03_classification_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.3\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Homework\n",
    "\n",
    "> Note: sometimes your answer doesn't match one of the options exactly. \n",
    "> That's fine. \n",
    "> Select the option that's closest to your solution.\n",
    "\n",
    "\n",
    "### Dataset\n",
    "\n",
    "In this homework, we will use the Bank Marketing dataset. Download it from [here](https://archive.ics.uci.edu/static/public/222/bank+marketing.zip).\n",
    "\n",
    "Or you can do it with `wget`:\n",
    "\n",
    "```bash\n",
    "wget https://archive.ics.uci.edu/static/public/222/bank+marketing.zip\n",
    "```\n",
    "\n",
    "We need to take `bank/bank-full.csv` file from the downloaded zip-file.  \n",
    "In this dataset our desired target for classification task will be `y` variable - has the client subscribed a term deposit or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!curl -O https://archive.ics.uci.edu/static/public/222/bank+marketing.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!unzip bank+marketing.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!unzip bank.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>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",
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       "      <td>no</td>\n",
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       "      <td>entrepreneur</td>\n",
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       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>5</td>\n",
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       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
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       "      <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",
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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>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</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",
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       "      <td>yes</td>\n",
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       "      <td>retired</td>\n",
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       "      <td>2</td>\n",
       "      <td>-1</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</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>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</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>no</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</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 × 17 columns</p>\n",
       "</div>"
      ],
      "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",
       "45206   51    technician   married   tertiary      no      825      no   no   \n",
       "45207   71       retired  divorced    primary      no     1729      no   no   \n",
       "45208   72       retired   married  secondary      no     5715      no   no   \n",
       "45209   57   blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37  entrepreneur   married  secondary      no     2971      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  \n",
       "...          ...  ...   ...       ...       ...    ...       ...      ...  ...  \n",
       "45206   cellular   17   nov       977         3     -1         0  unknown  yes  \n",
       "45207   cellular   17   nov       456         2     -1         0  unknown  yes  \n",
       "45208   cellular   17   nov      1127         5    184         3  success  yes  \n",
       "45209  telephone   17   nov       508         4     -1         0  unknown   no  \n",
       "45210   cellular   17   nov       361         2    188        11    other   no  \n",
       "\n",
       "[45211 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bank-full.csv', sep=';', header=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
    "### Data preparation\n",
    "\n",
    "* Select only the features from above.\n",
    "* Check if the missing values are presented in the features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "needed_cols = ['age', 'job', 'marital', 'education', 'balance', 'housing', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>33</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>1</td>\n",
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       "      <td>no</td>\n",
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       "      <td>tertiary</td>\n",
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       "      <td>no</td>\n",
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       "      <td>17</td>\n",
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       "      <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",
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       "      <td>yes</td>\n",
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       "    <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[needed_cols]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "What is the most frequent observation (mode) for the column `education`?\n",
