cohorts/2024/03-classification/homework_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7f165fc",
   "metadata": {},
   "source": [
    "# Homework #3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbc91ba",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e10879",
   "metadata": {},
   "source": [
    "In this homework, we will use Bank Marketing dataset. Download it from [here](https://archive.ics.uci.edu/static/public/222/bank+marketing.zip)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b801170a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LogisticRegression, Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mutual_info_score, accuracy_score, mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bc70f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "392e58e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('bank+marketing/bank/bank-full.csv', delimiter=';')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9afaf70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f278e656",
   "metadata": {
    "tags": []
   },
   "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>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</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>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</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>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</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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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def93194",
   "metadata": {},
   "source": [
    "## Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc979aa",
   "metadata": {},
   "source": [
    "For the rest of the homework, you'll need to use only these columns:\n",
    "\n",
    "* `age`,\n",
    "* `job`,\n",
    "* `marital`,\n",
    "* `education`,\n",
    "* `balance`,\n",
    "* `housing`,\n",
    "* `contact`,\n",
    "* `day`,\n",
    "* `month`,\n",
    "* `duration`,\n",
    "* `campaign`,\n",
    "* `pdays`,\n",
    "* `previous`,\n",
    "* `poutcome`,\n",
    "* `y`\n",
    "\n",
    "Select only them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2cf4f54",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = [\n",
    "    'age', 'job', 'marital', 'education', 'balance', 'housing', 'contact', \n",
    "    'day', 'month', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'y'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "203b58d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea672c17",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 15 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   balance    45211 non-null  int64 \n",
      " 5   housing    45211 non-null  object\n",
      " 6   contact    45211 non-null  object\n",
      " 7   day        45211 non-null  int64 \n",
      " 8   month      45211 non-null  object\n",
      " 9   duration   45211 non-null  int64 \n",
      " 10  campaign   45211 non-null  int64 \n",
      " 11  pdays      45211 non-null  int64 \n",
      " 12  previous   45211 non-null  int64 \n",
      " 13  poutcome   45211 non-null  object\n",
      " 14  y          45211 non-null  object\n",
      "dtypes: int64(7), object(8)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee382c0a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            77\n",
       "job            12\n",
       "marital         3\n",
       "education       4\n",
       "balance      7168\n",
       "housing         2\n",
       "contact         3\n",
       "day            31\n",
       "month          12\n",
       "duration     1573\n",
       "campaign       48\n",
       "pdays         559\n",
       "previous       41\n",
       "poutcome        4\n",
       "y               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db2a5810",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "balance      0\n",
       "housing      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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dca32631",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>tertiary</td>\n",
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       "      <th>2</th>\n",
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       "      <td>entrepreneur</td>\n",
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       "      <td>2</td>\n",
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       "      <td>blue-collar</td>\n",
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       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</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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       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</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>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job   marital  education  balance housing    contact  \\\n",
       "0       58    management   married   tertiary     2143     yes    unknown   \n",
       "1       44    technician    single  secondary       29     yes    unknown   \n",
       "2       33  entrepreneur   married  secondary        2     yes    unknown   \n",
       "3       47   blue-collar   married    unknown     1506     yes    unknown   \n",
       "4       33       unknown    single    unknown        1      no    unknown   \n",
       "...    ...           ...       ...        ...      ...     ...        ...   \n",
       "45206   51    technician   married   tertiary      825      no   cellular   \n",
       "45207   71       retired  divorced    primary     1729      no   cellular   \n",
       "45208   72       retired   married  secondary     5715      no   cellular   \n",
       "45209   57   blue-collar   married  secondary      668      no  telephone   \n",
       "45210   37  entrepreneur   married  secondary     2971      no   cellular   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous poutcome    y  \n",
       "0        5   may       261         1     -1         0  unknown   no  \n",
       "1        5   may       151         1     -1         0  unknown   no  \n",
       "2        5   may        76         1     -1         0  unknown   no  \n",
       "3        5   may        92         1     -1         0  unknown   no  \n",
