Logistic Regression - final version (edited) (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 - import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import category_encoders as ce\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as st\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 - load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>Gender</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>age</th>\n",
       "      <th>type_of_travel</th>\n",
       "      <th>customer_class</th>\n",
       "      <th>flight_distance</th>\n",
       "      <th>inflight_wifi_service</th>\n",
       "      <th>departure_arrival_time_convenient</th>\n",
       "      <th>ease_of_online_booking</th>\n",
       "      <th>...</th>\n",
       "      <th>inflight_entertainment</th>\n",
       "      <th>onboard_service</th>\n",
       "      <th>leg_room_service</th>\n",
       "      <th>baggage_handling</th>\n",
       "      <th>checkin_service</th>\n",
       "      <th>inflight_service</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>departure_delay_in_minutes</th>\n",
       "      <th>arrival_delay_in_minutes</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>13</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco Plus</td>\n",
       "      <td>460</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>235</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>26</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1142</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>562</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>61</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Gender      customer_type  age   type_of_travel customer_class  \\\n",
       "0           0    Male     Loyal Customer   13  Personal Travel       Eco Plus   \n",
       "1           1    Male  disloyal Customer   25  Business travel       Business   \n",
       "2           2  Female     Loyal Customer   26  Business travel       Business   \n",
       "3           3  Female     Loyal Customer   25  Business travel       Business   \n",
       "4           4    Male     Loyal Customer   61  Business travel       Business   \n",
       "\n",
       "   flight_distance  inflight_wifi_service  departure_arrival_time_convenient  \\\n",
       "0              460                      3                                  4   \n",
       "1              235                      3                                  2   \n",
       "2             1142                      2                                  2   \n",
       "3              562                      2                                  5   \n",
       "4              214                      3                                  3   \n",
       "\n",
       "   ease_of_online_booking  ...  inflight_entertainment  onboard_service  \\\n",
       "0                       3  ...                       5                4   \n",
       "1                       3  ...                       1                1   \n",
       "2                       2  ...                       5                4   \n",
       "3                       5  ...                       2                2   \n",
       "4                       3  ...                       3                3   \n",
       "\n",
       "   leg_room_service  baggage_handling  checkin_service  inflight_service  \\\n",
       "0                 3                 4                4                 5   \n",
       "1                 5                 3                1                 4   \n",
       "2                 3                 4                4                 4   \n",
       "3                 5                 3                1                 4   \n",
       "4                 4                 4                3                 3   \n",
       "\n",
       "   cleanliness  departure_delay_in_minutes  arrival_delay_in_minutes  \\\n",
       "0            5                          25                      18.0   \n",
       "1            1                           1                       6.0   \n",
       "2            5                           0                       0.0   \n",
       "3            2                          11                       9.0   \n",
       "4            3                           0                       0.0   \n",
       "\n",
       "              satisfaction  \n",
       "0  neutral or dissatisfied  \n",
       "1  neutral or dissatisfied  \n",
       "2                satisfied  \n",
       "3  neutral or dissatisfied  \n",
       "4                satisfied  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aps represents air passengers satisfaction\n",
    "aps_df = pd.read_csv(\"airline_passenger_satisfaction.csv\")\n",
    "aps_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of variables\n",
    "aps_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 129880 entries, 0 to 129879\n",
      "Data columns (total 24 columns):\n",
      " #   Column                             Non-Null Count   Dtype  \n",
      "---  ------                             --------------   -----  \n",
      " 0   Unnamed: 0                         129880 non-null  int64  \n",
      " 1   Gender                             129880 non-null  object \n",
      " 2   customer_type                      129880 non-null  object \n",
      " 3   age                                129880 non-null  int64  \n",
      " 4   type_of_travel                     129880 non-null  object \n",
      " 5   customer_class                     129880 non-null  object \n",
      " 6   flight_distance                    129880 non-null  int64  \n",
      " 7   inflight_wifi_service              129880 non-null  int64  \n",
      " 8   departure_arrival_time_convenient  129880 non-null  int64  \n",
      " 9   ease_of_online_booking             129880 non-null  int64  \n",
      " 10  gate_location                      129880 non-null  int64  \n",
      " 11  food_and_drink                     129880 non-null  int64  \n",
      " 12  online_boarding                    129880 non-null  int64  \n",
      " 13  seat_comfort                       129880 non-null  int64  \n",
      " 14  inflight_entertainment             129880 non-null  int64  \n",
      " 15  onboard_service                    129880 non-null  int64  \n",
      " 16  leg_room_service                   129880 non-null  int64  \n",
      " 17  baggage_handling                   129880 non-null  int64  \n",
      " 18  checkin_service                    129880 non-null  int64  \n",
      " 19  inflight_service                   129880 non-null  int64  \n",
      " 20  cleanliness                        129880 non-null  int64  \n",
      " 21  departure_delay_in_minutes         129880 non-null  int64  \n",
      " 22  arrival_delay_in_minutes           129487 non-null  float64\n",
      " 23  satisfaction                       129880 non-null  object \n",
      "dtypes: float64(1), int64(18), object(5)\n",
      "memory usage: 23.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# check for object (non-numerical type) data\n",
    "aps_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>flight_distance</th>\n",
       "      <th>inflight_wifi_service</th>\n",
       "      <th>departure_arrival_time_convenient</th>\n",
       "      <th>ease_of_online_booking</th>\n",
       "      <th>gate_location</th>\n",
       "      <th>food_and_drink</th>\n",
       "      <th>online_boarding</th>\n",
       "      <th>seat_comfort</th>\n",
       "      <th>inflight_entertainment</th>\n",
       "      <th>onboard_service</th>\n",
       "      <th>leg_room_service</th>\n",
       "      <th>baggage_handling</th>\n",
       "      <th>checkin_service</th>\n",
       "      <th>inflight_service</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>departure_delay_in_minutes</th>\n",
       "      <th>arrival_delay_in_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129487.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64939.500000</td>\n",
       "      <td>39.427957</td>\n",
       "      <td>1190.316392</td>\n",
       "      <td>2.728696</td>\n",
       "      <td>3.057599</td>\n",
       "      <td>2.756876</td>\n",
       "      <td>2.976925</td>\n",
       "      <td>3.204774</td>\n",
       "      <td>3.252633</td>\n",
       "      <td>3.441361</td>\n",
       "      <td>3.358077</td>\n",
       "      <td>3.383023</td>\n",
       "      <td>3.350878</td>\n",
       "      <td>3.632114</td>\n",
       "      <td>3.306267</td>\n",
       "      <td>3.642193</td>\n",
       "      <td>3.286326</td>\n",
       "      <td>14.713713</td>\n",
       "      <td>15.091129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>37493.270818</td>\n",
       "      <td>15.119360</td>\n",
       "      <td>997.452477</td>\n",
       "      <td>1.329340</td>\n",
       "      <td>1.526741</td>\n",
       "      <td>1.401740</td>\n",
       "      <td>1.278520</td>\n",
