{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n",
      "['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y']\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"../coal-price-data/banking.csv\", header=0)\n",
    "data = data.dropna()\n",
    "print(data.shape)\n",
    "print(list(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>...</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>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'unknown', 'university.degree', 'high.school',\n",
       "       'basic.9y', 'professional.course', 'basic.6y', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"education\"] = np.where(\n",
    "    data[\"education\"] == \"basic.9y\", \"Basic\", data[\"education\"]\n",
    ")\n",
    "data[\"education\"] = np.where(\n",
    "    data[\"education\"] == \"basic.6y\", \"Basic\", data[\"education\"]\n",
    ")\n",
    "data[\"education\"] = np.where(\n",
    "    data[\"education\"] == \"basic.4y\", \"Basic\", data[\"education\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic', 'unknown', 'university.degree', 'high.school',\n",
       "       'professional.course', 'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "0    36548\n",
       "1     4640\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/fj/ycln97zn6b1ckstg6ksdmgl80000gp/T/ipykernel_62188/2225886973.py:1: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(x=\"y\", data=data, palette=\"hls\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"y\", data=data, palette=\"hls\")\n",
    "plt.show()\n",
    "plt.savefig(\"count_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no subscription is 88.73458288821988\n",
      "percentage of subscription 11.265417111780131\n"
     ]
    }
   ],
   "source": [
    "count_no_sub = len(data[data[\"y\"] == 0])\n",
    "count_sub = len(data[data[\"y\"] == 1])\n",
    "pct_of_no_sub = count_no_sub / (count_no_sub + count_sub)\n",
    "print(\"percentage of no subscription is\", pct_of_no_sub * 100)\n",
    "pct_of_sub = count_sub / (count_no_sub + count_sub)\n",
    "print(\"percentage of subscription\", pct_of_sub * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.911185</td>\n",
       "      <td>220.844807</td>\n",
       "      <td>2.633085</td>\n",
       "      <td>984.113878</td>\n",
       "      <td>0.132374</td>\n",
       "      <td>0.248875</td>\n",
       "      <td>93.603757</td>\n",
       "      <td>-40.593097</td>\n",
       "      <td>3.811491</td>\n",
       "      <td>5176.166600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.913147</td>\n",
       "      <td>553.191164</td>\n",
       "      <td>2.051724</td>\n",
       "      <td>792.035560</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>-1.233448</td>\n",
       "      <td>93.354386</td>\n",
       "      <td>-39.789784</td>\n",
       "      <td>2.123135</td>\n",
       "      <td>5095.115991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "y                                                                        \n",
       "0  39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "1  40.913147  553.191164  2.051724  792.035560  0.492672     -1.233448   \n",
       "\n",
       "   cons_price_idx  cons_conf_idx  euribor3m  nr_employed  \n",
       "y                                                         \n",
       "0       93.603757     -40.593097   3.811491  5176.166600  \n",
       "1       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"y\").mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
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       "      <th>emp_var_rate</th>\n",
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       "      <th>y</th>\n",
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       "    <tr>\n",
       "      <th>job</th>\n",
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       "  <tbody>\n",
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       "      <th>admin.</th>\n",
       "      <td>38.187296</td>\n",
       "      <td>254.312128</td>\n",
       "      <td>2.623489</td>\n",
       "      <td>954.319229</td>\n",
       "      <td>0.189023</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>93.534054</td>\n",
       "      <td>-40.245433</td>\n",
       "      <td>3.550274</td>\n",
       "      <td>5164.125350</td>\n",
       "      <td>0.129726</td>\n",
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       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>39.555760</td>\n",
       "      <td>264.542360</td>\n",
       "      <td>2.558461</td>\n",
       "      <td>985.160363</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.248995</td>\n",
       "      <td>93.656656</td>\n",
       "      <td>-41.375816</td>\n",
