Arvato Project Workbook.ipynb

{
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  {
   "cell_type": "markdown",
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   "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": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "#imports to help me plot my venn diagrams\n",
    "import matplotlib_venn as venn2\n",
    "from matplotlib_venn import venn2\n",
    "from pylab import rcParams\n",
    "\n",
    "# import the util.py file where I define my functions\n",
    "from utils import *\n",
    "\n",
    "# sklearn\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import confusion_matrix,precision_recall_fscore_support\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier as gbm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import StratifiedKFold, learning_curve\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "\n",
    "import skopt\n",
    "from skopt import BayesSearchCV\n",
    "class BayesSearchCV(BayesSearchCV):\n",
    "    def _run_search(self, x): raise BaseException('Use newer skopt')\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Get to Know the Data\n",
    "\n",
    "There are four data files associated with this project:\n",
    "\n",
    "- `Udacity_AZDIAS_052018.csv`: Demographics data for the general population of Germany; 891 211 persons (rows) x 366 features (columns).\n",
    "- `Udacity_CUSTOMERS_052018.csv`: Demographics data for customers of a mail-order company; 191 652 persons (rows) x 369 features (columns).\n",
    "- `Udacity_MAILOUT_052018_TRAIN.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 982 persons (rows) x 367 (columns).\n",
    "- `Udacity_MAILOUT_052018_TEST.csv`: Demographics data for individuals who were targets of a marketing campaign; 42 833 persons (rows) x 366 (columns).\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. Use the information from the first two files to figure out how customers (\"CUSTOMERS\") are similar to or differ from the general population at large (\"AZDIAS\"), then use your analysis to make predictions on the other two files (\"MAILOUT\"), predicting which recipients are most likely to become a customer for the mail-order company.\n",
    "\n",
    "The \"CUSTOMERS\" file contains three extra columns ('CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'), which provide broad information about the customers depicted in the file. The original \"MAILOUT\" file included one additional column, \"RESPONSE\", which indicated whether or not each recipient became a customer of the company. For the \"TRAIN\" subset, this column has been retained, but in the \"TEST\" subset it has been removed; it is against that withheld column that your final predictions will be assessed in the Kaggle competition.\n",
    "\n",
    "Otherwise, all of the remaining columns are the same between the three data files. For more information about the columns depicted in the files, you can refer to two Excel spreadsheets provided in the workspace. [One of them](./DIAS Information Levels - Attributes 2017.xlsx) is a top-level list of attributes and descriptions, organized by informational category. [The other](./DIAS Attributes - Values 2017.xlsx) is a detailed mapping of data values for each feature in alphabetical order.\n",
    "\n",
    "In the below cell, we've provided some initial code to load in the first two datasets. Note for all of the `.csv` data files in this project that they're semicolon (`;`) delimited, so an additional argument in the [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call has been included to read in the data properly. Also, considering the size of the datasets, it may take some time for them to load completely.\n",
    "\n",
    "You'll notice when the data is loaded in that a warning message will immediately pop up. Before you really start digging into the modeling and analysis, you're going to need to perform some cleaning. Take some time to browse the structure of the data and look over the informational spreadsheets to understand the data values. Make some decisions on which features to keep, which features to drop, and if any revisions need to be made on data formats. It'll be a good idea to create a function with pre-processing steps, since you'll need to clean all of the datasets before you work with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the data\n",
    "'''\n",
    "There are 2 warnings when we read in the datasets:\n",
    "DtypeWarning: Columns (19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
    "interactivity=interactivity, compiler=compiler, result=result)\n",
    "\n",
    "This warning happens when pandas attempts to guess datatypes on particular columns, I will address this on \n",
    "the pre-processing steps\n",
    "'''\n",
    "azdias = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\azdias.csv\")\n",
    "customers = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\customers.csv\")\n",
    "attributes = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\features.csv\")\n",
    "dias_xls = pd.read_excel(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\DIAS Attributes - Values 2017.xlsx\", header = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to use the dias_xls file to help me find the values that correspond to missing or unknow so I will perform a few fixes.\n",
    "Namely, havind row 1 be the header for the dataframe and removing the extra unnammed column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Description</th>\n",
       "      <th>Value</th>\n",
       "      <th>Meaning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>best-ager typology</td>\n",
       "      <td>-1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>no classification possible</td>\n",
       "    </tr>\n",
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       "      <td>2</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>passive elderly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>cultural elderly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>experience-driven elderly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attribute         Description Value                     Meaning\n",
       "0  AGER_TYP  best-ager typology    -1                     unknown\n",
       "1  AGER_TYP                 NaN     0  no classification possible\n",
       "2  AGER_TYP                 NaN     1             passive elderly\n",
       "3  AGER_TYP                 NaN     2            cultural elderly\n",
       "4  AGER_TYP                 NaN     3   experience-driven elderly"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dias_xls.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "dias_xls['Attribute'] = dias_xls['Attribute'].ffill()\n",
    "dias_xls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CAMEO_DEUG_2015', 'CAMEO_INTL_2015'], dtype='object')\n",
      "Index(['CAMEO_DEUG_2015', 'CAMEO_INTL_2015'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# I will now check what is the problem with the columns 19 and 20\n",
    "# getting the name of these columns\n",
    "print(azdias.iloc[:,19:21].columns)\n",
    "print(customers.iloc[:,19:21].columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the mixed type issue comes from that  X that appears in these columns.\n",
    "There are ints, floats and strings all in the mix, I also want to do a quick fix on CAMEO_DEU_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>Comment</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ALTER_HH</td>\n",
       "      <td>household</td>\n",
       "      <td>interval</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANZ_HAUSHALTE_AKTIV</td>\n",
       "      <td>building</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
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       "              attribute information_level         type missing_or_unknown  \\\n",
       "0              AGER_TYP            person  categorical             [-1,0]   \n",
       "1              ALTER_HH         household     interval                [0]   \n",
       "2  ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]   \n",
       "3             ANREDE_KZ            person  categorical             [-1,0]   \n",
       "4   ANZ_HAUSHALTE_AKTIV          building      numeric                [0]   \n",
       "\n",
       "  Comment  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = special_feature_handler(azdias)\n",
    "customers = special_feature_handler(customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking if values were fixed\n",
    "#### Change this cell to code if you want to perform the checks\n",
    "\n",
    "azdias.CAMEO_DEUG_2015.unique()\n",
    "customers.CAMEO_INTL_2015.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the appearance of these mixed type data entries I created a function to check the dtype of the different attributes\n",
    "\n",
    "This might be useful in case some attributes have too many category values, which might fragment the data clustering too much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGER_TYP                      5\n",
      "ANREDE_KZ                     2\n",
      "CAMEO_DEU_2015               45\n",
      "CAMEO_DEUG_2015               9\n",
      "CJT_GESAMTTYP                 6\n",
      "D19_BANKEN_DATUM             10\n",
      "D19_BANKEN_OFFLINE_DATUM     10\n",
      "D19_BANKEN_ONLINE_DATUM      10\n",
      "D19_GESAMT_DATUM             10\n",
      "D19_GESAMT_OFFLINE_DATUM     10\n",
      "D19_GESAMT_ONLINE_DATUM      10\n",
      "D19_KONSUMTYP                 7\n",
      "D19_TELKO_DATUM              10\n",
      "D19_TELKO_OFFLINE_DATUM      10\n",
      "D19_TELKO_ONLINE_DATUM       10\n",
      "D19_VERSAND_DATUM            10\n",
      "D19_VERSAND_OFFLINE_DATUM    10\n",
