Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "952f4090",
   "metadata": {},
   "source": [
    "# Building A Logistic Regression in Python, Step by Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "906d320d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(CVXPY) Aug 12 05:55:55 PM: Encountered unexpected exception importing solver GLOP:\n",
      "RuntimeError('Unrecognized new version of ortools (9.5.2237). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n",
      "(CVXPY) Aug 12 05:55:55 PM: Encountered unexpected exception importing solver PDLP:\n",
      "RuntimeError('Unrecognized new version of ortools (9.5.2237). Expected < 9.5.0.Please open a feature request on cvxpy to enable support for this version.')\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report, auc, roc_auc_score, roc_curve, accuracy_score\n",
    "from optbinning import OptimalBinning\n",
    "from optbinning import BinningProcess\n",
    "from optbinning.scorecard import plot_auc_roc, plot_cap, plot_ks\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "# Config the matlotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0990ea92",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902d5f1d",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3438666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n",
      "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y']\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
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       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>59</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.866</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>31</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.860</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>42</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>48</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>92.431</td>\n",
       "      <td>-26.9</td>\n",
       "      <td>0.742</td>\n",
       "      <td>5017.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>25</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.859</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education  default housing loan  \\\n",
       "0       44  blue-collar  married             basic.4y  unknown     yes   no   \n",
       "1       53   technician  married              unknown       no      no   no   \n",
       "2       28   management   single    university.degree       no     yes   no   \n",
       "3       39     services  married          high.school       no      no   no   \n",
       "4       55      retired  married             basic.4y       no     yes   no   \n",
       "...    ...          ...      ...                  ...      ...     ...  ...   \n",
       "41183   59      retired  married          high.school  unknown      no  yes   \n",
       "41184   31    housemaid  married             basic.4y  unknown      no   no   \n",
       "41185   42       admin.   single    university.degree  unknown     yes  yes   \n",
       "41186   48   technician  married  professional.course       no      no  yes   \n",
       "41187   25      student   single          high.school       no      no   no   \n",
       "\n",
       "         contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "0       cellular   aug         thu  ...         1    999         0   \n",
       "1       cellular   nov         fri  ...         1    999         0   \n",
       "2       cellular   jun         thu  ...         3      6         2   \n",
       "3       cellular   apr         fri  ...         2    999         0   \n",
       "4       cellular   aug         fri  ...         1      3         1   \n",
       "...          ...   ...         ...  ...       ...    ...       ...   \n",
       "41183  telephone   jun         thu  ...         1    999         0   \n",
       "41184  telephone   may         thu  ...         2    999         0   \n",
       "41185  telephone   may         wed  ...         3    999         0   \n",
       "41186  telephone   oct         tue  ...         2    999         0   \n",
       "41187  telephone   may         fri  ...         4    999         0   \n",
       "\n",
       "          poutcome emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "0      nonexistent          1.4          93.444          -36.1      4.963   \n",
       "1      nonexistent         -0.1          93.200          -42.0      4.021   \n",
       "2          success         -1.7          94.055          -39.8      0.729   \n",
       "3      nonexistent         -1.8          93.075          -47.1      1.405   \n",
       "4          success         -2.9          92.201          -31.4      0.869   \n",
       "...            ...          ...             ...            ...        ...   \n",
       "41183  nonexistent          1.4          94.465          -41.8      4.866   \n",
       "41184  nonexistent          1.1          93.994          -36.4      4.860   \n",
       "41185  nonexistent          1.1          93.994          -36.4      4.857   \n",
       "41186  nonexistent         -3.4          92.431          -26.9      0.742   \n",
       "41187  nonexistent          1.1          93.994          -36.4      4.859   \n",
       "\n",
       "       nr_employed  y  \n",
       "0           5228.1  0  \n",
       "1           5195.8  0  \n",
       "2           4991.6  1  \n",
       "3           5099.1  0  \n",
       "4           5076.2  1  \n",
       "...            ... ..  \n",
       "41183       5228.1  0  \n",
       "41184       5191.0  0  \n",
       "41185       5191.0  0  \n",
       "41186       5017.5  0  \n",
       "41187       5191.0  0  \n",
       "\n",
       "[41188 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data = pd.read_csv('banking.csv', header = 0)\n",
    "# data.to_hdf('banking.h5', key='data', mode='w') \n",
    "data = pd.read_hdf('banking.h5', 'data')\n",
    "print(data.shape)\n",
    "print(list(data.columns))\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52ef4a0",
   "metadata": {},
   "source": [
    "#### Input variables\n",
    "\n",
    "* age (numeric)\n",
    "* job : type of job (categorical: “admin”, “blue-collar”, “entrepreneur”, “housemaid”, “management”, “retired”, “self-employed”, “services”, “student”, “technician”, “unemployed”, “unknown”)\n",
    "* marital : marital status (categorical: “divorced”, “married”, “single”, “unknown”)\n",
    "* education (categorical: “basic.4y”, “basic.6y”, “basic.9y”, “high.school”, “illiterate”, “professional.course”, “university.degree”, “unknown”)\n",
    "* default: has credit in default? (categorical: “no”, “yes”, “unknown”)\n",
    "* housing: has housing loan? (categorical: “no”, “yes”, “unknown”)\n",
    "* loan: has personal loan? (categorical: “no”, “yes”, “unknown”)\n",
    "* contact: contact communication type (categorical: “cellular”, “telephone”)\n",
    "* month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)\n",
    "* day_of_week: last contact day of the week (categorical: “mon”, “tue”, “wed”, “thu”, “fri”)\n",
    "* duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y=’no’). The duration is not known before a call is performed, also, after the end of the call, y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model\n",
    "* campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "* pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "* previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* poutcome: outcome of the previous marketing campaign (categorical: “failure”, “nonexistent”, “success”)\n",
    "* emp.var.rate: employment variation rate — (numeric)\n",
    "* cons.price.idx: consumer price index — (numeric)\n",
    "* cons.conf.idx: consumer confidence index — (numeric)\n",
    "* euribor3m: euribor 3 month rate — (numeric)\n",
    "* nr.employed: number of employees — (numeric)\n",
    "\n",
    "Predict variable (desired target):\n",
    "* y — has the client subscribed a term deposit? (binary: “1”, means “Yes”, “0” means “No”)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "659304eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age :  int64\n",
      "job :  object\n",
      "marital :  object\n",
      "education :  object\n",
      "default :  object\n",
      "housing :  object\n",
      "loan :  object\n",
      "contact :  object\n",
      "month :  object\n",
      "day_of_week :  object\n",
      "duration :  int64\n",
      "campaign :  int64\n",
      "pdays :  int64\n",
      "previous :  int64\n",
      "poutcome :  object\n",
      "emp_var_rate :  float64\n",
      "cons_price_idx :  float64\n",
