ShinkansenTravelExperience.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2ffec8ce",
   "metadata": {},
   "source": [
    "## **Importing the libraries required**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "755bada1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the basic libraries we will require for the project\n",
    "\n",
    "# Libraries to help with reading and manipulating data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Libaries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# Code to ignore warnings from function usage\n",
    "import warnings;\n",
    "import numpy as np\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da2d0c3d",
   "metadata": {},
   "source": [
    "## **Loading the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "619d74b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_data_original = pd.read_csv(\"Surveydata_train.csv\")\n",
    "travel_data_original = pd.read_csv(\"Traveldata_train.csv\")\n",
    "survey_data_original_test = pd.read_csv(\"Surveydata_test.csv\")\n",
    "travel_data_original_test = pd.read_csv(\"Traveldata_test.csv\")\n",
    "# Copying data to another variable to avoid any changes to original data\n",
    "survey_data = survey_data_original.copy()\n",
    "travel_data = travel_data_original.copy()\n",
    "survey_data_test = survey_data_original_test.copy()\n",
    "travel_data_test = travel_data_original_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59698d40",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
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       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
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       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
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       "      <th>CheckIn_Service</th>\n",
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       "      <td>Needs Improvement</td>\n",
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       "             ID  Overall_Experience       Seat_Comfort Seat_Class  \\\n",
       "0      98800001                   0  Needs Improvement  Green Car   \n",
       "1      98800002                   0               Poor   Ordinary   \n",
       "2      98800003                   1  Needs Improvement  Green Car   \n",
       "3      98800004                   0         Acceptable   Ordinary   \n",
       "4      98800005                   1         Acceptable   Ordinary   \n",
       "...         ...                 ...                ...        ...   \n",
       "94374  98894375                   0               Poor   Ordinary   \n",
       "94375  98894376                   1               Good   Ordinary   \n",
       "94376  98894377                   1  Needs Improvement  Green Car   \n",
       "94377  98894378                   0  Needs Improvement   Ordinary   \n",
       "94378  98894379                   0         Acceptable   Ordinary   \n",
       "\n",
       "      Arrival_Time_Convenient           Catering  Platform_Location  \\\n",
       "0                   Excellent          Excellent    Very Convenient   \n",
       "1                   Excellent               Poor  Needs Improvement   \n",
       "2           Needs Improvement  Needs Improvement  Needs Improvement   \n",
       "3           Needs Improvement                NaN  Needs Improvement   \n",
       "4                  Acceptable         Acceptable         Manageable   \n",
       "...                       ...                ...                ...   \n",
       "94374                    Good               Good         Convenient   \n",
       "94375                    Good               Good         Convenient   \n",
       "94376       Needs Improvement  Needs Improvement  Needs Improvement   \n",
       "94377                     NaN  Needs Improvement         Convenient   \n",
       "94378                    Poor         Acceptable         Manageable   \n",
       "\n",
       "      Onboard_Wifi_Service Onboard_Entertainment Online_Support  \\\n",
       "0                     Good     Needs Improvement     Acceptable   \n",
       "1                     Good                  Poor           Good   \n",
       "2        Needs Improvement                  Good      Excellent   \n",
       "3               Acceptable     Needs Improvement     Acceptable   \n",
       "4        Needs Improvement                  Good      Excellent   \n",
       "...                    ...                   ...            ...   \n",
       "94374                 Poor                  Poor           Poor   \n",
       "94375    Needs Improvement             Excellent      Excellent   \n",
       "94376                 Good             Excellent           Good   \n",
       "94377                 Good     Needs Improvement           Good   \n",
       "94378           Acceptable            Acceptable     Acceptable   \n",
       "\n",
       "      Ease_of_Online_Booking    Onboard_Service            Legroom  \\\n",
       "0          Needs Improvement  Needs Improvement         Acceptable   \n",
       "1                       Good          Excellent  Needs Improvement   \n",
       "2                  Excellent          Excellent          Excellent   \n",
       "3                 Acceptable         Acceptable         Acceptable   \n",
       "4                       Good               Good               Good   \n",
       "...                      ...                ...                ...   \n",
       "94374                   Poor               Good               Good   \n",
       "94375             Acceptable         Acceptable         Acceptable   \n",
       "94376                   Good               Good               Good   \n",
       "94377                   Good         Acceptable               Good   \n",
       "94378             Acceptable               Poor               Good   \n",
       "\n",
       "        Baggage_Handling    CheckIn_Service        Cleanliness Online_Boarding  \n",
       "0      Needs Improvement               Good  Needs Improvement            Poor  \n",
       "1                   Poor  Needs Improvement               Good            Good  \n",
       "2              Excellent               Good          Excellent       Excellent  \n",
       "3             Acceptable               Good         Acceptable      Acceptable  \n",
       "4                   Good               Good               Good            Good  \n",
       "...                  ...                ...                ...             ...  \n",
       "94374               Good  Needs Improvement               Good            Poor  \n",
       "94375         Acceptable               Good         Acceptable            Good  \n",
       "94376               Good         Acceptable               Good      Acceptable  \n",