       "      <td>3.771996</td>\n",
       "      <td>5175.615150</td>\n",
       "      <td>0.068943</td>\n",
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       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>41.723214</td>\n",
       "      <td>263.267857</td>\n",
       "      <td>2.535714</td>\n",
       "      <td>981.267170</td>\n",
       "      <td>0.138736</td>\n",
       "      <td>0.158723</td>\n",
       "      <td>93.605372</td>\n",
       "      <td>-41.283654</td>\n",
       "      <td>3.791120</td>\n",
       "      <td>5176.313530</td>\n",
       "      <td>0.085165</td>\n",
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       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>250.454717</td>\n",
       "      <td>2.639623</td>\n",
       "      <td>960.579245</td>\n",
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       "      <td>0.433396</td>\n",
       "      <td>93.676576</td>\n",
       "      <td>-39.495283</td>\n",
       "      <td>4.009645</td>\n",
       "      <td>5179.529623</td>\n",
       "      <td>0.100000</td>\n",
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       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>42.362859</td>\n",
       "      <td>257.058140</td>\n",
       "      <td>2.476060</td>\n",
       "      <td>962.647059</td>\n",
       "      <td>0.185021</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>93.522755</td>\n",
       "      <td>-40.489466</td>\n",
       "      <td>3.611316</td>\n",
       "      <td>5166.650513</td>\n",
       "      <td>0.112175</td>\n",
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       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>62.027326</td>\n",
       "      <td>273.712209</td>\n",
       "      <td>2.476744</td>\n",
       "      <td>897.936047</td>\n",
       "      <td>0.327326</td>\n",
       "      <td>-0.698314</td>\n",
       "      <td>93.430786</td>\n",
       "      <td>-38.573081</td>\n",
       "      <td>2.770066</td>\n",
       "      <td>5122.262151</td>\n",
       "      <td>0.252326</td>\n",
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       "      <th>self-employed</th>\n",
       "      <td>39.949331</td>\n",
       "      <td>264.142153</td>\n",
       "      <td>2.660802</td>\n",
       "      <td>976.621393</td>\n",
       "      <td>0.143561</td>\n",
       "      <td>0.094159</td>\n",
       "      <td>93.559982</td>\n",
       "      <td>-40.488107</td>\n",
       "      <td>3.689376</td>\n",
       "      <td>5170.674384</td>\n",
       "      <td>0.104856</td>\n",
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       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>37.926430</td>\n",
       "      <td>258.398085</td>\n",
       "      <td>2.587805</td>\n",
       "      <td>979.974049</td>\n",
       "      <td>0.154951</td>\n",
       "      <td>0.175359</td>\n",
       "      <td>93.634659</td>\n",
       "      <td>-41.290048</td>\n",
       "      <td>3.699187</td>\n",
       "      <td>5171.600126</td>\n",
       "      <td>0.081381</td>\n",
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       "      <th>student</th>\n",
       "      <td>25.894857</td>\n",
       "      <td>283.683429</td>\n",
       "      <td>2.104000</td>\n",
       "      <td>840.217143</td>\n",
       "      <td>0.524571</td>\n",
       "      <td>-1.408000</td>\n",
       "      <td>93.331613</td>\n",
       "      <td>-40.187543</td>\n",
       "      <td>1.884224</td>\n",
       "      <td>5085.939086</td>\n",
       "      <td>0.314286</td>\n",
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       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>38.507638</td>\n",
       "      <td>250.232241</td>\n",
       "      <td>2.577339</td>\n",
       "      <td>964.408127</td>\n",
       "      <td>0.153789</td>\n",
       "      <td>0.274566</td>\n",
       "      <td>93.561471</td>\n",
       "      <td>-39.927569</td>\n",
       "      <td>3.820401</td>\n",
       "      <td>5175.648391</td>\n",
       "      <td>0.108260</td>\n",
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       "      <th>unemployed</th>\n",
       "      <td>39.733728</td>\n",
       "      <td>249.451677</td>\n",
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       "      <td>935.316568</td>\n",
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       "      <td>93.563781</td>\n",
       "      <td>-40.007594</td>\n",
       "      <td>3.466583</td>\n",
       "      <td>5157.156509</td>\n",
       "      <td>0.142012</td>\n",
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       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.563636</td>\n",