      "cons_conf_idx :  float64\n",
      "euribor3m :  float64\n",
      "nr_employed :  float64\n",
      "y :  int64\n"
     ]
    }
   ],
   "source": [
    "#check dtype of all columns\n",
    "dtypes_dict = dict(data.dtypes)\n",
    "\n",
    "for key, value in dtypes_dict.items():\n",
    "    print(key, ': ', value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c887e47d",
   "metadata": {},
   "source": [
    "The education column of the dataset has many categories and we need to reduce the categories for a better modelling. The education column has the following categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48bea727",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>education</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>basic.4y</td>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>basic.6y</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>basic.9y</td>\n",
       "      <td>6045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high.school</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>illiterate</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>professional.course</td>\n",
       "      <td>5243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>university.degree</td>\n",
       "      <td>12168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             education  counts\n",
       "0             basic.4y    4176\n",
       "1             basic.6y    2292\n",
       "2             basic.9y    6045\n",
       "3          high.school    9515\n",
       "4           illiterate      18\n",
       "5  professional.course    5243\n",
       "6    university.degree   12168\n",
       "7              unknown    1731"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['education']).size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58d98a69",
   "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>education</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic</td>\n",
       "      <td>12513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high.school</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>illiterate</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>professional.course</td>\n",
       "      <td>5243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>university.degree</td>\n",
       "      <td>12168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             education  counts\n",
       "0                Basic   12513\n",
       "1          high.school    9515\n",
       "2           illiterate      18\n",
       "3  professional.course    5243\n",
       "4    university.degree   12168\n",
       "5              unknown    1731"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education']=np.where(data['education'] =='basic.9y', 'Basic', data['education'])\n",
    "data['education']=np.where(data['education'] =='basic.6y', 'Basic', data['education'])\n",
    "data['education']=np.where(data['education'] =='basic.4y', 'Basic', data['education'])\n",
    "\n",
    "data.groupby(['education']).size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675f202e",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c647587f",
   "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>y</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>36548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y  counts\n",
       "0  0   36548\n",
       "1  1    4640"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['y']).size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd782eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='y', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'y', data = data, palette = 'hls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c7d3bc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no subscription is 88.7346\n",
      "percentage of subscription 11.2654\n"
     ]
    }
   ],
   "source": [
    "count_no_sub = len(data[data['y']==0])\n",
    "count_sub = len(data[data['y']==1])\n",
    "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of no subscription is\", round(pct_of_no_sub*100, 4))\n",
    "pct_of_sub = 1 - pct_of_no_sub\n",
    "print(\"percentage of subscription\", round(pct_of_sub*100, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06490f95",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c3ea8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.job,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Job Title')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6959262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion of Customers')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.marital,data.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Marital Status vs Purchase')\n",
    "plt.xlabel('Marital Status')\n",
    "plt.ylabel('Proportion of Customers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8e4e774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion of Customers')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.education,data.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Education vs Purchase')\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('Proportion of Customers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d107b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.day_of_week,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Day of Week')\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "04b9d02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.month,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Month')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2cbd8b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist()\n",
    "plt.title('Histogram of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ecf7e80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.poutcome,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Poutcome')\n",
    "plt.xlabel('Poutcome')\n",
    "plt.ylabel('Frequency of Purchase')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169b11bb",
   "metadata": {},
   "source": [
    "### Implementing the model\n",
    "\n",
    "### Logistic Regression Assumptions\n",
    "* Binary logistic regression requires the dependent variable to be binary.\n",
    "* For a binary regression, the factor level 1 of the dependent variable should represent the desired outcome.\n",
    "* Only the meaningful variables should be included.\n",
    "* The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.\n",
    "* The independent variables are linearly related to the log odds.\n",
    "* Logistic regression requires quite large sample sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c16ae4c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'optbinning.binning.binning_statistics.BinningTable'>\n",
      "OPTIMAL\n",
      "[28.5 30.5 34.5 38.5 43.5 45.5 49.5 57.5]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bin</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count (%)</th>\n",
       "      <th>Non-event</th>\n",
       "      <th>Event</th>\n",
       "      <th>Event rate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "      <th>JS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-inf, 28.50)</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.102360</td>\n",
       "      <td>3480</td>\n",
       "      <td>736</td>\n",
       "      <td>0.174573</td>\n",
       "      <td>-0.510355</td>\n",
       "      <td>0.032358</td>\n",
       "      <td>0.004001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[28.50, 30.50)</td>\n",
       "      <td>3167</td>\n",
       "      <td>0.076891</td>\n",
       "      <td>2779</td>\n",
       "      <td>388</td>\n",
       "      <td>0.122513</td>\n",
       "      <td>-0.095071</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[30.50, 34.50)</td>\n",
       "      <td>7371</td>\n",
       "      <td>0.178960</td>\n",
       "      <td>6573</td>\n",
       "      <td>798</td>\n",
       "      <td>0.108262</td>\n",
       "      <td>0.044705</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[34.50, 38.50)</td>\n",
       "      <td>6421</td>\n",
       "      <td>0.155895</td>\n",
       "      <td>5820</td>\n",
       "      <td>601</td>\n",
       "      <td>0.093599</td>\n",
       "      <td>0.206549</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>0.000766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[38.50, 43.50)</td>\n",
       "      <td>6068</td>\n",
       "      <td>0.147324</td>\n",
       "      <td>5578</td>\n",
       "      <td>490</td>\n",
       "      <td>0.080751</td>\n",
       "      <td>0.368268</td>\n",
       "      <td>0.017315</td>\n",
