task_03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Importing Important libraries:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Loding Data:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = \"C:/Users/22831/Downloads/bank_set.csv\"\n",
    "\n",
    "df = pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Exploratory data analysis:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857         5191  no  \n",
       "1          93.994          -36.4      4.857         5191  no  \n",
       "2          93.994          -36.4      4.857         5191  no  \n",
       "3          93.994          -36.4      4.857         5191  no  \n",
       "4          93.994          -36.4      4.857         5191  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>41183</th>\n",
       "      <td>73</td>\n",
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       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
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       "      <td>999</td>\n",
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       "      <td>-50.8</td>\n",
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       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</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",
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       "      <td>-1.1</td>\n",
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       "      <td>-50.8</td>\n",
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       "      <td>4964</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</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>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4964</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</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>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4964</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4964</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "41183   73      retired  married  professional.course      no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course      no      no   no   \n",
       "41185   56      retired  married    university.degree      no     yes   no   \n",
       "41186   44   technician  married  professional.course      no      no   no   \n",
       "41187   74      retired  married  professional.course      no     yes   no   \n",
       "\n",
       "        contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "41183  cellular   nov         fri  ...         1    999         0   \n",
       "41184  cellular   nov         fri  ...         1    999         0   \n",
       "41185  cellular   nov         fri  ...         2    999         0   \n",
       "41186  cellular   nov         fri  ...         1    999         0   \n",
       "41187  cellular   nov         fri  ...         3    999         1   \n",
       "\n",
       "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "41183  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41184  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41185  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41186  nonexistent         -1.1          94.767          -50.8      1.028   \n",
       "41187      failure         -1.1          94.767          -50.8      1.028   \n",
       "\n",
       "       nr.employed    y  \n",
       "41183         4964  yes  \n",
       "41184         4964   no  \n",
       "41185         4964   no  \n",
       "41186         4964  yes  \n",
       "41187         4964   no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  int64  \n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</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>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.019010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.178074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4964.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   5167.019010  \n",
       "std        1.570960        0.578840       4.628198      1.734447     72.178074  \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   4964.000000  \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   5099.000000  \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   5228.000000  \n",
       "max        1.400000       94.767000     -26.900000      5.045000   5228.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_From above we can say that there are 21 variables in dataset and there are 10 categorical variables.They are {job,education,marital,y,default,housing,contact,month,day_of_week,poutcome}._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job\n",
      "admin.           10422\n",
      "blue-collar       9254\n",
      "technician        6743\n",
      "services          3969\n",
      "management        2924\n",
      "retired           1720\n",
      "entrepreneur      1456\n",
      "self-employed     1421\n",
      "housemaid         1060\n",
      "unemployed        1014\n",
      "student            875\n",
      "unknown            330\n",
      "Name: count, dtype: int64\n",
      "education\n",
      "university.degree      12168\n",
      "high.school             9515\n",
      "basic.9y                6045\n",
      "professional.course     5243\n",
      "basic.4y                4176\n",
      "basic.6y                2292\n",
      "unknown                 1731\n",
      "illiterate                18\n",
      "Name: count, dtype: int64\n",
      "marital\n",
      "married     24928\n",
      "single      11568\n",
      "divorced     4612\n",
      "unknown        80\n",
      "Name: count, dtype: int64\n",
      "y\n",
      "no     36548\n",
      "yes     4640\n",
      "Name: count, dtype: int64\n",
      "default\n",
      "no         32588\n",
      "unknown     8597\n",
      "yes            3\n",
      "Name: count, dtype: int64\n",
      "housing\n",
      "yes        21576\n",
      "no         18622\n",
      "unknown      990\n",
      "Name: count, dtype: int64\n",
      "contact\n",
      "cellular     26144\n",
      "telephone    15044\n",
      "Name: count, dtype: int64\n",
      "month\n",
      "may    13769\n",
      "jul     7174\n",
      "aug     6178\n",
      "jun     5318\n",
      "nov     4101\n",
      "apr     2632\n",
      "oct      718\n",
      "sep      570\n",
      "mar      546\n",
      "dec      182\n",