    "\n",
    "- `unknown`\n",
    "- `primary`\n",
    "- `secondary`\n",
    "- `tertiary`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>secondary</td>\n",
       "      <td>23202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tertiary</td>\n",
       "      <td>13301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>primary</td>\n",
       "      <td>6851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unknown</td>\n",
       "      <td>1857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  count\n",
       "0  secondary  23202\n",
       "1   tertiary  13301\n",
       "2    primary   6851\n",
       "3    unknown   1857"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.value_counts().reset_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Create the [correlation matrix](https://www.google.com/search?q=correlation+matrix) for the numerical features of your dataset. \n",
    "In a correlation matrix, you compute the correlation coefficient between every pair of features.\n",
    "\n",
    "What are the two features that have the biggest correlation?\n",
    "\n",
    "- `age` and `balance`\n",
    "- `day` and `campaign`\n",
    "- `day` and `pdays`\n",
    "- `pdays` and `previous`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals = list(df.dtypes[df.dtypes != 'object'].index)\n",
    "numericals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_dict = {}\n",
    "for i in numericals:\n",
    "    for j in numericals:\n",
    "        if i != j:\n",
    "            corr = df[i].corr(df[j])\n",
    "            corr_dict[f'{i} and {j} coorrelation is: '] = corr\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest correlation is pdays and previous coorrelation is:  with a value of 0.45481963548050097\n"
     ]
    }
   ],
   "source": [
    "# Find the key with the maximum value in the dictionary\n",
    "max_key = max(corr_dict, key=corr_dict.get)\n",
    "\n",
    "# Get the maximum value\n",
    "max_value = corr_dict[max_key]\n",
    "\n",
    "# Print the key and the value\n",
    "print(f'The highest correlation is {max_key} with a value of {max_value}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097783</td>\n",
       "      <td>-0.009120</td>\n",
       "      <td>-0.004648</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>-0.023758</td>\n",
       "      <td>0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.097783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.014578</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.016674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.009120</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030206</td>\n",
       "      <td>0.162490</td>\n",
       "      <td>-0.093044</td>\n",
       "      <td>-0.051710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    "corr_matrix = df[numericals].corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pdays     previous    0.45482\n",
      "previous  pdays       0.45482\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    " #Get the two largest correlations (excluding the diagonal)\n",
    "corr_matrix_no_diag = corr_matrix.where(~np.eye(corr_matrix.shape[0], dtype=bool))\n",
    "largest_corrs = corr_matrix_no_diag.unstack().nlargest(2)\n",
    "\n",
    "# Print the two largest correlations\n",
    "print(largest_corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       age         1.000000\n",
       "          balance     0.097783\n",
       "          day        -0.009120\n",
       "          duration   -0.004648\n",
       "          campaign    0.004760\n",
       "          pdays      -0.023758\n",
       "          previous    0.001288\n",
       "balance   age         0.097783\n",
       "          balance     1.000000\n",
       "          day         0.004503\n",
       "          duration    0.021560\n",
       "          campaign   -0.014578\n",
       "          pdays       0.003435\n",
       "          previous    0.016674\n",
       "day       age        -0.009120\n",
       "          balance     0.004503\n",
       "          day         1.000000\n",
       "          duration   -0.030206\n",
       "          campaign    0.162490\n",
       "          pdays      -0.093044\n",
       "          previous   -0.051710\n",
       "duration  age        -0.004648\n",
       "          balance     0.021560\n",
       "          day        -0.030206\n",
       "          duration    1.000000\n",
       "          campaign   -0.084570\n",
       "          pdays      -0.001565\n",
       "          previous    0.001203\n",
       "campaign  age         0.004760\n",
       "          balance    -0.014578\n",
       "          day         0.162490\n",
       "          duration   -0.084570\n",
       "          campaign    1.000000\n",
       "          pdays      -0.088628\n",
       "          previous   -0.032855\n",
       "pdays     age        -0.023758\n",
       "          balance     0.003435\n",
       "          day        -0.093044\n",
       "          duration   -0.001565\n",
       "          campaign   -0.088628\n",
       "          pdays       1.000000\n",