      "D19_VERSAND_ONLINE_DATUM     10\n",
      "D19_VERSI_DATUM              10\n",
      "D19_VERSI_OFFLINE_DATUM      10\n",
      "D19_VERSI_ONLINE_DATUM       10\n",
      "FINANZTYP                     6\n",
      "GEBAEUDETYP                   7\n",
      "GFK_URLAUBERTYP              12\n",
      "GREEN_AVANTGARDE              2\n",
      "KBA05_BAUMAX                  6\n",
      "KK_KUNDENTYP                  6\n",
      "LP_FAMILIE_FEIN              12\n",
      "LP_FAMILIE_GROB               6\n",
      "LP_STATUS_FEIN               10\n",
      "LP_STATUS_GROB                5\n",
      "NATIONALITAET_KZ              4\n",
      "OST_WEST_KZ                   2\n",
      "PLZ8_BAUMAX                   5\n",
      "SHOPPER_TYP                   5\n",
      "SOHO_KZ                       2\n",
      "TITEL_KZ                      6\n",
      "VERS_TYP                      3\n",
      "WOHNLAGE                      8\n",
      "ZABEOTYP                      6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#doing a quick check of categorical features and see if some are too granular to be maintained\n",
    "cat_check = categorical_checker(azdias, attributes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the categorical info it might be a good idea do drop CAMEO_DEU_2015 column, it is far too fragmented with 45 different category values, this is an idea to revisit after testing the models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an extra column called Unnamed that seems like an index duplication, I will drop it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have 3 columns that are different between azdias and customers:\n",
    "\n",
    "'CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'\n",
    "\n",
    "I will drop those to harmonize the 2 datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = customers.drop(['CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP'], inplace=False, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I will now check overal shapes of the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Azdias Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing dataframe shape\n",
      "(891221, 366)\n",
      "________________________________________________________\n"
     ]
    },
    {
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>910215</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>910225</td>\n",
       "      <td>-1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>910226</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>910241</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 366 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LNR  AGER_TYP  AKT_DAT_KL  ALTER_HH  ALTER_KIND1  ALTER_KIND2  \\\n",
       "0  910215        -1         NaN       NaN          NaN          NaN   \n",
       "1  910220        -1         9.0       0.0          NaN          NaN   \n",
       "2  910225        -1         9.0      17.0          NaN          NaN   \n",
       "3  910226         2         1.0      13.0          NaN          NaN   \n",
       "4  910241        -1         1.0      20.0          NaN          NaN   \n",
       "\n",
       "   ALTER_KIND3  ALTER_KIND4  ALTERSKATEGORIE_FEIN  ANZ_HAUSHALTE_AKTIV  ...  \\\n",
       "0          NaN          NaN                   NaN                  NaN  ...   \n",
       "1          NaN          NaN                  21.0                 11.0  ...   \n",
       "2          NaN          NaN                  17.0                 10.0  ...   \n",
       "3          NaN          NaN                  13.0                  1.0  ...   \n",
       "4          NaN          NaN                  14.0                  3.0  ...   \n",
       "\n",
       "   VHN  VK_DHT4A  VK_DISTANZ  VK_ZG11  W_KEIT_KIND_HH  WOHNDAUER_2008  \\\n",
       "0  NaN       NaN         NaN      NaN             NaN             NaN   \n",
       "1  4.0       8.0        11.0     10.0             3.0             9.0   \n",
       "2  2.0       9.0         9.0      6.0             3.0             9.0   \n",
       "3  0.0       7.0        10.0     11.0             NaN             9.0   \n",
       "4  2.0       3.0         5.0      4.0             2.0             9.0   \n",
       "\n",
       "   WOHNLAGE ZABEOTYP  ANREDE_KZ  ALTERSKATEGORIE_GROB  \n",
       "0       NaN        3          1                     2  \n",
       "1       4.0        5          2                     1  \n",
       "2       2.0        5          2                     3  \n",
       "3       7.0        3          2                     4  \n",
       "4       3.0        4          1                     3  \n",
       "\n",
       "[5 rows x 366 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking how the azdias dataframe looks like\n",
    "print('Printing dataframe shape')\n",
    "print(azdias.shape)\n",
    "print('________________________________________________________')\n",
    "\n",
    "azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customers Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing dataframe shape\n",
      "(191652, 366)\n",
      "________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<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",
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       "      <td>0</td>\n",
       "      <td>9626</td>\n",
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       "      <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",
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       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</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>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</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>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>6.0</td>\n",
       "      <td>4.0</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>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>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>1</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    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",
       "   VHN  VK_DHT4A  VK_DISTANZ  VK_ZG11  W_KEIT_KIND_HH  WOHNDAUER_2008  \\\n",
       "0  3.0       5.0         3.0      2.0             6.0             9.0   \n",
       "1  NaN       6.0         6.0      3.0             0.0             9.0   \n",
       "2  4.0      10.0        13.0     11.0             6.0             9.0   \n",
       "3  2.0       6.0         4.0      2.0             NaN             9.0   \n",
       "4  4.0       3.0         5.0      4.0             2.0             9.0   \n",
       "\n",
       "   WOHNLAGE ZABEOTYP  ANREDE_KZ  ALTERSKATEGORIE_GROB  \n",
       "0       7.0        3          1                     4  \n",
       "1       NaN        3          1                     4  \n",
       "2       2.0        3          2                     4  \n",
       "3       7.0        1          1                     4  \n",
       "4       3.0        1          1                     3  \n",
       "\n",
       "[5 rows x 366 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking how the customer dataframe looks like\n",
    "print('Printing dataframe shape')\n",
    "print(customers.shape)\n",
    "print('________________________________________________________')\n",
    "\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Attributes shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(332, 5)\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>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AGER_TYP</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ALTER_HH</td>\n",
       "      <td>household</td>\n",
       "      <td>interval</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ALTERSKATEGORIE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>ordinal</td>\n",
       "      <td>[-1,0,9]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANREDE_KZ</td>\n",
       "      <td>person</td>\n",
       "      <td>categorical</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANZ_HAUSHALTE_AKTIV</td>\n",
       "      <td>building</td>\n",
       "      <td>numeric</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              attribute information_level         type missing_or_unknown  \\\n",
       "0              AGER_TYP            person  categorical             [-1,0]   \n",
       "1              ALTER_HH         household     interval                [0]   \n",
       "2  ALTERSKATEGORIE_GROB            person      ordinal           [-1,0,9]   \n",
       "3             ANREDE_KZ            person  categorical             [-1,0]   \n",
       "4   ANZ_HAUSHALTE_AKTIV          building      numeric                [0]   \n",
       "\n",
       "  Comment  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the summary csv file\n",
    "print(attributes.shape)\n",
    "attributes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On the dataframe shapes:\n",
    "\n",
    "#### For now it is noted that the 2 initial working dataframes are harmonized in terms of  number of columns:\n",
    "#### azdias: (891221, 366)\n",
    "#### customers: (191652, 366)\n",
    "#### attributes: (332, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the unique attribute names to lists\n",
    "attributes_list = attributes.attribute.unique().tolist()\n",
    "azdias_list = list(azdias.columns)\n",
    "customers_list = list(customers.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of items common to all 3 daframes: 327\n",
      "No of items exclusive to azdias: 0\n",
      "No of items exclusive to customers: 0\n",
      "No of items exclusive to attributes: 5\n",
      "No of items overlapping between azdias and attributes: 327\n",
      "No of items exclusive to attributes vs azdias: 5\n",
      "No of items exclusive to azdias vs attributes: 5\n"
     ]
