Training_on_Bank_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DqCqqqIxOHVl"
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WJIcHBJNWc5"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "jNWmbBLH0G-B"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "id": "_lzPMfrt1YGd",
    "outputId": "4bc3bb54-3243-40d8-e724-dc2743bd72ce"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
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       "      <td>30</td>\n",
       "      <td>management</td>\n",
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       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4516</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-333</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>30</td>\n",
       "      <td>jul</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4517</th>\n",
       "      <td>57</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>-3313</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4518</th>\n",
       "      <td>57</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>295</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4519</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1137</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>feb</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>211</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>44</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1136</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>3</td>\n",
       "      <td>apr</td>\n",
       "      <td>345</td>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "      <td>7</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4521 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age            job  marital  education default  balance housing loan  \\\n",
       "0      30     unemployed  married    primary      no     1787      no   no   \n",
       "1      33       services  married  secondary      no     4789     yes  yes   \n",
       "2      35     management   single   tertiary      no     1350     yes   no   \n",
       "3      30     management  married   tertiary      no     1476     yes  yes   \n",
       "4      59    blue-collar  married  secondary      no        0     yes   no   \n",
       "...   ...            ...      ...        ...     ...      ...     ...  ...   \n",
       "4516   33       services  married  secondary      no     -333     yes   no   \n",
       "4517   57  self-employed  married   tertiary     yes    -3313     yes  yes   \n",
       "4518   57     technician  married  secondary      no      295      no   no   \n",
       "4519   28    blue-collar  married  secondary      no     1137      no   no   \n",
       "4520   44   entrepreneur   single   tertiary      no     1136     yes  yes   \n",
       "\n",
       "       contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0     cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1     cellular   11   may       220         1    339         4  failure  no  \n",
       "2     cellular   16   apr       185         1    330         1  failure  no  \n",
       "3      unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4      unknown    5   may       226         1     -1         0  unknown  no  \n",
       "...        ...  ...   ...       ...       ...    ...       ...      ...  ..  \n",
       "4516  cellular   30   jul       329         5     -1         0  unknown  no  \n",
       "4517   unknown    9   may       153         1     -1         0  unknown  no  \n",
       "4518  cellular   19   aug       151        11     -1         0  unknown  no  \n",
       "4519  cellular    6   feb       129         4    211         3    other  no  \n",
       "4520  cellular    3   apr       345         2    249         7    other  no  \n",
       "\n",
       "[4521 rows x 17 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n",
    "df = pd.read_csv('bank.csv', sep = ';')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e0eN71NbNhP9"
   },
   "source": [
    "## Verification of dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PD_of-rM3o_f",
    "outputId": "4da7e99a-ab73-48dc-901b-e0c3fc07034b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age          0\n",
      "job          0\n",
      "marital      0\n",
      "education    0\n",
      "default      0\n",
      "balance      0\n",
      "housing      0\n",
      "loan         0\n",
      "contact      0\n",
      "day          0\n",
      "month        0\n",
      "duration     0\n",
      "campaign     0\n",
      "pdays        0\n",
      "previous     0\n",
      "poutcome     0\n",
      "y            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kFHBZC6v45yz",
    "outputId": "d4603045-765d-415c-c0f0-e98e02f57761"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            67\n",
       "job            12\n",
       "marital         3\n",
       "education       4\n",
       "default         2\n",
       "balance      2353\n",
       "housing         2\n",
       "loan            2\n",
       "contact         3\n",