       "      <td>1.329933</td>\n",
       "      <td>1.350719</td>\n",
       "      <td>1.319289</td>\n",
       "      <td>1.334049</td>\n",
       "      <td>1.287099</td>\n",
       "      <td>1.316252</td>\n",
       "      <td>1.180025</td>\n",
       "      <td>1.266185</td>\n",
       "      <td>1.176669</td>\n",
       "      <td>1.313682</td>\n",
       "      <td>38.071126</td>\n",
       "      <td>38.465650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32469.750000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64939.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>844.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>97409.250000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1744.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>129879.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1592.000000</td>\n",
       "      <td>1584.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0            age  flight_distance  inflight_wifi_service  \\\n",
       "count  129880.000000  129880.000000    129880.000000          129880.000000   \n",
       "mean    64939.500000      39.427957      1190.316392               2.728696   \n",
       "std     37493.270818      15.119360       997.452477               1.329340   \n",
       "min         0.000000       7.000000        31.000000               0.000000   \n",
       "25%     32469.750000      27.000000       414.000000               2.000000   \n",
       "50%     64939.500000      40.000000       844.000000               3.000000   \n",
       "75%     97409.250000      51.000000      1744.000000               4.000000   \n",
       "max    129879.000000      85.000000      4983.000000               5.000000   \n",
       "\n",
       "       departure_arrival_time_convenient  ease_of_online_booking  \\\n",
       "count                      129880.000000           129880.000000   \n",
       "mean                            3.057599                2.756876   \n",
       "std                             1.526741                1.401740   \n",
       "min                             0.000000                0.000000   \n",
       "25%                             2.000000                2.000000   \n",
       "50%                             3.000000                3.000000   \n",
       "75%                             4.000000                4.000000   \n",
       "max                             5.000000                5.000000   \n",
       "\n",
       "       gate_location  food_and_drink  online_boarding   seat_comfort  \\\n",
       "count  129880.000000   129880.000000    129880.000000  129880.000000   \n",
       "mean        2.976925        3.204774         3.252633       3.441361   \n",
       "std         1.278520        1.329933         1.350719       1.319289   \n",
       "min         0.000000        0.000000         0.000000       0.000000   \n",
       "25%         2.000000        2.000000         2.000000       2.000000   \n",
       "50%         3.000000        3.000000         3.000000       4.000000   \n",
       "75%         4.000000        4.000000         4.000000       5.000000   \n",
       "max         5.000000        5.000000         5.000000       5.000000   \n",
       "\n",
       "       inflight_entertainment  onboard_service  leg_room_service  \\\n",
       "count           129880.000000    129880.000000     129880.000000   \n",
       "mean                 3.358077         3.383023          3.350878   \n",
       "std                  1.334049         1.287099          1.316252   \n",
       "min                  0.000000         0.000000          0.000000   \n",
       "25%                  2.000000         2.000000          2.000000   \n",
       "50%                  4.000000         4.000000          4.000000   \n",
       "75%                  4.000000         4.000000          4.000000   \n",
       "max                  5.000000         5.000000          5.000000   \n",
       "\n",
       "       baggage_handling  checkin_service  inflight_service    cleanliness  \\\n",
       "count     129880.000000    129880.000000     129880.000000  129880.000000   \n",
       "mean           3.632114         3.306267          3.642193       3.286326   \n",
       "std            1.180025         1.266185          1.176669       1.313682   \n",
       "min            1.000000         0.000000          0.000000       0.000000   \n",
       "25%            3.000000         3.000000          3.000000       2.000000   \n",
       "50%            4.000000         3.000000          4.000000       3.000000   \n",
       "75%            5.000000         4.000000          5.000000       4.000000   \n",
       "max            5.000000         5.000000          5.000000       5.000000   \n",
       "\n",
       "       departure_delay_in_minutes  arrival_delay_in_minutes  \n",
       "count               129880.000000             129487.000000  \n",
       "mean                    14.713713                 15.091129  \n",
       "std                     38.071126                 38.465650  \n",
       "min                      0.000000                  0.000000  \n",
       "25%                      0.000000                  0.000000  \n",
       "50%                      0.000000                  0.000000  \n",
       "75%                     12.000000                 13.000000  \n",
       "max                   1592.000000               1584.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to show description of the data in the DataFrame\n",
    "aps_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neutral or dissatisfied', 'satisfied'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aps_df[\"satisfaction\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender            2\n",
      "customer_type     2\n",
      "type_of_travel    2\n",
      "customer_class    3\n",
      "satisfaction      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Convert Object into int/float\n",
    "\n",
    "cat_columns = aps_df.select_dtypes(include = ['object'])\n",
    "unique_values = cat_columns.nunique(dropna=False)\n",
    "print (unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the column \"Unnamed: 0\"\n",
    "aps_df = aps_df.drop('Unnamed: 0', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Data pre-processing - identify the missing value for each variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                 0\n",
       "customer_type                          0\n",
       "age                                    0\n",
       "type_of_travel                         0\n",
       "customer_class                         0\n",
       "flight_distance                        0\n",
       "inflight_wifi_service                  0\n",
       "departure_arrival_time_convenient      0\n",
       "ease_of_online_booking                 0\n",
       "gate_location                          0\n",
       "food_and_drink                         0\n",
       "online_boarding                        0\n",
       "seat_comfort                           0\n",
       "inflight_entertainment                 0\n",
       "onboard_service                        0\n",
       "leg_room_service                       0\n",
       "baggage_handling                       0\n",
       "checkin_service                        0\n",
       "inflight_service                       0\n",
       "cleanliness                            0\n",
       "departure_delay_in_minutes             0\n",
       "arrival_delay_in_minutes             393\n",
       "satisfaction                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check for NaN value\n",
    "aps_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>age</th>\n",
       "      <th>type_of_travel</th>\n",
       "      <th>customer_class</th>\n",
       "      <th>flight_distance</th>\n",
       "      <th>inflight_wifi_service</th>\n",
       "      <th>departure_arrival_time_convenient</th>\n",
       "      <th>ease_of_online_booking</th>\n",
       "      <th>gate_location</th>\n",
       "      <th>...</th>\n",
       "      <th>inflight_entertainment</th>\n",
       "      <th>onboard_service</th>\n",
       "      <th>leg_room_service</th>\n",
       "      <th>baggage_handling</th>\n",
       "      <th>checkin_service</th>\n",
       "      <th>inflight_service</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>departure_delay_in_minutes</th>\n",
       "      <th>arrival_delay_in_minutes</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>13</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco Plus</td>\n",