    }
   ],
   "source": [
    "#establishing uniqueness of the attributes accross the datasets in work\n",
    "common_to_all = (set(attributes_list) & set(azdias_list) & set(customers_list))\n",
    "unique_to_azdias = (set(azdias_list) - set(attributes_list) - set(customers_list))\n",
    "unique_to_customers = (set(customers_list) - set(attributes_list) - set(azdias_list))\n",
    "unique_to_attributes = (set(attributes_list) - set(customers_list) - set(azdias_list))\n",
    "unique_to_attributes_vs_azdias = (set(attributes_list) - set(azdias_list))\n",
    "unique_to_azdias_vs_attributes = (set(attributes_list) - set(azdias_list))\n",
    "common_azdias_attributes = (set(azdias_list) & set(attributes_list))\n",
    "\n",
    "print(\"No of items common to all 3 daframes: \" + str(len(common_to_all)))\n",
    "print(\"No of items exclusive to azdias: \" + str(len(unique_to_azdias)))\n",
    "print(\"No of items exclusive to customers: \" + str(len(unique_to_customers)))\n",
    "print(\"No of items exclusive to attributes: \" + str(len(unique_to_attributes)))\n",
    "print(\"No of items overlapping between azdias and attributes: \" + str(len(common_azdias_attributes)))\n",
    "print(\"No of items exclusive to attributes vs azdias: \" + str(len(unique_to_attributes_vs_azdias)))\n",
    "print(\"No of items exclusive to azdias vs attributes: \" + str(len(unique_to_azdias_vs_attributes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 8, 8\n",
    "\n",
    "ax = plt.axes()\n",
    "ax.set_facecolor('lightgrey')\n",
    "v = venn2([len(azdias_list), len(attributes_list), len(common_azdias_attributes)], \n",
    "      set_labels=('Azdias', 'Attributes'),\n",
    "         set_colors = ['cyan', 'grey']);\n",
    "\n",
    "plt.title(\"Attribute presence on Azdias vs DIAS Attributes \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From this little exploration we got quite a little bit of information:\n",
    "- There are 3 extra features in the customers dataset, it corresponds to the columns 'CUSTOMER_GROUP', 'ONLINE_PURCHASE', and 'PRODUCT_GROUP'\n",
    "\n",
    "- All the datasets share 327 features between them\n",
    "\n",
    "- The attributes file has 5 columns corresponding to feature information that does not exist in the other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "Now that I have a birds-eye view of the data I will proceed with cleaning and handling missing calues, re-encode features (since the first portion of this project will involve unsupervised learning), perform some feature enginnering and scaling.\n",
    "Assessing missing data and replacing it with nan.\n",
    "I will also decide on which strategy to use before scaling features:\n",
    "-remove nulls -> scalling -> put nulls back \n",
    "or\n",
    "-remove nulls -> scalling -> impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_pre_cleanup = azdias.copy()\n",
    "customers_pre_cleanup = customers.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknowns_to_NANs(azdias, dias_xls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknowns_to_NANs(customers, dias_xls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the next step involves dropping columns missing data over a threshold it is important to check if there is a column match between azdias and customers before and after the cleanup process\n",
    "\n",
    "There is a chance that some columns are missing too much data in one dataframe and being dropped while they are abundant in the other, causing a discrepancy in the shape between the 2 dataframes\n",
    "\n",
    "It is always hard to define a threshold on how much missing data is too much, my first approach will consider over 30% too much\n",
    "\n",
    "Based on model performance this is an idea to revisit and adjust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  True\n"
     ]
    }
   ],
   "source": [
    "balance_checker(azdias, customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prior to cleanup customers and azdias match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_missing_azdias_df = percentage_of_missing(azdias)\n",
    "percent_missing_azdias_pc_df = percentage_of_missing(azdias_pre_cleanup)\n",
    "\n",
    "percent_missing_customers_df = percentage_of_missing(customers)\n",
    "percent_missing_customers_pc_df = percentage_of_missing(customers_pre_cleanup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Identified missing data in Azdias: \n",
      "Pre-cleanup: 33493669 Post_cleanup: 37088263\n",
      "Identified missing data in Customers: \n",
      "Pre-cleanup: 13864774 Post_cleanup: 14488721\n"
     ]
    }
   ],
   "source": [
    "print('Identified missing data in Azdias: ')\n",
    "print('Pre-cleanup: ' + str(azdias_pre_cleanup.isnull().sum().sum()) + \n",
    "      ' Post_cleanup: ' + str(azdias.isnull().sum().sum()))\n",
    "\n",
    "print('Identified missing data in Customers: ')\n",
    "print('Pre-cleanup: ' + str(customers_pre_cleanup.isnull().sum().sum()) +\n",
    "      ' Post_cleanup: ' + str(customers.isnull().sum().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azdias columns not missing values(percentage):\n",
      "Pre-cleanup:  87\n",
      "Post-cleanup:  93\n",
      "Customers columns not missing values(percentage):\n",
      "Pre-cleanup:  87\n",
      "Post-cleanup:  93\n"
     ]
    }
   ],
   "source": [
    "print('Azdias columns not missing values(percentage):')\n",
    "print('Pre-cleanup: ', (percent_missing_azdias_df['percent_missing'] == 0.0).sum())\n",
    "print('Post-cleanup: ', (percent_missing_azdias_pc_df['percent_missing'] == 0.0).sum())\n",
    "\n",
    "print('Customers columns not missing values(percentage):')\n",
    "print('Pre-cleanup: ', (percent_missing_customers_df['percent_missing'] == 0.0).sum())\n",
    "print('Post-cleanup: ', (percent_missing_customers_pc_df['percent_missing'] == 0.0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deciding on what data to maintain based on the percentage missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing more or less than 30% of the data\n",
    "azdias_missing_over_30 = split_on_percentage(percent_missing_azdias_df, 30, '>')\n",
    "azdias_missing_less_30 = split_on_percentage(percent_missing_azdias_df, 30, '<=')\n",
    "\n",
    "customers_missing_over_30 = split_on_percentage(percent_missing_customers_df, 30, '>')\n",
    "customers_missing_less_30 = split_on_percentage(percent_missing_customers_df, 30, '<=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting select features and their missing data percentages\n",
    "figure, axes = plt.subplots(4, 1, figsize = (15,15), squeeze = False)\n",
    "\n",
    "azdias_missing_over_30.sort_values(by = 'percent_missing', ascending = False).plot(kind = 'bar', x = 'column_name', y = 'percent_missing',\n",
    "                                                                                ax = axes[0][0], color = 'cyan', title = 'Azdias percentage of missing values over 30%' )\n",
    "\n",
    "#due to the sheer amount of data points to be plotted this does not make an appealing vis so I will restrict\n",
    "#the number of plotted points to 40\n",
    "azdias_missing_less_30.sort_values(by = 'percent_missing', ascending = False)[:40].plot(kind = 'bar', x = 'column_name', y = 'percent_missing',\n",
    "                                                                                ax = axes[1][0], color = 'cyan', title = 'Azdias percentage of missing values less 30%' )\n",
    "\n",
    "customers_missing_over_30.sort_values(by = 'percent_missing', ascending = False).plot(kind = 'bar', x = 'column_name', y = 'percent_missing',\n",
    "                                                                                ax = axes[2][0], color = 'grey', title = 'Customers percentage of missing values over 30%' )\n",
    "\n",
    "#due to the sheer amount of data points to be plotted this does not make an appealing vis so I will restrict\n",
    "#the number of plotted points to 40\n",
    "customers_missing_less_30.sort_values(by = 'percent_missing', ascending = False)[:40].plot(kind = 'bar', x = 'column_name', y = 'percent_missing',\n",
    "                                                                                ax = axes[3][0],color = 'grey', title = 'Customers percentage of missing values less 30%' )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The vast majority of the columns with missing values have a percent of missing under 30%\n",
    "### Based on this information I will remove columns with more than 30% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting column names with more than 30% values missing so we can drop them from azdias df\n",
    "azdias_col_delete = columns_to_delete(azdias_missing_over_30)\n",
    "\n",
    "#extracting column names with more than 30% values missing so we can drop them from customers df\n",
    "customers_col_delete = columns_to_delete(customers_missing_over_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the columns identified in the previous lists\n",
    "\n",
    "azdias = azdias.drop(azdias_col_delete, axis = 1)\n",
    "customers = customers.drop(customers_col_delete, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  False\n",
      "Your first argument df differs from the second on the following columns: \n",
      "{'REGIOTYP', 'KKK'}\n",
      "Your second argument df differs from the first on the following columns: \n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "#since I just dropped several columns I will do another balance check\n",
    "balance_checker(azdias, customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now that we dropped columns missing more than 30% of their data let's check if we should also drop rows based on a particular threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting distribution of null values\n",