       "day            31\n",
       "month          12\n",
       "duration      875\n",
       "campaign       32\n",
       "pdays         292\n",
       "previous       24\n",
       "poutcome        4\n",
       "y               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HKwVYhOgNnHU"
   },
   "source": [
    "## Ordinal Encoding for Education and Month Columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x7rgu6-H86Ap",
    "outputId": "0347a877-c711-4f89-922f-d9a06ee504bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['primary', 'secondary', 'tertiary', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "XfVdTulQ9X4k"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "cat = ['unknown', 'primary', 'secondary', 'tertiary'] # [0, 1, 2, 3]\n",
    "encoder = OrdinalEncoder(categories = [cat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "SQECcXyYCcRs",
    "outputId": "80db260f-fe16-4d7d-8753-c8b4b6f09ace"
   },
   "outputs": [
    {
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       "    <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>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "      <th>0</th>\n",
       "      <td>30</td>\n",
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       "      <td>services</td>\n",
       "      <td>married</td>\n",
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       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4516</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>-333</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>30</td>\n",
       "      <td>jul</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
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       "      <td>-3313</td>\n",
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       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4518</th>\n",
       "      <td>57</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>295</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4519</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1137</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>feb</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>211</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>44</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>no</td>\n",
       "      <td>1136</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>3</td>\n",
       "      <td>apr</td>\n",
       "      <td>345</td>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "      <td>7</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4521 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age            job  marital  education default  balance housing loan  \\\n",
       "0      30     unemployed  married        1.0      no     1787      no   no   \n",
       "1      33       services  married        2.0      no     4789     yes  yes   \n",
       "2      35     management   single        3.0      no     1350     yes   no   \n",
       "3      30     management  married        3.0      no     1476     yes  yes   \n",
       "4      59    blue-collar  married        2.0      no        0     yes   no   \n",
       "...   ...            ...      ...        ...     ...      ...     ...  ...   \n",
       "4516   33       services  married        2.0      no     -333     yes   no   \n",
       "4517   57  self-employed  married        3.0     yes    -3313     yes  yes   \n",
       "4518   57     technician  married        2.0      no      295      no   no   \n",
       "4519   28    blue-collar  married        2.0      no     1137      no   no   \n",
       "4520   44   entrepreneur   single        3.0      no     1136     yes  yes   \n",
       "\n",
       "       contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0     cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1     cellular   11   may       220         1    339         4  failure  no  \n",
       "2     cellular   16   apr       185         1    330         1  failure  no  \n",
       "3      unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4      unknown    5   may       226         1     -1         0  unknown  no  \n",
       "...        ...  ...   ...       ...       ...    ...       ...      ...  ..  \n",
       "4516  cellular   30   jul       329         5     -1         0  unknown  no  \n",
       "4517   unknown    9   may       153         1     -1         0  unknown  no  \n",
       "4518  cellular   19   aug       151        11     -1         0  unknown  no  \n",
       "4519  cellular    6   feb       129         4    211         3    other  no  \n",
       "4520  cellular    3   apr       345         2    249         7    other  no  \n",
       "\n",
       "[4521 rows x 17 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"education\"] = encoder.fit_transform(df[[\"education\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "QLp0mEl-ItlR",
    "outputId": "e2f40e7e-f87a-43f1-edea-5944cb444d27"
   },
   "outputs": [
    {
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       "      <td>no</td>\n",