       "      <td>0.002152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[43.50, 45.50)</td>\n",
       "      <td>2114</td>\n",
       "      <td>0.051326</td>\n",
       "      <td>1945</td>\n",
       "      <td>169</td>\n",
       "      <td>0.079943</td>\n",
       "      <td>0.379206</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.000791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[45.50, 49.50)</td>\n",
       "      <td>3776</td>\n",
       "      <td>0.091677</td>\n",
       "      <td>3487</td>\n",
       "      <td>289</td>\n",
       "      <td>0.076536</td>\n",
       "      <td>0.426458</td>\n",
       "      <td>0.014126</td>\n",
       "      <td>0.001753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[49.50, 57.50)</td>\n",
       "      <td>5823</td>\n",
       "      <td>0.141376</td>\n",
       "      <td>5253</td>\n",
       "      <td>570</td>\n",
       "      <td>0.097888</td>\n",
       "      <td>0.157006</td>\n",
       "      <td>0.003279</td>\n",
       "      <td>0.000409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[57.50, inf)</td>\n",
       "      <td>2232</td>\n",
       "      <td>0.054191</td>\n",
       "      <td>1633</td>\n",
       "      <td>599</td>\n",
       "      <td>0.268369</td>\n",
       "      <td>-1.061</td>\n",
       "      <td>0.089563</td>\n",
       "      <td>0.010698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Special</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Totals</th>\n",
       "      <td></td>\n",
       "      <td>41188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36548</td>\n",
       "      <td>4640</td>\n",
       "      <td>0.112654</td>\n",
       "      <td></td>\n",
       "      <td>0.170221</td>\n",
       "      <td>0.020705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Bin  Count  Count (%)  Non-event  Event  Event rate  \\\n",
       "0        (-inf, 28.50)   4216   0.102360       3480    736    0.174573   \n",
       "1       [28.50, 30.50)   3167   0.076891       2779    388    0.122513   \n",
       "2       [30.50, 34.50)   7371   0.178960       6573    798    0.108262   \n",
       "3       [34.50, 38.50)   6421   0.155895       5820    601    0.093599   \n",
       "4       [38.50, 43.50)   6068   0.147324       5578    490    0.080751   \n",
       "5       [43.50, 45.50)   2114   0.051326       1945    169    0.079943   \n",
       "6       [45.50, 49.50)   3776   0.091677       3487    289    0.076536   \n",
       "7       [49.50, 57.50)   5823   0.141376       5253    570    0.097888   \n",
       "8         [57.50, inf)   2232   0.054191       1633    599    0.268369   \n",
       "9              Special      0   0.000000          0      0    0.000000   \n",
       "10             Missing      0   0.000000          0      0    0.000000   \n",
       "Totals                  41188   1.000000      36548   4640    0.112654   \n",
       "\n",
       "             WoE        IV        JS  \n",
       "0      -0.510355  0.032358  0.004001  \n",
       "1      -0.095071  0.000721  0.000090  \n",
       "2       0.044705  0.000352  0.000044  \n",
       "3       0.206549  0.006138  0.000766  \n",
       "4       0.368268  0.017315  0.002152  \n",
       "5       0.379206  0.006369  0.000791  \n",
       "6       0.426458  0.014126  0.001753  \n",
       "7       0.157006  0.003279  0.000409  \n",
       "8         -1.061  0.089563  0.010698  \n",
       "9            0.0  0.000000  0.000000  \n",
       "10           0.0  0.000000  0.000000  \n",
       "Totals            0.170221  0.020705  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'age'\n",
    "x = data[col]\n",
    "y = data['y']\n",
    "optb = OptimalBinning(name = col, dtype = \"numerical\", solver = \"cp\")\n",
    "optb.fit(x, y)\n",
    "binning_table = optb.binning_table\n",
    "print(type(binning_table))\n",
    "print(optb.status)\n",
    "print(optb.splits)\n",
    "display(binning_table.build())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3449631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binning_table.plot(metric=\"woe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac9f8606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binning_table.plot(metric=\"event_rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "221f0b69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "OptimalBinning: Binary Binning Table Analysis\n",
      "---------------------------------------------\n",
      "\n",
      "  General metrics\n",
      "\n",
      "    Gini index               0.20843003\n",
      "    IV (Jeffrey)             0.17022083\n",
      "    JS (Jensen-Shannon)      0.02070507\n",
      "    Hellinger                0.02098438\n",
      "    Triangular               0.08073794\n",
      "    KS                       0.08441386\n",
      "    HHI                      0.12838705\n",
      "    HHI (normalized)         0.04122575\n",
      "    Cramer's V               0.14594549\n",
      "    Quality score            0.01989344\n",
      "\n",
      "  Monotonic trend                valley\n",
      "\n",
      "  Significance tests\n",
      "\n",
      "    Bin A  Bin B  t-statistic      p-value      P[A > B]     P[B > A]\n",
      "        0      1    37.976288 7.160966e-10  1.000000e+00 1.477707e-13\n",
      "        1      2     4.504568 3.380444e-02  9.824470e-01 1.755296e-02\n",
      "        2      3     8.094810 4.439219e-03  9.981266e-01 1.873393e-03\n",
      "        3      4     6.459063 1.103878e-02  9.949222e-01 5.077838e-03\n",
      "        4      5     0.013830 9.063838e-01  5.518493e-01 4.481507e-01\n",
      "        5      6     0.219395 6.395012e-01  6.772816e-01 3.227184e-01\n",
      "        6      7    12.816335 3.436061e-04  1.195917e-04 9.998804e-01\n",
      "        7      8   377.989255 3.409848e-84 6.009220e-183 1.000000e+00\n",
      "\n"
     ]
    }
   ],
   "source": [
    "binning_table.analysis(pvalue_test=\"chi2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c865f385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed']\n",
      "['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']\n"
     ]
    }
   ],
   "source": [
    "all_variables = data.columns\n",
    "all_dependent_variables = list(all_variables)\n",
    "all_dependent_variables.remove('y')\n",
    "\n",
    "category_variables = []\n",
    "\n",
    "dtypes_dict = dict(data[all_dependent_variables].dtypes)\n",
    "\n",
    "for key, value in dtypes_dict.items():\n",
    "    if value == 'object':\n",
    "        category_variables.append(key)\n",
    "        \n",
    "print(all_dependent_variables)        \n",
    "print(category_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea12c04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[all_dependent_variables]\n",
    "y = data[['y']]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd90b8e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                                      &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;,\n",
       "                                      &#x27;day_of_week&#x27;, &#x27;poutcome&#x27;],\n",
       "               max_n_prebins=10, n_jobs=-1,\n",
       "               selection_criteria={&#x27;gini&#x27;: {&#x27;min&#x27;: 0.2},\n",
       "                                   &#x27;iv&#x27;: {&#x27;max&#x27;: 1, &#x27;min&#x27;: 0.3}},\n",
       "               variable_names=[&#x27;age&#x27;, &#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;,\n",
       "                               &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;,\n",
       "                               &#x27;day_of_week&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;,\n",
       "                               &#x27;previous&#x27;, &#x27;poutcome&#x27;, &#x27;emp_var_rate&#x27;,\n",
       "                               &#x27;cons_price_idx&#x27;, &#x27;cons_conf_idx&#x27;, &#x27;euribor3m&#x27;,\n",
       "                               &#x27;nr_employed&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BinningProcess</label><div class=\"sk-toggleable__content\"><pre>BinningProcess(categorical_variables=[&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;,\n",
       "                                      &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;,\n",
       "                                      &#x27;day_of_week&#x27;, &#x27;poutcome&#x27;],\n",
       "               max_n_prebins=10, n_jobs=-1,\n",
       "               selection_criteria={&#x27;gini&#x27;: {&#x27;min&#x27;: 0.2},\n",
       "                                   &#x27;iv&#x27;: {&#x27;max&#x27;: 1, &#x27;min&#x27;: 0.3}},\n",