    "row_hist(azdias, customers, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on this visualization we deduct 2 things\n",
    "##### - most of the rows have less than 100 missing values \n",
    "##### - both customer and azdias have probably overlapping rows in which they are missing the same info "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#deleting rows based on the information acquired in the previous histogram \n",
    "azdias = row_dropper(azdias, 100)\n",
    "customers = row_dropper(customers, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#plotting null values distribution after cleanup\n",
    "row_hist(azdias, customers, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "balance_checker(azdias, customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It seems like dropping the rows makes the clustering perform worse so for now I will skip it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Encoding\n",
    "\n",
    "### Like I previously checked using the categorical_checker there are many features in need of re-encoding for the unsupervised learning portion \n",
    "\n",
    "- numerical features will be kept as is\n",
    "- ordinal features will be kept as is\n",
    "- categorical features and mixed type features will have to be re-encoded (label or dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>type</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>CAMEO_INTL_2015</td>\n",
       "      <td>microcell_rr4</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,XX]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>286</td>\n",
       "      <td>LP_LEBENSPHASE_FEIN</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>287</td>\n",
       "      <td>LP_LEBENSPHASE_GROB</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>303</td>\n",
       "      <td>PRAEGENDE_JUGENDJAHRE</td>\n",
       "      <td>person</td>\n",
       "      <td>mixed</td>\n",
       "      <td>[-1,0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 attribute information_level   type missing_or_unknown Comment\n",
       "15         CAMEO_INTL_2015     microcell_rr4  mixed            [-1,XX]     NaN\n",
       "286    LP_LEBENSPHASE_FEIN            person  mixed                [0]     NaN\n",
       "287    LP_LEBENSPHASE_GROB            person  mixed                [0]     NaN\n",
       "303  PRAEGENDE_JUGENDJAHRE            person  mixed             [-1,0]     NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for mixed type features\n",
    "attributes[attributes.type == 'mixed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'ANREDE_KZ',\n",
       " 'CAMEO_DEU_2015',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'D19_BANKEN_DATUM',\n",
       " 'D19_BANKEN_OFFLINE_DATUM',\n",
       " 'D19_BANKEN_ONLINE_DATUM',\n",
       " 'D19_GESAMT_DATUM',\n",
       " 'D19_GESAMT_OFFLINE_DATUM',\n",
       " 'D19_GESAMT_ONLINE_DATUM',\n",
       " 'D19_KONSUMTYP',\n",
       " 'D19_TELKO_DATUM',\n",
       " 'D19_TELKO_OFFLINE_DATUM',\n",
       " 'D19_TELKO_ONLINE_DATUM',\n",
       " 'D19_VERSAND_DATUM',\n",
       " 'D19_VERSAND_OFFLINE_DATUM',\n",
       " 'D19_VERSAND_ONLINE_DATUM',\n",
       " 'D19_VERSI_DATUM',\n",
       " 'D19_VERSI_OFFLINE_DATUM',\n",
       " 'D19_VERSI_ONLINE_DATUM',\n",
       " 'FINANZTYP',\n",
       " 'GEBAEUDETYP',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'GREEN_AVANTGARDE',\n",
       " 'HAUSHALTSSTRUKTUR',\n",
       " 'KBA05_BAUMAX',\n",
       " 'KK_KUNDENTYP',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'LP_STATUS_FEIN',\n",
       " 'LP_STATUS_GROB',\n",
       " 'NATIONALITAET_KZ',\n",
       " 'OST_WEST_KZ',\n",
       " 'PLZ8_BAUMAX',\n",
       " 'SHOPPER_TYP',\n",
       " 'SOHO_KZ',\n",
       " 'TITEL_KZ',\n",
       " 'VERS_TYP',\n",
       " 'WACHSTUMSGEBIET_NB',\n",
       " 'WOHNLAGE',\n",
       " 'ZABEOTYP']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#retrieve a list of categorical features for future encoding\n",
    "cats = attributes[attributes.type == 'categorical']\n",
    "list(cats['attribute'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point I already dealt with the CAMEO_INTL_2015 column by converting XX to nan\n",
    "\n",
    "PRAEGENDE_JUGENDJAHRE has 3 dimentions: generation decade, if people are mainstream or avant-garde and if they are from east or west, I will create new features out of this particular column\n",
    "\n",
    "LP_LEBENSPHASE_GROB seems to encode the same information as the CAMEO column and it is divided between gross(grob) and fine (fein) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  False\n",
      "Your first argument df differs from the second on the following columns: \n",
      "{'REGIOTYP', 'KKK'}\n",
      "Your second argument df differs from the first on the following columns: \n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "balance_checker(azdias, customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "#### Based on the previous exploration there are a few features that are good candidates for novel feature creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating PRAEGENDE_JUGENDJAHRE_DECADE feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_MOVEMENT feature\n",
      "Creating WOHNLAGE_QUALITY feature\n",
      "Creating WOHNLAGE_AREA feature\n",
      "Creating Wealth and Family feature\n",
      "Creating LP_LEBENSPHASE_FEIN_life_stage and LP_LEBENSPHASE_FEIN_fine_scale feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_DECADE feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_MOVEMENT feature\n",
      "Creating WOHNLAGE_QUALITY feature\n",
      "Creating WOHNLAGE_AREA feature\n",
      "Creating Wealth and Family feature\n",
      "Creating LP_LEBENSPHASE_FEIN_life_stage and LP_LEBENSPHASE_FEIN_fine_scale feature\n"
     ]
    }
   ],
   "source": [
    "azdias_eng = azdias.copy()\n",
    "customers_eng = customers.copy()\n",
    "azdias_eng = feat_eng(azdias_eng)\n",
    "customers_eng = feat_eng(customers_eng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "balance_checker(azdias, customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that I am done with creating new features and dealing with the most obvious columns I might need to encode the remaining categorical features\n",
    "#### Considering this post: https://stats.stackexchange.com/questions/224051/one-hot-vs-dummy-encoding-in-scikit-learn there are advantages and drawbacks with chosing one-hot-encoding vs dummy encoding.\n",
    "#### There are also concerns regarding using dummies all together https://towardsdatascience.com/one-hot-encoding-is-making-your-tree-based-ensembles-worse-heres-why-d64b282b5769 so I will keep this in mind while moving forward\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature scaling\n",
    "### Before moving on to dimentionality reduction I need to apply feature scaling, this way principal component vectors won't be affected by the variation that naturally occurs in the data\n",
    "### Before applying the scaler there should be no missing values in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "azdias_eng.to_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\azdias_eng.csv\")\n",
    "customers_eng.to_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\customers_eng.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframes using StandardScaler\n",
    "azdias_SS = feature_scaling(azdias_eng, 'StandardScaler')\n",
    "customers_SS = feature_scaling(customers_eng, 'StandardScaler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframes using MinMaxScaler\n",
    "azdias_MMS = feature_scaling(azdias_eng, 'MinMaxScaler')\n",
    "customers_MMS = feature_scaling(customers_eng, 'MinMaxScaler')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction\n",
    "### Finally I will use PCA (linear technique) to select only the components that seem to be more impactfull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components_azdias = len(azdias_SS.columns.values)\n",
    "n_components_customers = len(customers_SS.columns.values)\n",
    "\n",
    "azdias_SS_pca = pca_model(azdias_SS, n_components_azdias)\n",
    "customers_SS_pca = pca_model(customers_SS, n_components_customers)\n",
    "\n",
    "azdias_MMS_pca = pca_model(azdias_MMS, n_components_azdias)\n",
    "customers_MMS_pca = pca_model(customers_MMS, n_components_customers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change to code if intending to save file progress for other notebooks\n",
    "azdias_SS.to_pickle(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\azdias_SS.pickle\")\n",
    "azdias_MMS.to_pickle(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\azdias_MMS.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scree_plots(azdias_SS_pca, azdias_MMS_pca, ' azdias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scree_plots(customers_SS_pca, customers_MMS_pca, ' customers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each principal component is a directional vector pointing to the highest variance. The greater the distance from 0 the more the vector points to a feature.\n",
    "\n",
    "Lets check some interesting features in up to the third dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest: \n",
      "\tCAMEO_DEUG_2015      -0.126\n",
      "\tKBA13_ANTG3          -0.124\n",
      "\tPLZ8_ANTG3           -0.124\n",
      "Highest: \n",
      "\tMOBI_RASTER          0.124\n",
      "\tKBA13_ANTG1          0.124\n",
      "\tPLZ8_ANTG1           0.125\n"
     ]
    }
   ],
   "source": [
    "display_interesting_features(azdias_SS, azdias_SS_pca, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest: \n",
      "\tD19_GESAMT_ONLINE_DATUM -0.143\n",
      "\tD19_GESAMT_DATUM     -0.140\n",
      "\tD19_VERSAND_ONLINE_DATUM -0.138\n",
      "Highest: \n",