       "          previous    0.454820\n",
       "previous  age         0.001288\n",
       "          balance     0.016674\n",
       "          day        -0.051710\n",
       "          duration    0.001203\n",
       "          campaign   -0.032855\n",
       "          pdays       0.454820\n",
       "          previous    1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_series = corr_matrix.unstack()\n",
    "corr_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       balance     0.097783\n",
       "          day        -0.009120\n",
       "          duration   -0.004648\n",
       "          campaign    0.004760\n",
       "          pdays      -0.023758\n",
       "          previous    0.001288\n",
       "balance   age         0.097783\n",
       "          day         0.004503\n",
       "          duration    0.021560\n",
       "          campaign   -0.014578\n",
       "          pdays       0.003435\n",
       "          previous    0.016674\n",
       "day       age        -0.009120\n",
       "          balance     0.004503\n",
       "          duration   -0.030206\n",
       "          campaign    0.162490\n",
       "          pdays      -0.093044\n",
       "          previous   -0.051710\n",
       "duration  age        -0.004648\n",
       "          balance     0.021560\n",
       "          day        -0.030206\n",
       "          campaign   -0.084570\n",
       "          pdays      -0.001565\n",
       "          previous    0.001203\n",
       "campaign  age         0.004760\n",
       "          balance    -0.014578\n",
       "          day         0.162490\n",
       "          duration   -0.084570\n",
       "          pdays      -0.088628\n",
       "          previous   -0.032855\n",
       "pdays     age        -0.023758\n",
       "          balance     0.003435\n",
       "          day        -0.093044\n",
       "          duration   -0.001565\n",
       "          campaign   -0.088628\n",
       "          previous    0.454820\n",
       "previous  age         0.001288\n",
       "          balance     0.016674\n",
       "          day        -0.051710\n",
       "          duration    0.001203\n",
       "          campaign   -0.032855\n",
       "          pdays       0.454820\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_series = corr_series[corr_series < 1]\n",
    "corr_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('pdays', 'previous')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_corr = corr_series.idxmax()\n",
    "max_corr\n",
    "#//NOTE: the answer is pdays and previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4548196354805043"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value = corr_series.max()\n",
    "max_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target encoding\n",
    "\n",
    "* Now we want to encode the `y` variable.\n",
    "* Let's replace the values `yes`/`no` with `1`/`0`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5f/8m68q7196_78q45h5j1_83980000gn/T/ipykernel_61000/2070099191.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['y'] = (df['y'] == 'yes').astype(int)\n"
     ]
    }
   ],
   "source": [
    "df['y'] = (df['y'] == 'yes').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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>2143</td>\n",
       "      <td>yes</td>\n",
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       "      <td>1</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>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
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       "      <td>151</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data\n",
    "\n",
    "* 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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27126, 9042, 9043)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full_train, df_test = train_test_split(df, test_size = 0.2, random_state= 42)\n",
    "df_train, df_val = train_test_split(df_full_train, test_size = 0.25, random_state= 42)\n",
    "\n",
    "len(df_train), len(df_val), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_full_train = df_full_train['y'].values\n",
    "y_train = df_train['y'].values\n",
    "y_val = df_val['y'].values\n",
    "y_test = df_test['y'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_train['y']\n",
    "del df_val['y']\n",
    "del df_test['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "* 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",
    "\n",
    "Which of these variables has the biggest mutual information score?\n",
    "  \n",
    "- `contact`\n",
    "- `education`\n",
    "- `housing`\n",
    "- `poutcome`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mutual_info_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job', 'marital', 'education', 'housing', 'contact', 'month', 'poutcome']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricals = list(df.dtypes[df.dtypes == 'object'].index)\n",