       "4        5   may       198         1     -1         0  unknown   no  \n",
       "...    ...   ...       ...       ...    ...       ...      ...  ...  \n",
       "45206   17   nov       977         3     -1         0  unknown  yes  \n",
       "45207   17   nov       456         2     -1         0  unknown  yes  \n",
       "45208   17   nov      1127         5    184         3  success  yes  \n",
       "45209   17   nov       508         4     -1         0  unknown   no  \n",
       "45210   17   nov       361         2    188        11    other   no  \n",
       "\n",
       "[45211 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfa73b6",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74099f56",
   "metadata": {},
   "source": [
    "What is the most frequent observation (mode) for the column `education`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "177af65f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>housing</th>\n",
       "      <th>contact</th>\n",
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       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>45211</td>\n",
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       "      <td>45211</td>\n",
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       "      <td>45211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9732</td>\n",
       "      <td>27214</td>\n",
       "      <td>23202</td>\n",
       "      <td>25130</td>\n",
       "      <td>29285</td>\n",
       "      <td>13766</td>\n",
       "      <td>36959</td>\n",
       "      <td>39922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                job  marital  education housing   contact  month poutcome  \\\n",
       "count         45211    45211      45211   45211     45211  45211    45211   \n",
       "unique           12        3          4       2         3     12        4   \n",
       "top     blue-collar  married  secondary     yes  cellular    may  unknown   \n",
       "freq           9732    27214      23202   25130     29285  13766    36959   \n",
       "\n",
       "            y  \n",
       "count   45211  \n",
       "unique      2  \n",
       "top        no  \n",
       "freq    39922  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=[\"O\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bd24972",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "secondary    23202\n",
       "tertiary     13301\n",
       "primary       6851\n",
       "unknown       1857\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d345600",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cfc5d6",
   "metadata": {},
   "source": [
    "* Create the correlation matrix for the numerical features of your dataset\n",
    "* In a correlation matrix, you compute the correlation coefficient between every pair of features in the dataset\n",
    "* What are the two features that have the biggest correlation in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d266a309",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
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       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numeric = data.copy()\n",
    "data_numeric = data.drop(\n",
    "    ['job', 'marital', 'education', 'housing', 'contact', 'month', 'poutcome', 'y'], axis=1\n",
    ")\n",
    "data_numeric.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c789d653",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.023758</td>\n",
       "      <td>0.001288</td>\n",
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       "      <td>0.097783</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.021560</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.162490</td>\n",
       "      <td>-0.093044</td>\n",
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       "      <th>duration</th>\n",
       "      <td>-0.004648</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.030206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084570</td>\n",
       "      <td>-0.001565</td>\n",
       "      <td>0.001203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0.004760</td>\n",
       "      <td>-0.014578</td>\n",
       "      <td>0.162490</td>\n",
       "      <td>-0.084570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088628</td>\n",
       "      <td>-0.032855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.023758</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>-0.093044</td>\n",
       "      <td>-0.001565</td>\n",
       "      <td>-0.088628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.454820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.016674</td>\n",
       "      <td>-0.051710</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>-0.032855</td>\n",
       "      <td>0.454820</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "               age   balance       day  duration  campaign     pdays  previous\n",
       "age       1.000000  0.097783 -0.009120 -0.004648  0.004760 -0.023758  0.001288\n",
       "balance   0.097783  1.000000  0.004503  0.021560 -0.014578  0.003435  0.016674\n",
       "day      -0.009120  0.004503  1.000000 -0.030206  0.162490 -0.093044 -0.051710\n",
       "duration -0.004648  0.021560 -0.030206  1.000000 -0.084570 -0.001565  0.001203\n",
       "campaign  0.004760 -0.014578  0.162490 -0.084570  1.000000 -0.088628 -0.032855\n",
       "pdays    -0.023758  0.003435 -0.093044 -0.001565 -0.088628  1.000000  0.454820\n",
       "previous  0.001288  0.016674 -0.051710  0.001203 -0.032855  0.454820  1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numeric.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f39d4ab1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "sns.heatmap(data_numeric.corr(),annot=True,linewidths=.5, cmap=\"Blues\")\n",
    "plt.title('Heatmap showing correlations between numerical data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3752b5a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       age         1.000000\n",
       "balance   balance     1.000000\n",
       "pdays     pdays       1.000000\n",
       "campaign  campaign    1.000000\n",
       "day       day         1.000000\n",