      "Name: count, dtype: int64\n",
      "day_of_week\n",
      "thu    8623\n",
      "mon    8514\n",
      "wed    8134\n",
      "tue    8090\n",
      "fri    7827\n",
      "Name: count, dtype: int64\n",
      "poutcome\n",
      "nonexistent    35563\n",
      "failure         4252\n",
      "success         1373\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "col_names = [\n",
    "    \"job\",\n",
    "    \"education\",\n",
    "    \"marital\",\n",
    "    \"y\",\n",
    "    \"default\",\n",
    "    \"housing\",\n",
    "    \"contact\",\n",
    "    \"month\",\n",
    "    \"day_of_week\",\n",
    "    \"poutcome\",\n",
    "]\n",
    "\n",
    "for col in col_names:\n",
    "    print(df[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "no     36548\n",
       "yes     4640\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _checking for missing values:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Declare feature vector and target variables*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"y\"], axis=1)\n",
    "\n",
    "Y = df[\"y\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Splitting data into separate training and test sets:_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X, Y, test_size=0.30, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28831, 20), (12357, 20))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>39075</th>\n",
       "      <td>29</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>dec</td>\n",
       "      <td>mon</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>92.713</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>0.709</td>\n",
       "      <td>5024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34855</th>\n",
       "      <td>29</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</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>12</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7107</th>\n",
       "      <td>45</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>277</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31614</th>\n",
       "      <td>34</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.327</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34878</th>\n",
       "      <td>32</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>1181</td>\n",
       "      <td>9</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital          education  default housing loan  \\\n",
       "39075   29       admin.  married  university.degree       no      no   no   \n",
       "34855   29   technician   single  university.degree       no      no   no   \n",
       "7107    45  blue-collar  married           basic.6y  unknown     yes   no   \n",
       "31614   34     services  married  university.degree       no      no   no   \n",
       "34878   32       admin.   single        high.school       no      no   no   \n",
       "\n",
       "         contact month day_of_week  duration  campaign  pdays  previous  \\\n",
       "39075   cellular   dec         mon        77         3    999         1   \n",
       "34855  telephone   may         fri        12         4    999         0   \n",
       "7107   telephone   may         thu       277         2    999         0   \n",
       "31614   cellular   may         thu        70         1    999         1   \n",
       "34878   cellular   may         fri      1181         9    999         0   \n",
       "\n",
       "          poutcome  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "39075      failure          -3.0          92.713          -33.0      0.709   \n",
       "34855  nonexistent          -1.8          92.893          -46.2      1.250   \n",
       "7107   nonexistent           1.1          93.994          -36.4      4.860   \n",
       "31614      failure          -1.8          92.893          -46.2      1.327   \n",
       "34878  nonexistent          -1.8          92.893          -46.2      1.250   \n",
       "\n",
       "       nr.employed  \n",
       "39075         5024  \n",
       "34855         5099  \n",
       "7107          5191  \n",
       "31614         5099  \n",
       "34878         5099  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.OrdinalEncoder(\n",
    "    cols=[\n",
    "        \"age\",\n",
    "        \"job\",\n",
    "        \"marital\",\n",
    "        \"education\",\n",
    "        \"default\",\n",
    "        \"housing\",\n",
    "        \"loan\",\n",
    "        \"contact\",\n",
    "        \"month\",\n",
    "        \"day_of_week\",\n",
    "        \"duration\",\n",
    "        \"campaign\",\n",
    "        \"pdays\",\n",
    "        \"previous\",\n",
    "        \"poutcome\",\n",
    "        \"emp.var.rate\",\n",
    "        \"cons.price.idx\",\n",
    "        \"cons.conf.idx\",\n",
    "        \"euribor3m\",\n",
    "        \"nr.employed\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "X_train = encoder.fit_transform(X_train)\n",
    "X_test = encoder.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>39075</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34855</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7107</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31614</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34878</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "39075    1    1        1          1        1        1     1        1      1   \n",
       "34855    1    2        2          1        1        1     1        2      2   \n",
       "7107     2    3        1          2        2        2     1        2      2   \n",
       "31614    3    4        1          1        1        1     1        1      2   \n",
       "34878    4    1        2          3        1        1     1        1      2   \n",
       "\n",
       "       day_of_week  duration  campaign  pdays  previous  poutcome  \\\n",
       "39075            1         1         1      1         1         1   \n",
       "34855            2         2         2      1         2         2   \n",
       "7107             3         3         3      1         2         2   \n",
       "31614            3         4         4      1         1         1   \n",
       "34878            2         5         5      1         2         2   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "39075             1               1              1          1            1  \n",