       "      <td>460</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>235</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>26</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1142</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>562</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>61</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129875</th>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>34</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>526</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129876</th>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>23</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>646</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129877</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>17</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>828</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129878</th>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>14</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1127</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129879</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>42</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>264</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129487 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender      customer_type  age   type_of_travel customer_class  \\\n",
       "0         Male     Loyal Customer   13  Personal Travel       Eco Plus   \n",
       "1         Male  disloyal Customer   25  Business travel       Business   \n",
       "2       Female     Loyal Customer   26  Business travel       Business   \n",
       "3       Female     Loyal Customer   25  Business travel       Business   \n",
       "4         Male     Loyal Customer   61  Business travel       Business   \n",
       "...        ...                ...  ...              ...            ...   \n",
       "129875    Male  disloyal Customer   34  Business travel       Business   \n",
       "129876    Male     Loyal Customer   23  Business travel       Business   \n",
       "129877  Female     Loyal Customer   17  Personal Travel            Eco   \n",
       "129878    Male     Loyal Customer   14  Business travel       Business   \n",
       "129879  Female     Loyal Customer   42  Personal Travel            Eco   \n",
       "\n",
       "        flight_distance  inflight_wifi_service  \\\n",
       "0                   460                      3   \n",
       "1                   235                      3   \n",
       "2                  1142                      2   \n",
       "3                   562                      2   \n",
       "4                   214                      3   \n",
       "...                 ...                    ...   \n",
       "129875              526                      3   \n",
       "129876              646                      4   \n",
       "129877              828                      2   \n",
       "129878             1127                      3   \n",
       "129879              264                      2   \n",
       "\n",
       "        departure_arrival_time_convenient  ease_of_online_booking  \\\n",
       "0                                       4                       3   \n",
       "1                                       2                       3   \n",
       "2                                       2                       2   \n",
       "3                                       5                       5   \n",
       "4                                       3                       3   \n",
       "...                                   ...                     ...   \n",
       "129875                                  3                       3   \n",
       "129876                                  4                       4   \n",
       "129877                                  5                       1   \n",
       "129878                                  3                       3   \n",
       "129879                                  5                       2   \n",
       "\n",
       "        gate_location  ...  inflight_entertainment  onboard_service  \\\n",
       "0                   1  ...                       5                4   \n",
       "1                   3  ...                       1                1   \n",
       "2                   2  ...                       5                4   \n",
       "3                   5  ...                       2                2   \n",
       "4                   3  ...                       3                3   \n",
       "...               ...  ...                     ...              ...   \n",
       "129875              1  ...                       4                3   \n",
       "129876              4  ...                       4                4   \n",
       "129877              5  ...                       2                4   \n",
       "129878              3  ...                       4                3   \n",
       "129879              5  ...                       1                1   \n",
       "\n",
       "        leg_room_service  baggage_handling  checkin_service  inflight_service  \\\n",
       "0                      3                 4                4                 5   \n",
       "1                      5                 3                1                 4   \n",
       "2                      3                 4                4                 4   \n",
       "3                      5                 3                1                 4   \n",
       "4                      4                 4                3                 3   \n",
       "...                  ...               ...              ...               ...   \n",
       "129875                 2                 4                4                 5   \n",
       "129876                 5                 5                5                 5   \n",
       "129877                 3                 4                5                 4   \n",
       "129878                 2                 5                4                 5   \n",
       "129879                 2                 1                1                 1   \n",
       "\n",
       "        cleanliness  departure_delay_in_minutes  arrival_delay_in_minutes  \\\n",
       "0                 5                          25                      18.0   \n",
       "1                 1                           1                       6.0   \n",
       "2                 5                           0                       0.0   \n",
       "3                 2                          11                       9.0   \n",
       "4                 3                           0                       0.0   \n",
       "...             ...                         ...                       ...   \n",
       "129875            4                           0                       0.0   \n",
       "129876            4                           0                       0.0   \n",
       "129877            2                           0                       0.0   \n",
       "129878            4                           0                       0.0   \n",
       "129879            1                           0                       0.0   \n",
       "\n",
       "                   satisfaction  \n",
       "0       neutral or dissatisfied  \n",
       "1       neutral or dissatisfied  \n",
       "2                     satisfied  \n",
       "3       neutral or dissatisfied  \n",
       "4                     satisfied  \n",
       "...                         ...  \n",
       "129875  neutral or dissatisfied  \n",
       "129876                satisfied  \n",
       "129877  neutral or dissatisfied  \n",
       "129878                satisfied  \n",
       "129879  neutral or dissatisfied  \n",
       "\n",
       "[129487 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dealing with the NaN value\n",
    "# drop the row/data with NaN value\n",
    "aps_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 129880 entries, 0 to 129879\n",
      "Data columns (total 23 columns):\n",
      " #   Column                             Non-Null Count   Dtype  \n",
      "---  ------                             --------------   -----  \n",
      " 0   Gender                             129880 non-null  object \n",
      " 1   customer_type                      129880 non-null  object \n",
      " 2   age                                129880 non-null  int64  \n",
      " 3   type_of_travel                     129880 non-null  object \n",
      " 4   customer_class                     129880 non-null  object \n",
      " 5   flight_distance                    129880 non-null  int64  \n",
      " 6   inflight_wifi_service              129880 non-null  int64  \n",
      " 7   departure_arrival_time_convenient  129880 non-null  int64  \n",
      " 8   ease_of_online_booking             129880 non-null  int64  \n",
      " 9   gate_location                      129880 non-null  int64  \n",
      " 10  food_and_drink                     129880 non-null  int64  \n",
      " 11  online_boarding                    129880 non-null  int64  \n",
      " 12  seat_comfort                       129880 non-null  int64  \n",
      " 13  inflight_entertainment             129880 non-null  int64  \n",
