Arvato Project Workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project: Create a Customer Segmentation Report for Arvato Financial Services\n",
    "\n",
    "In this project, you will analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. You'll use unsupervised learning techniques to perform customer segmentation, identifying the parts of the population that best describe the core customer base of the company. Then, you'll apply what you've learned on a third dataset with demographics information for targets of a marketing campaign for the company, and use a model to predict which individuals are most likely to convert into becoming customers for the company. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "If you completed the first term of this program, you will be familiar with the first part of this project, from the unsupervised learning project. The versions of those two datasets used in this project will include many more features and has not been pre-cleaned. You are also free to choose whatever approach you'd like to analyzing the data rather than follow pre-determined steps. In your work on this project, make sure that you carefully document your steps and decisions, since your main deliverable for this project will be a blog post reporting your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import skew\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, FunctionTransformer, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_component_to_features(pca, component, column_names):\n",
    "    \"\"\"\n",
    "    Returns weights of features for a selected component.\n",
    "    \n",
    "    Input:\n",
    "    pca - fitted PCA object\n",
    "    component - PCA component of interest\n",
    "    column_names - list of original feature names\n",
    "    \n",
    "    Output:\n",
    "    df_features - sorted DataFrame with feature weigths\n",
    "    \"\"\"\n",
    "    \n",
    "    weights_array = pca.components_[component]\n",
    "    df_features = pd.DataFrame(weights_array, index = column_names, columns=['weight'])\n",
    "    return df_features.sort_values(by='weight', ascending=False).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_learning_curves(X, y, estimator, num_trainings):\n",
    "    \"\"\"\n",
    "    Draw learning curve that shows the validation and training auc_score of an estimator \n",
    "    for varying numbers of training samples.\n",
    "    \n",
    "    Input:\n",
    "        X: array like sample\n",
    "        y: array like target relative to X2 sample\n",
    "        estimator: object type that implements the “fit” and “predict” methods\n",
    "        num_trainings (int): number of training samples to plot\n",
    "        \n",
    "    Output:\n",
    "        None\n",
    "    \"\"\"\n",
    "    \n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, scoring = 'roc_auc', train_sizes=np.linspace(.1, 1.0, num_trainings))\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    print(\"AUC train score = {}\".format(train_scores_mean[-1].round(2)))\n",
    "    print(\"AUC validation score = {}\".format(test_scores_mean[-1].round(2)))\n",
    "    plt.grid()\n",
    "\n",
    "    plt.title(\"Learning Curves\")\n",
    "    plt.xlabel(\"% of training set\")\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    plt.plot(np.linspace(.1, 1.0, num_trainings)*100, train_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(np.linspace(.1, 1.0, num_trainings)*100, test_scores_mean, 'o-', color=\"y\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.yticks(np.arange(0.45, 1.02, 0.05))\n",
    "    plt.xticks(np.arange(0., 100.05, 10))\n",
    "    plt.legend(loc=\"best\")\n",
    "    print(\"\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomize(df):\n",
    "    \"\"\"\n",
    "    Returns randomized dataframes/series X and y.\n",
    "    \n",
    "    Input: df DataFrame\n",
    "    Output: randomized dataframes/series X and y\n",
    "    \"\"\"\n",
    "    \n",
    "    df_randomized = df.sample(frac=1)\n",
    "    y_rand = df_randomized['RESPONSE']\n",
    "    X_rand = df_randomized.drop(['RESPONSE'],axis=1)\n",
    "    return X_rand, y_rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_keys_to_nan(df, keys_dict):\n",
    "    \"\"\"\n",
    "    Replaces given keys from keys_dict to np.nan in df inplace\n",
    "    \n",
    "    Input:\n",
    "        df (DataFrame): Dataset for which keys to np.nan need to be converted\n",
    "        keys_dict: dictionary of attributes with keys that needs to be converted to np.nan\n",
    "        \n",
    "    Output:\n",
    "        None\n",
    "    \"\"\"\n",
    "    for attribute in keys_dict:\n",
    "        \n",
    "        # Check if attribute is in dataframe\n",
    "        if attribute in df.columns:       \n",
    "            keys_array = keys_dict[attribute]\n",
    "            for key in keys_array:\n",
    "                if key == 'X' or key == 'XX':\n",
    "                    key = str(key)\n",
    "                else:\n",
    "                    key = int(key)\n",
    "                \n",
    "                df[attribute].replace(key, np.NaN, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataframe(df, drop_cols, test_df=False):\n",
    "    \"\"\"\n",
    "    Returns cleaned dataframe.\n",
    "    \n",
    "    Input: \n",
    "    df - Dataframe to be cleaned\n",
    "    drop_cols - Columns of df to be dropped\n",
    "    test_df - True if dataframe is mailout_test dataframe\n",
    "    \n",
    "    Output:\n",
    "    df_clean - cleaned dataframe\n",
    "    \"\"\"\n",
    "    \n",
    "    # drop columns\n",
    "    df_clean = df.drop(drop_cols, axis=1)\n",
    "    \n",
    "    # Convert columns 'EINGEFUEGT_AM' and 'OST_WEST_KZ'\n",
    "    #df_clean = pd.concat([df_clean.drop(['CAMEO_DEU_2015'], axis=1), pd.get_dummies(df_clean['CAMEO_DEU_2015'], prefix='CAMEO_DEU_2015', drop_first=True, dummy_na=True)], axis=1)\n",
    "    df_clean['EINGEFUEGT_AM'] = pd.to_datetime(df_clean['EINGEFUEGT_AM'], format='%Y-%m-%d ').dt.year\n",
    "    df_clean['OST_WEST_KZ'].replace('O', 0, inplace=True)\n",
    "    df_clean['OST_WEST_KZ'].replace('W', 1, inplace=True)\n",
    "    df_clean['OST_WEST_KZ'] = pd.to_numeric(df_clean['OST_WEST_KZ'], errors = 'coerce')\n",
    "    \n",
    "    # Drop rows with more than 16 missing values if not mailout_test dataframe\n",
    "    if test_df==False:\n",
    "        rows_dropped = sum(df_clean.isnull().sum(axis=1) > 16)\n",
    "        df_clean = df_clean[df_clean.isnull().sum(axis=1) <= 16]\n",
    "    else:\n",
    "        rows_dropped = 0\n",
    "    \n",
    "    # Print new shape and datatypes\n",
    "    print(\"Old shape: {}\".format(df.shape))\n",
    "    print(\"{} rows and {} columns dropped.\".format(rows_dropped, len(drop_cols)))\n",
    "    print(\"New shape: {} \\n\".format(df_clean.shape))\n",
    "    print(\"Datatypes:\")\n",
    "    print(df_clean.get_dtype_counts())\n",
    "    \n",
    "    return df_clean    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca_scree_plot(pca):\n",
    "    \"\"\"\n",
    "    Plots PCA scree plot.\n",
    "    \n",
    "    Input: pca - fitted PCA object\n",
    "    Output: scree plot\n",
    "    \"\"\"\n",
    "    \n",
    "    num_components = len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    "    cumvals = np.cumsum(vals)\n",
    "\n",
    "    fig = plt.figure(figsize=(16,6))\n",
    "    ax = plt.subplot()\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "\n",
    "    plt.xlabel('Number of components')\n",
    "    plt.ylabel('Cumulative explained variance')\n",
    "    plt.title('Cumulative explained variance ratio plot')\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clusters_attributes(cluster_pipeline, numerical_cols, skewed_cols, column_names):\n",
    "    \"\"\"\n",
    "    Function transforms clusters centers, by performin pca inverse transform,\n",
    "    reverse scale for numerical(num_attributes) and atrributes with logarithmic transformation (log_attributes),\n",
    "    and exponential transformation for log_attributes\n",
    "    \n",
    "    Input:\n",
    "        cluster_pipeline: object of cluster_pipeline\n",
    "        num_attributes: list of numerical attributes which were rescaled\n",
    "        log_attributes: list of attributes wich had logarithmic transformation and also were rescaled\n",
    "        column_names: names of all columns after Column Transformer operation\n",
    "        \n",
    "        \n",
    "    Output:\n",
    "        cluster_centers_df (DataFrame): DataFrame of cluster_centers with their attributes values\n",
    "        \n",
    "    \"\"\"\n",
    "\n",
    "    pca_components = cluster_pipeline.named_steps['reduce']\n",
    "    kmeans = cluster_pipeline.named_steps['cluster']\n",
    "    transformer =  cluster_pipeline.named_steps['transform']\n",
    "\n",
    "    cluster_centers = pca_components.inverse_transform(kmeans.cluster_centers_)\n",
    "    cluster_centers_df = pd.DataFrame(cluster_centers, columns=column_names)\n",
    "\n",
    "    num_scale = transformer.named_transformers_['numerical'].named_steps['num_scale']\n",
    "    log_scale = transformer.named_transformers_['skewed'].named_steps['log_scale']\n",
    "    log_transform = transformer.named_transformers_['skewed'].named_steps['log_transform']\n",
    "\n",
    "    cluster_centers_df[numerical_cols] = num_scale.inverse_transform(cluster_centers_df[numerical_cols])\n",
    "    cluster_centers_df[skewed_cols] = log_scale.inverse_transform(cluster_centers_df[skewed_cols])\n",
    "    cluster_centers_df[skewed_cols] = log_transform.inverse_transform(cluster_centers_df[skewed_cols])\n",
    "\n",
    "    return cluster_centers_df  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Get to Know the Data\n",
    "\n",
    "There are four data files associated with this project:\n",
    "\n",
    "- `Udacity_AZDIAS_052018.csv`: Demographics data for the general population of Germany; 891 211 persons (rows) x 366 features (columns).\n",
    "- `Udacity_CUSTOMERS_052018.csv`: Demographics data for customers of a mail-order company; 191 652 persons (rows) x 369 features (columns).\n",
    "- `Udacity_MAILOUT_052018_TRAIN.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 982 persons (rows) x 367 (columns).\n",
    "- `Udacity_MAILOUT_052018_TEST.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 833 persons (rows) x 366 (columns).\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. Use the information from the first two files to figure out how customers (\"CUSTOMERS\") are similar to or differ from the general population at large (\"AZDIAS\"), then use your analysis to make predictions on the other two files (\"MAILOUT\"), predicting which recipients are most likely to become a customer for the mail-order company.\n",
    "\n",
    "The \"CUSTOMERS\" file contains three extra columns ('CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'), which provide broad information about the customers depicted in the file. The original \"MAILOUT\" file included one additional column, \"RESPONSE\", which indicated whether or not each recipient became a customer of the company. For the \"TRAIN\" subset, this column has been retained, but in the \"TEST\" subset it has been removed; it is against that withheld column that your final predictions will be assessed in the Kaggle competition.\n",
    "\n",
    "Otherwise, all of the remaining columns are the same between the three data files. For more information about the columns depicted in the files, you can refer to two Excel spreadsheets provided in the workspace. [One of them](./DIAS Information Levels - Attributes 2017.xlsx) is a top-level list of attributes and descriptions, organized by informational category. [The other](./DIAS Attributes - Values 2017.xlsx) is a detailed mapping of data values for each feature in alphabetical order.\n",
    "\n",
    "In the below cell, we've provided some initial code to load in the first two datasets. Note for all of the `.csv` data files in this project that they're semicolon (`;`) delimited, so an additional argument in the [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call has been included to read in the data properly. Also, considering the size of the datasets, it may take some time for them to load completely.\n",
    "\n",
    "You'll notice when the data is loaded in that a warning message will immediately pop up. Before you really start digging into the modeling and analysis, you're going to need to perform some cleaning. Take some time to browse the structure of the data and look over the informational spreadsheets to understand the data values. Make some decisions on which features to keep, which features to drop, and if any revisions need to be made on data formats. It'll be a good idea to create a function with pre-processing steps, since you'll need to clean all of the datasets before you work with them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "#azdias = pd.read_csv('Udacity_AZDIAS_052018.csv', sep=';', low_memory=False)\n",
    "#customers = pd.read_csv('Udacity_CUSTOMERS_052018.csv', sep=';', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle dataframes to speed-up data loading next time\n",
    "#azdias.to_pickle('azdias.pickle')\n",
    "#customers.to_pickle('customers.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pickled data\n",
    "azdias = pd.read_pickle('azdias.pickle')\n",
    "customers = pd.read_pickle('customers.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration & Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are four data files associated with this project:\n",
    "\n",
    "- `Udacity_AZDIAS_052018.csv`: Demographics data for the general population of Germany; 891 211 persons (rows) x 366 features (columns).\n",
    "- `Udacity_CUSTOMERS_052018.csv`: Demographics data for customers of a mail-order company; 191 652 persons (rows) x 369 features (columns).\n",
    "- `Udacity_MAILOUT_052018_TRAIN.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 982 persons (rows) x 367 (columns).\n",
    "- `Udacity_MAILOUT_052018_TEST.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 833 persons (rows) x 366 (columns).\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shape of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of general population dataset: 891221 rows, 366 columns\n"
     ]
    },
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>910241</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 366 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LNR  AGER_TYP  AKT_DAT_KL  ALTER_HH  ALTER_KIND1  ALTER_KIND2  \\\n",
       "0  910215        -1         NaN       NaN          NaN          NaN   \n",
       "1  910220        -1         9.0       0.0          NaN          NaN   \n",
       "2  910225        -1         9.0      17.0          NaN          NaN   \n",
       "3  910226         2         1.0      13.0          NaN          NaN   \n",
       "4  910241        -1         1.0      20.0          NaN          NaN   \n",
       "\n",
       "   ALTER_KIND3  ALTER_KIND4  ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV  \\\n",
       "0          NaN          NaN                   NaN                  NaN   \n",
       "1          NaN          NaN                  21.0                 11.0   \n",
       "2          NaN          NaN                  17.0                 10.0   \n",
       "3          NaN          NaN                  13.0                  1.0   \n",
       "4          NaN          NaN                  14.0                  3.0   \n",
       "\n",
       "          ...           VHN  VK_DHT4A  VK_DISTANZ  VK_ZG11  W_KEIT_KIND_HH  \\\n",
       "0         ...           NaN       NaN         NaN      NaN             NaN   \n",