    "categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>housing</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>may</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>may</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>nov</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                job   marital  education housing    contact month poutcome\n",
       "0        management   married   tertiary     yes    unknown   may  unknown\n",
       "1        technician    single  secondary     yes    unknown   may  unknown\n",
       "2      entrepreneur   married  secondary     yes    unknown   may  unknown\n",
       "3       blue-collar   married    unknown     yes    unknown   may  unknown\n",
       "4           unknown    single    unknown      no    unknown   may  unknown\n",
       "...             ...       ...        ...     ...        ...   ...      ...\n",
       "45206    technician   married   tertiary      no   cellular   nov  unknown\n",
       "45207       retired  divorced    primary      no   cellular   nov  unknown\n",
       "45208       retired   married  secondary      no   cellular   nov  success\n",
       "45209   blue-collar   married  secondary      no  telephone   nov  unknown\n",
       "45210  entrepreneur   married  secondary      no   cellular   nov    other\n",
       "\n",
       "[45211 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categoricals]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.007316082778474635"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info_score(df_train.job, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutual_information_subscribed_score(series):\n",
    "    return mutual_info_score(series, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis = df_train[categoricals].apply(mutual_information_subscribed_score)\n",
    "mis.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "* 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.\n",
    "\n",
    "What accuracy did you get?\n",
    "\n",
    "- 0.6\n",
    "- 0.7\n",
    "- 0.8\n",
    "- 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 32,\n",
       "  'balance': 1100,\n",
       "  'day': 11,\n",
       "  'duration': 67,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'entrepreneur',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 38,\n",
       "  'balance': 0,\n",
       "  'day': 17,\n",
       "  'duration': 258,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 49,\n",
       "  'balance': 3309,\n",
       "  'day': 15,\n",
       "  'duration': 349,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'housemaid',\n",
       "  'marital': 'married',\n",
       "  'education': 'primary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 37,\n",
       "  'balance': 2410,\n",
       "  'day': 4,\n",
       "  'duration': 315,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'self-employed',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 31,\n",
       "  'balance': 3220,\n",
       "  'day': 26,\n",
       "  'duration': 74,\n",
       "  'campaign': 4,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'services',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 32,\n",
       "  'balance': 11287,\n",
       "  'day': 21,\n",
       "  'duration': 92,\n",
       "  'campaign': 7,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'success',\n",
       "  'age': 35,\n",
       "  'balance': 0,\n",
       "  'day': 11,\n",
       "  'duration': 229,\n",
       "  'campaign': 2,\n",
       "  'pdays': 96,\n",
       "  'previous': 3},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 39,\n",
       "  'balance': -17,\n",
       "  'day': 28,\n",
       "  'duration': 908,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 60,\n",
       "  'balance': 2934,\n",
       "  'day': 8,\n",
       "  'duration': 233,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 48,\n",
       "  'balance': 3,\n",
       "  'day': 19,\n",
       "  'duration': 289,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'unknown',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 58,\n",
       "  'balance': -205,\n",
       "  'day': 3,\n",
       "  'duration': 497,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'self-employed',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 30,\n",
       "  'balance': 299,\n",
       "  'day': 16,\n",
       "  'duration': 679,\n",
       "  'campaign': 17,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'entrepreneur',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 42,\n",
       "  'balance': -480,\n",
       "  'day': 2,\n",
       "  'duration': 351,\n",
       "  'campaign': 6,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'primary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'telephone',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 50,\n",
       "  'balance': 3764,\n",
       "  'day': 2,\n",
       "  'duration': 108,\n",
       "  'campaign': 4,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'single',\n",
       "  'education': 'primary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 39,\n",
       "  'balance': 366,\n",
       "  'day': 7,\n",
       "  'duration': 166,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 53,\n",