       "duration  duration    1.000000\n",
       "previous  previous    1.000000\n",
       "          pdays       0.454820\n",
       "pdays     previous    0.454820\n",
       "campaign  day         0.162490\n",
       "day       campaign    0.162490\n",
       "age       balance     0.097783\n",
       "balance   age         0.097783\n",
       "          duration    0.021560\n",
       "duration  balance     0.021560\n",
       "balance   previous    0.016674\n",
       "previous  balance     0.016674\n",
       "age       campaign    0.004760\n",
       "campaign  age         0.004760\n",
       "balance   day         0.004503\n",
       "day       balance     0.004503\n",
       "balance   pdays       0.003435\n",
       "pdays     balance     0.003435\n",
       "previous  age         0.001288\n",
       "age       previous    0.001288\n",
       "duration  previous    0.001203\n",
       "previous  duration    0.001203\n",
       "pdays     duration   -0.001565\n",
       "duration  pdays      -0.001565\n",
       "age       duration   -0.004648\n",
       "duration  age        -0.004648\n",
       "age       day        -0.009120\n",
       "day       age        -0.009120\n",
       "balance   campaign   -0.014578\n",
       "campaign  balance    -0.014578\n",
       "pdays     age        -0.023758\n",
       "age       pdays      -0.023758\n",
       "duration  day        -0.030206\n",
       "day       duration   -0.030206\n",
       "previous  campaign   -0.032855\n",
       "campaign  previous   -0.032855\n",
       "day       previous   -0.051710\n",
       "previous  day        -0.051710\n",
       "duration  campaign   -0.084570\n",
       "campaign  duration   -0.084570\n",
       "pdays     campaign   -0.088628\n",
       "campaign  pdays      -0.088628\n",
       "day       pdays      -0.093044\n",
       "pdays     day        -0.093044\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numeric.corr().unstack().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76464e1",
   "metadata": {},
   "source": [
    "`pdays` and `previous`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b68b43",
   "metadata": {},
   "source": [
    "## Target encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60490dc",
   "metadata": {},
   "source": [
    "* Now we want to encode the `y` variable\n",
    "* Let's replace the values `yes`/`no` with `1`/`0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df2a9d99",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
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       "      <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>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</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>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</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>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</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>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</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>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</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>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job   marital  education  balance housing    contact  \\\n",
       "0       58    management   married   tertiary     2143     yes    unknown   \n",
       "1       44    technician    single  secondary       29     yes    unknown   \n",
       "2       33  entrepreneur   married  secondary        2     yes    unknown   \n",
       "3       47   blue-collar   married    unknown     1506     yes    unknown   \n",
       "4       33       unknown    single    unknown        1      no    unknown   \n",
       "...    ...           ...       ...        ...      ...     ...        ...   \n",
       "45206   51    technician   married   tertiary      825      no   cellular   \n",
       "45207   71       retired  divorced    primary     1729      no   cellular   \n",
       "45208   72       retired   married  secondary     5715      no   cellular   \n",
       "45209   57   blue-collar   married  secondary      668      no  telephone   \n",
       "45210   37  entrepreneur   married  secondary     2971      no   cellular   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous poutcome  y  \n",
       "0        5   may       261         1     -1         0  unknown  0  \n",
       "1        5   may       151         1     -1         0  unknown  0  \n",
       "2        5   may        76         1     -1         0  unknown  0  \n",
       "3        5   may        92         1     -1         0  unknown  0  \n",
       "4        5   may       198         1     -1         0  unknown  0  \n",
       "...    ...   ...       ...       ...    ...       ...      ... ..  \n",
       "45206   17   nov       977         3     -1         0  unknown  1  \n",
       "45207   17   nov       456         2     -1         0  unknown  1  \n",
       "45208   17   nov      1127         5    184         3  success  1  \n",
       "45209   17   nov       508         4     -1         0  unknown  0  \n",
       "45210   17   nov       361         2    188        11    other  0  \n",
       "\n",
       "[45211 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.y = (data.y == 'yes').astype(int)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a213a5c",
   "metadata": {},
   "source": [
    "## Split the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199bdbda",
   "metadata": {},
   "source": [
    "* Split your data in train/val/test sets with 60%/20%/20% distribution\n",
    "* Use Scikit-Learn for that (the `train_test_split` function) and set the seed to `42`\n",
    "* Make sure that the target value `y` is not in your dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf07feae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e641c553",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_full_train, df_test = train_test_split(data, test_size=0.2, random_state=SEED)\n",
    "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=SEED)\n",
    "\n",
