airline_passenger_satisfaction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "      <th>Departure and Arrival Time Convenience</th>\n",
       "      <th>...</th>\n",
       "      <th>On-board Service</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Leg Room Service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Food and Drink</th>\n",
       "      <th>In-flight Service</th>\n",
       "      <th>In-flight Wifi Service</th>\n",
       "      <th>In-flight Entertainment</th>\n",
       "      <th>Baggage Handling</th>\n",
       "      <th>Satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>First-time</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>821</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Neutral or Dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>821</td>\n",
       "      <td>26</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>1905</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>3470</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Gender  Age Customer Type Type of Travel     Class  Flight Distance  \\\n",
       "0   1    Male   48    First-time       Business  Business              821   \n",
       "1   2  Female   35     Returning       Business  Business              821   \n",
       "2   3    Male   41     Returning       Business  Business              853   \n",
       "3   4    Male   50     Returning       Business  Business             1905   \n",
       "4   5  Female   49     Returning       Business  Business             3470   \n",
       "\n",
       "   Departure Delay  Arrival Delay  Departure and Arrival Time Convenience  \\\n",
       "0                2            5.0                                       3   \n",
       "1               26           39.0                                       2   \n",
       "2                0            0.0                                       4   \n",
       "3                0            0.0                                       2   \n",
       "4                0            1.0                                       3   \n",
       "\n",
       "   ...  On-board Service  Seat Comfort  Leg Room Service  Cleanliness  \\\n",
       "0  ...                 3             5                 2            5   \n",
       "1  ...                 5             4                 5            5   \n",
       "2  ...                 3             5                 3            5   \n",
       "3  ...                 5             5                 5            4   \n",
       "4  ...                 3             4                 4            5   \n",
       "\n",
       "   Food and Drink  In-flight Service  In-flight Wifi Service  \\\n",
       "0               5                  5                       3   \n",
       "1               3                  5                       2   \n",
       "2               5                  3                       4   \n",
       "3               4                  5                       2   \n",
       "4               4                  3                       3   \n",
       "\n",
       "   In-flight Entertainment  Baggage Handling             Satisfaction  \n",
       "0                        5                 5  Neutral or Dissatisfied  \n",
       "1                        5                 5                Satisfied  \n",
       "2                        3                 3                Satisfied  \n",
       "3                        5                 5                Satisfied  \n",
       "4                        3                 3                Satisfied  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('airline_passenger_satisfaction.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "      <th>Departure and Arrival Time Convenience</th>\n",
       "      <th>Ease of Online Booking</th>\n",
       "      <th>...</th>\n",
       "      <th>On-board Service</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Leg Room Service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Food and Drink</th>\n",
       "      <th>In-flight Service</th>\n",
       "      <th>In-flight Wifi Service</th>\n",
       "      <th>In-flight Entertainment</th>\n",
       "      <th>Baggage Handling</th>\n",
       "      <th>Satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Male</td>\n",
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       "      <td>First-time</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>821</td>\n",
       "      <td>26</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <td>Satisfied</td>\n",
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       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>1905</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>Returning</td>\n",
       "      <td>Business</td>\n",
       "      <td>Business</td>\n",
       "      <td>3470</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Customer Type Type of Travel     Class  Flight Distance  \\\n",
       "0    Male   48    First-time       Business  Business              821   \n",
       "1  Female   35     Returning       Business  Business              821   \n",
       "2    Male   41     Returning       Business  Business              853   \n",
       "3    Male   50     Returning       Business  Business             1905   \n",
       "4  Female   49     Returning       Business  Business             3470   \n",
       "\n",
       "   Departure Delay  Arrival Delay  Departure and Arrival Time Convenience  \\\n",
       "0                2            5.0                                       3   \n",
       "1               26           39.0                                       2   \n",
       "2                0            0.0                                       4   \n",
       "3                0            0.0                                       2   \n",
       "4                0            1.0                                       3   \n",
       "\n",
       "   Ease of Online Booking  ...  On-board Service  Seat Comfort  \\\n",