       "      <td>1137</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>211</td>\n",
       "      <td>3</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
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       "      <td>yes</td>\n",
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       "      <td>7</td>\n",
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       "  </tbody>\n",
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       "<p>4521 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age            job  marital  education default  balance housing loan  \\\n",
       "0      30     unemployed  married        1.0      no     1787      no   no   \n",
       "1      33       services  married        2.0      no     4789     yes  yes   \n",
       "2      35     management   single        3.0      no     1350     yes   no   \n",
       "3      30     management  married        3.0      no     1476     yes  yes   \n",
       "4      59    blue-collar  married        2.0      no        0     yes   no   \n",
       "...   ...            ...      ...        ...     ...      ...     ...  ...   \n",
       "4516   33       services  married        2.0      no     -333     yes   no   \n",
       "4517   57  self-employed  married        3.0     yes    -3313     yes  yes   \n",
       "4518   57     technician  married        2.0      no      295      no   no   \n",
       "4519   28    blue-collar  married        2.0      no     1137      no   no   \n",
       "4520   44   entrepreneur   single        3.0      no     1136     yes  yes   \n",
       "\n",
       "       contact  day  month  duration  campaign  pdays  previous poutcome   y  \n",
       "0     cellular   19    1.0        79         1     -1         0  unknown  no  \n",
       "1     cellular   11    8.0       220         1    339         4  failure  no  \n",
       "2     cellular   16    7.0       185         1    330         1  failure  no  \n",
       "3      unknown    3    9.0       199         4     -1         0  unknown  no  \n",
       "4      unknown    5    8.0       226         1     -1         0  unknown  no  \n",
       "...        ...  ...    ...       ...       ...    ...       ...      ...  ..  \n",
       "4516  cellular   30   10.0       329         5     -1         0  unknown  no  \n",
       "4517   unknown    9    8.0       153         1     -1         0  unknown  no  \n",
       "4518  cellular   19   11.0       151        11     -1         0  unknown  no  \n",
       "4519  cellular    6    5.0       129         4    211         3    other  no  \n",
       "4520  cellular    3    7.0       345         2    249         7    other  no  \n",
       "\n",
       "[4521 rows x 17 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"month\"].unique()\n",
    "cat = ['sep', 'oct', 'nov', 'dec', 'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug'] #\n",
    "encoder = OrdinalEncoder(categories = [cat])\n",
    "df[\"month\"] = encoder.fit_transform(df[[\"month\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ht2EIe1INywz"
   },
   "source": [
    "## Label Encoding for binary Columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "MbuMPS7PDpPz",
    "outputId": "1844848a-5c9d-42e8-f921-dbc65c8f65b5"
   },
   "outputs": [
    {
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       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4516</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4517</th>\n",
       "      <td>57</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3313</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4518</th>\n",
       "      <td>57</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>151</td>\n",
       "      <td>11</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4519</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>211</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>44</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>345</td>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "      <td>7</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4521 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age            job  marital  education  default  balance  housing  loan  \\\n",
       "0      30     unemployed  married        1.0        0     1787        0     0   \n",
       "1      33       services  married        2.0        0     4789        1     1   \n",
       "2      35     management   single        3.0        0     1350        1     0   \n",
       "3      30     management  married        3.0        0     1476        1     1   \n",
       "4      59    blue-collar  married        2.0        0        0        1     0   \n",
       "...   ...            ...      ...        ...      ...      ...      ...   ...   \n",
       "4516   33       services  married        2.0        0     -333        1     0   \n",
       "4517   57  self-employed  married        3.0        1    -3313        1     1   \n",
       "4518   57     technician  married        2.0        0      295        0     0   \n",
       "4519   28    blue-collar  married        2.0        0     1137        0     0   \n",