       "1         ...           4.0       8.0        11.0     10.0             3.0   \n",
       "2         ...           2.0       9.0         9.0      6.0             3.0   \n",
       "3         ...           0.0       7.0        10.0     11.0             NaN   \n",
       "4         ...           2.0       3.0         5.0      4.0             2.0   \n",
       "\n",
       "   WOHNDAUER_2008  WOHNLAGE ZABEOTYP ANREDE_KZ ALTERSKATEGORIE_GROB  \n",
       "0             NaN       NaN        3         1                    2  \n",
       "1             9.0       4.0        5         2                    1  \n",
       "2             9.0       2.0        5         2                    3  \n",
       "3             9.0       7.0        3         2                    4  \n",
       "4             9.0       3.0        4         1                    3  \n",
       "\n",
       "[5 rows x 366 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of general population dataset\n",
    "print(\"Shape of general population dataset: {} rows, {} columns\".format(azdias.shape[0], azdias.shape[1]))\n",
    "azdias.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of customers dataset: 191652 rows, 369 columns\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNR</th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <th>ALTER_HH</th>\n",
       "      <th>ALTER_KIND1</th>\n",
       "      <th>ALTER_KIND2</th>\n",
       "      <th>ALTER_KIND3</th>\n",
       "      <th>ALTER_KIND4</th>\n",
       "      <th>ALTERSKATEGORIE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>...</th>\n",
       "      <th>VK_ZG11</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <th>PRODUCT_GROUP</th>\n",
       "      <th>CUSTOMER_GROUP</th>\n",
       "      <th>ONLINE_PURCHASE</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9626</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9628</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>SINGLE_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143872</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143873</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>COSMETIC</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143874</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LNR  AGER_TYP  AKT_DAT_KL  ALTER_HH  ALTER_KIND1  ALTER_KIND2  \\\n",
       "0    9626         2         1.0      10.0          NaN          NaN   \n",
       "1    9628        -1         9.0      11.0          NaN          NaN   \n",
       "2  143872        -1         1.0       6.0          NaN          NaN   \n",
       "3  143873         1         1.0       8.0          NaN          NaN   \n",
       "4  143874        -1         1.0      20.0          NaN          NaN   \n",
       "\n",
       "   ALTER_KIND3  ALTER_KIND4  ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV  \\\n",
       "0          NaN          NaN                  10.0                  1.0   \n",
       "1          NaN          NaN                   NaN                  NaN   \n",
       "2          NaN          NaN                   0.0                  1.0   \n",
       "3          NaN          NaN                   8.0                  0.0   \n",
       "4          NaN          NaN                  14.0                  7.0   \n",
       "\n",
       "          ...           VK_ZG11  W_KEIT_KIND_HH  WOHNDAUER_2008  WOHNLAGE  \\\n",
       "0         ...               2.0             6.0             9.0       7.0   \n",
       "1         ...               3.0             0.0             9.0       NaN   \n",
       "2         ...              11.0             6.0             9.0       2.0   \n",
       "3         ...               2.0             NaN             9.0       7.0   \n",
       "4         ...               4.0             2.0             9.0       3.0   \n",
       "\n",
       "   ZABEOTYP      PRODUCT_GROUP  CUSTOMER_GROUP ONLINE_PURCHASE ANREDE_KZ  \\\n",
       "0         3  COSMETIC_AND_FOOD     MULTI_BUYER               0         1   \n",
       "1         3               FOOD    SINGLE_BUYER               0         1   \n",
       "2         3  COSMETIC_AND_FOOD     MULTI_BUYER               0         2   \n",
       "3         1           COSMETIC     MULTI_BUYER               0         1   \n",
       "4         1               FOOD     MULTI_BUYER               0         1   \n",
       "\n",
       "  ALTERSKATEGORIE_GROB  \n",
       "0                    4  \n",
       "1                    4  \n",
       "2                    4  \n",
       "3                    4  \n",
       "4                    3  \n",
       "\n",
       "[5 rows x 369 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of customers dataset\n",
    "print(\"Shape of customers dataset: {} rows, {} columns\".format(customers.shape[0], customers.shape[1]))\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differences in columns\n",
    "azdias_columns = set(azdias.columns)\n",
    "customers_columns = set(customers.columns)\n",
    "customers_columns - azdias_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The customers dataset has three columns more: 'CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    267\n",
       "int64       93\n",
       "object       6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show dtypes and corrsp. number of columns\n",
    "azdias.get_dtype_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>CAMEO_DEU_2015</th>\n",
       "      <th>CAMEO_DEUG_2015</th>\n",
       "      <th>CAMEO_INTL_2015</th>\n",
       "      <th>D19_LETZTER_KAUF_BRANCHE</th>\n",
       "      <th>EINGEFUEGT_AM</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8A</td>\n",
       "      <td>8</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-02-10 00:00:00</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4C</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>D19_UNBEKANNT</td>\n",
       "      <td>1992-02-12 00:00:00</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2A</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>D19_UNBEKANNT</td>\n",
       "      <td>1997-04-21 00:00:00</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6B</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>D19_SCHUHE</td>\n",
       "      <td>1992-02-12 00:00:00</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CAMEO_DEU_2015 CAMEO_DEUG_2015 CAMEO_INTL_2015 D19_LETZTER_KAUF_BRANCHE  \\\n",
       "0            NaN             NaN             NaN                      NaN   \n",
       "1             8A               8              51                      NaN   \n",
       "2             4C               4              24            D19_UNBEKANNT   \n",
       "3             2A               2              12            D19_UNBEKANNT   \n",
       "4             6B               6              43               D19_SCHUHE   \n",
       "\n",
       "         EINGEFUEGT_AM OST_WEST_KZ  \n",
       "0                  NaN         NaN  \n",
       "1  1992-02-10 00:00:00           W  \n",
       "2  1992-02-12 00:00:00           W  \n",
       "3  1997-04-21 00:00:00           W  \n",
       "4  1992-02-12 00:00:00           W  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print categorical columns\n",
    "azdias.select_dtypes(['object']).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    267\n",
       "int64       94\n",
       "object       8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.get_dtype_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>CAMEO_DEU_2015</th>\n",
       "      <th>CAMEO_DEUG_2015</th>\n",
       "      <th>CAMEO_INTL_2015</th>\n",
       "      <th>D19_LETZTER_KAUF_BRANCHE</th>\n",
       "      <th>EINGEFUEGT_AM</th>\n",
       "      <th>OST_WEST_KZ</th>\n",
       "      <th>PRODUCT_GROUP</th>\n",
       "      <th>CUSTOMER_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1A</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>D19_UNBEKANNT</td>\n",
       "      <td>1992-02-12 00:00:00</td>\n",
       "      <td>W</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D19_BANKEN_GROSS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>SINGLE_BUYER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5D</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>D19_UNBEKANNT</td>\n",
       "      <td>1992-02-10 00:00:00</td>\n",
       "      <td>W</td>\n",
       "      <td>COSMETIC_AND_FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4C</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>D19_NAHRUNGSERGAENZUNG</td>\n",
       "      <td>1992-02-10 00:00:00</td>\n",
       "      <td>W</td>\n",
       "      <td>COSMETIC</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7B</td>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "      <td>D19_SCHUHE</td>\n",
       "      <td>1992-02-12 00:00:00</td>\n",
       "      <td>W</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>MULTI_BUYER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CAMEO_DEU_2015 CAMEO_DEUG_2015 CAMEO_INTL_2015 D19_LETZTER_KAUF_BRANCHE  \\\n",
       "0             1A               1              13            D19_UNBEKANNT   \n",
       "1            NaN             NaN             NaN         D19_BANKEN_GROSS   \n",
       "2             5D               5              34            D19_UNBEKANNT   \n",
       "3             4C               4              24   D19_NAHRUNGSERGAENZUNG   \n",
       "4             7B               7              41               D19_SCHUHE   \n",
       "\n",
       "         EINGEFUEGT_AM OST_WEST_KZ      PRODUCT_GROUP CUSTOMER_GROUP  \n",
       "0  1992-02-12 00:00:00           W  COSMETIC_AND_FOOD    MULTI_BUYER  \n",
       "1                  NaN         NaN               FOOD   SINGLE_BUYER  \n",
       "2  1992-02-10 00:00:00           W  COSMETIC_AND_FOOD    MULTI_BUYER  \n",
       "3  1992-02-10 00:00:00           W           COSMETIC    MULTI_BUYER  \n",
       "4  1992-02-12 00:00:00           W               FOOD    MULTI_BUYER  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.select_dtypes(['object']).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azdias dataset has 6 categorical columns of which one is a date column. Customers has two more columns which are not in the Azdias dataset.\n",
    "\n",
    "The common categorical columns are:\n",
    "* CAMEO_DEU_2015, CAMEO_DEUG_2015, CAMEO_INTL_2015: These describe the sociodemographic background of a household (cluster) based on the CAMEO classification system.\n",
    "* D19_LETZTER_KAUF_BRANCHE: No description is provided for this column. However, based on the column name it could be referred to the industry of the latest purchase of the household (shoes, food, etc)\n",
    "* EINGEFUEGT_AM: Again, no description provided but seems like the date when the household entry was added to the database.\n",
    "* OST_WEST_KZ: Indicates if the building is located (was constructed?) in the former GDR/East Germany (O) or FDR/West Germany (W)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNR</th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <th>ALTER_HH</th>\n",
       "      <th>ALTER_KIND1</th>\n",
       "      <th>ALTER_KIND2</th>\n",
       "      <th>ALTER_KIND3</th>\n",
       "      <th>ALTER_KIND4</th>\n",
       "      <th>ALTERSKATEGORIE_FEIN</th>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>...</th>\n",
       "      <th>VHN</th>\n",
       "      <th>VK_DHT4A</th>\n",
       "      <th>VK_DISTANZ</th>\n",
       "      <th>VK_ZG11</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.912210e+05</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>817722.000000</td>\n",
       "      <td>817722.000000</td>\n",
       "      <td>81058.000000</td>\n",
       "      <td>29499.000000</td>\n",
       "      <td>6170.000000</td>\n",
       "      <td>1205.000000</td>\n",
       "      <td>628274.000000</td>\n",
       "      <td>798073.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>770025.000000</td>\n",
       "      <td>815304.000000</td>\n",
       "      <td>815304.000000</td>\n",
       "      <td>815304.000000</td>\n",
       "      <td>783619.000000</td>\n",
       "      <td>817722.000000</td>\n",
       "      <td>798073.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "      <td>891221.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.372630e+05</td>\n",
       "      <td>-0.358435</td>\n",
       "      <td>4.421928</td>\n",
       "      <td>10.864126</td>\n",
       "      <td>11.745392</td>\n",
       "      <td>13.402658</td>\n",
       "      <td>14.476013</td>\n",
       "      <td>15.089627</td>\n",
       "      <td>13.700717</td>\n",
       "      <td>8.287263</td>\n",
       "      <td>...</td>\n",
       "      <td>2.417322</td>\n",
       "      <td>6.001214</td>\n",
       "      <td>7.532130</td>\n",
       "      <td>5.945972</td>\n",
       "      <td>3.933406</td>\n",
       "      <td>7.908791</td>\n",
       "      <td>4.052836</td>\n",
       "      <td>3.362438</td>\n",
       "      <td>1.522098</td>\n",
       "      <td>2.777398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.572735e+05</td>\n",
       "      <td>1.198724</td>\n",
       "      <td>3.638805</td>\n",
       "      <td>7.639683</td>\n",
       "      <td>4.097660</td>\n",
       "      <td>3.243300</td>\n",
       "      <td>2.712427</td>\n",
       "      <td>2.452932</td>\n",
       "      <td>5.079849</td>\n",
       "      <td>15.628087</td>\n",
       "      <td>...</td>\n",
       "      <td>1.166572</td>\n",
       "      <td>2.856091</td>\n",
       "      <td>3.247789</td>\n",
       "      <td>2.771464</td>\n",
       "      <td>1.964701</td>\n",
       "      <td>1.923137</td>\n",
       "      <td>1.949539</td>\n",
       "      <td>1.352704</td>\n",
       "      <td>0.499512</td>\n",
       "      <td>1.068775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.916530e+05</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.144580e+05</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.372630e+05</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.600680e+05</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.082873e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 360 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                LNR       AGER_TYP     AKT_DAT_KL       ALTER_HH  \\\n",
       "count  8.912210e+05  891221.000000  817722.000000  817722.000000   \n",
       "mean   6.372630e+05      -0.358435       4.421928      10.864126   \n",
       "std    2.572735e+05       1.198724       3.638805       7.639683   \n",
       "min    1.916530e+05      -1.000000       1.000000       0.000000   \n",
       "25%    4.144580e+05      -1.000000       1.000000       0.000000   \n",
       "50%    6.372630e+05      -1.000000       3.000000      13.000000   \n",
       "75%    8.600680e+05      -1.000000       9.000000      17.000000   \n",
       "max    1.082873e+06       3.000000       9.000000      21.000000   \n",
       "\n",
       "        ALTER_KIND1   ALTER_KIND2  ALTER_KIND3  ALTER_KIND4  \\\n",
       "count  81058.000000  29499.000000  6170.000000  1205.000000   \n",
       "mean      11.745392     13.402658    14.476013    15.089627   \n",
       "std        4.097660      3.243300     2.712427     2.452932   \n",
       "min        2.000000      2.000000     4.000000     7.000000   \n",
       "25%        8.000000     11.000000    13.000000    14.000000   \n",
       "50%       12.000000     14.000000    15.000000    15.000000   \n",
       "75%       15.000000     16.000000    17.000000    17.000000   \n",
       "max       18.000000     18.000000    18.000000    18.000000   \n",
       "\n",
       "       ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV          ...           \\\n",
       "count         628274.000000        798073.000000          ...            \n",
