main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"6\"> <div style=\"text-align: center\"> **Multi Classification predictor on Payment's Timeliness**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this task is to build a predictor that informs whether a payment occurs ahead of time, on time or if it's delayed, given an input data containing transactional and master data.\n",
    "\n",
    "A payment is considered to be on time if it occurs within the same month of the due date.\n",
    "\n",
    "To tackle this problem we will do the following steps:\n",
    "\n",
    "- Data Exploration and Analysis\n",
    "- Feature  and Data Transformation\n",
    "- Forecasting models using ML classification algoritms - Supervised methods to build a predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Imports</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-Upload\" data-toc-modified-id=\"Data-Upload-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Data Upload</a></span></li></ul></li><li><span><a href=\"#Exploratory-Data-Analysis\" data-toc-modified-id=\"Exploratory-Data-Analysis-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Exploratory Data Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#First-Look\" data-toc-modified-id=\"First-Look-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>First Look</a></span></li><li><span><a href=\"#Some-Variable's-Analysis\" data-toc-modified-id=\"Some-Variable's-Analysis-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Some Variable's Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Company,-Country,-City-and-profit-Center\" data-toc-modified-id=\"Company,-Country,-City-and-profit-Center-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Company, Country, City and profit Center</a></span></li><li><span><a href=\"#Document-Type\" data-toc-modified-id=\"Document-Type-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>Document Type</a></span></li><li><span><a href=\"#Credit-Risk\" data-toc-modified-id=\"Credit-Risk-2.2.3\"><span class=\"toc-item-num\">2.2.3&nbsp;&nbsp;</span>Credit Risk</a></span></li></ul></li><li><span><a href=\"#Further-variable-transformations\" data-toc-modified-id=\"Further-variable-transformations-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Further variable transformations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dependent-Variable-Creation\" data-toc-modified-id=\"Dependent-Variable-Creation-2.3.1\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>Dependent Variable Creation</a></span></li><li><span><a href=\"#DUE_DATE_SOURCE-and-MANSP\" data-toc-modified-id=\"DUE_DATE_SOURCE-and-MANSP-2.3.2\"><span class=\"toc-item-num\">2.3.2&nbsp;&nbsp;</span><code>DUE_DATE_SOURCE</code> and <code>MANSP</code></a></span></li><li><span><a href=\"#One-Hot-Encoding---BLART-and-TBSLT_LTEXT\" data-toc-modified-id=\"One-Hot-Encoding---BLART-and-TBSLT_LTEXT-2.3.3\"><span class=\"toc-item-num\">2.3.3&nbsp;&nbsp;</span>One Hot Encoding - <code>BLART</code> and <code>TBSLT_LTEXT</code></a></span></li></ul></li><li><span><a href=\"#Data-Cleaning\" data-toc-modified-id=\"Data-Cleaning-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Data Cleaning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Outliers\" data-toc-modified-id=\"Outliers-2.4.1\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>Outliers</a></span></li><li><span><a href=\"#Multicollinearity-Check\" data-toc-modified-id=\"Multicollinearity-Check-2.4.2\"><span class=\"toc-item-num\">2.4.2&nbsp;&nbsp;</span>Multicollinearity Check</a></span></li><li><span><a href=\"#Recursive-Feature-Elimination\" data-toc-modified-id=\"Recursive-Feature-Elimination-2.4.3\"><span class=\"toc-item-num\">2.4.3&nbsp;&nbsp;</span>Recursive Feature Elimination</a></span></li></ul></li></ul></li><li><span><a href=\"#Data-Transformation\" data-toc-modified-id=\"Data-Transformation-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Data Transformation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Normalization\" data-toc-modified-id=\"Normalization-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Normalization</a></span></li></ul></li><li><span><a href=\"#Modelling\" data-toc-modified-id=\"Modelling-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Modelling</a></span><ul class=\"toc-item\"><li><span><a href=\"#Model-Evaluation\" data-toc-modified-id=\"Model-Evaluation-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Model Evaluation</a></span></li><li><span><a href=\"#Simple-Models\" data-toc-modified-id=\"Simple-Models-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Simple Models</a></span><ul class=\"toc-item\"><li><span><a href=\"#Logistic-Regression\" data-toc-modified-id=\"Logistic-Regression-4.2.1\"><span class=\"toc-item-num\">4.2.1&nbsp;&nbsp;</span>Logistic Regression</a></span></li><li><span><a href=\"#Neural-Networks\" data-toc-modified-id=\"Neural-Networks-4.2.2\"><span class=\"toc-item-num\">4.2.2&nbsp;&nbsp;</span>Neural Networks</a></span></li><li><span><a href=\"#Random-Forest\" data-toc-modified-id=\"Random-Forest-4.2.3\"><span class=\"toc-item-num\">4.2.3&nbsp;&nbsp;</span>Random Forest</a></span></li></ul></li><li><span><a href=\"#Tuned-Model\" data-toc-modified-id=\"Tuned-Model-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Tuned Model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Random-Forest\" data-toc-modified-id=\"Random-Forest-4.3.1\"><span class=\"toc-item-num\">4.3.1&nbsp;&nbsp;</span>Random Forest</a></span></li></ul></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import itertools\n",
    "import random\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot \n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix,mean_squared_error, plot_confusion_matrix, plot_roc_curve, roc_auc_score\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>KNA1_LAND1</th>\n",
       "      <th>KNA1_ORT01</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>...</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>MANSP</th>\n",
       "      <th>CTLPC</th>\n",
       "      <th>HISTORICRATING</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>DATUM</th>\n",
       "      <th>DUE_DATE</th>\n",
       "      <th>GJAHR2</th>\n",
       "      <th>DOCUMENT_DATE</th>\n",
       "      <th>PAYMENT_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NL</td>\n",
       "      <td>KATWIJK</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.512086e+15</td>\n",
       "      <td>20180109</td>\n",
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       "      <td>11</td>\n",
       "      <td>19521</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>WARNSVELD</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-10</td>\n",
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       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>OSS</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.512086e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>KATWIJK</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.514851e+15</td>\n",
       "      <td>20180109</td>\n",
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       "      <td>310</td>\n",
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       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>OSS</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.514851e+15</td>\n",
       "      <td>20180109</td>\n",
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       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10946</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>STANGE</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R4+</td>\n",
       "      <td>R6+</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
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       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10983</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>PORSGRUNN</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R7+</td>\n",
       "      <td>R7+</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
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       "      <td>34</td>\n",
       "      <td>NO</td>\n",
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       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R7+</td>\n",
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       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.546387e+15</td>\n",
       "      <td>20190215</td>\n",
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       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>310</td>\n",
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       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12221</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>LYSAKER</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R4-</td>\n",
       "      <td>R5-</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
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       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>SANDVIKA</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R6+</td>\n",
       "      <td>R6+</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MANDT  BUKRS  GJAHR  HKONT  KUNNR  PRCTR KNA1_LAND1 KNA1_ORT01  ZTERM  \\\n",
       "0         310      3   2018     11  18724      1         NL    KATWIJK     63   \n",
       "1         310      3   2018     11  19521      1         NL  WARNSVELD     63   \n",
       "2         310      3   2018     11   5420      1         NL        OSS     63   \n",
       "3         310      3   2018     11  18724      1         NL    KATWIJK     63   \n",
       "4         310      3   2018     11   5420      1         NL        OSS     63   \n",
       "...       ...    ...    ...    ...    ...    ...        ...        ...    ...   \n",
       "266088    310      4   2019      1  10946     34         NO     STANGE     25   \n",
       "266089    310      4   2019      1  10983     34         NO  PORSGRUNN     29   \n",
       "266090    310      4   2019      1  22904     34         NO       OSLO     29   \n",
       "266091    310      4   2019      1  12221     34         NO    LYSAKER     25   \n",
       "266092    310      4   2019      1  10766     34         NO   SANDVIKA     25   \n",
       "\n",
       "       DUE_DATE_SOURCE  ...  TBSLT_LTEXT  MANSP  CTLPC HISTORICRATING  \\\n",
       "0                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "1                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "2                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "3                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "4                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "...                ...  ...          ...    ...    ...            ...   \n",
       "266088           ZBD1T  ...      Invoice    NaN    3.0            R4+   \n",
       "266089           ZBD1T  ...      Invoice    NaN    3.0            R7+   \n",
       "266090           ZBD1T  ...      Invoice    NaN    3.0            R7+   \n",
       "266091           ZBD1T  ...      Invoice    NaN    3.0            R4-   \n",
       "266092           ZBD1T  ...      Invoice    NaN    3.0            R6+   \n",
       "\n",
       "       CURRENTRATING       DATUM    DUE_DATE  GJAHR2 DOCUMENT_DATE  \\\n",
       "0                NaN  2017-12-01  2017-12-23    2019  1.512086e+15   \n",
       "1                NaN  2018-01-02  2018-01-10    2016  1.514851e+15   \n",
       "2                NaN  2017-12-01  2017-12-27    2019  1.512086e+15   \n",
       "3                NaN  2018-01-02  2018-01-27    2016  1.514851e+15   \n",
       "4                NaN  2018-01-02  2018-01-07    2016  1.514851e+15   \n",
       "...              ...         ...         ...     ...           ...   \n",
       "266088           R6+  2019-02-01  2019-02-13    2018  1.548979e+15   \n",
       "266089           R7+  2019-02-01  2019-02-13    2019  1.548979e+15   \n",
       "266090           R7+  2019-01-02  2019-01-13    2019  1.546387e+15   \n",
       "266091           R5-  2019-02-01  2019-01-28    2015  1.548979e+15   \n",
       "266092           R6+  2019-02-01  2019-02-20    2017  1.548979e+15   \n",
       "\n",
       "       PAYMENT_DATE  \n",
       "0          20180109  \n",
       "1          20180109  \n",
       "2          20180109  \n",
       "3          20180109  \n",
       "4          20180109  \n",
       "...             ...  \n",
       "266088     20190215  \n",
       "266089     20190215  \n",
       "266090     20190215  \n",
       "266091     20190215  \n",
       "266092     20190215  \n",
       "\n",
       "[266093 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('training_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266093, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To describe dataset structure - nº of observations(rows) and features(columns)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'BUKRS', 'GJAHR', 'HKONT', 'KUNNR', 'PRCTR', 'KNA1_LAND1',\n",
       "       'KNA1_ORT01', 'ZTERM', 'DUE_DATE_SOURCE', 'VALUE_EUR', 'MWSTS', 'MWST2',\n",
       "       'BLART', 'T003T_LTEXT', 'TBSLT_LTEXT', 'MANSP', 'CTLPC',\n",
       "       'HISTORICRATING', 'CURRENTRATING', 'DATUM', 'DUE_DATE', 'GJAHR2',\n",
       "       'DOCUMENT_DATE', 'PAYMENT_DATE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To identify dataset features (columns)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266093 entries, 0 to 266092\n",
      "Data columns (total 25 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   MANDT            266093 non-null  int64  \n",
      " 1   BUKRS            266093 non-null  int64  \n",
      " 2   GJAHR            266093 non-null  int64  \n",
      " 3   HKONT            266093 non-null  int64  \n",
      " 4   KUNNR            266093 non-null  int64  \n",
      " 5   PRCTR            266093 non-null  int64  \n",
      " 6   KNA1_LAND1       266093 non-null  object \n",
      " 7   KNA1_ORT01       266093 non-null  object \n",
      " 8   ZTERM            266093 non-null  int64  \n",
      " 9   DUE_DATE_SOURCE  266093 non-null  object \n",
      " 10  VALUE_EUR        266093 non-null  int64  \n",
      " 11  MWSTS            266093 non-null  float64\n",
      " 12  MWST2            266093 non-null  float64\n",
      " 13  BLART            266093 non-null  object \n",
      " 14  T003T_LTEXT      266093 non-null  object \n",
      " 15  TBSLT_LTEXT      266093 non-null  object \n",
      " 16  MANSP            7619 non-null    object \n",
      " 17  CTLPC            153913 non-null  float64\n",
      " 18  HISTORICRATING   160122 non-null  object \n",
      " 19  CURRENTRATING    160121 non-null  object \n",
      " 20  DATUM            266093 non-null  object \n",
      " 21  DUE_DATE         266093 non-null  object \n",
      " 22  GJAHR2           266093 non-null  int64  \n",
      " 23  DOCUMENT_DATE    266093 non-null  float64\n",
      " 24  PAYMENT_DATE     266093 non-null  int64  \n",
      "dtypes: float64(4), int64(10), object(11)\n",
      "memory usage: 50.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# To check dtypes and missing values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">All but **4 columns** have null records: `MANSP`, `CTLPC`, `HISTORICRATING` and `CURRENTRATING`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MANDT</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>3.100000e+02</td>\n",
       "      <td>3.100000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUKRS</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>3.049047e+00</td>\n",
       "      <td>1.841819e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJAHR</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>2.018348e+03</td>\n",
       "      <td>4.763053e-01</td>\n",
       "      <td>2.018000e+03</td>\n",
       "      <td>2.018000e+03</td>\n",
       "      <td>2.018000e+03</td>\n",
       "      <td>2.019000e+03</td>\n",
       "      <td>2.019000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKONT</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>4.939735e+00</td>\n",
       "      <td>2.712330e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUNNR</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>9.589260e+03</td>\n",
       "      <td>6.401649e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.008000e+03</td>\n",
       "      <td>9.103000e+03</td>\n",
       "      <td>1.535900e+04</td>\n",
       "      <td>2.382300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRCTR</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>9.110297e+01</td>\n",
       "      <td>7.524370e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>1.420000e+02</td>\n",
       "      <td>2.290000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTERM</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>5.572431e+01</td>\n",
       "      <td>3.209570e+01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>8.900000e+01</td>\n",
       "      <td>1.540000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>1.932597e+02</td>\n",
       "      <td>1.473406e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>3.012410e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWSTS</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>5.524894e+02</td>\n",
       "      <td>1.238005e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.750000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWST2</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTLPC</th>\n",
       "      <td>153913.0</td>\n",
       "      <td>2.362192e+00</td>\n",
       "      <td>6.722468e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJAHR2</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>2.017503e+03</td>\n",
       "      <td>1.710152e+00</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>2.018000e+03</td>\n",