       "4520   44   entrepreneur   single        3.0        0     1136        1     1   \n",
       "\n",
       "       contact  day  month  duration  campaign  pdays  previous poutcome  y  \n",
       "0     cellular   19    1.0        79         1     -1         0  unknown  0  \n",
       "1     cellular   11    8.0       220         1    339         4  failure  0  \n",
       "2     cellular   16    7.0       185         1    330         1  failure  0  \n",
       "3      unknown    3    9.0       199         4     -1         0  unknown  0  \n",
       "4      unknown    5    8.0       226         1     -1         0  unknown  0  \n",
       "...        ...  ...    ...       ...       ...    ...       ...      ... ..  \n",
       "4516  cellular   30   10.0       329         5     -1         0  unknown  0  \n",
       "4517   unknown    9    8.0       153         1     -1         0  unknown  0  \n",
       "4518  cellular   19   11.0       151        11     -1         0  unknown  0  \n",
       "4519  cellular    6    5.0       129         4    211         3    other  0  \n",
       "4520  cellular    3    7.0       345         2    249         7    other  0  \n",
       "\n",
       "[4521 rows x 17 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "binaryVariables = [\"default\", \"housing\", \"loan\", \"y\"]\n",
    "\n",
    "for var in binaryVariables:\n",
    "  df[var] = encoder.fit_transform(df[var])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.iloc[:,:-1]\n",
    "y = df[\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4383</th>\n",
       "      <td>26</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>609</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>31</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4052</th>\n",
       "      <td>52</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>54</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>26</td>\n",
       "      <td>8.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3088</th>\n",
       "      <td>31</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>29</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>534</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "      <td>382</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3264</th>\n",
       "      <td>36</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>53</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>388</td>\n",
       "      <td>3</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2607</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>524</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2732</th>\n",
       "      <td>45</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-322</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>225</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3616 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age          job  marital  education  default  balance  housing  loan  \\\n",
       "4383   26       admin.   single        2.0        0      609        0     0   \n",
       "502    31   management  married        3.0        0       13        0     0   \n",
       "4052   52   unemployed  married        1.0        0       20        1     0   \n",
       "3634   54      retired  married        2.0        0      153        1     1   \n",
       "3088   31   technician   single        2.0        0     2544        0     0   \n",
       "...   ...          ...      ...        ...      ...      ...      ...   ...   \n",
       "1033   29   technician   single        2.0        0      534        1     0   \n",
       "3264   36   management   single        3.0        0      613        0     0   \n",
       "1653   53  blue-collar  married        1.0        0      306        0     0   \n",
       "2607   32  blue-collar  married        1.0        0      642        0     1   \n",
       "2732   45   technician  married        1.0        0     -322        0     0   \n",
       "\n",
       "        contact  day  month  duration  campaign  pdays  previous poutcome  \n",
       "4383  telephone   28    4.0       112         1     -1         0  unknown  \n",
       "502    cellular   28   11.0       110         5     -1         0  unknown  \n",
       "4052  telephone   28    4.0       172         1     -1         0  unknown  \n",
       "3634    unknown   26    8.0        74         1     -1         0  unknown  \n",
       "3088   cellular   15   10.0       252         1    184         3  success  \n",
       "...         ...  ...    ...       ...       ...    ...       ...      ...  \n",
       "1033   cellular   18    8.0       382         1     -1         0  unknown  \n",
       "3264   cellular    7   11.0        86         2     -1         0  unknown  \n",
       "1653   cellular   28   10.0       388         3    181         1  success  \n",
       "2607   cellular    8   10.0       524         1     -1         0  unknown  \n",
       "2732    unknown    9    9.0       225         2     -1         0  unknown  \n",
       "\n",