      "\tD19_GESAMT_ANZ_12    0.139\n",
      "\tONLINE_AFFINITAET    0.140\n",
      "\tD19_GESAMT_ANZ_24    0.147\n"
     ]
    }
   ],
   "source": [
    "display_interesting_features(azdias_SS, azdias_SS_pca, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest: \n",
      "\tPRAEGENDE_JUGENDJAHRE_DECADE -0.196\n",
      "\tFINANZ_UNAUFFAELLIGER -0.191\n",
      "\tFINANZ_SPARER        -0.191\n",
      "Highest: \n",
      "\tLP_LEBENSPHASE_FEIN_life_stage 0.147\n",
      "\tALTERSKATEGORIE_GROB 0.160\n",
      "\tFINANZ_VORSORGER     0.168\n"
     ]
    }
   ],
   "source": [
    "display_interesting_features(azdias_SS, azdias_SS_pca, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The highest the weight of an attribute the more relevant it is considered to be, lets take a look on the most important features for a few dimensions considering that positive weights might relate to a positive relationship and negative weights a negative one.:\n",
    "- dimension 1(0) using standard scaler:\n",
    "\n",
    "    These are some of features related to positive weights:\n",
    "    \n",
    "    - MOBI_RASTER: refers to the individual's mobility\n",
    "    - KBA13_ANTG1: lower share of car owners\n",
    "    - PLZ8_ANTG1 : lower number of 1-2 family houses\n",
    "    \n",
    "    And these are some of the feaures related to negative weights:\n",
    "    \n",
    "    - KBA13_ANTG4: refers to posession of higher number of cars\n",
    "    - PLZ-ANTG3: number of 6-10 family houses in the PLZ8\n",
    "    - CAMEO_DEU_2015: detailed classification os cultural and living status \n",
    "    \n",
    "So overall the first dimension refers to the social status and living conditions of the individuals present in the dataset.\n",
    "Interesting to see that even though different features were selected for dimensionality reduction but they overall have the same meaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on these plots:\n",
    "- using standard scaler with 150 principal components 90% of the original variance can be represented\n",
    "- using minmax scaler with 150 components we represent 90% of the original variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Moving on I will pick the standard scaler PCA and I will re-fit with with a number of components that explains over 80% of the explained variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Customer Segmentation Report\n",
    "\n",
    "The main bulk of your analysis will come in this part of the project. Here, you should use unsupervised learning techniques to describe the relationship between the demographics of the company's existing customers and the general population of Germany. By the end of this part, you should be able to describe parts of the general population that are more likely to be part of the mail-order company's main customer base, and which parts of the general population are less so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After a lot of data Pre-Processing we are finally getting to the analysis, I will start by attempting KMeans to find relevant clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that I have reduced the number of components to use, it is important to select the number of clusters to aim at for kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(150)\n",
    "azdias_SS_pca = pca.fit_transform(azdias_SS)\n",
    "customers_SS_PCA = pca.fit_transform(customers_SS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The elbow method (https://bl.ocks.org/rpgove/0060ff3b656618e9136b) is a way to validate the optimal number of clusters to use for a particular dataset.\n",
    "It can take some time training the dataset, optimising for the optimal n of clusters means that less resources are used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the elbow plot 9 clusters should be enough to proceed with the kmeans training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# refitting using just 9 clusters\n",
    "kmeans = KMeans(9)\n",
    "kmodel = kmeans.fit(azdias_SS_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#and now we can compare the customer data to the general demographics\n",
    "azdias_clusters = kmodel.predict(pca.transform(azdias_SS))\n",
    "customers_clusters = kmodel.predict(pca.transform(customers_SS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting clusters for the LNRs for the customers\n",
    "cluster_map = pd.DataFrame()\n",
    "cluster_map['LNR'] = azdias_eng.index.values\n",
    "cluster_map['cluster'] = kmodel.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimenting with visualization of the clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#preparing cluster visualization\n",
    "from collections import Counter\n",
    "\n",
    "azdias_labels = kmeans.labels_\n",
    "customers_labels = kmeans.labels_\n",
    "\n",
    "model_feat = list(azdias_eng.columns)\n",
    "cust_feat = list(customers_eng.columns)\n",
    "\n",
    "model_feat_df = pd.DataFrame()\n",
    "model_feat_df['model_feat'] = model_feat\n",
    "\n",
    "#Find model features not in customer features\n",
    "model_feat_notin_cust = [feat for feat in model_feat if feat not in cust_feat]\n",
    "len(model_feat_notin_cust)\n",
    "\n",
    "\n",
    "customers_pca = pca.transform(customers_eng)\n",
    "customers_labels = kmeans.predict(customers_pca)\n",
    "\n",
    "counts_customer = Counter(customers_labels)\n",
    "n_customers = customers_pca.shape[0]\n",
    "\n",
    "customer_freqs = {label: 100*(freq / n_customers) for label, freq in counts_customer.items()}\n",
    "\n",
    "counts_population = Counter(azdias_labels)\n",
    "n_population = azdias_SS_pca.shape[0]\n",
    "\n",
    "population_freqs = {label: 100*(freq / n_population) for label, freq in counts_population.items()}\n",
    "\n",
    "\n",
    "customer_clusters = pd.DataFrame.from_dict(customer_freqs, orient='index', columns=['% of data'])\n",
    "customer_clusters['Cluster'] = customer_clusters.index\n",
    "customer_clusters['DataSet'] = 'Customers Data'\n",
    "population_clusters = pd.DataFrame.from_dict(population_freqs, orient='index', columns=['% of data'])\n",
    "population_clusters['Cluster'] = population_clusters.index\n",
    "population_clusters['DataSet'] = 'General Population'\n",
    "\n",
    "all_clusters = pd.concat([customer_clusters, population_clusters])\n",
    "all_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 491.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Cluster', y='% of data', hue='DataSet', data=all_clusters, kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform the customers using pca\n",
    "customers_pca = pca.transform(customers_SS)\n",
    "\n",
    "#predict clustering using the kmeans\n",
    "predict_customers = kmodel.predict(customers_pca)\n",
    "\n",
    "#cluster and center prediction and info\n",
    "clust_preds = kmodel.predict(azdias_SS_pca)\n",
    "centers = kmodel.cluster_centers_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHdbsee95z3u47bbbAJg5c+abzrv99tvZuHEjS5cu5emnn2bIkCGMGTOGmTNnAvDkk0+yfPlyhgwZ8pb77rrrrqxZs6bZz6y9x5w5c+jbty+77bZbi99FkrT9c8xLne2///5ceOGFHHHEEXTt2pURI0bwpS99iRNPPJHRo0czbty4xlaEOXPmcOWVV7LTTjvRq1evxtaOc845h4aGBkaOHMnMmTOZPHkyo0ePBuDss89mxIgRjd1Cm3PRRRfxL//yLzQ0NJCZDBgwoHGsSK2WsjSnZ8+eLFq0iAMPPJDevXvzne98p9nzrr32WqZOncqVV15Jv379mDFjRuOxIUOGcMQRR7By5UpuuOEGevTowfnnn8+5557L0KFD6datGzfddNObWks2aWhooFu3bgwbNozJkyczYsT/PEnikksuYcqUKTQ0NLDLLrtw8803F/o5SZK2X9Fa03yZjBo1KufPn/+mfU888URjl4jqp1evXtv0ROrJkydz/PHHc8opp7Rhqrbh7yFJqrG1i71u5aKuEfFwZo5qun/H6jaSJEmlZ7eRCjv44IN5/fXX37Tvlltu2aZWF6is2yJJUlGdvnhpbraPts4DDzzQ0RHaVWfpUpWkzqZTdxv16NGDVatW+ZeQtlhmsmrVKnr06NHRUSRJTXTqlpf+/fuzYsWKxuXqpS3Ro0cP+vfv39ExJElNdOriZaeddmLgwIEdHUOSJLWhTt1tJEmSOh+LF0mSVCoWL5IkqVQsXiRJUqlYvEiSpFKxeJEkSaVi8SJJkkrF4kWSJJWKxYskSSoVixdJklQqFi+SJKlULF4kSVKpWLxIkqRSsXiRJEmlYvEiSZJKxeJFkiSVisWLJEkqFYsXSZJUKhYvkiSpVCxeJElSqVi8SJKkUrF4kSRJpWLxIkmSSsXiRZIklYrFiyRJKhWLF0mSVCoWL5IkqVQsXiRJUqlYvEiSpFKxeJEkSaVi8SJJkkql1eIlIrpExO/aK4wkSdLmtFq8ZOZG4LcRsVc75ZEkSWpVtwLn/C2wKCIeBF7dtDMzJ9QtlSRJUguKFC+X1j2FJElSQZstXjJzbkTsDQzOzJ9FxC5A1/pHkyRJeqvNzjaKiI8CdwDfqO7aE5hVz1CSJEktKTJV+p+AQ4GXATLzKWCPIjePiGMj4vcRsSQiLmjm+Ccj4vGIWBARP6+28Gw6Nikinqq+JhX7OpIkqbMrUry8npl/3bQREd2A3NxFEdEVuB54H/Au4IyIeFeT0x4FRmVmA5XWnS9Xr+0DXAwcDIwGLo6I3QtklSRJnVyR4mVuRHwW2DkijgZuB35Y4LrRwJLMfLpa/NwGnFh7Qmbel5lrq5u/AfpX3x8D/DQzV2fmC8BPgWMLfKYkSerkihQvFwDPAwuBjwF3ZeaFBa7bE/hjzfaK6r6WfAT4yZZcGxHnRMT8iJj//PPPF4gkSZLKrshU6U9k5jXA/920IyL+ubqvNdHMvma7myLiTGAUcMSWXJuZ04HpAKNGjdpsV5YkSSq/Ii0vzQ2WnVzguhXAO2u2+wPPNj0pIt4LXAhMyMzXt+RaSZK042mx5SUizgA+BAyMiNk1h3YDVhW490PA4IgYCPwJOL16v9rPGEFlCvaxmfnfNYfuBr5YM0h3PPCZAp8pSZI6uda6jX4FPAf0Ba6u2b8GWLC5G2fm+oj4OJVCpCtwY2YuiojLgPmZORu4EugF3B4RAMszc0Jmro6Iz1MpgAAuy8zVW/jdJElSJ9Ri8ZKZzwDPVLt1XsvMjRGxD7AvlcG7m5WZdwF3Ndn3uZr3723l2huBG4t8jiRJ2nEUGfPyC6BHROwJ/ByYAtxUz1CSJEktKVK8RHUtlpOBr2XmB6gsOidJktTuChUvEfFu4MPAj6v7ikyxliRJanNFipd/oTLT5/vVAbeDgPvqG0uSJKl5m21Bycy5wNya7aeBafUMJUmS1JLNFi8RcR/Nr257VF0SSZIktaLI2JVP17zvAfwjsL4+cSRJklpXpNvo4Sa7fhkRc5s9WZIkqc6KdBv1qdnsAhwI/K+6JZIkSWpFkW6jh6mMeQkq3UV/AD5Sz1CSJEktKdJtNLA9gkiSJBXR2lOlT27twsz8XtvHkSRJal1rLS8ntHIsAYsXSZLU7lp7qvSU9gwiSZJUxGYfDxARX4yIt9ds7x4RX6hvLEmSpOYVebbR+zLzxU0bmfkC8P76RZIkSWpZkeKla0R037QRETsD3Vs5X5IkqW6KrPPybeDnETGDykDdqcDNdU0lSZLUgiLrvHw5IhYA76WyUN3nM/PuuieTJElqRpGWFzLzv4D/qnMWSZKkzSoy5kWSJGm7YfEiSZJKxeJFkiSVymbHvETEocAlwN7V8wPIzBxU32iSJElvVWTA7jeB/w08DGyobxxJkqTWFSleXsrMn9Q9iSRJUgEtFi8RMbL69r6IuJLKU6Rf33Q8Mx+pczZJksrlkt5bed1LbZujk2ut5eXqJtujat4ncFTbx5EkSWpdi8VLZh7ZnkEkSZKK2OxU6Yj4YkS8vWZ794j4Qn1jSZIkNa/IOi/vy8wXN21k5gvA++sXSZIkqWVFipeuEdF900ZE7Ax0b+V8SZKkuikyVfrbwM8jYgaVgbpTgZvrmkqSJKkFmy1eMvPLEbEQGEdldd3PZ+bddU8mSZLUjCItL1QXqXOhOkmS1OGKzDY6JCIeiohXIuKvEbEhIl5uj3CSJElNFRmwex1wBvAUsDNwNvC1eoaSJElqSdFuoyUR0TUzNwAzIuJXdc4lSZLUrCLFy9qIeBvwWER8GXgO6FnfWJIkSc0r0m00sXrex4FXgXcC/1jPUJIkSS0pMlX6merCdH+bmZe2QyZJkqQWFZltdALwGPBf1e3hETG73sEkSZKaU6Tb6BJgNPAiQGY+BgyoXyRJkqSWFRmwuz4zX4qIuoeRpB3eJb238rqX2jaHtB0rUrz8LiI+ROUBjYOBaYBTpSVJUoco0m30CWB/4HXgVuAl4F/qGUqSJKklRWYbrQUurL4kSZI6VJGWF0mSpO2GxYskSSoVixdJklQqmx3zEhH9gI9SWdul8fzMnFq/WJIkSc0rMlX6B8D9wM+ADfWNI0mS1Loixcsumfn/bM3NI+JY4BqgK/AfmXlFk+NjgH8HGoDTM/OOmmMbgIXVzeWZOWFrMkiSpM6lSPHyo4h4f2betSU3joiuwPXA0cAK4KGImJ2Zj9ecthyYDHy6mVu8lpnDt+QzJUlS51ekePln4LMR8TrwBhBAZuZum7luNLAkM58GiIjbgBOBxuIlM5dVj23c8uiSWuQS85I6sc3ONsrMXTOzS2bunJm7Vbc3V7gA7An8sWZ7RXVfUT0iYn5E/CYiTmruhIg4p3rO/Oeff34Lbi1JksqqxZaXiNg3MxdHxMjmjmfmI5u5d3NPcswtyLZXZj4bEYOAeyNiYWYubZJhOjAdYNSoUVtyb0mSVFKtdRt9EjgHuLqZYwkctZl7rwDeWbPdH3i2aLDMfLb669MRMQcYASxt9SJJktTptVi8ZOY51V+P3Mp7PwQMjoiBwJ+A04EPFbkwInYH1mbm6xHRFzgU+PJW5pAkSZ1I3VbYzcz1wMeBu4EngO9m5qKIuCwiJgBExEERsQI4FfhGRCyqXr4fMD8ifgvcB1zRZJaSJEnaQRWZbbTVqtOr72qy73M17x+i0p3U9LpfAUPrmU2SJJWTzzaSJEmlstniJSIOjYie1fdnRsRXImLv+keTJEl6qyItL/8fsDYihgH/BjwDfKuuqSRJklpQZMzL+szMiDgRuCYzvxkRk+odTCpiwAU/3qrrll1xXBsnkSS1lyLFy5qI+AxwJjCm+syineobS5IkqXlFuo1OA14HPpKZf6ayxP+VdU0lSZLUgs22vFQLlq/UbC/HMS+SJKmDFJltdHJEPBURL0XEyxGxJiJebo9wkiRJTRUZ8/Jl4ITMfKLeYSSps9jqweQ92jiItor//bZvRca8rLRwkSRJ24siLS/zI+I7wCwqA3cByMzv1S2VJElSC4oUL7sBa4HxNfsSsHiRJEntrshsoyntEUSSJKmIIrON+kfE9yPivyNiZUTcGRFveRK0JElSeygyYHcGMBv4OyoL1P2wuk+SJKndFSle+mXmjMxcX33dBPSrcy5JkqRmFSle/hIRZ0ZE1+rrTGBVvYNJkiQ1p0jxMhX4IPDn6uuU6j5JkqR2V2S20XJgQjtkkSRJ2qwis42+HBG7RcROEfHziPhLtetIkiSp3RXpNhqfmS8DxwMrgH2Af61rKkmSpBYUKV52qv76fuA/M3N1HfNIkiS1qsjjAWZHxGLgNeD8iOgHrKtvLEmSpOa1WrxERBcqi9J9GXg5MzdExFrgxPYIpw50Se+tvO6lts0hSWpzAy748VZdt6xHGwfZSq12G2XmRuDqzHwhMzdU972amX9ul3SSJElNFBnzck9E/GNERN3TSJIkbUaRMS+fBHoCGyLiNSCAzMzd6ppMkiSpGUUWqdu1PYJIkiQVUWSRuqg+2+ii6vY7I2J0/aNJkiS9VZExL18H3g18qLr9CnB93RJJkiS1osiYl4Mzc2REPAqQmS9ExNvqnEuSJKlZRVpe3oiIrkACVBep21jXVJIkSS0oUrxcC3wf+JuIuByYB3yxrqkkSZJaUGS20cyIeBgYV911UmY+Ud9YkiRJzSsy5gVgF2BT19HO9YsjSZLUuiJTpT8H3Az0AfoCMyLi/9Q7mCRJUnOKtLycAYzIzHUAEXEF8AjwhXoGkyRJak6RAbvLgNrnSHYHltYljSRJ0mYUaXl5HVgUET+lMublaGBeRFwLkJnT6phPkiTpTYoUL9+vvjaZU58oknY0Ay748VZdt+yK49o4iaQyKTJV+ub2CCJJklREkTEvkiRJ2w2LF0mSVCotFi8RcUv1139uvziSJEmta63l5cCI2BuYGhG7R0Sf2ld7BZQkSarV2oDdG4D/AgYBDwNRcyyr+yVJktpViy0vmXltZu4H3JiZgzJzYM3LwkWSJHWIIlOlz4uIYcDh1V2/yMwF9Y0lSZLUvCIPZpwGzAT2qL5mRsQn6h1MkiSpOUVW2D0bODgzXwWIiC8Bvwa+Vs9gkiRJzSmyzksAG2q2N/DmwbuSJEntpkjLywzggYjY9Hyjk4Bv1i+SJElSyzbb8pKZXwGmAKuBF4ApmfnvRW4eEcdGxO8jYklEXNDM8TER8UhErI+IU5ocmxQRT1Vfk4p9HUmS1NkVaXkhMx8BHtmSG0dEV+B64GhgBfBQRMzOzMdrTlsOTAY+3eTaPsDFwCgqa8o8XL32hS3JIEmSOp96PttoNLAkM5/OzL8CtwEn1p6Qmcuq0643Nrn2GOCnmbm6WrD8FDi2jlklSVJJFGp52Up7An+s2V4BHLwN1+7Z9KSIOAc4B2CvvfbaupTaMV3Seyuve6ltc0iStlg9W16am5GUbXltZk7PzFGZOapfv35bFE6SJJVTkUXqTq4Omn0pIl6OiDUR8XKBe68A3lmz3R94tmCubblWkiR1YkVaXr4MTMjM3pm5W2bumpm7FbjuIWBwRAyMiLcBpwOzC+a6GxhffZr17sD46j5JkrSDK1K8rMzMJ7b0xpm5Hvg4laLjCeC7mbkoIi6LiAkAEXFQRKwATgW+ERGLqteuBj5PpQB6CLisuk+SJO3gigzYnR8R3wFmAa9v2pmZ39vchZl5F3BXk32fq3n/EJUuoeauvRG4sUA+SZK0AylSvOwGrKXSdbNJApstXiRJktraZouXzJzSHkEkSZKKKDLbqH9EfD8i/jsiVkbEnRHRbFePJElSvRUZsDuDyiyhv6OyUNwPq/skSZLaXZHipV9mzsjM9dXXTYArwkmSpA5RpHj5S0ScGRFdq68zgVX1DiZJktScIrONpgLXAV+lMsvoV9V9KoEBF/x4q65b1qONg0iS1EaKzDZaDkxohyySJEmb1WLxEhH/lplfjoiv0fxDEafVNZkkabu11a26VxzXxkm0I2qt5WXTIwHmt0cQSZKkIlosXjLzh9W3azPz9tpjEXFqXVNJkiS1oMhso88U3CdJklR3rY15eR/wfmDPiLi25tBuwPp6B5MkSWpOa2NenqUy3mUC8HDN/jXA/65nKEmSpJa0NtDCzA4AAA1QSURBVObltxHxO2B8Zt7cjpkkSZJa1OqYl8zcALwjIt7WTnkkSZJaVWSF3WeAX0bEbODVTTsz8yt1SyUJcIVkSWpOkeLl2eqrC7BrfeNIkiS1rsjjAS4FiIhdK5v5St1TSZIktWCzxUtEHADcAvSpbv8FOCszF9U5W7twiWtJksqlyCJ104FPZubembk38Cng/9Y3liRJUvOKFC89M/O+TRuZOQfoWbdEkiRJrSgyYPfpiLiIStcRwJnAH+oXSZIkqWVFWl6mAv2A7wHfr76fUs9QkiRJLSky2+gFYFpE9AY2Zuaa+seSJElq3mZbXiLioIhYCPwWWBgRv42IA+sfTZIk6a2KjHn5JnB+Zt4PEBGHATOAhnoGkyRJak6RMS9rNhUuAJk5j8qTpSVJktpdkZaXByPiG8B/AgmcBsyJiJEAmflIHfNJkiS9SZHiZXj114ub7H8PlWLmqDZNJEmS1Iois42ObI8gkiRJRRSZbdQ7Ir4SEfOrr6ur06YlSZLaXZEBuzdSGaD7werrZSqzjSRJktpdkTEvf5+Z/1izfWlEPFavQJIkSa0p0vLyWnVtFwAi4lDgtfpFkiRJalmRlpdzgW/VjHN5AZhUv0iSJEkta7V4iYguwJDMHBYRuwFk5svtkkySJKkZrXYbZeZG4OPV9y9buEiSpI5WZMzLTyPi0xHxzojos+lV92SSJEnNKDLmZWr113+q2ZfAoLaPI0mS1LoiK+wObI8gkiRJRWy2eImIHsD5wGFUWlzuB27IzHV1ziZJkvQWRbqNvkVlhd2vVbfPAG4BTq1XKEmSpJYUKV6GZOawmu37IuK39QokSZLUmiKzjR6NiEM2bUTEwcAv6xdJkiSpZUVaXg4GzoqI5dXtvYAnImIhkJnZULd0kiRJTRQpXo6tewpJkqSCikyVfqY9gkiSJBVRZMyLJEnSdsPiRZIklYrFiyRJKhWLF0mSVCp1LV4i4tiI+H1ELImIC5o53j0ivlM9/kBEDKjuHxARr0XEY9XXDfXMKUmSyqPIVOmtEhFdgeuBo4EVwEMRMTszH6857SPAC5n5DxFxOvAl4LTqsaWZObxe+SRJUjnVs+VlNLAkM5/OzL8CtwEnNjnnRODm6vs7gHEREXXMJEmSSq6excuewB9rtldU9zV7TmauB14C3lE9NjAiHo2IuRFxeHMfEBHnRMT8iJj//PPPt216SZK0Xapn8dJcC0oWPOc5YK/MHAF8Erg1InZ7y4mZ0zNzVGaO6tev3zYHliRJ2796Fi8rgHfWbPcHnm3pnIjoBvQGVmfm65m5CiAzHwaWAvvUMaskSSqJehYvDwGDI2JgRLwNOB2Y3eSc2cCk6vtTgHszMyOiX3XALxExCBgMPF3HrJIkqSTqNtsoM9dHxMeBu4GuwI2ZuSgiLgPmZ+Zs4JvALRGxBFhNpcABGANcFhHrgQ3AuZm5ul5ZJUlSedSteAHIzLuAu5rs+1zN+3XAqc1cdydwZz2zSZKkcnKFXUmSVCoWL5IkqVQsXiRJUqlYvEiSpFKxeJEkSaVi8SJJkkrF4kWSJJWKxYskSSoVixdJklQqFi+SJKlULF4kSVKpWLxIkqRSsXiRJEmlYvEiSZJKxeJFkiSVisWLJEkqFYsXSZJUKhYvkiSpVCxeJElSqVi8SJKkUrF4kSRJpWLxIkmSSsXiRZIklYrFiyRJKhWLF0mSVCoWL5IkqVQsXiRJUqlYvEiSpFKxeJEkSaVi8SJJkkrF4kWSJJWKxYskSSoVixdJklQqFi+SJKlULF4kSVKpWLxIkqRSsXiRJEmlYvEiSZJKxeJFkiSVisWLJEkqlW4dHaC0Lum9lde91LY5JEnawVi8SCof//Eg7dDsNpIkSaViy4skqf3YaqY2YMuLJEkqFYsXSZJUKhYvkiSpVCxeJElSqVi8SJKkUrF4kSRJpWLxIkmSSsXiRZIklUpdi5eIODYifh8RSyLigmaOd4+I71SPPxARA2qOfaa6//cRcUw9c0qSpPKoW/ESEV2B64H3Ae8CzoiIdzU57SPAC5n5D8BXgS9Vr30XcDqwP3As8PXq/SRJ0g6uni0vo4Elmfl0Zv4VuA04sck5JwI3V9/fAYyLiKjuvy0zX8/MPwBLqveTJEk7uMjM+tw44hTg2Mw8u7o9ETg4Mz9ec87vquesqG4vBQ4GLgF+k5nfru7/JvCTzLyjyWecA5xT3RwC/L4uX6Z5fYG/tOPntTe/X7n5/cqrM3838PuVXXt/v70zs1/TnfV8MGM0s69ppdTSOUWuJTOnA9O3PNq2i4j5mTmqIz67Pfj9ys3vV16d+buB36/stpfvV89uoxXAO2u2+wPPtnRORHQDegOrC14rSZJ2QPUsXh4CBkfEwIh4G5UBuLObnDMbmFR9fwpwb1b6sWYDp1dnIw0EBgMP1jGrJEkqibp1G2Xm+oj4OHA30BW4MTMXRcRlwPzMnA18E7glIpZQaXE5vXrtooj4LvA4sB74p8zcUK+sW6lDuqvakd+v3Px+5dWZvxv4/cpuu/h+dRuwK0mSVA+usCtJkkrF4kWSJJWKxYskSSoVixdJklQqFi8CICL2jYhxEdGryf5jOypTW4qI0RFxUPX9uyLikxHx/o7OVQ8R8a2OzlAvEXFY9b/d+I7O0hYi4uCI2K36fueIuDQifhgRX4qI3h2db1tFxLSIeOfmzyyniHhbRJwVEe+tbn8oIq6LiH+KiJ06Ot+2ioi/j4hPR8Q1EXF1RJy7vfy+dLbRNoqIKZk5o6NzbIuImAb8E/AEMBz458z8QfXYI5k5siPzbauIuJjKA0K7AT+l8giKOcB7gbsz8/KOS7dtIqLp2kkBHAncC5CZE9o9VBuKiAczc3T1/Uep/D79PjAe+GFmXtGR+bZVRCwChlWXlpgOrKX6nLfq/pM7NOA2ioiXgFeBpcB/Ardn5vMdm6rtRMRMKn+u7AK8CPQCvkflv19k5qRWLt+uVf9eOAGYC7wfeAx4AfgAcH5mzum4dBYv2ywilmfmXh2dY1tExELg3Zn5SkQMoPKH5y2ZeU1EPJqZIzo04Daqfr/hQHfgz0D/zHw5InYGHsjMhg4NuA0i4hEq6yH9B//zaI3/5H/WTJrbcem2Xe3vv4h4CHh/Zj4fET2pPP9saMcm3DYR8URm7ld9/6Z/KETEY5k5vOPSbbuIeBQ4kMo/FE4DJgAPU/k9+r3MXNOB8bZZRCzIzIbqCvF/Av4uMzdUHzD825L/2bIQGF79PrsAd2Xm2IjYC/hBR/+9UM9nG3UaEbGgpUPA37RnljrpmpmvAGTmsogYC9wREXvT/HOmymZ9dZHDtRGxNDNfBsjM1yJiYwdn21ajgH8GLgT+NTMfi4jXyl601OgSEbtT6eKOTf9qz8xXI2J9x0ZrE7+rab39bUSMysz5EbEP8EZHh2sDmZkbgXuAe6pdKe8DzgCuAt7ywL2S6VJdQb4nldaXTY+46Q6UvtuISo2wgcr32RUgM5dvD11iFi/F/A1wDJUms1oB/Kr947S5P0fE8Mx8DKDaAnM8cCNQ6n/ZVv01InbJzLVU/hUIQLXvttTFS/Uvhq9GxO3VX1fSuf6/7k3lX+oBZET8r8z8c3VsVmcorM8GromI/0PlSb2/jog/An+sHiu7N/03ysw3qDz+ZXa15bPsvgksprKK/IXA7RHxNHAIcFtHBmsD/wE8FBG/AcYAXwKIiH5UCrQOZbdRARHxTWBGZs5r5titmfmhDojVZiKiP5XWiT83c+zQzPxlB8RqMxHRPTNfb2Z/X+BvM3NhB8Sqi4g4Djg0Mz/b0VnqqdqM/TeZ+YeOztIWImJXYBCVwnNFZq7s4EhtIiL2ycwnOzpHPUXE3wFk5rMR8XYqXWTLM7P0z+OLiP2B/YDfZebijs5Ty+JFkiSVilOlJUlSqVi8SJKkUrF4kdThIuKSiPj0Vlz39og4vx6ZJG2/LF4kldnbgS0qXqLCP/ukEvN/YEntrrqk+oKI+G1E3NLk2JyIGFV93zcillXf7x8RD0bEY9VrBwNXAH9f3Xdl9bx/jYiHqudcWt03ICKeiIivA48AnXbJemlH0JnWg5BUAtXplxdSmdL9l4joA0wrcOm5wDWZObO6MFhX4ALggE0r0UblmUeDgdFU1hiZHRFjgOXAEGBKZtrNJJWcxYuk9nYUcEdm/gUgM1dXVlPfrF8DF1bXJfpeZj7VzHXjq69Hq9u9qBQzy4FnMvM3bZBfUgez20hSewsqz2FqyXr+58+mHpt2ZuatVJ6N8xpwd0Qc1cK9/9/MHF59/UNmfrN67NVtjy5pe2DxIqm9/Rz4YES8A6DabVRrGf/zGIdTNu2MiEHA05l5LZUl5huANVSfuVJ1NzC1+vgAImLPiNijHl9CUsex20hSu8rMRRFxOTA3IjZQ6eJZVnPKVcB3I2IicG/N/tOAMyPiDSpPB7+s2uX0y4j4HfCTzPzXiNiPyjOCAF4BzqTycDlJnYSPB5AkSaVit5EkSSoVixdJklQqFi+SJKlULF4kSVKpWLxIkqRSsXiRJEmlYvEiSZJK5f8HFl2lbLUzrvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "general_pop = []\n",
    "customers_pop = []\n",
    "x = [i+1 for i in range(9)]\n",
    "for i in range(9):\n",
    "    general_pop.append((clust_preds == i).sum()/len(clust_preds))\n",
    "    customers_pop.append((predict_customers == i).sum()/len(predict_customers))\n",
    "\n",
    "\n",
    "df_general = pd.DataFrame({'cluster' : x, 'general_proportion' : general_pop, 'customers_proportion':customers_pop})\n",
    "\n",
    "df_general.plot(x='cluster', y = ['general_proportion', 'customers_proportion'], kind='bar', figsize=(9,6))\n",
    "plt.ylabel('proportion of persons in each cluster')\n",
    "plt.show()"
   ]
  },
  {
   "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": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "mailout_train = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\mailout_train.csv\")\n",
    "mailout_test = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\mailout_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating PRAEGENDE_JUGENDJAHRE_DECADE feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_MOVEMENT feature\n",
      "Creating WOHNLAGE_QUALITY feature\n",
      "Creating WOHNLAGE_AREA feature\n",
      "Creating Wealth and Family feature\n",
      "Creating LP_LEBENSPHASE_FEIN_life_stage and LP_LEBENSPHASE_FEIN_fine_scale feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_DECADE feature\n",
      "Creating PRAEGENDE_JUGENDJAHRE_MOVEMENT feature\n",
      "Creating WOHNLAGE_QUALITY feature\n",
      "Creating WOHNLAGE_AREA feature\n",
      "Creating Wealth and Family feature\n",
      "Creating LP_LEBENSPHASE_FEIN_life_stage and LP_LEBENSPHASE_FEIN_fine_scale feature\n"
     ]
    }
   ],
   "source": [
    "#running all the cleaning and feature transformation functions\n",
    "#fixing the mixed type columns\n",
    "mailout_train = special_feature_handler(mailout_train)\n",
    "mailout_test = special_feature_handler(mailout_test)\n",
    "\n",
    "#dealing with missing and unknowns\n",