       "      <td>239.675758</td>\n",
       "      <td>2.648485</td>\n",
       "      <td>938.727273</td>\n",
       "      <td>0.154545</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>93.718942</td>\n",
       "      <td>-38.797879</td>\n",
       "      <td>3.949033</td>\n",
       "      <td>5172.931818</td>\n",
       "      <td>0.112121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    duration  campaign       pdays  previous  \\\n",
       "job                                                                    \n",
       "admin.         38.187296  254.312128  2.623489  954.319229  0.189023   \n",
       "blue-collar    39.555760  264.542360  2.558461  985.160363  0.122542   \n",
       "entrepreneur   41.723214  263.267857  2.535714  981.267170  0.138736   \n",
       "housemaid      45.500000  250.454717  2.639623  960.579245  0.137736   \n",
       "management     42.362859  257.058140  2.476060  962.647059  0.185021   \n",
       "retired        62.027326  273.712209  2.476744  897.936047  0.327326   \n",
       "self-employed  39.949331  264.142153  2.660802  976.621393  0.143561   \n",
       "services       37.926430  258.398085  2.587805  979.974049  0.154951   \n",
       "student        25.894857  283.683429  2.104000  840.217143  0.524571   \n",
       "technician     38.507638  250.232241  2.577339  964.408127  0.153789   \n",
       "unemployed     39.733728  249.451677  2.564103  935.316568  0.199211   \n",
       "unknown        45.563636  239.675758  2.648485  938.727273  0.154545   \n",
       "\n",
       "               emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "job                                                                     \n",
       "admin.             0.015563       93.534054     -40.245433   3.550274   \n",
       "blue-collar        0.248995       93.656656     -41.375816   3.771996   \n",
       "entrepreneur       0.158723       93.605372     -41.283654   3.791120   \n",
       "housemaid          0.433396       93.676576     -39.495283   4.009645   \n",
       "management        -0.012688       93.522755     -40.489466   3.611316   \n",
       "retired           -0.698314       93.430786     -38.573081   2.770066   \n",
       "self-employed      0.094159       93.559982     -40.488107   3.689376   \n",
       "services           0.175359       93.634659     -41.290048   3.699187   \n",
       "student           -1.408000       93.331613     -40.187543   1.884224   \n",
       "technician         0.274566       93.561471     -39.927569   3.820401   \n",
       "unemployed        -0.111736       93.563781     -40.007594   3.466583   \n",
       "unknown            0.357879       93.718942     -38.797879   3.949033   \n",
       "\n",
       "               nr_employed         y  \n",
       "job                                   \n",
       "admin.         5164.125350  0.129726  \n",
       "blue-collar    5175.615150  0.068943  \n",
       "entrepreneur   5176.313530  0.085165  \n",
       "housemaid      5179.529623  0.100000  \n",
       "management     5166.650513  0.112175  \n",
       "retired        5122.262151  0.252326  \n",
       "self-employed  5170.674384  0.104856  \n",
       "services       5171.600126  0.081381  \n",
       "student        5085.939086  0.314286  \n",
       "technician     5175.648391  0.108260  \n",
       "unemployed     5157.156509  0.142012  \n",
       "unknown        5172.931818  0.112121  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"job\").mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>marital</th>\n",
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       "      <th>single</th>\n",
       "      <td>33.158714</td>\n",
       "      <td>261.524378</td>\n",
       "      <td>2.53380</td>\n",
       "      <td>949.909578</td>\n",
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       "      <td>0.150000</td>\n",
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      ],
      "text/plain": [
       "                age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "marital                                                                         \n",
       "divorced  44.899393  253.790330   2.61340  968.639853  0.168690      0.163985   \n",
       "married   42.307165  257.438623   2.57281  967.247673  0.155608      0.183625   \n",
       "single    33.158714  261.524378   2.53380  949.909578  0.211359     -0.167989   \n",
       "unknown   40.275000  312.725000   3.18750  937.100000  0.275000     -0.221250   \n",
       "\n",
       "          cons_price_idx  cons_conf_idx  euribor3m  nr_employed         y  \n",
       "marital                                                                    \n",