       "94377               Good               Good          Excellent            Good  \n",
       "94378               Good               Poor               Good      Acceptable  \n",
       "\n",
       "[94379 rows x 17 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c05386ca",
   "metadata": {},
   "outputs": [
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       "      <td>28.0</td>\n",
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       "             ID  Gender   Customer_Type   Age      Type_Travel Travel_Class  \\\n",
       "0      98800001  Female  Loyal Customer  52.0              NaN     Business   \n",
       "1      98800002    Male  Loyal Customer  48.0  Personal Travel          Eco   \n",
       "2      98800003  Female  Loyal Customer  43.0  Business Travel     Business   \n",
       "3      98800004  Female  Loyal Customer  44.0  Business Travel     Business   \n",
       "4      98800005  Female  Loyal Customer  50.0  Business Travel     Business   \n",
       "...         ...     ...             ...   ...              ...          ...   \n",
       "94374  98894375    Male  Loyal Customer  32.0  Business Travel     Business   \n",
       "94375  98894376    Male  Loyal Customer  44.0  Business Travel     Business   \n",
       "94376  98894377    Male             NaN  63.0  Business Travel     Business   \n",
       "94377  98894378    Male  Loyal Customer  16.0  Personal Travel          Eco   \n",
       "94378  98894379    Male  Loyal Customer  54.0              NaN          Eco   \n",
       "\n",
       "       Travel_Distance  Departure_Delay_in_Mins  Arrival_Delay_in_Mins  \n",
       "0                  272                      0.0                    5.0  \n",
       "1                 2200                      9.0                    0.0  \n",
       "2                 1061                     77.0                  119.0  \n",
       "3                  780                     13.0                   18.0  \n",
       "4                 1981                      0.0                    0.0  \n",
       "...                ...                      ...                    ...  \n",
       "94374             1357                     83.0                  125.0  \n",
       "94375              592                      5.0                   11.0  \n",
       "94376             2794                      0.0                    0.0  \n",
       "94377             2744                      0.0                    0.0  \n",
       "94378             2107                     28.0                   28.0  \n",
       "\n",
       "[94379 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "travel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "282776b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(survey_data, travel_data, how='inner', on = 'ID')\n",
    "data_test = pd.merge(survey_data_test, travel_data_test, how='inner', on = 'ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec09d377",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop([\"ID\"], axis=1)\n",
    "Submission=pd.DataFrame(data_test['ID'])\n",
    "data_test = data_test.drop([\"ID\"], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9bf4337",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>...</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_Boarding</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type_Travel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>Departure_Delay_in_Mins</th>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Female</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1425</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>...</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Male</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1610</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>Good</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Poor</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Male</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Inconvenient</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>420</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>Good</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Good</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35602 rows &times; 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Seat_Comfort Seat_Class Arrival_Time_Convenient  \\\n",
       "0             Acceptable  Green Car              Acceptable   \n",
       "1         Extremely Poor   Ordinary                    Good   \n",
       "2              Excellent   Ordinary               Excellent   \n",
       "3             Acceptable  Green Car               Excellent   \n",
       "4              Excellent   Ordinary          Extremely Poor   \n",
       "...                  ...        ...                     ...   \n",
       "35597  Needs Improvement  Green Car               Excellent   \n",
       "35598  Needs Improvement   Ordinary       Needs Improvement   \n",
       "35599               Good  Green Car          Extremely Poor   \n",
       "35600          Excellent   Ordinary               Excellent   \n",
       "35601               Good   Ordinary              Acceptable   \n",
       "\n",
       "                Catering  Platform_Location Onboard_Wifi_Service  \\\n",
       "0             Acceptable         Manageable    Needs Improvement   \n",
       "1                   Poor         Manageable           Acceptable   \n",
       "2              Excellent    Very Convenient            Excellent   \n",
       "3             Acceptable    Very Convenient                 Poor   \n",
       "4              Excellent  Needs Improvement            Excellent   \n",
       "...                  ...                ...                  ...   \n",
       "35597  Needs Improvement         Manageable           Acceptable   \n",
       "35598               Good  Needs Improvement           Acceptable   \n",
       "35599               Good  Needs Improvement    Needs Improvement   \n",
       "35600          Excellent       Inconvenient           Acceptable   \n",
       "35601               Good         Manageable                 Poor   \n",
       "\n",
       "      Onboard_Entertainment Online_Support Ease_of_Online_Booking  \\\n",
       "0                 Excellent           Good              Excellent   \n",
       "1                      Poor     Acceptable             Acceptable   \n",
       "2                 Excellent      Excellent      Needs Improvement   \n",
       "3                Acceptable      Excellent                   Poor   \n",
       "4                 Excellent      Excellent              Excellent   \n",
       "...                     ...            ...                    ...   \n",
       "35597     Needs Improvement     Acceptable             Acceptable   \n",