    "assert len(data) == (len(df_train) + len(df_val) + len(df_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8adcc7d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27126, 9042, 9043)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3416c2ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = df_train.reset_index(drop=True)\n",
    "df_val = df_val.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f8073275",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train = df_train.y.values\n",
    "y_val = df_val.y.values\n",
    "y_test = df_test.y.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3c270f",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77812a2",
   "metadata": {},
   "source": [
    "* Calculate the mutual information score between `y` and other categorical variables in the dataset. Use the training set only\n",
    "* Round the scores to 2 decimals using `round(score, 2)`\n",
    "* Which of these variables has the biggest score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a3b6ab2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_mi(series):\n",
    "    return mutual_info_score(series, df_train.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "20f1887a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = ['job', 'marital', 'education', 'housing', 'contact', 'month', 'poutcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0d100616",
   "metadata": {
    "tags": []
   },
   "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>MI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>0.029533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.025090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>0.013356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>0.010343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>0.007316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>0.002697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>0.002050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MI\n",
       "poutcome   0.029533\n",
       "month      0.025090\n",
       "contact    0.013356\n",
       "housing    0.010343\n",
       "job        0.007316\n",
       "education  0.002697\n",
       "marital    0.002050"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mi = df_train[cat].apply(calculate_mi)\n",
    "df_mi = df_mi.sort_values(ascending=False).to_frame(name='MI')\n",
    "df_mi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067dc099",
   "metadata": {},
   "source": [
    "`poutcome` has the biggest score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ab12289",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = df_train.drop('y', axis=1)\n",
    "df_val = df_val.drop('y', axis=1)\n",
    "df_test = df_test.drop('y', axis=1)\n",
    "\n",
    "assert 'y' not in df_train.columns\n",
    "assert 'y' not in df_val.columns\n",
    "assert 'y' not in df_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe5bfa7",
   "metadata": {},
   "source": [
    "## Question 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cac43da",
   "metadata": {},
   "source": [
    "* Now let's train a logistic regression\n",
    "* Remember that we have several categorical variables in the dataset. Include them using one-hot encoding\n",
    "* Fit the model on the training dataset:\n",
    "    * To make sure the results are reproducible across different versions of Scikit-Learn, fit the model with these parameters:\n",
    "    * `model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000, random_state=42)`\n",
    "* Calculate the accuracy on the validation dataset and round it to 2 decimal digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7bf5a1f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "train_dict = df_train.to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eec786c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/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(max_iter=1000, random_state=42, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42, solver='liblinear')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver='liblinear', max_iter=1000, C=1.0, random_state=SEED)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb0ae94c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "val_dict = df_val.to_dict(orient='records')\n",
    "X_val = dv.transform(val_dict)\n",
    "\n",
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ea66aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9011280690112807"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_score = accuracy_score(y_val, y_pred)\n",
    "original_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "96ed5589",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.9\n"
     ]
    }
   ],
   "source": [
    "accuracy = np.round(original_score, 2)\n",
    "print(f'Accuracy = {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fa18b1",
   "metadata": {},
   "source": [
    "## Question 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e247849e",
   "metadata": {},
   "source": [
    "* Let's find the least useful feature using the _feature elimination_ technique\n",
    "* Train a model with all these features (using the same parameters as in Q4)\n",
    "* Now exclude each feature from this set and train a model without it. Record the accuracy for each model\n",
    "* For each feature, calculate the difference between the original accuracy and the accuracy without the feature\n",
    "* Which of following feature has the smallest difference?\n",
    "    - `age`\n",
    "    - `balance`\n",
    "    - `marital`\n",
    "    - `previous`\n",
    "    \n",
    "> **note:** the difference doesn't have to be positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "748a7867",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'job',\n",
       " 'marital',\n",
       " 'education',\n",
       " 'balance',\n",
       " 'housing',\n",
       " 'contact',\n",
       " 'day',\n",
       " 'month',\n",
       " 'duration',\n",
       " 'campaign',\n",
       " 'pdays',\n",
       " 'previous',\n",
       " 'poutcome']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df_train.columns.to_list()\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cabc785c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scores = pd.DataFrame(columns=['eliminated_feature', 'accuracy', 'difference'])\n",
    "for feature in features:\n",