       "      <td>2.019000e+03</td>\n",
       "      <td>2.020000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOCUMENT_DATE</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>1.532360e+15</td>\n",
       "      <td>1.378414e+13</td>\n",
       "      <td>1.491178e+15</td>\n",
       "      <td>1.519862e+15</td>\n",
       "      <td>1.533082e+15</td>\n",
       "      <td>1.546387e+15</td>\n",
       "      <td>1.561939e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYMENT_DATE</th>\n",
       "      <td>266093.0</td>\n",
       "      <td>2.018235e+07</td>\n",
       "      <td>5.378721e+03</td>\n",
       "      <td>2.017100e+07</td>\n",
       "      <td>2.018031e+07</td>\n",
       "      <td>2.018080e+07</td>\n",
       "      <td>2.019010e+07</td>\n",
       "      <td>2.019092e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std           min  \\\n",
       "MANDT          266093.0  3.100000e+02  0.000000e+00  3.100000e+02   \n",
       "BUKRS          266093.0  3.049047e+00  1.841819e+00  0.000000e+00   \n",
       "GJAHR          266093.0  2.018348e+03  4.763053e-01  2.018000e+03   \n",
       "HKONT          266093.0  4.939735e+00  2.712330e+00  0.000000e+00   \n",
       "KUNNR          266093.0  9.589260e+03  6.401649e+03  0.000000e+00   \n",
       "PRCTR          266093.0  9.110297e+01  7.524370e+01  0.000000e+00   \n",
       "ZTERM          266093.0  5.572431e+01  3.209570e+01 -1.000000e+00   \n",
       "VALUE_EUR      266093.0  1.932597e+02  1.473406e+03  0.000000e+00   \n",
       "MWSTS          266093.0  5.524894e+02  1.238005e+04  0.000000e+00   \n",
       "MWST2          266093.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "CTLPC          153913.0  2.362192e+00  6.722468e-01  0.000000e+00   \n",
       "GJAHR2         266093.0  2.017503e+03  1.710152e+00  2.015000e+03   \n",
       "DOCUMENT_DATE  266093.0  1.532360e+15  1.378414e+13  1.491178e+15   \n",
       "PAYMENT_DATE   266093.0  2.018235e+07  5.378721e+03  2.017100e+07   \n",
       "\n",
       "                        25%           50%           75%           max  \n",
       "MANDT          3.100000e+02  3.100000e+02  3.100000e+02  3.100000e+02  \n",
       "BUKRS          1.000000e+00  3.000000e+00  5.000000e+00  5.000000e+00  \n",
       "GJAHR          2.018000e+03  2.018000e+03  2.019000e+03  2.019000e+03  \n",
       "HKONT          5.000000e+00  5.000000e+00  5.000000e+00  1.300000e+01  \n",
       "KUNNR          4.008000e+03  9.103000e+03  1.535900e+04  2.382300e+04  \n",
       "PRCTR          3.000000e+01  8.000000e+01  1.420000e+02  2.290000e+02  \n",
       "ZTERM          2.900000e+01  3.300000e+01  8.900000e+01  1.540000e+02  \n",
       "VALUE_EUR      1.000000e+01  3.300000e+01  1.000000e+02  3.012410e+05  \n",
       "MWSTS          0.000000e+00  0.000000e+00  0.000000e+00  1.750000e+06  \n",
       "MWST2          0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "CTLPC          2.000000e+00  2.000000e+00  3.000000e+00  1.000000e+01  \n",
       "GJAHR2         2.016000e+03  2.018000e+03  2.019000e+03  2.020000e+03  \n",
       "DOCUMENT_DATE  1.519862e+15  1.533082e+15  1.546387e+15  1.561939e+15  \n",
       "PAYMENT_DATE   2.018031e+07  2.018080e+07  2.019010e+07  2.019092e+07  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To describe the main statistical informartion about the numerical features\n",
    "\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MANDT</th>\n",
       "      <td>266093</td>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>266093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUKRS</th>\n",
       "      <td>266093</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>91623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJAHR</th>\n",
       "      <td>266093</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>173519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HKONT</th>\n",
       "      <td>266093</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>186617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUNNR</th>\n",
       "      <td>266093</td>\n",
       "      <td>23067</td>\n",
       "      <td>17812</td>\n",
       "      <td>2654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRCTR</th>\n",
       "      <td>266093</td>\n",
       "      <td>226</td>\n",
       "      <td>142</td>\n",
       "      <td>34943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNA1_LAND1</th>\n",
       "      <td>266093</td>\n",
       "      <td>50</td>\n",
       "      <td>SE</td>\n",
       "      <td>90751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNA1_ORT01</th>\n",
       "      <td>266093</td>\n",
       "      <td>3311</td>\n",
       "      <td>OSLO</td>\n",
       "      <td>9459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTERM</th>\n",
       "      <td>266093</td>\n",
       "      <td>150</td>\n",
       "      <td>29</td>\n",
       "      <td>111656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <td>266093</td>\n",
       "      <td>4</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>222594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <td>266093</td>\n",
       "      <td>4659</td>\n",
       "      <td>1</td>\n",
       "      <td>9145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWSTS</th>\n",
       "      <td>266093</td>\n",
       "      <td>2401</td>\n",
       "      <td>0</td>\n",
       "      <td>260868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MWST2</th>\n",
       "      <td>266093</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>266093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLART</th>\n",
       "      <td>266093</td>\n",
       "      <td>3</td>\n",
       "      <td>RV</td>\n",
       "      <td>261743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T003T_LTEXT</th>\n",
       "      <td>266093</td>\n",
       "      <td>3</td>\n",
       "      <td>Billing doc.transfer</td>\n",
       "      <td>261743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <td>266093</td>\n",
       "      <td>8</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>247875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANSP</th>\n",
       "      <td>7619</td>\n",
       "      <td>11</td>\n",
       "      <td>H</td>\n",
       "      <td>4788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTLPC</th>\n",
       "      <td>153913</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>104205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HISTORICRATING</th>\n",
       "      <td>160122</td>\n",
       "      <td>20</td>\n",
       "      <td>R5+</td>\n",
       "      <td>39647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <td>160121</td>\n",
       "      <td>20</td>\n",
       "      <td>R5+</td>\n",
       "      <td>37930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATUM</th>\n",
       "      <td>266093</td>\n",
       "      <td>28</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>22540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUE_DATE</th>\n",
       "      <td>266093</td>\n",
       "      <td>576</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>4950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJAHR2</th>\n",
       "      <td>266093</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>44634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOCUMENT_DATE</th>\n",
       "      <td>266093</td>\n",
       "      <td>28</td>\n",
       "      <td>1.54639e+15</td>\n",
       "      <td>22540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYMENT_DATE</th>\n",
       "      <td>266093</td>\n",
       "      <td>567</td>\n",
       "      <td>20190308</td>\n",
       "      <td>4058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count unique                   top    freq\n",
       "MANDT            266093      1                   310  266093\n",
       "BUKRS            266093      6                     5   91623\n",
       "GJAHR            266093      2                  2018  173519\n",
       "HKONT            266093     14                     5  186617\n",
       "KUNNR            266093  23067                 17812    2654\n",
       "PRCTR            266093    226                   142   34943\n",
       "KNA1_LAND1       266093     50                    SE   90751\n",
       "KNA1_ORT01       266093   3311                  OSLO    9459\n",
       "ZTERM            266093    150                    29  111656\n",
       "DUE_DATE_SOURCE  266093      4                 ZBD1T  222594\n",
       "VALUE_EUR        266093   4659                     1    9145\n",
       "MWSTS            266093   2401                     0  260868\n",
       "MWST2            266093      1                     0  266093\n",
       "BLART            266093      3                    RV  261743\n",
       "T003T_LTEXT      266093      3  Billing doc.transfer  261743\n",
       "TBSLT_LTEXT      266093      8               Invoice  247875\n",
       "MANSP              7619     11                     H    4788\n",
       "CTLPC            153913      9                     2  104205\n",
       "HISTORICRATING   160122     20                   R5+   39647\n",
       "CURRENTRATING    160121     20                   R5+   37930\n",
       "DATUM            266093     28            2019-01-02   22540\n",
       "DUE_DATE         266093    576            2019-03-08    4950\n",
       "GJAHR2           266093      6                  2020   44634\n",
       "DOCUMENT_DATE    266093     28           1.54639e+15   22540\n",
       "PAYMENT_DATE     266093    567              20190308    4058"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To describe object statistical information on all columns\n",
    "\n",
    "df.astype(object).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "- `MANDT` and `MWST2` (client code and tax information) have only one value each variable - irrelevant and so it will be deleted\n",
    "- `GJAHR` and `GJAHR2` correspond to fiscal year information on each document, however it is not relevant for the purpose of this problem - it will be deleted\n",
    "- `DATUM` and `DOCUMENT_DATE` represent the date in which the document was issued (the first one only the date and the second one in unix timestamp format) and both are irrelevant since we only want to see differences between the payment date (`PAYMENT_DATE`) and the date when payment has to be made (`DUE_DATE`) - it will be deleted   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Variable's Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">Now, let's compare interesting relationships between some groups of variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Company, Country, City and profit Center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">First, let's check for the relationship between **4 variables** about the location and specifics of the payable company: `KUNNR`, `PRCTR`, `KNA1_LAND1` and `KNA1_ORT01`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>KNA1_LAND1</th>\n",
       "      <th>KNA1_ORT01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUNNR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[NL]</td>\n",
       "      <td>[BARENDRECHT]</td>\n",
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       "      <th>1</th>\n",
       "      <td>[NL]</td>\n",
       "      <td>[SASSENHEIM]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[NL]</td>\n",
       "      <td>['S-GRAVENHAGE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[NL]</td>\n",
       "      <td>[VOORBURG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[DK]</td>\n",
       "      <td>[Ballerup]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23819</th>\n",
       "      <td>[NO]</td>\n",
       "      <td>[FORNEBU]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23820</th>\n",
       "      <td>[NO]</td>\n",
       "      <td>[STAVANGER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23821</th>\n",
       "      <td>[NO]</td>\n",
       "      <td>[OSLO]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23822</th>\n",
       "      <td>[NO]</td>\n",
       "      <td>[KOLBOTN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23823</th>\n",
       "      <td>[NO]</td>\n",
       "      <td>[OSLO]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23067 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      KNA1_LAND1       KNA1_ORT01\n",
       "          unique           unique\n",
       "KUNNR                            \n",
       "0           [NL]    [BARENDRECHT]\n",
       "1           [NL]     [SASSENHEIM]\n",
       "2           [NL]  ['S-GRAVENHAGE]\n",
       "3           [NL]       [VOORBURG]\n",
       "4           [DK]       [Ballerup]\n",
       "...          ...              ...\n",
       "23819       [NO]        [FORNEBU]\n",
       "23820       [NO]      [STAVANGER]\n",
       "23821       [NO]           [OSLO]\n",
       "23822       [NO]        [KOLBOTN]\n",
       "23823       [NO]           [OSLO]\n",
       "\n",
       "[23067 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.groupby(['KUNNR', 'KNA1_LAND1', 'KNA1_ORT01']).count().reset_index().nunique()\n",
    "a = df.groupby('KUNNR')['KNA1_LAND1','KNA1_ORT01'].agg(['unique'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNA1_LAND1</th>\n",
       "      <th>unique</th>\n",
       "      <td>23067</td>\n",
       "      <td>50</td>\n",
       "      <td>[SE]</td>\n",
       "      <td>7923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNA1_ORT01</th>\n",
       "      <th>unique</th>\n",
       "      <td>23067</td>\n",
       "      <td>3311</td>\n",
       "      <td>[Stockholm]</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count unique          top  freq\n",
       "KNA1_LAND1 unique  23067     50         [SE]  7923\n",
       "KNA1_ORT01 unique  23067   3311  [Stockholm]   664"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.astype(object).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">The above indicates us that every `KUNNR` corresponds to one country and one city only because this group by contains 23067 unique values, which is equal to those unique values, as seen previously. This means, that for each customer information, there's only one combination in country and city, and, thus, it facilitates our job onwards, as both categorical fields (country and city) can be removed, while the center code is kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KUNNR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[142, 92]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23819</th>\n",
       "      <td>[34, 33]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23820</th>\n",
       "      <td>[37, 33, 36, 35]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23821</th>\n",
       "      <td>[34, 36, 35]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23822</th>\n",
       "      <td>[34, 32, 31, 30, 37]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23823</th>\n",
       "      <td>[30]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23067 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     unique\n",
       "KUNNR                      \n",
       "0                      [24]\n",
       "1                      [24]\n",
       "2                      [24]\n",
       "3                      [24]\n",
       "4                 [142, 92]\n",
       "...                     ...\n",
       "23819              [34, 33]\n",
       "23820      [37, 33, 36, 35]\n",
       "23821          [34, 36, 35]\n",
       "23822  [34, 32, 31, 30, 37]\n",
       "23823                  [30]\n",
       "\n",
       "[23067 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df.groupby('KUNNR')['PRCTR'].agg(['unique'])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>23067</td>\n",
       "      <td>5688</td>\n",
       "      <td>[7]</td>\n",
       "      <td>4877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count unique  top  freq\n",
       "unique  23067   5688  [7]  4877"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.astype(object).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">Above, it is visible that each company code `KUNNR` can have more than one profit center (`PRCTR`) as there are 5688 unique values grouped by the first column but only 226 unique profit centers, meanng that different countries and cities can have the same profit center and this information can be useful for the predictive model, therefore, I will keep this column as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Document Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Other interesting comparison can be between three categorical variables relative to each record's document/operation type: `BLART`, `T003T_LTEXT` and `TBSLT_LTEXT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='BLART', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# relation between 'BLART' and 'T003T_LTEXT'\n",
    "sns.countplot(x='BLART', hue='T003T_LTEXT', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RV    0.983652\n",
       "ZF    0.009440\n",
       "ZA    0.006907\n",
       "Name: BLART, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of unique values in 'BLART'\n",
    "df['BLART'].value_counts()/df['BLART'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "- From the first countplot, we see that each value from `BLART` corresponds to a unique value from `T003T_LTEXT`, therefore they have the exact same information and one can be removed.\n",
    "- Secondly, we see an extreme dominance in the **'RV'** type or **'Billing doc transfer'** with about 98.4% of total records. Further ahead I will check if it still makes sense to keep this column (depending on the impact onto the output variable). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invoice                 0.931535\n",