       "35598             Excellent      Excellent                   Good   \n",
       "35599                  Good           Poor      Needs Improvement   \n",
       "35600             Excellent           Good              Excellent   \n",
       "35601                  Good           Poor                   Poor   \n",
       "\n",
       "         Onboard_Service  ...        Cleanliness    Online_Boarding  Gender  \\\n",
       "0              Excellent  ...          Excellent               Poor  Female   \n",
       "1              Excellent  ...          Excellent         Acceptable  Female   \n",
       "2      Needs Improvement  ...  Needs Improvement          Excellent    Male   \n",
       "3             Acceptable  ...          Excellent               Poor  Female   \n",
       "4                    NaN  ...          Excellent          Excellent    Male   \n",
       "...                  ...  ...                ...                ...     ...   \n",
       "35597               Good  ...               Good         Acceptable    Male   \n",
       "35598               Good  ...               Good               Good  Female   \n",
       "35599               Poor  ...          Excellent  Needs Improvement    Male   \n",
       "35600          Excellent  ...          Excellent               Good  Female   \n",
       "35601         Acceptable  ...               Good               Poor    Male   \n",
       "\n",
       "           Customer_Type   Age      Type_Travel Travel_Class  Travel_Distance  \\\n",
       "0                    NaN  36.0  Business Travel     Business              532   \n",
       "1      Disloyal Customer  21.0  Business Travel     Business             1425   \n",
       "2         Loyal Customer  60.0  Business Travel     Business             2832   \n",
       "3         Loyal Customer  29.0  Personal Travel          Eco             1352   \n",
       "4      Disloyal Customer  18.0  Business Travel     Business             1610   \n",
       "...                  ...   ...              ...          ...              ...   \n",
       "35597     Loyal Customer   8.0  Personal Travel          Eco             1334   \n",
       "35598     Loyal Customer  53.0  Business Travel     Business             1772   \n",
       "35599  Disloyal Customer  22.0  Business Travel          Eco             1180   \n",
       "35600     Loyal Customer  67.0  Personal Travel          Eco              420   \n",
       "35601                NaN  20.0  Personal Travel          Eco             1680   \n",
       "\n",
       "      Departure_Delay_in_Mins Arrival_Delay_in_Mins  \n",
       "0                         0.0                   0.0  \n",
       "1                         9.0                  28.0  \n",
       "2                         0.0                   0.0  \n",
       "3                         0.0                   0.0  \n",
       "4                        17.0                   0.0  \n",
       "...                       ...                   ...  \n",
       "35597                     0.0                   0.0  \n",
       "35598                     0.0                   0.0  \n",
       "35599                     0.0                   0.0  \n",
       "35600                    23.0                  16.0  \n",
       "35601                     0.0                   0.0  \n",
       "\n",
       "[35602 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "988c730a",
   "metadata": {},
   "source": [
    "## **Overview of the dataset**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f38416a8",
   "metadata": {},
   "source": [
    "### **The first and last 5 rows of the dataset**\n",
    "\n",
    "Let's **view the first few rows and last few rows** of the dataset in order to understand its structure a little better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96cd0e5b",
   "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>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>...</th>\n",
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       "      <th>Arrival_Delay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
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       "      <td>Needs Improvement</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business</td>\n",
       "      <td>272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2200</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1061</td>\n",
       "      <td>77.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>780</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows &times; 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall_Experience       Seat_Comfort Seat_Class Arrival_Time_Convenient  \\\n",
       "0                   0  Needs Improvement  Green Car               Excellent   \n",
       "1                   0               Poor   Ordinary               Excellent   \n",
       "2                   1  Needs Improvement  Green Car       Needs Improvement   \n",
       "3                   0         Acceptable   Ordinary       Needs Improvement   \n",
       "4                   1         Acceptable   Ordinary              Acceptable   \n",
       "\n",
       "            Catering  Platform_Location Onboard_Wifi_Service  \\\n",
       "0          Excellent    Very Convenient                 Good   \n",
       "1               Poor  Needs Improvement                 Good   \n",
       "2  Needs Improvement  Needs Improvement    Needs Improvement   \n",
       "3                NaN  Needs Improvement           Acceptable   \n",
       "4         Acceptable         Manageable    Needs Improvement   \n",
       "\n",
       "  Onboard_Entertainment Online_Support Ease_of_Online_Booking  ...  \\\n",
       "0     Needs Improvement     Acceptable      Needs Improvement  ...   \n",
       "1                  Poor           Good                   Good  ...   \n",
       "2                  Good      Excellent              Excellent  ...   \n",
       "3     Needs Improvement     Acceptable             Acceptable  ...   \n",
       "4                  Good      Excellent                   Good  ...   \n",
       "\n",
       "         Cleanliness Online_Boarding  Gender   Customer_Type   Age  \\\n",
       "0  Needs Improvement            Poor  Female  Loyal Customer  52.0   \n",
       "1               Good            Good    Male  Loyal Customer  48.0   \n",
       "2          Excellent       Excellent  Female  Loyal Customer  43.0   \n",
       "3         Acceptable      Acceptable  Female  Loyal Customer  44.0   \n",
       "4               Good            Good  Female  Loyal Customer  50.0   \n",
       "\n",
       "       Type_Travel Travel_Class Travel_Distance  Departure_Delay_in_Mins  \\\n",
       "0              NaN     Business             272                      0.0   \n",