       "0                       3  ...                 3             5   \n",
       "1                       2  ...                 5             4   \n",
       "2                       4  ...                 3             5   \n",
       "3                       2  ...                 5             5   \n",
       "4                       3  ...                 3             4   \n",
       "\n",
       "   Leg Room Service  Cleanliness  Food and Drink  In-flight Service  \\\n",
       "0                 2            5               5                  5   \n",
       "1                 5            5               3                  5   \n",
       "2                 3            5               5                  3   \n",
       "3                 5            4               4                  5   \n",
       "4                 4            5               4                  3   \n",
       "\n",
       "   In-flight Wifi Service  In-flight Entertainment  Baggage Handling  \\\n",
       "0                       3                        5                 5   \n",
       "1                       2                        5                 5   \n",
       "2                       4                        3                 3   \n",
       "3                       2                        5                 5   \n",
       "4                       3                        3                 3   \n",
       "\n",
       "              Satisfaction  \n",
       "0  Neutral or Dissatisfied  \n",
       "1                Satisfied  \n",
       "2                Satisfied  \n",
       "3                Satisfied  \n",
       "4                Satisfied  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns='ID', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 23)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "      <th>Departure and Arrival Time Convenience</th>\n",
       "      <th>Ease of Online Booking</th>\n",
       "      <th>Check-in Service</th>\n",
       "      <th>Online Boarding</th>\n",
       "      <th>Gate Location</th>\n",
       "      <th>On-board Service</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Leg Room Service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Food and Drink</th>\n",
       "      <th>In-flight Service</th>\n",
       "      <th>In-flight Wifi Service</th>\n",
       "      <th>In-flight Entertainment</th>\n",
       "      <th>Baggage Handling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129487.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "      <td>129880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.43</td>\n",
       "      <td>1190.32</td>\n",
       "      <td>14.71</td>\n",
       "      <td>15.09</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.12</td>\n",
       "      <td>997.45</td>\n",
       "      <td>38.07</td>\n",
       "      <td>38.47</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.00</td>\n",
       "      <td>414.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.00</td>\n",
       "      <td>844.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.00</td>\n",
       "      <td>1744.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85.00</td>\n",
       "      <td>4983.00</td>\n",
       "      <td>1592.00</td>\n",
       "      <td>1584.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age  Flight Distance  Departure Delay  Arrival Delay  \\\n",
       "count  129880.00        129880.00        129880.00      129487.00   \n",
       "mean       39.43          1190.32            14.71          15.09   \n",
       "std        15.12           997.45            38.07          38.47   \n",
       "min         7.00            31.00             0.00           0.00   \n",
       "25%        27.00           414.00             0.00           0.00   \n",
       "50%        40.00           844.00             0.00           0.00   \n",
       "75%        51.00          1744.00            12.00          13.00   \n",
       "max        85.00          4983.00          1592.00        1584.00   \n",
       "\n",
       "       Departure and Arrival Time Convenience  Ease of Online Booking  \\\n",
       "count                               129880.00               129880.00   \n",
       "mean                                     3.06                    2.76   \n",
       "std                                      1.53                    1.40   \n",
       "min                                      0.00                    0.00   \n",
       "25%                                      2.00                    2.00   \n",
       "50%                                      3.00                    3.00   \n",
       "75%                                      4.00                    4.00   \n",
       "max                                      5.00                    5.00   \n",
       "\n",
       "       Check-in Service  Online Boarding  Gate Location  On-board Service  \\\n",
       "count         129880.00        129880.00      129880.00         129880.00   \n",
       "mean               3.31             3.25           2.98              3.38   \n",
       "std                1.27             1.35           1.28              1.29   \n",
       "min                0.00             0.00           0.00              0.00   \n",
       "25%                3.00             2.00           2.00              2.00   \n",
       "50%                3.00             3.00           3.00              4.00   \n",
       "75%                4.00             4.00           4.00              4.00   \n",
       "max                5.00             5.00           5.00              5.00   \n",
       "\n",
       "       Seat Comfort  Leg Room Service  Cleanliness  Food and Drink  \\\n",
       "count     129880.00         129880.00    129880.00       129880.00   \n",
       "mean           3.44              3.35         3.29            3.20   \n",
       "std            1.32              1.32         1.31            1.33   \n",
       "min            0.00              0.00         0.00            0.00   \n",