       "mean              13.700717             8.287263          ...            \n",
       "std                5.079849            15.628087          ...            \n",
       "min                0.000000             0.000000          ...            \n",
       "25%               11.000000             1.000000          ...            \n",
       "50%               14.000000             4.000000          ...            \n",
       "75%               17.000000             9.000000          ...            \n",
       "max               25.000000           595.000000          ...            \n",
       "\n",
       "                 VHN       VK_DHT4A     VK_DISTANZ        VK_ZG11  \\\n",
       "count  770025.000000  815304.000000  815304.000000  815304.000000   \n",
       "mean        2.417322       6.001214       7.532130       5.945972   \n",
       "std         1.166572       2.856091       3.247789       2.771464   \n",
       "min         0.000000       1.000000       1.000000       1.000000   \n",
       "25%         2.000000       3.000000       5.000000       4.000000   \n",
       "50%         2.000000       6.000000       8.000000       6.000000   \n",
       "75%         3.000000       9.000000      10.000000       8.000000   \n",
       "max         4.000000      11.000000      13.000000      11.000000   \n",
       "\n",
       "       W_KEIT_KIND_HH  WOHNDAUER_2008       WOHNLAGE       ZABEOTYP  \\\n",
       "count   783619.000000   817722.000000  798073.000000  891221.000000   \n",
       "mean         3.933406        7.908791       4.052836       3.362438   \n",
       "std          1.964701        1.923137       1.949539       1.352704   \n",
       "min          0.000000        1.000000       0.000000       1.000000   \n",
       "25%          2.000000        8.000000       3.000000       3.000000   \n",
       "50%          4.000000        9.000000       3.000000       3.000000   \n",
       "75%          6.000000        9.000000       5.000000       4.000000   \n",
       "max          6.000000        9.000000       8.000000       6.000000   \n",
       "\n",
       "           ANREDE_KZ  ALTERSKATEGORIE_GROB  \n",
       "count  891221.000000         891221.000000  \n",
       "mean        1.522098              2.777398  \n",
       "std         0.499512              1.068775  \n",
       "min         1.000000              1.000000  \n",
       "25%         1.000000              2.000000  \n",
       "50%         2.000000              3.000000  \n",
       "75%         2.000000              4.000000  \n",
       "max         2.000000              9.000000  \n",
       "\n",
       "[8 rows x 360 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LNR</th>\n",
       "      <th>AGER_TYP</th>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <th>ALTER_HH</th>\n",
       "      <th>ALTER_KIND1</th>\n",
       "      <th>ALTER_KIND2</th>\n",
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       "      <th>ALTER_KIND4</th>\n",
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       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <th>...</th>\n",
       "      <th>VK_DHT4A</th>\n",
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       "      <th>VK_ZG11</th>\n",
       "      <th>W_KEIT_KIND_HH</th>\n",
       "      <th>WOHNDAUER_2008</th>\n",
       "      <th>WOHNLAGE</th>\n",
       "      <th>ZABEOTYP</th>\n",
       "      <th>ONLINE_PURCHASE</th>\n",
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       "      <th>count</th>\n",
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       "      <td>191652.000000</td>\n",
       "      <td>145056.000000</td>\n",
       "      <td>145056.000000</td>\n",
       "      <td>11766.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>139810.000000</td>\n",
       "      <td>141725.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>143781.000000</td>\n",
       "      <td>143781.000000</td>\n",
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       "      <td>191652.000000</td>\n",
       "      <td>191652.000000</td>\n",
       "      <td>191652.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95826.500000</td>\n",
       "      <td>0.344359</td>\n",
       "      <td>1.747525</td>\n",
       "      <td>11.352009</td>\n",
       "      <td>12.337243</td>\n",
       "      <td>13.672353</td>\n",
       "      <td>14.647059</td>\n",
       "      <td>15.377119</td>\n",
       "      <td>10.331579</td>\n",
       "      <td>4.965863</td>\n",
       "      <td>...</td>\n",
       "      <td>4.374417</td>\n",
       "      <td>4.564769</td>\n",
       "      <td>3.168868</td>\n",
       "      <td>4.152716</td>\n",
       "      <td>8.646371</td>\n",
       "      <td>3.723133</td>\n",
       "      <td>2.576806</td>\n",
       "      <td>0.090247</td>\n",
       "      <td>1.376432</td>\n",
       "      <td>3.060907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>55325.311233</td>\n",
       "      <td>1.391672</td>\n",
       "      <td>1.966334</td>\n",
       "      <td>6.275026</td>\n",
       "      <td>4.006050</td>\n",
       "      <td>3.243335</td>\n",
       "      <td>2.753787</td>\n",
       "      <td>2.307653</td>\n",
       "      <td>4.134828</td>\n",
       "      <td>14.309694</td>\n",
       "      <td>...</td>\n",
       "      <td>2.924355</td>\n",
       "      <td>2.887035</td>\n",
       "      <td>2.233516</td>\n",
       "      <td>1.974375</td>\n",
       "      <td>1.154001</td>\n",
       "      <td>2.095540</td>\n",
       "      <td>1.168486</td>\n",
       "      <td>0.286536</td>\n",
       "      <td>0.484492</td>\n",
       "      <td>1.086254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47913.750000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>95826.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>143739.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>191652.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 361 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LNR       AGER_TYP     AKT_DAT_KL       ALTER_HH  \\\n",
       "count  191652.000000  191652.000000  145056.000000  145056.000000   \n",
       "mean    95826.500000       0.344359       1.747525      11.352009   \n",
       "std     55325.311233       1.391672       1.966334       6.275026   \n",
       "min         1.000000      -1.000000       1.000000       0.000000   \n",
       "25%     47913.750000      -1.000000       1.000000       8.000000   \n",
       "50%     95826.500000       0.000000       1.000000      11.000000   \n",
       "75%    143739.250000       2.000000       1.000000      16.000000   \n",
       "max    191652.000000       3.000000       9.000000      21.000000   \n",
       "\n",
       "        ALTER_KIND1  ALTER_KIND2  ALTER_KIND3  ALTER_KIND4  \\\n",
       "count  11766.000000  5100.000000  1275.000000   236.000000   \n",
       "mean      12.337243    13.672353    14.647059    15.377119   \n",
       "std        4.006050     3.243335     2.753787     2.307653   \n",
       "min        2.000000     2.000000     5.000000     8.000000   \n",
       "25%        9.000000    11.000000    13.000000    14.000000   \n",
       "50%       13.000000    14.000000    15.000000    16.000000   \n",
       "75%       16.000000    16.000000    17.000000    17.000000   \n",
       "max       18.000000    18.000000    18.000000    18.000000   \n",
       "\n",
       "       ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV          ...           \\\n",
       "count         139810.000000        141725.000000          ...            \n",
       "mean              10.331579             4.965863          ...            \n",
       "std                4.134828            14.309694          ...            \n",
       "min                0.000000             0.000000          ...            \n",
       "25%                9.000000             1.000000          ...            \n",
       "50%               10.000000             1.000000          ...            \n",
       "75%               13.000000             4.000000          ...            \n",
       "max               25.000000           523.000000          ...            \n",
       "\n",
       "            VK_DHT4A     VK_DISTANZ        VK_ZG11  W_KEIT_KIND_HH  \\\n",
       "count  143781.000000  143781.000000  143781.000000   137910.000000   \n",
       "mean        4.374417       4.564769       3.168868        4.152716   \n",
       "std         2.924355       2.887035       2.233516        1.974375   \n",
       "min         1.000000       1.000000       1.000000        0.000000   \n",
       "25%         2.000000       2.000000       1.000000        2.000000   \n",
       "50%         4.000000       4.000000       3.000000        5.000000   \n",
       "75%         7.000000       7.000000       4.000000        6.000000   \n",
       "max        11.000000      13.000000      11.000000        6.000000   \n",
       "\n",
       "       WOHNDAUER_2008       WOHNLAGE       ZABEOTYP  ONLINE_PURCHASE  \\\n",
       "count   145056.000000  141725.000000  191652.000000    191652.000000   \n",
       "mean         8.646371       3.723133       2.576806         0.090247   \n",
       "std          1.154001       2.095540       1.168486         0.286536   \n",
       "min          1.000000       0.000000       1.000000         0.000000   \n",
       "25%          9.000000       2.000000       1.000000         0.000000   \n",
       "50%          9.000000       3.000000       3.000000         0.000000   \n",
       "75%          9.000000       5.000000       3.000000         0.000000   \n",
       "max          9.000000       8.000000       6.000000         1.000000   \n",
       "\n",
       "           ANREDE_KZ  ALTERSKATEGORIE_GROB  \n",
       "count  191652.000000         191652.000000  \n",
       "mean        1.376432              3.060907  \n",
       "std         0.484492              1.086254  \n",
       "min         1.000000              1.000000  \n",
       "25%         1.000000              3.000000  \n",
       "50%         1.000000              3.000000  \n",
       "75%         2.000000              4.000000  \n",
       "max         2.000000              9.000000  \n",
       "\n",
       "[8 rows x 361 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some attributes a value denotes a missing information according to the \"DIAS Attributes - Values 2017.xlsx\". Firstly, the corresponding value(s) needs to be identified for each attribute. The respective value(s) can then be replaced by nans in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Excel and filter for missing value classes\n",
    "attr_values = pd.read_excel('DIAS Attributes - Values 2017.xlsx', usecols='B:E', dtype='str')\n",
    "missing_keys = attr_values[attr_values[\"Meaning\"].isin([\"unknown\",\"unknown / no main age detectable\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dict of missing attribute-value combinations\n",
    "missing_keys_dict = {}\n",
    "for _, row in missing_keys.iterrows():\n",
    "    key = row[\"Attribute\"]\n",
    "    missing_keys_dict[key] = row[\"Value\"].split(\", \")\n",
    "\n",
    "# Manually add missing codes from other/similar attributes \n",
    "missing_keys_dict[\"CAMEO_INTL_2015\"] = ['XX']\n",
    "missing_keys_dict[\"CAMEO_DEUG_2015\"] = ['X','XX']\n",
    "missing_keys_dict[\"CAMEO_DEU_2015\"] =['XX']\n",
    "missing_keys_dict[\"GEBURTSJAHR\"] = ['0']\n",
    "missing_keys_dict[\"CAMEO_INTL_2015\"] == missing_keys_dict[\"CAMEO_DEUINTL_2015\"]\n",
    "missing_keys_dict[\"KBA13_CCM_1401_2500\"] = missing_keys_dict[\"KBA13_CCM_1400_2500\"]\n",
    "missing_keys_dict[\"KBA13_BAUMAX\"] = missing_keys_dict[\"KBA05_BAUMAX\"]\n",
    "missing_keys_dict[\"KBA13_ANTG1\"] =  missing_keys_dict[\"KBA05_ANTG1\"]\n",
    "missing_keys_dict[\"KBA13_ANTG2\"] =  missing_keys_dict[\"KBA05_ANTG2\"]\n",
    "missing_keys_dict[\"KBA13_ANTG3\"] =  missing_keys_dict[\"KBA05_ANTG3\"]\n",
    "missing_keys_dict[\"KBA13_ANTG4\"] =  missing_keys_dict[\"KBA05_ANTG4\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_keys_to_nan(azdias, missing_keys_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Missing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count and share of missing values\n",
    "azdias_null = azdias.isnull().sum()\n",
    "azdias_null_share = azdias_null/len(azdias) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 100 columns with highest share of missing values\n",
    "ax = azdias_null_share.sort_values(ascending=False).head(100).plot(kind='bar', figsize=(16,8), title='Top 100 missing columns')\n",
    "ax.set_xlabel(\"Column name\")\n",
    "ax.set_ylabel(\"Share of missing values %\")\n",
    "ax.figure.savefig('top_missing.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8 columns have 60% or more missing values. Most of the columns have less than 20% missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of missing values\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.hist(azdias_null_share, bins=np.linspace(5,100))\n",
    "\n",
    "plt.xticks(np.arange(0, 100, 5))\n",
    "plt.ylabel('# of columns')\n",
    "plt.xlabel('% of missing data')\n",
    "plt.title('Distribution of missing data')\n",
    "plt.savefig('distribution_missing.png', dpi=500);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    366.000000\n",
       "mean      11.486055\n",
       "std       13.829709\n",
       "min        0.000000\n",
       "25%        0.544646\n",
       "50%       11.871354\n",
       "75%       13.073637\n",
       "max       99.864792\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_null_share.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, columns have a missing share of 11.5%.\n"
     ]
    }
   ],
   "source": [
    "print(\"On average, columns have a missing share of {}%.\".format(round(azdias_null_share.mean(),1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97% of columns have a missing share of 29.8% or less.\n"
     ]
    }
   ],
   "source": [
    "print(\"97% of columns have a missing share of {}% or less.\".format(round(azdias_null_share.quantile(0.97),1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 columns that have more than 30% missing values will be dropped: ['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'EXTSEL992', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ']\n"
     ]
    }
   ],
   "source": [
    "drop_null_cols = list(azdias_null_share[azdias_null_share>30].index)\n",
    "print(\"{} columns that have more than 30% missing values will be dropped: {}\".format(len(drop_null_cols),drop_null_cols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Other columns to drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_columns = azdias.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column CAMEO_DEU_2015: 44 unique values\n",
      "Column CAMEO_DEUG_2015: 9 unique values\n",
      "Column CAMEO_INTL_2015: 21 unique values\n",
      "Column D19_LETZTER_KAUF_BRANCHE: 35 unique values\n",
      "Column EINGEFUEGT_AM: 5162 unique values\n",
      "Column OST_WEST_KZ: 2 unique values\n"
     ]
    }
   ],
   "source": [
    "for col in cat_columns:\n",
    "    print(\"Column {}: {} unique values\".format(col, azdias[col].nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 'CAMEO_DEU_2015' has many unique values but will be kept as it might be a powerful feature. \n",
    "* 'CAMEO_DEUG_2015' and 'CAMEO_INTL_2015' will be dropped as they are similar attributes to 'CAMEO_DEU_2015'.\n",
    "* 'D19_LETZTER_KAUF_BRANCHE' will be dropped due to many unique values.\n",
    "* 'EINGEFUEGT_AM' will be transformed to datetime year.\n",