       "               variable_names=[&#x27;age&#x27;, &#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;default&#x27;,\n",
       "                               &#x27;housing&#x27;, &#x27;loan&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;,\n",
       "                               &#x27;day_of_week&#x27;, &#x27;duration&#x27;, &#x27;campaign&#x27;, &#x27;pdays&#x27;,\n",
       "                               &#x27;previous&#x27;, &#x27;poutcome&#x27;, &#x27;emp_var_rate&#x27;,\n",
       "                               &#x27;cons_price_idx&#x27;, &#x27;cons_conf_idx&#x27;, &#x27;euribor3m&#x27;,\n",
       "                               &#x27;nr_employed&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BinningProcess(categorical_variables=['job', 'marital', 'education', 'default',\n",
       "                                      'housing', 'loan', 'contact', 'month',\n",
       "                                      'day_of_week', 'poutcome'],\n",
       "               max_n_prebins=10, n_jobs=-1,\n",
       "               selection_criteria={'gini': {'min': 0.2},\n",
       "                                   'iv': {'max': 1, 'min': 0.3}},\n",
       "               variable_names=['age', 'job', 'marital', 'education', 'default',\n",
       "                               'housing', 'loan', 'contact', 'month',\n",
       "                               'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "                               'previous', 'poutcome', 'emp_var_rate',\n",
       "                               'cons_price_idx', 'cons_conf_idx', 'euribor3m',\n",
       "                               'nr_employed'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection_criteria = {\n",
    "    \"iv\": {\"min\": 0.3, \"max\": 1},\n",
    "    \"gini\": {\"min\": 0.2}\n",
    "}\n",
    "\n",
    "binning_process = BinningProcess(variable_names = all_dependent_variables\n",
    "                                 , max_n_prebins = 10\n",
    "                                 , categorical_variables = category_variables\n",
    "                                 , selection_criteria = selection_criteria\n",
    "                                 , n_jobs = -1)\n",
    "\n",
    "binning_process.fit(X_train, y_train['y'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b9570202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>status</th>\n",
       "      <th>selected</th>\n",
       "      <th>n_bins</th>\n",
       "      <th>iv</th>\n",
       "      <th>js</th>\n",
       "      <th>gini</th>\n",
       "      <th>quality_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>month</td>\n",
       "      <td>categorical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.421748</td>\n",
       "      <td>0.050012</td>\n",
       "      <td>0.313663</td>\n",
       "      <td>0.030792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>emp_var_rate</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.909388</td>\n",
       "      <td>0.107198</td>\n",
       "      <td>0.481004</td>\n",
       "      <td>0.188605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cons_price_idx</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>0.630392</td>\n",
       "      <td>0.072419</td>\n",
       "      <td>0.400818</td>\n",
       "      <td>0.687148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cons_conf_idx</td>\n",
       "      <td>numerical</td>\n",
       "      <td>OPTIMAL</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0.743231</td>\n",
       "      <td>0.084778</td>\n",
       "      <td>0.417969</td>\n",
       "      <td>0.396901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name        dtype   status  selected n_bins        iv        js  \\\n",
       "8            month  categorical  OPTIMAL      True      6  0.421748  0.050012   \n",
       "15    emp_var_rate    numerical  OPTIMAL      True      4  0.909388  0.107198   \n",
       "16  cons_price_idx    numerical  OPTIMAL      True      6  0.630392  0.072419   \n",
       "17   cons_conf_idx    numerical  OPTIMAL      True      5  0.743231  0.084778   \n",
       "\n",
       "        gini quality_score  \n",
       "8   0.313663      0.030792  \n",
       "15  0.481004      0.188605  \n",
       "16  0.400818      0.687148  \n",
       "17  0.417969      0.396901  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary = binning_process.summary()\n",
    "summary = summary[summary['selected'] == True]\n",
    "display(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b3bde8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Bin</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count (%)</th>\n",
       "      <th>Non-event</th>\n",
       "      <th>Event</th>\n",
       "      <th>Event rate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "      <th>JS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[may]</td>\n",
       "      <td>9628</td>\n",
       "      <td>0.333946</td>\n",
       "      <td>9020</td>\n",
       "      <td>608</td>\n",
       "      <td>0.063149</td>\n",
       "      <td>0.633136</td>\n",
       "      <td>0.104712</td>\n",
       "      <td>0.012875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[jul]</td>\n",
       "      <td>4999</td>\n",
       "      <td>0.173390</td>\n",
       "      <td>4551</td>\n",
       "      <td>448</td>\n",
       "      <td>0.089618</td>\n",
       "      <td>0.25442</td>\n",
       "      <td>0.010167</td>\n",
       "      <td>0.001267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[nov]</td>\n",
       "      <td>2847</td>\n",
       "      <td>0.098748</td>\n",
       "      <td>2555</td>\n",
       "      <td>292</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.105165</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[jun]</td>\n",
       "      <td>3730</td>\n",
       "      <td>0.129375</td>\n",
       "      <td>3336</td>\n",
       "      <td>394</td>\n",
       "      <td>0.105630</td>\n",
       "      <td>0.072288</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[aug]</td>\n",
       "      <td>4361</td>\n",
       "      <td>0.151261</td>\n",
       "      <td>3896</td>\n",
       "      <td>465</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>0.06178</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[apr, oct, sep, mar, dec]</td>\n",
       "      <td>3266</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>2225</td>\n",
       "      <td>1041</td>\n",
       "      <td>0.318739</td>\n",
       "      <td>-1.304314</td>\n",
       "      <td>0.304600</td>\n",
       "      <td>0.035587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Special</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Totals</th>\n",
       "      <td></td>\n",
       "      <td>28831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25583</td>\n",
       "      <td>3248</td>\n",
       "      <td>0.112657</td>\n",
       "      <td></td>\n",
       "      <td>0.421748</td>\n",
       "      <td>0.050012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Bin  Count  Count (%)  Non-event  Event  \\\n",
       "0                           [may]   9628   0.333946       9020    608   \n",
       "1                           [jul]   4999   0.173390       4551    448   \n",
       "2                           [nov]   2847   0.098748       2555    292   \n",
       "3                           [jun]   3730   0.129375       3336    394   \n",
       "4                           [aug]   4361   0.151261       3896    465   \n",
       "5       [apr, oct, sep, mar, dec]   3266   0.113281       2225   1041   \n",
       "6                         Special      0   0.000000          0      0   \n",
       "7                         Missing      0   0.000000          0      0   \n",
       "Totals                             28831   1.000000      25583   3248   \n",
       "\n",
       "        Event rate       WoE        IV        JS  \n",
       "0         0.063149  0.633136  0.104712  0.012875  \n",
       "1         0.089618   0.25442  0.010167  0.001267  \n",
       "2         0.102564  0.105165  0.001048  0.000131  \n",
       "3         0.105630  0.072288  0.000657  0.000082  \n",
       "4         0.106627   0.06178  0.000564  0.000070  \n",
       "5         0.318739 -1.304314  0.304600  0.035587  \n",
       "6         0.000000       0.0  0.000000  0.000000  \n",
       "7         0.000000       0.0  0.000000  0.000000  \n",
       "Totals    0.112657            0.421748  0.050012  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = binning_process.get_binned_variable('month')\n",