       "34855             2               2              2          2            2  \n",
       "7107              3               3              3          3            3  \n",
       "31614             2               2              2          4            2  \n",
       "34878             2               2              2          2            2  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()  # type:ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>32884</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32206</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9403</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14020</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "32884    1    1        1          1        1        1     1        1      1   \n",
       "3169     2    2        1          2        2        2     2        2      1   \n",
       "32206    3    3        1          3        1        1     2        1      1   \n",
       "9403     4    4        1          1        1        1     2        2      2   \n",
       "14020    5    5        1          1        1        2     2        1      3   \n",
       "\n",
       "       day_of_week  duration  campaign  pdays  previous  poutcome  \\\n",
       "32884            1         1         1      1         1         1   \n",
       "3169             2         2         2      1         2         2   \n",
       "32206            3         3         1      1         1         1   \n",
       "9403             3         4         3      1         2         2   \n",
       "14020            3         5         2      1         2         2   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "32884             1               1              1          1            1  \n",
       "3169              2               2              2          2            2  \n",
       "32206             1               1              1          3            1  \n",
       "9403              3               3              3          4            3  \n",
       "14020             3               4              4          5            3  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()  # type:ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Decision Tree Classifier with criterion gini index*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gini = DecisionTreeClassifier(criterion=\"gini\", max_depth=3, random_state=0)\n",
    "clf_gini.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gini = clf_gini.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model acceurary score with criterion gini index : 0.8894\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(\n",
    "    \"model acceurary score with criterion gini index : {0:0.4f}\".format(\n",
    "        accuracy_score(Y_test, y_pred_gini)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'no', 'no', ..., 'yes', 'no', 'no'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train_gini = clf_gini.predict(X_train)\n",
    "y_pred_train_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set acurecy score :0.9028\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Training-set acurecy score :{0:0.4f}\".format(\n",
    "        accuracy_score(Y_train, y_pred_train_gini)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set acurecy score :0.9028\n",
      "Model acceurary score with criterion gini index : 0.8894\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Training-set acurecy score :{0:0.4f}\".format(\n",
    "        accuracy_score(Y_train, y_pred_train_gini)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Model acceurary score with criterion gini index : {0:0.4f}\".format(\n",
    "        accuracy_score(Y_test, y_pred_gini)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'x[19] <= 5.5\\ngini = 0.2\\nsamples = 28831\\nvalue = [25580, 3251]'),\n",
       " Text(0.25, 0.625, 'x[10] <= 709.5\\ngini = 0.128\\nsamples = 25455\\nvalue = [23700, 1755]'),\n",
       " Text(0.375, 0.75, 'True  '),\n",
       " Text(0.125, 0.375, 'x[18] <= 67.5\\ngini = 0.079\\nsamples = 23355\\nvalue = [22388, 967]'),\n",
       " Text(0.0625, 0.125, 'gini = 0.061\\nsamples = 22304\\nvalue = [21597, 707]'),\n",
       " Text(0.1875, 0.125, 'gini = 0.372\\nsamples = 1051\\nvalue = [791, 260]'),\n",
       " Text(0.375, 0.375, 'x[10] <= 1046.5\\ngini = 0.469\\nsamples = 2100\\nvalue = [1312, 788]'),\n",
       " Text(0.3125, 0.125, 'gini = 0.431\\nsamples = 1481\\nvalue = [1016.0, 465.0]'),\n",
       " Text(0.4375, 0.125, 'gini = 0.499\\nsamples = 619\\nvalue = [296, 323]'),\n",
       " Text(0.75, 0.625, 'x[12] <= 1.5\\ngini = 0.494\\nsamples = 3376\\nvalue = [1880, 1496]'),\n",
       " Text(0.625, 0.75, '  False'),\n",
       " Text(0.625, 0.375, 'x[10] <= 536.5\\ngini = 0.459\\nsamples = 2543\\nvalue = [1636.0, 907.0]'),\n",
       " Text(0.5625, 0.125, 'gini = 0.427\\nsamples = 2112\\nvalue = [1459, 653]'),\n",
       " Text(0.6875, 0.125, 'gini = 0.484\\nsamples = 431\\nvalue = [177.0, 254.0]'),\n",
       " Text(0.875, 0.375, 'x[10] <= 347.5\\ngini = 0.414\\nsamples = 833\\nvalue = [244.0, 589.0]'),\n",
       " Text(0.8125, 0.125, 'gini = 0.462\\nsamples = 480\\nvalue = [174, 306]'),\n",
       " Text(0.9375, 0.125, 'gini = 0.318\\nsamples = 353\\nvalue = [70, 283]')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(clf_gini.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.1.0 (20240811.2233)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1194pt\" height=\"447pt\"\n",
       " viewBox=\"0.00 0.00 1193.75 447.25\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 443.25)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-443.25 1189.75,-443.25 1189.75,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#e89152\" stroke=\"black\" d=\"M666.75,-439.25C666.75,-439.25 544.25,-439.25 544.25,-439.25 538.25,-439.25 532.25,-433.25 532.25,-427.25 532.25,-427.25 532.25,-364.5 532.25,-364.5 532.25,-358.5 538.25,-352.5 544.25,-352.5 544.25,-352.5 666.75,-352.5 666.75,-352.5 672.75,-352.5 678.75,-358.5 678.75,-364.5 678.75,-364.5 678.75,-427.25 678.75,-427.25 678.75,-433.25 672.75,-439.25 666.75,-439.25\"/>\n",
       "<text text-anchor=\"start\" x=\"551.5\" y=\"-421.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">nr.employed ≤ 5.5</text>\n",