      " 14  onboard_service                    129880 non-null  int64  \n",
      " 15  leg_room_service                   129880 non-null  int64  \n",
      " 16  baggage_handling                   129880 non-null  int64  \n",
      " 17  checkin_service                    129880 non-null  int64  \n",
      " 18  inflight_service                   129880 non-null  int64  \n",
      " 19  cleanliness                        129880 non-null  int64  \n",
      " 20  departure_delay_in_minutes         129880 non-null  int64  \n",
      " 21  arrival_delay_in_minutes           129487 non-null  float64\n",
      " 22  satisfaction                       129880 non-null  object \n",
      "dtypes: float64(1), int64(17), object(5)\n",
      "memory usage: 22.8+ MB\n"
     ]
    }
   ],
   "source": [
    "aps_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "aps_df.satisfaction = [1 if v == 'satisfied' else 0 for v in aps_df.satisfaction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Gender', 'customer_type', 'type_of_travel', 'customer_class'],\n",
       " ['age',\n",
       "  'flight_distance',\n",
       "  'inflight_wifi_service',\n",
       "  'departure_arrival_time_convenient',\n",
       "  'ease_of_online_booking',\n",
       "  'gate_location',\n",
       "  'food_and_drink',\n",
       "  'online_boarding',\n",
       "  'seat_comfort',\n",
       "  'inflight_entertainment',\n",
       "  'onboard_service',\n",
       "  'leg_room_service',\n",
       "  'baggage_handling',\n",
       "  'checkin_service',\n",
       "  'inflight_service',\n",
       "  'cleanliness',\n",
       "  'departure_delay_in_minutes',\n",
       "  'arrival_delay_in_minutes'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_numeric = []\n",
    "columns_cat = []\n",
    "\n",
    "for col in aps_df.columns:\n",
    "    \n",
    "    if col in ['satisfaction']:\n",
    "        continue\n",
    "    \n",
    "    if aps_df[col].dtype in ['int64', 'float64']:\n",
    "        columns_numeric.append(col)\n",
    "    else: \n",
    "        columns_cat.append(col)\n",
    "\n",
    "columns_cat, columns_numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                  2\n",
       "customer_type                           2\n",
       "age                                    75\n",
       "type_of_travel                          2\n",
       "customer_class                          3\n",
       "flight_distance                      3821\n",
       "inflight_wifi_service                   6\n",
       "departure_arrival_time_convenient       6\n",
       "ease_of_online_booking                  6\n",
       "gate_location                           6\n",
       "food_and_drink                          6\n",
       "online_boarding                         6\n",
       "seat_comfort                            6\n",
       "inflight_entertainment                  6\n",
       "onboard_service                         6\n",
       "leg_room_service                        6\n",
       "baggage_handling                        5\n",
       "checkin_service                         6\n",
       "inflight_service                        6\n",
       "cleanliness                             6\n",
       "departure_delay_in_minutes            466\n",
       "arrival_delay_in_minutes              473\n",
       "satisfaction                            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find unqiue values\n",
    "aps_df.apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - Data  Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between departure and arrival delays:  0.9652911835467097\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix heatmap- to determine if two variables in a dataset are related in any way\n",
    "\n",
    "corrmat = aps_df.corr()\n",
    "plt.subplots(figsize=(18,10))\n",
    "sns.heatmap(corrmat, annot=True, fmt='.3f')\n",
    "print ('Correlation between departure and arrival delays: ', corrmat['arrival_delay_in_minutes']['departure_delay_in_minutes'])\n",
    "plt.savefig(\"lr1.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                               0\n",
      "customer_type                        0\n",
      "age                                  0\n",
      "type_of_travel                       0\n",
      "customer_class                       0\n",
      "flight_distance                      0\n",
      "inflight_wifi_service                0\n",
      "departure_arrival_time_convenient    0\n",
      "ease_of_online_booking               0\n",
      "gate_location                        0\n",
      "food_and_drink                       0\n",
      "online_boarding                      0\n",
      "seat_comfort                         0\n",
      "inflight_entertainment               0\n",
      "onboard_service                      0\n",
      "leg_room_service                     0\n",
      "baggage_handling                     0\n",
      "checkin_service                      0\n",
      "inflight_service                     0\n",
      "cleanliness                          0\n",
      "departure_delay_in_minutes           0\n",
      "arrival_delay_in_minutes             0\n",
      "satisfaction                         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Fix data by imputation\n",
    "aps_df['arrival_delay_in_minutes'].fillna(aps_df['departure_delay_in_minutes'], inplace = True)\n",
    "print (aps_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 - Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the graph\n",
    "# Seaborn count plot - to show the number of occurrences or frequency for each categorical data using bars\n",
    "ax = sns.countplot(aps_df[\"satisfaction\"])\n",
    "ax.set_xticklabels(['Neutral/Dissatisfied','Satisfied'])\n",
    "for p in ax.patches:\n",
    "        ax.annotate(str(p.get_height())+' ('+str((p.get_height()/len(aps_df)*100).round(1))+'%)', (p.get_x()+0.1, p.get_height()+400))\n",
    "plt.xlabel('Satisfaction',weight='bold',fontsize='10')   \n",
    "plt.ylabel('No. of Passengers',weight='bold',fontsize='10')  \n",
    "sns.despine()\n",
    "plt.title(\"Target\");\n",
    "plt.savefig(\"lr2.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "for i, col in enumerate(columns_cat):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    sns.countplot(data= aps_df, x=col)\n",
    "    plt.subplot(2, 4, i+5)  \n",
    "    sns.countplot(data= aps_df, x=col, hue='satisfaction')\n",
    "    \n",
    "plt.savefig(\"lr3.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1656x1296 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(23, 18))\n",
    "for i, col in enumerate(columns_numeric):\n",
    "    plt.subplot(4, 5, i+1)\n",
    "    aps_df[col][aps_df.satisfaction == 0].hist(bins=20)\n",
    "    aps_df[col][aps_df.satisfaction == 1].hist(bins=20, alpha=0.6)    \n",
    "    plt.title(f'{col}')\n",
    "\n",
    "plt.savefig(\"lr4.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping count customer for each class\n",
    "df_eco = aps_df[aps_df['customer_class']=='Eco']\n",
    "\n",
    "# Show distribution graph each class by age (customer)\n",
    "## show the economy class\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.3)\n",
    "fig,ax = plt.subplots(1,1,figsize=(18,9))\n",
    "sns.countplot(data=df_eco,x='age')\n",
    "ax.set_title('Customer Count by Age in Economy Class')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig(\"lr5.jpg\", dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_eco1 = aps_df[aps_df['customer_class']=='Eco Plus']\n",
    "## show the economy plus class\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.3)\n",
    "fig,ax = plt.subplots(1,1,figsize=(18,9))\n",
    "sns.countplot(data=df_eco1,x='age')\n",
    "ax.set_title('Customer Count by Age in Economy Plus Class')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig(\"lr6.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bus = aps_df[aps_df['customer_class']=='Business']\n",
    "## show the business class\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context(\"paper\", font_scale=1.3)\n",
    "fig,ax = plt.subplots(1,1,figsize=(18,9))\n",
    "sns.countplot(data=df_bus,x='age')\n",
    "ax.set_title('Customer Count by Age in Business Class')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig(\"lr7.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6 - Data distribution- Training and Testing data preparation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummy encoding to prepare separate columns for all unique values under the same targeted column\n",
    "def dummy_encode(df,column):\n",
    "    df=df.copy()\n",
    "    dummies=pd.get_dummies(df[column],prefix=column)\n",
    "    df=pd.concat([df,dummies],axis=1)#concate new columns to the dataframe\n",
    "    df=df.drop(column,axis=1)#drop original data\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ordinal encoding to transform object type dependent variables into int type in one column\n",