       "25%            2.00              2.00         2.00            2.00   \n",
       "50%            4.00              4.00         3.00            3.00   \n",
       "75%            5.00              4.00         4.00            4.00   \n",
       "max            5.00              5.00         5.00            5.00   \n",
       "\n",
       "       In-flight Service  In-flight Wifi Service  In-flight Entertainment  \\\n",
       "count          129880.00               129880.00                129880.00   \n",
       "mean                3.64                    2.73                     3.36   \n",
       "std                 1.18                    1.33                     1.33   \n",
       "min                 0.00                    0.00                     0.00   \n",
       "25%                 3.00                    2.00                     2.00   \n",
       "50%                 4.00                    3.00                     4.00   \n",
       "75%                 5.00                    4.00                     4.00   \n",
       "max                 5.00                    5.00                     5.00   \n",
       "\n",
       "       Baggage Handling  \n",
       "count         129880.00  \n",
       "mean               3.63  \n",
       "std                1.18  \n",
       "min                1.00  \n",
       "25%                3.00  \n",
       "50%                4.00  \n",
       "75%                5.00  \n",
       "max                5.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                     object\n",
       "Age                                         int64\n",
       "Customer Type                              object\n",
       "Type of Travel                             object\n",
       "Class                                      object\n",
       "Flight Distance                             int64\n",
       "Departure Delay                             int64\n",
       "Arrival Delay                             float64\n",
       "Departure and Arrival Time Convenience      int64\n",
       "Ease of Online Booking                      int64\n",
       "Check-in Service                            int64\n",
       "Online Boarding                             int64\n",
       "Gate Location                               int64\n",
       "On-board Service                            int64\n",
       "Seat Comfort                                int64\n",
       "Leg Room Service                            int64\n",
       "Cleanliness                                 int64\n",
       "Food and Drink                              int64\n",
       "In-flight Service                           int64\n",
       "In-flight Wifi Service                      int64\n",
       "In-flight Entertainment                     int64\n",
       "Baggage Handling                            int64\n",
       "Satisfaction                               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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   Age                                     129880 non-null  int64  \n",
      " 2   Customer Type                           129880 non-null  object \n",
      " 3   Type of Travel                          129880 non-null  object \n",
      " 4   Class                                   129880 non-null  object \n",
      " 5   Flight Distance                         129880 non-null  int64  \n",
      " 6   Departure Delay                         129880 non-null  int64  \n",
      " 7   Arrival Delay                           129487 non-null  float64\n",
      " 8   Departure and Arrival Time Convenience  129880 non-null  int64  \n",
      " 9   Ease of Online Booking                  129880 non-null  int64  \n",
      " 10  Check-in Service                        129880 non-null  int64  \n",
      " 11  Online Boarding                         129880 non-null  int64  \n",
      " 12  Gate Location                           129880 non-null  int64  \n",
      " 13  On-board Service                        129880 non-null  int64  \n",
      " 14  Seat Comfort                            129880 non-null  int64  \n",
      " 15  Leg Room Service                        129880 non-null  int64  \n",
      " 16  Cleanliness                             129880 non-null  int64  \n",
      " 17  Food and Drink                          129880 non-null  int64  \n",
      " 18  In-flight Service                       129880 non-null  int64  \n",
      " 19  In-flight Wifi Service                  129880 non-null  int64  \n",
      " 20  In-flight Entertainment                 129880 non-null  int64  \n",
      " 21  Baggage Handling                        129880 non-null  int64  \n",
      " 22  Satisfaction                            129880 non-null  object \n",
      "dtypes: float64(1), int64(17), object(5)\n",
      "memory usage: 22.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                      0\n",
       "Age                                         0\n",
       "Customer Type                               0\n",
       "Type of Travel                              0\n",
       "Class                                       0\n",
       "Flight Distance                             0\n",
       "Departure Delay                             0\n",
       "Arrival Delay                             393\n",
       "Departure and Arrival Time Convenience      0\n",
       "Ease of Online Booking                      0\n",
       "Check-in Service                            0\n",
       "Online Boarding                             0\n",
       "Gate Location                               0\n",
       "On-board Service                            0\n",
       "Seat Comfort                                0\n",
       "Leg Room Service                            0\n",
       "Cleanliness                                 0\n",
       "Food and Drink                              0\n",
       "In-flight Service                           0\n",
       "In-flight Wifi Service                      0\n",