       "[3616 rows x 16 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TGWBuyrNOiba"
   },
   "source": [
    "## Frequency Encoding for job Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fZxqDsjtOh08",
    "outputId": "e58a303d-45db-4dfa-eac4-c4609a0a3599"
   },
   "outputs": [],
   "source": [
    "# Warning : apply only to train set\n",
    "enc_job = (X_train.groupby('job').size())/len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21045353982300885\n"
     ]
    }
   ],
   "source": [
    "print(enc_job[\"blue-collar\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "zmMDf3eNQhae",
    "outputId": "3a702619-7ff3-4f3e-ba63-5ba1032d32a8"
   },
   "outputs": [
    {
     "data": {
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       "\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>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4383</th>\n",
       "      <td>26</td>\n",
       "      <td>0.105088</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>609</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>31</td>\n",
       "      <td>0.211007</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4052</th>\n",
       "      <td>52</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>54</td>\n",
       "      <td>0.050332</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>26</td>\n",
       "      <td>8.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3088</th>\n",
       "      <td>31</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>29</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>534</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "      <td>382</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3264</th>\n",
       "      <td>36</td>\n",
       "      <td>0.211007</td>\n",
       "      <td>single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>53</td>\n",
       "      <td>0.210454</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>388</td>\n",
       "      <td>3</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2607</th>\n",
       "      <td>32</td>\n",
       "      <td>0.210454</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>524</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2732</th>\n",
       "      <td>45</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-322</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>225</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3616 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age       job  marital  education  default  balance  housing  loan  \\\n",
       "4383   26  0.105088   single        2.0        0      609        0     0   \n",
       "502    31  0.211007  married        3.0        0       13        0     0   \n",
       "4052   52  0.029867  married        1.0        0       20        1     0   \n",
       "3634   54  0.050332  married        2.0        0      153        1     1   \n",
       "3088   31  0.173119   single        2.0        0     2544        0     0   \n",
       "...   ...       ...      ...        ...      ...      ...      ...   ...   \n",
       "1033   29  0.173119   single        2.0        0      534        1     0   \n",
       "3264   36  0.211007   single        3.0        0      613        0     0   \n",
       "1653   53  0.210454  married        1.0        0      306        0     0   \n",
       "2607   32  0.210454  married        1.0        0      642        0     1   \n",
       "2732   45  0.173119  married        1.0        0     -322        0     0   \n",
       "\n",
       "        contact  day  month  duration  campaign  pdays  previous poutcome  \n",
       "4383  telephone   28    4.0       112         1     -1         0  unknown  \n",
       "502    cellular   28   11.0       110         5     -1         0  unknown  \n",
       "4052  telephone   28    4.0       172         1     -1         0  unknown  \n",
       "3634    unknown   26    8.0        74         1     -1         0  unknown  \n",
       "3088   cellular   15   10.0       252         1    184         3  success  \n",
       "...         ...  ...    ...       ...       ...    ...       ...      ...  \n",
       "1033   cellular   18    8.0       382         1     -1         0  unknown  \n",
       "3264   cellular    7   11.0        86         2     -1         0  unknown  \n",
       "1653   cellular   28   10.0       388         3    181         1  success  \n",
       "2607   cellular    8   10.0       524         1     -1         0  unknown  \n",
       "2732    unknown    9    9.0       225         2     -1         0  unknown  \n",
       "\n",
       "[3616 rows x 16 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(X_train)):\n",
    "    X_train.iloc[i,1] = enc_job[X_train.iloc[i,1]]\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>4274</th>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>31</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>30</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>42</td>\n",
       "      <td>0.105088</td>\n",