       "1  Personal Travel          Eco            2200                      9.0   \n",
       "2  Business Travel     Business            1061                     77.0   \n",
       "3  Business Travel     Business             780                     13.0   \n",
       "4  Business Travel     Business            1981                      0.0   \n",
       "\n",
       "  Arrival_Delay_in_Mins  \n",
       "0                   5.0  \n",
       "1                   0.0  \n",
       "2                 119.0  \n",
       "3                  18.0  \n",
       "4                   0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f38e234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>...</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_Boarding</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type_Travel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>Departure_Delay_in_Mins</th>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>0</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1357</td>\n",
       "      <td>83.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Good</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>592</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>1</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>0</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2107</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows &times; 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience       Seat_Comfort Seat_Class  \\\n",
       "94374                   0               Poor   Ordinary   \n",
       "94375                   1               Good   Ordinary   \n",
       "94376                   1  Needs Improvement  Green Car   \n",
       "94377                   0  Needs Improvement   Ordinary   \n",
       "94378                   0         Acceptable   Ordinary   \n",
       "\n",
       "      Arrival_Time_Convenient           Catering  Platform_Location  \\\n",
       "94374                    Good               Good         Convenient   \n",
       "94375                    Good               Good         Convenient   \n",
       "94376       Needs Improvement  Needs Improvement  Needs Improvement   \n",
       "94377                     NaN  Needs Improvement         Convenient   \n",
       "94378                    Poor         Acceptable         Manageable   \n",
       "\n",
       "      Onboard_Wifi_Service Onboard_Entertainment Online_Support  \\\n",
       "94374                 Poor                  Poor           Poor   \n",
       "94375    Needs Improvement             Excellent      Excellent   \n",
       "94376                 Good             Excellent           Good   \n",
       "94377                 Good     Needs Improvement           Good   \n",
       "94378           Acceptable            Acceptable     Acceptable   \n",
       "\n",
       "      Ease_of_Online_Booking  ... Cleanliness Online_Boarding Gender  \\\n",
       "94374                   Poor  ...        Good            Poor   Male   \n",
       "94375             Acceptable  ...  Acceptable            Good   Male   \n",
       "94376                   Good  ...        Good      Acceptable   Male   \n",
       "94377                   Good  ...   Excellent            Good   Male   \n",
       "94378             Acceptable  ...        Good      Acceptable   Male   \n",
       "\n",
       "        Customer_Type   Age      Type_Travel Travel_Class Travel_Distance  \\\n",
       "94374  Loyal Customer  32.0  Business Travel     Business            1357   \n",
       "94375  Loyal Customer  44.0  Business Travel     Business             592   \n",
       "94376             NaN  63.0  Business Travel     Business            2794   \n",
       "94377  Loyal Customer  16.0  Personal Travel          Eco            2744   \n",
       "94378  Loyal Customer  54.0              NaN          Eco            2107   \n",
       "\n",
       "       Departure_Delay_in_Mins Arrival_Delay_in_Mins  \n",
       "94374                     83.0                 125.0  \n",
       "94375                      5.0                  11.0  \n",
       "94376                      0.0                   0.0  \n",
       "94377                      0.0                   0.0  \n",
       "94378                     28.0                  28.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a3e28505",
   "metadata": {},
   "source": [
    "### **The shape of the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f7a78ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(94379, 24)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "36418311",
   "metadata": {},
   "source": [
    "### **Check the data types of the columns for the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b44b46c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 94379 entries, 0 to 94378\n",
      "Data columns (total 24 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Overall_Experience       94379 non-null  int64  \n",
      " 1   Seat_Comfort             94318 non-null  object \n",
      " 2   Seat_Class               94379 non-null  object \n",
      " 3   Arrival_Time_Convenient  85449 non-null  object \n",
      " 4   Catering                 85638 non-null  object \n",
      " 5   Platform_Location        94349 non-null  object \n",
      " 6   Onboard_Wifi_Service     94349 non-null  object \n",
      " 7   Onboard_Entertainment    94361 non-null  object \n",
      " 8   Online_Support           94288 non-null  object \n",
      " 9   Ease_of_Online_Booking   94306 non-null  object \n",
      " 10  Onboard_Service          86778 non-null  object \n",
      " 11  Legroom                  94289 non-null  object \n",
      " 12  Baggage_Handling         94237 non-null  object \n",
      " 13  CheckIn_Service          94302 non-null  object \n",
      " 14  Cleanliness              94373 non-null  object \n",
      " 15  Online_Boarding          94373 non-null  object \n",
      " 16  Gender                   94302 non-null  object \n",
      " 17  Customer_Type            85428 non-null  object \n",
      " 18  Age                      94346 non-null  float64\n",
      " 19  Type_Travel              85153 non-null  object \n",
      " 20  Travel_Class             94379 non-null  object \n",
      " 21  Travel_Distance          94379 non-null  int64  \n",
      " 22  Departure_Delay_in_Mins  94322 non-null  float64\n",
      " 23  Arrival_Delay_in_Mins    94022 non-null  float64\n",
      "dtypes: float64(3), int64(2), object(19)\n",
      "memory usage: 18.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "41850c48",
   "metadata": {},
   "source": [
    "### **Data types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fb6b3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Overall_Experience           int64\n",