    "dictionary_satisfication=[{'col':'satisfaction','mapping':{'satisfied':1,'neutral or dissatisfied':0}}]\n",
    "dictionary_custype=[{'col':'customer_type','mapping':{'Loyal Customer':1,'disloyal Customer':0}}]\n",
    "\n",
    "encoder1=ce.OrdinalEncoder(cols='satisfaction',mapping=dictionary_satisfication)\n",
    "encoder2=ce.OrdinalEncoder(cols='customer_type',mapping=dictionary_custype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the categorical data of independent variables:Gender,type_of_travel,customer_class\n",
    "df1=dummy_encode(aps_df,'Gender')\n",
    "df1=dummy_encode(df1,'customer_class')\n",
    "df1=dummy_encode(df1,'type_of_travel')\n",
    "#encoding the categorical data of dependent variables:satisfaction,customer_type\n",
    "aps_df=encoder1.fit_transform(df1)\n",
    "aps_df=encoder2.fit_transform(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 129880 entries, 0 to 129879\n",
      "Data columns (total 27 columns):\n",
      " #   Column                             Non-Null Count   Dtype  \n",
      "---  ------                             --------------   -----  \n",
      " 0   customer_type                      129880 non-null  int32  \n",
      " 1   age                                129880 non-null  int64  \n",
      " 2   flight_distance                    129880 non-null  int64  \n",
      " 3   inflight_wifi_service              129880 non-null  int64  \n",
      " 4   departure_arrival_time_convenient  129880 non-null  int64  \n",
      " 5   ease_of_online_booking             129880 non-null  int64  \n",
      " 6   gate_location                      129880 non-null  int64  \n",
      " 7   food_and_drink                     129880 non-null  int64  \n",
      " 8   online_boarding                    129880 non-null  int64  \n",
      " 9   seat_comfort                       129880 non-null  int64  \n",
      " 10  inflight_entertainment             129880 non-null  int64  \n",
      " 11  onboard_service                    129880 non-null  int64  \n",
      " 12  leg_room_service                   129880 non-null  int64  \n",
      " 13  baggage_handling                   129880 non-null  int64  \n",
      " 14  checkin_service                    129880 non-null  int64  \n",
      " 15  inflight_service                   129880 non-null  int64  \n",
      " 16  cleanliness                        129880 non-null  int64  \n",
      " 17  departure_delay_in_minutes         129880 non-null  int64  \n",
      " 18  arrival_delay_in_minutes           129880 non-null  float64\n",
      " 19  satisfaction                       129880 non-null  int64  \n",
      " 20  Gender_Female                      129880 non-null  uint8  \n",
      " 21  Gender_Male                        129880 non-null  uint8  \n",
      " 22  customer_class_Business            129880 non-null  uint8  \n",
      " 23  customer_class_Eco                 129880 non-null  uint8  \n",
      " 24  customer_class_Eco Plus            129880 non-null  uint8  \n",
      " 25  type_of_travel_Business travel     129880 non-null  uint8  \n",
      " 26  type_of_travel_Personal Travel     129880 non-null  uint8  \n",
      "dtypes: float64(1), int32(1), int64(18), uint8(7)\n",
      "memory usage: 20.2 MB\n"
     ]
    }
   ],
   "source": [
    "# to print a concise summary of a DataFrame\n",
    "aps_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 7 - Data Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for the train data:  [0.875, 0.8747016476747768, 0.8745572836464429, 0.874576532183554, 0.875750692947336, 0.8747305204804435, 0.8750096242685556, 0.8750769941484448, 0.8738547120418848, 0.8753079765937789, 0.8745957807206652, 0.8740760702186634, 0.8744995380351094, 0.8742108099784416, 0.8742396827841085, 0.8740087003387742, 0.8742878041268863, 0.8745957807206652, 0.8748652602402217, 0.875423467816446, 0.8744321681552202, 0.8747112719433323, 0.8747978903603326, 0.874461040960887, 0.8739509547274407, 0.874461040960887, 0.8739798275331074, 0.8747112719433323, 0.8737488450877733, 0.8745091623036649, 0.8749037573144441, 0.8751058669541115, 0.8735948567908839, 0.8741434400985525, 0.8747497690175546, 0.8741819371727748, 0.8738547120418848, 0.8743840468124423, 0.8742781798583308, 0.874461040960887, 0.8742300585155528, 0.8750673698798891, 0.8741241915614413, 0.8740279488758854, 0.8746054049892208, 0.8739124576532183, 0.8742685555897752, 0.8740664459501077, 0.8757891900215584, 0.8748941330458885, 0.8748075146288882, 0.8743455497382199, 0.8743263012011088, 0.8740664459501077, 0.8747497690175546, 0.8746823991376655, 0.8740568216815522, 0.8737488450877733, 0.8740183246073299, 0.8748845087773329, 0.8739605789959963, 0.8743263012011088, 0.8743840468124423, 0.8752887280566677, 0.8747112719433323, 0.8743936710809979, 0.8753464736680012, 0.874201185709886, 0.874201185709886, 0.8744995380351094, 0.8740760702186634, 0.8752309824453341, 0.8744514166923314, 0.8739702032645519, 0.8744225438866646, 0.8743936710809979, 0.8742878041268863, 0.874297428395442, 0.8748267631659994, 0.8747208962118879, 0.8745669079149985, 0.8750384970742223, 0.8737873421619957, 0.8739220819217739, 0.8742781798583308, 0.8746342777948876, 0.8742108099784416, 0.8746631506005543, 0.8741338158299969, 0.8736718509393286, 0.8749326301201109, 0.8739124576532183, 0.8745091623036649, 0.8752983523252232, 0.8744032953495534, 0.8750096242685556, 0.874297428395442, 0.8752021096396674, 0.8739124576532183, 0.8739605789959963]\n",
      "Accuracy for the test data:  [0.8721897135817678, 0.8746535263319988, 0.8753079765937789, 0.8745380351093317, 0.870495842315984, 0.8739990760702187, 0.8727671696951032, 0.8736910994764397, 0.8770788420080073, 0.8720742223591007, 0.8746535263319988, 0.875538959039113, 0.8734216199568833, 0.8755774561133354, 0.8747690175546659, 0.8765398829688944, 0.875423467816446, 0.874461040960887, 0.8733446258084385, 0.8718432399137666, 0.8750769941484448, 0.8742685555897752, 0.8728056667693256, 0.8741915614413305, 0.8762319063751155, 0.874461040960887, 0.8768478595626732, 0.8739990760702187, 0.8775793039728981, 0.8743840468124423, 0.872112719433323, 0.8722282106559901, 0.8786187249769017, 0.8758854327071143, 0.873498614105328, 0.8755774561133354, 0.8772328303048968, 0.8737295965506622, 0.8747305204804435, 0.8739605789959963, 0.8763089005235603, 0.8714967662457653, 0.875423467816446, 0.8762704034493378, 0.8746920234062211, 0.8757699414844472, 0.8753849707422235, 0.8753464736680012, 0.8694949183862026, 0.8737295965506622, 0.8731521404373268, 0.8747690175546659, 0.8752309824453341, 0.8756929473360024, 0.8735756082537727, 0.8736910994764397, 0.8763473975977826, 0.877463812750231, 0.876924853711118, 0.8736526024022174, 0.8760394210040037, 0.8746150292577765, 0.8752694795195565, 0.8706883276870958, 0.8729981521404373, 0.8740760702186634, 0.8707653218355405, 0.8762319063751155, 0.8766938712657838, 0.874153064367108, 0.8758084385586695, 0.8709963042808746, 0.8743840468124423, 0.8766938712657838, 0.8743455497382199, 0.8754619648906683, 0.8762704034493378, 0.8755774561133354, 0.8740760702186634, 0.8742685555897752, 0.8738835848475516, 0.8718817369879889, 0.8767708654142286, 0.8763473975977826, 0.8751924853711118, 0.874037573144441, 0.8749615029257777, 0.874153064367108, 0.8758854327071143, 0.8779642747151216, 0.8731906375115491, 0.8768478595626732, 0.8748460117031106, 0.871034801355097, 0.8743070526639976, 0.8730366492146597, 0.8751924853711118, 0.8728056667693256, 0.8764243917462273, 0.8766553741915615]\n",
      "Precision for the train data:  [0.8701002419633599, 0.870295961242624, 0.8695381780490614, 0.8696304849884526, 0.8710836712126797, 0.8705294104030186, 0.8710317918407868, 0.8701809598741149, 0.8698806836504354, 0.8706968158744809, 0.8700965670193596, 0.8694591484464902, 0.8702748745338184, 0.8695181998067277, 0.8697174178392943, 0.8699418644729888, 0.8697241379310345, 0.8700821094021047, 0.8705792612454348, 0.8703370682897589, 0.8697132863914584, 0.870004625346901, 0.8709062514353957, 0.8699926158390253, 0.869629390466087, 0.870416608868544, 0.8693722843671998, 0.8706978030844418, 0.8690561253889593, 0.8696442960745943, 0.8701307671056266, 0.8706080224869248, 0.8688671650373778, 0.8694078871551386, 0.8707454679985202, 0.8700690164577707, 0.8691670703296196, 0.8704899880040602, 0.8701104119125003, 0.8701874249838427, 0.8697869786978698, 0.8704746580852776, 0.8696194635059263, 0.8693419901459686, 0.8700788311718335, 0.8695612009237875, 0.869711494146926, 0.8696314416930142, 0.8714384349827388, 0.870558141678573, 0.8705151793928243, 0.8706123671898407, 0.8699792003697712, 0.8703934320372676, 0.8700481644504874, 0.8701793256248702, 0.8699312635512294, 0.8693122792437149, 0.8694981228551029, 0.8701912001842893, 0.8695961613517919, 0.8701878799042181, 0.8699819836466947, 0.8704897949749208, 0.87040931888044, 0.8701955262125165, 0.8712441395875193, 0.8690440446220292, 0.869438114310706, 0.870322119486269, 0.8697388774732759, 0.870756279326525, 0.8700156552168709, 0.8696603339090385, 0.8692089940392627, 0.8706400036861264, 0.8691409854216645, 0.8690972782374226, 0.8704195432819968, 0.8698918269230769, 0.8704433838279034, 0.8707101479378707, 0.8694022988505747, 0.869516155033047, 0.8697676562788125, 0.8703922249219022, 0.8698913843888071, 0.8702766143508077, 0.8697114166609088, 0.8691245729060855, 0.8702187687896027, 0.8692890469811059, 0.8696304157246343, 0.8710109763142692, 0.8695893195361943, 0.8711436692625887, 0.8699207592370773, 0.8704386288241799, 0.8693234050376908, 0.8692298810760882]\n",