       "<text text-anchor=\"start\" x=\"577.75\" y=\"-406.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.2</text>\n",
       "<text text-anchor=\"start\" x=\"553.38\" y=\"-390.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28831</text>\n",
       "<text text-anchor=\"start\" x=\"540.25\" y=\"-374.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [25580, 3251]</text>\n",
       "<text text-anchor=\"start\" x=\"574.75\" y=\"-358.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#e78a48\" stroke=\"black\" d=\"M515.75,-316.5C515.75,-316.5 393.25,-316.5 393.25,-316.5 387.25,-316.5 381.25,-310.5 381.25,-304.5 381.25,-304.5 381.25,-241.75 381.25,-241.75 381.25,-235.75 387.25,-229.75 393.25,-229.75 393.25,-229.75 515.75,-229.75 515.75,-229.75 521.75,-229.75 527.75,-235.75 527.75,-241.75 527.75,-241.75 527.75,-304.5 527.75,-304.5 527.75,-310.5 521.75,-316.5 515.75,-316.5\"/>\n",
       "<text text-anchor=\"start\" x=\"405.75\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 709.5</text>\n",
       "<text text-anchor=\"start\" x=\"419.25\" y=\"-283.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.128</text>\n",
       "<text text-anchor=\"start\" x=\"402.38\" y=\"-267.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 25455</text>\n",
       "<text text-anchor=\"start\" x=\"389.25\" y=\"-251.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [23700, 1755]</text>\n",
       "<text text-anchor=\"start\" x=\"423.75\" y=\"-236.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M552.26,-352.3C540.83,-343.16 528.64,-333.42 516.9,-324.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"519.2,-321.38 509.2,-317.87 514.83,-326.85 519.2,-321.38\"/>\n",
       "<text text-anchor=\"middle\" x=\"510.79\" y=\"-336.34\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"#fae5d7\" stroke=\"black\" d=\"M808,-316.5C808,-316.5 693,-316.5 693,-316.5 687,-316.5 681,-310.5 681,-304.5 681,-304.5 681,-241.75 681,-241.75 681,-235.75 687,-229.75 693,-229.75 693,-229.75 808,-229.75 808,-229.75 814,-229.75 820,-235.75 820,-241.75 820,-241.75 820,-304.5 820,-304.5 820,-310.5 814,-316.5 808,-316.5\"/>\n",
       "<text text-anchor=\"start\" x=\"715.62\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">pdays ≤ 1.5</text>\n",
       "<text text-anchor=\"start\" x=\"715.25\" y=\"-283.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.494</text>\n",
       "<text text-anchor=\"start\" x=\"702.12\" y=\"-267.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3376</text>\n",
       "<text text-anchor=\"start\" x=\"689\" y=\"-251.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1880, 1496]</text>\n",
       "<text text-anchor=\"start\" x=\"719.75\" y=\"-236.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>0&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M656.62,-352.3C667.67,-343.1 679.44,-333.3 690.78,-323.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"692.74,-326.78 698.19,-317.69 688.26,-321.4 692.74,-326.78\"/>\n",
       "<text text-anchor=\"middle\" x=\"697.08\" y=\"-336.19\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#e68642\" stroke=\"black\" d=\"M277,-193.75C277,-193.75 162,-193.75 162,-193.75 156,-193.75 150,-187.75 150,-181.75 150,-181.75 150,-119 150,-119 150,-113 156,-107 162,-107 162,-107 277,-107 277,-107 283,-107 289,-113 289,-119 289,-119 289,-181.75 289,-181.75 289,-187.75 283,-193.75 277,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"168.12\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">euribor3m ≤ 67.5</text>\n",
       "<text text-anchor=\"start\" x=\"184.25\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.079</text>\n",
       "<text text-anchor=\"start\" x=\"167.38\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 23355</text>\n",
       "<text text-anchor=\"start\" x=\"158\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [22388, 967]</text>\n",
       "<text text-anchor=\"start\" x=\"188.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M380.79,-234.25C354.89,-220.94 325.68,-205.93 299.3,-192.38\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"301.18,-189.41 290.69,-187.95 297.98,-195.64 301.18,-189.41\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#f5cdb0\" stroke=\"black\" d=\"M508.25,-193.75C508.25,-193.75 400.75,-193.75 400.75,-193.75 394.75,-193.75 388.75,-187.75 388.75,-181.75 388.75,-181.75 388.75,-119 388.75,-119 388.75,-113 394.75,-107 400.75,-107 400.75,-107 508.25,-107 508.25,-107 514.25,-107 520.25,-113 520.25,-119 520.25,-119 520.25,-181.75 520.25,-181.75 520.25,-187.75 514.25,-193.75 508.25,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"402\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 1046.5</text>\n",
       "<text text-anchor=\"start\" x=\"419.25\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.469</text>\n",
       "<text text-anchor=\"start\" x=\"406.12\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2100</text>\n",
       "<text text-anchor=\"start\" x=\"396.75\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1312, 788]</text>\n",
       "<text text-anchor=\"start\" x=\"423.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>1&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M454.5,-229.55C454.5,-221.71 454.5,-213.42 454.5,-205.29\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"458,-205.48 454.5,-195.48 451,-205.48 458,-205.48\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#e6853f\" stroke=\"black\" d=\"M127,-71C127,-71 12,-71 12,-71 6,-71 0,-65 0,-59 0,-59 0,-12 0,-12 0,-6 6,0 12,0 12,0 127,0 127,0 133,0 139,-6 139,-12 139,-12 139,-59 139,-59 139,-65 133,-71 127,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"34.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.061</text>\n",
       "<text text-anchor=\"start\" x=\"17.38\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 22304</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [21597, 707]</text>\n",
       "<text text-anchor=\"start\" x=\"38.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>2&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M162.79,-106.7C150.24,-97.26 136.96,-87.27 124.48,-77.87\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"126.86,-75.28 116.77,-72.07 122.65,-80.88 126.86,-75.28\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#eeaa7a\" stroke=\"black\" d=\"M269.5,-71C269.5,-71 169.5,-71 169.5,-71 163.5,-71 157.5,-65 157.5,-59 157.5,-59 157.5,-12 157.5,-12 157.5,-6 163.5,0 169.5,0 169.5,0 269.5,0 269.5,0 275.5,0 281.5,-6 281.5,-12 281.5,-12 281.5,-59 281.5,-59 281.5,-65 275.5,-71 269.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"184.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.372</text>\n",