       "Seat_Comfort                object\n",
       "Seat_Class                  object\n",
       "Arrival_Time_Convenient     object\n",
       "Catering                    object\n",
       "Platform_Location           object\n",
       "Onboard_Wifi_Service        object\n",
       "Onboard_Entertainment       object\n",
       "Online_Support              object\n",
       "Ease_of_Online_Booking      object\n",
       "Onboard_Service             object\n",
       "Legroom                     object\n",
       "Baggage_Handling            object\n",
       "CheckIn_Service             object\n",
       "Cleanliness                 object\n",
       "Online_Boarding             object\n",
       "Gender                      object\n",
       "Customer_Type               object\n",
       "Age                        float64\n",
       "Type_Travel                 object\n",
       "Travel_Class                object\n",
       "Travel_Distance              int64\n",
       "Departure_Delay_in_Mins    float64\n",
       "Arrival_Delay_in_Mins      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a99e0c75",
   "metadata": {},
   "source": [
    "### **Data head**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ee06fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>Legroom</th>\n",
       "      <th>Baggage_Handling</th>\n",
       "      <th>CheckIn_Service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_Boarding</th>\n",
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       "      <th>Type_Travel</th>\n",
       "      <th>Travel_Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
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       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Seat_Comfort Seat_Class Arrival_Time_Convenient           Catering  \\\n",
       "0  Needs Improvement  Green Car               Excellent          Excellent   \n",
       "1               Poor   Ordinary               Excellent               Poor   \n",
       "2  Needs Improvement  Green Car       Needs Improvement  Needs Improvement   \n",
       "3         Acceptable   Ordinary       Needs Improvement                NaN   \n",
       "4         Acceptable   Ordinary              Acceptable         Acceptable   \n",
       "\n",
       "   Platform_Location Onboard_Wifi_Service Onboard_Entertainment  \\\n",
       "0    Very Convenient                 Good     Needs Improvement   \n",
       "1  Needs Improvement                 Good                  Poor   \n",
       "2  Needs Improvement    Needs Improvement                  Good   \n",
       "3  Needs Improvement           Acceptable     Needs Improvement   \n",
       "4         Manageable    Needs Improvement                  Good   \n",
       "\n",
       "  Online_Support Ease_of_Online_Booking    Onboard_Service            Legroom  \\\n",
       "0     Acceptable      Needs Improvement  Needs Improvement         Acceptable   \n",
       "1           Good                   Good          Excellent  Needs Improvement   \n",
       "2      Excellent              Excellent          Excellent          Excellent   \n",
       "3     Acceptable             Acceptable         Acceptable         Acceptable   \n",
       "4      Excellent                   Good               Good               Good   \n",
       "\n",
       "    Baggage_Handling    CheckIn_Service        Cleanliness Online_Boarding  \\\n",
       "0  Needs Improvement               Good  Needs Improvement            Poor   \n",
       "1               Poor  Needs Improvement               Good            Good   \n",
       "2          Excellent               Good          Excellent       Excellent   \n",
       "3         Acceptable               Good         Acceptable      Acceptable   \n",
       "4               Good               Good               Good            Good   \n",
       "\n",
       "   Gender   Customer_Type      Type_Travel Travel_Class  \n",
       "0  Female  Loyal Customer              NaN     Business  \n",
       "1    Male  Loyal Customer  Personal Travel          Eco  \n",
       "2  Female  Loyal Customer  Business Travel     Business  \n",
       "3  Female  Loyal Customer  Business Travel     Business  \n",
       "4  Female  Loyal Customer  Business Travel     Business  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df = data.select_dtypes(include=['object']).copy()\n",
    "obj_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1b7904e",
   "metadata": {},
   "source": [
    "### **Look for the presence of null values in the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87e46d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Overall_Experience            0\n",
       "Seat_Comfort                 61\n",
       "Seat_Class                    0\n",
       "Arrival_Time_Convenient    8930\n",
       "Catering                   8741\n",
       "Platform_Location            30\n",
       "Onboard_Wifi_Service         30\n",
       "Onboard_Entertainment        18\n",
       "Online_Support               91\n",
       "Ease_of_Online_Booking       73\n",
       "Onboard_Service            7601\n",
       "Legroom                      90\n",
       "Baggage_Handling            142\n",
       "CheckIn_Service              77\n",
       "Cleanliness                   6\n",
       "Online_Boarding               6\n",
       "Gender                       77\n",
       "Customer_Type              8951\n",
       "Age                          33\n",
       "Type_Travel                9226\n",
       "Travel_Class                  0\n",
       "Travel_Distance               0\n",
       "Departure_Delay_in_Mins      57\n",
       "Arrival_Delay_in_Mins       357\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4c7c50ef",
   "metadata": {},
   "source": [
    "### Listing of column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c32d969a",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['Seat_Class', 'Gender', 'Customer_Type', 'Type_Travel', 'Travel_Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c4dd6c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode(col_list, dataframe):\n",
    "    for column in col_list:\n",
    "        dataframe[column].fillna('sin_valor', inplace=True)\n",
    "        # Get one hot encoding of column\n",
    "        one_hot = pd.get_dummies(dataframe[column], prefix=column)\n",
    "        # Drop column as it is now encoded\n",
    "        dataframe = dataframe.drop(column,axis = 1)\n",
    "        # Join the encoded df\n",
    "        dataframe = dataframe.join(one_hot)\n",
    "    return dataframe\n",
    "data = one_hot_encode(categorical_columns,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b9dcd4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Overall_Experience', 'Seat_Comfort', 'Arrival_Time_Convenient',\n",