       "In-flight Entertainment                     0\n",
       "Baggage Handling                            0\n",
       "Satisfaction                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(15.09112883918849)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Arrival Delay'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pelik\\AppData\\Local\\Temp\\ipykernel_14000\\899276898.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Arrival Delay'].fillna(df['Arrival Delay'].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['Arrival Delay'].fillna(df['Arrival Delay'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                    0\n",
       "Age                                       0\n",
       "Customer Type                             0\n",
       "Type of Travel                            0\n",
       "Class                                     0\n",
       "Flight Distance                           0\n",
       "Departure Delay                           0\n",
       "Arrival Delay                             0\n",
       "Departure and Arrival Time Convenience    0\n",
       "Ease of Online Booking                    0\n",
       "Check-in Service                          0\n",
       "Online Boarding                           0\n",
       "Gate Location                             0\n",
       "On-board Service                          0\n",
       "Seat Comfort                              0\n",
       "Leg Room Service                          0\n",
       "Cleanliness                               0\n",
       "Food and Drink                            0\n",
       "In-flight Service                         0\n",
       "In-flight Wifi Service                    0\n",
       "In-flight Entertainment                   0\n",
       "Baggage Handling                          0\n",
       "Satisfaction                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arrival Delay is 0\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['Satisfaction'].value_counts(), labels=['Neutral or Dissatisfied', 'Satisfied'], autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['Gender', 'Customer Type', 'Type of Travel', 'Class', 'Satisfaction']\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "for i, col in enumerate(cols):\n",
    "    plt.subplot(3, 2, i + 1)\n",
    "    sns.countplot(x=col, data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=20, figsize=(20, 20), color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column Data Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male' 'Female']\n",
      "['First-time' 'Returning']\n",
      "['Business' 'Personal']\n",
      "['Business' 'Economy' 'Economy Plus']\n",
      "['Neutral or Dissatisfied' 'Satisfied']\n"
     ]
    }
   ],
   "source": [
    "items = df.select_dtypes(include='object').columns\n",
    "\n",
    "for i in items:\n",
    "    print(df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OPTION 1\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "columns = df.select_dtypes(include='object').drop(columns='Satisfaction').columns\n",
    "\n",
    "for column in columns:\n",
    "    df[column] = label_encoder.fit_transform(df[column])\n",
    "\n",
    "df.head()\n",
    "df['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OPTION 2\n",
    "\n",
    "# df.replace({\n",
    "#     'Gender': {\n",
    "#         'Male': 1,\n",
    "#         'Female': 2\n",
    "#     },\n",
    "#     'Customer Type': {\n",
    "#         'First-time': 1,\n",
    "#         'Returning': 2\n",
    "#     },\n",
    "#     'Type of Travel': {\n",
    "#         'Business': 1,\n",
    "#         'Personal': 2\n",
    "#     },\n",
    "#     'Class': {\n",
    "#         'Business': 1,\n",
    "#         'Economy': 2,\n",
    "#         'Economy Plus': 3\n",
    "#     }\n",
    "# }, inplace=True)\n",
    "\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                                      int64\n",
       "Age                                         int64\n",
       "Customer Type                               int64\n",
       "Type of Travel                              int64\n",
       "Class                                       int64\n",
       "Flight Distance                             int64\n",
       "Departure Delay                             int64\n",
       "Arrival Delay                             float64\n",
       "Departure and Arrival Time Convenience      int64\n",
       "Ease of Online Booking                      int64\n",
       "Check-in Service                            int64\n",
       "Online Boarding                             int64\n",
       "Gate Location                               int64\n",
       "On-board Service                            int64\n",
       "Seat Comfort                                int64\n",
       "Leg Room Service                            int64\n",
       "Cleanliness                                 int64\n",
       "Food and Drink                              int64\n",
       "In-flight Service                           int64\n",
       "In-flight Wifi Service                      int64\n",
       "In-flight Entertainment                     int64\n",
       "Baggage Handling                            int64\n",
       "Satisfaction                               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "sns.heatmap(df.drop(columns='Satisfaction').corr(), annot=True, fmt='.2f', cmap='Greens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b3303d7190>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1804.62x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='Age', height=4, aspect=4, kind='count', hue='Satisfaction', order=range(7, 73))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b333df6b90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1804.62x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='On-board Service', height=4, aspect=4, kind='count', hue='Satisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b33062d1d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1804.62x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='Gender', height=4, aspect=4, kind='count', hue='Satisfaction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Type of Travel\n",