       "<text text-anchor=\"start\" x=\"171.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1051</text>\n",
       "<text text-anchor=\"start\" x=\"165.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [791, 260]</text>\n",
       "<text text-anchor=\"start\" x=\"188.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>2&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M219.5,-106.7C219.5,-98.82 219.5,-90.55 219.5,-82.57\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"223,-82.67 219.5,-72.67 216,-82.67 223,-82.67\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#f1bb94\" stroke=\"black\" d=\"M441.5,-71C441.5,-71 311.5,-71 311.5,-71 305.5,-71 299.5,-65 299.5,-59 299.5,-59 299.5,-12 299.5,-12 299.5,-6 305.5,0 311.5,0 311.5,0 441.5,0 441.5,0 447.5,0 453.5,-6 453.5,-12 453.5,-12 453.5,-59 453.5,-59 453.5,-65 447.5,-71 441.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"341.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.431</text>\n",
       "<text text-anchor=\"start\" x=\"328.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1481</text>\n",
       "<text text-anchor=\"start\" x=\"307.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1016.0, 465.0]</text>\n",
       "<text text-anchor=\"start\" x=\"345.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M425.01,-106.7C419.12,-98.17 412.92,-89.2 406.99,-80.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"409.87,-78.64 401.31,-72.4 404.12,-82.62 409.87,-78.64\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#eef7fd\" stroke=\"black\" d=\"M583.5,-71C583.5,-71 483.5,-71 483.5,-71 477.5,-71 471.5,-65 471.5,-59 471.5,-59 471.5,-12 471.5,-12 471.5,-6 477.5,0 483.5,0 483.5,0 583.5,0 583.5,0 589.5,0 595.5,-6 595.5,-12 595.5,-12 595.5,-59 595.5,-59 595.5,-65 589.5,-71 583.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"498.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.499</text>\n",
       "<text text-anchor=\"start\" x=\"488.88\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 619</text>\n",
       "<text text-anchor=\"start\" x=\"479.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [296, 323]</text>\n",
       "<text text-anchor=\"start\" x=\"502.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>5&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M484.37,-106.7C490.4,-98.08 496.75,-89.01 502.81,-80.35\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"505.51,-82.6 508.38,-72.4 499.77,-78.58 505.51,-82.6\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<path fill=\"#f3c7a7\" stroke=\"black\" d=\"M815.5,-193.75C815.5,-193.75 685.5,-193.75 685.5,-193.75 679.5,-193.75 673.5,-187.75 673.5,-181.75 673.5,-181.75 673.5,-119 673.5,-119 673.5,-113 679.5,-107 685.5,-107 685.5,-107 815.5,-107 815.5,-107 821.5,-107 827.5,-113 827.5,-119 827.5,-119 827.5,-181.75 827.5,-181.75 827.5,-187.75 821.5,-193.75 815.5,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"701.75\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 536.5</text>\n",
       "<text text-anchor=\"start\" x=\"715.25\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.459</text>\n",
       "<text text-anchor=\"start\" x=\"702.12\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2543</text>\n",
       "<text text-anchor=\"start\" x=\"681.5\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1636.0, 907.0]</text>\n",
       "<text text-anchor=\"start\" x=\"719.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M750.5,-229.55C750.5,-221.71 750.5,-213.42 750.5,-205.29\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"754,-205.48 750.5,-195.48 747,-205.48 754,-205.48\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"#8bc6f0\" stroke=\"black\" d=\"M1050.75,-193.75C1050.75,-193.75 928.25,-193.75 928.25,-193.75 922.25,-193.75 916.25,-187.75 916.25,-181.75 916.25,-181.75 916.25,-119 916.25,-119 916.25,-113 922.25,-107 928.25,-107 928.25,-107 1050.75,-107 1050.75,-107 1056.75,-107 1062.75,-113 1062.75,-119 1062.75,-119 1062.75,-181.75 1062.75,-181.75 1062.75,-187.75 1056.75,-193.75 1050.75,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"940.75\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 347.5</text>\n",
       "<text text-anchor=\"start\" x=\"954.25\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.414</text>\n",
       "<text text-anchor=\"start\" x=\"944.88\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 833</text>\n",
       "<text text-anchor=\"start\" x=\"924.25\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [244.0, 589.0]</text>\n",
       "<text text-anchor=\"start\" x=\"958.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M820.27,-236.87C847.02,-223.36 877.82,-207.8 905.75,-193.69\"/>\n",
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       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#f1b992\" stroke=\"black\" d=\"M733.25,-71C733.25,-71 625.75,-71 625.75,-71 619.75,-71 613.75,-65 613.75,-59 613.75,-59 613.75,-12 613.75,-12 613.75,-6 619.75,0 625.75,0 625.75,0 733.25,0 733.25,0 739.25,0 745.25,-6 745.25,-12 745.25,-12 745.25,-59 745.25,-59 745.25,-65 739.25,-71 733.25,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"644.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.427</text>\n",
       "<text text-anchor=\"start\" x=\"631.5\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2112</text>\n",
       "<text text-anchor=\"start\" x=\"621.75\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1459, 653]</text>\n",
       "<text text-anchor=\"start\" x=\"648.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
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       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M723.66,-106.7C718.35,-98.27 712.77,-89.39 707.43,-80.9\"/>\n",
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       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"#c3e1f7\" stroke=\"black\" d=\"M897.75,-71C897.75,-71 775.25,-71 775.25,-71 769.25,-71 763.25,-65 763.25,-59 763.25,-59 763.25,-12 763.25,-12 763.25,-6 769.25,0 775.25,0 775.25,0 897.75,0 897.75,0 903.75,0 909.75,-6 909.75,-12 909.75,-12 909.75,-59 909.75,-59 909.75,-65 903.75,-71 897.75,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"801.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.484</text>\n",