       "  'balance': -31,\n",
       "  'day': 21,\n",
       "  'duration': 82,\n",
       "  'campaign': 8,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'retired',\n",
       "  'marital': 'married',\n",
       "  'education': 'primary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 54,\n",
       "  'balance': 1156,\n",
       "  'day': 16,\n",
       "  'duration': 64,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 44,\n",
       "  'balance': 111,\n",
       "  'day': 25,\n",
       "  'duration': 356,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 35,\n",
       "  'balance': 3297,\n",
       "  'day': 29,\n",
       "  'duration': 193,\n",
       "  'campaign': 15,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'primary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 50,\n",
       "  'balance': 171,\n",
       "  'day': 14,\n",
       "  'duration': 75,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'telephone',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 33,\n",
       "  'balance': 804,\n",
       "  'day': 18,\n",
       "  'duration': 212,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 42,\n",
       "  'balance': 2124,\n",
       "  'day': 15,\n",
       "  'duration': 161,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'primary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 56,\n",
       "  'balance': -116,\n",
       "  'day': 8,\n",
       "  'duration': 1014,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'feb',\n",
       "  'poutcome': 'other',\n",
       "  'age': 33,\n",
       "  'balance': 647,\n",
       "  'day': 4,\n",
       "  'duration': 124,\n",
       "  'campaign': 1,\n",
       "  'pdays': 273,\n",
       "  'previous': 1},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 28,\n",
       "  'balance': 38,\n",
       "  'day': 23,\n",
       "  'duration': 31,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'failure',\n",
       "  'age': 29,\n",
       "  'balance': -219,\n",
       "  'day': 15,\n",
       "  'duration': 85,\n",
       "  'campaign': 3,\n",
       "  'pdays': 368,\n",
       "  'previous': 2},\n",
       " {'job': 'admin.',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'oct',\n",
       "  'poutcome': 'failure',\n",
       "  'age': 55,\n",
       "  'balance': 0,\n",
       "  'day': 7,\n",
       "  'duration': 122,\n",
       "  'campaign': 1,\n",
       "  'pdays': 245,\n",
       "  'previous': 4},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'success',\n",
       "  'age': 46,\n",
       "  'balance': 3434,\n",
       "  'day': 8,\n",
       "  'duration': 148,\n",
       "  'campaign': 2,\n",
       "  'pdays': 101,\n",
       "  'previous': 1},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'sep',\n",
       "  'poutcome': 'other',\n",
       "  'age': 32,\n",
       "  'balance': 260,\n",
       "  'day': 5,\n",
       "  'duration': 13,\n",
       "  'campaign': 1,\n",
       "  'pdays': 656,\n",
       "  'previous': 7},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'primary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 47,\n",
       "  'balance': 52,\n",
       "  'day': 15,\n",
       "  'duration': 140,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'apr',\n",
       "  'poutcome': 'failure',\n",
       "  'age': 32,\n",
       "  'balance': 298,\n",
       "  'day': 17,\n",
       "  'duration': 163,\n",
       "  'campaign': 2,\n",
       "  'pdays': 323,\n",
       "  'previous': 7},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'failure',\n",
       "  'age': 31,\n",
       "  'balance': 846,\n",
       "  'day': 26,\n",
       "  'duration': 405,\n",
       "  'campaign': 3,\n",
       "  'pdays': 205,\n",
       "  'previous': 2},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'single',\n",
       "  'education': 'primary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 34,\n",
       "  'balance': 709,\n",
       "  'day': 20,\n",
       "  'duration': 106,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'services',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 26,\n",
       "  'balance': 549,\n",
       "  'day': 3,\n",
       "  'duration': 149,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'failure',\n",
       "  'age': 37,\n",
       "  'balance': 4596,\n",
       "  'day': 6,\n",
       "  'duration': 323,\n",
       "  'campaign': 1,\n",
       "  'pdays': 91,\n",
       "  'previous': 6},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 31,\n",
       "  'balance': 33,\n",
       "  'day': 15,\n",
       "  'duration': 214,\n",
       "  'campaign': 4,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'admin.',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'success',\n",
       "  'age': 45,\n",
       "  'balance': 205,\n",
       "  'day': 4,\n",
       "  'duration': 180,\n",
       "  'campaign': 1,\n",
       "  'pdays': 92,\n",
       "  'previous': 3},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 32,\n",
       "  'balance': 130,\n",
       "  'day': 6,\n",