       "0       1   48               0\n",
       "1       0   35               0\n",
       "2       1   41               0\n",
       "3       1   50               0\n",
       "4       0   49               0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Gender', 'Age', 'Type of Travel']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>3788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Flight Distance\n",
       "2       1   41              853\n",
       "3       1   50             1905\n",
       "4       0   49             3470\n",
       "5       1   43             3788"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2:5, ['Gender', 'Age', 'Flight Distance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Flight Distance\n",
       "7        0   60              853\n",
       "14       1   52              853\n",
       "15       1   70              821\n",
       "17       0   61              821\n",
       "21       0   70              853"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age'] > 50, ['Gender', 'Age', 'Flight Distance']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>2607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>2168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>2035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Flight Distance\n",
       "3        1   50             1905\n",
       "8        1   50             2607\n",
       "26       1   50             2168\n",
       "66       1   50               67\n",
       "91       0   50             2035"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age'] == 50, ['Gender', 'Age', 'Flight Distance']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>421</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>453</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>853</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Customer Type  Type of Travel  Class  Flight Distance  \\\n",
       "10   28              0               0      0              821   \n",
       "11   27              0               0      0              421   \n",
       "12   24              0               0      1              453   \n",
       "13    9              1               1      0              853   \n",
       "14   52              1               1      1              853   \n",
       "\n",
       "    Departure Delay  \n",
       "10                0  \n",
       "11               20  \n",
       "12               16  \n",
       "13               68  \n",
       "14                0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[10:15, 1:7].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Customer Type', 'Type of Travel', 'Class',\n",
       "       'Flight Distance', 'Departure Delay', 'Arrival Delay',\n",
       "       'Departure and Arrival Time Convenience', 'Ease of Online Booking',\n",
       "       'Check-in Service', 'Online Boarding', 'Gate Location',\n",
       "       'On-board Service', 'Seat Comfort', 'Leg Room Service', 'Cleanliness',\n",
       "       'Food and Drink', 'In-flight Service', 'In-flight Wifi Service',\n",
       "       'In-flight Entertainment', 'Baggage Handling', 'Satisfaction'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "      <th>Departure and Arrival Time Convenience</th>\n",
       "      <th>Ease of Online Booking</th>\n",
       "      <th>...</th>\n",
       "      <th>Gate Location</th>\n",
       "      <th>On-board Service</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Leg Room Service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Food and Drink</th>\n",
       "      <th>In-flight Service</th>\n",
       "      <th>In-flight Wifi Service</th>\n",
       "      <th>In-flight Entertainment</th>\n",
       "      <th>Baggage Handling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>26</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Customer Type  Type of Travel  Class  Flight Distance  \\\n",
       "0       1   48              0               0      0              821   \n",
       "1       0   35              1               0      0              821   \n",
       "2       1   41              1               0      0              853   \n",
       "\n",
       "   Departure Delay  Arrival Delay  Departure and Arrival Time Convenience  \\\n",
       "0                2            5.0                                       3   \n",
       "1               26           39.0                                       2   \n",
       "2                0            0.0                                       4   \n",
       "\n",
       "   Ease of Online Booking  ...  Gate Location  On-board Service  Seat Comfort  \\\n",
       "0                       3  ...              3                 3             5   \n",
       "1                       2  ...              2                 5             4   \n",
       "2                       4  ...              4                 3             5   \n",
       "\n",
       "   Leg Room Service  Cleanliness  Food and Drink  In-flight Service  \\\n",
       "0                 2            5               5                  5   \n",
       "1                 5            5               3                  5   \n",
       "2                 3            5               5                  3   \n",
       "\n",
       "   In-flight Wifi Service  In-flight Entertainment  Baggage Handling  \n",
       "0                       3                        5                 5  \n",
       "1                       2                        5                 5  \n",
       "2                       4                        3                 3  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns='Satisfaction')\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Neutral or Dissatisfied\n",
       "1                  Satisfied\n",