       "<text text-anchor=\"start\" x=\"791.88\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 431</text>\n",
       "<text text-anchor=\"start\" x=\"771.25\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [177.0, 254.0]</text>\n",
       "<text text-anchor=\"start\" x=\"805.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>9&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M783.01,-106.7C789.65,-97.99 796.64,-88.81 803.3,-80.07\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"805.9,-82.44 809.18,-72.36 800.33,-78.19 805.9,-82.44\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"#aad5f4\" stroke=\"black\" d=\"M1039.5,-71C1039.5,-71 939.5,-71 939.5,-71 933.5,-71 927.5,-65 927.5,-59 927.5,-59 927.5,-12 927.5,-12 927.5,-6 933.5,0 939.5,0 939.5,0 1039.5,0 1039.5,0 1045.5,0 1051.5,-6 1051.5,-12 1051.5,-12 1051.5,-59 1051.5,-59 1051.5,-65 1045.5,-71 1039.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"954.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.462</text>\n",
       "<text text-anchor=\"start\" x=\"944.88\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 480</text>\n",
       "<text text-anchor=\"start\" x=\"935.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [174, 306]</text>\n",
       "<text text-anchor=\"start\" x=\"958.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M989.5,-106.7C989.5,-98.82 989.5,-90.55 989.5,-82.57\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"993,-82.67 989.5,-72.67 986,-82.67 993,-82.67\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#6ab5eb\" stroke=\"black\" d=\"M1173.75,-71C1173.75,-71 1081.25,-71 1081.25,-71 1075.25,-71 1069.25,-65 1069.25,-59 1069.25,-59 1069.25,-12 1069.25,-12 1069.25,-6 1075.25,0 1081.25,0 1081.25,0 1173.75,0 1173.75,0 1179.75,0 1185.75,-6 1185.75,-12 1185.75,-12 1185.75,-59 1185.75,-59 1185.75,-65 1179.75,-71 1173.75,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"1092.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.318</text>\n",
       "<text text-anchor=\"start\" x=\"1082.88\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 353</text>\n",
       "<text text-anchor=\"start\" x=\"1077.25\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [70, 283]</text>\n",
       "<text text-anchor=\"start\" x=\"1096.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>12&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1041.67,-106.7C1052.99,-97.44 1064.96,-87.65 1076.25,-78.42\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1078.43,-81.16 1083.96,-72.12 1074,-75.74 1078.43,-81.16\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x184b3102810>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(\n",
    "    clf_gini,\n",
    "    out_file=None,\n",
    "    feature_names=X_train.columns,  # type: ignore\n",
    "    class_names=Y_train,\n",
    "    filled=True,\n",
    "    rounded=True,\n",
    "    special_characters=True,\n",
    ")\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Decision Tree Classifier with criterion entropy*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_en = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3, random_state=0)\n",
    "clf_en.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_en = clf_en.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with criterian entropy :0.8953\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(\n",
    "    \"Model accuracy score with criterian entropy :{0:0.4f}\".format(\n",
    "        accuracy_score(Y_test, y_pred_en)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'no', 'no', ..., 'yes', 'no', 'no'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train_en = clf_en.predict(X_train)\n",
    "\n",
    "y_pred_train_en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 0.8992\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Training-set accuracy score: {0:0.4f}\".format(\n",
    "        accuracy_score(Y_train, y_pred_train_en)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.8992\n",
      "Test set score: 0.8953\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set score: {:.4f}\".format(clf_en.score(X_train, Y_train)))\n",
    "\n",
    "print(\"Test set score: {:.4f}\".format(clf_en.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'x[19] <= 5.5\\nentropy = 0.508\\nsamples = 28831\\nvalue = [25580, 3251]'),\n",
       " Text(0.25, 0.625, 'x[10] <= 603.5\\nentropy = 0.362\\nsamples = 25455\\nvalue = [23700, 1755]'),\n",
       " Text(0.375, 0.75, 'True  '),\n",
       " Text(0.125, 0.375, 'x[18] <= 67.5\\nentropy = 0.223\\nsamples = 22288\\nvalue = [21488, 800]'),\n",
       " Text(0.0625, 0.125, 'entropy = 0.176\\nsamples = 21276\\nvalue = [20715, 561]'),\n",
       " Text(0.1875, 0.125, 'entropy = 0.789\\nsamples = 1012\\nvalue = [773, 239]'),\n",
       " Text(0.375, 0.375, 'x[10] <= 901.5\\nentropy = 0.883\\nsamples = 3167\\nvalue = [2212.0, 955.0]'),\n",
       " Text(0.3125, 0.125, 'entropy = 0.755\\nsamples = 2069\\nvalue = [1620, 449]'),\n",
       " Text(0.4375, 0.125, 'entropy = 0.996\\nsamples = 1098\\nvalue = [592, 506]'),\n",
       " Text(0.75, 0.625, 'x[12] <= 1.5\\nentropy = 0.991\\nsamples = 3376\\nvalue = [1880, 1496]'),\n",
       " Text(0.625, 0.75, '  False'),\n",
       " Text(0.625, 0.375, 'x[10] <= 333.5\\nentropy = 0.94\\nsamples = 2543\\nvalue = [1636.0, 907.0]'),\n",
       " Text(0.5625, 0.125, 'entropy = 0.847\\nsamples = 1557\\nvalue = [1131, 426]'),\n",
       " Text(0.6875, 0.125, 'entropy = 1.0\\nsamples = 986\\nvalue = [505, 481]'),\n",
       " Text(0.875, 0.375, 'x[10] <= 403.0\\nentropy = 0.872\\nsamples = 833\\nvalue = [244.0, 589.0]'),\n",
       " Text(0.8125, 0.125, 'entropy = 0.938\\nsamples = 528\\nvalue = [187, 341]'),\n",
       " Text(0.9375, 0.125, 'entropy = 0.695\\nsamples = 305\\nvalue = [57, 248]')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(clf_en.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1148pt\" height=\"447pt\"\n",
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       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 443.25)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-443.25 1143.75,-443.25 1143.75,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
