notebooks/mod_03_topic_05_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування логістичної регресії. EDA датасету Rain in Australia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Знайомство з даними"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 23)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../datasets/mod_03_topic_05_weather_data.csv.gz')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  ...  Temp3pm  RainToday  RainTomorrow\n",
       "0  2008-12-01   Albury     13.4  ...     21.8         No            No\n",
       "1  2008-12-02   Albury      7.4  ...     24.3         No            No\n",
       "2  2008-12-03   Albury     12.9  ...     23.2         No            No\n",
       "3  2008-12-04   Albury      9.2  ...     26.5         No            No\n",
       "4  2008-12-05   Albury     17.5  ...     29.7         No            No\n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "Location          object\n",
       "MinTemp          float64\n",
       "MaxTemp          float64\n",
       "Rainfall         float64\n",
       "Evaporation      float64\n",
       "Sunshine         float64\n",
       "WindGustDir       object\n",
       "WindGustSpeed    float64\n",
       "WindDir9am        object\n",
       "WindDir3pm        object\n",
       "WindSpeed9am     float64\n",
       "WindSpeed3pm     float64\n",
       "Humidity9am      float64\n",
       "Humidity3pm      float64\n",
       "Pressure9am      float64\n",
       "Pressure3pm      float64\n",
       "Cloud9am         float64\n",
       "Cloud3pm         float64\n",
       "Temp9am          float64\n",
       "Temp3pm          float64\n",
       "RainToday         object\n",
       "RainTomorrow      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Перевірка на наявність пропущених значень"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunshine         0.480098\n",
       "Evaporation      0.431665\n",
       "Cloud3pm         0.408071\n",
       "Cloud9am         0.384216\n",
       "Pressure9am      0.103568\n",
       "Pressure3pm      0.103314\n",
       "WindDir9am       0.072639\n",
       "WindGustDir      0.070989\n",
       "WindGustSpeed    0.070555\n",
       "Humidity3pm      0.030984\n",
       "WindDir3pm       0.029066\n",
       "Temp3pm          0.024811\n",
       "RainTomorrow     0.022460\n",
       "Rainfall         0.022419\n",
       "RainToday        0.022419\n",
       "WindSpeed3pm     0.021050\n",
       "Humidity9am      0.018246\n",
       "Temp9am          0.012148\n",
       "WindSpeed9am     0.012148\n",
       "MinTemp          0.010209\n",
       "MaxTemp          0.008669\n",
       "Location         0.000000\n",
       "Date             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "    tmp = (data\n",
    "           .groupby('Location')\n",
    "           .apply(lambda x:\n",
    "                  x.drop(['Location', 'Date'], axis=1)\n",
    "                  .isna()\n",
    "                  .mean()))\n",
    "\n",
    "plt.figure(figsize=(9, 13))\n",
    "\n",
    "ax = sns.heatmap(tmp,\n",
    "                 cmap='Blues',\n",
    "                 linewidth=0.5,\n",
    "                 square=True,\n",
    "                 cbar_kws=dict(\n",
    "                     location=\"bottom\",\n",
    "                     pad=0.01,\n",
    "                     shrink=0.25))\n",
    "\n",
    "ax.xaxis.tick_top()\n",
    "ax.tick_params(axis='x', labelrotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.columns[data.isna().mean().lt(0.35)]]\n",
    "\n",
    "data = data.dropna(subset='RainTomorrow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Огляд даних за типами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_num = data.select_dtypes(include=np.number)\n",
    "data_cat = data.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x648 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "melted = data_num.melt()\n",
    "\n",
    "g = sns.FacetGrid(melted,\n",
    "                  col='variable',\n",
    "                  col_wrap=4,\n",
    "                  sharex=False,\n",
    "                  sharey=False,\n",
    "                  aspect=1.25)\n",
    "\n",
    "g.map(sns.histplot, 'value')\n",
    "\n",
    "g.set_titles(col_template='{col_name}')\n",
    "\n",
    "g.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date            3436\n",
       "Location          49\n",
       "WindGustDir       16\n",
       "WindDir9am        16\n",
       "WindDir3pm        16\n",
       "RainToday          2\n",
       "RainTomorrow       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date            [2008-12-01, 2008-12-02, 2008-12-03, 2008-12-0...\n",
       "Location        [Albury, BadgerysCreek, Cobar, CoffsHarbour, M...\n",
