkCrNYkyRJqrD/D5K70iyk8cx2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac24c1978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "sns.barplot(data=clusters_cnt, x = 'clusters', y ='percentage %',  hue = 'dataset')\n",
    "plt.title('Clusters Visualizations for General and Customer Demographics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select over and under represented clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pivot cluster tables\n",
    "clusters_pivot = clusters_cnt.pivot(index = 'clusters',  columns= 'dataset', values= 'percentage %')\n",
    "clusters_pivot['difference %'] = clusters_pivot['Customers'] - clusters_pivot['General']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getCentroids(centroids_nums, feature_names, customers):\n",
    "    \"\"\"Get the features given the cluster centroid numbers\"\"\"\n",
    "    centroids = customers[customers['clusters'].isin(centroids_nums)]\n",
    "    return pd.DataFrame(centroids.iloc[:, :-1].mean()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getMeanRest(centroids_nums, customers):\n",
    "    \"\"\"Get the mean of the rest of the data\"\"\"\n",
    "    rest_customers = customers[~customers['clusters'].isin(centroids_nums)]\n",
    "    return pd.DataFrame(rest_customers.iloc[:, :-1].mean()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Overrepresented population**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the top 2 over represented clusters, 18 and 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "overrepresented  = list(clusters_pivot['difference %'].sort_values(ascending = False).head(2).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_diff = pd.concat([getCentroids([overrepresented[0]], feature_names,customers_impute ), \n",
    "                           getCentroids([overrepresented[1]], feature_names, customers_impute), \n",
    "                           getMeanRest(overrepresented, customers_impute)])\n",
    "clusters_diff.set_index(pd.Series(overrepresented + ['rest']), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find representative features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_diff = (abs(clusters_diff.loc[overrepresented[0]] - clusters_diff.loc['rest']) \n",
    "                + abs(clusters_diff.loc[overrepresented[1]] - clusters_diff.loc['rest']))/2\n",
    "top5_diff = list(average_diff.sort_values(ascending = False).head(5).index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize cluster the top 5 different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>KBA13_ANZAHL_PKW</th>\n",
       "      <th>DECADES</th>\n",
       "      <th>GEBURTSJAHR</th>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>619.698207</td>\n",
       "      <td>73.328046</td>\n",
       "      <td>1967.102689</td>\n",
       "      <td>16.332161</td>\n",
       "      <td>8.354924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>694.675669</td>\n",
       "      <td>55.834402</td>\n",
       "      <td>1953.619264</td>\n",
       "      <td>28.864037</td>\n",
       "      <td>1.553170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rest</th>\n",
       "      <td>656.748831</td>\n",
       "      <td>60.466167</td>\n",
       "      <td>1956.264147</td>\n",
       "      <td>22.644504</td>\n",
       "      <td>6.186470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      KBA13_ANZAHL_PKW    DECADES  GEBURTSJAHR  LP_LEBENSPHASE_FEIN  \\\n",
       "9           619.698207  73.328046  1967.102689            16.332161   \n",
       "24          694.675669  55.834402  1953.619264            28.864037   \n",
       "rest        656.748831  60.466167  1956.264147            22.644504   \n",
       "\n",
       "      ANZ_HAUSHALTE_AKTIV  \n",
       "9                8.354924  \n",
       "24               1.553170  \n",
       "rest             6.186470  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_diff[top5_diff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1xGAlSZLUEIOVJElSQwxWkiRJDTFYSZIkNcRgJUmS1BCDlSRJUkMMVpIkSQ0x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac2ba1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_diff[top5_diff].apply(lambda x: x/ max(x)).T.plot(kind = 'bar', \n",
    "                                                    figsize = (10, 6),\n",
    "                                                    title = 'Top 5 different features (Scaled), Overrepresented vs. Rest')\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 9, the most over represented cluster, is showing signnificant differences in these 5 features with the rest of populations. In some features dimensions, the second most overrepresented cluster 24, also have significant differences with the rest of the populations in a different way. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular, cluster 9 has the following characteristics besides the rest\n",
    "\n",
    "* Lower number of cars in region (**KBA13_ANZAHL_PKW**)   --- Lower income\n",
    "* Higher value in birth decade (**DECADES**), lower birth year (1967) (**GEBURTSJAHR**)  --- Mid aged population\n",
    "* Life stage (**LP_LEBENSPHASE_FEIN**) close to 16 -- Average earners couples\n",
    "\n",
    "cluster 24 has the following characteristics besides the rest \n",
    "\n",