       "      <td>married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>529</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>193</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>41</td>\n",
       "      <td>0.210454</td>\n",
       "      <td>married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>187</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>34</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>26</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2591</th>\n",
       "      <td>34</td>\n",
       "      <td>0.211007</td>\n",
       "      <td>married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2892</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1165</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>27</td>\n",
       "      <td>0.090708</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>34</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>347</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>154</td>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>6</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>32</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>11.0</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>905 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age       job  marital  education  default  balance  housing  loan  \\\n",
       "4274   35  0.210454  married        1.0        0      378        1     0   \n",
       "72     31  0.211007   single        3.0        0     6248        1     0   \n",
       "1065   30  0.029867  married        3.0        0        0        1     0   \n",
       "3185   42  0.105088  married        2.0        0      529        1     1   \n",
       "130    41  0.210454  married        1.0        0      168        1     0   \n",
       "...   ...       ...      ...        ...      ...      ...      ...   ...   \n",
       "1523   34  0.173119   single        2.0        0     2178        1     0   \n",
       "2591   34  0.211007  married        3.0        0     2892        1     1   \n",
       "1224   27  0.090708   single        2.0        0      201        1     0   \n",
       "3071   34  0.173119   single        2.0        0      347        1     0   \n",
       "1680   32  0.173119   single        2.0        0        0        0     0   \n",
       "\n",
       "       contact  day  month  duration  campaign  pdays  previous poutcome  \n",
       "4274   unknown   16    8.0       427         3     -1         0  unknown  \n",
       "72     unknown    9    8.0       154         2     -1         0  unknown  \n",
       "1065  cellular   18    2.0       756         1     -1         0  unknown  \n",
       "3185  cellular   19    2.0       193         3     -1         0  unknown  \n",
       "130   cellular   18    2.0       187         3     -1         0  unknown  \n",
       "...        ...  ...    ...       ...       ...    ...       ...      ...  \n",
       "1523  cellular   26    8.0        41         1     -1         0  unknown  \n",
       "2591  cellular   30   10.0      1165         7     -1         0  unknown  \n",
       "1224   unknown    4    9.0       172         2     -1         0  unknown  \n",
       "3071  cellular    6    5.0       154         2    197         6  failure  \n",
       "1680  cellular   18   11.0       594         4     -1         0  unknown  \n",
       "\n",
       "[905 rows x 16 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(X_test)):\n",
    "    X_test.iloc[i,1] = enc_job[X_test.iloc[i,1]]\n",
    "\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jbacb\\AppData\\Local\\Temp\\ipykernel_9956\\1550115466.py:6: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(train_dataset.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "train_dataset = X_train.copy()\n",
    "train_dataset[\"y\"] = y_train\n",
    "\n",
    "sns.heatmap(train_dataset.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling of relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4274</th>\n",
       "      <td>-0.579101</td>\n",
       "      <td>0.210454</td>\n",
       "      <td>married</td>\n",
       "      <td>-1.383193</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.014415</td>\n",
       "      <td>0.103682</td>\n",
       "      <td>0.667576</td>\n",
       "      <td>0.063387</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>-0.954863</td>\n",
       "      <td>0.211007</td>\n",
       "      <td>single</td>\n",
       "      <td>1.198291</td>\n",
       "      <td>0</td>\n",
       "      <td>1.582336</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>-0.834147</td>\n",
       "      <td>0.103682</td>\n",
       "      <td>-0.420324</td>\n",
       "      <td>-0.261730</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>-1.048804</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>married</td>\n",
       "      <td>1.198291</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.466780</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>0.256862</td>\n",
       "      <td>-2.030560</td>\n",
       "      <td>1.978636</td>\n",
       "      <td>-0.586848</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>0.078483</td>\n",