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       "<text text-anchor=\"start\" x=\"506.38\" y=\"-390.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28831</text>\n",
       "<text text-anchor=\"start\" x=\"493.25\" y=\"-374.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [25580, 3251]</text>\n",
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       "</g>\n",
       "<!-- 1 -->\n",
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       "<title>1</title>\n",
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       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
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       "<title>8</title>\n",
       "<path fill=\"#fae5d7\" stroke=\"black\" d=\"M752,-316.5C752,-316.5 637,-316.5 637,-316.5 631,-316.5 625,-310.5 625,-304.5 625,-304.5 625,-241.75 625,-241.75 625,-235.75 631,-229.75 637,-229.75 637,-229.75 752,-229.75 752,-229.75 758,-229.75 764,-235.75 764,-241.75 764,-241.75 764,-304.5 764,-304.5 764,-310.5 758,-316.5 752,-316.5\"/>\n",
       "<text text-anchor=\"start\" x=\"659.62\" y=\"-299.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">pdays ≤ 1.5</text>\n",
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       "<!-- 0&#45;&gt;8 -->\n",
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       "<title>0&#45;&gt;8</title>\n",
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       "<polygon fill=\"black\" stroke=\"black\" points=\"640.24,-326.98 645.39,-317.72 635.59,-321.75 640.24,-326.98\"/>\n",
       "<text text-anchor=\"middle\" x=\"645.04\" y=\"-336.25\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
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       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#e68640\" stroke=\"black\" d=\"M277,-193.75C277,-193.75 162,-193.75 162,-193.75 156,-193.75 150,-187.75 150,-181.75 150,-181.75 150,-119 150,-119 150,-113 156,-107 162,-107 162,-107 277,-107 277,-107 283,-107 289,-113 289,-119 289,-119 289,-181.75 289,-181.75 289,-187.75 283,-193.75 277,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"168.12\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">euribor3m ≤ 67.5</text>\n",
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       "<!-- 1&#45;&gt;2 -->\n",
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       "<title>1&#45;&gt;2</title>\n",
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       "<title>5</title>\n",
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       "<text text-anchor=\"start\" x=\"423.75\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 901.5</text>\n",
       "<text text-anchor=\"start\" x=\"425.25\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.883</text>\n",
       "<text text-anchor=\"start\" x=\"424.12\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3167</text>\n",
       "<text text-anchor=\"start\" x=\"403.5\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2212.0, 955.0]</text>\n",
       "<text text-anchor=\"start\" x=\"441.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
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       "<!-- 1&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>1&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M472.5,-229.55C472.5,-221.71 472.5,-213.42 472.5,-205.29\"/>\n",
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       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#e6843e\" stroke=\"black\" d=\"M127,-71C127,-71 12,-71 12,-71 6,-71 0,-65 0,-59 0,-59 0,-12 0,-12 0,-6 6,0 12,0 12,0 127,0 127,0 133,0 139,-6 139,-12 139,-12 139,-59 139,-59 139,-65 133,-71 127,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"22.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.176</text>\n",
       "<text text-anchor=\"start\" x=\"17.38\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21276</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [20715, 561]</text>\n",
       "<text text-anchor=\"start\" x=\"38.75\" y=\"-6.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
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       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>2&#45;&gt;3</title>\n",
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       "<title>4</title>\n",
       "<path fill=\"#eda876\" stroke=\"black\" d=\"M269.5,-71C269.5,-71 169.5,-71 169.5,-71 163.5,-71 157.5,-65 157.5,-59 157.5,-59 157.5,-12 157.5,-12 157.5,-6 163.5,0 169.5,0 169.5,0 269.5,0 269.5,0 275.5,0 281.5,-6 281.5,-12 281.5,-12 281.5,-59 281.5,-59 281.5,-65 275.5,-71 269.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"172.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.789</text>\n",
       "<text text-anchor=\"start\" x=\"171.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1012</text>\n",
       "<text text-anchor=\"start\" x=\"165.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [773, 239]</text>\n",
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       "<!-- 2&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>2&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M219.5,-106.7C219.5,-98.82 219.5,-90.55 219.5,-82.57\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"223,-82.67 219.5,-72.67 216,-82.67 223,-82.67\"/>\n",
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       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
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       "<text text-anchor=\"start\" x=\"318.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.755</text>\n",
       "<text text-anchor=\"start\" x=\"317.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2069</text>\n",
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       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M432.05,-106.7C423.53,-97.72 414.54,-88.23 406.02,-79.25\"/>\n",
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       "<title>7</title>\n",
       "<path fill=\"#fbede2\" stroke=\"black\" d=\"M561.5,-71C561.5,-71 461.5,-71 461.5,-71 455.5,-71 449.5,-65 449.5,-59 449.5,-59 449.5,-12 449.5,-12 449.5,-6 455.5,0 461.5,0 461.5,0 561.5,0 561.5,0 567.5,0 573.5,-6 573.5,-12 573.5,-12 573.5,-59 573.5,-59 573.5,-65 567.5,-71 561.5,-71\"/>\n",
       "<text text-anchor=\"start\" x=\"464.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.996</text>\n",
       "<text text-anchor=\"start\" x=\"463.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1098</text>\n",