       "       'Catering', 'Platform_Location', 'Onboard_Wifi_Service',\n",
       "       'Onboard_Entertainment', 'Online_Support', 'Ease_of_Online_Booking',\n",
       "       'Onboard_Service', 'Legroom', 'Baggage_Handling', 'CheckIn_Service',\n",
       "       'Cleanliness', 'Online_Boarding', 'Age', 'Travel_Distance',\n",
       "       'Departure_Delay_in_Mins', 'Arrival_Delay_in_Mins',\n",
       "       'Seat_Class_Green Car', 'Seat_Class_Ordinary', 'Gender_Female',\n",
       "       'Gender_Male', 'Gender_sin_valor', 'Customer_Type_Disloyal Customer',\n",
       "       'Customer_Type_Loyal Customer', 'Customer_Type_sin_valor',\n",
       "       'Type_Travel_Business Travel', 'Type_Travel_Personal Travel',\n",
       "       'Type_Travel_sin_valor', 'Travel_Class_Business', 'Travel_Class_Eco'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f10096c",
   "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>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Gender_sin_valor</th>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <th>Customer_Type_Loyal Customer</th>\n",
       "      <th>Customer_Type_sin_valor</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Type_Travel_sin_valor</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>0</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>1</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>1</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>0</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Convenient</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94379 rows &times; 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience       Seat_Comfort Arrival_Time_Convenient  \\\n",
       "0                       0  Needs Improvement               Excellent   \n",
       "1                       0               Poor               Excellent   \n",
       "2                       1  Needs Improvement       Needs Improvement   \n",
       "3                       0         Acceptable       Needs Improvement   \n",
       "4                       1         Acceptable              Acceptable   \n",
       "...                   ...                ...                     ...   \n",
       "94374                   0               Poor                    Good   \n",
       "94375                   1               Good                    Good   \n",
       "94376                   1  Needs Improvement       Needs Improvement   \n",
       "94377                   0  Needs Improvement                     NaN   \n",
       "94378                   0         Acceptable                    Poor   \n",
       "\n",
       "                Catering  Platform_Location Onboard_Wifi_Service  \\\n",
       "0              Excellent    Very Convenient                 Good   \n",
       "1                   Poor  Needs Improvement                 Good   \n",
       "2      Needs Improvement  Needs Improvement    Needs Improvement   \n",
       "3                    NaN  Needs Improvement           Acceptable   \n",
       "4             Acceptable         Manageable    Needs Improvement   \n",
       "...                  ...                ...                  ...   \n",
       "94374               Good         Convenient                 Poor   \n",
       "94375               Good         Convenient    Needs Improvement   \n",
       "94376  Needs Improvement  Needs Improvement                 Good   \n",
       "94377  Needs Improvement         Convenient                 Good   \n",
       "94378         Acceptable         Manageable           Acceptable   \n",
       "\n",
       "      Onboard_Entertainment Online_Support Ease_of_Online_Booking  \\\n",
       "0         Needs Improvement     Acceptable      Needs Improvement   \n",
       "1                      Poor           Good                   Good   \n",
       "2                      Good      Excellent              Excellent   \n",
       "3         Needs Improvement     Acceptable             Acceptable   \n",
       "4                      Good      Excellent                   Good   \n",
       "...                     ...            ...                    ...   \n",
       "94374                  Poor           Poor                   Poor   \n",
       "94375             Excellent      Excellent             Acceptable   \n",
       "94376             Excellent           Good                   Good   \n",
       "94377     Needs Improvement           Good                   Good   \n",
       "94378            Acceptable     Acceptable             Acceptable   \n",
       "\n",
       "         Onboard_Service  ... Gender_Male Gender_sin_valor  \\\n",
       "0      Needs Improvement  ...           0                0   \n",
       "1              Excellent  ...           1                0   \n",
       "2              Excellent  ...           0                0   \n",
       "3             Acceptable  ...           0                0   \n",
       "4                   Good  ...           0                0   \n",
       "...                  ...  ...         ...              ...   \n",
       "94374               Good  ...           1                0   \n",
       "94375         Acceptable  ...           1                0   \n",
       "94376               Good  ...           1                0   \n",
       "94377         Acceptable  ...           1                0   \n",
       "94378               Poor  ...           1                0   \n",
       "\n",
       "      Customer_Type_Disloyal Customer Customer_Type_Loyal Customer  \\\n",
       "0                                   0                            1   \n",
       "1                                   0                            1   \n",
       "2                                   0                            1   \n",
       "3                                   0                            1   \n",
       "4                                   0                            1   \n",
       "...                               ...                          ...   \n",
       "94374                               0                            1   \n",
       "94375                               0                            1   \n",
       "94376                               0                            0   \n",
       "94377                               0                            1   \n",
       "94378                               0                            1   \n",
       "\n",
       "      Customer_Type_sin_valor  Type_Travel_Business Travel  \\\n",
       "0                           0                            0   \n",
       "1                           0                            0   \n",
       "2                           0                            1   \n",
       "3                           0                            1   \n",