      "Precision for the test data:  [0.8307270960332185, 0.8307333691949077, 0.8332891129388874, 0.8330405761461444, 0.8266678463988675, 0.837090972892359, 0.8336283185840708, 0.8337698620569234, 0.8414100847835787, 0.8326278659611993, 0.8371192111287199, 0.8382274426054458, 0.8341354723707665, 0.8333333333333334, 0.8333925715301226, 0.8391093553600287, 0.8367857142857142, 0.8340791436342897, 0.8377046294666431, 0.8295474555379468, 0.8344447408197742, 0.8330704521556257, 0.8356866624696465, 0.8328914810887239, 0.8416105001773678, 0.83994708994709, 0.8367634124890062, 0.8353631433905236, 0.8372463896518119, 0.8333924454690548, 0.8276871230932955, 0.8324468085106383, 0.8385170662151323, 0.8326841359773371, 0.8388859509959458, 0.8393268379096546, 0.8363684582743989, 0.8371535181236673, 0.8365623889086384, 0.8396710293597454, 0.8383561643835616, 0.8305387085265786, 0.8387352474898714, 0.8342958685028876, 0.8330697592690213, 0.8418263711030646, 0.8345157857396239, 0.8366047745358091, 0.8313767342582711, 0.8324607329842932, 0.835625, 0.8376594416198376, 0.8371212121212122, 0.8410123874877462, 0.8321975396052748, 0.8361581920903954, 0.8382744541097107, 0.8387980556783031, 0.837643295121301, 0.8328313253012049, 0.8395150426582847, 0.8371678259318709, 0.8379188712522045, 0.8275650573906949, 0.8327903495641454, 0.837137296532201, 0.8353491657102499, 0.8348245614035088, 0.838070705276248, 0.8375787698588799, 0.8420680512593902, 0.8311063602351684, 0.8376190052495773, 0.8379052369077307, 0.8338202744577247, 0.8386751182930096, 0.8369718309859155, 0.8341566690190544, 0.8371703272470671, 0.8349846558526962, 0.8365299236370094, 0.8309037900874635, 0.8364352183249821, 0.8375011123965471, 0.8376030493750554, 0.8331723592940556, 0.8373773416592328, 0.836347438752784, 0.8342374388617163, 0.8434298834334157, 0.8305738063950941, 0.840718879393886, 0.8356104010577347, 0.8337147887323944, 0.8326300984528833, 0.8357773851590106, 0.8390968086052093, 0.8297495796832138, 0.8359703226959864, 0.8382806306703074]\n",
      "Recall for the train data:  [0.8370391717839012, 0.8372134116711578, 0.836661421511048, 0.835997513431908, 0.8379204892966361, 0.8351506806724556, 0.8359998227264669, 0.8361275403566505, 0.8350839174756208, 0.8369410929737402, 0.8356556467716885, 0.8359814118167737, 0.836201557246505, 0.8362690860809914, 0.8359675919776863, 0.8359240450430558, 0.8364950915362165, 0.8353800715062957, 0.8357298184884392, 0.8375859773685378, 0.8365463358491402, 0.835680646881109, 0.8369418879251362, 0.835742152863983, 0.8351346562721474, 0.8350115330021292, 0.8348351014248302, 0.8360078797671484, 0.8352495514055958, 0.8366112956810631, 0.8370614812189936, 0.8369584477717729, 0.8355785608423474, 0.8357927856066649, 0.8353223015837806, 0.8350835216447339, 0.8352025888819931, 0.8353404764013106, 0.8355764122542944, 0.8355496453900709, 0.8353911289428768, 0.8375353857041755, 0.8350268447441984, 0.8358754123038098, 0.8355236868978377, 0.8347633300077597, 0.8358876683203402, 0.8351877299525688, 0.8379957507082153, 0.836533138466308, 0.8368709648890068, 0.8354158834066834, 0.8351229035406869, 0.834870705864136, 0.8365795829732544, 0.8360088691796009, 0.8351047340684646, 0.8347039655974996, 0.835661317100166, 0.8368409392999557, 0.8362660896827324, 0.8358915380191976, 0.8353663946061036, 0.8366100188825947, 0.8355705442524017, 0.8353869337824661, 0.8364337819560542, 0.8356356045127477, 0.8361965906575161, 0.8357875157768871, 0.8350364639904241, 0.8375071899473474, 0.8362654628338755, 0.8355088495575221, 0.8368156337934549, 0.8355849382006324, 0.8360477500665661, 0.8363124279251308, 0.8360204918941696, 0.8359060924416409, 0.835849975645397, 0.8363519475049325, 0.8357420666489879, 0.8354982186718595, 0.8358030433915875, 0.8351428761739825, 0.8359945154584457, 0.8366037652368206, 0.8357568111900494, 0.8346709826179496, 0.8359807166818475, 0.8349047185926304, 0.8361244815118781, 0.8363583917635573, 0.835656574624878, 0.8357054181076528, 0.835897208880232, 0.8372814501296342, 0.8360999977896156, 0.8350970604547976]\n",
      "Recall for the test data:  [0.8700842046821504, 0.8713560278352455, 0.8743504083147736, 0.8747579083279535, 0.8692780052102717, 0.8707793392954919, 0.8686030428769018, 0.8739818797474147, 0.8694208779048321, 0.8688690530965307, 0.8711746380795309, 0.8694047069681587, 0.867698868904135, 0.8732748974263335, 0.8718973691549688, 0.8685066443639067, 0.8693877551020408, 0.874034571533652, 0.8681138270704123, 0.8711974110032362, 0.8715638930163447, 0.87476996687523, 0.8628470610084502, 0.8725236067394927, 0.869127209451415, 0.8681979764834564, 0.8763010039605784, 0.8686448511739693, 0.8755674974520522, 0.8716498191597886, 0.8712658700522778, 0.867917552454016, 0.8752558139534884, 0.8757914338919925, 0.8671647230320699, 0.8698366073067744, 0.8760996388554496, 0.8668813247470101, 0.8693202807536018, 0.8666484118291348, 0.8726770929162834, 0.8662325581395349, 0.8714311859443631, 0.8743830896731539, 0.8749653963273969, 0.8690736688548505, 0.8728318337816529, 0.8719130114264652, 0.862202545655783, 0.8707880813143971, 0.865532229723481, 0.8676090169992609, 0.8722349701698027, 0.8672119095754457, 0.871374293392642, 0.8692300633201798, 0.8717002030644269, 0.8747465437788018, 0.8731011485735457, 0.8707854761022601, 0.867080975790743, 0.8677326924854423, 0.8714233308877476, 0.8716315983757844, 0.8710628108307239, 0.868723033140549, 0.8637940478364068, 0.8772237072541248, 0.8723994452149791, 0.8676903273262229, 0.8687881827300082, 0.8650904033379694, 0.8674898636196093, 0.8718376424798443, 0.8716335030078667, 0.8680465717981889, 0.8747010119595217, 0.8749074759437454, 0.8694576768046903, 0.8731090125607408, 0.8678150331613854, 0.8693040022183196, 0.8719335882846749, 0.8716310086135037, 0.8701537894833778, 0.873676457048154, 0.8682019977802442, 0.8676524953789279, 0.8733823666325295, 0.8723967848008769, 0.874550318236325, 0.8727821474300348, 0.8724461623412479, 0.8663556531284303, 0.8743653650881565, 0.8679816513761468, 0.8682733879127955, 0.8717114437110719, 0.8718187750536031, 0.8743224621038126]\n"
     ]
    }
   ],
   "source": [
    "# create several empty lists\n",
    "acc_logr_train_data_list = []\n",
    "acc_logr_test_data_list = []\n",
    "pre_logr_train_data_list = []\n",
    "pre_logr_test_data_list = []\n",
    "recall_logr_train_data_list = []\n",
    "recall_logr_test_data_list = []\n",
    "\n",
    "# Prepare for modeling - migrate to Numpy and split to training and test sets\n",
    "used_data = aps_df.drop (['satisfaction'],axis = 1)\n",
    "x = used_data.values \n",
    "y = aps_df['satisfaction'].values\n",
    "\n",
    "# Normalize features\n",
    "for feature in range (x.shape[1]):\n",
    "    min = x[:,feature].min()\n",
    "    max = x[:,feature].max()\n",
    "    x[:,feature] = (x[:,feature]-min) / (max-min)\n",
    "\n",
    "    \n",
    "# generate 100 different training/testing dataset (using random_state=1 to random_state=100), and repeat the same experiments\n",
    "for seed in range(0,100):\n",
    "    x_train, x_test, y_train, y_test = train_test_split (x, y, test_size=0.20, random_state=seed)\n",
    "    \n",
    "    # Building a Logistic Regression model\n",
    "    #logr = make_pipeline(StandardScaler(), LogisticRegression())\n",
    "    logr = LogisticRegression(max_iter = 1000)\n",
    "    logr.fit(x_train, y_train)  # apply scaling on training data\n",
    "    \n",
    "    # Model Evaluation - Training data Evaluation\n",
    "    x_train_prediction = logr.predict(x_train)\n",
    "    logr_train_data_acc_score = accuracy_score(y_train,x_train_prediction)\n",
    "    logr_train_data_pre_score = precision_score(y_train,x_train_prediction)\n",
    "    logr_train_data_recall = recall_score(y_train,x_train_prediction)\n",
    "    \n",
    "    # Model Evaluation - Testing data Evaluation\n",
    "    x_test_prediction = logr.predict(x_test)\n",
    "    logr_test_data_acc_score = accuracy_score(x_test_prediction,y_test)\n",
    "    logr_test_data_pre_score = precision_score(x_test_prediction,y_test)\n",
    "    logr_test_data_recall = recall_score(x_test_prediction,y_test)\n",
    "    \n",
    "    # add Accuracy score, Precision and Recall and and show the output for both training and testing data\n",
    "    \n",
    "    acc_logr_train_data_list.append(logr_train_data_acc_score)\n",
    "    acc_logr_test_data_list.append(logr_test_data_acc_score)\n",