    "* 'OST_WEST_KZ' will be converted to 0/1 with 0 = OST and 1 = WEST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'LNR' is the unique identifier of each person and will be dropped as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 columns will be dropped from the dataset: ['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'EXTSEL992', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE', 'LNR']\n"
     ]
    }
   ],
   "source": [
    "# append to drop cols\n",
    "drop_cols = drop_null_cols + ['CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE', 'LNR']\n",
    "print(\"{} columns will be dropped from the dataset: {}\".format(len(drop_cols), drop_cols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Missing rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aaI0sKa6qi4HHDSnfd5L6BRwxyb4TgBOGlC8BHj2k/OfAfuvYZUmSJElSz4zlnmJJkiRJkmYjk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeMimWJEmSJPWWSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIkSZKk3jIpliRJkiT1lkmxJEmSJKm3TIolSZIkSb1lUixJkiRJ6i2TYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9ZZJsSRJkiSpt0yKJUmSJEm9ZVIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeGllSnOTeSc5PclGSy5K8rZXvmuS8JFcm+WSSzVv5Fu31srZ/wUBbb2jlVyQ5YKB8UStbluSogfKhMSRJkiRJGjTKkeLbgX2r6rHA7sCiJPsA7wLeW1ULgZuAw1r9w4CbquphwHtbPZLsBhwKPApYBPxbkjlJ5gDvBw4EdgNe2OoyRQxJkiRJklYbWVJcnV+0l5u1RwH7Aqe18pOAQ9r2we01bf9+SdLKT6mq26vqR8AyYO/2WFZVV1XVb4FTgIPbMZPFkCRJkiRptZHeU9xGdJcCK4CzgB8CN1fVHa3KcmCntr0TcA1A238L8IDB8gnHTFb+gCliTOzf4UmWJFmycuXKe3KqkiRJkqSN0EiT4qq6s6p2B+bTjew+cli19pxJ9m2o8mH9+0BV7VVVe82bN29YFUmSJEnSJmwsq09X1c3A2cA+wDZJ5rZd84Fr2/ZyYGeAtn9r4MbB8gnHTFZ+wxQxJEmSJElabZSrT89Lsk3b3hJ4BnA58HXgua3aYuBzbfv09pq2/2tVVa380LY69a7AQuB84AJgYVtpenO6xbhOb8dMFkOSJEmSpNXmrr3KetsROKmtEn0v4NSq+kKS7wGnJPl74LvA8a3+8cBHkiyjGyE+FKCqLktyKvA94A7giKq6EyDJkcCZwBzghKq6rLX1+kliSJIkSZK02siS4qq6GHjckPKr6O4vnlj+G+B5k7R1NHD0kPIzgDOmG0OSJEmSpEFjuadYkiRJkqTZyKRYkiRJktRbJsWSJEmSpN4yKZYkSZIk9ZZJsSRJkiSpt0yKJUmSJEm9ZVIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeMimWJEmSJPWWSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIkSZKk3jIpliRJkiT1lkmxJEmSJKm3TIolSZIkSb1lUixJkiRJ6i2TYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9ZZJsSRJkiSpt0aWFCfZOcnXk1ye5LIkr27lb03y0yRL2+OggWPekGRZkiuSHDBQvqiVLUty1ED5rknOS3Jlkk8m2byVb9FeL2v7F4zqPCVJkiRJG69RjhTfAby2qh4J7AMckWS3tu+9VbV7e5wB0PYdCjwKWAT8W5I5SeYA7wcOBHYDXjjQzrtaWwuBm4DDWvlhwE1V9TDgva2eJEmSJElrGFlSXFXXVdV32vZtwOXATlMccjBwSlXdXlU/ApYBe7fHsqq6qqp+C5wCHJwkwL7Aae34k4BDBto6qW2fBuzX6kuSJEmStNpY7ilu05cfB5zXio5McnGSE5Js28p2Aq4ZOGx5K5us/AHAzVV1x4TyNdpq+29p9SVJkiRJWm3kSXGS+wGfBl5TVbcCxwEPBXYHrgPevarqkMNrPcqnamti3w5PsiTJkpUrV055HpIkSZKkTc9Ik+Ikm9ElxB+rqs8AVNX1VXVnVd0FfJBuejR0I707Dxw+H7h2ivIbgG2SzJ1QvkZbbf/WwI0T+1dVH6iqvapqr3nz5t3T05UkSZIkbWRGufp0gOOBy6vqPQPlOw5Uew5wads+HTi0rRy9K7AQOB+4AFjYVprenG4xrtOrqoCvA89txy8GPjfQ1uK2/Vzga62+JEmSJEmrzV17lfX2JOBPgUuSLG1lb6RbPXp3uunMVwN/AVBVlyU5Ffge3crVR1TVnQBJjgTOBOYAJ1TVZa291wOnJPl74Lt0STjt+SNJltGNEB86wvOUJEmSJG2kRpYUV9W3GX5v7xlTHHM0cPSQ8jOGHVdVV/H76deD5b8Bnrcu/ZUkSZIk9c86TZ9Osm2Sx4yqM5IkSZIkjdNak+IkZyfZKsl2wEXAh5O8Z23HSZIkSZI0201npHjr9qeU/ifw4araE3jGaLslSZIkSdLoTScpnttWjH4+8IUR90eSJEmSpLGZTlL8drqVn5dV1QVJHgJcOdpuSZIkSZI0etNZffrzVfWpVS/ais9/PLouSZIkSZI0HtNJii9Ncj3wLeCbwP+tqltG2y1JkiRJkkZvrdOnq+phwAuBS4BnARclWTrqjkmSJEmSNGprHSlOMh94EvAU4LHAZcC3R9wvSZIkSZJGbjrTp38CXAC8s6peMeL+SJIkSZI0NtNZffpxwMnAnyQ5J8nJSQ4bcb8kSZIkSRq5tY4UV9VFSX4I/JBuCvWLgacCx4+4b5IkSZIkjdR07ileAmwB/BfdvcRPraofj7pjkiRJkiSN2nTuKT6wqlaOvCeSJEmSJI3ZdO4p/m2S9yRZ0h7vTrL1yHsmSZIkSdKITScpPgG4DXh+e9wKfHiUnZIkSZIkaRymM336oVX1xwOv35Zk6ag6JEmSJEnSuExnpPjXSZ686kWSJwG/Hl2XJEmSJEkaj+mMFL8COHngPuKbgMWj65IkSZIkSeMxZVKc5F7Aw6vqsUm2AqiqW8fSM0mSJEmSRmzK6dNVdRdwZNu+1YRYkiRJkrQpmc49xWcl+ZskOyfZbtVj5D2TJEmSJGnEpnNP8cva8xEDZQU8ZMN3R5IkSZKk8VlrUlxVu46jI5IkSZIkjdt0pk9LkiRJkrRJMimWJEmSJPXWpElxkie15y3G1x1JkiRJksZnqpHiY9vzOePoiCRJkiRJ4zbVQlu/S/JhYKckx07cWVWvGl23JEmSJEkavalGip8FnAn8BrhwyGNK7e8afz3J5UkuS/LqVr5dkrOSXNmet23lSXJskmVJLk6yx0Bbi1v9K5MsHijfM8kl7Zhjk2SqGJIkSZIkDZo0Ka6qG6rqFODZVXXSxMc02r4DeG1VPRLYBzgiyW7AUcBXq2oh8NX2GuBAYGF7HA4cB12CC7wFeAKwN/CWgST3uFZ31XGLWvlkMSRJkiRJWm06q0//PMlnk6xIcn2STyeZv7aDquq6qvpO274NuBzYCTgYWJVUnwQc0rYPBk6uzrnANkl2BA4AzqqqG6vqJuAsYFHbt1VVnVNVBZw8oa1hMSRJkiRJWm06SfGHgdOBB9EltZ9vZdOWZAHwOOA8YIequg66xBl4YKu2E3DNwGHLW9lU5cuHlDNFDEmSJEmSVptOUvzAqvpwVd3RHicC86YbIMn9gE8Dr6mqW6eqOqSs1qN82pIcnmRJkiUrV65cl0MlSZIkSZuA6STFK5O8OMmc9ngx8PPpNJ5kM7qE+GNV9ZlWfH2b+kx7XtHKlwM7Dxw+H7h2LeXzh5RPFWMNVfWBqtqrqvaaN2/aeb4kSZIkaRMxnaT4ZcDzgZ8B1wHPbWVTaitBHw9cXlXvGdh1OrBqBenFwOcGyl/SVqHeB7ilTX0+E9g/ybZtga39gTPbvtuS7NNivWRCW8NiSJIkSZK02lR/pxiAqvoJ8Oz1aPtJwJ8ClyRZ2sreCBwDnJrkMOAnwPPavjOAg4BlwK+Al7b4NyZ5B3BBq/f2qrqxbb8SOBHYEvhSezBFDEmSJEmSVltrUry+qurbDL/vF2C/IfULOGKStk4AThhSvgR49JDynw+LIUmSJEnSoOlMn5YkSZIkaZNkUixJkiRJ6q21JsVJ3jywvcVouyNJkiRJ0vhMmhQneV2SJ9KtNr3KOaPvkiRJkiRJ4zHVQltX0K3a/JAk3wIuBx6Q5OFVdcVYeidJkiRJ0ghNNX36Jro/obQMeBpwbCs/Ksl/jbhfkiRJkiSN3FQjxYuAtwAPBd4DXAT8sqpeOo6OSZIkSZI0apOOFFfVG6tqP+Bq4KN0CfS8JN9O8vkx9U+SJEmSpJGZaqR4lTOr6gLggiSvrKonJ9l+1B2TJEmSJGnU1vonmarqdQMv/6yV3TCqDkmSJEmSNC5rTYoHVdVFo+qIJEmSJEnjtk5JsSRJkiRJmxKTYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9ZZJsSRJkiSpt0yKJUmSJEm9ZVIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeMimWJEmSJPWWSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknprZElxkhOSrEhy6UDZW5P8NMnS9jhoYN8bkixLckWSAwbKF7WyZUmOGijfNcl5Sa5M8skkm7fyLdrrZW3/glGdoyRJkiRp4zbKkeITgUVDyt9bVbu3xxkASXYDDgUe1Y75tyRzkswB3g8cCOwGvLDVBXhXa2shcBNwWCs/DLipqh4GvLfVkyRJkiTpbkaWFFfVN4Ebp1n9YOCUqrq9qn4ELAP2bo9lVXVVVf0WOAU4OEmAfYHT2vEnAYcMtHVS2z4N2K/VlyRJkiRpDTNxT/GRSS5u06u3bWU7AdcM1FneyiYrfwBwc1XdMaF8jbba/lta/btJcniSJUmWrFy58p6fmSRJkiRpozLupPg44KHA7sB1wLtb+bCR3FqP8qnaunth1Qeqaq+q2mvevHlT9VuSJEmStAkaa1JcVddX1Z1VdRfwQbrp0dCN9O48UHU+cO0U5TcA2ySZO6F8jbba/q2Z/jRuSZIkSVKPjDUpTrLjwMvnAKtWpj4dOLStHL0rsBA4H7gAWNhWmt6cbjGu06uqgK8Dz23HLwY+N9DW4rb9XOBrrb4kSZIkSWuYu/Yq6yfJJ4CnAdsnWQ68BXhakt3ppjNfDfwFQFVdluRU4HvAHcARVXVna+dI4ExgDnBCVV3WQrweOCXJ3wPfBY5v5ccDH0myjG6E+NBRnaMkSZIkaeM2sqS4ql44pPj4IWWr6h8NHD2k/AzgjCHlV/H76deD5b8BnrdOnZUkSZIk9dJMrD4tSZIkSdKsYFIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeMimWJEmSJPWWSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIkSZKk3jIpliRJkiT1lkmxJEmSJKm3TIolSZIkSb1lUixJkiRJ6q25M90BTd+Co744012QJEmSpE2KI8WSJEmSpN5ypFhjtSFGu68+5pkboCeSJEmS5EixJEmSJKnHTIolSZIkSb1lUixJkiRJ6i2TYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9dbIkuIkJyRZkeTSgbLtkpyV5Mr2vG0rT5JjkyxLcnGSPQaOWdzqX5lk8UD5nkkuacccmyRTxZAkSZIkaaJRjhSfCCyaUHYU8NWqWgh8tb0GOBBY2B6HA8dBl+ACbwGeAOwNvGUgyT2u1V113KK1xJAkSZIkaQ0jS4qr6pvAjROKDwZOatsnAYcMlJ9cnXOBbZLsCBwAnFVVN1bVTcBZwKK2b6uqOqeqCjh5QlvDYkiSJEmStIZx31O8Q1VdB9CeH9jKdwKuGai3vJVNVb58SPlUMe4myeFJliRZsnLlyvU+KUmSJEnSxmm2LLSVIWW1HuXrpKo+UFV7VdVe8+bNW9fDJUmSJEkbuXEnxde3qc+05xWtfDmw80C9+cC1aymfP6R8qhiSJEmSJK1h3Enx6cCqFaQXA58bKH9JW4V6H+CWNvX5TGD/JNu2Bbb2B85s+25Lsk9bdfolE9oaFkOSJEmSpDXMHVXDST4BPA3YPslyulWkjwFOTXIY8BPgea36GcBBwDLgV8BLAUsK4zcAABM9SURBVKrqxiTvAC5o9d5eVasW73ol3QrXWwJfag+miCFJkiRJ0hpGlhRX1Qsn2bXfkLoFHDFJOycAJwwpXwI8ekj5z4fFkCRJkiRpotmy0JYkSZIkSWNnUixJkiRJ6i2TYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9ZZJsSRJkiSpt0yKJUmSJEm9ZVIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTeMimWJEmSJPXW3JnugCRJkiRp/S046ov3uI2rj3nmBujJxsmkWOtkQ/yDkyRJkqTZwunTkiRJkqTeMimWJEmSJPWWSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIkSZKk3jIpliRJkiT1lkmxJEmSJKm3TIolSZIkSb01I0lxkquTXJJkaZIlrWy7JGclubI9b9vKk+TYJMuSXJxkj4F2Frf6VyZZPFC+Z2t/WTs24z9LSZIkSdJsN5MjxU+vqt2raq/2+ijgq1W1EPhqew1wILCwPQ4HjoMuiQbeAjwB2Bt4y6pEutU5fOC4RaM/HUmSJEnSxmY2TZ8+GDipbZ8EHDJQfnJ1zgW2SbIjcABwVlXdWFU3AWcBi9q+rarqnKoq4OSBtiRJkiRJWm2mkuICvpLkwiSHt7Idquo6gPb8wFa+E3DNwLHLW9lU5cuHlEuSJEmStIa5MxT3SVV1bZIHAmcl+f4UdYfdD1zrUX73hruE/HCABz/4wVP3WJIkSZK0yZmRkeKqurY9rwA+S3dP8PVt6jPteUWrvhzYeeDw+cC1aymfP6R8WD8+UFV7VdVe8+bNu6enJUmSJEnayIw9KU5y3yT3X7UN7A9cCpwOrFpBejHwubZ9OvCStgr1PsAtbXr1mcD+SbZtC2ztD5zZ9t2WZJ+26vRLBtqSJEmSJGm1mZg+vQPw2fZXkuYCH6+qLye5ADg1yWHAT4DntfpnAAcBy4BfAS8FqKobk7wDuKDVe3tV3di2XwmcCGwJfKk9JEmSJElaw9iT4qq6CnjskPKfA/sNKS/giEnaOgE4YUj5EuDR97izkiRJkqRN2mz6k0ySJEmSJI2VSbEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIkSZKk3jIpliRJkiT1lkmxJEmSJKm3TIolSZIkSb1lUixJkiRJ6i2TYkmSJElSb5kUS5IkSZJ6y6RYkiRJktRbJsWSJEmSpN4yKZYkSZIk9dbcme6AtK4WHPXFe3T81cc8cwP1RJIkSdLGzpFiSZIkSVJvmRRLkiRJknrL6dOSes8p+ZIkSf3lSLEkSZIkqbdMiiVJkiRJvWVSLEmSJEnqLe8plqR76J7ekwzelyxJkjRTTIolbdQ2REIqSZKk/nL6tCRJkiSptxwpliRJkrTO/JOG2lSYFEuSJEkaO9fk0GyxyU6fTrIoyRVJliU5aqb7I0mSJEmafTbJkeIkc4D3A/8dWA5ckOT0qvrezPZM0kQulNXx23JJGg+n/GqQ//8KNtGkGNgbWFZVVwEkOQU4GDAplhc/Sb3gtU6SpOnZVJPinYBrBl4vB54wQ32R7sZfVjUKs2HU3Z/LDWc2fJ6zoQ/+TGnQbPiZnA19AP9trDJbPg9t3FJVM92HDS7J84ADqurl7fWfAntX1V9OqHc4cHh7+XDgirF2dN1tD9zQo7gzGbuP5zyTsft4zjMZ23M29qYat6+xPWdjb6px+xq7j+c8LrtU1byJhZvqSPFyYOeB1/OBaydWqqoPAB8YV6fuqSRLqmqvvsSdydh9POeZjN3Hc57J2J6zsTfVuH2N7Tkbe1ON29fYfTznmbaprj59AbAwya5JNgcOBU6f4T5JkiRJkmaZTXKkuKruSHIkcCYwBzihqi6b4W5JkiRJkmaZTTIpBqiqM4AzZrofG9hMTfWeySnmnnM/YvfxnGcytuds7E01bl9je87G3lTj9jV2H895Rm2SC21JkiRJkjQdm+o9xZIkSZIkrZVJ8SyX5IQkK5JcOlPxkjwvyWVJ7koyktXokuyc5OtJLm+xXj3G2PdOcn6Si1qst7XyI5MsS1JJth9F7BZnTpLvJvnCmONeneSSJEuTLGllI3+/W5xtkpyW5PvtM3/iqGMneXg711WPW5O8Zozn/FctzqVJPtF+7kb+WSd5dYt5WZLXtLKRnPMk14/tkpyV5Mr2vG0rf0SSc5LcnuRvRhR76HkmeUC73vwiyftGFPuf2s/3xUk+m2SbDR17krjvaDGXJvlKkge18pG/3wP7/mbwZ3pMn/Vbk/x04N/3Qa18pO93K//LJFe0n7V/3NBxJ4ud5JMD53t1kqUbOvYkcXdPcm6LuyTJ3q18HJ/zY1uMS5J8PslWrXxDnvNkv4+M/Fo2ReyRX8umiD3Sa9kUcUd+LZss9sD+kVzLpjjncVzHJj3njOFaNqtVlY9Z/ACeCuwBXDpT8YBH0v0d57OBvUYUd0dgj7Z9f+AHwG5jih3gfm17M+A8YB/gccAC4Gpg+xG+538NfBz4Qns9rrh3a38c73eLcxLw8ra9ObDNuGK3mHOAnwG7jOlnbCfgR8CW7fWpwJ+N+rMGHg1cCtyHbg2J/wQWjuqcJ7l+/CNwVNs+CnhX234g8HjgaOBvRhR76HkC9wWeDLwCeN+IYu8PzG3b7xo47w0We5K4Ww1svwr493G93618Z7pFLn+86md6TJ/1W4e1PYb3++nt39UWq851XD9jE/a/G/i7MZ3zV4AD2/ZBwNlj/JwvAP6obb8MeMcIznmy30dGfi2bIvbIr2VTxB7ptWyKuCO/lk0Wu70e2bVsinN+67B2x/Q5j+VaNpsfjhTPclX1TeDGmYxXVZdX1RUjjntdVX2nbd8GXA7sNKbYVVW/aC83a4+qqu9W1dWjjJ1kPvBM4EMD/Rl53MmM4/1u3+w/FTi+xfxtVd08jtgD9gN+WFU/HmPcucCWSebSJanXjuGzfiRwblX9qqruAL4BPGdU5zzJ9epgui9BaM+HtLorquoC4Hejij3ZeVbVL6vq28BvRhj7K+09BzgXmL+hY08S99aBl/cFqpWP/P1u3gu8blXcMcceVnek7zfwSuCYqrq91VmxoeNOERuAJAGeD3xiQ8eeJG4BW7XtrYFrW91xfM4PB77Zts8C/rjV3ZDnPPT3EcZwLVvX34XGcd6jvpZNEXfk17IpPmsY4bVsLXGH1R/Hz/dYrmWzmUmxZp0kC+hG0M4bY8w56aaerQDOqqpxxf4XuovuXWOKN6iAryS5MMnhY4z7EGAl8OF008Y/lOS+Y4wP3d8u/8S4glXVT4F/Bn4CXAfcUlVfGUPoS4GntulP96Eb1dl5DHEH7VBV10H3nzHdN+198zLgS+MKluToJNcALwL+boxxnw38tKouGlfMCY5s0y1PWDW1dQz+EHhKkvOSfCPJ48cUd9BTgOur6soxxXsN8E/tZ+yfgTeMKS5017Rnt+3nMeLr2YTfR8Z6LZuJ34WmEXuk17KJccd5LRuMPc5r2ZD3emzXsQmxZ8O1bEaZFGtWSXI/4NPAayZ8SzhSVXVnVe1O9w3o3kkePeqYSZ4FrKiqC0cdaxJPqqo9gAOBI5I8dUxx59JNiTuuqh4H/JJuKtpYJNmc7peqT40x5rZ0owy7Ag8C7pvkxaOOW1WX0013Owv4MnARcMeUB2mDSvImuvf8Y+OKWVVvqqqdW8wjxxGzfenyJsaYhE9wHPBQYHe6L57ePaa4c4Ft6W65+Vvg1DZyO04vZIxf8tGNKP1V+xn7K9qsnzF5Gd3/VxfSTf387agCzdTvI7M19qivZcPijutaNhib7hzHci0bcs5ju44NiT0brmUzyqRYs0aSzej+gX6sqj4zE32oqpvp7tlZNIZwTwKeneRq4BRg3yQfHUNcAKpq9ZQ34LPA3mMKvRxYPjAafxpdkjwuBwLfqarrxxjzGcCPqmplVf0O+Azw38YRuKqOr6o9quqpdFMRxzWatMr1SXYEaM8rxhx/xiRZDDwLeFFVzcTfP/w4bXrpGDyU7kufi9o1bT7wnSR/MI7gVXV9+3LzLuCDjPd69pnqnE8362dkCyRO1G7H+J/AJ8cVE1hMdw2D7svFcb3XVNX3q2r/qtqT7ouAH44iziS/j4zlWjaTvwtNFnvU17JpnPPIrmVDYo/lWjbsnMd1HZvk/Z7Ra9lsYFKsWaF9G3U8cHlVvWfMsefl96spbkmXwHx/1HGr6g1VNb+qFtBN5/1aVY189BAgyX2T3H/VNt1CGmNZ4byqfgZck+ThrWg/4HvjiN2Me1QFumnT+yS5T/tZ34/uPp6RS/LA9vxgul+ex33up9P9Ek17/tyY48+IJIuA1wPPrqpfjTHuwoGXz2YM1zKAqrqkqh5YVQvaNW053WIuPxtH/FXJSvMcxnQ9A/4D2Lf14Q/pFg68YUyxof1/VVXLxxjzWuCP2va+jPGLtoHr2b2ANwP/PoIYk/0+MvJr2Qz/LjQ09qivZVPEHfm1bFjscVzLpjjnkV/HpvgZm+lr2cyrWbDal4/JH3S/wF5Hd1P/cuCwccej+4e5HLgduB44cwRxn0x3j+vFwNL2OGhMsR8DfLfFvpTfr+D5qhb7DrpfAj40wvf9afx+9emRx6W7r/ei9rgMeFMrH/n73eLsDixp7/l/0E3ZGcdnfR/g58DWA2XjOue30f2nfinwEWCLMX3W36L70uEiYL9RnvMk148HAF+l+8X5q8B2re4ftDq3Aje37a02cOxJz5Nuxe8bgV+0Ortt4NjLgGv4/fXs3zd07Enifrr9jF0MfJ5uwZqxvN8T9l/N71dsHcdn/RHgknbepwM7jun93hz4aHvPvwPsO66fsVZ+IvCKIfVHec5PBi6ku6acB+w5xs/51XSr5f4AOAbICM55st9HRn4tmyL2yK9lU8Qe6bVsirgjv5ZNFnvIv6UNei2b4pzHcR2bLPZYrmWz+ZF2spIkSZIk9Y7TpyVJkiRJvWVSLEmSJEnqLZNiSZIkSVJvmRRLkiRJknrLpFiSJEmS1FsmxZIk3QNJ/iHJ05IckuSodTx2XpLzknw3yVPWI/aDkpy2Hsd9KMlu63rchpDkFzMRV5KkyfgnmSRJugeSfA14JvBO4LSq+r/rcOyhwIFVtXhU/Zttkvyiqu430/2QJGkVR4olSVoPSf4pycXA44FzgJcDxyX5uyF1d0ny1SQXt+cHJ9kd+EfgoCRLk2w54Zirk7wzyTlJliTZI8mZSX6Y5BWtzoIkl7btRyU5v7V1cZKFSe6b5ItJLkpyaZIXtLpnJ9mrbf8iydGtzrlJdmjlD22vL0jy9mEjvEneleR/Dbx+a5LXJrlfO8/vJLkkycFDjn1aki8MvH5fkj9r23sm+UaSC9s579jKX5Xke+38TlmnD0ySpEmYFEuStB6q6m/pEuET6RLji6vqMVX19iHV3wecXFWPAT4GHFtVS4G/Az5ZVbtX1a+HHHdNVT0R+FaL81xgH2BYjFcA/1pVuwN7AcuBRcC1VfXYqno08OUhx90XOLeqHgt8E/jzVv6vrb3HA9dO8jacArxg4PXzgU8BvwGeU1V7AE8H3p0kk7SxhiSbAf8HeG5V7QmcABzddh8FPK69j6+YTnuSJK2NSbEkSevvccBS4BHA96ao90Tg4237I8CTp9n+6e35EuC8qrqtqlYCv0myzYS65wBvTPJ6YJeWZF8CPKON6D6lqm4ZEuO3wKoR2wuBBQN9/lTb/jhDVNV3gQe2e5sfC9xUVT8BAryzjaT/J7ATsMM0z/nhwKOBs5IsBd4MzG/7LgY+luTFwB3TbE+SpCnNnekOSJK0sWlTn0+kS9ZuAO7TFWcp8MRJRn0HTXdBj9vb810D26ter/F/eFV9PMl5dPc3n5nk5VX1tSR7AgcB/5DkK0NGsn9Xv19g5M6J7U7DaXQj2H9AN3IM8CJgHrBnVf0uydXAvSccdwdrfjm/an+Ay9oI+UTPBJ4KPBv430keVVUmx5Kke8SRYkmS1lFVLW3TlH8A7AZ8DThgimnQ/wUc2rZfBHx7Q/cpyUOAq6rqWLoR5sckeRDwq6r6KPDPwB7r0OS5wB+37UOnqHdK2/9cugQZYGtgRUuInw7sMuS4HwO7JdkiydbAfq38CmBekie289qs3S99L2Dnqvo68DpgG8AFuyRJ95gjxZIkrYck8+imC9+V5BFVNdX06VcBJyT5W2Al8NIRdOkFwIuT/A74Gd19x48H/inJXcDvgFeuQ3uvAT6a5LXAF4FhU6+pqsuS3B/4aVVd14o/Bnw+yRK66eXfH3LcNUlOpZsSfSXw3Vb+2yTPBY5tyfJc4F/ovoD4aCsL8N6qunkdzkeSpKH8k0ySJOluktwH+HVVVfvTUS+sqrutIi1J0sbOkWJJkjTMnsD72qrRNwMvm+H+SJI0Eo4US5IkSZJ6y4W2JEmSJEm9ZVIsSZIkSeotk2JJkiRJUm+ZFEuSJEmSesukWJIkSZLUWybFkiRJkqTe+n9txqkH7ceyjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of missing values\n",
    "x = azdias.isnull().sum(axis=1)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.hist(x, bins=np.linspace(min(x), max(x)+1))\n",
    "\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 10))\n",
    "plt.ylabel('# of rows')\n",
    "plt.xlabel('# of missing values')\n",
    "plt.title('Distribution of missing values');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_null_rows = azdias.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = (azdias_null_rows.value_counts().sort_index().cumsum()/len(azdias)) * 100\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(x)\n",
    "\n",
    "plt.yticks(np.arange(0, 100+5, 5))\n",
    "plt.xticks(np.arange(min(x.index), max(x.index)+1, 10))\n",
    "plt.ylabel('Share of rows %')\n",
    "plt.xlabel('# of missing values')\n",
    "plt.title('Cumulative Distribution of missing values')\n",
    "plt.savefig('dist_missing_rows.png', dpi=500);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891221.000000\n",
       "mean         42.038960\n",
       "std          76.354371\n",
       "min           1.000000\n",
       "25%           7.000000\n",
       "50%          10.000000\n",
       "75%          20.000000\n",
       "max         265.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias_null_rows.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16    64.306721\n",
       "17    66.954437\n",
       "18    69.518559\n",
       "19    71.136228\n",
       "20    75.611885\n",
       "21    79.330267\n",
       "22    79.977806\n",
       "23    80.487668\n",
       "24    80.799712\n",
       "25    81.115458\n",
       "26    81.729896\n",
       "27    82.211258\n",
       "28    82.347813\n",
       "29    82.437577\n",
       "30    82.505237\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[15:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~82% of rows have 16 or less missing values. Dropping all rows with more than 16 missing values results in a loss of 18% of rows. This is were the line is drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old shape: (891221, 366)\n",
      "159296 rows and 15 columns dropped.\n",
      "New shape: (731925, 351) \n",
      "\n",
      "Datatypes:\n",
      "float64    265\n",
      "int64       85\n",
      "object       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "azdias_clean = clean_dataframe(azdias, drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old shape: (191652, 366)\n",
      "59550 rows and 15 columns dropped.\n",
      "New shape: (132102, 351) \n",
      "\n",
      "Datatypes:\n",