       "  'duration': 302,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'services',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 37,\n",
       "  'balance': 78,\n",
       "  'day': 7,\n",
       "  'duration': 295,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'admin.',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 37,\n",
       "  'balance': -557,\n",
       "  'day': 15,\n",
       "  'duration': 284,\n",
       "  'campaign': 15,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 53,\n",
       "  'balance': 6164,\n",
       "  'day': 17,\n",
       "  'duration': 101,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 30,\n",
       "  'balance': 0,\n",
       "  'day': 14,\n",
       "  'duration': 47,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'student',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'mar',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 23,\n",
       "  'balance': 4126,\n",
       "  'day': 25,\n",
       "  'duration': 96,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 36,\n",
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       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 45,\n",
       "  'balance': 1003,\n",
       "  'day': 4,\n",
       "  'duration': 71,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'services',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'telephone',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 39,\n",
       "  'balance': 293,\n",
       "  'day': 28,\n",
       "  'duration': 148,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'entrepreneur',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jan',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 31,\n",
       "  'balance': 137,\n",
       "  'day': 29,\n",
       "  'duration': 445,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'apr',\n",
       "  'poutcome': 'other',\n",
       "  'age': 37,\n",
       "  'balance': -129,\n",
       "  'day': 20,\n",
       "  'duration': 123,\n",
       "  'campaign': 4,\n",
       "  'pdays': 328,\n",
       "  'previous': 1},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'telephone',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 48,\n",
       "  'balance': 1893,\n",
       "  'day': 29,\n",
       "  'duration': 538,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'admin.',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'feb',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 39,\n",
       "  'balance': 7,\n",
       "  'day': 2,\n",
       "  'duration': 550,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'unemployed',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'feb',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 31,\n",
       "  'balance': 381,\n",
       "  'day': 9,\n",
       "  'duration': 42,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 45,\n",
       "  'balance': 546,\n",
       "  'day': 9,\n",
       "  'duration': 152,\n",
       "  'campaign': 4,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'single',\n",
       "  'education': 'unknown',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'apr',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 37,\n",
       "  'balance': 504,\n",
       "  'day': 16,\n",
       "  'duration': 555,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 55,\n",
       "  'balance': 328,\n",
       "  'day': 27,\n",
       "  'duration': 335,\n",
       "  'campaign': 4,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'services',\n",
       "  'marital': 'married',\n",
       "  'education': 'unknown',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 51,\n",
       "  'balance': 224,\n",
       "  'day': 29,\n",
       "  'duration': 52,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'student',\n",
       "  'marital': 'single',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 29,\n",
       "  'balance': 1124,\n",
       "  'day': 24,\n",
       "  'duration': 26,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'self-employed',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 32,\n",
       "  'balance': 518,\n",
       "  'day': 27,\n",
       "  'duration': 462,\n",
       "  'campaign': 5,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'admin.',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 47,\n",
       "  'balance': 869,\n",
       "  'day': 29,\n",
       "  'duration': 216,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'married',\n",
       "  'education': 'unknown',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 29,\n",
       "  'balance': 356,\n",
       "  'day': 4,\n",
       "  'duration': 169,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'single',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 31,\n",
       "  'balance': 8436,\n",
       "  'day': 19,\n",
       "  'duration': 162,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'nov',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 53,\n",
       "  'balance': 453,\n",
       "  'day': 17,\n",
       "  'duration': 148,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'unknown',\n",