      "Credit memo             0.039685\n",
      "Payment difference      0.021421\n",
      "Down payment request    0.006907\n",
      "advances Third Party    0.000241\n",
      "Reverse invoice         0.000165\n",
      "Incoming payment        0.000023\n",
      "Reverse credit memo     0.000023\n",
      "Name: TBSLT_LTEXT, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Invoice                 247875\n",
       "Credit memo              10560\n",
       "Payment difference        5700\n",
       "Down payment request      1838\n",
       "advances Third Party        64\n",
       "Reverse invoice             44\n",
       "Incoming payment             6\n",
       "Reverse credit memo          6\n",
       "Name: TBSLT_LTEXT, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of unique values in 'TBSLT_LTEXT' and unique count\n",
    "\n",
    "print(df['TBSLT_LTEXT'].value_counts()/df['TBSLT_LTEXT'].count())\n",
    "df['TBSLT_LTEXT'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">With `TBSLT_LTEXT` we see again a very dominant value which is 'Invoice' as a type of payment with about 93% of total records. Ahead, I will check again if it worth to keep this column or how to transform it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit Risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "- Here, we'll compare `CTLPC`, `HISTORICRATING` and `CURRENTRATING`. They all seem related to credit risk ratings, being the first one more related with levels. First let's transform the **rating** variables into numbers only (by removing the 'R' and the signal, so I will consider a rating R2+ equal to R2- and R2, for instance) and, then, see correlation between them, which should be really high.\n",
    "- For null values, let's fill these values as a value **11** to indicate that they are 'Not Rated' or missing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>KNA1_LAND1</th>\n",
       "      <th>KNA1_ORT01</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>...</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>MANSP</th>\n",
       "      <th>CTLPC</th>\n",
       "      <th>HISTORICRATING</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>DATUM</th>\n",
       "      <th>DUE_DATE</th>\n",
       "      <th>GJAHR2</th>\n",
       "      <th>DOCUMENT_DATE</th>\n",
       "      <th>PAYMENT_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>KATWIJK</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.512086e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>19521</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>WARNSVELD</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.514851e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>OSS</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.512086e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>KATWIJK</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.514851e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>NL</td>\n",
       "      <td>OSS</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.514851e+15</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10946</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>STANGE</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10983</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>PORSGRUNN</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>22904</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>OSLO</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.546387e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12221</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>LYSAKER</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>NO</td>\n",
       "      <td>SANDVIKA</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>...</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.548979e+15</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MANDT  BUKRS  GJAHR  HKONT  KUNNR  PRCTR KNA1_LAND1 KNA1_ORT01  ZTERM  \\\n",
       "0         310      3   2018     11  18724      1         NL    KATWIJK     63   \n",
       "1         310      3   2018     11  19521      1         NL  WARNSVELD     63   \n",
       "2         310      3   2018     11   5420      1         NL        OSS     63   \n",
       "3         310      3   2018     11  18724      1         NL    KATWIJK     63   \n",
       "4         310      3   2018     11   5420      1         NL        OSS     63   \n",
       "...       ...    ...    ...    ...    ...    ...        ...        ...    ...   \n",
       "266088    310      4   2019      1  10946     34         NO     STANGE     25   \n",
       "266089    310      4   2019      1  10983     34         NO  PORSGRUNN     29   \n",
       "266090    310      4   2019      1  22904     34         NO       OSLO     29   \n",
       "266091    310      4   2019      1  12221     34         NO    LYSAKER     25   \n",
       "266092    310      4   2019      1  10766     34         NO   SANDVIKA     25   \n",
       "\n",
       "       DUE_DATE_SOURCE  ...  TBSLT_LTEXT  MANSP  CTLPC HISTORICRATING  \\\n",
       "0                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "1                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "2                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "3                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "4                ZBD2T  ...      Invoice    NaN    NaN            NaN   \n",
       "...                ...  ...          ...    ...    ...            ...   \n",
       "266088           ZBD1T  ...      Invoice    NaN    3.0              4   \n",
       "266089           ZBD1T  ...      Invoice    NaN    3.0              7   \n",
       "266090           ZBD1T  ...      Invoice    NaN    3.0              7   \n",
       "266091           ZBD1T  ...      Invoice    NaN    3.0              4   \n",
       "266092           ZBD1T  ...      Invoice    NaN    3.0              6   \n",
       "\n",
       "       CURRENTRATING       DATUM    DUE_DATE  GJAHR2 DOCUMENT_DATE  \\\n",
       "0                NaN  2017-12-01  2017-12-23    2019  1.512086e+15   \n",
       "1                NaN  2018-01-02  2018-01-10    2016  1.514851e+15   \n",
       "2                NaN  2017-12-01  2017-12-27    2019  1.512086e+15   \n",
       "3                NaN  2018-01-02  2018-01-27    2016  1.514851e+15   \n",
       "4                NaN  2018-01-02  2018-01-07    2016  1.514851e+15   \n",
       "...              ...         ...         ...     ...           ...   \n",
       "266088             6  2019-02-01  2019-02-13    2018  1.548979e+15   \n",
       "266089             7  2019-02-01  2019-02-13    2019  1.548979e+15   \n",
       "266090             7  2019-01-02  2019-01-13    2019  1.546387e+15   \n",
       "266091             5  2019-02-01  2019-01-28    2015  1.548979e+15   \n",
       "266092             6  2019-02-01  2019-02-20    2017  1.548979e+15   \n",
       "\n",
       "       PAYMENT_DATE  \n",
       "0          20180109  \n",
       "1          20180109  \n",
       "2          20180109  \n",
       "3          20180109  \n",
       "4          20180109  \n",
       "...             ...  \n",
       "266088     20190215  \n",
       "266089     20190215  \n",
       "266090     20190215  \n",
       "266091     20190215  \n",
       "266092     20190215  \n",
       "\n",
       "[266093 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform 'Historicrating' and 'currentrating'\n",
    "\n",
    "df['HISTORICRATING'] = df['HISTORICRATING'].str[1]\n",
    "df['CURRENTRATING'] = df['CURRENTRATING'].str[1]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        CTLPC HISTORICRATING CURRENTRATING\n",
      "0        11.0             11            11\n",
      "1        11.0             11            11\n",
      "2        11.0             11            11\n",
      "3        11.0             11            11\n",
      "4        11.0             11            11\n",
      "...       ...            ...           ...\n",
      "266088    3.0              4             6\n",
      "266089    3.0              7             7\n",
      "266090    3.0              7             7\n",
      "266091    3.0              4             5\n",
      "266092    3.0              6             6\n",
      "\n",
      "[266093 rows x 3 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266093 entries, 0 to 266092\n",
      "Data columns (total 25 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   MANDT            266093 non-null  int64  \n",
      " 1   BUKRS            266093 non-null  int64  \n",
      " 2   GJAHR            266093 non-null  int64  \n",
      " 3   HKONT            266093 non-null  int64  \n",
      " 4   KUNNR            266093 non-null  int64  \n",
      " 5   PRCTR            266093 non-null  int64  \n",
      " 6   KNA1_LAND1       266093 non-null  object \n",
      " 7   KNA1_ORT01       266093 non-null  object \n",
      " 8   ZTERM            266093 non-null  int64  \n",
      " 9   DUE_DATE_SOURCE  266093 non-null  object \n",
      " 10  VALUE_EUR        266093 non-null  int64  \n",
      " 11  MWSTS            266093 non-null  float64\n",
      " 12  MWST2            266093 non-null  float64\n",
      " 13  BLART            266093 non-null  object \n",
      " 14  T003T_LTEXT      266093 non-null  object \n",
      " 15  TBSLT_LTEXT      266093 non-null  object \n",
      " 16  MANSP            7619 non-null    object \n",
      " 17  CTLPC            266093 non-null  float64\n",
      " 18  HISTORICRATING   266093 non-null  object \n",
      " 19  CURRENTRATING    266093 non-null  object \n",
      " 20  DATUM            266093 non-null  object \n",
      " 21  DUE_DATE         266093 non-null  object \n",
      " 22  GJAHR2           266093 non-null  int64  \n",
      " 23  DOCUMENT_DATE    266093 non-null  float64\n",
      " 24  PAYMENT_DATE     266093 non-null  int64  \n",
      "dtypes: float64(4), int64(10), object(11)\n",
      "memory usage: 50.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Replace nulls with the value 11\n",
    "\n",
    "df.update(df[['CTLPC', 'HISTORICRATING', 'CURRENTRATING']].fillna(11))\n",
    "print(df[['CTLPC', 'HISTORICRATING', 'CURRENTRATING']])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266093 entries, 0 to 266092\n",
      "Data columns (total 25 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   MANDT            266093 non-null  int64  \n",
      " 1   BUKRS            266093 non-null  int64  \n",
      " 2   GJAHR            266093 non-null  int64  \n",
      " 3   HKONT            266093 non-null  int64  \n",
      " 4   KUNNR            266093 non-null  int64  \n",
      " 5   PRCTR            266093 non-null  int64  \n",
      " 6   KNA1_LAND1       266093 non-null  object \n",
      " 7   KNA1_ORT01       266093 non-null  object \n",
      " 8   ZTERM            266093 non-null  int64  \n",
      " 9   DUE_DATE_SOURCE  266093 non-null  object \n",
      " 10  VALUE_EUR        266093 non-null  int64  \n",
      " 11  MWSTS            266093 non-null  float64\n",
      " 12  MWST2            266093 non-null  float64\n",
      " 13  BLART            266093 non-null  object \n",
      " 14  T003T_LTEXT      266093 non-null  object \n",
      " 15  TBSLT_LTEXT      266093 non-null  object \n",
      " 16  MANSP            7619 non-null    object \n",
      " 17  CTLPC            266093 non-null  int32  \n",
      " 18  HISTORICRATING   266093 non-null  int32  \n",
      " 19  CURRENTRATING    266093 non-null  int32  \n",
      " 20  DATUM            266093 non-null  object \n",
      " 21  DUE_DATE         266093 non-null  object \n",
      " 22  GJAHR2           266093 non-null  int64  \n",
      " 23  DOCUMENT_DATE    266093 non-null  float64\n",
      " 24  PAYMENT_DATE     266093 non-null  int64  \n",
      "dtypes: float64(3), int32(3), int64(10), object(9)\n",
      "memory usage: 47.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# transform these columns into integer\n",
    "\n",
    "df[['CTLPC', 'HISTORICRATING', 'CURRENTRATING']] = df[['CTLPC', 'HISTORICRATING', 'CURRENTRATING']].astype(int)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See Pearson's correlation between these 3 columns\n",
    "\n",
    "def heatmap(df=df):\n",
    "    corr_df = df.corr(method='pearson')\n",
    "\n",
    "    mask = np.zeros_like(corr_df, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
    "    sns.heatmap(corr_df, annot=True, square=False, mask=mask)\n",
    "    return plt.show()\n",
    "\n",
    "heatmap(df[['CTLPC', 'HISTORICRATING', 'CURRENTRATING']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> As seen above and as expected, all variables are extremely well correlated (especially the historical and current rating). Therefore, I will remove `HISTORICRATING` to avoid multicollinearity issues further on the model but still keep `CTLPC` for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
       "      <th>BLART</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>MANSP</th>\n",
       "      <th>CTLPC</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>DUE_DATE</th>\n",
       "      <th>PAYMENT_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>19521</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>20180109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10946</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10983</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>22904</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12221</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>20190215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "0           3     11  18724      1     63           ZBD2T        607    0.0   \n",
       "1           3     11  19521      1     63           ZBD2T          1    0.0   \n",
       "2           3     11   5420      1     63           ZBD2T         14    0.0   \n",
       "3           3     11  18724      1     63           ZBD2T         50    0.0   \n",
       "4           3     11   5420      1     63           ZBD2T         27    0.0   \n",
       "...       ...    ...    ...    ...    ...             ...        ...    ...   \n",
       "266088      4      1  10946     34     25           ZBD1T          6    0.0   \n",
       "266089      4      1  10983     34     29           ZBD1T          6    0.0   \n",
       "266090      4      1  22904     34     29           ZBD1T         49    0.0   \n",
       "266091      4      1  12221     34     25           ZBD1T         75    0.0   \n",
       "266092      4      1  10766     34     25           ZBD1T         22    0.0   \n",
       "\n",
       "       BLART TBSLT_LTEXT MANSP  CTLPC  CURRENTRATING    DUE_DATE  PAYMENT_DATE  \n",
       "0         RV     Invoice   NaN     11             11  2017-12-23      20180109  \n",
       "1         RV     Invoice   NaN     11             11  2018-01-10      20180109  \n",
       "2         RV     Invoice   NaN     11             11  2017-12-27      20180109  \n",
       "3         RV     Invoice   NaN     11             11  2018-01-27      20180109  \n",
       "4         RV     Invoice   NaN     11             11  2018-01-07      20180109  \n",
       "...      ...         ...   ...    ...            ...         ...           ...  \n",
       "266088    RV     Invoice   NaN      3              6  2019-02-13      20190215  \n",
       "266089    RV     Invoice   NaN      3              7  2019-02-13      20190215  \n",
       "266090    ZF     Invoice   NaN      3              7  2019-01-13      20190215  \n",
       "266091    RV     Invoice   NaN      3              5  2019-01-28      20190215  \n",
       "266092    RV     Invoice   NaN      3              6  2019-02-20      20190215  \n",
       "\n",
       "[266093 rows x 15 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete all columns so far\n",
    "\n",
    "df1 = df.drop(columns=['MANDT', 'MWST2', 'GJAHR', 'GJAHR2', 'DATUM', 'DOCUMENT_DATE', \\\n",
    "                      'KNA1_LAND1', 'KNA1_ORT01', 'T003T_LTEXT', 'HISTORICRATING'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further variable transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependent Variable Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "Finally, let's create the dependent variable which is the ouput for our predictor. <br>\n",
    "This is a ternary classification problem because we have three outputs for calculations:\n",
    "- A payment done in time considering that it was done on the same month as the due_date (Value **0**)\n",
    "- A payment done ahead of time considering that it was done one or more months before the due_date month (Value **1**)\n",
    "- A delayed payment considering that it was done one or more months after the due_date month (Value **2**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>PAYMENT_DATE</th>\n",
       "      <th>DUE_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>2017-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>2018-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>2017-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>2018-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>2018-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>2019-02-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>2019-02-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>2019-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>2019-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>2019-02-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PAYMENT_DATE   DUE_DATE\n",
       "0        2018-01-09 2017-12-23\n",
       "1        2018-01-09 2018-01-10\n",
       "2        2018-01-09 2017-12-27\n",
       "3        2018-01-09 2018-01-27\n",
       "4        2018-01-09 2018-01-07\n",
       "...             ...        ...\n",
       "266088   2019-02-15 2019-02-13\n",
       "266089   2019-02-15 2019-02-13\n",