      "float64    260\n",
      "int64       90\n",
      "object       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "customers_clean = clean_dataframe(customers.drop(['CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP'], axis=1), drop_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute, transform, and standardize features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classify attribute types and build pipelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation should be based on the type of attribute:\n",
    "* **Skewed continuous:** There are (at least) 7 attributes in the dataset that are continuous numbers, like number of people in household. In contrast most other numerical values are discrete and denote a specific classification. Continuous numbers that show highly skewed distribution should be log-transformed first before imputation. Missing values are then median imputed after transformation.\n",
    "* **Binary:** Some attributes only take 2 (non-missing) values. Missing values should be imputed based on the most common value.\n",
    "* **Categorical:** These will be one-hot encoded first and then imputed like binary attributes (most common value).\n",
    "* **Numerical:** Missing values of numerical attributes will be imputed based on the median value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Skewed continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_cols = ['ANZ_HAUSHALTE_AKTIV', 'ANZ_HH_TITEL', 'ANZ_PERSONEN', 'ANZ_TITEL', 'GEBURTSJAHR', 'KBA13_ANZAHL_PKW', 'MIN_GEBAEUDEJAHR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANZ_HAUSHALTE_AKTIV, Skewness: 8.7\n",
      "ANZ_HH_TITEL, Skewness: 22.4\n",
      "ANZ_PERSONEN, Skewness: 1.8\n",
      "ANZ_TITEL, Skewness: 18.1\n",
      "KBA13_ANZAHL_PKW, Skewness: 1.3\n",
      "MIN_GEBAEUDEJAHR, Skewness: 3.1\n"
     ]
    }
   ],
   "source": [
    "skewed_cols = []\n",
    "for col in continuous_cols:\n",
    "    try:\n",
    "        skewness = skew(azdias_clean[col].dropna())\n",
    "        print(\"{}, Skewness: {}\".format(col, round(skewness,1)))\n",
    "        if abs(skewness) > 1:\n",
    "                        skewed_cols.append(col)\n",
    "    except:\n",
    "         pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ANZ_HAUSHALTE_AKTIV',\n",
       " 'ANZ_HH_TITEL',\n",
       " 'ANZ_PERSONEN',\n",
       " 'ANZ_TITEL',\n",
       " 'KBA13_ANZAHL_PKW',\n",
       " 'MIN_GEBAEUDEJAHR']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewed_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform-impute Pipeline\n",
    "skew_pipeline = Pipeline([\n",
    "    ('log_transform', FunctionTransformer(np.log1p)),\n",
    "    ('log_impute', SimpleImputer(missing_values=np.nan, strategy='median')),\n",
    "    ('log_scale', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = azdias_clean.select_dtypes(['float64', 'int64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_cols = []\n",
    "for col in num_cols:\n",
    "    n_unique = azdias_clean[col].dropna().nunique()\n",
    "    if n_unique == 2:\n",
    "        binary_cols.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DSL_FLAG',\n",
       " 'GREEN_AVANTGARDE',\n",
       " 'HH_DELTA_FLAG',\n",
       " 'KBA05_SEG6',\n",
       " 'KONSUMZELLE',\n",
       " 'OST_WEST_KZ',\n",
       " 'SOHO_KZ',\n",
       " 'UNGLEICHENN_FLAG',\n",
       " 'VERS_TYP',\n",
       " 'ANREDE_KZ']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform-impute Pipeline\n",
    "binary_pipeline = Pipeline([('bin_impute', SimpleImputer(missing_values=np.nan, strategy='most_frequent'))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CAMEO_DEU_2015']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = list(azdias_clean.select_dtypes(['object']).columns)\n",
    "categorical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_pipeline = Pipeline([\n",
    "    ('bin_impute', SimpleImputer(missing_values=np.nan, strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "numerical_cols = list(set(azdias_clean.columns) - set(skewed_cols) - set(binary_cols) - set(categorical_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform-impute Pipeline\n",
    "numerical_pipeline = Pipeline([\n",
    "    ('num_impute', SimpleImputer(missing_values=np.nan, strategy='median')),\n",
    "    ('num_scale', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformers = [('skewed', skew_pipeline, skewed_cols),\n",
    "               ('binary', binary_pipeline, binary_cols),\n",
    "               ('categorical', categorical_pipeline, categorical_cols),\n",
    "               ('numerical', numerical_pipeline, numerical_cols)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_transformer = ColumnTransformer(transformers = transformers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_trans = column_transformer.fit_transform(azdias_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get column names from one hot encoded features\n",
    "dummy_cols = list(column_transformer.transformers_[2][1].named_steps['onehot'].get_feature_names(categorical_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create list of column names for transformed dataframe\n",
    "column_names = skewed_cols + binary_cols + dummy_cols + numerical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create transformed dataframe\n",
    "azdias_trans = pd.DataFrame(azdias_trans, columns=[column_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    394\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all features are numerical\n",
    "azdias_trans.get_dtype_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Customer Segmentation Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cleaned and transformed Azdias dataset has 394 different features. The feature range can be reduced by applying PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit PCA object\n",
    "pca = PCA().fit(azdias_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scree plot\n",
    "fig = pca_scree_plot(pca)\n",
    "fig.savefig('scree_plot.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_variance = pd.DataFrame(np.cumsum(pca.explained_variance_ratio_), columns=['cumulative_variance'])\n",
    "cum_variance.set_index(cum_variance.index+1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220 components explain 95% of variance in the dataset.\n"
     ]
    }
   ],
   "source": [
    "num_components = cum_variance[cum_variance['cumulative_variance']>.95].index[0]\n",
    "print('{} components explain 95% of variance in the dataset.'.format(num_components))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the close to 400 features in the dataset there are **220** that explain 95% of the variance. The dataset can be reduced to almost half of its feature range while only losing 5% of variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=220).fit(azdias_trans)\n",
    "azdias_reduced = pd.DataFrame(pca.transform(azdias_trans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_three_pca = pca.explained_variance_ratio_[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first three components explain 19.0% of variance.\n"
     ]
    }
   ],
   "source": [
    "print(\"The first three components explain {:.1%} of variance.\".format(top_three_pca.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 1 explains 8.6% of variance and is determined by: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MOBI_REGIO</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANTG1</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_FEIN</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA05_ANTG1</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                weight\n",
       "MOBI_REGIO        0.14\n",
       "PLZ8_ANTG1        0.13\n",
       "KBA13_ANTG1       0.13\n",
       "LP_STATUS_FEIN    0.13\n",
       "KBA05_ANTG1       0.13"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Component 1 explains {:.1%} of variance and is determined by: \".format(top_three_pca[0]))\n",
    "map_component_to_features(pca, 0, azdias_trans.columns).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANTG4</th>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_ANTG3</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_HAUSHALTE_AKTIV</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     weight\n",
       "PLZ8_ANTG4            -0.12\n",
       "KBA13_ANTG4           -0.12\n",
       "KBA13_ANTG3           -0.13\n",
       "PLZ8_ANTG3            -0.13\n",
       "ANZ_HAUSHALTE_AKTIV   -0.13"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_component_to_features(pca, 0, azdias_trans.columns).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Component 1 is mainly determined by the mobility and social status of the person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 2 explains 5.3% of variance and is determined by: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ONLINE_AFFINITAET</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_ANZ_24</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_TYP_2</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       weight\n",
       "ONLINE_AFFINITAET        0.15\n",
       "PRAEGENDE_JUGENDJAHRE    0.15\n",
       "D19_GESAMT_ANZ_24        0.14\n",
       "CJT_TYP_2                0.14\n",
       "FINANZ_SPARER            0.14"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Component 2 explains {:.1%} of variance and is determined by: \".format(top_three_pca[1]))\n",
    "map_component_to_features(pca, 1, azdias_trans.columns).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_DATUM</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CJT_TYP_5</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_VERSAND_ONLINE_DATUM</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_ONLINE_DATUM</th>\n",
       "      <td>-0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          weight\n",
       "D19_GESAMT_DATUM           -0.13\n",
       "FINANZ_VORSORGER           -0.13\n",
       "CJT_TYP_5                  -0.13\n",
       "D19_VERSAND_ONLINE_DATUM   -0.13\n",
       "D19_GESAMT_ONLINE_DATUM    -0.14"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_component_to_features(pca, 1, azdias_trans.columns).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Component 2 is determined by online affinity, a dominating movement in the person's youth, and the person's financial behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 3 explains 5.1% of variance and is determined by: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KBA13_HERST_BMW_BENZ</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_SEG_OBEREMITTELKLASSE</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_MERCEDES</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_BMW</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_SITZE_4</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             weight\n",
       "KBA13_HERST_BMW_BENZ           0.18\n",
       "KBA13_SEG_OBEREMITTELKLASSE    0.15\n",
       "KBA13_MERCEDES                 0.15\n",
       "KBA13_BMW                      0.15\n",
       "KBA13_SITZE_4                  0.14"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Component 3 explains {:.1%} of variance and is determined by: \".format(top_three_pca[2]))\n",
    "map_component_to_features(pca, 2, azdias_trans.columns).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KBA13_HALTER_25</th>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_KMH_180</th>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_KMH_140_210</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_SEG_KLEINWAGEN</th>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KBA13_SITZE_5</th>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      weight\n",
       "KBA13_HALTER_25        -0.12\n",
       "KBA13_KMH_180          -0.12\n",
       "KBA13_KMH_140_210      -0.13\n",
       "KBA13_SEG_KLEINWAGEN   -0.13\n",
       "KBA13_SITZE_5          -0.15"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_component_to_features(pca, 2, azdias_trans.columns).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Component 3 is determined by the car a person owns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The people in the general population dataset can be clustered by applying K-means clustering. Applying k-Means++ algorithm increases the efficiency. The final number of clusters is determined by using the elbow method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "sse = []\n",
    "list_k = list(range(1, 31))\n",
    "\n",
    "for k in list_k:\n",
    "    km = KMeans(n_clusters=k, init='k-means++')\n",
    "    km.fit(azdias_reduced.sample(20000))\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sse against k\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(list_k, sse, '-o')\n",
    "plt.title('k-means Clustering Elbow Plot')\n",
    "plt.xlabel(\"Number of clusters k\")\n",
    "plt.ylabel(\"Sum of squared distance\")\n",
    "plt.xticks(list(range(1,31)))\n",
    "plt.savefig('elbow_plot.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no clear elbow in the plot. The inertia or sum of squared distances drops significantly until around 6 clusters and then basically flattens out with a few further drops at 14, 20, 23, ... clusters. At 13, inertia increases a bit so 12 clusters are chosen as final number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform, reduce and cluster cleaned customers dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set PCA and KMeans parameters\n",
    "pca_components = 220\n",
    "kmeans_clusters = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create cluster pipeline\n",
    "cluster_pipeline = Pipeline([\n",
    "    ('transform', column_transformer),\n",
    "    ('reduce', PCA(n_components = pca_components)),\n",
    "    ('cluster', KMeans(n_clusters = kmeans_clusters, init = 'k-means++'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit cluster pipeline and create clustered general population dataframe\n",
    "cluster_pipeline.fit(azdias_clean)\n",
    "azdias_clustered = pd.DataFrame(cluster_pipeline.predict(azdias_clean), columns = ['Cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create clustered customers dataframe\n",
    "customers_clustered = pd.DataFrame(cluster_pipeline.predict(customers_clean), columns = ['Cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_clustered.to_pickle('azdias_clustered')\n",
    "customers_clustered.to_pickle('customers_clustered')\n",
    "#azdias_clustered = pd.read_pickle('azdias_clustered')\n",
    "#customers_clustered = pd.read_pickle('customers_clustered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get cluster sizes in each dataset\n",
    "population_clusters = azdias_clustered['Cluster'].value_counts().sort_index()\n",
    "customer_clusters = customers_clustered['Cluster'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concat both dataframes to one\n",
    "clusters = pd.concat([population_clusters, customer_clusters], axis=1).reset_index()\n",
    "clusters.columns = ['cluster', 'population_count', 'customers_count']\n",
    "clusters['cluster']+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate share of each cluster\n",
    "clusters['population_share'] = clusters['population_count']/clusters['population_count'].sum()*100\n",
    "clusters['customers_share'] = clusters['customers_count']/clusters['customers_count'].sum()*100\n",
    "clusters['diff_share'] = clusters['customers_share'] - clusters['population_share']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot clusters \n",
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "\n",
    "ind = np.arange(kmeans_clusters)    # the x locations for the groups\n",
    "width = 0.35         # the width of the bars\n",
    "\n",