       "  'month': 'jun',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 29,\n",
       "  'balance': 620,\n",
       "  'day': 18,\n",
       "  'duration': 72,\n",
       "  'campaign': 3,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'unknown',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jan',\n",
       "  'poutcome': 'other',\n",
       "  'age': 34,\n",
       "  'balance': 975,\n",
       "  'day': 30,\n",
       "  'duration': 263,\n",
       "  'campaign': 2,\n",
       "  'pdays': 191,\n",
       "  'previous': 1},\n",
       " {'job': 'management',\n",
       "  'marital': 'married',\n",
       "  'education': 'tertiary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'other',\n",
       "  'age': 33,\n",
       "  'balance': 643,\n",
       "  'day': 2,\n",
       "  'duration': 203,\n",
       "  'campaign': 3,\n",
       "  'pdays': 94,\n",
       "  'previous': 1},\n",
       " {'job': 'technician',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'no',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'aug',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 43,\n",
       "  'balance': 159,\n",
       "  'day': 11,\n",
       "  'duration': 160,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'blue-collar',\n",
       "  'marital': 'divorced',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'may',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 42,\n",
       "  'balance': 498,\n",
       "  'day': 11,\n",
       "  'duration': 173,\n",
       "  'campaign': 2,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " {'job': 'technician',\n",
       "  'marital': 'married',\n",
       "  'education': 'secondary',\n",
       "  'housing': 'yes',\n",
       "  'contact': 'cellular',\n",
       "  'month': 'jul',\n",
       "  'poutcome': 'unknown',\n",
       "  'age': 53,\n",
       "  'balance': 768,\n",
       "  'day': 10,\n",
       "  'duration': 218,\n",
       "  'campaign': 1,\n",
       "  'pdays': -1,\n",
       "  'previous': 0},\n",
       " ...]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dicts = df_train[categoricals + numericals].to_dict(orient= 'records')\n",
    "train_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = dv.fit_transform(train_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'balance', 'campaign', 'contact=cellular',\n",
       "       'contact=telephone', 'contact=unknown', 'day', 'duration',\n",
       "       'education=primary', 'education=secondary', 'education=tertiary',\n",
       "       'education=unknown', 'housing=no', 'housing=yes', 'job=admin.',\n",
       "       'job=blue-collar', 'job=entrepreneur', 'job=housemaid',\n",
       "       'job=management', 'job=retired', 'job=self-employed',\n",
       "       'job=services', 'job=student', 'job=technician', 'job=unemployed',\n",
       "       'job=unknown', 'marital=divorced', 'marital=married',\n",
       "       'marital=single', 'month=apr', 'month=aug', 'month=dec',\n",
       "       'month=feb', 'month=jan', 'month=jul', 'month=jun', 'month=mar',\n",
       "       'month=may', 'month=nov', 'month=oct', 'month=sep', 'pdays',\n",
       "       'poutcome=failure', 'poutcome=other', 'poutcome=success',\n",
       "       'poutcome=unknown', 'previous'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dicts = df_val[categoricals + numericals].to_dict(orient= 'records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27126, 47), (9042, 47))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96831215, 0.03168785],\n",
       "       [0.96890418, 0.03109582],\n",
       "       [0.93337122, 0.06662878],\n",
       "       ...,\n",
       "       [0.99121962, 0.00878038],\n",
       "       [0.2376122 , 0.7623878 ],\n",
       "       [0.86165266, 0.13834734]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01249077, 0.00926422, 0.14663636, ..., 0.05021294, 0.00872845,\n",
       "       0.27989186])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(X_val)[:, 1]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscribe_decision = (y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscribe_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9014598540145985"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_val == subscribe_decision).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#//NOTE: nézzük meg ,hogy mi az eredmény ha predict_proba, helyett csak a predicttel nézem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_val)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9014598540145985"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_val == y_pred).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 \n",
    "\n",
    "* 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",
    "\n",
    "Which of following feature has the smallest difference?\n",
    "\n",
    "- `age`\n",
    "- `balance`\n",
    "- `marital`\n",
    "- `previous`\n",
    "\n",
    "> **Note**: The difference doesn't have to be positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>balance</th>\n",
       "      <th>housing</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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education  balance housing  contact  day month  \\\n",