       "266090   2019-02-15 2019-01-13\n",
       "266091   2019-02-15 2019-01-28\n",
       "266092   2019-02-15 2019-02-20\n",
       "\n",
       "[266093 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert `PAYMENT_DATE` column from UNIX timestamp to date and `DUE_DATE` from object to date\n",
    "\n",
    "df1['PAYMENT_DATE'] = pd.to_datetime(df1['PAYMENT_DATE'], format='%Y%m%d')\n",
    "df1['DUE_DATE'] = pd.to_datetime(df1['DUE_DATE'])\n",
    "\n",
    "df1[['PAYMENT_DATE', 'DUE_DATE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
       "      <th>BLART</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>MANSP</th>\n",
       "      <th>CTLPC</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>DUE_DATE</th>\n",
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       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>607</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>ZBD2T</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-01-09</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-12-27</td>\n",
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       "      <th>3</th>\n",
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       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>ZBD2T</td>\n",
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       "      <td>11</td>\n",
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       "      <td>2018-01-07</td>\n",
       "      <td>2018-01-09</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10946</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>4</td>\n",
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       "      <td>ZBD1T</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>22904</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7</td>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12221</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>ZBD1T</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "0           3     11  18724      1     63           ZBD2T        607    0.0   \n",
       "1           3     11  19521      1     63           ZBD2T          1    0.0   \n",
       "2           3     11   5420      1     63           ZBD2T         14    0.0   \n",
       "3           3     11  18724      1     63           ZBD2T         50    0.0   \n",
       "4           3     11   5420      1     63           ZBD2T         27    0.0   \n",
       "...       ...    ...    ...    ...    ...             ...        ...    ...   \n",
       "266088      4      1  10946     34     25           ZBD1T          6    0.0   \n",
       "266089      4      1  10983     34     29           ZBD1T          6    0.0   \n",
       "266090      4      1  22904     34     29           ZBD1T         49    0.0   \n",
       "266091      4      1  12221     34     25           ZBD1T         75    0.0   \n",
       "266092      4      1  10766     34     25           ZBD1T         22    0.0   \n",
       "\n",
       "       BLART TBSLT_LTEXT MANSP  CTLPC  CURRENTRATING   DUE_DATE PAYMENT_DATE  \\\n",
       "0         RV     Invoice   NaN     11             11 2017-12-23   2018-01-09   \n",
       "1         RV     Invoice   NaN     11             11 2018-01-10   2018-01-09   \n",
       "2         RV     Invoice   NaN     11             11 2017-12-27   2018-01-09   \n",
       "3         RV     Invoice   NaN     11             11 2018-01-27   2018-01-09   \n",
       "4         RV     Invoice   NaN     11             11 2018-01-07   2018-01-09   \n",
       "...      ...         ...   ...    ...            ...        ...          ...   \n",
       "266088    RV     Invoice   NaN      3              6 2019-02-13   2019-02-15   \n",
       "266089    RV     Invoice   NaN      3              7 2019-02-13   2019-02-15   \n",
       "266090    ZF     Invoice   NaN      3              7 2019-01-13   2019-02-15   \n",
       "266091    RV     Invoice   NaN      3              5 2019-01-28   2019-02-15   \n",
       "266092    RV     Invoice   NaN      3              6 2019-02-20   2019-02-15   \n",
       "\n",
       "        diff_months  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  \n",
       "...             ...  \n",
       "266088            0  \n",
       "266089            0  \n",
       "266090            1  \n",
       "266091            1  \n",
       "266092            0  \n",
       "\n",
       "[266093 rows x 16 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create column to see difference in months (payment date - due date)\n",
    "\n",
    "df1['diff_months'] = (df1['PAYMENT_DATE'].dt.year - df1['DUE_DATE'].dt.year) * 12 + \\\n",
    "           (df1['PAYMENT_DATE'].dt.month - df1['DUE_DATE'].dt.month)\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    182366\n",
       "2     50260\n",
       "1     33467\n",
       "Name: RESP, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create `Resp` Variable\n",
    "\n",
    "df1['RESP'] = np.where(df1['diff_months'] == 0, 0, np.where(df1['diff_months'] >  0, 2, 1)) \n",
    "\n",
    "df1['RESP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize in a pie chart\n",
    "data1 = df1['RESP'].value_counts()\n",
    "pie, ax = plt.subplots(figsize=[8,8])\n",
    "labels = data1.keys()\n",
    "\n",
    "plt.pie(x=data1, autopct=lambda p : '{:.2f}%  ({:,.0f})'.format(p,p * sum(data1)/100), labels=labels)\n",
    "plt.title('Response Variable Distribution', size=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> We can see a predominence in payments done in time (in the same month as the due date) and, then, more delayed payments than upfront ones, which makes sense and, therefore, we can't consider this variable too much unbalanced, which might be better for the modelling phase. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `DUE_DATE_SOURCE` and `MANSP`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "Since `DUE_DATE_SOURCE` and `MANSP` are categorical variables, let's transform them into numerical.\n",
    "- For the first one, I will keep only the key number (0 to 3), being 0 = 'ZFBDT'. This information can be useful to tell us what source it was used for due date calculations (with or without cash discounts)\n",
    "- For `MANSP`, since it has a lot of missing values and the value 'H' is dominant in valid values, I will consider 0 all nulls (meaning there wasn't any notice to the client) and 1 to all non-blank values (considering that the customer was contacted (dunning process)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1         2\n",
       "2         2\n",
       "3         2\n",
       "4         2\n",
       "         ..\n",
       "266088    1\n",
       "266089    1\n",
       "266090    1\n",
       "266091    1\n",
       "266092    1\n",
       "Name: DUE_DATE_SOURCE, Length: 266093, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode values in `DUE_DATE_SOURCE`\n",
    "\n",
    "df2 = df1\n",
    "df2.replace({'DUE_DATE_SOURCE' : { 'ZFBDT' : 0, 'ZBD1T' : 1, 'ZBD2T' : 2, 'ZBD3T': 3 }}, inplace=True)\n",
    "\n",
    "df2['DUE_DATE_SOURCE']    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`MANSP` variable has 97.1% missing values.\n"
     ]
    }
   ],
   "source": [
    "# print number of missing values in `MANSP`\n",
    "\n",
    "print('`MANSP` variable has %.1f%% missing values.' % (df1['MANSP'].isnull().mean()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Although only 2.9% of values are valid, this variable might be useful to predict essentially delayed payments, since its the objective, therefore I will keep using it, encoding all non-null as 1 and null as 0 like explained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         0\n",
      "1         0\n",
      "2         0\n",
      "3         0\n",
      "4         0\n",
      "         ..\n",
      "266088    0\n",
      "266089    0\n",
      "266090    0\n",
      "266091    0\n",
      "266092    0\n",
      "Name: MANSP, Length: 266093, dtype: int32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    258474\n",
       "1      7619\n",
       "Name: MANSP, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode values in `MANSP`\n",
    "\n",
    "df3 = df1\n",
    "df3['MANSP'] = (df3['MANSP'].notnull()).astype('int')\n",
    "print(df3['MANSP'])\n",
    "df3['MANSP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MANSP', ylabel='count'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# relation between 'MANSP' and 'RESP'\n",
    "sns.countplot(x='MANSP', hue='RESP', data=df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> This graph actually concludes the hypothesis in our sample that dunning a client (`MANSP` = 1) is more prevalent when the client is going to pay earlier than the due date, than on the same month or even when the payment comes months later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding - `BLART` and `TBSLT_LTEXT`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Now it is time to transform our two categorical variables into multiple binary using the One Hot Encoding method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 5000.0)]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# relation between 'BLART' and 'RESP' amplified\n",
    "\n",
    "ax = sns.countplot(x='BLART', hue='RESP', data=df3)\n",
    "ax.set(ylim=(0, 5000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform `BLART` variable\n",
    "\n",
    "list_bin = [1 if value == 'RV' else 0 for value in eval('df3[' + '\\'' + 'BLART' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'RV' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'ZA' else 0 for value in eval('df3[' + '\\'' + 'BLART' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'ZA' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'ZF' else 0 for value in eval('df3[' + '\\'' + 'BLART' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'ZF' + '\\'' + '] = list_bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
       "      <th>BLART</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>...</th>\n",
       "      <th>ZA</th>\n",
       "      <th>ZF</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>advances Third Party</th>\n",
       "      <th>Credit memo</th>\n",
       "      <th>Down payment request</th>\n",
       "      <th>Incoming payment</th>\n",
       "      <th>Payment difference</th>\n",
       "      <th>Reverse credit memo</th>\n",
       "      <th>Reverse invoice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>19521</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10946</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10983</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>22904</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12221</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266093 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM  DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "0           3     11  18724      1     63                2        607    0.0   \n",
       "1           3     11  19521      1     63                2          1    0.0   \n",
       "2           3     11   5420      1     63                2         14    0.0   \n",
       "3           3     11  18724      1     63                2         50    0.0   \n",
       "4           3     11   5420      1     63                2         27    0.0   \n",
       "...       ...    ...    ...    ...    ...              ...        ...    ...   \n",
       "266088      4      1  10946     34     25                1          6    0.0   \n",
       "266089      4      1  10983     34     29                1          6    0.0   \n",
       "266090      4      1  22904     34     29                1         49    0.0   \n",
       "266091      4      1  12221     34     25                1         75    0.0   \n",
       "266092      4      1  10766     34     25                1         22    0.0   \n",
       "\n",
       "       BLART TBSLT_LTEXT  ...  ZA  ZF  Invoice advances Third Party  \\\n",
       "0         RV     Invoice  ...   0   0        1                    0   \n",
       "1         RV     Invoice  ...   0   0        1                    0   \n",
       "2         RV     Invoice  ...   0   0        1                    0   \n",
       "3         RV     Invoice  ...   0   0        1                    0   \n",
       "4         RV     Invoice  ...   0   0        1                    0   \n",
       "...      ...         ...  ...  ..  ..      ...                  ...   \n",
       "266088    RV     Invoice  ...   0   0        1                    0   \n",
       "266089    RV     Invoice  ...   0   0        1                    0   \n",
       "266090    ZF     Invoice  ...   0   1        1                    0   \n",
       "266091    RV     Invoice  ...   0   0        1                    0   \n",
       "266092    RV     Invoice  ...   0   0        1                    0   \n",
       "\n",
       "       Credit memo  Down payment request  Incoming payment  \\\n",
       "0                0                     0                 0   \n",
       "1                0                     0                 0   \n",
       "2                0                     0                 0   \n",
       "3                0                     0                 0   \n",
       "4                0                     0                 0   \n",
       "...            ...                   ...               ...   \n",
       "266088           0                     0                 0   \n",
       "266089           0                     0                 0   \n",
       "266090           0                     0                 0   \n",
       "266091           0                     0                 0   \n",
       "266092           0                     0                 0   \n",
       "\n",
       "        Payment difference  Reverse credit memo  Reverse invoice  \n",
       "0                        0                    0                0  \n",
       "1                        0                    0                0  \n",
       "2                        0                    0                0  \n",
       "3                        0                    0                0  \n",
       "4                        0                    0                0  \n",
       "...                    ...                  ...              ...  \n",
       "266088                   0                    0                0  \n",
       "266089                   0                    0                0  \n",
       "266090                   0                    0                0  \n",
       "266091                   0                    0                0  \n",
       "266092                   0                    0                0  \n",
       "\n",
       "[266093 rows x 28 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform `TBSLT_LTEXT` variable\n",
    "\n",
    "list_bin = [1 if value == 'Invoice' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Invoice' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'advances Third Party' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'advances Third Party' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Credit memo' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Credit memo' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Down payment request' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Down payment request' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Incoming payment' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Incoming payment' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Payment difference' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Payment difference' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Reverse credit memo' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Reverse credit memo' + '\\'' + '] = list_bin')\n",
    "\n",
    "list_bin = [1 if value == 'Reverse invoice' else 0 for value in eval('df3[' + '\\'' + 'TBSLT_LTEXT' + '\\'' + ']')]\n",
    "exec('df3.loc[:, ' + '\\'' + 'Reverse invoice' + '\\'' + '] = list_bin')\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Now let's inspect for outliers in every numerical variable by visualizing each distribution in whisker's boxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266093 entries, 0 to 266092\n",
      "Data columns (total 24 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   BUKRS                 266093 non-null  int64  \n",
      " 1   HKONT                 266093 non-null  int64  \n",
      " 2   KUNNR                 266093 non-null  int64  \n",
      " 3   PRCTR                 266093 non-null  int64  \n",
      " 4   ZTERM                 266093 non-null  int64  \n",
      " 5   DUE_DATE_SOURCE       266093 non-null  int64  \n",
      " 6   VALUE_EUR             266093 non-null  int64  \n",
      " 7   MWSTS                 266093 non-null  float64\n",
      " 8   MANSP                 266093 non-null  int32  \n",
      " 9   CTLPC                 266093 non-null  int32  \n",
      " 10  CURRENTRATING         266093 non-null  int32  \n",
      " 11  diff_months           266093 non-null  int64  \n",
      " 12  RESP                  266093 non-null  int32  \n",
      " 13  RV                    266093 non-null  int64  \n",
      " 14  ZA                    266093 non-null  int64  \n",
      " 15  ZF                    266093 non-null  int64  \n",
      " 16  Invoice               266093 non-null  int64  \n",
      " 17  advances Third Party  266093 non-null  int64  \n",
      " 18  Credit memo           266093 non-null  int64  \n",
      " 19  Down payment request  266093 non-null  int64  \n",
      " 20  Incoming payment      266093 non-null  int64  \n",
      " 21  Payment difference    266093 non-null  int64  \n",
      " 22  Reverse credit memo   266093 non-null  int64  \n",
      " 23  Reverse invoice       266093 non-null  int64  \n",