    "display(month.binning_table.build())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "825edc38",
   "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>month</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.633136</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>1.449272</td>\n",
       "      <td>1.449272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.633136</td>\n",
       "      <td>-0.693353</td>\n",
       "      <td>-0.203949</td>\n",
       "      <td>0.106440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.254420</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>0.667055</td>\n",
       "      <td>0.106440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.304314</td>\n",
       "      <td>-0.693353</td>\n",
       "      <td>-0.203949</td>\n",
       "      <td>-0.835571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.254420</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>0.667055</td>\n",
       "      <td>0.106440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28826</th>\n",
       "      <td>0.633136</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>1.449272</td>\n",
       "      <td>1.449272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28827</th>\n",
       "      <td>0.061780</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>0.220814</td>\n",
       "      <td>0.861410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28828</th>\n",
       "      <td>0.061780</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>0.220814</td>\n",
       "      <td>0.861410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28829</th>\n",
       "      <td>-1.304314</td>\n",
       "      <td>-0.693353</td>\n",
       "      <td>-0.203949</td>\n",
       "      <td>-0.835571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28830</th>\n",
       "      <td>0.633136</td>\n",
       "      <td>0.983820</td>\n",
       "      <td>1.449272</td>\n",
       "      <td>1.449272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28831 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          month  emp_var_rate  cons_price_idx  cons_conf_idx\n",
       "0      0.633136      0.983820        1.449272       1.449272\n",
       "1      0.633136     -0.693353       -0.203949       0.106440\n",
       "2      0.254420      0.983820        0.667055       0.106440\n",
       "3     -1.304314     -0.693353       -0.203949      -0.835571\n",
       "4      0.254420      0.983820        0.667055       0.106440\n",
       "...         ...           ...             ...            ...\n",
       "28826  0.633136      0.983820        1.449272       1.449272\n",
       "28827  0.061780      0.983820        0.220814       0.861410\n",
       "28828  0.061780      0.983820        0.220814       0.861410\n",
       "28829 -1.304314     -0.693353       -0.203949      -0.835571\n",
       "28830  0.633136      0.983820        1.449272       1.449272\n",
       "\n",
       "[28831 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_transform = binning_process.transform(X_train, metric = \"woe\")\n",
    "display(data_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7f240810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.8849\n",
      "intercept: [-2.05995484]\n",
      "coefficients: [[-0.41406786 -0.82562576  0.02450444 -0.12565878]]\n"
     ]
    }
   ],
   "source": [
    "X = binning_process.transform(X_train, metric = \"woe\")\n",
    "y = y_train['y'].values\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "r_sq = model.score(X, y)\n",
    "print('coefficient of determination:', round(r_sq, 4))\n",
    "intercept, coefficients = model.intercept_, model.coef_\n",
    "print('intercept:', intercept)\n",
    "print('coefficients:', coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "99a92424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "X2 = binning_process.transform(X_test, metric = \"woe\")\n",
    "y_pred = model.predict(X2)\n",
    "print('predicted response:', y_pred, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b24e724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit statistics of logistic regression classifier on test set\n",
      "AUC: 0.75394\n",
      "Gini: 0.50788\n"
     ]
    }
   ],
   "source": [
    "X2 = binning_process.transform(X_test, metric = \"woe\")\n",
    "probs = model.predict_proba(X2)\n",
    "preds = probs[:,1]\n",
    "y = y_test['y'].values\n",
    "\n",
    "# Calculate the false positive rate (FPR), true positive rate (TPR), and thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y, preds)\n",
    "\n",
    "# Calculate the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "gini = 2.0*roc_auc-1.0\n",
    "print('Fit statistics of logistic regression classifier on test set')\n",
    "print('AUC:', round(roc_auc, 5))\n",
    "print('Gini:', round(gini, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "431fd983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.301134\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                28831\n",
      "Model:                          Logit   Df Residuals:                    28826\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Sat, 12 Aug 2023   Pseudo R-squ.:                  0.1446\n",
      "Time:                        17:55:58   Log-Likelihood:                -8682.0\n",
      "converged:                       True   LL-Null:                       -10149.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const             -2.0601      0.021    -98.177      0.000      -2.101      -2.019\n",
      "month             -0.4146      0.035    -11.750      0.000      -0.484      -0.345\n",
      "emp_var_rate      -0.8266      0.033    -24.749      0.000      -0.892      -0.761\n",
      "cons_price_idx     0.0251      0.048      0.526      0.599      -0.068       0.119\n",
      "cons_conf_idx     -0.1252      0.049     -2.567      0.010      -0.221      -0.030\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "X = binning_process.transform(X_train, metric = \"woe\")\n",
    "X2 = sm.add_constant(X)\n",
    "y = y_train['y'].values\n",
    "logit_model = sm.Logit(y, X2)\n",
    "result = logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f2753b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regression coefficients: const            -2.060065\n",
      "month            -0.414555\n",
      "emp_var_rate     -0.826566\n",
      "cons_price_idx    0.025118\n",
      "cons_conf_idx    -0.125168\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('regression coefficients:', result.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39c77f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "0        0.048554\n",
      "1        0.300107\n",
      "2        0.050893\n",
      "3        0.145806\n",
      "4        0.275115\n",
      "           ...   \n",
      "12352    0.036238\n",
      "12353    0.048554\n",
      "12354    0.036238\n",
      "12355    0.145806\n",
      "12356    0.176608\n",
      "Length: 12357, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = binning_process.transform(X_test, metric = \"woe\")\n",
    "X2 = sm.add_constant(X)\n",
    "print('predicted response:', result.predict(X2), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "86ef0a55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit statistics of logistic regression classifier on test set\n",
      "AUC: 0.75394\n",
      "Gini: 0.50788\n"
     ]
    }
   ],
   "source": [
    "X = binning_process.transform(X_test, metric = \"woe\")\n",
    "X2 = sm.add_constant(X)\n",
    "preds = result.predict(X2)\n",
    "y = y_test['y'].values\n",
    "\n",
    "# Calculate the false positive rate (FPR), true positive rate (TPR), and thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y, preds)\n",
    "\n",
    "# Calculate the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "gini = 2.0*roc_auc-1.0\n",
    "print('Fit statistics of logistic regression classifier on test set')\n",
    "print('AUC:', round(roc_auc, 5))\n",
    "print('Gini:', round(gini, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780518f2",
   "metadata": {},
   "source": [
    "### Compute precision, recall, F-measure and support\n",
    "\n",
    "* The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier to not label a sample as positive if it is negative.\n",
    "\n",
    "* The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.\n",
    "\n",
    "* The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.\n",
    "\n",
