72. Airline Passenger Satisfaction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c9c4099",
   "metadata": {},
   "source": [
    "# Airline Passenger Satisfaction\n",
    "\n",
    "## Imporing required libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fdb993e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from statistics import mode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cbd2de",
   "metadata": {},
   "source": [
    "## Getting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f86d8ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>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>Food and drink</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Inflight entertainment</th>\n",
       "      <th>On-board 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>70172</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>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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>5047</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>110028</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>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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>24026</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>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>119299</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>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</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",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      id  ... Arrival Delay in Minutes             satisfaction\n",
       "0           0   70172  ...                     18.0  neutral or dissatisfied\n",
       "1           1    5047  ...                      6.0  neutral or dissatisfied\n",
       "2           2  110028  ...                      0.0                satisfied\n",
       "3           3   24026  ...                      9.0  neutral or dissatisfied\n",
       "4           4  119299  ...                      0.0                satisfied\n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/proj72/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e59e097",
   "metadata": {},
   "source": [
    "## Cleaning dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e41c7d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                             0\n",
       "id                                     0\n",
       "Gender                                 0\n",
       "Customer Type                          0\n",
       "Age                                    0\n",
       "Type of Travel                         0\n",
       "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",
       "On-board 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             310\n",
       "satisfaction                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7b69369",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataCleaner(df, method):\n",
    "    if method == \"mean\":\n",
    "        for column in df.keys():\n",
    "            if pd.api.types.is_object_dtype(df[column]) == False:\n",
    "                df[column].fillna(np.mean(df[column]), inplace=True)\n",
    "    \n",
    "    if method == \"median\":\n",
    "        for column in df.keys():\n",
    "            if pd.api.types.is_object_dtype(df[column]) == False:\n",
    "                df[column].fillna(np.median(df[column]), inplace=True)\n",
    "    \n",
    "    if method == \"mode\":\n",
    "        for column in df.keys():\n",
    "            if pd.api.types.is_object_dtype(df[column]) == False:\n",
    "                df[column].fillna(mode(df[column]), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9063d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataCleaner(df, method=\"mean\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78a166a",
   "metadata": {},
   "source": [
    "## Pre-Processing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f7b7c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessData(df):\n",
    "    classes = {}\n",
    "    for column in df.keys():\n",
    "        if pd.api.types.is_object_dtype(df[column]):\n",
    "            le = LabelEncoder()\n",
    "            df[column] = le.fit_transform(df[column])\n",
    "            classes[column] = le.classes_\n",
    "    \n",
    "    return classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24548b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Gender': array(['Female', 'Male'], dtype=object),\n",
       " 'Customer Type': array(['Loyal Customer', 'disloyal Customer'], dtype=object),\n",
       " 'Type of Travel': array(['Business travel', 'Personal Travel'], dtype=object),\n",
       " 'Class': array(['Business', 'Eco', 'Eco Plus'], dtype=object),\n",
       " 'satisfaction': array(['neutral or dissatisfied', 'satisfied'], dtype=object)}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = preprocessData(df)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28a290d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"satisfaction\", axis=1)\n",
    "y = df[\"satisfaction\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1574874",
   "metadata": {},
   "source": [
    "## Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d08f110",
   "metadata": {},
   "outputs": [],
   "source": [
    "rclf = RandomForestClassifier()\n",
    "rclf.fit(X_train, y_train)\n",
    "\n",
    "calibrated_clf = CalibratedClassifierCV(rclf, cv=5, method='sigmoid')\n",
    "calibrated_clf.fit(X_train, y_train)\n",
    "predictions = calibrated_clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6754da1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9638612193830903"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calibrated_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1f668770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9657379336894278"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xclf = XGBClassifier()\n",
    "xclf.fit(X_train.values, y_train.values)\n",
    "xclf.score(X_test.values, y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36343c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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