       "2                  Satisfied\n",
       "Name: Satisfaction, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Satisfaction']\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 22)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103904, 22)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25976, 22)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Satisfied', 'Neutral or Dissatisfied', 'Neutral or Dissatisfied',\n",
       "       ..., 'Neutral or Dissatisfied', 'Satisfied',\n",
       "       'Neutral or Dissatisfied'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9424468740375731"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score = accuracy_score(y_test, predictions)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614259316291962"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions\n",
    "\n",
    "model_score = accuracy_score(y_test, predictions)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7502309824453341"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = KNeighborsClassifier()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions\n",
    "\n",
    "model_score = accuracy_score(y_test, predictions)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8744995380351094"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = LogisticRegression(max_iter=10000)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions\n",
    "\n",
    "model_score = accuracy_score(y_test, predictions)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Without Voting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>26</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1905</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3470</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Customer Type  Type of Travel  Class  Flight Distance  \\\n",
       "0       1   48              0               0      0              821   \n",
       "1       0   35              1               0      0              821   \n",
       "2       1   41              1               0      0              853   \n",
       "3       1   50              1               0      0             1905   \n",
       "4       0   49              1               0      0             3470   \n",
       "\n",
       "   Departure Delay  Arrival Delay  \n",
       "0                2            5.0  \n",
       "1               26           39.0  \n",
       "2                0            0.0  \n",
       "3                0            0.0  \n",
       "4                0            1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Gender', 'Age', 'Customer Type', 'Type of Travel', 'Class', 'Flight Distance', 'Departure Delay', 'Arrival Delay']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Neutral or Dissatisfied\n",
       "1                  Satisfied\n",
       "2                  Satisfied\n",
       "3                  Satisfied\n",
       "4                  Satisfied\n",
       "Name: Satisfaction, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Satisfaction']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7676701570680629"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "predictions\n",
    "\n",
    "model_score = accuracy_score(y_test, predictions)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Gender': {},\n",
       " 'Age': {},\n",
       " 'Customer Type': {},\n",
       " 'Type of Travel': {},\n",
       " 'Class': {},\n",
       " 'Flight Distance': {},\n",
       " 'Departure Delay': {},\n",
       " 'Arrival Delay': {}}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[:0].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Customer Type  Type of Travel  Class  Flight Distance  \\\n",
       "0       1   35              0               0      1             1200   \n",
       "1       0   25              1               0      1              600   \n",
       "\n",
       "   Departure Delay  Arrival Delay  \n",
       "0                0              0  \n",
       "1               55              0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_iputs = {\n",
    "    'Gender': [1, 0],\n",
    "    'Age': [35, 25],\n",
    "    'Customer Type': [0, 1],\n",
    "    'Type of Travel': [0, 0],\n",
    "    'Class': [1, 1],\n",
    "    'Flight Distance': [1200, 600],\n",
    "    'Departure Delay': [0, 55],\n",
    "    'Arrival Delay': [0, 0]\n",
    "}\n",
    "\n",
    "test_df = pd.DataFrame(test_iputs)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Neutral or Dissatisfied', 'Neutral or Dissatisfied'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airline_passenger_satisfaction.joblib']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(model, 'airline_passenger_satisfaction.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Age</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Departure Delay</th>\n",
       "      <th>Arrival Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Customer Type  Type of Travel  Class  Flight Distance  \\\n",
       "0       1   35              0               0      1             1200   \n",
       "1       0   25              1               0      1              600   \n",
       "\n",
       "   Departure Delay  Arrival Delay  \n",
       "0                0              0  \n",
       "1               55              0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_iputs = {\n",
    "    'Gender': [1, 0],\n",
    "    'Age': [35, 25],\n",
    "    'Customer Type': [0, 1],\n",
    "    'Type of Travel': [0, 0],\n",
    "    'Class': [1, 1],\n",
    "    'Flight Distance': [1200, 600],\n",
    "    'Departure Delay': [0, 55],\n",
    "    'Arrival Delay': [0, 0]\n",
    "}\n",
    "\n",
    "test_df = pd.DataFrame(test_iputs)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Neutral or Dissatisfied', 'Neutral or Dissatisfied'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trained_model = joblib.load('airline_passenger_satisfaction.joblib')\n",
    "trained_model.predict(test_df)"
   ]
  }
 ],
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