      "dtypes: float64(1), int32(4), int64(19)\n",
      "memory usage: 44.7 MB\n"
     ]
    }
   ],
   "source": [
    "# first, select all numeric variables\n",
    "\n",
    "numerics = ['int32', 'int64', 'float32', 'float64']\n",
    "dfo = df3.select_dtypes(include=numerics)\n",
    "\n",
    "dfo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x2160 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes= plt.subplots(figsize=(16,30))\n",
    "whiskers=dict()\n",
    "for o in range(len(dfo.columns)):\n",
    "    plt.subplot(6, 4, o+1)\n",
    "    C1=plt.boxplot(x = dfo.columns[o], data = dfo)\n",
    "    plt.title(dfo.columns[o])\n",
    "    w=[item.get_ydata() for item in C1['whiskers']]\n",
    "    whiskers[dfo.columns[o]] = (w[0][1],w[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "    \n",
    "- As expected very few columns have outliers and those only columns with real and interpretable extreme values are `VALUE_EUR` and `MWSTS`: Value to be paid and tax amount.\n",
    "- With the analysis of the summary statistics with these variables it is noticeable that there are a lot of 0s and therefore, the median, 25 and 75 percentile are closer to 0 (or they are really low when compared to max and extreme values), which push the standard deviation to really high values.\n",
    "- Since the standard deviation is really high, I will delete all records whose values are above (and below if exists) 3 standard deviations comparing to the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
       "      <th>BLART</th>\n",
       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>...</th>\n",
       "      <th>ZA</th>\n",
       "      <th>ZF</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>advances Third Party</th>\n",
       "      <th>Credit memo</th>\n",
       "      <th>Down payment request</th>\n",
       "      <th>Incoming payment</th>\n",
       "      <th>Payment difference</th>\n",
       "      <th>Reverse credit memo</th>\n",
       "      <th>Reverse invoice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5186</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5186</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5270</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>4100</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>11062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5969</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>17812</td>\n",
       "      <td>220</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>5535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10451</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9195</td>\n",
       "      <td>219</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4924</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Credit memo</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244372</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>453</td>\n",
       "      <td>48</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>16304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244536</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>13757</td>\n",
       "      <td>11</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>4898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244537</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>13757</td>\n",
       "      <td>11</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>20297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253736</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5186</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>8783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263742</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11915</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>8495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Credit memo</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1292 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM  DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "1743        3     11   5186      1     61                1       5701    0.0   \n",
       "1746        3     11   5186      1     61                1       5270    0.0   \n",
       "2014        3     11   4100      1     69                1      11062    0.0   \n",
       "5969        1     11  17812    220    101                0       5535    0.0   \n",
       "10451       1     11   9195    219    100                0       4924    0.0   \n",
       "...       ...    ...    ...    ...    ...              ...        ...    ...   \n",
       "244372      0      6    453     48    116                1      16304    0.0   \n",
       "244536      3      7  13757     11     69                1       4898    0.0   \n",
       "244537      3      7  13757     11     69                1      20297    0.0   \n",
       "253736      3      1   5186      1     61                1       8783    0.0   \n",
       "263742      3      1  11915      4     96                0       8495    0.0   \n",
       "\n",
       "       BLART  TBSLT_LTEXT  ...  ZA  ZF  Invoice advances Third Party  \\\n",
       "1743      RV      Invoice  ...   0   0        1                    0   \n",
       "1746      RV      Invoice  ...   0   0        1                    0   \n",
       "2014      RV      Invoice  ...   0   0        1                    0   \n",
       "5969      RV      Invoice  ...   0   0        1                    0   \n",
       "10451     RV  Credit memo  ...   0   0        0                    0   \n",
       "...      ...          ...  ...  ..  ..      ...                  ...   \n",
       "244372    RV      Invoice  ...   0   0        1                    0   \n",
       "244536    ZF      Invoice  ...   0   1        1                    0   \n",
       "244537    ZF      Invoice  ...   0   1        1                    0   \n",
       "253736    RV      Invoice  ...   0   0        1                    0   \n",
       "263742    RV  Credit memo  ...   0   0        0                    0   \n",
       "\n",
       "       Credit memo  Down payment request  Incoming payment  \\\n",
       "1743             0                     0                 0   \n",
       "1746             0                     0                 0   \n",
       "2014             0                     0                 0   \n",
       "5969             0                     0                 0   \n",
       "10451            1                     0                 0   \n",
       "...            ...                   ...               ...   \n",
       "244372           0                     0                 0   \n",
       "244536           0                     0                 0   \n",
       "244537           0                     0                 0   \n",
       "253736           0                     0                 0   \n",
       "263742           1                     0                 0   \n",
       "\n",
       "        Payment difference  Reverse credit memo  Reverse invoice  \n",
       "1743                     0                    0                0  \n",
       "1746                     0                    0                0  \n",
       "2014                     0                    0                0  \n",
       "5969                     0                    0                0  \n",
       "10451                    0                    0                0  \n",
       "...                    ...                  ...              ...  \n",
       "244372                   0                    0                0  \n",
       "244536                   0                    0                0  \n",
       "244537                   0                    0                0  \n",
       "253736                   0                    0                0  \n",
       "263742                   0                    0                0  \n",
       "\n",
       "[1292 rows x 28 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df3['VALUE_EUR']\n",
    "\n",
    "out_value = df3[(value >= (value.mean() + 3*value.std())) | (value < (value.mean() - 3*value.std()))]\n",
    "out_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
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       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>...</th>\n",
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       "      <th>Payment difference</th>\n",
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       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>669</td>\n",
       "      <td>47500.00</td>\n",
       "      <td>RV</td>\n",
       "      <td>Payment difference</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28173</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>20560</td>\n",
       "      <td>91</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2123</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>RV</td>\n",
       "      <td>Payment difference</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
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       "      <td>20560</td>\n",
       "      <td>91</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1786</td>\n",
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       "      <td>RV</td>\n",
       "      <td>Payment difference</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>1752</td>\n",
       "      <td>43661.25</td>\n",
       "      <td>RV</td>\n",
       "      <td>Payment difference</td>\n",
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       "      <td>0</td>\n",
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       "      <td>47</td>\n",
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       "      <td>2857</td>\n",
       "      <td>57500.00</td>\n",
       "      <td>ZA</td>\n",
       "      <td>Down payment request</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>793</td>\n",
       "      <td>44071.88</td>\n",
       "      <td>RV</td>\n",
       "      <td>Payment difference</td>\n",
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       "      <td>ZA</td>\n",
       "      <td>Down payment request</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>18024</td>\n",
       "      <td>56</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>327</td>\n",
       "      <td>44925.00</td>\n",
       "      <td>ZA</td>\n",
       "      <td>Down payment request</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>875 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM  DUE_DATE_SOURCE  VALUE_EUR  \\\n",
       "28131       5     13  12377     91     29                1       1247   \n",
       "28142       5     13   7231     91     29                1       2477   \n",
       "28148       5     13   7194     91     29                1        669   \n",
       "28173       5     13  20560     91     29                1       2123   \n",
       "28184       5     13  20560     91     29                1       1786   \n",
       "...       ...    ...    ...    ...    ...              ...        ...   \n",
       "229386      5     13   8794     76     29                1       1752   \n",
       "229512      0      4    342     47     97                1       2857   \n",
       "232903      0      5   1496     61    114                1        793   \n",
       "241041      0      4  18024     56    116                1       2845   \n",
       "241042      0      4  18024     56    116                1        327   \n",
       "\n",
       "           MWSTS BLART           TBSLT_LTEXT  ...  ZA  ZF  Invoice  \\\n",
       "28131   41662.50    RV    Payment difference  ...   0   0        0   \n",
       "28142   73750.00    RV    Payment difference  ...   0   0        0   \n",
       "28148   47500.00    RV    Payment difference  ...   0   0        0   \n",
       "28173   75000.00    RV    Payment difference  ...   0   0        0   \n",
       "28184   62500.00    RV    Payment difference  ...   0   0        0   \n",
       "...          ...   ...                   ...  ...  ..  ..      ...   \n",
       "229386  43661.25    RV    Payment difference  ...   0   0        0   \n",
       "229512  57500.00    ZA  Down payment request  ...   1   0        0   \n",
       "232903  44071.88    RV    Payment difference  ...   0   0        0   \n",
       "241041  43315.00    ZA  Down payment request  ...   1   0        0   \n",
       "241042  44925.00    ZA  Down payment request  ...   1   0        0   \n",
       "\n",
       "       advances Third Party Credit memo  Down payment request  \\\n",
       "28131                     0           0                     0   \n",
       "28142                     0           0                     0   \n",
       "28148                     0           0                     0   \n",
       "28173                     0           0                     0   \n",
       "28184                     0           0                     0   \n",
       "...                     ...         ...                   ...   \n",
       "229386                    0           0                     0   \n",
       "229512                    0           0                     1   \n",
       "232903                    0           0                     0   \n",
       "241041                    0           0                     1   \n",
       "241042                    0           0                     1   \n",
       "\n",
       "        Incoming payment  Payment difference  Reverse credit memo  \\\n",
       "28131                  0                   1                    0   \n",
       "28142                  0                   1                    0   \n",
       "28148                  0                   1                    0   \n",
       "28173                  0                   1                    0   \n",
       "28184                  0                   1                    0   \n",
       "...                  ...                 ...                  ...   \n",
       "229386                 0                   1                    0   \n",
       "229512                 0                   0                    0   \n",
       "232903                 0                   1                    0   \n",
       "241041                 0                   0                    0   \n",
       "241042                 0                   0                    0   \n",
       "\n",
       "        Reverse invoice  \n",
       "28131                 0  \n",
       "28142                 0  \n",
       "28148                 0  \n",
       "28173                 0  \n",
       "28184                 0  \n",
       "...                 ...  \n",
       "229386                0  \n",
       "229512                0  \n",
       "232903                0  \n",
       "241041                0  \n",
       "241042                0  \n",
       "\n",
       "[875 rows x 28 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = df3['MWSTS']\n",
    "\n",
    "out_val = df3[(val >= (val.mean() + 3*val.std())) | (val < (val.mean() - 3*val.std()))]\n",
    "out_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BUKRS</th>\n",
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       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
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       "      <th>TBSLT_LTEXT</th>\n",
       "      <th>...</th>\n",
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       "      <th>Down payment request</th>\n",
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       "      <th>Reverse invoice</th>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZF</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
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       "      <td>Invoice</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10766</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RV</td>\n",
       "      <td>Invoice</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264047 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BUKRS  HKONT  KUNNR  PRCTR  ZTERM  DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "0           3     11  18724      1     63                2        607    0.0   \n",
       "1           3     11  19521      1     63                2          1    0.0   \n",
       "2           3     11   5420      1     63                2         14    0.0   \n",
       "3           3     11  18724      1     63                2         50    0.0   \n",
       "4           3     11   5420      1     63                2         27    0.0   \n",
       "...       ...    ...    ...    ...    ...              ...        ...    ...   \n",
       "266088      4      1  10946     34     25                1          6    0.0   \n",
       "266089      4      1  10983     34     29                1          6    0.0   \n",
       "266090      4      1  22904     34     29                1         49    0.0   \n",
       "266091      4      1  12221     34     25                1         75    0.0   \n",
       "266092      4      1  10766     34     25                1         22    0.0   \n",
       "\n",
       "       BLART TBSLT_LTEXT  ...  ZA  ZF  Invoice advances Third Party  \\\n",
       "0         RV     Invoice  ...   0   0        1                    0   \n",
       "1         RV     Invoice  ...   0   0        1                    0   \n",
       "2         RV     Invoice  ...   0   0        1                    0   \n",
       "3         RV     Invoice  ...   0   0        1                    0   \n",
       "4         RV     Invoice  ...   0   0        1                    0   \n",
       "...      ...         ...  ...  ..  ..      ...                  ...   \n",
       "266088    RV     Invoice  ...   0   0        1                    0   \n",
       "266089    RV     Invoice  ...   0   0        1                    0   \n",