    "    pre_logr_train_data_list.append(logr_train_data_pre_score)\n",
    "    pre_logr_test_data_list.append(logr_test_data_pre_score)\n",
    "    recall_logr_train_data_list.append(logr_train_data_recall)\n",
    "    recall_logr_test_data_list.append(logr_test_data_recall)\n",
    "\n",
    "print(\"Accuracy for the train data: \", acc_logr_train_data_list)\n",
    "print(\"Accuracy for the test data: \", acc_logr_test_data_list)\n",
    "print(\"Precision for the train data: \", pre_logr_train_data_list)\n",
    "print(\"Precision for the test data: \", pre_logr_test_data_list)\n",
    "print(\"Recall for the train data: \", recall_logr_train_data_list)\n",
    "print(\"Recall for the test data: \", recall_logr_test_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted coefficients for features:\n",
      "1 online_boarding 3.01\n",
      "2 customer_type 2.03\n",
      "3 inflight_wifi_service 1.97\n",
      "4 checkin_service 1.62\n",
      "5 onboard_service 1.51\n",
      "6 type_of_travel_Business travel 1.36\n",
      "7 leg_room_service 1.27\n",
      "8 cleanliness 1.11\n",
      "9 inflight_service 0.58\n",
      "10 baggage_handling 0.54\n",
      "11 customer_class_Business 0.5\n",
      "12 seat_comfort 0.35\n",
      "13 inflight_entertainment 0.3\n",
      "14 gate_location 0.12\n",
      "15 Gender_Male 0.02\n",
      "16 Gender_Female -0.04\n",
      "17 flight_distance -0.08\n",
      "18 food_and_drink -0.13\n",
      "19 customer_class_Eco -0.2\n",
      "20 customer_class_Eco Plus -0.3\n",
      "21 age -0.61\n",
      "22 departure_arrival_time_convenient -0.64\n",
      "23 ease_of_online_booking -0.73\n"
     ]
    }
   ],
   "source": [
    "# Get most important tree features\n",
    "\n",
    "features = used_data.columns\n",
    "coefficients = logr.coef_[0]\n",
    "leading_indices = (-coefficients).argsort()[:23]\n",
    "print (\"Sorted coefficients for features:\")\n",
    "for i in range (23):\n",
    "   print (i+1, features[leading_indices[i]], round(coefficients[leading_indices[i]],2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 8 - Model Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean Absolute Error for the training data\n",
    "\n",
    "training_data_mean_absolute_error = mean_absolute_error(x_train_prediction, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error score of the training data:  0.1260394210040037\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error score of the training data: \", training_data_mean_absolute_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean Absolute Error for the testing data\n",
    "\n",
    "testing_data_mean_absolute_error = mean_absolute_error(x_test_prediction, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error score of the testing data:  0.12334462580843857\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error score of the testing data: \", testing_data_mean_absolute_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Train data Accuracy  Test data Accuracy  Train data Precision  Test data Precision  Train data Recall  Test data Recall\n",
      "0                0.8750              0.8722                0.8701               0.8307             0.8370            0.8701\n",
      "1                0.8747              0.8747                0.8703               0.8307             0.8372            0.8714\n",
      "2                0.8746              0.8753                0.8695               0.8333             0.8367            0.8744\n",
      "3                0.8746              0.8745                0.8696               0.8330             0.8360            0.8748\n",
      "4                0.8758              0.8705                0.8711               0.8267             0.8379            0.8693\n",
      "..                  ...                 ...                   ...                  ...                ...               ...\n",
      "95               0.8750              0.8730                0.8711               0.8358             0.8357            0.8680\n",
      "96               0.8743              0.8752                0.8699               0.8391             0.8359            0.8683\n",
      "97               0.8752              0.8728                0.8704               0.8297             0.8373            0.8717\n",
      "98               0.8739              0.8764                0.8693               0.8360             0.8361            0.8718\n",
      "99               0.8740              0.8767                0.8692               0.8383             0.8351            0.8743\n",
      "\n",
      "[100 rows x 6 columns]\n"
     ]
    },
    {
     "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>Train data Accuracy</th>\n",
       "      <th>Test data Accuracy</th>\n",
       "      <th>Train data Precision</th>\n",
       "      <th>Test data Precision</th>\n",
       "      <th>Train data Recall</th>\n",
       "      <th>Test data Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.8745</td>\n",
       "      <td>0.8746</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>0.8359</td>\n",
       "      <td>0.8707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.8736</td>\n",
       "      <td>0.8695</td>\n",
       "      <td>0.8689</td>\n",
       "      <td>0.8267</td>\n",
       "      <td>0.8347</td>\n",
       "      <td>0.8622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.8741</td>\n",
       "      <td>0.8737</td>\n",
       "      <td>0.8696</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.8354</td>\n",
       "      <td>0.8683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.8744</td>\n",
       "      <td>0.8747</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.8363</td>\n",
       "      <td>0.8359</td>\n",
       "      <td>0.8712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.8747</td>\n",
       "      <td>0.8758</td>\n",
       "      <td>0.8704</td>\n",
       "      <td>0.8378</td>\n",
       "      <td>0.8364</td>\n",
       "      <td>0.8728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.8758</td>\n",
       "      <td>0.8786</td>\n",
       "      <td>0.8714</td>\n",
       "      <td>0.8434</td>\n",
       "      <td>0.8380</td>\n",
       "      <td>0.8772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Train data Accuracy  Test data Accuracy  Train data Precision  \\\n",
       "count             100.0000            100.0000              100.0000   \n",
       "mean                0.8745              0.8746                0.8700   \n",
       "std                 0.0005              0.0018                0.0006   \n",
       "min                 0.8736              0.8695                0.8689   \n",
       "25%                 0.8741              0.8737                0.8696   \n",
       "50%                 0.8744              0.8747                0.8700   \n",
       "75%                 0.8747              0.8758                0.8704   \n",
       "max                 0.8758              0.8786                0.8714   \n",
       "\n",
       "       Test data Precision  Train data Recall  Test data Recall  \n",
       "count             100.0000           100.0000          100.0000  \n",
       "mean                0.8356             0.8359            0.8707  \n",
       "std                 0.0033             0.0007            0.0031  \n",
       "min                 0.8267             0.8347            0.8622  \n",
       "25%                 0.8333             0.8354            0.8683  \n",
       "50%                 0.8363             0.8359            0.8712  \n",
       "75%                 0.8378             0.8364            0.8728  \n",
       "max                 0.8434             0.8380            0.8772  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[acc_logr_train_data_list[0],acc_logr_test_data_list[0], \n",
    "                     pre_logr_train_data_list[0], pre_logr_test_data_list[0],\n",
    "                     recall_logr_train_data_list[0],  recall_logr_test_data_list[0]]],\n",
    "                     columns = [\"Train data Accuracy\",\"Test data Accuracy\",\"Train data Precision\",\"Test data Precision\"\n",
    "                               ,\"Train data Recall\",\"Test data Recall\"])\n",
    "\n",
    "for x in range(1,100):\n",
    "        to_append = [acc_logr_train_data_list[x], acc_logr_test_data_list[x], \n",
    "                     pre_logr_train_data_list[x], pre_logr_test_data_list[x],\n",
    "                     recall_logr_train_data_list[x],  recall_logr_test_data_list[x]]\n",
    "        alpha_data = pd.Series(to_append, index = df.columns)\n",
    "        df = df.append(alpha_data, ignore_index = True)\n",
    "                   \n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" %x) \n",
    "with pd.option_context('expand_frame_repr',False):\n",
    "     print(df)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data Accuracy mean :  0.8744779796735448\n",