       "divorced       93.606563     -40.707069   3.715603  5170.878643  0.103209  \n",
       "married        93.597367     -40.270659   3.745832  5171.848772  0.101573  \n",
       "single         93.517300     -40.918698   3.317447  5155.199265  0.140041  \n",
       "unknown        93.471250     -40.820000   3.313038  5157.393750  0.150000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"marital\").mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>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",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basic</th>\n",
       "      <td>42.163910</td>\n",
       "      <td>263.043874</td>\n",
       "      <td>2.559498</td>\n",
       "      <td>974.877967</td>\n",
       "      <td>0.141053</td>\n",
       "      <td>0.191329</td>\n",
       "      <td>93.639933</td>\n",
       "      <td>-40.927595</td>\n",
       "      <td>3.729654</td>\n",
       "      <td>5172.014113</td>\n",
       "      <td>0.087029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>37.998213</td>\n",
       "      <td>260.886810</td>\n",
       "      <td>2.568576</td>\n",
       "      <td>964.358382</td>\n",
       "      <td>0.185917</td>\n",
       "      <td>0.032937</td>\n",
       "      <td>93.584857</td>\n",
       "      <td>-40.940641</td>\n",
       "      <td>3.556157</td>\n",
       "      <td>5164.994735</td>\n",
       "      <td>0.108355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>276.777778</td>\n",
       "      <td>2.277778</td>\n",
       "      <td>943.833333</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>93.317333</td>\n",
       "      <td>-39.950000</td>\n",
       "      <td>3.516556</td>\n",
       "      <td>5171.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>40.080107</td>\n",
       "      <td>252.533855</td>\n",
       "      <td>2.586115</td>\n",
       "      <td>960.765974</td>\n",
       "      <td>0.163075</td>\n",
       "      <td>0.173012</td>\n",
       "      <td>93.569864</td>\n",
       "      <td>-40.124108</td>\n",
       "      <td>3.710457</td>\n",
       "      <td>5170.155979</td>\n",
       "      <td>0.113485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>38.879191</td>\n",
       "      <td>253.223373</td>\n",
       "      <td>2.563527</td>\n",
       "      <td>951.807692</td>\n",
       "      <td>0.192390</td>\n",
       "      <td>-0.028090</td>\n",
       "      <td>93.493466</td>\n",
       "      <td>-39.975805</td>\n",
       "      <td>3.529663</td>\n",
       "      <td>5163.226298</td>\n",
       "      <td>0.137245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>43.481225</td>\n",
       "      <td>262.390526</td>\n",
       "      <td>2.596187</td>\n",
       "      <td>942.830734</td>\n",
       "      <td>0.226459</td>\n",
       "      <td>0.059099</td>\n",
       "      <td>93.658615</td>\n",
       "      <td>-39.877816</td>\n",
       "      <td>3.571098</td>\n",
       "      <td>5159.549509</td>\n",
       "      <td>0.145003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.163910  263.043874  2.559498  974.877967  0.141053   \n",
       "high.school          37.998213  260.886810  2.568576  964.358382  0.185917   \n",
       "illiterate           48.500000  276.777778  2.277778  943.833333  0.111111   \n",
       "professional.course  40.080107  252.533855  2.586115  960.765974  0.163075   \n",
       "university.degree    38.879191  253.223373  2.563527  951.807692  0.192390   \n",
       "unknown              43.481225  262.390526  2.596187  942.830734  0.226459   \n",
       "\n",
       "                     emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.191329       93.639933     -40.927595   3.729654   \n",
       "high.school              0.032937       93.584857     -40.940641   3.556157   \n",
       "illiterate              -0.133333       93.317333     -39.950000   3.516556   \n",
       "professional.course      0.173012       93.569864     -40.124108   3.710457   \n",
       "university.degree       -0.028090       93.493466     -39.975805   3.529663   \n",
       "unknown                  0.059099       93.658615     -39.877816   3.571098   \n",
       "\n",
       "                     nr_employed         y  \n",
       "education                                   \n",
       "Basic                5172.014113  0.087029  \n",
       "high.school          5164.994735  0.108355  \n",
       "illiterate           5171.777778  0.222222  \n",
       "professional.course  5170.155979  0.113485  \n",
       "university.degree    5163.226298  0.137245  \n",