       "266090    ZF     Invoice  ...   0   1        1                    0   \n",
       "266091    RV     Invoice  ...   0   0        1                    0   \n",
       "266092    RV     Invoice  ...   0   0        1                    0   \n",
       "\n",
       "       Credit memo  Down payment request  Incoming payment  \\\n",
       "0                0                     0                 0   \n",
       "1                0                     0                 0   \n",
       "2                0                     0                 0   \n",
       "3                0                     0                 0   \n",
       "4                0                     0                 0   \n",
       "...            ...                   ...               ...   \n",
       "266088           0                     0                 0   \n",
       "266089           0                     0                 0   \n",
       "266090           0                     0                 0   \n",
       "266091           0                     0                 0   \n",
       "266092           0                     0                 0   \n",
       "\n",
       "        Payment difference  Reverse credit memo  Reverse invoice  \n",
       "0                        0                    0                0  \n",
       "1                        0                    0                0  \n",
       "2                        0                    0                0  \n",
       "3                        0                    0                0  \n",
       "4                        0                    0                0  \n",
       "...                    ...                  ...              ...  \n",
       "266088                   0                    0                0  \n",
       "266089                   0                    0                0  \n",
       "266090                   0                    0                0  \n",
       "266091                   0                    0                0  \n",
       "266092                   0                    0                0  \n",
       "\n",
       "[264047 rows x 28 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove outliers\n",
    "\n",
    "df4 = df3.drop(out_value.index | out_val.index)\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check one last time multicollinearity issues with all variables in this case\n",
    "\n",
    "heatmap(df4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Here, we see an interesting medium correlation between `ZTERM` and the rating variables. Other than that, we see some strong negative correlations between some binary variables created but these don't have a practical meaning and therefore I will keep all columns right until the last dimension reduction exercise (Recursive Feature Elimination)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> But first, let's remove already useless columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>BUKRS</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>DUE_DATE_SOURCE</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>MWSTS</th>\n",
       "      <th>MANSP</th>\n",
       "      <th>CTLPC</th>\n",
       "      <th>...</th>\n",
       "      <th>ZA</th>\n",
       "      <th>ZF</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>advances Third Party</th>\n",
       "      <th>Credit memo</th>\n",
       "      <th>Down payment request</th>\n",
       "      <th>Incoming payment</th>\n",
       "      <th>Payment difference</th>\n",
       "      <th>Reverse credit memo</th>\n",
       "      <th>Reverse invoice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>19521</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>18724</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5420</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BUKRS  HKONT  KUNNR  PRCTR  ZTERM  DUE_DATE_SOURCE  VALUE_EUR  MWSTS  \\\n",
       "0      3     11  18724      1     63                2        607    0.0   \n",
       "1      3     11  19521      1     63                2          1    0.0   \n",
       "2      3     11   5420      1     63                2         14    0.0   \n",
       "3      3     11  18724      1     63                2         50    0.0   \n",
       "4      3     11   5420      1     63                2         27    0.0   \n",
       "\n",
       "   MANSP  CTLPC  ...  ZA  ZF  Invoice  advances Third Party  Credit memo  \\\n",
       "0      0     11  ...   0   0        1                     0            0   \n",
       "1      0     11  ...   0   0        1                     0            0   \n",
       "2      0     11  ...   0   0        1                     0            0   \n",
       "3      0     11  ...   0   0        1                     0            0   \n",
       "4      0     11  ...   0   0        1                     0            0   \n",
       "\n",
       "   Down payment request  Incoming payment  Payment difference  \\\n",
       "0                     0                 0                   0   \n",
       "1                     0                 0                   0   \n",
       "2                     0                 0                   0   \n",
       "3                     0                 0                   0   \n",
       "4                     0                 0                   0   \n",
       "\n",
       "   Reverse credit memo  Reverse invoice  \n",
       "0                    0                0  \n",
       "1                    0                0  \n",
       "2                    0                0  \n",
       "3                    0                0  \n",
       "4                    0                0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.drop(columns =['BLART', 'TBSLT_LTEXT', 'DUE_DATE', 'PAYMENT_DATE', 'diff_months'])\n",
    "df5.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> For this step I am going to use a Decision Tree classifier as the method for feature importance to choose for the final treated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df5.drop(columns =['RESP'])\n",
    "y = df5['RESP']\n",
    "\n",
    "# for here let's split the traditional way with 80% train, 20% test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_gini = DecisionTreeClassifier(criterion = 'gini').fit(x_train, y_train)\n",
    "\n",
    "def plot_feature_importances(model):\n",
    "    n_features = x_train.shape[1]\n",
    "    plt.figure(figsize=(20,10))\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), x_train.columns)\n",
    "    plt.xlabel('Feature importance')\n",
    "    plt.ylabel('Feature')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances(model_gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_entropy = DecisionTreeClassifier(criterion = 'entropy').fit(x_train, y_train)\n",
    "\n",
    "plot_feature_importances(model_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> I decided to test two models for RFE, choosing the 'gini' or 'entropy' criterion for the Decision Tree and the results were very similar in terms of assessing the most important features. I will choose as a fair threshold the value of 0.02 and therefore only the following 8 variables will be used further on and so our model will remove any kind of noise and irrelevant features:<br>\n",
    "- `Invoice`, `CURRENTRATING`, `VALUE_EUR`, `ZTERM`, `PRCTR`, `KUNNR`, `HKONT` and `BUKRS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>RESP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>607</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>18724</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>19521</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>5420</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>18724</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>5420</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>10946</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>10983</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>22904</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>12221</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>10766</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264047 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice  CURRENTRATING  VALUE_EUR  ZTERM  PRCTR  KUNNR  HKONT  BUKRS  \\\n",
       "0             1             11        607     63      1  18724     11      3   \n",
       "1             1             11          1     63      1  19521     11      3   \n",
       "2             1             11         14     63      1   5420     11      3   \n",
       "3             1             11         50     63      1  18724     11      3   \n",
       "4             1             11         27     63      1   5420     11      3   \n",
       "...         ...            ...        ...    ...    ...    ...    ...    ...   \n",
       "266088        1              6          6     25     34  10946      1      4   \n",
       "266089        1              7          6     29     34  10983      1      4   \n",
       "266090        1              7         49     29     34  22904      1      4   \n",
       "266091        1              5         75     25     34  12221      1      4   \n",
       "266092        1              6         22     25     34  10766      1      4   \n",
       "\n",
       "        RESP  \n",
       "0          2  \n",
       "1          0  \n",
       "2          2  \n",
       "3          0  \n",
       "4          0  \n",
       "...      ...  \n",
       "266088     0  \n",
       "266089     0  \n",
       "266090     2  \n",
       "266091     2  \n",
       "266092     0  \n",
       "\n",
       "[264047 rows x 9 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_treated = df5[['Invoice', 'CURRENTRATING', 'VALUE_EUR', 'ZTERM', 'PRCTR', 'KUNNR', 'HKONT', 'BUKRS', 'RESP']]\n",
    "df_treated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "\n",
    "- Now, that every variable was treated and analysed, the following table provides a summary of every column description and decision made:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Column          \t| Description                                      \t| Data Info and Importance                                                           \t| Action                   \t|\n",
    "|-----------------\t|--------------------------------------------------\t|------------------------------------------------------------------------------------\t|--------------------------\t|\n",
    "| MANDT           \t| Client                                           \t| Always the same value                                                              \t| Remove                   \t|\n",
    "| BUKRS           \t| Company Code                                     \t| Numerical values with low/medium   importance                                      \t| Keep                     \t|\n",
    "| GJAHR           \t| Fiscal Year                                      \t| Only two year values - not relevant   for this purpose                             \t| Remove                   \t|\n",
    "| HKONT           \t| General Ledger                                   \t| 14 values with some importance                                                     \t| Keep                     \t|\n",
    "| KUNNR           \t| Customer information                             \t| Several numerical values and has   really good importance                          \t| Keep                     \t|\n",
    "| PRCTR           \t| Profit Center                                    \t| Several numerical values and has good   importance                                 \t| Remove                   \t|\n",
    "| KNA1_LAND1      \t| Country code                                     \t| Categorical values, but related with   'PRCTR'                                     \t| Remove                   \t|\n",
    "| KNA1_ORT01      \t| City code                                        \t| Categorical values, but related with   'PRCTR'                                     \t| Keep                     \t|\n",
    "| ZTERM           \t| Term of payment Key                              \t| Several numerical values and has good   importance                                 \t| Keep                     \t|\n",
    "| DUE_DATE_SOURCE \t| Due Date Calculation term keys                   \t| 4 Categorical values without   importance                                          \t| Remove                   \t|\n",
    "| VALUE_EUR       \t| Payment in euros                                 \t| Several numerical values and it's the   most important variable with some outliers \t| Keep                     \t|\n",
    "| MWSTS           \t| Tax   Amount in Local Currency                   \t| Several numerical values with some   outliers, however, it's not important         \t| Remove                   \t|\n",
    "| MWST2           \t| LC2   tax amount information                     \t| Always the same value                                                              \t| Remove                   \t|\n",
    "| BLART           \t| Document type code                               \t| 3 Categorical values without   importance                                          \t| Remove                   \t|\n",
    "| T003T_LTEXT     \t| Document type description                        \t| 3 Categorical values which correspond   to same values in 'BLART'                  \t| Remove                   \t|\n",
    "| TBSLT_LTEXT     \t| Payment type operation                           \t| 8 Categorical values in which it only   matter if it's an 'Invoice' or   not       \t| Remove all but 'Invoice' \t|\n",
    "| MANSP           \t| Dunning   lock reason information                \t| 97 % blanks and overall has low   importance                                       \t| Remove                   \t|\n",
    "| CTLPC           \t| Credit risk levels                               \t| 11 values and several blanks with low   importance                                 \t| Remove                   \t|\n",
    "| HISTORICRATING  \t| Historical credit rating                         \t| Alphanumerical column with almost   perfect correlation with 'CURRENTRATING'       \t| Remove                   \t|\n",
    "| CURRENTRATING   \t| Current credit rating                            \t| Alphanumerical column with medium   importance but with several blanks             \t| Keep                     \t|\n",
    "| DATUM           \t| Date of document emission                        \t| Date column irrelevant for this   problem                                          \t| Remove                   \t|\n",
    "| DUE_DATE        \t| Date where invoice has to be paid                \t| Date column only relevant for the   predictor variable creation                    \t| Remove                   \t|\n",
    "| GJAHR2          \t| Fiscal Year 2                                    \t| 6 year values - not relevant for this   purpose                                    \t| Remove                   \t|\n",
    "| DOCUMENT_DATE   \t| Date of document emission (timestamp   included) \t| Timestamp column irrelevant for this   problem                                     \t| Remove                   \t|\n",
    "| PAYMENT_DATE    \t| Date when payment was done                       \t| Timestamp column only relevant for   the predictor variable creation               \t| Remove                   \t|\n",
    "|                 \t|                                                  \t|                                                                                    \t|                          \t|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "    \n",
    "- Now, the final step for the modelling phase is to normalize our dataset, since variables like `VALUE_EUR` and `KUNNR` have really higher unit dimensions when compared to remaining features. <br>\n",
    "- The method for keeping all variables at the same level that I choose is the min-max normalization (0 to 1) which will keep every column with values ranging from 0 to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>CURRENTRATING</th>\n",
       "      <th>VALUE_EUR</th>\n",
       "      <th>ZTERM</th>\n",
       "      <th>PRCTR</th>\n",
       "      <th>KUNNR</th>\n",
       "      <th>HKONT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>RESP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.003036</td>\n",
       "      <td>0.412903</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010841</td>\n",
       "      <td>0.412903</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>0.785963</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>0.412903</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>0.227511</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>266088</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.167742</td>\n",
       "      <td>0.148472</td>\n",
       "      <td>0.459472</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266089</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.148472</td>\n",
       "      <td>0.461025</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>266090</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.148472</td>\n",
       "      <td>0.961424</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266091</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.016262</td>\n",
       "      <td>0.167742</td>\n",
       "      <td>0.148472</td>\n",
       "      <td>0.512992</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>266092</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.004770</td>\n",
       "      <td>0.167742</td>\n",
       "      <td>0.148472</td>\n",
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       "      <td>0.076923</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>264047 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice  CURRENTRATING  VALUE_EUR     ZTERM     PRCTR     KUNNR  \\\n",