    "* Higher number of cars in region (**KBA13_ANZAHL_PKW**)   --- Higher income\n",
    "* Lower value in birth decade (**DECADES**), lower birth year (1953) (**GEBURTSJAHR**)  --- Elderly population\n",
    "* Life stage (**LP_LEBENSPHASE_FEIN**) close to 28 -- Top earners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arvato's primary customers seems to be individuals who are middle-aged to elderly, has a family, average to top earners."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Underrepresented Clusters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the top 2 under represented clusters, 15 and 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "underrepresented = list(clusters_pivot['difference %'].sort_values(ascending = False).dropna().tail(2).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_diff = pd.concat([getCentroids([underrepresented[0]], feature_names ,customers_impute), \n",
    "                           getCentroids([underrepresented[1]], feature_names,customers_impute), \n",
    "                           getMeanRest(underrepresented, customers_impute)])\n",
    "clusters_diff.set_index(pd.Series(underrepresented + ['rest']), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_diff = (abs(clusters_diff.loc[underrepresented[0]] - clusters_diff.loc['rest']) \n",
    "                + abs(clusters_diff.loc[underrepresented[1]] - clusters_diff.loc['rest']))/2\n",
    "top5_diff = list(average_diff.sort_values(ascending=False).head(5).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>KBA13_ANZAHL_PKW</th>\n",
       "      <th>DECADES</th>\n",
       "      <th>GEBURTSJAHR</th>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>721.796890</td>\n",
       "      <td>83.814499</td>\n",
       "      <td>1974.850683</td>\n",
       "      <td>13.001715</td>\n",
       "      <td>4.692734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>487.111996</td>\n",
       "      <td>84.919094</td>\n",
       "      <td>1975.162247</td>\n",
       "      <td>12.537518</td>\n",
       "      <td>13.771688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rest</th>\n",
       "      <td>655.004609</td>\n",
       "      <td>62.308560</td>\n",
       "      <td>1958.020757</td>\n",
       "      <td>22.319477</td>\n",
       "      <td>5.923502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      KBA13_ANZAHL_PKW    DECADES  GEBURTSJAHR  LP_LEBENSPHASE_FEIN  \\\n",
       "7           721.796890  83.814499  1974.850683            13.001715   \n",
       "2           487.111996  84.919094  1975.162247            12.537518   \n",
       "rest        655.004609  62.308560  1958.020757            22.319477   \n",
       "\n",
       "      ANZ_HAUSHALTE_AKTIV  \n",
       "7                4.692734  \n",
       "2               13.771688  \n",
       "rest             5.923502  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_diff[top5_diff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y2lQje1bVDvNcWdICs4+VpEFU1Zxqs6adqjqdkabLvuyxU1MaSYsSmwIlSZIGYlOgJEnS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\nNLQk90hyQpKXpF3+AOAvtB3/b2mXTVm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AVvPNB/oQuqwHSRpOu+gVWKoY3gaG2n6+3BCc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\nLg0AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fac21b24a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_diff[top5_diff].apply(lambda x: x/ max(x)).T.plot(kind = 'bar', \n",
    "                                                    figsize = (10, 6),\n",
    "                                                    title = 'Top 5 different features (Scaled), Underrepresented vs. Rest')\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 7 and 2 are significantly under represented. The also have significant differences with the rest of the population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular, cluster 9 has the following characteristics\n",
    "\n",
    "* Significantly higher number of cars in region (**KBA13_ANZAHL_PKW**), even more than cluster 24   --- Significantly higher income\n",
    "* Significantly Higher value in birth decade (**DECADES**), higher birth year (1973) (**GEBURTSJAHR**) (higher than cluster 7) --- Younger middle-aged population\n",
    "* Life stage (**LP_LEBENSPHASE_FEIN**) number close to 13 ---- Single top earners at higher age\n",
    "\n",
    "Cluster 2 has the following characteristics\n",
    "\n",
    "* Significant lower number of cars in region (**KBA13_ANZAHL_PKW**), even less than cluster 9   --- Significantly lower income\n",
    "* Significantly Higher value in birth decade (**DECADES**), higher birth year (1975) (**GEBURTSJAHR**) (higher than cluster 7) --- Younger middle-aged populatio\n",
    "* Life stage (**LP_LEBENSPHASE_FEIN**) number close to 12 ---- single homeowners at retirement age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two types of individuals are not significantly represented in Arvato's customer base. Those who do not have a family, on the more extreme side of income generation abilities (either extremely rich or pretty poor), are not very interested in Arvato's products. These people are on the younger spectrum of middle aged group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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