       "unknown              5159.549509  0.145003  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"education\").mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.crosstab(data.job,data.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Job Title')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('Frequency of Purchase')\n",
    "plt.savefig('purchase_fre_job')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.crosstab(data.marital, data.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.title(\"Stacked Bar Chart of Marital Status vs Purchase\")\n",
    "plt.xlabel(\"Marital Status\")\n",
    "plt.ylabel(\"Proportion of Customers\")\n",
    "plt.savefig(\"mariral_vs_pur_stack\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.crosstab(data.education, data.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.title(\"Stacked Bar Chart of Education vs Purchase\")\n",
    "plt.xlabel(\"Education\")\n",
    "plt.ylabel(\"Proportion of Customers\")\n",
    "plt.savefig(\"edu_vs_pur_stack\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.day_of_week, data.y).plot(kind=\"bar\")\n",
    "plt.title(\"Purchase Frequency for Day of Week\")\n",
    "plt.xlabel(\"Day of Week\")\n",
    "plt.ylabel(\"Frequency of Purchase\")\n",
    "plt.savefig(\"pur_dayofweek_bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.month, data.y).plot(kind=\"bar\")\n",
    "plt.title(\"Purchase Frequency for Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Frequency of Purchase\")\n",
    "plt.savefig(\"pur_fre_month_bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist()\n",
    "plt.title(\"Histogram of Age\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"hist_age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.poutcome, data.y).plot(kind=\"bar\")\n",
    "plt.title(\"Purchase Frequency for Poutcome\")\n",
    "plt.xlabel(\"Poutcome\")\n",
    "plt.ylabel(\"Frequency of Purchase\")\n",
    "plt.savefig(\"pur_fre_pout_bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = [\n",
    "    \"job\",\n",
    "    \"marital\",\n",
    "    \"education\",\n",
    "    \"default\",\n",
    "    \"housing\",\n",
    "    \"loan\",\n",
    "    \"contact\",\n",
    "    \"month\",\n",
    "    \"day_of_week\",\n",
    "    \"poutcome\",\n",
    "]\n",
    "for var in cat_vars:\n",
    "    cat_list = \"var\" + \"_\" + var\n",
    "    cat_list = pd.get_dummies(data[var], prefix=var)\n",
    "    data1 = data.join(cat_list)\n",
    "    data = data1\n",
    "\n",
    "cat_vars = [\n",
    "    \"job\",\n",
    "    \"marital\",\n",
    "    \"education\",\n",
    "    \"default\",\n",
    "    \"housing\",\n",
    "    \"loan\",\n",
    "    \"contact\",\n",
    "    \"month\",\n",
    "    \"day_of_week\",\n",
    "    \"poutcome\",\n",
    "]\n",
    "data_vars = data.columns.values.tolist()\n",
    "to_keep = [i for i in data_vars if i not in cat_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp_var_rate',\n",
       "       'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y',\n",
       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired',\n",
       "       'job_self-employed', 'job_services', 'job_student',\n",
       "       'job_technician', 'job_unemployed', 'job_unknown',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_Basic', 'education_high.school',\n",
       "       'education_illiterate', 'education_professional.course',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'default_yes', 'housing_no', 'housing_unknown',\n",
       "       'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes',\n",
       "       'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug',\n",
       "       'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may',\n",
       "       'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri',\n",
       "       'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n",
       "       'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent',\n",
       "       'poutcome_success'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final = data[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  51134\n",
      "Number of no subscription in oversampled data 25567\n",
      "Number of subscription 25567\n",
      "Proportion of no subscription data in oversampled data is  0.5\n",
      "Proportion of subscription data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "X = data_final.loc[:, data_final.columns != \"y\"]\n",
    "y = data_final.loc[:, data_final.columns == \"y\"]\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "os = SMOTE(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "columns = X_train.columns\n",
    "\n",
    "os_data_X, os_data_y = os.fit_resample(X_train, y_train)\n",
    "os_data_X = pd.DataFrame(data=os_data_X, columns=columns)\n",
    "os_data_y = pd.DataFrame(data=os_data_y, columns=[\"y\"])\n",