       "4                           0                            1   \n",
       "...                       ...                          ...   \n",
       "94374                       0                            1   \n",
       "94375                       0                            1   \n",
       "94376                       1                            1   \n",
       "94377                       0                            0   \n",
       "94378                       0                            0   \n",
       "\n",
       "       Type_Travel_Personal Travel  Type_Travel_sin_valor  \\\n",
       "0                                0                      1   \n",
       "1                                1                      0   \n",
       "2                                0                      0   \n",
       "3                                0                      0   \n",
       "4                                0                      0   \n",
       "...                            ...                    ...   \n",
       "94374                            0                      0   \n",
       "94375                            0                      0   \n",
       "94376                            0                      0   \n",
       "94377                            1                      0   \n",
       "94378                            0                      1   \n",
       "\n",
       "       Travel_Class_Business  Travel_Class_Eco  \n",
       "0                          1                 0  \n",
       "1                          0                 1  \n",
       "2                          1                 0  \n",
       "3                          1                 0  \n",
       "4                          1                 0  \n",
       "...                      ...               ...  \n",
       "94374                      1                 0  \n",
       "94375                      1                 0  \n",
       "94376                      1                 0  \n",
       "94377                      0                 1  \n",
       "94378                      0                 1  \n",
       "\n",
       "[94379 rows x 32 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "983c3e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_list = survey_data.columns.tolist()\n",
    "cols_list.remove('ID')\n",
    "cols_list_similar = cols_list.copy()\n",
    "cols_list_similar.remove('Seat_Class')\n",
    "cols_list_similar.remove('Platform_Location')\n",
    "cols_list_similar.remove('Overall_Experience')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26a6a788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Seat_Comfort',\n",
       " 'Arrival_Time_Convenient',\n",
       " 'Catering',\n",
       " 'Onboard_Wifi_Service',\n",
       " 'Onboard_Entertainment',\n",
       " 'Online_Support',\n",
       " 'Ease_of_Online_Booking',\n",
       " 'Onboard_Service',\n",
       " 'Legroom',\n",
       " 'Baggage_Handling',\n",
       " 'CheckIn_Service',\n",
       " 'Cleanliness',\n",
       " 'Online_Boarding']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_list_similar"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "df3da9d8",
   "metadata": {},
   "source": [
    "### **Encode the categorical object variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b13916f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_cols_list_similar(cols_list_similar,dataset):\n",
    "    for col_name in cols_list_similar:\n",
    "        dataset[col_name].replace(['Extremely Poor', 'Poor', 'Needs Improvement', 'Acceptable', 'Good', 'Excellent'],\n",
    "                             [0, 1, 2, 3, 4, 5], inplace=True)\n",
    "    return dataset    \n",
    "data=encode_cols_list_similar(cols_list_similar,data)\n",
    "data['Platform_Location'].replace(['Very Inconvenient', 'Inconvenient', 'Needs Improvement', 'Manageable', 'Convenient', 'Very Convenient'],\n",
    "                             [0, 1, 2, 3, 4, 5], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "42dc298b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
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       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
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       "      <th>Customer_Type_Loyal Customer</th>\n",
       "      <th>Customer_Type_sin_valor</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Type_Travel_sin_valor</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
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       "<p>94379 rows &times; 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience  Seat_Comfort  Arrival_Time_Convenient  Catering  \\\n",
       "0                       0           2.0                      5.0       5.0   \n",
       "1                       0           1.0                      5.0       1.0   \n",
       "2                       1           2.0                      2.0       2.0   \n",
       "3                       0           3.0                      2.0       NaN   \n",
       "4                       1           3.0                      3.0       3.0   \n",
       "...                   ...           ...                      ...       ...   \n",
       "94374                   0           1.0                      4.0       4.0   \n",
       "94375                   1           4.0                      4.0       4.0   \n",
       "94376                   1           2.0                      2.0       2.0   \n",
       "94377                   0           2.0                      NaN       2.0   \n",
       "94378                   0           3.0                      1.0       3.0   \n",
       "\n",
       "       Platform_Location  Onboard_Wifi_Service  Onboard_Entertainment  \\\n",
       "0                    5.0                   4.0                    2.0   \n",
       "1                    2.0                   4.0                    1.0   \n",
       "2                    2.0                   2.0                    4.0   \n",
       "3                    2.0                   3.0                    2.0   \n",
       "4                    3.0                   2.0                    4.0   \n",
       "...                  ...                   ...                    ...   \n",
       "94374                4.0                   1.0                    1.0   \n",
       "94375                4.0                   2.0                    5.0   \n",
       "94376                2.0                   4.0                    5.0   \n",
       "94377                4.0                   4.0                    2.0   \n",
       "94378                3.0                   3.0                    3.0   \n",
       "\n",
       "       Online_Support  Ease_of_Online_Booking  Onboard_Service  ...  \\\n",