       "WindGustDir                                [W, WNW, WSW, NE, NNW]\n",
       "WindDir9am                                  [W, NNW, SE, ENE, SW]\n",
       "WindDir3pm                                   [WNW, WSW, E, NW, W]\n",
       "RainToday                                          [No, Yes, nan]\n",
       "RainTomorrow                                            [No, Yes]\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.apply(lambda x: x.unique()[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Month\n",
       "0  2008    12\n",
       "1  2008    12\n",
       "2  2008    12\n",
       "3  2008    12\n",
       "4  2008    12"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat['Date'] = pd.to_datetime(data['Date'])\n",
    "\n",
    "data_cat[['Year', 'Month']] = (data_cat['Date']\n",
    "                               .apply(lambda x:\n",
    "                                      pd.Series([x.year, x.month])))\n",
    "\n",
    "data_cat.drop('Date', axis=1, inplace=True)\n",
    "\n",
    "data_cat[['Year', 'Month']] = data_cat[['Year', 'Month']].astype(str)\n",
    "\n",
    "data_cat[['Year', 'Month']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування логістичної регресії. Підготовка й обробка даних"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розбиття на тренувальну й тестову вибірки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_num, X_test_num, X_train_cat,  X_test_cat, y_train, y_test = (\n",
    "    train_test_split(\n",
    "        data_num,\n",
    "        data_cat.drop('RainTomorrow', axis=1),\n",
    "        data['RainTomorrow'],\n",
    "        test_size=0.2,\n",
    "        random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Відновлення пропущених значень"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "WindGustSpeed    0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_imputer = SimpleImputer().set_output(transform='pandas')\n",
    "X_train_num = num_imputer.fit_transform(X_train_num)\n",
    "X_test_num = num_imputer.transform(X_test_num)\n",
    "\n",
    "pd.concat([X_train_num, X_test_num]).isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location       0\n",
       "WindGustDir    0\n",
       "WindDir9am     0\n",
       "WindDir3pm     0\n",
       "RainToday      0\n",
       "Year           0\n",
       "Month          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_imputer = SimpleImputer(\n",
    "    strategy='most_frequent').set_output(transform='pandas')\n",
    "X_train_cat = cat_imputer.fit_transform(X_train_cat)\n",
    "X_test_cat = cat_imputer.transform(X_test_cat)\n",
    "\n",
    "pd.concat([X_train_cat, X_test_cat]).isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Нормалізація змінних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().set_output(transform='pandas')\n",
    "\n",
    "X_train_num = scaler.fit_transform(X_train_num)\n",
    "X_test_num = scaler.transform(X_test_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Onehot кодування категоріальних змінних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113754, 121)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = (OneHotEncoder(drop='if_binary',\n",
    "                         sparse_output=False)\n",
    "           .set_output(transform='pandas'))\n",
    "\n",
    "X_train_cat = encoder.fit_transform(X_train_cat)\n",
    "X_test_cat = encoder.transform(X_test_cat)\n",
    "\n",
    "X_train_cat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113754, 133)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.concat([X_train_num, X_train_cat], axis=1)\n",
    "X_test = pd.concat([X_test_num, X_test_cat], axis=1)\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування логістичної регресії. Навчання й оцінка моделі"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розподіл цільової змінної"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RainTomorrow\n",
       "No     0.775516\n",
       "Yes    0.224484\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = (LogisticRegression(solver='liblinear',\n",
    "                          class_weight='balanced',\n",
    "                          random_state=42)\n",
    "       .fit(X_train, y_train))\n",
    "\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оцінювання точності моделі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.92      0.79      0.85     22098\n",
      "         Yes       0.51      0.76      0.61      6341\n",
      "\n",
      "    accuracy                           0.79     28439\n",
      "   macro avg       0.72      0.78      0.73     28439\n",
      "weighted avg       0.83      0.79      0.80     28439\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}