    "unknowns_to_NANs(mailout_train, dias_xls)\n",
    "unknowns_to_NANs(mailout_test, dias_xls)\n",
    "\n",
    "#getting percentages of missing\n",
    "percent_missing_train = percentage_of_missing(mailout_train)\n",
    "percent_missing_test = percentage_of_missing(mailout_test)\n",
    "\n",
    "#getting missing over 30%\n",
    "train_missing_over_30 = split_on_percentage(percent_missing_train, 30, '>')\n",
    "test_missing_over_30 = split_on_percentage(percent_missing_test, 30, '>')\n",
    "\n",
    "#getting columns to delete\n",
    "train_col_delete = columns_to_delete(train_missing_over_30)\n",
    "test_col_delete = columns_to_delete(test_missing_over_30)\n",
    "\n",
    "#dropping cols\n",
    "mailout_train = mailout_train.drop(train_col_delete, axis = 1)\n",
    "mailout_test = mailout_test.drop(test_col_delete, axis = 1)\n",
    "\n",
    "#feature engineering\n",
    "mailout_train = feat_eng(mailout_train)\n",
    "mailout_test = feat_eng(mailout_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mailout_train.to_csv('mailout_train_clean.csv')\n",
    "mailout_test.to_csv('mailout_test_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#since I just dropped several columns I will do another balance check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  False\n",
      "Your first argument df differs from the second on the following columns: \n",
      "set()\n",
      "Your second argument df differs from the first on the following columns: \n",
      "{'RESPONSE'}\n"
     ]
    }
   ],
   "source": [
    "balance_checker(azdias_eng, mailout_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  False\n",
      "Your first argument df differs from the second on the following columns: \n",
      "{'RESPONSE'}\n",
      "Your second argument df differs from the first on the following columns: \n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "balance_checker(mailout_train, mailout_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before moving on to the model I want to experiment a bit with the identified clusters and how the responses align with the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging mailout and cluster map on the LNR column\n",
    "clusters_mailout = pd.merge(mailout_train, cluster_map, on = 'LNR')\n",
    "response_and_cluster = clusters_mailout[['LNR', 'RESPONSE', 'cluster']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"cluster\", hue=\"RESPONSE\", data=response_and_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258d0a0f608>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 = response_and_cluster.groupby(['cluster', 'RESPONSE'])['RESPONSE'].count().unstack('RESPONSE')\n",
    "plot1[[0, 1]].plot(kind = 'bar', stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pos_response = response_and_cluster[response_and_cluster['RESPONSE'] == 1]\n",
    "df_neg_response = response_and_cluster[response_and_cluster['RESPONSE'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 1, figsize = (15,15), squeeze = False)\n",
    "\n",
    "pos = df_pos_response['cluster'].value_counts().plot(kind='bar',figsize=(15,10), color = 'C1', ax = axes[0][0], title = 'Clusters with positive responses')\n",
    "neg = df_neg_response['cluster'].value_counts().plot(kind='bar',figsize=(15,10), ax = axes[1][0], title = 'Clusters with negative responses')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change to code if intending to save file progress for other notebooks\n",
    "mailout_train.to_pickle(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\mailout_train.pickle\")\n",
    "mailout_test.to_pickle(r\"C:\\Users\\sousa\\Desktop\\github\\Arvato\\data\\mailout_test.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting to the models for response prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the target\n",
    "target = mailout_train['RESPONSE']\n",
    "mailout_train_clean = mailout_train.drop(['RESPONSE'], inplace=False, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping LNR\n",
    "mailout_test_clean = mailout_test.copy()\n",
    "mailout_train_clean.drop(['LNR'], inplace = True, axis = 1)\n",
    "mailout_test_clean.drop(['LNR'], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature balance between dfs?:  True\n"
     ]
    }
   ],
   "source": [
    "balance_checker(mailout_train_clean, mailout_test_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Label distribution')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the label distribution\n",
    "\n",
    "sns.countplot(target).set_title(\"Label distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this plot these datasets are quite imbalanced, there are quite a few more responses corresponding to 0 than to 1.\n",
    "\n",
    "Considering this accuracy will not be an appropriate metric, ROC-AUC is a better option (1 being a perfect score, and 0.5 just random chance)\n",
    "\n",
    "Lets try model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 stratified folds\n",
    "skf = StratifiedKFold(n_splits=5, random_state=SEED, shuffle=True)\n",
    "skf.get_n_splits(mailout_train_clean, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best performing model seem to be XGB so from here on I will continue the analysis with this model.\n",
    "There also seems to be no difference beetween standard scaler and Min Max Scaler. since the second is a bit faster I will stick with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(mailout_train_clean.astype('float'))\n",
    "\n",
    "mailout_train_scaled = scaler.transform(mailout_train_clean)\n",
    "mailout_test_scaled = scaler.transform(mailout_test_clean)\n",
    "\n",
    "# map back to dfs\n",
    "mailout_train_scaled = pd.DataFrame(data=mailout_train_scaled, columns=mailout_train_clean.columns)\n",
    "mailout_test_scaled = pd.DataFrame(data=mailout_test_scaled, columns=mailout_test_clean.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "balance_checker(mailout_train, mailout_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I experimented with multiple models to determine which one was the best performing.\n",
    "### The best performer was xgboost so I decided to use the Bayesian Optimization algorithm to fine tune the hyperparameters that produce the best results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#submission 1 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=1,\n",
    "                              colsample_bytree=0.6872852588903648, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=1.0,\n",
    "                              learning_rate=0.014017007043823785, \n",
    "                              max_delta_step=0, \n",
    "                              max_depth=7,\n",
    "                              min_child_weight=1, \n",
    "                              missing=None, \n",
    "                              n_estimators=236, \n",
    "                              n_jobs=-1,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=1e-09, \n",
    "                              reg_lambda=1, \n",
    "                              scale_pos_weight=1, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 2 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=1,\n",
    "                              colsample_bytree=1, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=0,\n",
    "                              learning_rate=0.04788137748021131, \n",
    "                              max_delta_step=0, \n",
    "                              max_depth=1,\n",
    "                              min_child_weight=10, \n",
    "                              missing=None, \n",
    "                              n_estimators=429, \n",
    "                              n_jobs=-1,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=0.17651703245342792, \n",
    "                              reg_lambda=1, \n",
    "                              scale_pos_weight=1, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 3 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=1,\n",
    "                              colsample_bytree=0.9889952021544406, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=0.7810813412544743,\n",
    "                              learning_rate=0.044734814719961276, \n",
    "                              max_delta_step=0, \n",
    "                              max_depth=1,\n",
    "                              min_child_weight=10, \n",
    "                              missing=None, \n",
    "                              n_estimators=454, \n",
    "                              n_jobs=-1,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=1.0, \n",
    "                              reg_lambda=1, \n",
    "                              scale_pos_weight=1, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 4 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=1,\n",
    "                              colsample_bytree=0.8, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=0,\n",
    "                              learning_rate=0.1, \n",
    "                              max_delta_step=0, \n",
    "                              max_depth=5,\n",
    "                              min_child_weight=1, \n",
    "                              missing=None, \n",
    "                              n_estimators=454, \n",
    "                              n_jobs=2,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=1.0, \n",
    "                              reg_lambda=1, \n",
    "                              scale_pos_weight=1, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 5 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=1,\n",
    "                              colsample_bytree=0.6872852588903648, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=1,\n",
    "                              learning_rate=0.01, \n",