    "* The F-beta score weights the recall more than the precision by a factor of beta. beta = 1.0 means recall and precision are equally important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7a9b0810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col_0      0    1\n",
      "row_0            \n",
      "0      10804  161\n",
      "1       1299   93\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.94     10965\n",
      "           1       0.37      0.07      0.11      1392\n",
      "\n",
      "    accuracy                           0.88     12357\n",
      "   macro avg       0.63      0.53      0.52     12357\n",
      "weighted avg       0.83      0.88      0.84     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = binning_process.transform(X_test, metric = \"woe\")\n",
    "X2 = sm.add_constant(X)\n",
    "preds = result.predict(X2)\n",
    "y_pred = np.where(preds < 0.5, 0, 1)\n",
    "y = y_test['y'].values\n",
    "\n",
    "print(pd.crosstab(y, y_pred))\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e92d426",
   "metadata": {},
   "source": [
    "### ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e90f0b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = binning_process.transform(X_test, metric = \"woe\")\n",
    "X2 = sm.add_constant(X)\n",
    "preds = result.predict(X2)\n",
    "y = y_test['y'].values\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y, preds)\n",
    "fpr, tpr, thresholds = roc_curve(y, preds)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (AUC = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.axis(\"square\")\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "43d96926",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27a76ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']\n",
      "['age', 'duration', 'campaign', 'pdays', 'previous', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed']\n"
     ]
    }
   ],
   "source": [
    "all_variables = data.columns\n",
    "all_dependent_variables = list(all_variables)\n",
    "all_dependent_variables.remove('y')\n",
    "\n",
    "numeric_variables = []\n",
    "category_variables = []\n",
    "\n",
    "dtypes_dict = dict(data[all_dependent_variables].dtypes)\n",
    "\n",
    "for key, value in dtypes_dict.items():\n",
    "    if value == 'object':\n",
    "        category_variables.append(key)\n",
    "    else:\n",
    "        numeric_variables.append(key)\n",
    "         \n",
    "print(category_variables)\n",
    "print(numeric_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7fc85ba8",
   "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",
       "\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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40442</th>\n",
       "      <td>38</td>\n",
       "      <td>0.112784</td>\n",
       "      <td>0.101596</td>\n",
       "      <td>0.115741</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.116385</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>0.063149</td>\n",
       "      <td>0.120787</td>\n",
       "      <td>287</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26951</th>\n",
       "      <td>27</td>\n",
       "      <td>0.127483</td>\n",
       "      <td>0.100604</td>\n",
       "      <td>0.106393</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.063149</td>\n",
       "      <td>0.120787</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.266</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13935</th>\n",
       "      <td>50</td>\n",
       "      <td>0.127483</td>\n",
       "      <td>0.101596</td>\n",
       "      <td>0.138563</td>\n",
       "      <td>0.049062</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.109842</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.089618</td>\n",
       "      <td>0.099017</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36198</th>\n",
       "      <td>37</td>\n",
       "      <td>0.112784</td>\n",
       "      <td>0.101596</td>\n",
       "      <td>0.085890</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.116385</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.197826</td>\n",
       "      <td>0.099017</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.466</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20808</th>\n",
       "      <td>45</td>\n",
       "      <td>0.084497</td>\n",
       "      <td>0.100604</td>\n",
       "      <td>0.106393</td>\n",
       "      <td>0.049062</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.089618</td>\n",
       "      <td>0.117281</td>\n",
       "      <td>210</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5228.1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35766</th>\n",
       "      <td>47</td>\n",
       "      <td>0.069056</td>\n",
       "      <td>0.100604</td>\n",
       "      <td>0.085890</td>\n",
       "      <td>0.049062</td>\n",
       "      <td>0.109971</td>\n",
       "      <td>0.109971</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>0.063149</td>\n",
       "      <td>0.117281</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31108</th>\n",
       "      <td>38</td>\n",
       "      <td>0.127483</td>\n",
       "      <td>0.142594</td>\n",
       "      <td>0.138563</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.116385</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>0.120787</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7820</th>\n",
       "      <td>33</td>\n",
       "      <td>0.127483</td>\n",
       "      <td>0.100604</td>\n",
       "      <td>0.138563</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.106627</td>\n",
       "      <td>0.114835</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.964</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14188</th>\n",
       "      <td>35</td>\n",
       "      <td>0.084497</td>\n",
       "      <td>0.100604</td>\n",
       "      <td>0.106393</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.116385</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.147258</td>\n",
       "      <td>0.197826</td>\n",
       "      <td>0.120787</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.410</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23167</th>\n",
       "      <td>27</td>\n",
       "      <td>0.112784</td>\n",
       "      <td>0.142594</td>\n",
       "      <td>0.106393</td>\n",
       "      <td>0.049062</td>\n",
       "      <td>0.108479</td>\n",
       "      <td>0.109842</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>0.063149</td>\n",
       "      <td>0.117281</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.856</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28831 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age       job   marital  education   default   housing      loan  \\\n",
       "40442   38  0.112784  0.101596   0.115741  0.129636  0.116385  0.113252   \n",
       "26951   27  0.127483  0.100604   0.106393  0.129636  0.108479  0.113252   \n",
       "13935   50  0.127483  0.101596   0.138563  0.049062  0.108479  0.109842   \n",
       "36198   37  0.112784  0.101596   0.085890  0.129636  0.116385  0.113252   \n",
       "20808   45  0.084497  0.100604   0.106393  0.049062  0.108479  0.113252   \n",
       "...    ...       ...       ...        ...       ...       ...       ...   \n",
       "35766   47  0.069056  0.100604   0.085890  0.049062  0.109971  0.109971   \n",
       "31108   38  0.127483  0.142594   0.138563  0.129636  0.116385  0.113252   \n",
       "7820    33  0.127483  0.100604   0.138563  0.129636  0.108479  0.113252   \n",
       "14188   35  0.084497  0.100604   0.106393  0.129636  0.116385  0.113252   \n",
       "23167   27  0.112784  0.142594   0.106393  0.049062  0.108479  0.109842   \n",
       "\n",
       "        contact     month  day_of_week  duration  campaign  pdays  previous  \\\n",
       "40442  0.052150  0.063149     0.120787       287         4    999         0   \n",
       "26951  0.147258  0.063149     0.120787       244         1    999         1   \n",
       "13935  0.147258  0.089618     0.099017        85         9    999         0   \n",
       "36198  0.147258  0.197826     0.099017       247         1    999         1   \n",
       "20808  0.147258  0.089618     0.117281       210         2    999         0   \n",
       "...         ...       ...          ...       ...       ...    ...       ...   \n",
       "35766  0.052150  0.063149     0.117281        66         2    999         0   \n",
       "31108  0.147258  0.106627     0.120787       118         2    999         0   \n",
       "7820   0.147258  0.106627     0.114835        88         1    999         0   \n",