    "p1 = ax.bar(ind, clusters['population_share'], width, bottom=0)\n",
    "p2 = ax.bar(ind + width, clusters['customers_share'], width, bottom=0)\n",
    "\n",
    "ax.set_title('Share of clusters in Population vs Customers dataset')\n",
    "ax.set_ylabel('Share of total (%)')\n",
    "ax.set_xlabel('Cluster')\n",
    "\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(clusters['cluster'])\n",
    "ax.legend((p1[0], p2[0]), ('Population', 'Customers'))\n",
    "ax.autoscale_view()\n",
    "\n",
    "plt.savefig('clusters_1.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows how clusters are distributed across both datasets. Clusters are equally spread across the general population and show only small differences in size. This can be expected from dataset that represents the general population very well.\n",
    "\n",
    "In contrast, the customers dataset exhibits a large imbalance across clusters. Cluster 9 and 10 stand out specifically: More than 50% of customers are mapped to these two clusters. The remaining share is distributed across clusters 5 and 12 while all other clusters only contain a small share of customers.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cluster differences\n",
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "\n",
    "ind = np.arange(kmeans_clusters)    # the x locations for the groups\n",
    "width = 0.35         # the width of the bars\n",
    "\n",
    "bar_colors = ('grey','red','grey','grey','grey','grey','grey','red','green','green','grey','grey')\n",
    "\n",
    "p1 = ax.bar(ind, clusters['diff_share'], width, color = bar_colors)\n",
    "\n",
    "ax.set_title('Overrepresentation (green) and Underrepresentation (red) of clusters in Customers dataset vs Population')\n",
    "ax.set_ylabel('Difference in share (%)')\n",
    "ax.set_xlabel('Cluster')\n",
    "\n",
    "ax.axhline(linewidth=.5, color='black')\n",
    "\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(clusters['cluster'])\n",
    "\n",
    "plt.savefig('clusters_2.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters 9 and 10 are significantly overrepresented in the Customers dataset. These should be the focus of a marketing campaign for the firm. On the other hand, clusters 2 and 8 show the largest underrepresentation compared to the general population and are outside of the focus group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_centers_df = get_clusters_attributes(cluster_pipeline, numerical_cols, skewed_cols, column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison_df = cluster_centers_df.T[[8,9,1,7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison_df.columns=['interested_cluster_9','interested_cluster_10','not_interested_cluster_2','not_interested_cluster_8']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "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>interested_cluster_9</th>\n",
       "      <th>interested_cluster_10</th>\n",
       "      <th>not_interested_cluster_2</th>\n",
       "      <th>not_interested_cluster_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_GROB</th>\n",
       "      <td>8.004706</td>\n",
       "      <td>4.718162</td>\n",
       "      <td>2.525255</td>\n",
       "      <td>3.350580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EINGEZOGENAM_HH_JAHR</th>\n",
       "      <td>1999.979827</td>\n",
       "      <td>2000.728004</td>\n",
       "      <td>2005.356248</td>\n",
       "      <td>2005.628696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_LEBENSPHASE_FEIN</th>\n",
       "      <td>27.026184</td>\n",
       "      <td>16.490010</td>\n",
       "      <td>7.400173</td>\n",
       "      <td>10.764894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANZ_STATISTISCHE_HAUSHALTE</th>\n",
       "      <td>2.360956</td>\n",
       "      <td>2.137019</td>\n",
       "      <td>12.066082</td>\n",
       "      <td>4.434107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_ONLINE_QUOTE_12</th>\n",
       "      <td>5.546279</td>\n",
       "      <td>0.627578</td>\n",
       "      <td>0.265035</td>\n",
       "      <td>0.747452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VK_ZG11</th>\n",
       "      <td>3.126616</td>\n",
       "      <td>5.810008</td>\n",
       "      <td>8.031530</td>\n",
       "      <td>7.756917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_KONSUMTYP_MAX</th>\n",
       "      <td>2.464837</td>\n",
       "      <td>6.535185</td>\n",
       "      <td>7.834241</td>\n",
       "      <td>7.223877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKT_DAT_KL</th>\n",
       "      <td>2.063873</td>\n",
       "      <td>3.448639</td>\n",
       "      <td>6.885743</td>\n",
       "      <td>6.474712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_ONLINE_DATUM</th>\n",
       "      <td>5.139734</td>\n",
       "      <td>9.062734</td>\n",
       "      <td>9.458925</td>\n",
       "      <td>9.040411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_GESAMT_DATUM</th>\n",
       "      <td>4.082363</td>\n",
       "      <td>8.190865</td>\n",
       "      <td>9.078505</td>\n",
       "      <td>8.562975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALTERSKATEGORIE_FEIN</th>\n",
       "      <td>12.234505</td>\n",
       "      <td>10.555965</td>\n",
       "      <td>15.770613</td>\n",
       "      <td>16.068201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP_STATUS_FEIN</th>\n",
       "      <td>8.134305</td>\n",
       "      <td>7.838847</td>\n",
       "      <td>1.992815</td>\n",
       "      <td>4.506373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_VERSAND_ONLINE_QUOTE_12</th>\n",
       "      <td>5.360404</td>\n",
       "      <td>0.386713</td>\n",
       "      <td>0.182866</td>\n",
       "      <td>0.593306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_VERSAND_ONLINE_DATUM</th>\n",
       "      <td>5.405279</td>\n",
       "      <td>9.339377</td>\n",
       "      <td>9.607193</td>\n",
       "      <td>9.245365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VK_DISTANZ</th>\n",
       "      <td>4.156976</td>\n",
       "      <td>8.132599</td>\n",
       "      <td>9.877294</td>\n",
       "      <td>9.341777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEMEINDETYP</th>\n",
       "      <td>22.619327</td>\n",
       "      <td>32.285191</td>\n",
       "      <td>17.430234</td>\n",
       "      <td>29.704421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE</th>\n",
       "      <td>7.728952</td>\n",
       "      <td>5.909049</td>\n",
       "      <td>11.944965</td>\n",
       "      <td>12.310745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D19_VERSAND_DATUM</th>\n",
       "      <td>4.818272</td>\n",
       "      <td>9.015984</td>\n",
       "      <td>9.523110</td>\n",
       "      <td>9.136726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             interested_cluster_9  interested_cluster_10  \\\n",
       "LP_LEBENSPHASE_GROB                      8.004706               4.718162   \n",
       "EINGEZOGENAM_HH_JAHR                  1999.979827            2000.728004   \n",
       "LP_LEBENSPHASE_FEIN                     27.026184              16.490010   \n",
       "ANZ_STATISTISCHE_HAUSHALTE               2.360956               2.137019   \n",
       "D19_GESAMT_ONLINE_QUOTE_12               5.546279               0.627578   \n",
       "VK_ZG11                                  3.126616               5.810008   \n",
       "D19_KONSUMTYP_MAX                        2.464837               6.535185   \n",
       "AKT_DAT_KL                               2.063873               3.448639   \n",
       "D19_GESAMT_ONLINE_DATUM                  5.139734               9.062734   \n",
       "D19_GESAMT_DATUM                         4.082363               8.190865   \n",
       "ALTERSKATEGORIE_FEIN                    12.234505              10.555965   \n",
       "LP_STATUS_FEIN                           8.134305               7.838847   \n",
       "D19_VERSAND_ONLINE_QUOTE_12              5.360404               0.386713   \n",
       "D19_VERSAND_ONLINE_DATUM                 5.405279               9.339377   \n",
       "VK_DISTANZ                               4.156976               8.132599   \n",
       "GEMEINDETYP                             22.619327              32.285191   \n",
       "PRAEGENDE_JUGENDJAHRE                    7.728952               5.909049   \n",
       "D19_VERSAND_DATUM                        4.818272               9.015984   \n",
       "\n",
       "                             not_interested_cluster_2  \\\n",
       "LP_LEBENSPHASE_GROB                          2.525255   \n",
       "EINGEZOGENAM_HH_JAHR                      2005.356248   \n",
       "LP_LEBENSPHASE_FEIN                          7.400173   \n",
       "ANZ_STATISTISCHE_HAUSHALTE                  12.066082   \n",
       "D19_GESAMT_ONLINE_QUOTE_12                   0.265035   \n",
       "VK_ZG11                                      8.031530   \n",
       "D19_KONSUMTYP_MAX                            7.834241   \n",
       "AKT_DAT_KL                                   6.885743   \n",
       "D19_GESAMT_ONLINE_DATUM                      9.458925   \n",
       "D19_GESAMT_DATUM                             9.078505   \n",
       "ALTERSKATEGORIE_FEIN                        15.770613   \n",
       "LP_STATUS_FEIN                               1.992815   \n",
       "D19_VERSAND_ONLINE_QUOTE_12                  0.182866   \n",
       "D19_VERSAND_ONLINE_DATUM                     9.607193   \n",
       "VK_DISTANZ                                   9.877294   \n",
       "GEMEINDETYP                                 17.430234   \n",
       "PRAEGENDE_JUGENDJAHRE                       11.944965   \n",
       "D19_VERSAND_DATUM                            9.523110   \n",
       "\n",
       "                             not_interested_cluster_8  \n",
       "LP_LEBENSPHASE_GROB                          3.350580  \n",
       "EINGEZOGENAM_HH_JAHR                      2005.628696  \n",
       "LP_LEBENSPHASE_FEIN                         10.764894  \n",
       "ANZ_STATISTISCHE_HAUSHALTE                   4.434107  \n",
       "D19_GESAMT_ONLINE_QUOTE_12                   0.747452  \n",
       "VK_ZG11                                      7.756917  \n",
       "D19_KONSUMTYP_MAX                            7.223877  \n",
       "AKT_DAT_KL                                   6.474712  \n",
       "D19_GESAMT_ONLINE_DATUM                      9.040411  \n",
       "D19_GESAMT_DATUM                             8.562975  \n",
       "ALTERSKATEGORIE_FEIN                        16.068201  \n",
       "LP_STATUS_FEIN                               4.506373  \n",
       "D19_VERSAND_ONLINE_QUOTE_12                  0.593306  \n",
       "D19_VERSAND_ONLINE_DATUM                     9.245365  \n",
       "VK_DISTANZ                                   9.341777  \n",
       "GEMEINDETYP                                 29.704421  \n",
       "PRAEGENDE_JUGENDJAHRE                       12.310745  \n",
       "D19_VERSAND_DATUM                            9.136726  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_df[comparison_df.std(axis=1)>2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at attributes with the highest differences among the 4 focus clusters allows to define specific characteristics of the groups. \n",
    "\n",
    "Interested clusters:\n",
    "* Are high-income couples or families.\n",
    "* Live in more densily populated, probably more exclusive areas.\n",
    "* Are used to buying things online.\n",
    "* Are house owners and/or hold an academic title.\n",
    "* Are 50-60 years old, grew up in the 60s/70s, and likely were part of the politically active generation 68/student protesters or Berlin wall opponents.\n",
    "\n",
    "In contrast, not-interested clusters:\n",
    "* Tend to be low- to average-income singles of advanced to higher age.\n",
    "* Live in more crowded but rural areas.\n",
    "* Rarely buy or order things online.\n",
    "* Grew up in Eastern Germany in the 80s and likely participated in the communist party youth organisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "interested_cluster_9        1.499231\n",
       "interested_cluster_10       0.711209\n",
       "not_interested_cluster_2    0.075782\n",
       "not_interested_cluster_8    0.162990\n",
       "Name: D19_SOZIALES, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_df.loc['D19_SOZIALES']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison_df.to_pickle('comparison_df')\n",
    "#comparison_df = pd.read_pickle('comparison_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Supervised Learning Model\n",
    "\n",
    "Now that you've found which parts of the population are more likely to be customers of the mail-order company, it's time to build a prediction model. Each of the rows in the \"MAILOUT\" data files represents an individual that was targeted for a mailout campaign. Ideally, we should be able to use the demographic information from each individual to decide whether or not it will be worth it to include that person in the campaign.\n",
    "\n",
    "The \"MAILOUT\" data has been split into two approximately equal parts, each with almost 43 000 data rows. In this part, you can verify your model with the \"TRAIN\" partition, which includes a column, \"RESPONSE\", that states whether or not a person became a customer of the company following the campaign. In the next part, you'll need to create predictions on the \"TEST\" partition, where the \"RESPONSE\" column has been withheld."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "mailout_train = pd.read_csv('Udacity_MAILOUT_052018_TRAIN.csv', sep=';', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42962, 367)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mailout_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old shape: (42962, 366)\n",
      "9327 rows and 15 columns dropped.\n",
      "New shape: (33635, 351) \n",
      "\n",
      "Datatypes:\n",
      "float64    260\n",
      "int64       90\n",
      "object       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mailout_train_clean = pd.concat([clean_dataframe(mailout_train.drop(['RESPONSE'], axis=1), drop_cols), mailout_train['RESPONSE']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = mailout_train_clean.drop(['RESPONSE'], axis=1)\n",
    "y = mailout_train_clean['RESPONSE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {'RandomForestClassifier': RandomForestClassifier(), \n",
    "          'AdaBoostClassifier': AdaBoostClassifier(), \n",
    "          'GradientBoostingClassifier': GradientBoostingClassifier(),\n",
    "          'XGBClassifier': xgb.XGBClassifier()\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier\n",
      "Roc_auc train score = 0.95\n",
      "Roc_auc validation score = 0.58\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier\n",
      "Roc_auc train score = 0.83\n",
      "Roc_auc validation score = 0.73\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier\n",
      "Roc_auc train score = 0.89\n",
      "Roc_auc validation score = 0.75\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier\n",
      "Roc_auc train score = 0.97\n",
      "Roc_auc validation score = 0.66\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model_key in models.keys():\n",
    "    print(model_key)\n",
    "    ml_pipeline = Pipeline([\n",
    "        ('transform', column_transformer),\n",
    "        ('model', models[model_key])\n",
    "    ])\n",
    "    X, y = randomize(mailout_train_clean)\n",
    "    draw_learning_curves(X, y, ml_pipeline, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Random Forest Classifier** does not only show the lowest mean validation score but also a consisently high train score, implying high model bias. In other words, the model is strongly overfitted on the training data and provides a bad predictor for unseen data. The same can be summarized for the **XGBoost Classifier**.\n",