       "0           1.0       1.000000   0.131613  0.412903  0.004367  0.785963   \n",
       "1           1.0       1.000000   0.000217  0.412903  0.004367  0.819418   \n",
       "2           1.0       1.000000   0.003036  0.412903  0.004367  0.227511   \n",
       "3           1.0       1.000000   0.010841  0.412903  0.004367  0.785963   \n",
       "4           1.0       1.000000   0.005854  0.412903  0.004367  0.227511   \n",
       "...         ...            ...        ...       ...       ...       ...   \n",
       "266088      1.0       0.545455   0.001301  0.167742  0.148472  0.459472   \n",
       "266089      1.0       0.636364   0.001301  0.193548  0.148472  0.461025   \n",
       "266090      1.0       0.636364   0.010624  0.193548  0.148472  0.961424   \n",
       "266091      1.0       0.454545   0.016262  0.167742  0.148472  0.512992   \n",
       "266092      1.0       0.545455   0.004770  0.167742  0.148472  0.451916   \n",
       "\n",
       "           HKONT  BUKRS  RESP  \n",
       "0       0.846154    0.6     2  \n",
       "1       0.846154    0.6     0  \n",
       "2       0.846154    0.6     2  \n",
       "3       0.846154    0.6     0  \n",
       "4       0.846154    0.6     0  \n",
       "...          ...    ...   ...  \n",
       "266088  0.076923    0.8     0  \n",
       "266089  0.076923    0.8     0  \n",
       "266090  0.076923    0.8     2  \n",
       "266091  0.076923    0.8     2  \n",
       "266092  0.076923    0.8     0  \n",
       "\n",
       "[264047 rows x 9 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# include all but the Response variable\n",
    "df6 = df_treated.iloc[:,:-1]\n",
    "\n",
    "# Create an instance of the MinMaxScaler class\n",
    "min_max_scaler = MinMaxScaler()\n",
    "\n",
    "# Normalize our dataset (returns an array)\n",
    "normalized_df = min_max_scaler.fit_transform(df6)\n",
    "\n",
    "# Convert the array above back into a pandas DataFrame\n",
    "normalized_df = pd.DataFrame(data=normalized_df, columns=df6.columns, index=df6.index)\n",
    "\n",
    "#include `RESP` now\n",
    "normalized_df['RESP'] = df_treated['RESP']\n",
    "normalized_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> <ins> Notice that now the target variable is the only one which is not normalized, however it would be the same to do so, it's just for same interpretability reasons. </ins>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "\n",
    "- Now, it comes the modelling phase, where I'm going to compute and compare 3 simple models: 1 **Logistic Regression**, 1 **Random Forest** and 1 **Neural Network** and, therefore, fine-tune the best model in order to try to achieve the best possible in terms of accuracy and other metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate X and Y variables\n",
    "y = normalized_df['RESP']\n",
    "x = normalized_df.iloc[:,:-1]\n",
    "\n",
    "# again, split the same way\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> I'll begin with the model evaluation functions in order to compare model results right away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification reports and confusion matrix \n",
    "\n",
    "def model_eval(model):\n",
    "    m = model.fit(X_train, y_train)\n",
    "    pred = m.predict(X_test)\n",
    "    print('\\nClassification Report for Train:\\n', classification_report(y_train, model.predict(X_train)))\n",
    "    print('\\nClassification Report for Test:\\n',classification_report(y_test, model.predict(X_test)))\n",
    "    plot_confusion_matrix(model, X_test, y_test)\n",
    "    plt.title('Confusion Matrix for Test')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model definition \n",
    "model_lr_simple = LogisticRegression(random_state=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report for Train:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.96      0.83    145402\n",
      "           1       0.61      0.30      0.41     25792\n",
      "           2       0.45      0.08      0.14     40043\n",
      "\n",
      "    accuracy                           0.71    211237\n",
      "   macro avg       0.60      0.45      0.46    211237\n",
      "weighted avg       0.66      0.71      0.64    211237\n",
      "\n",
      "\n",
      "Classification Report for Test:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.96      0.83     36351\n",
      "           1       0.62      0.30      0.41      6448\n",
      "           2       0.46      0.08      0.14     10011\n",
      "\n",
      "    accuracy                           0.71     52810\n",
      "   macro avg       0.60      0.45      0.46     52810\n",
      "weighted avg       0.66      0.71      0.64     52810\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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2JJkt6xNo1yUvbHHXteNP3sm2jfHkZscSKIpi1rRUju3rHVlcev0mmqcWMvH+jhWuu2ROMw7vmE+zlML6DLlWeI84jQlqaozqMilWNSJUSlUnqmo/Ve3XumXtD9nuyIouHUzJ3ycs+K4p3Xrt451FS3ljzjLemLOM+MRiXvtheek6G1bFszs3hh799paWbd8cS/4+7z/frh3RLJ2bTPsj89m3J4qsbd4vP1AEc75uRodu+bW+H+E01HfoDNC8pfdFFlEuuW4zn7/d2itPLSQqyusoO7xDHkd0yWPLhvj6D7ieZGyO45jj9xCfEACUPoNz2bg6gREXZXDCkFwevr4b6mstte2Uh5dSoNtxe4iNK2ZnTmNMHEIgyKkxqsvfyFxqMPJT27K3xfLYDR0pLvYGR4act4OBZ+yscp3/fJLCqSNzEN/vdMOqeF6e0NXrJ1e44JrtdDk2j5ztMdxzeVcKC7z6ew/ezbmjMyutu7GJTwzQ95Rcnrljf7/Y0N9kcd7oDABmfZnCV++1AqDngF2MvnkTRYWCqvDsHZ3ZndsYv/TBWbGwCTO/SOG5z5YSKBJWL0vii8mH8fHSeWzbFM+TH3rd57O+TOGfz7bj5BHZnP7bLIqKhII84aHrutEYR+UUqr1apTETrcMhMBE5B3gK75ScSa4ztFL9eifonGkdqlrkkDai0wF9z6a8qMaXZOrTT/lfsLM466A+pPY9m+u1750U1LJ3HPfF/KpO3m6I6vTfeE1GfowxDZuqRHRLMXKPbYwxdcIbaImAS7YqYUnRGBMie0aLMcaU8gZaIrfv1pKiMSZktXFFS0NlSdEYE5KSK1oilSVFY0zIIvnBVZYUjTEhUYXCYkuKxhgDlBw+W1I0xphSjfW65mBYUjTGhMROyTHGmDLs8NkYY8qojWe0NFSWFI0xIfFGn+3aZ2OMAezkbWOMOYAdPhtjjBPpo8+RO4RkjKkzxRoV1FQVEekgIt+KyDIRWSoiN7jye0Rkk4gsdNM5vnVuF5E0EVkhImf5yke4sjQRGe8r7yIis135uyISV92+WVI0xoREVSjSqKCmahQBt6hqD2AgcK2I9HDznlTVPm6aCuDmXQwcB4wAXhCRaBGJBp4HzgZ6AJf46nnE1dUNyAGuqi4oS4rGmJDVxnOfVXWLqi5wr3cBy6n62fAjgXdUNV9V1wJpQH83panqGlUtAN4BRoqIAMOAf7n1XwdGVbdvlhSNMSEp6VM82KToJyKdgeOB2a5onIj8LCKTRCTFlbUDNvpWS3dllZW3BHaoalG58ipZUjTGhCyEpNhKROb5prHl6xKRJsAHwI2quhN4ETgS6ANsAR6vvz2z0WdjTIhCPE8xs6pHnIpILF5CfFtVPwRQ1W2++S8Dn7m3mwD/M5DbuzIqKc8CWohIjGst+pevlLUUjTEhK0aCmqri+vxeBZar6hO+8ra+xc4HlrjXU4CLRSReRLoA3YE5wFyguxtpjsMbjJmi3kPtvwUucOuPAT6pbt+spWiMCYkqFNXOTWZPAi4DFovIQld2B97ocR+87st1wNXednWpiLwHLMMbub5WVQMAIjIOmAZEA5NUdamr7zbgHRG5H/gvXhKukiVFY0zIauPkbVX9HipsTk6tYp0HgAcqKJ9a0XqqugZvdDpolhSNMSGxa5+NMaYctaRojDH72Q0hjDHGUY3sG0JYUjTGhEgI2CNOjTFmP+tTrCerVqZwzum/D3cYDVZUs8xwh9DgBbKywx1Cw6Z68FVgh8/GGLOf1kpubbAsKRpjQmajz8YY46gNtBhjTFl2+GyMMT42+myMMY6qJUVjjCnDTskxxhgf61M0xhhHEYpt9NkYY/aL4IaiJUVjTIhsoMUYY8qJ4KaiJUVjTMgOyZaiiDxLFf8PVPX6OonIGNOgKVBcfAgmRWBevUVhjGk8FDgUW4qq+rr/vYgkqereug/JGNPQRfJ5itWebCQig0RkGfCLe99bRF6o88iMMQ2XBjlVQUQ6iMi3IrJMRJaKyA2uPFVEpovIKvczxZWLiDwjImki8rOI9PXVNcYtv0pExvjKTxCRxW6dZ0Sk2iZuMGdgPgWcBWQBqOoiYEgQ6xljIpKgGtxUjSLgFlXtAQwErhWRHsB44BtV7Q58494DnA10d9NY4EXwkihwNzAA78H3d5ckUrfMn3zrjaguqKBOS1fVjeWKAsGsZ4yJULXQUlTVLaq6wL3eBSwH2gEjgZLuu9eBUe71SOAN9fwEtBCRtniNtumqmq2qOcB0YISb10xVf1JVBd7w1VWpYE7J2SgigwEVkVjgBhe8MeZQpKDBjz63EhH/oO1EVZ1YfiER6QwcD8wG2qjqFjdrK9DGvW4H+Bto6a6sqvL0CsqrFExSvAZ42lW2GZgGXBvEesaYiBV0UsxU1X5V1iTSBPgAuFFVd/q7/VRVRaReh3WqTYqqmglcWg+xGGMai1pKU+7o8wPgbVX90BVvE5G2qrrFHQJnuPJNQAff6u1d2SZgaLnyGa68fQXLVymY0eeuIvKpiGwXkQwR+UREula3njEmgtXO6LMArwLLVfUJ36wpQMkI8hjgE1/5aDcKPRDIdYfZ04AzRSTFDbCcCUxz83aKyEC3rdG+uioVzOHzP4HngfPd+4uByXgjPcaYQ03tnbx9EnAZsFhEFrqyO4CHgfdE5CpgPVDyMPipwDlAGrAXuAJAVbNF5D5grltugqqWPAD8z8BrQCLwhZuqFExSTFLVN33v3xKRW4NYzxgToWrj5G1V/Z7KOyeHV7C8Usl4hqpOAiZVUD4P6BlKXFVd+5zqXn4hIuOBd/D+R1yEl7GNMYeqQ/Ta5/l4SbBk76/2zVPg9roKyhjTsNXveHD9qura5y71GYgxppEIYhClMQvqfooi0hPoASSUlKnqG3UVlDGmIZND8y45JUTkbrxzgHrg9SWeDXyPd8mMMeZQFMEtxWCufb4AbyRoq6peAfQGmtdpVMaYhq04yKkRCubweZ+qFotIkYg0wzu7vEN1KzVkUVHK0y98TVZmIvf87WRuuGUu3Y/KQQQ2pTfhiUf7k5e3/6M56ZR07rz7R27483BWrUylabN87rjrR446Opuvp3Xmxef6VrG1xuXGCcvpPySLHdlx/Pm3/QHoctRuxt21gsSkANs2JfDo+B7s2+N9Pp2P2s11d60gKbkIVeGGi08gKgpuf3wJbTvkURyA2f9pxWtPHRnO3aoTNz+xgQGn72JHZgxXDzu6zLzfXZ3B2Lu3cGHP49iZHUOvQbu55x9r2boxDoBZU5vz9pOHhyPsg3eo3mTWZ56ItABexhuR3g38WN1KIjIJOBfIUNWQzhOqayPPX8XGDU1JSioCYOKLfdi3NxaAP12zkPNGpfH+O8cAkJhYyMjzV/HL8tTS9QsKonnztZ507pxLp8659b8DdejrT9ry6eT23PLA/nt+3HDvL7zy+JEsmZfCGaO2cMEVG3jzua5ERRdz60PLeOz2Hqxd2YSmzQsJFEURFVfMh6915Oe5KcTEFPPgKwvpd3IW875vGcY9q31fvZvKlH+04tany95EqvURBfQ9dRfb0mPLlC+ZncxdYyLjYrBIHn2u9vBZVf+sqjtU9SXgDGCMO4yuzmsEce+y+tay1V5OHLCFaVP3/3GWJERQ4uIDZU5Mvezypbz/7jEUFESXluXnxbBsSasyZZFiyfwW7Mot+7+yXae9LJnXAoD//pjCSadvB6Dv4BzWrmzC2pVNANiVG0txsZCfF83Pc73b2RUVRbF6eVNatsmvv52oJ0tmN2FXzoHtiqvv2cyr9x8R0Xenro3L/BqqSpOiiPQtPwGpQIz/jreVUdXvgOzqlqtvV/95IZNe7kVxuV/YTX+Zy9vvf0r7Drv49ONuABzZLYfWh+1l7uy2YYi04Vi/OplBwzIBOOWsDFod7iW4dp32gsJ9Ly3kmXfncsEV6w9YN7lpIf2HZrJodsoB8yLRoLNyydway5pliQfMO/aEvbw4fQX3v7WGTkflhSE6E4yqDp8fr2KeAsNqIwARGYt3F10SYpvVRpWV6j9gMzt2JJC2KoVf9c4oM+/Jx04kKkq5Ztx/GTJ0I19/1Zk//e8innj0xDqNqTF46q5juGb8Ki6+eh2zv21FUaHXnxQdrfQ4PpcbLzmB/LxoHnxlIauWNWXRbK+rISq6mNseXcaUt9uzNf3AJBFp4hOLufi6DG6/5MBD5LTFiVzW/1jy9kZz4rCd3D1pLVeefGwYoqwdkXz4XNXJ26fVRwDuhpMTAZontq3Tj7pHzywGDtrMif23EBsXICmpiL+Mn81jD3v3tiguFr77tgMXXLSCWd+3p1PnXB55fAYAKal53DVhFhPuOolVK1Or2ErkSV+bzN+u7gN4rcMTh2QBkLktniXzW7Bzhzd4MG9mS7odu7s0KV5/9wo2rU/kk7ca9bhc0Np2yufwjgW8+PUKAFq3LeT5aSu5/pzu5Gzf378499/NGPdQOs1Si9iZ3Qgfva4cspf5RZzXXv0Vr736KwB+1TuD3124ksce7k/bI3azZXMTQBkwaDMbNzRl755YLvndyNJ1H358Bq/+vdchlxABmqcWkJsdh4hy8dh1TH3Pu3nxgh9SueCKDcQnBCgsFHr228HHb3i3rxt93RqSmwR4+u5jwhl6vVr3SyIX9Tqu9P3rs5dx3dlHsTM7hpTWheRsjwGEo/vsJSoKdmY34j7pQ7GleKgQgVv+Ooek5EIA1q5pwXNPV3+KzT/e+pykpEJiYosZdNJm7rxtCBs31O3hf3346yNL6XXiDpq1KOSNr3/grec7k5gU4NyLvXtzzvqmNdM/9k4l2b0zlo/e7MBTk+ehKsybmcrcma1o2SaPi8euZ8OaJJ55z7sT/WeT2zHtwyPCtl91YfwL6+k1aDfNU4t4a94y3ny8DdMmVzzCfsq5uZw7OpNAkZCfF8VD/9uJEO5e3eBE8uGzaB0NkYnIZLwrYVoB24C7VfXVqtZpnthWBx15ZZ3EExG2ZYY7ggYvkNXgxvYalNn6DTs1+6CycXyHDtr+xpuCWnbNX26ZX93jCBqaYC7zE7zHEXRV1Qki0hE4XFXnVLWeql5SSzEaYxqaCG4pBnOZ3wvAIKAkye3CuxO3MeYQJBr81BgF06c4QFX7ish/AVQ1R0Ti6jguY0xDdoiPPheKSDSuwSwirWm0l3obY2pDY20FBiOYw+dngI+Aw0TkAbzbhj1Yp1EZYxq2CL7ML5jnPr8tIvPxbh8mwChVXV7NasaYSNWI+wuDEczoc0e8xwl+6i9T1Q11GZgxpgGL4KQYzOHz58Bn7uc3wBqCeHaqMSZySXFwU7X1iEwSkQwRWeIru0dENonIQjed45t3u4ikicgKETnLVz7ClaW5p4+WlHcRkdmu/N1gBomDuXXYr1S1l/vZHehPEPdTNMaYILxGxbcYfFJV+7hpKoCI9AAuBo5z67wgItFuIPh5vEel9AAuccsCPOLq6gbkAFdVF1AwLcUyVHUBMCDU9YwxEaSWBlpCvMXgSOAdVc1X1bVAGl4jrT+QpqprVLUA7xn1I92FJ8OAf7n1XwdGVbeRYPoUb/a9jQL6ApuD3AljTKQJbaCllYjM872f6O6MVZ1xIjIamAfcoqo5QDvgJ98y6a4MYGO58gFAS2CHqhZVsHylgjlPsanvdRFe3+IHQaxnjIlUwSfFzBpc+/wicJ/byn1493att5siVJkU3bF6U1X9Sz3FY4xpDOpw9FlVt5W8FpGX8QZ6ATZR9qF57V0ZlZRnAS1EJMa1Fv3LV6qqxxHEqGoAOCmI/TDGHCKE2ht9rrB+Ef/zP84HSkampwAXi0i8iHQBugNzgLlAdzfSHIc3GDNFvVuAfYv3mGaAMcAn1W2/qpbiHLz+w4UiMgV4H9hTMlNVPwxi/4wxkaYWT97232JQRNKBu4GhItLH2xLrgKsBVHWpiLwHLMPryrvWNdwQkXHANCAamKSqS90mbgPeEZH7gf8CVd6+EILrU0zAa4YOc0GK+2lJ0ZhDVS0lxUpuMVhp4lLVB4AHKiifCkytoHwN3uh00KpKioe5kecl7E+GpdsKZSPGmAgTwRmgqqQYDTSh4numR/BHYoypzqF67fMWVZ1Qb5EYYxqPQzQpRu5dJI0xNac1H1luDKpKisPrLQpjTONyKLYUVdUei2aMqdCh2qdojDEVs6RojDFOI37UQDAsKRpjQiLY4bMxxpRhSdEYY/wsKRpjjI8lRWOMcQ71R5waY8wBLCkaY8x+h+plfvUukBDN7qNahDuMBivxl9XhDqHBk/j4cIfQsOXXzi0N7PDZGGNK2MnbxhhTjiVFY4zx2BUtxhhTjhRHbla0pGiMCY31KRpjTFl2+GyMMX4RnBSjwh2AMabxEQ1uqrYekUkikiEiS3xlqSIyXURWuZ8prlxE5BkRSRORn0Wkr2+dMW75VSIyxld+gogsdus8IyLVnqhpSdEYEzoNcqrea8CIcmXjgW9UtTvwjXsPcDbQ3U1jgRfBS6LA3cAAvAff312SSN0yf/KtV35bB7CkaIwJjXuaXzBTtVWpfgeUfx7USOB19/p1YJSv/A31/AS0EJG2wFnAdFXNVtUcYDowws1rpqo/qaoCb/jqqpT1KRpjQhLieYqtRGSe7/1EVZ1YzTptVHWLe70VaONetwM2+pZLd2VVladXUF4lS4rGmNBp0FkxU1X71XwzqiL1O9Zth8/GmJDV1kBLJba5Q1/czwxXvgno4FuuvSurqrx9BeVVsqRojAlNsIMsNU+KU4CSEeQxwCe+8tFuFHogkOsOs6cBZ4pIihtgOROY5ubtFJGBbtR5tK+uStnhszEmZLV1P0URmQwMxet7TMcbRX4YeE9ErgLWA793i08FzgHSgL3AFQCqmi0i9wFz3XITVLVk8ObPeCPcicAXbqqSJUVjTMhqKymq6iWVzBpewbIKXFtJPZOASRWUzwN6hhKTJUVjTGiUUAZaGh1LisaYkNm1z8YY42dJ0RhjPHaTWWOM8VO1m8waY0wZkZsTLSkaY0Jnh8/GGFNCATt8NsYYn8jNiZYUjTGhs8NnY4zxsdFnY4wpYY84NcaY/byTtyM3K1pSNMaErpbuktMQWVI0xoTMWooR5MJhizl38C8owppNqTz85hAev34qSfGFAKQ0zWP5+tbc+fcz6dN9Mw9e8xVbMpsC8N3CLrz+Rd9K6ykoiryPMypKeXbqL2RtjeWuy7tx27Nr6d5rL4FCYcXCZJ4e35FAkfco3V6DdnHNPenExCi5OTHcesFRYY6+7p1/5VZGXLQdVVi3IpHHb+3KTY+s5aheeygqFFYsSuaZOzsTKIrigrFbOG1kFgDR0UqHbvu46IS+7M5tZH831qdYMyLSAe+Rgm3wPsKJqvp0XW0vGK2a7+GCoUu47L4LKSiM4Z6rvmZYvzVc98RvSpe570/T+f7nTqXvf047nPEvjgiqni9/irwkMOqqDDamJZDUJADAvz9K5ZHrOgMw/rl1nH1JJp+92ZrkZkWMe2Ajd/5PN7ZvjqN5y8IwRl0/WrYpYOTlWxl7Ri8K8qO447k0hp6XxbeftOTRm7oCMP7p1Yy4aDufv92Gf01sy78mtgVgwPAczr9ya+NLiABE9rXPdfmMliLgFlXtAQwErhWRHnW4vaBERyvxsUVERxWTEFdEVm5S6bykhAL6Hr2ZmYs6H1Q9kaJV2wL6D9/JF/9sVVo299/NcV3trFiYRKu2BQCcNiqHWV+0YPvmOABys2LDEHH9i46GuIRioqKV+IQAWRlxzJ3RgtLPaFGT0s/Ib+h5Wcz4tGV9h1t7VIObGqE6+zflHhqzxb3eJSLL8Z65uqyutlmdzNxk3vm6F+/fP5mCwhjmLm/H3OX7H/Z1Su91zP+lHXvz4krLjuuSwaQ7PiAzN4kXPhzAui2p1dYTKa65J51XHmhX2kr0i45Rhv8um5fu9h6i1r5rHtExyqPvryQpOcDHrx7G1x804i99ELK2xfGvlw/nzVkLyc+LYsHM5iyY2bx0fnRMMcPPz+TFCZ3KrBefEKDfqbk8f3fneo64lmjtPY6gIaqXp/mJSGfgeGB2fWyvMk0S8zm51zouuutizr/9UhLiizij/6rS+cP7reabeUeWvl+5sRW//79LuPLB3/HhjON48OrpQdUTCQYMz2VHZgxpiytuAV/34AaWzG7CkjlNAIiOge699vJ/o4/kjku784cbt9KuS159hlzvmjQrYtAZOVw+pDeXDuxDQlKAYaMyS+ePu289i+c0ZencpmXWGzB8B0vnN22kh85OBLcU6zwpikgT4APgRlXdWcH8sSIyT0TmFeXvqdNY+h2ziS1ZTcndnUigOIrvFnamZ9dtADRPzuPYTtv5ccn+x8fuzYtjX753GPjT0o5ERxfTPDmvynoiRY8TdzPwzFxe/3EJtz+/lt4n7eKvz6wF4NKbttA8tYi/37u/dbx9Syzz/9OM/H3R7MyJYfHsJnTtsS9c4deL40/eybaN8eRmxxIoimLWtFSO7bsbgEuv30Tz1EIm3t/xgPVOPS+bGVNS6zvc2lW3jzgNqzpNiiISi5cQ31bVDytaRlUnqmo/Ve0XE59cl+GwLacJPTpnEB9bBCgnHL2Z9VtbAHBq3zX8uKRjmRHk1GZ7KfnNHtspgyhRcvfEV1lPpPjHw+34nxN/xZhBPXno2i4smtWUR6/vwohLMul36k4eGtcFVSld/sdpzTnuxD2ub62YY/rsYUNaQhj3oO5lbI7jmOP3EJ8QAJQ+g3PZuDqBERdlcMKQXB6+vluZzwggqWkRvQbs5MfpKeEJupZIcXFQU2NUl6PPArwKLFfVJ+pqO6FYvu4wZvy3K6/c/iGB4ihWbWzJp98fC8DwE9bw9le9yyw/9Pi1jDxlGYHiKPILY7h30nBAqqwn0l3/0Aa2pcfx1CcrAJj1RQvefqotG9MSmTejGS9NX44Ww5eTW7F+RWKYo61bKxY2YeYXKTz32VICRcLqZUl8MfkwPl46j22b4nnyQ6/7fNaXKfzz2XYAnHRmDvNnNid/X3Q4Qz84Sq2dvC0i64BdQAAoUtV+IpIKvAt0BtYBv1fVHJdTnsZ79vNe4HJVXeDqGQP8zVV7v6q+XuOYtI6O+0XkZGAmsJj9H+Edqjq1snWapLTXPqfdUCfxRILEKfPDHUKDJ7GNuJ+uHvyU/wU7i7Ok+iUr1zz5CB3Y4+qglv1q3j3zVbVfZfNdUuynqpm+skeBbFV9WETGAymqepuInANch5cUBwBPq+oAl0TnAf3wUvZ84ARVzanJ/tXl6PP3eOclGGMiTd0OoowEhrrXrwMzgNtc+RvqteR+EpEWItLWLTtdVbMBRGQ6MAKYXJON18voszEmwgQ/+tyqZCDVTWPL1wR8JSLzffPauFP6ALbiXQAC3il9G33rpruyysprxI41jDGhCa1PMbOqw2fgZFXdJCKHAdNF5Jcym1JVkfq9pa21FI0xIaut0WdV3eR+ZgAfAf2Bbe6wGPczwy2+CejgW729K6usvEYsKRpjQhTkoXM1/Y4ikiwiTUteA2cCS4ApwBi32BjgE/d6CjBaPAOBXHeYPQ04U0RSRCTF1TOtpntnh8/GmNAotTXQ0gb4yDvThhjgn6r6pYjMBd4TkauA9cDv3fJT8Uae0/BOybkCQFWzReQ+YK5bbkLJoEtNWFI0xoSuFs5TVNU1QO8KyrOA4RWUK3BtJXVNAiYdfFSWFI0xNWA3mTXGGD9LisYY46hCoHFe1xwMS4rGmNBZS9EYY3wsKRpjjKNABD+jxZKiMSZECmp9isYY41FsoMUYY8qwPkVjjPGxpGiMMSUa75P6gmFJ0RgTGgUa6UOpgmFJ0RgTOmspGmNMCbvMzxhj9lNQO0/RGGN87IoWY4zxsT5FY4xxVG302RhjyrCWojHGlFA0EAh3EHXGkqIxJjR26zBjjCnHTskxxhiPAmotRWOMcdRuMmuMMWVE8kCLaAMaWheR7cD6cMfh0wrIDHcQDZh9PtVraJ9RJ1VtfTAViMiXePsVjExVHXEw26tvDSopNjQiMk9V+4U7jobKPp/q2WfU+ESFOwBjjGlILCkaY4yPJcWqTQx3AA2cfT7Vs8+okbE+RWOM8bGWojHG+FhSNMYYH0uKFRCRESKyQkTSRGR8uONpaERkkohkiMiScMfSEIlIBxH5VkSWichSEbkh3DGZ4FmfYjkiEg2sBM4A0oG5wCWquiysgTUgIjIE2A28oao9wx1PQyMibYG2qrpARJoC84FR9jfUOFhL8UD9gTRVXaOqBcA7wMgwx9SgqOp3QHa442ioVHWLqi5wr3cBy4F24Y3KBMuS4oHaARt979OxP2hTQyLSGTgemB3mUEyQLCkaU0dEpAnwAXCjqu4MdzwmOJYUD7QJ6OB7396VGRM0EYnFS4hvq+qH4Y7HBM+S4oHmAt1FpIuIxAEXA1PCHJNpREREgFeB5ar6RLjjMaGxpFiOqhYB44BpeB3k76nq0vBG1bCIyGTgR+BoEUkXkavCHVMDcxJwGTBMRBa66ZxwB2WCY6fkGGOMj7UUjTHGx5KiMcb4WFI0xhgfS4rGGONjSdEYY3wsKTYiIhJwp3csEZH3RSTpIOp6TUQucK9fEZEeVSw7VEQG12Ab60TkgKe+VVZebpndIW7rHhH5S6gxGlOeJcXGZZ+q9nF3pikArvHPFJEaPcdbVf9YzR1chgIhJ0VjGiNLio3XTKCba8XNFJEpwDIRiRaR/ycic0XkZxG5GryrLETkOXefyK+Bw0oqEpEZItLPvR4hIgtEZJGIfONuaHANcJNrpZ4iIq1F5AO3jbkicpJbt6WIfOXuIfgKINXthIh8LCLz3Tpjy8170pV/IyKtXdmRIvKlW2emiBxTK5+mMU6NWhYmvFyL8GzgS1fUF+ipqmtdYslV1RNFJB6YJSJf4d2p5WigB9AGWAZMKldva+BlYIirK1VVs0XkJWC3qj7mlvsn8KSqfi8iHfGu/jkWuBv4XlUniMivgWCudLnSbSMRmCsiH6hqFpAMzFPVm0TkLlf3OLwHQV2jqqtEZADwAjCsBh+jMRWypNi4JIrIQvd6Jt71tYOBOaq61pWfCfQq6S8EmgPdgSHAZFUNAJtF5N8V1D8Q+K6kLlWt7J6JpwM9vEt8AWjm7ggzBPitW/dzEckJYp+uF5Hz3esOLtYsoBh415W/BXzotjEYeN+37fggtmFM0CwpNi77VLWPv8Alhz3+IuA6VZ1WbrnavPY2ChioqnkVxBI0ERmKl2AHqepeEZkBJFSyuLrt7ij/GRhTm6xPMfJMA/7X3boKETlKRJKB74CLXJ9jW+C0Ctb9CRgiIl3cuqmufBfQ1LfcV8B1JW9EpI97+R3wB1d2NpBSTazNgRyXEI/Ba6mWiAJKWrt/wDss3wmsFZEL3TZERHpXsw1jQmJJMfK8gtdfuEC8B0v9He+I4CNglZv3Bt5dbspQ1e3AWLxD1UXsP3z9FDi/ZKAFuB7o5wZylrF/FPxevKS6FO8wekM1sX4JxIjIcuBhvKRcYg/Q3+3DMGCCK78UuMrFtxR7VISpZXaXHGOM8bGWojHG+FhSNMYYH0uKxhjjY0nRGGN8LCkaY4yPJUVjjPGxpGiMMT7/Hz8RMnjAHBaEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_eval(model_lr_simple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Similar results for Train and Test, meaning that the model does not shows any signs of overfitting, however, the total accuracy and values for F1 score (especially when trying to predict the least frequent classes) are not the most satisfying and, thus, this is not probably the best model for this context."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model definition \n",
    "model_nn_simple = MLPClassifier(random_state=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report for Train:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.92      0.84    145402\n",
      "           1       0.70      0.36      0.47     25792\n",
      "           2       0.55      0.31      0.40     40043\n",
      "\n",
      "    accuracy                           0.74    211237\n",
      "   macro avg       0.67      0.53      0.57    211237\n",
      "weighted avg       0.72      0.74      0.71    211237\n",
      "\n",
      "\n",
      "Classification Report for Test:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.92      0.83     36351\n",
      "           1       0.71      0.35      0.47      6448\n",
      "           2       0.54      0.30      0.39     10011\n",
      "\n",
      "    accuracy                           0.73     52810\n",
      "   macro avg       0.67      0.52      0.56     52810\n",