       "14188  0.147258  0.197826     0.120787       137         1    999         1   \n",
       "23167  0.052150  0.063149     0.117281       351         2    999         0   \n",
       "\n",
       "       poutcome  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "40442  0.087924           1.1          93.994          -36.4      4.855   \n",
       "26951  0.146374          -1.8          92.893          -46.2      1.266   \n",
       "13935  0.087924           1.4          93.918          -42.7      4.962   \n",
       "36198  0.146374          -1.8          93.075          -47.1      1.466   \n",
       "20808  0.087924           1.4          93.918          -42.7      4.961   \n",
       "...         ...           ...             ...            ...        ...   \n",
       "35766  0.087924           1.1          93.994          -36.4      4.857   \n",
       "31108  0.087924           1.4          93.444          -36.1      4.963   \n",
       "7820   0.087924           1.4          93.444          -36.1      4.964   \n",
       "14188  0.146374          -1.8          93.075          -47.1      1.410   \n",
       "23167  0.087924           1.1          93.994          -36.4      4.856   \n",
       "\n",
       "       nr_employed  \n",
       "40442       5191.0  \n",
       "26951       5099.1  \n",
       "13935       5228.1  \n",
       "36198       5099.1  \n",
       "20808       5228.1  \n",
       "...            ...  \n",
       "35766       5191.0  \n",
       "31108       5228.1  \n",
       "7820        5228.1  \n",
       "14188       5099.1  \n",
       "23167       5191.0  \n",
       "\n",
       "[28831 rows x 20 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "X = X_train\n",
    "X2 = X_test\n",
    "y = y_train['y'].values\n",
    "\n",
    "ce_target = ce.TargetEncoder(cols = category_variables)\n",
    "ce_target.fit(X, y)\n",
    "XTE = ce_target.transform(X)\n",
    "XTE2 = ce_target.transform(X2)\n",
    "display(XTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85e32252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = tree.DecisionTreeClassifier(criterion = \"gini\", max_depth = 3)\n",
    "decision_tree.fit(XTE, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba7ddc36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- nr_employed <= 5087.65\n",
      "|   |--- duration <= 172.50\n",
      "|   |   |--- pdays <= 11.00\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- pdays >  11.00\n",
      "|   |   |   |--- class: 0\n",
      "|   |--- duration >  172.50\n",
      "|   |   |--- pdays <= 16.50\n",
      "|   |   |   |--- class: 1\n",
      "|   |   |--- pdays >  16.50\n",
      "|   |   |   |--- class: 1\n",
      "|--- nr_employed >  5087.65\n",
      "|   |--- duration <= 528.50\n",
      "|   |   |--- month <= 0.33\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- month >  0.33\n",
      "|   |   |   |--- class: 0\n",
      "|   |--- duration >  528.50\n",
      "|   |   |--- duration <= 827.50\n",
      "|   |   |   |--- class: 0\n",
      "|   |   |--- duration >  827.50\n",
      "|   |   |   |--- class: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "feature_names = XTE.columns.tolist()\n",
    "class_ = list(set(y))\n",
    "class_names = [str(x) for x in class_]\n",
    "\n",
    "tree_rules = tree.export_text(decision_tree, feature_names = feature_names)\n",
    "print(tree_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e3d1b084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'nr_employed <= 5087.65\\ngini = 0.2\\nsamples = 28831\\nvalue = [25583, 3248]\\nclass = 0'),\n",
       " Text(0.25, 0.625, 'duration <= 172.5\\ngini = 0.495\\nsamples = 3512\\nvalue = [1930, 1582]\\nclass = 0'),\n",
       " Text(0.125, 0.375, 'pdays <= 11.0\\ngini = 0.288\\nsamples = 1336\\nvalue = [1103, 233]\\nclass = 0'),\n",
       " Text(0.0625, 0.125, 'gini = 0.482\\nsamples = 202\\nvalue = [120, 82]\\nclass = 0'),\n",
       " Text(0.1875, 0.125, 'gini = 0.231\\nsamples = 1134\\nvalue = [983, 151]\\nclass = 0'),\n",
       " Text(0.375, 0.375, 'pdays <= 16.5\\ngini = 0.471\\nsamples = 2176\\nvalue = [827, 1349]\\nclass = 1'),\n",
       " Text(0.3125, 0.125, 'gini = 0.306\\nsamples = 642\\nvalue = [121, 521]\\nclass = 1'),\n",
       " Text(0.4375, 0.125, 'gini = 0.497\\nsamples = 1534\\nvalue = [706, 828]\\nclass = 1'),\n",
       " Text(0.75, 0.625, 'duration <= 528.5\\ngini = 0.123\\nsamples = 25319\\nvalue = [23653, 1666]\\nclass = 0'),\n",
       " Text(0.625, 0.375, 'month <= 0.326\\ngini = 0.047\\nsamples = 22633\\nvalue = [22083, 550]\\nclass = 0'),\n",
       " Text(0.5625, 0.125, 'gini = 0.039\\nsamples = 22391\\nvalue = [21948, 443]\\nclass = 0'),\n",
       " Text(0.6875, 0.125, 'gini = 0.493\\nsamples = 242\\nvalue = [135, 107]\\nclass = 0'),\n",
       " Text(0.875, 0.375, 'duration <= 827.5\\ngini = 0.486\\nsamples = 2686\\nvalue = [1570, 1116]\\nclass = 0'),\n",
       " Text(0.8125, 0.125, 'gini = 0.433\\nsamples = 1689\\nvalue = [1154, 535]\\nclass = 0'),\n",
       " Text(0.9375, 0.125, 'gini = 0.486\\nsamples = 997\\nvalue = [416, 581]\\nclass = 1')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(decision_tree\n",
    "          , feature_names = feature_names\n",
    "          , class_names = class_names\n",
    "          , filled = True\n",
    "          , rounded = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f612fde4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus\n",
    "from IPython.display import Image\n",
    "\n",
    "dot_data = tree.export_graphviz(decision_tree\n",
    "                                , out_file = None\n",
    "                                , feature_names = feature_names\n",
    "                                , class_names = class_names\n",
    "                                , filled = True\n",
    "                                , rounded = True)\n",
    "\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f6b746cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit statistics of decision tree classifier on test set\n",
      "AUC: 0.86818\n",
      "Gini: 0.73636\n"
     ]
    }
   ],
   "source": [
    "y = y_test['y'].values\n",
    "probs = decision_tree.predict_proba(XTE2)\n",
    "preds = probs[:,1]\n",
    "\n",
    "# Calculate the false positive rate (FPR), true positive rate (TPR), and thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y, preds)\n",
    "\n",
    "# Calculate the AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "gini = 2.0*roc_auc-1.0\n",
    "print('Fit statistics of decision tree classifier on test set')\n",
    "print('AUC:', round(roc_auc, 5))\n",
    "print('Gini:', round(gini, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61c8e4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_train\n",
    "X2 = X_test\n",
    "y = y_train['y'].values\n",
    "\n",
    "ce_target = ce.TargetEncoder(cols = category_variables)\n",
    "ce_target.fit(X, y)\n",
    "XTE = ce_target.transform(X)\n",
    "XTE2 = ce_target.transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "52598c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a4d099ff",
   "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>duration</th>\n",
       "      <th>pdays</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>287.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>1.266</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>247.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>1.466</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28826</th>\n",
       "      <td>66.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28827</th>\n",
       "      <td>118.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28828</th>\n",
       "      <td>88.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.964</td>\n",
       "      <td>5228.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28829</th>\n",
       "      <td>137.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.146374</td>\n",
       "      <td>1.410</td>\n",
       "      <td>5099.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28830</th>\n",
       "      <td>351.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.087924</td>\n",
       "      <td>4.856</td>\n",
       "      <td>5191.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28831 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration  pdays  poutcome  euribor3m  nr_employed\n",
       "0         287.0  999.0  0.087924      4.855       5191.0\n",
       "1         244.0  999.0  0.146374      1.266       5099.1\n",
       "2          85.0  999.0  0.087924      4.962       5228.1\n",