    "    subset = features.copy()\n",
    "    subset.remove(feature)\n",
    "    \n",
    "    dv = DictVectorizer(sparse=False)\n",
    "    train_dict = df_train[subset].to_dict(orient='records')\n",
    "    X_train = dv.fit_transform(train_dict)\n",
    "\n",
    "    model = LogisticRegression(solver='liblinear', max_iter=1000, C=1.0, random_state=SEED)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    val_dict = df_val[subset].to_dict(orient='records')\n",
    "    X_val = dv.transform(val_dict)\n",
    "    \n",
    "    y_pred = model.predict(X_val)\n",
    "    score = accuracy_score(y_val, y_pred)\n",
    "    \n",
    "    scores.loc[len(scores)] = [feature, score, original_score - score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7c253ea7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>eliminated_feature</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.901128</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>job</td>\n",
       "      <td>0.901128</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.900354</td>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education</td>\n",
       "      <td>0.900796</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.901128</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.900575</td>\n",
       "      <td>0.000553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.900796</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>day</td>\n",
       "      <td>0.900796</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>month</td>\n",
       "      <td>0.899690</td>\n",
       "      <td>0.001438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.890290</td>\n",
       "      <td>0.010838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.899801</td>\n",
       "      <td>0.001327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.900907</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.900686</td>\n",
       "      <td>0.000442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.893939</td>\n",
       "      <td>0.007189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eliminated_feature  accuracy  difference\n",
       "0                 age  0.901128    0.000000\n",
       "1                 job  0.901128    0.000000\n",
       "2             marital  0.900354    0.000774\n",
       "3           education  0.900796    0.000332\n",
       "4             balance  0.901128    0.000000\n",
       "5             housing  0.900575    0.000553\n",
       "6             contact  0.900796    0.000332\n",
       "7                 day  0.900796    0.000332\n",
       "8               month  0.899690    0.001438\n",
       "9            duration  0.890290    0.010838\n",
       "10           campaign  0.899801    0.001327\n",
       "11              pdays  0.900907    0.000221\n",
       "12           previous  0.900686    0.000442\n",
       "13           poutcome  0.893939    0.007189"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6fddefae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eliminated_feature</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.901128</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  eliminated_feature  accuracy  difference\n",
       "0                age  0.901128         0.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores[scores.index == scores.difference.abs().idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c45b41",
   "metadata": {},
   "source": [
    "`age` and `balance` features are the least important in our case. So, you we can choose one of them.\n",
    "\n",
    "> **note:** you can get other answers in Google Colab. Therefore, we decided to mark all the answers as correct."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5dede5e",
   "metadata": {},
   "source": [
    "## Question 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527539a6",
   "metadata": {},
   "source": [
    "* Now let's train a regularized logistic regression\n",
    "* Let's try the following values of the parameter `C`: `[0, 0.01, 0.1, 1, 10]`\n",
    "* Train models using all the features as in Q4\n",
    "* Calculate the accuracy on the validation dataset and round it to 3 decimal digits\n",
    "* Which of these `C` leads to the best accuracy on the validation set?\n",
    "> **note:** If there are multiple options, select the smallest `C`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee58b860",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27126,), (9042,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82bd46e8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "train_dict = df_train.to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dict)\n",
    "\n",
    "val_dict = df_val.to_dict(orient='records')\n",
    "X_val = dv.transform(val_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fbcdf686",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.01:\t Accuracy = 0.898363193983632\n",
      "C = 0.1:\t Accuracy = 0.9014598540145985\n",
      "C = 1:\t Accuracy = 0.9011280690112807\n",
      "C = 10:\t Accuracy = 0.9015704490157045\n",
      "C = 100:\t Accuracy = 0.9007962840079629\n"
     ]
    }
   ],
   "source": [
    "scores = {}\n",
    "for C in [0.01, 0.1, 1, 10, 100]:\n",
    "    model = LogisticRegression(solver='liblinear', max_iter=1000, C=C, random_state=SEED)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_val)\n",
    "    \n",
    "    score = accuracy_score(y_val, y_pred)\n",
    "    scores[C] = round(score, 3)\n",
    "    print(f'C = {C}:\\t Accuracy = {score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ceaa5761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.01: 0.898, 0.1: 0.901, 1: 0.901, 10: 0.902, 100: 0.901}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "11cabb9c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The smallest `C` is 10.\n"
     ]
    }
   ],
   "source": [
    "print(f'The smallest `C` is {max(scores, key=scores.get)}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384f76f9",
   "metadata": {},
   "source": [
    "`C = 0.1` is also a valid answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40a23b87",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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