    "                              max_delta_step=0, \n",
    "                              max_depth=3,\n",
    "                              min_child_weight=4, \n",
    "                              missing=None, \n",
    "                              n_estimators=454, \n",
    "                              n_jobs=2,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=1e-09, \n",
    "                              reg_lambda=1, \n",
    "                              scale_pos_weight=1, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.8)\n",
    "                              \n",
    "{'colsample_bylevel': 0.6207294509491699, 'colsample_bytree': 0.9623594386716922, 'gamma': 1.0, 'learning_rate': 0.001, 'max_delta_step': 1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 500, 'reg_alpha': 1e-09, 'reg_lambda': 0.0002004101751465717, 'scale_pos_weight': 56, 'subsample': 0.6092942995200367}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 6 parameters\n",
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, \n",
    "                              booster='gbtree', \n",
    "                              colsample_bylevel=0.6207294509491699,\n",
    "                              colsample_bytree=0.9623594386716922, \n",
    "                              eval_metric='auc', \n",
    "                              gamma=1,\n",
    "                              learning_rate=0.001, \n",
    "                              max_delta_step=1, \n",
    "                              max_depth=6,\n",
    "                              min_child_weight=1, \n",
    "                              missing=None, \n",
    "                              n_estimators=500, \n",
    "                              n_jobs=2,\n",
    "                              nthread=None, \n",
    "                              objective='binary:logistic', \n",
    "                              random_state=0,\n",
    "                              reg_alpha=1e-09, \n",
    "                              reg_lambda=0.0002004101751465717, \n",
    "                              scale_pos_weight=56, \n",
    "                              seed=None,\n",
    "                              silent=1, \n",
    "                              subsample=0.6092942995200367)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bayes_xgb = xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.5578105865941786,\n",
    "              eval_metric='auc', gamma=0.07148993933953135,\n",
    "              learning_rate=0.002858444321957567, max_delta_step=0, max_depth=4,\n",
    "              min_child_weight=1, missing=None, n_estimators=500, n_jobs=-1,\n",
    "              nthread=None, objective='binary:logistic', random_state=0,\n",
    "              reg_alpha=0.004635659074237927, reg_lambda=1, scale_pos_weight=41,\n",
    "              seed=None, silent=1, subsample=0.522846932344412, verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.5578105865941786,\n",
       "              eval_metric='auc', gamma=0.07148993933953135,\n",
       "              learning_rate=0.002858444321957567, max_delta_step=0, max_depth=4,\n",
       "              min_child_weight=1, missing=None, n_estimators=500, n_jobs=-1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0.004635659074237927, reg_lambda=1, scale_pos_weight=41,\n",
       "              seed=None, silent=1, subsample=0.522846932344412, verbosity=1)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_xgb.fit(mailout_train_scaled, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(model=bayes_xgb, model_type=\"XGBClassifier\", features=mailout_train_scaled.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#submission 1 lgbm\n",
    "bayes_lgbm = lgb.LGBMClassifier(application='binary', \n",
    "                                boosting_type='gbdt', \n",
    "                                class_weight=None,\n",
    "                                colsample_bytree=1.0, \n",
    "                                importance_type='split',\n",
    "                                learning_rate=0.09531171453332088, \n",
    "                                max_bin=100, \n",
    "                                max_depth=2,\n",
    "                                metric='auc', \n",
    "                                min_child_samples=14, \n",
    "                                min_child_weight=0.001,\n",
    "                                min_data_in_leaf=265, \n",
    "                                min_split_gain=0.0, \n",
    "                                n_estimators=24,\n",
    "                                n_jobs=-1, \n",
    "                                num_leaves=450, \n",
    "                                objective=None, \n",
    "                                random_state=None,\n",
    "                                reg_alpha=1e-09, \n",
    "                                reg_lambda=1.0,\n",
    "                                scale_pos_weight=69.87642288579819, \n",
    "                                silent=True, \n",
    "                                subsample=1.0,\n",
    "                                subsample_for_bin=200000, \n",
    "                                subsample_freq=0, \n",
    "                                verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#submission 2 lgbm\n",
    "bayes_lgbm = lgb.LGBMClassifier(application='binary', \n",
    "                                boosting_type='gbdt', \n",
    "                                class_weight=None,\n",
    "                                colsample_bytree=1.0, \n",
    "                                importance_type='split',\n",
    "                                learning_rate=0.0882410095694084, \n",
    "                                max_bin=826, \n",
    "                                max_depth=2,\n",
    "                                metric='auc', \n",
    "                                min_child_samples=0, \n",
    "                                min_child_weight=0.001,\n",
    "                                min_data_in_leaf=32, \n",
    "                                min_split_gain=0.0, \n",
    "                                n_estimators=24,\n",
    "                                n_jobs=-1, \n",
    "                                num_leaves=94, \n",
    "                                objective=None, \n",
    "                                random_state=None,\n",
    "                                reg_alpha=1.0, \n",
    "                                reg_lambda=1e-09,\n",
    "                                scale_pos_weight=90.0, \n",
    "                                silent=True, \n",
    "                                subsample=1.0,\n",
    "                                subsample_for_bin=200000, \n",
    "                                subsample_freq=0, \n",
    "                                verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(application='binary', boosting_type='gbdt', class_weight=None,\n",
       "               colsample_bytree=1.0, importance_type='split',\n",
       "               learning_rate=0.0882410095694084, max_bin=826, max_depth=2,\n",
       "               metric='auc', min_child_samples=0, min_child_weight=0.001,\n",
       "               min_data_in_leaf=32, min_split_gain=0.0, n_estimators=24,\n",
       "               n_jobs=-1, num_leaves=94, objective=None, random_state=None,\n",
       "               reg_alpha=1.0, reg_lambda=1e-09, scale_pos_weight=90.0,\n",
       "               silent=True, subsample=1.0, subsample_for_bin=200000,\n",
       "               subsample_freq=0, verbose=0)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_lgbm.fit(mailout_train_scaled, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(model=bayes_lgbm, model_type=\"LGBMClassifier\", features=mailout_train_scaled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_comparison_feature(column = 'D19_SOZIALES', df=mailout_train)"
   ]
  },
  {
   "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": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit and predict xgb\n",
    "bayes_xgb.fit(mailout_train_scaled, target)\n",
    "predictions = bayes_xgb.predict_proba(mailout_test_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit and predict lgbm\n",
    "bayes_lgbm.fit(mailout_train_scaled, target)\n",
    "predictions = bayes_lgbm.predict_proba(mailout_test_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>RESPONSE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LNR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1754</td>\n",
       "      <td>0.548138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1770</td>\n",
       "      <td>0.556485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1465</td>\n",
       "      <td>0.190174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1470</td>\n",
       "      <td>0.219252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1478</td>\n",
       "      <td>0.226788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RESPONSE\n",
       "LNR           \n",
       "1754  0.548138\n",
       "1770  0.556485\n",
       "1465  0.190174\n",
       "1470  0.219252\n",
       "1478  0.226788"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create submission file\n",
    "lnr = pd.DataFrame(mailout_test['LNR'].astype('int32'))\n",
    "predictions = pd.DataFrame(predictions)\n",
    "predictions = predictions.rename(columns={0: \"RESPONSE\"})\n",
    "dfs = [lnr, predictions]\n",
    "submission = pd.concat(dfs, sort=False, axis=1)\n",
    "submission.set_index('LNR', inplace = True)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42833, 1)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('kaggle_sub10.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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