       "3         247.0  999.0  0.146374      1.466       5099.1\n",
       "4         210.0  999.0  0.087924      4.961       5228.1\n",
       "...         ...    ...       ...        ...          ...\n",
       "28826      66.0  999.0  0.087924      4.857       5191.0\n",
       "28827     118.0  999.0  0.087924      4.963       5228.1\n",
       "28828      88.0  999.0  0.087924      4.964       5228.1\n",
       "28829     137.0  999.0  0.146374      1.410       5099.1\n",
       "28830     351.0  999.0  0.087924      4.856       5191.0\n",
       "\n",
       "[28831 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(XTE.columns)\n",
    "cols_selector = []\n",
    "selector = SelectKBest(f_classif, k = 5)\n",
    "selector.fit(XTE, y)\n",
    "cols_idxs = selector.get_support(indices=True)\n",
    "\n",
    "for x in cols_idxs:\n",
    "    cols_selector.append(cols[x])\n",
    "\n",
    "XTES = selector.transform(XTE)\n",
    "XTES = pd.DataFrame(XTES)\n",
    "XTES.columns = cols_selector\n",
    "\n",
    "XTE2S = selector.transform(XTE2)\n",
    "XTE2S = pd.DataFrame(XTE2S)\n",
    "XTE2S.columns = cols_selector\n",
    "\n",
    "display(XTES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d23cc23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "723ee6b6",
   "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>duration</th>\n",
       "      <th>pdays</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.110899</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.712892</td>\n",
       "      <td>0.333553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.055258</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>0.332423</td>\n",
       "      <td>-1.354284</td>\n",
       "      <td>-0.936399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.669651</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.774521</td>\n",
       "      <td>0.846233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.043665</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>0.332423</td>\n",
       "      <td>-1.239089</td>\n",
       "      <td>-0.936399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.186637</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.773945</td>\n",
       "      <td>0.846233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28826</th>\n",
       "      <td>-0.743070</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.714044</td>\n",
       "      <td>0.333553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28827</th>\n",
       "      <td>-0.542136</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.775097</td>\n",
       "      <td>0.846233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28828</th>\n",
       "      <td>-0.658059</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.775673</td>\n",
       "      <td>0.846233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28829</th>\n",
       "      <td>-0.468718</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>0.332423</td>\n",
       "      <td>-1.271343</td>\n",
       "      <td>-0.936399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28830</th>\n",
       "      <td>0.358202</td>\n",
       "      <td>0.195941</td>\n",
       "      <td>-0.243841</td>\n",
       "      <td>0.713468</td>\n",
       "      <td>0.333553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28831 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration     pdays  poutcome  euribor3m  nr_employed\n",
       "0      0.110899  0.195941 -0.243841   0.712892     0.333553\n",
       "1     -0.055258  0.195941  0.332423  -1.354284    -0.936399\n",
       "2     -0.669651  0.195941 -0.243841   0.774521     0.846233\n",
       "3     -0.043665  0.195941  0.332423  -1.239089    -0.936399\n",
       "4     -0.186637  0.195941 -0.243841   0.773945     0.846233\n",
       "...         ...       ...       ...        ...          ...\n",
       "28826 -0.743070  0.195941 -0.243841   0.714044     0.333553\n",
       "28827 -0.542136  0.195941 -0.243841   0.775097     0.846233\n",
       "28828 -0.658059  0.195941 -0.243841   0.775673     0.846233\n",
       "28829 -0.468718  0.195941  0.332423  -1.271343    -0.936399\n",
       "28830  0.358202  0.195941 -0.243841   0.713468     0.333553\n",
       "\n",
       "[28831 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = y_train['y'].values\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(XTES)\n",
    "\n",
    "XTESS = scaler.transform(XTES)\n",
    "XTESS = pd.DataFrame(XTESS)\n",
    "XTESS.columns = cols_selector\n",
    "\n",
    "XTE2SS = scaler.transform(XTE2S)\n",
    "XTE2SS = pd.DataFrame(XTE2SS)\n",
    "XTE2SS.columns = cols_selector\n",
    "\n",
    "display(XTESS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "21c01335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col_0      0    1\n",
      "row_0            \n",
      "0      10517  448\n",
      "1        664  728\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(30, 20, 10), max_iter = 100)\n",
    "mlp.fit(XTESS, y)\n",
    "\n",
    "predictions = mlp.predict(XTE2SS)\n",
    "y = y_test['y'].values\n",
    "tab = pd.crosstab(y, predictions)\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dc3751e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "56673913",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found:\n",
      " {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (20, 10, 5), 'learning_rate': 'adaptive', 'solver': 'adam'}\n",
      "col_0      1\n",
      "row_0       \n",
      "0      10965\n",
      "1       1392\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y = y_train['y'].values\n",
    "\n",
    "mlp = MLPClassifier(max_iter = 50)\n",
    "\n",
    "parameter_space = {\n",
    "    'hidden_layer_sizes': [(30,30,30), (20,10,5), (30,20,10)],\n",
    "    'activation': ['tanh', 'relu'],\n",
    "    'solver': ['sgd', 'adam'],\n",
    "    'alpha': [0.0001, 0.05],\n",
    "    'learning_rate': ['constant','adaptive'],\n",
    "}\n",
    "\n",
    "clf = GridSearchCV(mlp, parameter_space, n_jobs=-1, cv=3)\n",
    "clf.fit(XTESS, y)\n",
    "\n",
    "print('Best parameters found:\\n', clf.best_params_)\n",
    "\n",
    "predictions = clf.predict(XTE2S)\n",
    "y = y_test['y'].values\n",
    "tab = pd.crosstab(y, predictions)\n",
    "print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a7fdd0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "93349bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_train\n",
    "X2 = X_test\n",
    "y = y_train['y'].values\n",
    "\n",
    "ce_target = ce.TargetEncoder(cols = category_variables)\n",
    "ce_target.fit(X, y)\n",
    "XTE = ce_target.transform(X)\n",
    "XTE2 = ce_target.transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b2cb10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Nearest Neighbors | Accuracy: 90.60% (+/- 0.17%)\n",
      "          Linear SVM | Accuracy: 90.69% (+/- 0.18%)\n",
      "             RBF SVM | Accuracy: 89.81% (+/- 0.06%)\n"
     ]
    }
   ],
   "source": [
    "names = [\n",
    "    \"Nearest Neighbors\",\n",
    "    \"Linear SVM\",\n",
    "    \"RBF SVM\",\n",
    "    \"Gaussian Process\",\n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\",\n",
    "    \"Neural Net\",\n",
    "    \"AdaBoost\",\n",
    "    \"Naive Bayes\",\n",
    "    \"QDA\"\n",
    "]\n",
    "\n",
    "classifiers = [\n",
    "    {'name': \"Nearest Neighbors\", 'model': KNeighborsClassifier()},\n",
    "    {'name': \"Linear SVM\",        'model': SVC(kernel=\"linear\")},\n",
    "    {'name': \"RBF SVM\",           'model': SVC(kernel=\"rbf\")},\n",
    "    {'name': \"Gaussian Process\",  'model': GaussianProcessClassifier()},\n",
    "    {'name': \"Decision Tree\",     'model': DecisionTreeClassifier()},\n",
    "    {'name': \"Random Forest\",     'model': RandomForestClassifier()},\n",
    "    {'name': \"Neural Net\",        'model': MLPClassifier()},\n",
    "    {'name': \"AdaBoost\",          'model': AdaBoostClassifier()},\n",
    "    {'name': \"Naive Bayes\",       'model': GaussianNB()},\n",
    "    {'name': \"QDA\",               'model': QuadraticDiscriminantAnalysis()},\n",
    "]\n",
    "\n",
    "for classifier in classifiers:\n",
    "    model = classifier['model']\n",
    "    name = classifier['name']\n",
    "\n",
    "    scores = cross_val_score(model, XTE, y, cv=5)\n",
    "\n",
    "    accuracy = 100 * scores.mean()\n",
    "    stdev = 100 * scores.std()\n",
    "\n",
    "    print(f'{name:>20} | Accuracy: {accuracy:.2f}% (+/- {stdev:.2f}%)')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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