       "0   58    management  married   tertiary     2143     yes  unknown    5   may   \n",
       "1   44    technician   single  secondary       29     yes  unknown    5   may   \n",
       "2   33  entrepreneur  married  secondary        2     yes  unknown    5   may   \n",
       "3   47   blue-collar  married    unknown     1506     yes  unknown    5   may   \n",
       "4   33       unknown   single    unknown        1      no  unknown    5   may   \n",
       "\n",
       "   duration  campaign  pdays  previous poutcome  y  \n",
       "0       261         1     -1         0  unknown  0  \n",
       "1       151         1     -1         0  unknown  0  \n",
       "2        76         1     -1         0  unknown  0  \n",
       "3        92         1     -1         0  unknown  0  \n",
       "4       198         1     -1         0  unknown  0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = categoricals + numericals\n",
    "omitt = ['age', 'balance', 'marital', 'previous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy without age feature is 0.9007962840079629 compare to 0.9014598540145985 which is the \"original accuracy\"\n",
      "\n",
      "The accuracy without balance feature is 0.9007962840079629 compare to 0.9014598540145985 which is the \"original accuracy\"\n",
      "\n",
      "The accuracy without marital feature is 0.9000221190002212 compare to 0.9014598540145985 which is the \"original accuracy\"\n",
      "\n",
      "The accuracy without previous feature is 0.9012386640123866 compare to 0.9014598540145985 which is the \"original accuracy\"\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'marital': 0.0014377350143772727,\n",
       " 'age': 0.0006635700066356387,\n",
       " 'balance': 0.0006635700066356387,\n",
       " 'previous': 0.00022119000221187957}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_accuracy = (y_val == subscribe_decision).mean()\n",
    "\n",
    "fe_dictionary = {}\n",
    "\n",
    "for i in omitt:\n",
    "    df_train_omitt = df_train.copy()\n",
    "    df_val_omitt = df_val.copy()\n",
    "    del df_train_omitt[i]\n",
    "    del df_val_omitt[i]\n",
    "    train_dicts = df_train_omitt.to_dict(orient= 'records')\n",
    "    X_train = dv.fit_transform(train_dicts)\n",
    "    val_dicts = df_val_omitt.to_dict(orient= 'records')\n",
    "    X_val = dv.transform(val_dicts)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict_proba(X_val)[:, 1]\n",
    "    subscribe_decision = (y_pred > 0.5)\n",
    "    accuracy = (y_val == subscribe_decision).mean()\n",
    "    print(f'The accuracy without {i} feature is {accuracy} compare to {original_accuracy} which is the \"original accuracy\"')\n",
    "    print()\n",
    "    decrease_in_accuracy = original_accuracy - accuracy\n",
    "    fe_dictionary[i] = decrease_in_accuracy\n",
    "\n",
    "sorted_dict = dict(sorted(fe_dictionary.items(), key=lambda item: item[1], reverse=True))\n",
    "sorted_dict\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "* 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",
    "\n",
    "Which of these `C` leads to the best accuracy on the validation set?\n",
    "\n",
    "- 0.01\n",
    "- 0.1\n",
    "- 1\n",
    "- 10\n",
    "- 100\n",
    "\n",
    "> **Note**: If there are multiple options, select the smallest `C`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy with 0.001 regularization is 0.8904003539040035\n",
      "The accuracy with 0.01 regularization is 0.8976996239769962\n",
      "The accuracy with 0.1 regularization is 0.900575094005751\n",
      "The accuracy with 1 regularization is 0.9014598540145985\n",
      "The accuracy with 10 regularization is 0.9010174740101747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0.1: 0.901, 1: 0.901, 10: 0.901, 0.01: 0.898, 0.001: 0.89}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_accuracy = (y_val == subscribe_decision).mean()\n",
    "\n",
    "regs = [0.001, 0.01, 0.1, 1, 10]\n",
    "\n",
    "reg_dictionary = {}\n",
    "\n",
    "\n",
    "for i in regs:\n",
    "    model = LogisticRegression(solver='liblinear', C=i, max_iter=1000, random_state=42)\n",
    "    df_train = df_train.copy()\n",
    "    df_val = df_val.copy()\n",
    "    train_dicts = df_train[categoricals + numericals].to_dict(orient= 'records')\n",
    "    X_train = dv.fit_transform(train_dicts)\n",
    "    val_dicts = df_val[categoricals + numericals].to_dict(orient= 'records')\n",
    "    X_val = dv.transform(val_dicts)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict_proba(X_val)[:, 1]\n",
    "    subscribe_decision = (y_pred > 0.5)\n",
    "    accuracy = (y_val == subscribe_decision).mean()\n",
    "    print(f'The accuracy with {i} regularization is {accuracy}')\n",
    "    \n",
    "    reg_dictionary[i] = accuracy.round(3)\n",
    "\n",
    "\n",
    "sorted_reg_dict = dict(sorted(reg_dictionary.items(), key=lambda item: item[1], reverse=True))\n",
    "sorted_reg_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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