    "# we can Check the numbers of our data\n",
    "print(\"length of oversampled data is \", len(os_data_X))\n",
    "print(\n",
    "    \"Number of no subscription in oversampled data\", len(os_data_y[os_data_y[\"y\"] == 0])\n",
    ")\n",
    "print(\"Number of subscription\", len(os_data_y[os_data_y[\"y\"] == 1]))\n",
    "print(\n",
    "    \"Proportion of no subscription data in oversampled data is \",\n",
    "    len(os_data_y[os_data_y[\"y\"] == 0]) / len(os_data_X),\n",
    ")\n",
    "print(\n",
    "    \"Proportion of subscription data in oversampled data is \",\n",
    "    len(os_data_y[os_data_y[\"y\"] == 1]) / len(os_data_X),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True False  True\n",
      "  True False  True  True False  True  True  True False False False  True\n",
      "  True  True  True  True  True False False False False False False False\n",
      " False False False False False  True  True  True  True  True False False\n",
      " False]\n",
      "[24 25 21 26 18 16 20 22 30 23  1  1  1  1  1  1  1  1  1  1  1  1  2  1\n",
      "  1 29  1  1 27  1  1  1 17 28 32  1  1  1  1  1  1 19 31 10 13  9 12 11\n",
      "  6 15 14  8  7  1  1  1  1  1  4  5  3]\n"
     ]
    }
   ],
   "source": [
    "data_final_vars = data_final.columns.values.tolist()\n",
    "y = [\"y\"]\n",
    "X = [i for i in data_final_vars if i not in y]\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "rfe = RFE(logreg)\n",
    "rfe = rfe.fit(os_data_X, os_data_y.values.ravel())\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\n",
    "    \"euribor3m\",\n",
    "    \"job_blue-collar\",\n",
    "    \"job_housemaid\",\n",
    "    \"marital_unknown\",\n",
    "    \"education_illiterate\",\n",
    "    \"default_no\",\n",
    "    \"default_unknown\",\n",
    "    \"contact_cellular\",\n",
    "    \"contact_telephone\",\n",
    "    \"month_apr\",\n",
    "    \"month_aug\",\n",
    "    \"month_dec\",\n",
    "    \"month_jul\",\n",
    "    \"month_jun\",\n",
    "    \"month_mar\",\n",
    "    \"month_may\",\n",
    "    \"month_nov\",\n",
    "    \"month_oct\",\n",
    "    \"poutcome_failure\",\n",
    "    \"poutcome_success\",\n",
    "]\n",
    "X = os_data_X[cols]\n",
    "y = os_data_y[\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.442547\n",
      "         Iterations 7\n",
      "                           Results: Logit\n",
      "=====================================================================\n",
      "Model:                Logit             Method:            MLE       \n",
      "Dependent Variable:   y                 Pseudo R-squared:  0.362     \n",
      "Date:                 2024-04-06 21:11  AIC:               45298.4477\n",
      "No. Observations:     51134             BIC:               45475.2918\n",
      "Df Model:             19                Log-Likelihood:    -22629.   \n",
      "Df Residuals:         51114             LL-Null:           -35443.   \n",
      "Converged:            1.0000            LLR p-value:       0.0000    \n",
      "No. Iterations:       7.0000            Scale:             1.0000    \n",
      "---------------------------------------------------------------------\n",
      "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "---------------------------------------------------------------------\n",
      "euribor3m            -0.9276   0.0104 -89.3986 0.0000 -0.9479 -0.9072\n",
      "job_blue-collar       0.2944   0.0276  10.6790 0.0000  0.2403  0.3484\n",
      "job_housemaid         0.3491   0.0719   4.8587 0.0000  0.2083  0.4900\n",
      "marital_unknown       0.7634   0.2170   3.5179 0.0004  0.3381  1.1887\n",
      "education_illiterate  1.8983   0.3669   5.1738 0.0000  1.1792  2.6175\n",
      "default_no            0.7756   0.0479  16.2049 0.0000  0.6818  0.8694\n",
      "default_unknown       0.8449   0.0455  18.5609 0.0000  0.7556  0.9341\n",
      "contact_cellular     -0.8155   0.0511 -15.9708 0.0000 -0.9156 -0.7154\n",
      "contact_telephone     0.3087   0.0498   6.1957 0.0000  0.2110  0.4063\n",
      "month_apr             1.6227   0.0459  35.3165 0.0000  1.5326  1.7127\n",
      "month_aug             2.8544   0.0543  52.5638 0.0000  2.7479  2.9608\n",
      "month_dec             1.8739   0.1338  14.0063 0.0000  1.6117  2.1362\n",
      "month_jul             3.2465   0.0537  60.4650 0.0000  3.1412  3.3517\n",
      "month_jun             2.0927   0.0497  42.0806 0.0000  1.9952  2.1902\n",
      "month_mar             2.6971   0.0833  32.3795 0.0000  2.5338  2.8603\n",
      "month_may             1.0958   0.0416  26.3248 0.0000  1.0143  1.1774\n",
      "month_nov             2.7162   0.0544  49.9173 0.0000  2.6096  2.8229\n",