       "      <td>0.105088</td>\n",
       "      <td>married</td>\n",
       "      <td>-0.092451</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.293287</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>0.378085</td>\n",
       "      <td>-2.030560</td>\n",
       "      <td>-0.264910</td>\n",
       "      <td>0.063387</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>-0.015458</td>\n",
       "      <td>0.210454</td>\n",
       "      <td>married</td>\n",
       "      <td>-1.383193</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.411682</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>0.256862</td>\n",
       "      <td>-2.030560</td>\n",
       "      <td>-0.288820</td>\n",
       "      <td>0.063387</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>-0.673041</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>-0.092451</td>\n",
       "      <td>0</td>\n",
       "      <td>0.247525</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>1.226648</td>\n",
       "      <td>0.103682</td>\n",
       "      <td>-0.870628</td>\n",
       "      <td>-0.586848</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2591</th>\n",
       "      <td>-0.673041</td>\n",
       "      <td>0.211007</td>\n",
       "      <td>married</td>\n",
       "      <td>1.198291</td>\n",
       "      <td>0</td>\n",
       "      <td>0.481691</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>1.711541</td>\n",
       "      <td>0.815096</td>\n",
       "      <td>3.608495</td>\n",
       "      <td>1.363858</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>-1.330625</td>\n",
       "      <td>0.090708</td>\n",
       "      <td>single</td>\n",
       "      <td>-0.092451</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.400859</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>-1.440263</td>\n",
       "      <td>0.459389</td>\n",
       "      <td>-0.348595</td>\n",
       "      <td>-0.261730</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>-0.673041</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>-0.092451</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.352976</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>-1.197817</td>\n",
       "      <td>-0.963439</td>\n",
       "      <td>-0.420324</td>\n",
       "      <td>-0.261730</td>\n",
       "      <td>1.576479</td>\n",
       "      <td>3.133871</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>-0.860923</td>\n",
       "      <td>0.173119</td>\n",
       "      <td>single</td>\n",
       "      <td>-0.092451</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.466780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>0.256862</td>\n",
       "      <td>1.170803</td>\n",
       "      <td>1.333069</td>\n",
       "      <td>0.388505</td>\n",
       "      <td>-0.404991</td>\n",
       "      <td>-0.314372</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>905 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age       job  marital  education  default   balance  housing  \\\n",
       "4274 -0.579101  0.210454  married  -1.383193        0 -0.342809        1   \n",
       "72   -0.954863  0.211007   single   1.198291        0  1.582336        1   \n",
       "1065 -1.048804  0.029867  married   1.198291        0 -0.466780        1   \n",
       "3185  0.078483  0.105088  married  -0.092451        0 -0.293287        1   \n",
       "130  -0.015458  0.210454  married  -1.383193        0 -0.411682        1   \n",
       "...        ...       ...      ...        ...      ...       ...      ...   \n",
       "1523 -0.673041  0.173119   single  -0.092451        0  0.247525        1   \n",
       "2591 -0.673041  0.211007  married   1.198291        0  0.481691        1   \n",
       "1224 -1.330625  0.090708   single  -0.092451        0 -0.400859        1   \n",
       "3071 -0.673041  0.173119   single  -0.092451        0 -0.352976        1   \n",
       "1680 -0.860923  0.173119   single  -0.092451        0 -0.466780        0   \n",
       "\n",
       "      loan   contact       day     month  duration  campaign     pdays  \\\n",
       "4274     0   unknown  0.014415  0.103682  0.667576  0.063387 -0.404991   \n",
       "72       0   unknown -0.834147  0.103682 -0.420324 -0.261730 -0.404991   \n",
       "1065     0  cellular  0.256862 -2.030560  1.978636 -0.586848 -0.404991   \n",
       "3185     1  cellular  0.378085 -2.030560 -0.264910  0.063387 -0.404991   \n",
       "130      0  cellular  0.256862 -2.030560 -0.288820  0.063387 -0.404991   \n",
       "...    ...       ...       ...       ...       ...       ...       ...   \n",
       "1523     0  cellular  1.226648  0.103682 -0.870628 -0.586848 -0.404991   \n",
       "2591     1  cellular  1.711541  0.815096  3.608495  1.363858 -0.404991   \n",
       "1224     0   unknown -1.440263  0.459389 -0.348595 -0.261730 -0.404991   \n",
       "3071     0  cellular -1.197817 -0.963439 -0.420324 -0.261730  1.576479   \n",
       "1680     0  cellular  0.256862  1.170803  1.333069  0.388505 -0.404991   \n",