       "<text text-anchor=\"start\" x=\"457.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [592, 506]</text>\n",
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       "<!-- 5&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>5&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M487.24,-106.7C490.03,-98.63 492.96,-90.16 495.78,-82.01\"/>\n",
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       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<path fill=\"#f3c7a7\" stroke=\"black\" d=\"M759.5,-193.75C759.5,-193.75 629.5,-193.75 629.5,-193.75 623.5,-193.75 617.5,-187.75 617.5,-181.75 617.5,-181.75 617.5,-119 617.5,-119 617.5,-113 623.5,-107 629.5,-107 629.5,-107 759.5,-107 759.5,-107 765.5,-107 771.5,-113 771.5,-119 771.5,-119 771.5,-181.75 771.5,-181.75 771.5,-187.75 765.5,-193.75 759.5,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"645.75\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 333.5</text>\n",
       "<text text-anchor=\"start\" x=\"651\" y=\"-160.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.94</text>\n",
       "<text text-anchor=\"start\" x=\"646.12\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2543</text>\n",
       "<text text-anchor=\"start\" x=\"625.5\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1636.0, 907.0]</text>\n",
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       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M694.5,-229.55C694.5,-221.71 694.5,-213.42 694.5,-205.29\"/>\n",
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       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"#8bc6f0\" stroke=\"black\" d=\"M1004.75,-193.75C1004.75,-193.75 882.25,-193.75 882.25,-193.75 876.25,-193.75 870.25,-187.75 870.25,-181.75 870.25,-181.75 870.25,-119 870.25,-119 870.25,-113 876.25,-107 882.25,-107 882.25,-107 1004.75,-107 1004.75,-107 1010.75,-107 1016.75,-113 1016.75,-119 1016.75,-119 1016.75,-181.75 1016.75,-181.75 1016.75,-187.75 1010.75,-193.75 1004.75,-193.75\"/>\n",
       "<text text-anchor=\"start\" x=\"894.75\" y=\"-176.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">duration ≤ 403.0</text>\n",
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       "<text text-anchor=\"start\" x=\"898.88\" y=\"-144.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 833</text>\n",
       "<text text-anchor=\"start\" x=\"878.25\" y=\"-129.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [244.0, 589.0]</text>\n",
       "<text text-anchor=\"start\" x=\"912.75\" y=\"-113.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = no</text>\n",
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       "<!-- 8&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M764.19,-238.33C793.79,-223.97 828.56,-207.12 859.56,-192.08\"/>\n",
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       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
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       "<text text-anchor=\"start\" x=\"609.25\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.847</text>\n",
       "<text text-anchor=\"start\" x=\"608.12\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1557</text>\n",
       "<text text-anchor=\"start\" x=\"599.12\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1131, 426]</text>\n",
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       "<!-- 9&#45;&gt;10 -->\n",
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       "<title>9&#45;&gt;10</title>\n",
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       "<title>11</title>\n",
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       "<text text-anchor=\"start\" x=\"761.75\" y=\"-53.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\n",
       "<text text-anchor=\"start\" x=\"756.88\" y=\"-37.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 986</text>\n",
       "<text text-anchor=\"start\" x=\"747.5\" y=\"-22.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [505, 481]</text>\n",
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       "<!-- 9&#45;&gt;11 -->\n",
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       "<title>13</title>\n",
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       "<title>14</title>\n",
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       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>12&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M995.67,-106.7C1006.99,-97.44 1018.96,-87.65 1030.25,-78.42\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1032.43,-81.16 1037.96,-72.12 1028,-75.74 1032.43,-81.16\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x184b316b5c0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(\n",
    "    clf_en,\n",
    "    out_file=None,\n",
    "    feature_names=X_train.columns,  # type: ignore\n",
    "    class_names=Y_train,\n",
    "    filled=True,\n",
    "    rounded=True,\n",
    "    special_characters=True,\n",
    ")\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  *Report*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.90      0.99      0.94     10968\n",
      "         yes       0.70      0.12      0.21      1389\n",
      "\n",
      "    accuracy                           0.90     12357\n",
      "   macro avg       0.80      0.56      0.58     12357\n",
      "weighted avg       0.88      0.90      0.86     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(Y_test, y_pred_en))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *1.In this project, I build a Decision-Tree Classifier model to predict whether a customer will purchase a product or service. I build two models, one with criterion gini index and another one with criterion entropy. The model yields a very good performance as indicated by the model accuracy in both the cases which was found to be 0.9028.*\n",
    "##### *2.In the model with criterion gini index, the training-set accuracy score is 0.9028 while the test-set accuracy to be 0.8894. These two values are quite comparable. So, there is no sign of overfitting.*\n",
    "##### *3.Similarly, in the model with criterion entropy, the training-set accuracy score is 0.8992 while the test-set accuracy to be 0.8853.We get the same values as in the case with criterion gini. So, there is no sign of overfitting.*\n",
    "##### *4.The confusion matrix and classification report yields very good model performance.*"
   ]
  }
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