    "\n",
    "The **AdaBoost Classifier** and **Gradient Boosting Classifier** on the other hand show better performance. They demonstrate a decrease of model bias with increasing sample size. While AdaBoost curves already converged after ~80% of the sample is fed to the algorithm, learning curves for the Gradient Boosting Classifier keep converging with more data. In addition to that, Gradient Boosting provides the highest mean validation score, making it our choise of estimator for the forecasting model.\n",
    "\n",
    "Finally, the parameters for the Gradient Boosting Classifiers are hypertuned using grid-search on the following selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.718, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.6min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.745, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  3.3min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.793, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  4.9min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.735, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  6.5min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.760, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  8.1min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.727, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:  9.8min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.746, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed: 11.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.791, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed: 13.0min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.739, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed: 14.6min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.763, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed: 16.2min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.699, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed: 18.8min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.722, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed: 21.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.762, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed: 24.0min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.730, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed: 26.6min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.746, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed: 29.1min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.694, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed: 31.7min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.707, total=42.0min\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed: 73.7min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.777, total= 2.7min\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed: 76.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.726, total= 2.7min\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed: 79.1min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.1, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.731, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed: 81.8min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.689, total= 1.7min\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed: 83.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.713, total= 1.7min\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed: 85.1min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.784, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed: 86.7min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.708, total= 1.7min\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed: 88.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.729, total= 1.7min\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed: 90.0min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.707, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed: 91.7min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.703, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed: 93.3min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.763, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed: 95.0min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.719, total= 1.6min\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed: 96.6min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=3, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.705, total= 1.7min\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 98.3min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.671, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed: 100.9min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.652, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed: 103.6min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.688, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed: 106.2min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.692, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed: 108.8min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=2, gbc__n_estimators=100, score=0.688, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed: 111.5min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.662, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed: 114.1min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.633, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed: 116.7min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.697, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed: 119.4min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.668, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed: 122.0min remaining:    0.0s\n",
      "[CV] gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100 \n",
      "[CV]  gbc__learning_rate=0.2, gbc__max_depth=5, gbc__min_samples_split=4, gbc__n_estimators=100, score=0.701, total= 2.6min\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed: 124.7min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed: 124.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('transform',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('skewed',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('log_transform',\n",
       "                                                                                          FunctionTransformer(accept_sparse=False,\n",
       "                                                                                                              check_inverse=True,\n",
       "                                                                                                              func=<ufunc 'log1p'>,\n",
       "                                                                                                              inv_kw_args=None,\n",
       "                                                                                                              inve...\n",
       "                                                                   random_state=42,\n",
       "                                                                   subsample=1.0,\n",
       "                                                                   tol=0.0001,\n",
       "                                                                   validation_fraction=0.1,\n",
       "                                                                   verbose=0,\n",
       "                                                                   warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'gbc__learning_rate': [0.1, 0.2],\n",
       "                         'gbc__max_depth': [3, 5],\n",
       "                         'gbc__min_samples_split': [2, 4],\n",
       "                         'gbc__n_estimators': [100]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=50)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initialize with GradientBoostingClassifier\n",
    "gbc_pipeline = Pipeline([\n",
    "    ('transform', column_transformer),\n",
    "    ('gbc', GradientBoostingClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "parameters = {'gbc__learning_rate': [0.1, 0.2], 'gbc__n_estimators': [100],\n",
    "             'gbc__max_depth': [3, 5], 'gbc__min_samples_split': [2,4]}        \n",
    "        \n",
    "grid_obj = GridSearchCV(gbc_pipeline, parameters, scoring = 'roc_auc', verbose=50)\n",
    "\n",
    "# Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_obj.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gbc__learning_rate': 0.1, 'gbc__max_depth': 3, 'gbc__min_samples_split': 4, 'gbc__n_estimators': 100}\n",
      "ROC score: 0.8766\n",
      "Final ROC: 0.8779\n"
     ]
    }
   ],
   "source": [
    "# Get the estimator and predict\n",
    "print(grid_obj.best_params_)\n",
    "best_clf = grid_obj.best_estimator_\n",
    "best_predictions = best_clf.predict_proba(X)[:, 1]\n",
    "\n",
    "# Make predictions using unoptimized and the best model\n",
    "predictions = (gbc_pipeline.fit(X, y)).predict_proba(X)[:, 1]\n",
    "\n",
    "print(\"ROC score: {:.4f}\".format(roc_auc_score(y, predictions)))\n",
    "print(\"Final ROC score: {:.4f}\".format(roc_auc_score(y, best_predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ROC as scoring metric, the model performance can be increased by 0.13 %-points by using the best classifier parameters vs default settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to file in the current working directory\n",
    "pkl_filename = \"best_gradient_boosting.pkl\"  \n",
    "with open(pkl_filename, 'wb') as file:  \n",
    "    pickle.dump(best_clf, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load best GBC model\n",
    "with open(\"best_gradient_boosting.pkl\", \"rb\") as input_file:\n",
    "    best_clf = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=42, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.named_steps['gbc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the features that the classifier uses to predict customer response, D19_SOZIALES is by far the most important one. Unfortunately, the attribute is not described by the accompanying data guide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqatZMF6Dlw53X3sI17z5rpst4wDb96HNmugRJWu658yFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqakbCR5Izk+w21HZAktOT3JZkUe/ntV3/0iSLk5yf5L+SbN679u5u7JIk5yV5e5KVur7nJflWb+weSRYkuSjJxUk+0bUfmuSdQzUtTfKoXi2/SnJt7/njk1wwdM2f5klybDd+te75+kmW9sY+Lsm3klyRZGGSM5I8t+t7VNd3XpILk5zetc/uvUcXJjkuySpDNRzRrbvSaHUNvb71u8eV5Mu9vllJft1/7yRJmg4ztfNxPDBvqG0e8BHgiqqa0/s5rjdmp6p6MnAmcEiv/bZu7LbAC4AXAu8fXjTJdsCRwKur6gnAdsCVE9R690gtwNHA4b3nd07itd4NvH6UWlYHvg0cU1WPraodgbcCW3ZDPgD8R1VtX1VPBN7du/yKbv0nAZsCr+jNuxKwN/BL4LmTqG/EH4DtkqzRPX8BcO0UrpckaVJmKnycDOzZ2xGYDWwMXDPJ688BNhmto6puAPYH3pIkQ91/DxxWVRd3Y++qqs9Mufqp+RfgwCTDv8r+VcA5VfWNkYaquqCqju2ebkTv/aiq84cnrqq7gXO593uxE3ABcBQwf4q1fgd4Ufd4PoOQKEnStJqR8FFVNzH40Ny9a5oHnAgU8Nih2y6j/c82dgdOG2f+Kxm8tg2HurYDFo5T2oH9tRkEogfqauBs4DVD7dsCPxvnuk8Dn+9uxRyc5D61dLsnTwO+22seCQ2nMgh4qwxfN44TgHndvE8GfjKFayVJmpSZPHDav/Uyjz//LXv4tkv//1Z2RpIbgOcDX51g/uFdj8k4vL82cN0E42uS7R8G3sU473eSU5NckOTfAKrqewxuwXwW2Ab4eZINuuGP7cLRTcDVI7siSVZlcMvptKr6HYPwsOtka+3mmc0gwJw+Vq29mvfvzs8s+M2t/zvRcEmSgJkNH6cBuyTZAVijqsbbBRixE7A5sITBmYhRJdmSwVmLG4a6lgA73r9yR3UTsM5Q27rAjf2GqrocWETvbEZXyw69MXsD+3XXj7T9pqq+WlWvAX7Kn89wjJz52Ap4epKXdO27A48AFncHW5/Nn2+9jFbr2sBwavgG8Akmcculqo6pqrlVNXfdNR850XBJkoAZDB9VdQuDg6NfYApnC6rqNuAA4LVJ1h3u73YHjgaOrKrhv+1/HHhvksd3Y1dK8vb79wr+9BquT7JLN9+6DALA2aMMPwzof9vkq8CzesEBYM3e69g5yZrd47WBxzK4hdNf/3oGB1Hf0zXNB95YVbOrajawBbBrN88PgZd0c5Hkr4DzunMjfV8APlBViyf3LkiSNDUz/Xs+jge2Z3DWYMTwmY+3DV/UfegeD7y5a1pj5Ku2wH8C/w78v1GuO59BcDk+yUUMDmZu9ABfw2uBQ7rbID8A/l9VXTHK2kvonfHoQtSewJuSXJnkHAbf4PlQN2RHYEGS8xkcsP1cVf10lPVPA9ZM8pfAbgy+QTOyxh8YBKEXd6/9SODsrtY3AW8cpc5rquqIqb4JkiRNVu67OSBN3ZM32qZOf91nZ7qMB2zTj452vlmSNFVJFlbV3NH6ZnrnQ5IkrWAMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmZs10AVo+rLrJWv52UEnSpLjzIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpmbNdAFaPvzPlZfzT/vuOdNlPCDvOPFbM12CJK0Q3PmQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU8tF+EhyS+/xC5NcluQxSQ5Ncm2SRUkuTnJUkpV6Y2cluTHJR4bme0uSy5NUkvV77S9Ncn4334Ikz56grscnOb2b66IkX0vyqK7vqUl+mOSSrrbPJVkzyX7durv05tm7a9tnnLW+0s11QZIvJFmla39ekpu7mhcleV/vmt27ay5P8u5e+xZJftK9jycmWXWifwaSJE3WchE+RnQf2J8Cdq+qq7vmw6tqDvBE4EnAX/Yu2RW4BHhFkvTafwQ8H/jF0BLfB7bv5ns98Llxalkd+DZwVFVtVVVPAI4CNugCyEnAQVW1NfAE4LvA2t3li4H5venmAedN8PK/AmzTvcY1gDf2+s6qqjndzwe6+lYGPg3sweC9mZ/kid34f2Twvj0O+C3whgnWliRp0pab8JHkOcBngRdV1RWjDFkVWJ3Bh+mI+cARwNXA00caq+rnVbV0eIKquqWqqnv6MKCGx/S8Ejinqr7Zu/6MqroAeDPwpao6p2uvqjq5qv6nG3oW8NQkqyRZC9gKWDTOWlTV6d08BZwLbDreeOCpwOVVdWVV3QmcALy0C2E7Ayd3474E7DXBXJIkTdryEj5WA74O7FVVFw/1HZhkEXA9cGlVLQJIsgawC/At4HjuvdMwpu4WyMUMdjVeP87Q7YCF96MPBqHmP4HdgJcC35hMbV19qwCvYbCTMuIZSc5L8p0k23ZtmwC/7I25pmtbD/jfqrprqH20tfbvbj8t+MMdd062REnSCm55CR9/BH7M6LcHRm67bAg8LMm8rn1P4IyquhU4Bdi7uxUxrqo6taq2YbAb8MFpqX50JzC43TKPQTiarM8AP6yqs7rnPwM2r6rtGdySOq1rzyjX1jjt922sOqaq5lbV3Iet5rEQSdLkLC/h4x7gFcBfJHnvaAOq6o8MdgOe2zXNB56fZCmDXYj1gJ0mu2BV/RB4bP9A6pAlwI73o29k/nMZ7JCsX1WXTqamJO8HNgDe3pvnd1V1S/f4dGCVruZrgM16l28KXAfcCDwyyayhdkmSpsXyEj7odjD2BF6V5D47IN1ZhmcCVyR5OPBs4DFVNbuqZjM4hzHurZckW40cTE2yA4NzJDeNMfyrwDOTvKh3/e5JngQcCbwuydN6fa9O8uihOd4DjBqmRqntjQxu08yvqnt67Y/u1fxUBv/MbwJ+Cjyu+2bLqgx2WL7RnRk5Axj5Zs3rGNzSkiRpWiw34QOgqn4D7A4ckuSlXfPImY8LgFkMbkv8FfCDqrqjd/nXgZckWS3J25Jcw+Bv/ecnGflWy8uAC7r5Pg3s2zuAOlzLbQzC0Fu7r6xeCOwH3NAdLJ0HfKL7qutFwHOA3w3N8Z2qOmOSL/9o4FHAOUNfqd2nq/k84JPAvO5c6l3AW4DvARcBX6uqJd01BwFvT3I5gx2hz0+yBkmSJpQxPjulKdls3UfWAS8Y99eeLPPeceK3ZroESVpuJFlYVXNH61uudj4kSdKyb9bEQzSe7gzHl4ea76iqp402fhrWOxXYYqj5oKr63oOxniRJ083w8QBV1WJgTsP19m61liRJDwZvu0iSpKYMH5IkqSnDhyRJasrwIUmSmvLAqabFo7bcyt+TIUmaFHc+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1NWumC9Dy4YZf/J5Pv+kHM13G/fbmo3ee6RIkaYXhzockSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqatrCR5K9k1SSbbrns5NcMDTm6CSLklyY5Lbu8aLu2v8vyVW9trO6a96Y5Ndd28VJ3tab7wlJ/qvruyjJUV3785Oc1hv3kSTfTrJq9/xRSe5K8obemAXdPFf31luUZLMk1yRZ3Gs7vLsmSd7Z1XV+kvOSfCLJyl3/Ot3rujzJFUmOTfLwrm+r3ntwUdc3a7j+odc/8rP1GP8M+nOO/Kw81hzd+EW9NSvJHr35vpvk2ff/3wpJku5rOn+9+nzgbGAecOhoA6rqTTD4kAROrqo5I31JXgYcWFWnjXLpV6rqgCQbAJckOamqrgeOBD5WVd9OEmC74QuTHAr8BbBnVd3ZNe8LnNPV/Pmutrnd+DcC21XVAb05AJ5TVf87NP2bgZ2Ap1XVzUlWA94BrAbcCnwRWFBVr+7m+RDwr926AJdU1ZwudHwfeBlw4livf5T20VzSf1979d9nju6fQ98vgUOA70xyLUmSpmxadj6SrAU8C3gDg/DxoKiqXwNXAht1TRsB13R9VVWLh+o6CNgZeElV3d7rmg8cAGyZ5NEPoKT3Am+qqpu7Gu6oqg9X1a3d7sR2wId74w8FnpVk9tDrugv4KbDJA6hlOvwMuD3JTjNchyRpOTZdt132Ar5bVZcCv0myw/2c5/DebYHjhju7D+2VgZHbOf8M/DDJ6UkOSPKI3vDnAq8HXlRVtw7NsU5VLQROBl4xydrO6tX2tiTrAKtU1S/HGL8t8POqumekoQsZ5wFPHHpdazDYnfneGHO9auiWyarj1Ll1b9wn78cchzHY/ZhQkv2721ULbrl9eFNIkqTRTVf4mA+c0D0+gT/fVpiqA6tqTvfz2l77q5IsAS4H/nnk9klVfY7BB/nJwC7AOb0P1cuAVbr24VpHbm1Mpdbn9Gr7JJB+Z5IXdh/qv0jy1K6/Rpmn3751d+biJuDyqloyxtpf6a09p3f7aDSX9Ma9rdc+qTmq6gfA6kmeMc4aI2OPqaq5VTV3rdUfOdFwSZKAaQgfSdZjcGvjc0mWAu9icKYi4103RV+pqm2B5wFHJNlwpKOqrq2qL1TVixm8nid0XdcDLwSOTPLc3lzzgTd2tf4bsGOSLaZaUFX9BrgryWO656d3Zy0uAlYFlgA7JPnTe9wdRH1SNwb+fD5jK+Avk7xwqnU8SA4DDp7pIiRJy6fp2PnYBziuqjavqtlVtRlwFbDpNMx9L1V1NnA88FaAJLv3viGyMbAOcF1v/MUMDnEen+TJSZ4IrFxVm3S1zgY+zv0/p/IR4KiR2z3dodfVe2tfCLy7N/79wH9X1dKh13Ud8J7uZ8ZV1enAoxncOpIkaVpNR/iYD5w61HYKg8OYW2fwNdWRn5dPMFf/zMeika+sDvkog52LhwF7AEuSnAecDhzQHUr9k6r6CfBG4JtdTaPV+spJvM7+mY8vdm2fAn4I/DTJ+cCPgJ8wONcBsB+wXfc12yuA2cD+Y8x/MrDuGLc7hs9rPG0S9T7QOT7MgxAgJUlK1WjHEqSpecwGW9dBLztqpsu439589M4zXYIkLVeSLBz5NRbD/A2nkiSpqen8JWNqJMkc4Nih5lur6pkzUI4kSVNi+HgIqqpFwJwJB0qStAzytoskSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJaspvu2habLj52v6iLknSpLjzIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpmbNdAFaPtx+wRIu2uYJM13GpD3h4otmugRJWmG58yFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaWibCR5Jbeo9fmOSyJI9JcmiSa5MsSnJxkqOSrNQbOyvJjcO+kYUAAB54SURBVEk+MjTfW5JcnqSSrN9rf2mS87v5FiR59iRqOzDJ7Uke0Wt7Xjf3i3tt3+