      "Test data Accuracy mean :  0.874590391130274\n",
      "Train data Precision mean :  0.8700128811318385\n",
      "Test data Precision mean :  0.8356329555095181\n",
      "Train data Recall mean :  0.8359412309822776\n",
      "Test data Recall mean :  0.870681485009968\n"
     ]
    }
   ],
   "source": [
    "# compute the mean for data\n",
    "train_data_logr_acc_mean = np.mean(acc_logr_train_data_list)\n",
    "test_data_logr_acc_mean = np.mean(acc_logr_test_data_list)\n",
    "train_Data_logr_pre_mean = np.mean(pre_logr_train_data_list)\n",
    "test_data_logr_pre_mean = np.mean(pre_logr_test_data_list)\n",
    "train_data_logr_recall_mean = np.mean(recall_logr_train_data_list)\n",
    "test_data_logr_recall_mean = np.mean(recall_logr_test_data_list)\n",
    "\n",
    "# show the mean for data\n",
    "print('Train data Accuracy mean : ', train_data_logr_acc_mean)\n",
    "print('Test data Accuracy mean : ', test_data_logr_acc_mean)\n",
    "print('Train data Precision mean : ', train_Data_logr_pre_mean)\n",
    "print('Test data Precision mean : ', test_data_logr_pre_mean)\n",
    "print('Train data Recall mean : ', train_data_logr_recall_mean)\n",
    "print('Test data Recall mean : ', test_data_logr_recall_mean)\n",
    "\n",
    "# add the performance mean of model\n",
    "performance_logr = (train_data_logr_acc_mean,test_data_logr_acc_mean,train_Data_logr_pre_mean,test_data_logr_pre_mean\n",
    "                    ,train_data_logr_recall_mean,test_data_logr_recall_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Train data Accuracy  Test data Accuracy  Train data Precision  Test data Precision  Train data Recall  Test data Recall\n",
      "mean               0.8745              0.8746                0.8700               0.8356             0.8359            0.8707\n"
     ]
    }
   ],
   "source": [
    "mean_df = pd.DataFrame([[train_data_logr_acc_mean,test_data_logr_acc_mean,\n",
    "                       train_Data_logr_pre_mean, test_data_logr_pre_mean,\n",
    "                       train_data_logr_recall_mean, test_data_logr_recall_mean]],\n",
    "                       columns = ['Train data Accuracy', 'Test data Accuracy',\n",
    "                                  'Train data Precision', 'Test data Precision', \n",
    "                                  'Train data Recall','Test data Recall'])\n",
    "mean_df.index = ['mean']\n",
    "\n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" %x) \n",
    "with pd.option_context('expand_frame_repr',False):\n",
    "\n",
    "    print(mean_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence interval for train data accuracy score :  (0.8743860541260197, 0.8745699052210699)\n",
      "Confidence interval for test data accuracy score :  (0.8742359891473402, 0.8749447931132078)\n",
      "Confidence interval for train data precision score :  (0.8699026404061416, 0.8701231218575354)\n",
      "Confidence interval for test data precision score :  (0.8349711608462642, 0.836294750172772)\n",
      "Confidence interval for train data recall :  (0.8357953012949588, 0.8360871606695963)\n",
      "Confidence interval for test data recall :  (0.8700677986845374, 0.8712951713353986)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#compute confidence interval using 95% confidence level\n",
    "confidence_level = 0.95 \n",
    "\n",
    "# confidence interval for train data acc_score\n",
    "acc_train_confidence_interval = st.t.interval(alpha = confidence_level, df = len(acc_logr_train_data_list)-1,\n",
    "loc = np.mean(acc_logr_train_data_list), scale = st.sem(acc_logr_train_data_list))\n",
    "print('Confidence interval for train data accuracy score : ', acc_train_confidence_interval)\n",
    "\n",
    "# confidence interval for test data acc_score\n",
    "acc_test_confidence_interval = st.t.interval(alpha = confidence_level, df=len(acc_logr_test_data_list)-1,\n",
    "loc = np.mean(acc_logr_test_data_list),scale = st.sem(acc_logr_test_data_list))\n",
    "print('Confidence interval for test data accuracy score : ', acc_test_confidence_interval)\n",
    "\n",
    "# confidence interval for train data pre_score\n",
    "pre_train_confidence_interval = st.t.interval(alpha = confidence_level, df = len(pre_logr_train_data_list)-1,\n",
    "loc = np.mean(pre_logr_train_data_list), scale = st.sem(pre_logr_train_data_list))\n",
    "print('Confidence interval for train data precision score : ', pre_train_confidence_interval)\n",
    "\n",
    "#confidence interval for test data pre_score\n",
    "pre_test_confidence_interval = st.t.interval(alpha = confidence_level, df = len( pre_logr_test_data_list)-1,\n",
    "loc = np.mean( pre_logr_test_data_list), scale = st.sem( pre_logr_test_data_list))\n",
    "print('Confidence interval for test data precision score : ', pre_test_confidence_interval)\n",
    "\n",
    "#confidence interval for train data recall\n",
    "recall_train_confidence_interval = st.t.interval(alpha = confidence_level, df = len(recall_logr_train_data_list)-1,\n",
    "loc = np.mean(recall_logr_train_data_list), scale = st.sem(recall_logr_train_data_list))\n",
    "print('Confidence interval for train data recall : ', recall_train_confidence_interval)\n",
    "\n",
    "#confidence interval for test data recall\n",
    "recall_test_confidence_interval = st.t.interval(alpha = confidence_level, df = len(recall_logr_test_data_list)-1,\n",
    "loc = np.mean(recall_logr_test_data_list), scale = st.sem(recall_logr_test_data_list))\n",
    "print('Confidence interval for test data recall : ', recall_test_confidence_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Confidence Interval - Lower_Boundary  \\\n",
      "Train Data Accuracy Score                                 0.8744   \n",
      "Test Data Accuracy Score                                  0.8742   \n",
      "Train Data Precision Score                                0.8699   \n",
      "Test Data Precision Score                                 0.8350   \n",
      "Train Data Recall Score                                   0.8358   \n",
      "Test Data Recall Score                                    0.8701   \n",
      "\n",
      "                            Confidence Interval - Upper_Boundary  \n",
      "Train Data Accuracy Score                                 0.8746  \n",
      "Test Data Accuracy Score                                  0.8749  \n",
      "Train Data Precision Score                                0.8701  \n",
      "Test Data Precision Score                                 0.8363  \n",
      "Train Data Recall Score                                   0.8361  \n",
      "Test Data Recall Score                                    0.8713  \n"
     ]
    }
   ],
   "source": [
    "con_df = pd.DataFrame([[acc_train_confidence_interval[0],acc_train_confidence_interval[1]],\n",
    "                       [acc_test_confidence_interval[0], acc_test_confidence_interval[1]],\n",
    "                       [pre_train_confidence_interval[0], pre_train_confidence_interval[1]],\n",
    "                       [pre_test_confidence_interval[0],pre_test_confidence_interval[1]],\n",
    "                       [recall_train_confidence_interval[0], recall_train_confidence_interval[1]],\n",
    "                       [recall_test_confidence_interval[0], recall_test_confidence_interval[1]]], \n",
    "                       columns = ['Confidence Interval - Lower_Boundary', 'Confidence Interval - Upper_Boundary'])\n",
    "                       \n",
    "con_df.index = ['Train Data Accuracy Score', 'Test Data Accuracy Score', 'Train Data Precision Score',\n",
    "                'Test Data Precision Score', 'Train Data Recall Score', 'Test Data Recall Score']\n",
    "                \n",
    "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" %x) \n",
    "print(con_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x26c33c45220>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(logr, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.89     14623\n",
      "           1       0.87      0.84      0.86     11353\n",
      "\n",
      "    accuracy                           0.88     25976\n",
      "   macro avg       0.88      0.87      0.87     25976\n",
      "weighted avg       0.88      0.88      0.88     25976\n",
      "\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13255  1368]\n",
      " [ 1836  9517]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test,x_test_prediction))\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "ax=sns.heatmap(confusion_matrix(y_test, x_test_prediction),annot=True)\n",
    "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "## Label the confusion matrix table\n",
    "ax.xaxis.set_ticklabels(['False','True'])\n",
    "ax.yaxis.set_ticklabels(['False','True'])\n",
    "plt.show()\n",
    "\n",
    "print(confusion_matrix(y_test,x_test_prediction))\n",
    "plt.savefig(\"lr8.jpg\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}