       "\n",
       "      previous poutcome  \n",
       "4274 -0.314372  unknown  \n",
       "72   -0.314372  unknown  \n",
       "1065 -0.314372  unknown  \n",
       "3185 -0.314372  unknown  \n",
       "130  -0.314372  unknown  \n",
       "...        ...      ...  \n",
       "1523 -0.314372  unknown  \n",
       "2591 -0.314372  unknown  \n",
       "1224 -0.314372  unknown  \n",
       "3071  3.133871  failure  \n",
       "1680 -0.314372  unknown  \n",
       "\n",
       "[905 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "indexesToScale = [0, 3, 5, 9, 10,11, 12, 13, 14]\n",
    "\n",
    "scaler.fit(X_train.iloc[:,indexesToScale]) \n",
    "\n",
    "X_train.iloc[:,indexesToScale] = scaler.transform(X_train.iloc[:,indexesToScale])\n",
    "X_test.iloc[:,indexesToScale] = scaler.transform(X_test.iloc[:,indexesToScale])\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gLqD6zH2N6q_"
   },
   "source": [
    "## One Hot Encoding for marital, contact and poutcome Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "jjvZAYaLF9eJ",
    "outputId": "18540a9c-12d9-4cc2-cea6-753017e09b5d"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9   ... 13        14 15 16  \\\n",
       "0    0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  ...  0 -0.342809  1  0   \n",
       "1    0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  ...  0  1.582336  1  0   \n",
       "2    0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0  -0.46678  1  0   \n",
       "3    0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0 -0.293287  1  1   \n",
       "4    0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0 -0.411682  1  0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ..       ... .. ..   \n",
       "900  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0  0.247525  1  0   \n",
       "901  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0  0.481691  1  1   \n",
       "902  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  ...  0 -0.400859  1  0   \n",
       "903  0.0  0.0  1.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  0 -0.352976  1  0   \n",
       "904  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0  -0.46678  0  0   \n",
       "\n",
       "           17        18        19        20        21        22  \n",
       "0    0.014415  0.103682  0.667576  0.063387 -0.404991 -0.314372  \n",
       "1   -0.834147  0.103682 -0.420324  -0.26173 -0.404991 -0.314372  \n",
       "2    0.256862  -2.03056  1.978636 -0.586848 -0.404991 -0.314372  \n",
       "3    0.378085  -2.03056  -0.26491  0.063387 -0.404991 -0.314372  \n",
       "4    0.256862  -2.03056  -0.28882  0.063387 -0.404991 -0.314372  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "900  1.226648  0.103682 -0.870628 -0.586848 -0.404991 -0.314372  \n",
       "901  1.711541  0.815096  3.608495  1.363858 -0.404991 -0.314372  \n",
       "902 -1.440263  0.459389 -0.348595  -0.26173 -0.404991 -0.314372  \n",
       "903 -1.197817 -0.963439 -0.420324  -0.26173  1.576479  3.133871  \n",
       "904  0.256862  1.170803  1.333069  0.388505 -0.404991 -0.314372  \n",
       "\n",
       "[905 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [2, 8, 15])], remainder='passthrough')\n",
    "\n",
    "X_train = pd.DataFrame(columnTransformer.fit_transform(X_train))\n",
    "X_test = pd.DataFrame(columnTransformer.fit_transform(X_test))\n",
    "\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 0.903\n",
      "Confusion Matrix:\n",
      "[[3144  288]\n",
      " [  63  121]] \n",
      "\n",
      "Test Accuracy = 0.893\n",
      "Confusion Matrix:\n",
      "[[769  73]\n",
      " [ 24  39]]\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "Classifier = LogisticRegression()\n",
    "Classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = Classifier.predict(X_train)\n",
    "\n",
    "print(\"Train Accuracy =\", round(accuracy_score(y_pred, y_train), 3))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_pred, y_train), '\\n')\n",
    "\n",
    "y_pred = Classifier.predict(X_test)\n",
    "\n",
    "print(\"Test Accuracy =\", round(accuracy_score(y_pred, y_test), 3))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy = 1.0\n",
      "Confusion Matrix:\n",
      "[[3207    0]\n",
      " [   0  409]] \n",
      "\n",
      "Test Accuracy = 0.89\n",
      "Confusion Matrix:\n",
      "[[776  80]\n",
      " [ 17  32]]\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Classifier = RandomForestClassifier(n_estimators = 100, criterion = \"entropy\")\n",
    "Classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = Classifier.predict(X_train)\n",
    "\n",
    "print(\"Train Accuracy =\", round(accuracy_score(y_pred, y_train), 2))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_pred, y_train), '\\n')\n",
    "\n",
    "y_pred = Classifier.predict(X_test)\n",
    "\n",
    "print(\"Test Accuracy =\", round(accuracy_score(y_pred, y_test), 2))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_pred, y_test))"
   ]
  }
 ],
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