raN+vm7//8Lsk/9sY+pbt+t7Heh+75fkmO7B4fmuSdE9Xbjb27W/eCJCclWXN4/qH3+aYk6dqf0dW2aff8EUl+03/fJUl6IJapD5QkuwCfAnavqqu75sOrag7wROBJwF/2LtkVuAR4xciHZ+dHwPOBXwwt8X1g+26+1wOfm0RZ84GfAnsPtV8DHDw8uKp+WVVzRn6A1wA3A/8yNOfZ3Z8Phtu69bcD7gTe1O8c5X3+FTDyu9GfCfy8+xPg6cBPquqeB6lWSdIKZpkJH0meA3wWeFFVXTHKkFWB1YHf9trmA0cAVzP4kASgqn5eVUuHJ6iqW6qquqcPA2p4zFBNjwXWAg7hvkHhPODmJC8Y5/rVga8Ab66q67u2APsA+wG7dmMeTGcBW/VqGu19/hF/DhvPBA4fev7jB7lGSdIKZFkJH6sBXwf2qqqLh/oOTLIIuB64tKoWASRZA9gF+BZwPJPcRUiyd5KLgW8z2P0Yz/xu7rOArZNsONT/IQbBZCwfA35UVd/otT0LuKr74D8TeGGvb43+7RrgAxO+oHEkmQXsASzumsZ6n3/Mn8PGlsBJwNzu+TMZhBNJkqbFshI+/sjgA/ANo/SN3HbZEHhYknld+57AGVV1K3AKsHeSlSdaqKpOraptgL2AD04wfB5wQnfL4d+Alw/NdRb8aTfhXpLsweDWzzuGuuYDJ3SPT+Deoem2oVs275vo9YxhjS68LGCwK/T5rn2s9/lHwDOTbAEsrarbBy8hawE7AueOtkiS/buzMwt+c/dd97NUSdKKZlkJH/cArwD+Isl7RxtQVX8Evgs8t2uaDzw/yVJgIbAesNNkF6yqHwKP7R9I7UvyZOBxwH90a8xj9N2Vwxg6+5FkA+BfgVd14WikfWXgZcD7ujk/BeyRZO3J1j1J/RDz1qq6s2sf9X2uqsuAdYAXA+d0zQuBv2awS3Ovg7C9646pqrlVNXfdlWdN80uQJC2vlpXwQfchvSfwqiT32QHpzko8E7giycOBZwOPqarZVTUbeDMT3HpJslXvWx07MDhHctMYw+cDh47MX1UbA5sk2Xyo7n9n8MG9fa/5C8CnqurnQ3M+Hzivqjbr5tycwa7NXuPVPZ3GeZ/PAf4vfw4f5wAH4HkPSdI0W2bCB0BV/QbYHTgkyUu75pEzHxcAs4DPAH8F/KCq7uhd/nXgJUlWS/K2JNcAmwLnJxn5VsvLgAu6+T4N7Ns7gDpsHnDqUNupXfuww7q1SPIMBh/urx76uu3HGQSa4TlPAV451nsy5JAk14z8TPKa+xjjff4RsBmDWzUwCB9bYviQJE2zjP3ZK03edquvUSfNnj3TZUzaEy6+aKZLkKTlWpKFVTV3tL5laudDkiQt/1b4U4JJngR8eaj5jqp62kzUM1lJ1mPwS9OG7VJVY51jkSRpxq3w4aOqFgNzZrqOqeoCxkOubkmSvO0iSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkppa4b/toumx+nbb8oQFCyYeKEla4bnzIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpmbNdAFaPiy5aQlP+tKTZrqMSVn8usUzXYIkrdDc+ZAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJTMxI+kjw6yQlJrkhyYZLTkzy+6zswye1JHtEb/7wkleQNvbandG3v7J4fm+SqJIu6nx/3xu6V5PwkFydZnGSvCeobmeu8JJcmOS7JJr3+pd08I2t9sms/M8nc3rjZSS4YZ50XJFnYzbUwyc69vh279suTfDJJuvaXJ1mS5J5R1rqtV9PRE7zGVZMc072+i5O8bKj/vCTHjzeHJEn3R/P/t0v3IXoq8KWqmte1zQEeBVwKzAd+CuwNHNu7dDGwL/D57vk84Lyh6d9VVScPrbc98AngBVV1VZItgP9IcmVVnT9Oqe+qqpO7eg8AzkiyXVXd2fXvVFU3TuW1j+JG4MVVdV2S7YDvASMh5yhgf+C/gdOB3YHvABcAfwX86yjzXVFVcya59sHADVX1+CQrAeuOdCR5AoNg+twkD6uqP0z9pUmSNLqZ2PnYCfhjVf3pb+ZVtaiqzkryWGAt4BAGIaTvamD1JI/qAsHIh/FE3gl8uKqu6ta6CvgI8K7JFFsDhwO/AvaYzDWTVVU/r6rruqdLGLy+1ZJsBDy8qs6pqgKOA/bqrrmoqi6ZhuVfz+B9oKruGQpSrwS+DPw78JJpWEuSpD+ZifCxHbBwjL75wPHAWcDWSTYc6j8ZeDnwTOBnwB1D/R/v3Xb4Ste27SjrLejap+JnwDa952f01jpwinON5mXAz6vqDga7H9f0+q7hzzsi49kiyc+T/FeS54w1KMkju4cfTPKzJCcleVRvyL7AiQz+WQyHwP48+ydZkGTB3b+/exLlSZI0A7ddJjAP2Luq7knybwyCxqd7/V9j8KG4DYMPxmcOXX+f2y5AgJpE20Qy9Hy02y6jzTnhOkm2Bf4R2HWMtSYzz/XAY6rqpiQ7Aqcl2baqfjfK2FnApsCPqurtSd7O4NbUa5L8BfDrqvpFkmuALyRZp6p+e5+Cqo4BjgFYY4s1pvp+SpJWUDOx87EE2HG4McmTgccxOI+xlEEQudffuqvqV8AfgRcA35/CenOH2nYALpxS1fAU4KIJxtwErNN7vi6Dcx1jSrIpgzMwr62qK7rmaxiEgxGbAtcNX9tXVXdU1U3d44XAFcDjx6nz1m5dgJMYvCcweM+36f4ZXAE8nMGujCRJ02ImwscPgNWS/J+Rhu5v20cAh1bV7O5nY2CTJJsPXf8+4KCqmuw+/yeA9ySZ3a01G3gv8E+TuTgDbwM2Ar47wfAzgVePfDMFeB1wxjhzPxL4NvCeqvrRSHtVXQ/8PsnTu7leC3x9gjo3SLJy93hLBkHuytHGdudIvgk8r2vaBbiwO3j6cuDJI/8cgJcyzq0XSZKmqnn46D749gZekMFXbZcAhzL4IDx1aPipDHZA+tf/uKpOG2P6/pmPRUlWrapFwEHAN5NczOBD9++79vF8PMl5DL6B8xcMbrPc2evvn/k4rms7Bvg9cF537VoMws9Y3gJsBfxDb66Rcy5/C3wOuJzBDsR3AJLs3d0OeQbw7STf68Y/Fzi/W/dk4E1V9Ztx1j4IODTJ+cBrgHd0c1xbVdf2xv0QeGJ3CFaSpAcsgywgPTBrbLFGbXXoVjNdxqQsft3imS5BkpZ7SRZW1fCxB8DfcCpJkhpb1r7t0lSSTwPPGmo+oqq+OM3r7Mbg2yx9V1XV3tO5zhhr/wRYbaj5NVXlX/8lSTNihQ4fVfXmRut8j8FvL22uqp42E+tKkjQWb7tIkqSmDB+SJKkpw4ckSWrK8CFJkppaoQ+cavpsu962LHjdgpkuQ5L0EODOhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpmbNdAFaTlz3czj0ETNdxfgOvXmmK5Ak4c6HJElqzPAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwcT8k2TtJJdmmez67e/7W3pgjk+yXZOUki4Z+bkxy4jjzn5lkbm/uy5LsluR5Sb7Vte+X5J4kT+5dd0GS2d3jpUkWdz8XJvlQktV6c942VNNrh647P8l/Jdl8+t9BSdKKzPBx/8wHzgbm9dpuAP5vklX7A6vq7qqaM/ID7AHcBnxwokWSbAp8D3hHVX1vlCHXAAePM8VOVfUk4KnAlsAxvb4r+nVV1XFD1z0ZOBM4ZKI6JUmaCsPHFCVZC3gW8AbuHT5+DXwfeN041wb4EvDxqrpggqUeDfw7cEhVfWOMMd8Ctk2y9XgTVdUtwJuAvZKsO8G6fecAm0xhvCRJEzJ8TN1ewHer6lLgN0l26PV9FHhHkpXHuPZA4C7gU5NY5zjgyKo6aZwx9wAfA9470WRV9TvgKuBxXdNjh267PGeUy3YHThtrziT7J1mQZMGvb62JSpAkCTB83B/zgRO6xyd0zwGoqquAc4FXDl+UZHvgAOCvq2oyn9T/CbwmyZoTjPsq8PQkW0xizvQeD992OavXd0aSG4Dnd/OPqqqOqaq5VTV3gzUz1jBJku7F8DEFSdYDdgY+l2Qp8C5gX+79of5h4CB6722SNYCvAH9XVf8zyeU+BvwEOCnJrLEGVdVdwD91a45X+9rAbODSSay9E7A5sAT4wCTrlSRpUgwfU7MPcFxVbV5Vs6tqMwa3MjYdGVBVFwMXAnv2rvsE8F9V9a0prncg8Dvg8915kbEcy2CXYoPROrtzKp8BTquq305m4aq6jcFOzWuneE5EkqRxGT6mZj5w6lDbKdz3zMVhdIEkycbA3wE7D52x+MpEi3W3Z14HbMRgJ2SscXcCnwQ2HOo6I8kFDG4FXQ38Ta9v+MzH20aZ93rgeODNE9UqSdJkZXLHD6Txzd145Vqw/1ozXcb4Dr15piuQpBVGkoVVNXe0Pnc+JElSU2MeZNSDL8mpwPC3VA4a4xeKSZK0XDB8zKCq2numa5AkqTVvu0iSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpvy2i6bHxk+BQxfMdBWSpIcAdz4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktTUrJkuQMuHxdfezOx3f3umy7iPpR990UyXIEka4s6HJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmpowfCS5O8mi3s+7u/Yzk8ztHi9Nckrvmn2SHNt7vnuSc5Nc3M1xYpLHdH3HJrmqN/+Pu/YvDq27NMn/9Obcv5vv4m7uZ/f6ZiX5cJLLetcfPMnXtKA3bm6SM4fejyOSXJtkpV7bfkkqyS69tr27tn3GeW//9B52z2cnuaB7/Lwk3xoaf+wU53tKV8NuQ+NuGXq+X5Iju8eHdq9vUZILk8wfaz1Jku6Pyfy/XW6rqjmTGDc3ybZVtaTfmGQ74FPAS6rqoq7tJcBs4Opu2Luq6uT+dVX11705VgLOBI7rnu8J/A3w7Kq6MckOwGlJnlpVvwI+BDwaeFJV3Z5kbeAdk3xNGybZo6q+M9zR1bE38EvguV1NIxYD84Hvd8/nAeeNsUYr84Gzuz+/N4XrDq+qTyR5HLAwyclV9ccHpUJJ0gpnOm+7fAJ47yjtBwEfHgkeAFX1jar64RTmfi9wY1V9rjfnu6rqxm6+nwFfAt6cZE3g/wBvrarbu/7fV9Whk1zr48AhY/TtBFwAHMXgA73vLOCpSVZJshawFbBokmtOuyQB9gH2A3ZNsvpU56iqy4BbgXWmtzpJ0opsMuFjjaFbFPuOMe5rwA5Jthpq3xb42QRrfLw3/1f6HUmeCryx++nPuXBojgVd+1bA1VX1+/v5ms4B7kiy0yjXzQeOB04F9kyySq+vgP8EdgNeCnxjnPX7vjJSB3D6UN9z+nUCL5nknADPAq6qqisY7NC8sNe3xtC8Hxhtgm5H6bKqumGM/v2TLEiy4O5bb55CaZKkFdlkwsdtVTWn93PiGOPuZrBr8J6xJkqyXveBd2mSd/a63tWb/1W98WsBXwbeUFW/maDOMAgAw2v+dbfmL5NsNsnX9CGGdj+SrMrgA/y0qvod8BNg16HrTmBwu2Ueg5AyGa8aqYN7BwSAs/p1MvlAA4OgdEKvrv5OzW1D875v6NoDk1zC4DUeOtYCVXVMVc2tqrkrr/mIKZQmSVqRTfe3Xb7M4CzEY3ptS4AdAKrqpu7D7hhgrUnM9yngG1X1/aH2C4Edh9p26NovBx7TnfOgqr7YrXkzsPJkXkRV/QBYHXh6r3l34BHA4iRLgWczdOulqs4FtgPWr6pLJ7PWgyHJysDLgPd1tX4K2GPkPZmEw6tqa2Bf4Lj7c8tGkqSxTGv46A4lHg4c0Gv+GHBwkif02tacaK7uWx3bAweP0v0x4B+TrNeNncPgbMNnqupW4PPAkSMfmt2H8apTfDmHAX/fez4feGNVza6q2cAWDM5SDL+W9zD62ZeWng+cV1WbdfVuDpwC7DWVSarq3xjcznrdg1CjJGkFNZlvu6zRnQsY8d2qevc44z9P75ZFVS1O8n8Z/A16beAmBt9yeX/vmo8n6d/meCqDD/81gXMHZyf/5BlV9Y0kmwA/TlLA74FXV9X13ZiDgQ8CFyT5PXAbgwOp1032NVXV6Ul+DdAFjN0YfMNmpP8PSc4GXjx03X2+JdPQLOAOBkHp1KG+U4C/ZbA7NRUfAL6a5LNVdc8DL1GStKJL1X2OSeghKMlqDG45bVdVzU9/rrbR42qj1/1L62UntPSjL5rpEiRphZRkYVXNHa3P33C6HOh+sdgiBred/NqJJGmZNpnbLnqAkpzK4IxI30FVNZVf/DXRfAfc3/kkSWrJ8NFAVe29LM8nSVJL3naRJElNGT4kSVJThg9JktSU4UOSJDXlgVNNiydt8ggW+Ds1JEmT4M6HJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqKlU10zVoOZDk98AlM13HFKwP3DjTRUyRNbdhzQ++h1q9YM33x+ZVtcFoHbNaV6Ll1iVVNXemi5isJAseSvWCNbdizQ++h1q9YM3TzdsukiSpKcOHJElqyvCh6XLMTBcwRQ+1esGaW7HmB99DrV6w5mnlgVNJktSUOx+SJKkpw4fGlWT3JJckuTzJu0fpXy3JiV3/T5LM7vW9p2u/JMluy3rNSV6QZGGSxd2fOy/rNff6H5PkliTvfCjUnOTJSc5JsqR7v1dfVutNskqSL3V1XpTkPQ92rVOo+blJfpbkriT7DPW9Lsll3c/rlvWak8zp/TtxfpJ9l/Wae/0PT3JtkiOX9Xq7/1b8e/fv8oXD/y1ppqr88WfUH2Bl4ApgS2BV4DzgiUNj/g44uns8Dzixe/zEbvxqwBbdPCsv4zU/Bdi4e7wdcO2y/j73+k8BTgLeuazXzOAr/ucD23fP13uw/914gPW+Ejihe7wmsBSYvYy8x7OBJwPHAfv02tcFruz+XKd7vM4yXvPjgcd1jzcGrgceuSzX3Os/AvgqcOSyXi9wJvCC7vFawJoPds2j/bjzofE8Fbi8qq6sqjuBE4CXDo15KfCl7vHJwC5J0rWfUFV3/P/t3X/sVXUdx/HnS0D5URlu1SxlX1rilmQogeGkwpJ+rFmbbhYmYn80MnW5qf3QFdqPidJw5YpZOS35w2FqZK6vhJOQTUUEvoABkjBwsJiSGRgi8O6Pz+fW5cu9X+733nuOp+312O6+957v+Zzz+tyd8/1+zuf8+ETEVmBLXl5lM0fE6ojYmadvAIZLOqHKmQEkfZH0z2VDCVlrOsk8HeiLiLUAEfFKRByqcN4ARkkaCowADgCvFZy3pcwRsS0i+oDD/cp+GlgSEXsi4h/AEuAzVc4cEZsj4oX8fiewG2j4gKqqZAaQNBF4D/BYCVmhg7ySPggMjYgleb69EfF6SbmP4MaHDeR9wI66zy/laQ3niYiDwD9JR7KtlC1CJ5nrXQSsjog3CsrZME/WcmZJo4BvATeXkLNhnmww3/M4ICT15q7hGyqe9wFgH+lIfDswLyL2FB2YzvahKu9/xyRpMumo/m9dyjWQtjNLOg74CXB9Abma6eQ7Hge8KulBSasl3S5pSNcTtsBPOLWBqMG0/rdHNZunlbJF6CRz+qV0BjCXdIRehk4y3wzMj4i9uSOkLJ1kHgqcB0wCXgeWSloVEUu7G7GlLK3MMxk4RDoVMBpYLunPEfFidyMepZN9qMr738ALkE4GfgtcHhFH9TQUoJPMVwKPRsSOEve/TvIOBaaSTjFvB+4HZgG/7kqyQXDPhw3kJeDUus+nADubzZO7pU8E9rRYtgidZEbSKcBDwMyIKOOo64g82WAynwPcJmkb8E3gu5KuKjownW8byyLi5dzl+yhwdoXzzgD+FBFvRsRuYAVQxiOrO9mHqrz/NSXpHcAfgZsi4qkuZ2umk8xTgKvy/jcPmCnp1u7GO0qn28XqfMrmIPAwxe97DbnxYQNZCZwmaayk40kX4S3uN89ioHYl/cXA45GuZFoMfCnfQTAWOA14psqZJb2T9IfvOxGxooSsNW1njoipEdETET3AHcCPI6KMK+472TZ6gTMljcz/5D8OPF/hvNuB85WMAj4KbCw4b6uZm+kFpksaLWk0qRevt6Cc9drOnOd/CPhNRCwqMGN/bWeOiEsjYkze/64jZT/q7pMu62S7WAmMllS7luZ8it/3GnsrrnL16//nBXwO2Ew693pjnnYLcGF+P5x0l8UWUuPi/XVlb8zlNgGfrXpm4CbSuf01da93Vzlzv2XMoaS7XbqwbXyFdIHseuC2Kucl3RGwKOd9Hri+Qt/xJNLR7D7gFWBDXdmv5rpsAa6oeua8TbzZb/+bUOXM/ZYxixLudunCdnEB6W6zdcA9wPFlbRv1Lz/h1MzMzErl0y5mZmZWKjc+zMzMrFRufJiZmVmp3PgwMzOzUrnxYWZmZqVy48PMKkfSNXnUzYVtlO2RNKOIXHn5syXNLGr5TdY5S9J7y1ynWZF8q62ZVY6kjaRnw2xto+wnSM87+fwgyw2J4ge4G7Q89sZSUp2efavzmHWDez7MrFIkLSANF75Y0rWSRkm6W9LKPBjWF/J8PZKW58HpnpN0bl7ErcBUSWty+VmS7qxb/iO5gYKkvZJukfQ0MEXSREnLJK3KA9+d3CDfHEnX5fdPSJov6S+5p2ZSHrTrBUk/rMu5UdK9kvokPSBpZP7dJ3Od1uU6npCnb5P0PUlPAl8mPc59Ya7TiPy7lZLWS7pL+u8Ix09ImivpGUmbJU3N04dImpfX0yfp6jz9mPU1K4IbH2ZWKRExmzRWxbSImE96Uu7jETEJmAbcnh9zvhu4ICLOBi4BfpoX8W1geURMyOUHMgpYHxHnAE8DPwMujoiJwN3Aj1qIfCAiPgYsAH4PfAMYD8ySVBst+XTgrog4E3gNuFLScNITJi+JiA+RBv36et1y90fEeRFxH/AscGmu079JT9KcFBHjgRFAfS/P0IiYTBrr5/t52teAscBZOcNCScParK9ZxzyqrZlV3XTgwlpvA+kx6GNIDZQ7JU0gjTo7ro1lHwJ+l9+fTmo0LMkdCUOAXS0sozauxjrSY6x3AUh6kTQA2KvAjvjfeEH3AdcAS4CtEbE5T7+X1HC5I3++f4B1TpN0AzASOIn06Pc/5N89mH+uAnry+08BCyINJkZE7JE0vs36mnXMjQ8zqzoBF0XEpiMmSnOAvwMfJvXi7m9S/iBH9vIOr3u/v+46D5EaD1MGme+N/PNw3fva59rf2P4X1wWNh0avt6/RxNxj8nPgI5GGcp/DkXWqZThUt341yNBufc065tMuZlZ1vcDVddc1nJWnnwjsiojDwGWkI3eAfwFvryu/DZgg6ThJpwKTm6xnE/AuSVPyeoZJOqNLdRhTWy7pGo4nSSPj9kj6QJ5+GbCsSfn6OtUaGi9LehtpBN5jeQyYrTSKMJJOotj6mg3IjQ8zq7ofAMOAPknr82dIR/+XS3qKdMql1lPQBxyUtFbStcAKYCvptMg84LlGK4mIA6R/5HMlrSWNqnpuo3nb8NectY90muQXEbEfuAJYJGkdqadkQZPy9wALJK0h9Wz8MtfnYdIw6cfyK2A76TtcC8wouL5mA/KttmZmBZLUAzySLw41M9zzYWZmZiVzz4eZmZmVyj0fZmZmVio3PszMzKxUbnyYmZlZqdz4MDMzs1K58WFmZmalcuPDzMzMSvUfTCyIM0bLVEEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = best_clf.named_steps['gbc'].feature_importances_\n",
    "pd.Series(feature_importances, index=[''] + column_names).sort_values()[-10:].plot(kind='barh', figsize=(7,10))\n",
    "plt.xlabel('feature importance')\n",
    "plt.savefig('feature_importance.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the distribution of the D19_SOZIALES attribute in our focus clusters, we see a clear characteristic in these groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot D19_SOZIALES distribution in relevant clusters\n",
    "x1=customers_clean.iloc[customers_clustered[customers_clustered['Cluster'] == 8].index]['D19_SOZIALES']\n",
    "x2=customers_clean.iloc[customers_clustered[customers_clustered['Cluster'] == 9].index]['D19_SOZIALES']\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "\n",
    "width = 0.35 \n",
    "\n",
    "p1 = plt.hist(x1, width=width, label='Cluster 9')\n",
    "p2 = plt.hist(x2, width=width, label='Cluster 10')\n",
    "\n",
    "plt.title('Distribution of D19_SOZIALES among interested clusters')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Attribute value')\n",
    "plt.legend()\n",
    "plt.savefig('d19_distribution.png', dpi=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Kaggle Competition\n",
    "\n",
    "Now that you've created a model to predict which individuals are most likely to respond to a mailout campaign, it's time to test that model in competition through Kaggle. If you click on the link [here](http://www.kaggle.com/t/21e6d45d4c574c7fa2d868f0e8c83140), you'll be taken to the competition page where, if you have a Kaggle account, you can enter. If you're one of the top performers, you may have the chance to be contacted by a hiring manager from Arvato or Bertelsmann for an interview!\n",
    "\n",
    "Your entry to the competition should be a CSV file with two columns. The first column should be a copy of \"LNR\", which acts as an ID number for each individual in the \"TEST\" partition. The second column, \"RESPONSE\", should be some measure of how likely each individual became a customer – this might not be a straightforward probability. As you should have found in Part 2, there is a large output class imbalance, where most individuals did not respond to the mailout. Thus, predicting individual classes and using accuracy does not seem to be an appropriate performance evaluation method. Instead, the competition will be using AUC to evaluate performance. The exact values of the \"RESPONSE\" column do not matter as much: only that the higher values try to capture as many of the actual customers as possible, early in the ROC curve sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "mailout_test = pd.read_csv('Udacity_MAILOUT_052018_TEST.csv', sep=';', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42833, 366)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mailout_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set= set(mailout_train.columns)\n",
    "test_set= set(mailout_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RESPONSE'}"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set - test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old shape: (42833, 366)\n",
      "0 rows and 15 columns dropped.\n",
      "New shape: (42833, 351) \n",
      "\n",
      "Datatypes:\n",
      "float64    260\n",
      "int64       90\n",
      "object       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mailout_test_clean = clean_dataframe(mailout_test, drop_cols, test_df = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load from file\n",
    "pkl_filename = \"best_gradient_boosting.pkl\"  \n",
    "with open(pkl_filename, 'rb') as file:  \n",
    "    pickle_model = pickle.load(file) \n",
    "    \n",
    "#predict target values\n",
    "kaggle_predict = pickle_model.predict_proba(mailout_test_clean)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('Arvato_Capstone_Example_Submission.csv')\n",
    "submission['RESPONSE'] = kaggle_predict\n",
    "submission.to_csv('kaggle_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a last step, the model performance is evaluated by predicting the response of each person in the \"MAILOUT_TEST\" dataset and submit the results to the Arvato Kaggle competition. The submission scored 0.75706, positioning it among the top 150."
   ]
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    "### Conclusion"
   ]
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   "source": [
    "The Bertelsmann/Arvato Project proved to be a fun and engaging final exercise in the Data Science Nanodegree program. Handling the data was certainly among the biggest challenges in this project. Managing the high number of missing values and many different types of features can make a huge difference on final model performance.\n",
    "Customers and general population were clustered into 12 groups, of which 2 showed to hold over 50% of the company's customers. These two focus groups had specific characteristics: Broadly speaking, customers of this company are well-earning couples or families with a certain affinity for online purchases.\n",
    "\n",
    "To forecast customer responses, the Gradient Boosting Classifier proved to be the best-performing estimator. The Kaggle submission results shows that there is still room for improvement though. More specifically, the following changes to my approach could be considered to optimize project outcomes:\n",
    "* Drop fewer or more data points when preparing the datasets.\n",
    "* Test different approaches to standardize and scale features, like using Min-Max-Scaler instead of Standard Scaler.\n",
    "* Choose a smaller number of clusters to group customers and population.\n",
    "*Consider alternative classifiers than the ones described. Choose different parameters and ranges to hypertune the model.\n",
    "\n",
    "The project is far from finished and leaves ample room for further exploration!"
   ]
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