      "month_oct             2.8007   0.0794  35.2615 0.0000  2.6450  2.9564\n",
      "poutcome_failure     -0.3545   0.0344 -10.3019 0.0000 -0.4220 -0.2871\n",
      "poutcome_success      1.2213   0.0649  18.8104 0.0000  1.0941  1.3486\n",
      "=====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "logit_model = sm.Logit(y, X.astype(float))\n",
    "result = logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.452566\n",
      "         Iterations 7\n",
      "                           Results: Logit\n",
      "=====================================================================\n",
      "Model:                Logit             Method:            MLE       \n",
      "Dependent Variable:   y                 Pseudo R-squared:  0.347     \n",
      "Date:                 2024-04-06 21:12  AIC:               46314.9963\n",
      "No. Observations:     51134             BIC:               46456.4716\n",
      "Df Model:             15                Log-Likelihood:    -23141.   \n",
      "Df Residuals:         51118             LL-Null:           -35443.   \n",
      "Converged:            1.0000            LLR p-value:       0.0000    \n",
      "No. Iterations:       7.0000            Scale:             1.0000    \n",
      "---------------------------------------------------------------------\n",
      "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "---------------------------------------------------------------------\n",
      "euribor3m            -0.7705   0.0079 -97.4685 0.0000 -0.7860 -0.7550\n",
      "job_blue-collar       0.3321   0.0270  12.3210 0.0000  0.2793  0.3850\n",
      "job_housemaid         0.3507   0.0711   4.9350 0.0000  0.2114  0.4900\n",
      "marital_unknown       0.6770   0.2173   3.1160 0.0018  0.2512  1.1029\n",
      "education_illiterate  1.7304   0.3651   4.7396 0.0000  1.0148  2.4459\n",
      "month_apr             1.4746   0.0351  42.0361 0.0000  1.4059  1.5434\n",
      "month_aug             2.3491   0.0408  57.6216 0.0000  2.2692  2.4290\n",
      "month_dec             1.9147   0.1285  14.9013 0.0000  1.6629  2.1665\n",
      "month_jul             2.7454   0.0414  66.3519 0.0000  2.6643  2.8265\n",
      "month_jun             2.3108   0.0392  58.9984 0.0000  2.2340  2.3875\n",
      "month_mar             2.5770   0.0775  33.2690 0.0000  2.4252  2.7288\n",
      "month_may             1.1476   0.0282  40.7396 0.0000  1.0924  1.2028\n",
      "month_nov             2.3547   0.0422  55.7868 0.0000  2.2720  2.4374\n",
      "month_oct             2.9015   0.0745  38.9684 0.0000  2.7555  3.0474\n",
      "poutcome_failure     -0.4338   0.0323 -13.4275 0.0000 -0.4971 -0.3705\n",
      "poutcome_success      1.1880   0.0615  19.3210 0.0000  1.0675  1.3085\n",
      "=====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cols = [\n",
    "    \"euribor3m\",\n",
    "    \"job_blue-collar\",\n",
    "    \"job_housemaid\",\n",
    "    \"marital_unknown\",\n",
    "    \"education_illiterate\",\n",
    "    \"month_apr\",\n",
    "    \"month_aug\",\n",
    "    \"month_dec\",\n",
    "    \"month_jul\",\n",
    "    \"month_jun\",\n",
    "    \"month_mar\",\n",
    "    \"month_may\",\n",
    "    \"month_nov\",\n",
    "    \"month_oct\",\n",
    "    \"poutcome_failure\",\n",
    "    \"poutcome_success\",\n",
    "]\n",
    "X = os_data_X[cols]\n",
    "y = os_data_y[\"y\"]\n",
    "\n",
    "logit_model = sm.Logit(y, X.astype(float))\n",
    "result = logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>LogisticRegression()</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;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.82\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print(\n",
    "    \"Accuracy of logistic regression classifier on test set: {:.2f}\".format(\n",
    "        logreg.score(X_test, y_test)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6878  788]\n",
      " [2009 5666]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.90      0.83      7666\n",
      "           1       0.88      0.74      0.80      7675\n",
      "\n",
      "    accuracy                           0.82     15341\n",
      "   macro avg       0.83      0.82      0.82     15341\n",
      "weighted avg       0.83      0.82      0.82     15341\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:, 1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label=\"Logistic Regression (area = %0.2f)\" % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1], \"r--\")\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver operating characteristic\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"Log_ROC\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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