      "weighted avg       0.71      0.73      0.70     52810\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_eval(model_nn_simple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> This simple NN seems to be an improvement to the simple LR and still doesn't show signs of overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model definition \n",
    "model_rf_simple = RandomForestClassifier(random_state=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report for Train:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97    145402\n",
      "           1       0.96      0.91      0.93     25792\n",
      "           2       0.96      0.90      0.92     40043\n",
      "\n",
      "    accuracy                           0.96    211237\n",
      "   macro avg       0.96      0.93      0.94    211237\n",
      "weighted avg       0.96      0.96      0.96    211237\n",
      "\n",
      "\n",
      "Classification Report for Test:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.89      0.84     36351\n",
      "           1       0.65      0.53      0.58      6448\n",
      "           2       0.59      0.44      0.51     10011\n",
      "\n",
      "    accuracy                           0.76     52810\n",
      "   macro avg       0.68      0.62      0.64     52810\n",
      "weighted avg       0.74      0.76      0.75     52810\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_eval(model_rf_simple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Here, model improves, however, there is a sign of overfitting as the train results are really good but the test ones show us just medium to good results.<br>\n",
    "However, I will keep using this model and, in the next section, fine tune it in order to get the most out of it in terms of constructing a good predictor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuned Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> To finalize and to improve the RF model, I will fine tune its most relevant hyperparameters using the RandomizedSearchCV, as it is faster in computation time and has overall better performance in lower dimensionality problems (like this one), when compared with other search methods like GridSearchCV, for instance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using RandomizedSearchCV \n",
    "\n",
    "rf_params = {\n",
    "    'n_estimators': [10, 30, 100],\n",
    "    'max_depth': [2, 6, 10, 15],\n",
    "    'min_samples_split': [5, 10],\n",
    "    'min_samples_leaf': [3, 6]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Parameters: {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 6, 'max_depth': 15}\n"
     ]
    }
   ],
   "source": [
    "best_rf = RandomizedSearchCV(model_rf_simple, rf_params)\n",
    "\n",
    "best_rf.fit(X_train, y_train)\n",
    "\n",
    "print(f\"Optimal Parameters: {best_rf.best_params_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model definition \n",
    "\n",
    "best_rf = RandomForestClassifier(n_estimators=100 , max_depth=15 , min_samples_split=5, min_samples_leaf=6\\\n",
    "                                 , random_state=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report for Train:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.97      0.87    145402\n",
      "           1       0.81      0.45      0.58     25792\n",
      "           2       0.79      0.35      0.48     40043\n",
      "\n",
      "    accuracy                           0.79    211237\n",
      "   macro avg       0.80      0.59      0.64    211237\n",
      "weighted avg       0.79      0.79      0.76    211237\n",
      "\n",
      "\n",
      "Classification Report for Test:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.96      0.86     36351\n",
      "           1       0.80      0.44      0.57      6448\n",
      "           2       0.75      0.32      0.45     10011\n",
      "\n",
      "    accuracy                           0.78     52810\n",
      "   macro avg       0.78      0.57      0.62     52810\n",
      "weighted avg       0.77      0.78      0.75     52810\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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680U7Yrh46CG8PG8F7fYr4c6X1gGwoyCGOVNbk9R6Vytx1kfJDD116x4TKa8/2oncrFiue3htvZ5DQ+l7VD6DT8nlqBN+ID6hlOYtg9z81FoevrYHN57dB4AjhuWR0rNwj31/mN+S/fb/mVZtSsjLifiPWL05/Ng8MtMSyM32fhjmTm9L3yPz6XHIdlYu8SaZ/u/jttz3sjcWveWXeH5ckkSwJIbM9ATS1ybStUchq75vWhNS3itOo/f/tT6vU6xu8LOcqj6vqgNVdWCHdnX/NN+tWYHyyZSiHcLiWS3pddgO3vpuGa9+s5xXv1lOQrNSXp63AoDcrACl3hAkbz3dkVPOz65Q3lcftGH46K0V0qa90ZaFX7Xi1mfXERMlV37+68Gu/OGoXzFmSD8eGN+D7+a25OFre5RPDsTFl3LeVZl8/FoHALp0L8T7dYFe/bYTl6Dk5UTv05kBNmXEc/DhBSQkBgFlwNBc1qcm0qJlkK49dgBwxLF5pKU2A2DeZ204bLDXw2jVppiUHoVsXJ8QqervBSEY4tIU1We4Lx/kxAuGFwC/q8fjVSo7M45Hrtuf0lKhtBSGnbmVwSfv2fUt8/3/kpj8QBdElF8NKmD8X9PLt/2SFs/mjDgOG5JfYZ+nJnWjU8pOrj/zIACOOW0rf5iQWT8nFGHnXpnJoBNzkRj45NUOfDevJQDHnraVk87OpqREKCqM4a9X9oAm+ksRqpVLkpg9rQ1//3gZwRLhp+XNmfZmR7ZsjOf2Z1NRhfzcWB67uQcAi2a15sjjcvnnZ99TGhRefKAb27Y2vckWhVDuVmmyROtxmlBETgOeYNcg5x59fr+B/RP1m+ndqsuyTxuRcmSkq9DoSVz0duvqwtdF08grzdqrv1Yp/Vrr+HeOCSnvbYdOW1TddYqNUb3+BNVmkNMY07ipSlS3FO3PqjEmLN5ES/SOF1tQNMaEyd7RYowx5byJluidRLOgaIwJWx3d0dIoWVA0xoSlru5oaawsKBpjwmYvrjLGGEcVikstKBpjDFDWfbagaIwx5Zrqfc2hsKBojAmLXZJjjDEVWPfZGGMqqOt3tDQmFhSNMWHxZp/t3mdjjAHs4m1jjNmDdZ+NMcax2WdjjNmNzT4bY4yjKpREcVCM3jMzxtSbUpWQluqISDcRmSkiy0VkmYhc59LvFpENIrLELaf59rlVRFJFZKWIjPClj3RpqSIyyZfeQ0Tmu/S3RSS+pnOzoGiMCUvZmOLeBkWgBLhRVfsCg4HxItLXbXtcVQe4ZSqA23YBcCgwEnhWRAIiEgCeAU4F+gIX+sp5yJXVC8gBxtZUKQuKxpiw1UVQVNWNqrrYfd4GrAC6VrPLKOAtVS1S1bVAKnC0W1JVdY2q7gTeAkaJiAAnAP91+78CjK7p3CwoGmPCUnadYohBsb2ILPQt4yorU0S6A4cD813S1SLyvYhMFpE2Lq0rkObbLd2lVZXeDtiqqiW7pVfLgqIxJmylSEgLsEVVB/qW53cvS0SSgHeB61U1D3gOOBAYAGwEHm24M7PZZ2NMmFShpI4eMisicXgB8Q1Vfc8rXzN9218APnarG4Buvt1TXBpVpGcBySIS61qL/vxVspaiMSZsdTT7LMBLwApVfcyX3tmX7TfAD+7zFOACEUkQkR5Ab+AbYAHQ2800x+NNxkxRVQVmAue4/ccAH9Z0btZSNMaEpQ7vfT4G+COwVESWuLTb8GaPB+BNdK8DLgdQ1WUi8g6wHG/meryqBgFE5GpgOhAAJqvqMlfeLcBbInIf8C1eEK6WBUVjTNi0DoKiqs6BSm+inlrNPvcD91eSPrWy/VR1Dd7sdMgsKBpjwmYPhDDGGEfVHghhjDE+QtBecWqMMbvUxZhiY9WoguLqH5M5fehZka5GoxXbuaTmTPu4kg0Zka5C46a690Vg3WdjjNlF6yS2NloWFI0xYbPZZ2OMcdQmWowxpiLrPhtjjI/NPhtjjKNqQdEYYyqwS3KMMcbHxhSNMcZRhFKbfTbGmF2iuKFoQdEYEyabaDHGmN1EcVPRgqIxJmz7ZEtRRJ6mmr8HqnptvdTIGNOoKVBaug8GRWBhg9XCGNN0KLAvthRV9RX/uog0V9Xt9V8lY0xjF83XKdZ4sZGIDBGR5cCPbr2/iDxb7zUzxjReGuLSBIVyBeYTwAggC0BVvwOG1WOdjDGNmqAa2lJtKSLdRGSmiCwXkWUicp1LbysiM0Rktfu3jUsXEXlKRFJF5HsROcJX1hiXf7WIjPGlHykiS90+T4lIjf3+kC5LV9W03ZKCoexnjIlSddNSLAFuVNW+wGBgvIj0BSYBX6hqb+ALtw5wKtDbLeOA58ALosBdwCC8dzzfVRZIXZ7LfPuNrKlSoQTFNBEZCqiIxInITcCKEPYzxkQjBS2VkJZqi1HdqKqL3edteHGlKzAKKJvTeAUY7T6PAl5Vz9dAsoh0xuvJzlDVbFXNAWYAI922Vqr6taoq8KqvrCqFEhSvAMa7ymYAA9y6MWafJSEutBeRhb5lXKWliXQHDgfmA51UdaPb9AvQyX3uCvh7rekurbr09ErSq1XjxduqugX4fU35jDH7kNAnUbao6sDqMohIEvAucL2q5vmH/VRVRaRBp2xCmX3uKSIfichmEdkkIh+KSM+GqJwxppGqo9lnEYnDC4hvqOp7LjnTdX1x/25y6RuAbr7dU1xadekplaRXK5Tu87+Bd4DOQBfgP8CbIexnjIlGZRdvh7JUw80EvwSsUNXHfJumAGUzyGOAD33pF7lZ6MFArutmTwdOEZE2boLlFGC625YnIoPdsS7ylVWlUO59bq6qr/nWXxeRiSHsZ4yJUnV08fYxwB+BpSKyxKXdBjwIvCMiY4GfgfPctqnAaUAqsB24xKuLZovIvcACl+8eVc12n68CXgaaAdPcUq3q7n1u6z5OE5FJwFt4fyPOd5Uzxuyr6uDeZ1WdA1W+QPrESvIrVUzyqupkYHIl6QuBfuHUq7qW4iK8IFhW6cv9xwJuDedAxpjo0bBTHw2runufezRkRYwxTUQTvoUvFCE9T1FE+gF9gcSyNFV9tb4qZYxpzGqeRGnKagyKInIXMBwvKE7Fu9VmDt7V4caYfVEUtxRDuSTnHLxBz19U9RKgP9C6XmtljGncSkNcmqBQus87VLVUREpEpBXehZTdatqpMYqLD/LQs/OIiyslEChl7swuvPFSn/Ltl9/wAyefvp5zTjoNgFNHr+OMs9dRGhR27Ajw9EP9SVvXkuGnpHP2734q3697rzyuu2QYa1Y37b8V7Tvt4Ma7vye5bRGK8On73ZjyVnd6HpTH+Ek/EJ9QSrBEePahQ1m1PJnhIzdwzkVrEVF2bI/lmQcPZe3qVgCcdcE6RoxOQwSmf5DCh29G3xD1hMfWM+ikbWzdEsvlJ3g/Rz0P3cG1D6YTn+h9V3+/NYWVS5pz/G9yOG/8JkRgR0EMT09KYc3yZhE+g1raVx8y67NQRJKBF/BmpPOB/9W0k4hMBs4ANqlqWFPi9aV4Zwy3XTOEwh2xBAKl/O0fc1n4dUdWLmtDr4O3ktRyZ4X8X33WlWkfdAdg0LG/cNm1y7hzwmC++iyFrz7zLpQ/oGcedzy0oMkHRIBgifDiEwfz08rWNGtewpOvzuXb+e245Jof+feLvVk0rwMDh27ikmtXcusVg8jMaM6kyweRvy2OI4du5prbfmDCJUM54MBtjBidxoQxQykuEe59aiHfzO7IxvQWkT7FOvXZ222Z8q/2THxy1223l96eweuPdWLhzFYcdUIeY2/P4OZzepGZFs/Esw8kPzeWgcfncd3D6Vx3Ru8I1n7vRPPsc43dZ1W9SlW3quo/gJOBMa4bXZOXCeExPQ1LKNzh/R2IjS0lEFsKCjExytjxy5n8TN8KuXdsjyv/nNgsWOkFq78+eQOzPu9Sr7VuKDlZify00gvuO7bHkrYuiXYdilAVmrcoAaBFUgnZmxMAWPF9G/K3ed/RyqXJtOtYCEC37vms+iGZoqIApcEYli5uy9DjMyNwRvXrh/lJbMup2K5QhRYtvSfrtWgVJDvT+36WL2xBfq6X98fFzWnfueIf4CYnih8yW93F20dUt63skT9VUdVZ7skXjUpMjPLk5Fl0Tingk/e6s3J5G846bw3z53QiJytxj/yn/3Ytv7lwDbGxpdx2zZA9tg87KYN7bzmqIareoDp23k7PPnmsXNaaFx47hHueXsDY635ERLlp7J7fwymj0lg0rwMAP//UkouuXEXL1jvZWRhg4NDNpK5o+i3pUPzjzq789c01XHbnRkSUG87aszU48sJsFsxsFYHamVBU131+tJptCpxQFxVwjxIaB5AYaFkXRVartFS45uJf0yKpmNsfWMChA7I49vgMJl09tNL8n7zXg0/e68GvT07n/ItX8/h9h5dv69M3h6LCAD+via4f8MRmJfz5oW954bFD2FEQx2lXrOaFxw5h3sz9OPakjVx/x1L+PP7o8vyHHZnFKWelM/GywQCkrUviv6/25L6nF1C4I8CaVS0JNtFB93CdMSaLf97VhTlTkxl25lYmPJbGpPMPLN/ef2g+Iy7MZsLoXhGs5d7bJ7vPqnp8NUudBER3nOdVdaCqDowPNK+rYmtUkB/H94vbc9gRW+iSsp0X3/mSye9+TkJikBfe+WKP/LM+78qQYb9USBt20gb+b0aNj2drUgKBUm576FtmftqFeTP3A+DEMzYwb6b3SLs5n+/HQX23lufv3iuPa29fyj03Hcm23Pjy9M+mdOO6i47hlssHk78tjoz10TWeWJWTz81mzlSvVTzro9YcNGDXu956HLKD6x9J4+5LeuzR7W5SFO82v1CWJiik1xFEi1bJRbRIKgYgPj7IgKM2k/pjMn848xT+dPZJ/OnskygqDHDZed5tl11S8sv3PWpoJhlpu36xRZRjT9wYNeOJHuW6O5aStq4FH/x712xx9uYEfnWEd399/6Oyyr+HDp128OeHv+XRu/rvEfRatykqzzP0+Ey++jSavqeqZWXGcdiQAgAGHJtPxlpv/LVD153c+eI6/nbt/mxYkxDJKtaNfXFMMRq1bVfEhDu+JSZGkRiY80UXFszrVGX+M85Zx4CBmwmWxJC/LY7HfF3nfgOy2JKZyC8Z0dMC6ts/hxNPz2Dt6pY8/cYcAF555iCeur8fl9+4gpiAUrwzhqf/6l1McOGlqbRqvZOrblkGeLPX1485BoDbHvqWVq13UlISw3MP96UgP67ygzZhk579mcOG5NO6bQmvL1zOa4924omJKVx5TwaBgLKzKIYnJnpXKfz+hkxatgly9QPeg6CDJcI1px4UyervlWjuPovW0wtcReRNvDth2gOZwF2q+lJ1+7RO2E+HdrWHfFepuCTSNWj0SjZkRLoKjdp8/YI8zd6rfm1Ct26acv0NIeVdc9ONi2p68nZjE8ptfoL3OoKeqnqPiOwP7Keq31S3n6peWEd1NMY0NlHcUgxlTPFZYAhQFuS2Ac/UW42MMY2aaOhLUxTKmOIgVT1CRL4FUNUcEYmvaSdjTBRrojPLoQglKBaLSADXYBaRDjTZW72NMXWhqbYCQxFK9/kp4H2go4jcj/fYsL/Wa62MMY3bvnxJjqq+ISKL8B4fJsBoVV1R7zUzxjROTXi8MBShzD7vj/fmrI/8aaq6vj4rZoxpxPbloAh8wq4XWCUCPYCVwKH1WC9jTCMmUTyrEMqjw36lqoe5f3sDRxPC8xSNMaYmIjJZRDaJyA++tLtFZIOILHHLab5tt4pIqoisFJERvvSRLi3VvZK5LL2HiMx36W+HcuVM2Pc+u0eGDQp3P2NMFKm7iZaXqfy5q4+r6gC3TAUQkb7ABXi91JHAsyIScFfHPIP3/qi+wIUuL8BDrqxeQA4wtqYKhTKmOMG3GgMcAdi9VMbsq+pwoiXM566OAt5S1SJgrYik4vVcAVJVdQ2AiLwFjBKRFXiPOPydy/MKcDfwXHUHCaWl2NK3JOCNMY4K8SSMMdEo9JZiexFZ6FvGhXiEq0Xke9e9buPSugJpvjzpLq2q9HbAVlUt2S29WtW2FF2ztKWq3hTSaRhj9g2htxS31OKBEM8B97qj3Iv3wOs/hVlGrVX3OoJYVS0RkWMaqjLGmMZPqN/ZZ1Utf6GPiLwAfOxWN1DxTaIpLo0q0rOA5LJYtlv+KlXXfS57Cs4SEZkiIn8Ukd+WLTUVbIyJUvX8QAgR6exb/Q1QNjM9BbhARBJEpAfQGy9OLQB6u5nmeLzJmCnqPRdxJt676wHGAB/WdPxQrlNMxIu4J7DrekUF3gthX2NMNKqjiRb/c1dFJB24CxguIgPcUdYBlwOo6jIReQdYDpQA41U16Mq5GpgOBIDJqrrMHeIW4C0RuQ/4Fqj2ma5QfVDs6Gaef2BXMCwTxdezG2NqVHezz5U9d7XKwKWq9wP3V5I+FZhaSfoads1Qh6S6oBgAkqgYDMuPFc5BjDHRZV+993mjqt7TYDUxxjQd+2hQjN6nSBpjak+j+97n6oLiiQ1WC2NM07IvthRVNbshK2KMaTr21TFFY4ypnAVFY4xxmvCrBkJhQdEYExbBus/GGFOBBUVjjPGzoGiMMT4WFI0xxtnXX3FqjDF7sKBojDG77Ku3+TU4jQ+ws1u7SFej0YqZ/W2kq9DoxR7QreZM+zDJiKubcqylaIwxjl28bYwxu7GgaIwxHrujxRhjdiOl0RsVLSgaY8JjY4rGGFORdZ+NMcYvioNiTKQrYIxpeip78X1lS43liEwWkU0i8oMvra2IzBCR1e7fNi5dROQpEUkVke9F5AjfPmNc/tUiMsaXfqSILHX7PCUiNb57yoKiMSZ8GuJSs5eBkbulTQK+UNXewBduHeBUoLdbxgHPgRdEgbuAQXjveL6rLJC6PJf59tv9WHuwoGiMCY97m18oS41Fqc4Cdn8f1CjgFff5FWC0L/1V9XwNJItIZ2AEMENVs1U1B5gBjHTbWqnq16qqwKu+sqpkY4rGmLCEeZ1iexFZ6Ft/XlWfr2GfTqq60X3+BejkPncF0nz50l1adenplaRXy4KiMSZ8GnJU3KKqA2t/GFWRhp3rtu6zMSZsdTXRUoVM1/XF/bvJpW8A/E/8SHFp1aWnVJJeLQuKxpjwhDrJUvugOAUom0EeA3zoS7/IzUIPBnJdN3s6cIqItHETLKcA0922PBEZ7GadL/KVVSXrPhtjwlZXz1MUkTeB4Xhjj+l4s8gPAu+IyFjgZ+A8l30qcBqQCmwHLgFQ1WwRuRdY4PLdo6plkzdX4c1wNwOmuaVaFhSNMWGrq6CoqhdWsenESvIqML6KciYDkytJXwj0C6dOFhSNMeFRwploaXIsKBpjwmb3PhtjjJ8FRWOM8dhDZo0xxk/VHjJrjDEVRG9MtKBojAmfdZ+NMaaMAtZ9NsYYn+iNiRYUjTHhs+6zMcb42OyzMcaUsVecGmPMLt7F29EbFS0oGmPCV0dPyWmMLCgaY8JmLcUo0qJ5ERMun0f3bjmA8MhzxzCw/wZOO3E1uXkJAEx+80i+WZJCIFDKhMvn0rtHFoGAMmPWgbz1wWFVlrNidcfInVg96NBlJxOfXE9yhxJQmPp6Oz54qQOX3pHB4JPzKN4pbPw5nkdv2J+CvAB9Bmznur957w8S4LVH92Pep60jexJ1LC4+yEPPzSMurpRAQJk7szNvvNiHm+5eTO+DcykpiWHVimT+/uCvCAZjAOXyG5YxcOgmigoDPH7vAH5a1ZqevXO5auJSmrcoobRUePvl3sz+okukTy80NqZYOyLSDe+Vgp3wvsLnVfXJ+jpeqK66+BsWfteVex8/nthAkISEEgb238C7n/Tlvx9XfBblsMHriIsLMm7iaBLiS3jx0feZObcHmZtbVlpOtAmWCM/f04XUpc1p1iLI3z9dxeJZLVk8qyWT/9qZ0qAw9s8ZXHBNJi/d34V1KxO5euRBlAaFth2Lee7zVXw9oxWlwRrfP95kFO+M4barh1C4I5ZAoJS//XMeC//Xka+md+WRuw8H4Oa/fMuIs9Yz9f3uDByyiS7dCrjs3OPpc+hWxt+8lAmXHkthYYDH7hlARnoSbdsX8uS/ZrN4fgcK8uMifIahiO57n+vzHS0lwI2q2hcYDIwXkb71eLwaNW+2k18dksm0L3t7FQwGKNieUPUOCokJJcTElBIfX0JJSYDt2+PDL6eJyt4UR+rS5gDsKAiQlppI+87FLP6/luWBbsWiFrTvXAxA0Y6Y8vS4hNIofQ6pULjDa0vExiqB2FJQWPi/TrgpCFatSKZ9x0IABg/L5MtpKYCwclkbWiQV06ZdIRlpSWSkJwGQvSWRrTnxtE4uiswp1YZqaEsTVG8tRffSmI3u8zYRWYH3ztXl9XXMmnTuuI3cvEQmXjmHngfksHptO559+WgARo1YwcnDfmLVmnb887WjyC9IYNb87gw5aj1v//NtEuKD/OPVo9hWkMCBB2RVWk5hUVP4K187nVJ2cmC/Hfy4uHmF9BEXZvN/HyaXr/c5vIAbH0ujY0oxD1+zf1S1EsvExChP/ms2nVMK+OTd7qxc3qZ8WyBQyvEj03n+8UMBaNehkM2Zzcq3b9mcSLsOheRkJZanHdQ3h7g4ZeOGFg13EntD6+51BI1Rg7zNT0S6A4cD8xvieFUJBJTePbL4aMbBXDnpLAoLYzl/1FI+mnEwY649mytuOYvsnOZc/kfv/TcH99pMaWkMF1xxPhddczbnnLGM/Tpuq7KcaJXYPMgdL67jH3d2YXt+oDz9wmszCZbAl+8ll6et/LYF444/mGtO7c0F12QSlxB9vz2lpcI1Y4YxZtRJHNR3Kwf0zCvfdtXEpfywpB3LvmsXUllt2hVy451LePy+/qg2oT8gUdxSrPegKCJJwLvA9aqaV8n2cSKyUEQW7iwuqNe6bM5qzuas5vyY2gGAWfO707tHNltzm1GqMagKU7/sTZ9eWwA44Zi1LFzSlWAwhq15zVi2siMH9dxSZTnRKBCr3PHiOr58rw1zpyWXp598XjZHn5THQ1cfgNdtrCgtNZEdBQG69ylsuMo2sIL8OL5f3I4jB28G4MI/raJ18k5efHLXKFHW5kQ6dNpRvt6+QyFZm71WYrPmxdz96De8+s8+rFzWhialfl9xGlH1GhRFJA4vIL6hqu9VlkdVn1fVgao6MD6ufrsPObnN2ZzVgpTOuQAc3i+Dn9Nb0zZ5e3meY45az7q0ZAA2bWnBgH4bAUhMKOaQ3ptJy2hdZTnRR5nwaBppqxN57/kO5akDh+dx7lWbuPviHhTt2PUj1KlbETEB7zehY9eddOtVSGZ6fIPXuj61Si6iRZI3hhqfEGTAUVtI+zmJU85cz5GDN/PwXUdUaPHNn92JE05NB5Q+h+ZQUBBLTlYisbGl3P7QQr6clsLcmU1k1tlHSktDWpqi+px9FuAlYIWqPlZfxwnXM/8axK3XzCI2tpSNm5J45LljGX/xfA7sno2qkLk5iSdeGALAh9MPZuJVc3jhkQ8QUaZ/1Zu169tWWU60OfToAk46N4c1yxN5dsZKAP71QGeuuncDcQnKA2//BMCPi1rw1KQU+h1dwPlXr6WkRCgtFZ6+LYW87Oi66qttuyIm3LmEmBhFBOZ82ZkFczsxZfYnbPqlGY8+PxeAef+3H29OPogF8zoycOgmXvzPTIqKAjx+X38Ajjsxg34DsmnVqpiTTksH4PH7+rNmdRP446pE9cXbovXU7xeRY4HZwFJ2fYW3qerUqvZp1bKrHnVEpa91NUDM7G8jXYVGL/aAbpGuQqM2L+MNcosy92rwsnWLLjq47+Uh5f1s4d2LVHVgVdtFZB2wDQgCJao6UETaAm8D3YF1wHmqmuMaWk8CpwHbgYtVdbErZwxwuyv2PlV9pRanBtTv7PMcKhtsMsY0fXXbmDpeVbf41icBX6jqgyIyya3fApwK9HbLIOA5YJALoncBA/HasYtEZIqq5tSmMg0y+2yMiTL1O/s8Cihr6b0CjPalv6qer4FkEekMjABmqGq2C4QzgJG1PbgFRWNMeMrGFENZoH3Z1SVuGVdJaZ+JyCLftk7uOmeAX/DuigPvOuc0377pLq2q9FqJrlFwY0yDCGNmeUt1Y4rAsaq6QUQ6AjNE5Ef/RlVVkYZ9zre1FI0xYQqx6xxC91lVN7h/NwHvA0cDma5bjPt3k8u+AfDPpKW4tKrSa8WCojEmPEqdBEURaSEiLcs+A6cAPwBTgDEu2xjgQ/d5CnCReAYDua6bPR04RUTaiEgbV8702p6edZ+NMeGrm+sUOwHve1faEAv8W1U/FZEFwDsiMhb4GTjP5Z+KdzlOKt4lOZcAqGq2iNwLLHD57lHVWt9iZkHRGBO2unjIrKquAfpXkp4FnFhJugKVXsisqpOByXtdKSwoGmNqo4k+7CEUFhSNMeFRhWD03udnQdEYEz5rKRpjjI8FRWOMcRSI4ne0WFA0xoRJQW1M0RhjPIpNtBhjTAU2pmiMMT4WFI0xpkzTfVNfKCwoGmPCo0ATfSlVKCwoGmPCZy1FY4wpY7f5GWPMLgpq1ykaY4yP3dFijDE+NqZojDGOqs0+G2NMBdZSNMaYMooGg5GuRL2xoGiMCY89OswYY3Zjl+QYY4xHAbWWojHGOGoPmTXGmAqieaJFtBFNrYvIZuDnSNfDpz2wJdKVaMTs+6lZY/uODlDVDntTgIh8indeodiiqiP35ngNrVEFxcZGRBaq6sBI16Oxsu+nZvYdNT0xka6AMcY0JhYUjTHGx4Ji9Z6PdAUaOft+ambfURNjY4rGGONjLUVjjPGxoGiMMT4WFCshIiNFZKWIpIrIpEjXp7ERkckisklEfoh0XRojEekmIjNFZLmILBOR6yJdJxM6G1PcjYgEgFXAyUA6sAC4UFWXR7RijYiIDAPygVdVtV+k69PYiEhnoLOqLhaRlsAiYLT9DDUN1lLc09FAqqquUdWdwFvAqAjXqVFR1VlAdqTr0Vip6kZVXew+bwNWAF0jWysTKguKe+oKpPnW07EfaFNLItIdOByYH+GqmBBZUDSmnohIEvAucL2q5kW6PiY0FhT3tAHo5ltPcWnGhExE4vAC4huq+l6k62NCZ0FxTwuA3iLSQ0TigQuAKRGuk2lCRESAl4AVqvpYpOtjwmNBcTeqWgJcDUzHGyB/R1WXRbZWjYuIvAn8D+gjIukiMjbSdWpkjgH+CJwgIkvcclqkK2VCY5fkGGOMj7UUjTHGx4KiMcb4WFA0xhgfC4rGGONjQdEYY3wsKDYhIhJ0l3f8ICL/EZHme1HWyyJyjvv8ooj0rSbvcBEZWotjrBORPd76VlX6bnnywzzW3SJyU7h1NGZ3FhSblh2qOsA9mWYncIV/o4jU6j3eqnppDU9wGQ6EHRSNaYosKDZds4FerhU3W0SmAMtFJCAifxORBSLyvYhcDt5dFiLyd/ecyM+BjmUFichXIjLQfR4pIotF5DsR+cI90OAK4AbXSj1ORDqIyLvuGAtE5Bi3bzsR+cw9Q/BFQGo6CRH5QEQWuX3G7bbtcZf+hYh0cGkHisinbp/ZInJwnXybxji1almYyHItwlOBT13SEUA/VV3rAkuuqh4lIgnAXBH5DO9JLX2AvkAnYDkwebdyOwAvAMNcWW1VNVtE/gHkq+ojLt+/gcdVdY6I7I93988hwF3AHFW9R0ROB0K50+VP7hjNgAUi8q6qZgEtgIWqeoOI3OnKvhrvRVBXqOpqERkEPAucUIuv0ZhKWVBsWpqJyBL3eTbe/bVDgW9Uda1LPwU4rGy8EGgN9AaGAW+qahDIEJEvKyl/MDCrrCxVreqZiScBfb1bfAFo5Z4IMwz4rdv3ExHJCeGcrhWR37jP3Vxds4BS4G2X/jrwnjvGUOA/vmMnhHAMY0JmQbFp2aGqA/wJLjgU+JOAa1R1+m756vLe2xhgsKoWVlKXkInIcLwAO0RVt4vIV0BiFdnVHXfr7t+BMXXJxhSjz3TgSvfoKkTkIBFpAcwCzndjjp2B4yvZ92tgmIj0cPu2denbgJa+fJ8B15StiMgA93EW8DuXdirQpoa6tgZyXEA8GK+lWiYGKGvt/g6vW54HrBWRc90xRET613AMY8JiQTH6vIg3XrhYvBdL/ROvR/A+sNptexXvKTcVqOpmYBxeV/U7dnVfPwJ+UzbRAlwLDHQTOcvZNQv+F7ygugyvG72+hrp+CsSKyArgQbygXKYAONqdwwnAPS7998BYV79l2KsiTB2zp+QYY4yPtRSNMcbHgqIxxvhYUDTGGB8LisYY42NB0RhjfCwoGmOMjwVFY4zx+X+k3eQdJbhG9wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_eval(best_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Finally, the model has seen a nice improvement, especially because it is not overfitted anymore (train and test scores look similar) and the overall accuracy achieved with these variables in the model is about **78%**. Still, it lacks in terms of other metrics like 'Recall' or 'F1 Score' especially in less frequent labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\"> \n",
    "\n",
    "- The purpose of this work was, essentially, to try to understand the variables of the dataset and know how to pre-process them, since that the majority of work in machine learning comes from data scrapping and cleaning. <br><br>\n",
    "- Our best performing model in this dummy example in building a good predictor was the **Random Forest Classifier**. <br><br>\n",
    "- Although the estimator yields good results on payments done on time, we observed that it is the most difficult to predict whenever payments are delayed as the obtained metrics were not favourable. <br><br>\n",
    "- Further modelling and comparison with other type of models and parameter tuning should be made as next steps, in order to improve, essentially, **delayed and early payment** predictions.<br><br>\n",
    "- Model deployment should be done, finally, as last step to start using the model! "
   ]
  }
 ],
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