       "0                 3.0                     2.0              2.0  ...   \n",
       "1                 4.0                     4.0              5.0  ...   \n",
       "2                 5.0                     5.0              5.0  ...   \n",
       "3                 3.0                     3.0              3.0  ...   \n",
       "4                 5.0                     4.0              4.0  ...   \n",
       "...               ...                     ...              ...  ...   \n",
       "94374             1.0                     1.0              4.0  ...   \n",
       "94375             5.0                     3.0              3.0  ...   \n",
       "94376             4.0                     4.0              4.0  ...   \n",
       "94377             4.0                     4.0              3.0  ...   \n",
       "94378             3.0                     3.0              1.0  ...   \n",
       "\n",
       "       Gender_Male  Gender_sin_valor  Customer_Type_Disloyal Customer  \\\n",
       "0                0                 0                                0   \n",
       "1                1                 0                                0   \n",
       "2                0                 0                                0   \n",
       "3                0                 0                                0   \n",
       "4                0                 0                                0   \n",
       "...            ...               ...                              ...   \n",
       "94374            1                 0                                0   \n",
       "94375            1                 0                                0   \n",
       "94376            1                 0                                0   \n",
       "94377            1                 0                                0   \n",
       "94378            1                 0                                0   \n",
       "\n",
       "       Customer_Type_Loyal Customer  Customer_Type_sin_valor  \\\n",
       "0                                 1                        0   \n",
       "1                                 1                        0   \n",
       "2                                 1                        0   \n",
       "3                                 1                        0   \n",
       "4                                 1                        0   \n",
       "...                             ...                      ...   \n",
       "94374                             1                        0   \n",
       "94375                             1                        0   \n",
       "94376                             0                        1   \n",
       "94377                             1                        0   \n",
       "94378                             1                        0   \n",
       "\n",
       "       Type_Travel_Business Travel  Type_Travel_Personal Travel  \\\n",
       "0                                0                            0   \n",
       "1                                0                            1   \n",
       "2                                1                            0   \n",
       "3                                1                            0   \n",
       "4                                1                            0   \n",
       "...                            ...                          ...   \n",
       "94374                            1                            0   \n",
       "94375                            1                            0   \n",
       "94376                            1                            0   \n",
       "94377                            0                            1   \n",
       "94378                            0                            0   \n",
       "\n",
       "       Type_Travel_sin_valor  Travel_Class_Business  Travel_Class_Eco  \n",
       "0                          1                      1                 0  \n",
       "1                          0                      0                 1  \n",
       "2                          0                      1                 0  \n",
       "3                          0                      1                 0  \n",
       "4                          0                      1                 0  \n",
       "...                      ...                    ...               ...  \n",
       "94374                      0                      1                 0  \n",
       "94375                      0                      1                 0  \n",
       "94376                      0                      1                 0  \n",
       "94377                      0                      0                 1  \n",
       "94378                      1                      0                 1  \n",
       "\n",
       "[94379 rows x 32 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "685a6184",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_list = data.columns.tolist()\n",
    "#cols_list.remove('ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f22ee542",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_list.remove('Overall_Experience')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc32224f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Seat_Comfort',\n",
       " 'Arrival_Time_Convenient',\n",
       " 'Catering',\n",
       " 'Platform_Location',\n",
       " 'Onboard_Wifi_Service',\n",
       " 'Onboard_Entertainment',\n",
       " 'Online_Support',\n",
       " 'Ease_of_Online_Booking',\n",
       " 'Onboard_Service',\n",
       " 'Legroom',\n",
       " 'Baggage_Handling',\n",
       " 'CheckIn_Service',\n",
       " 'Cleanliness',\n",
       " 'Online_Boarding',\n",
       " 'Age',\n",
       " 'Travel_Distance',\n",
       " 'Departure_Delay_in_Mins',\n",
       " 'Arrival_Delay_in_Mins',\n",
       " 'Seat_Class_Green Car',\n",
       " 'Seat_Class_Ordinary',\n",
       " 'Gender_Female',\n",
       " 'Gender_Male',\n",
       " 'Gender_sin_valor',\n",
       " 'Customer_Type_Disloyal Customer',\n",
       " 'Customer_Type_Loyal Customer',\n",
       " 'Customer_Type_sin_valor',\n",
       " 'Type_Travel_Business Travel',\n",
       " 'Type_Travel_Personal Travel',\n",
       " 'Type_Travel_sin_valor',\n",
       " 'Travel_Class_Business',\n",
       " 'Travel_Class_Eco']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "854a7e12",
   "metadata": {},
   "source": [
    "### **Normalize the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d65ca30c",
   "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>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>...</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Gender_sin_valor</th>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <th>Customer_Type_Loyal Customer</th>\n",
       "      <th>Customer_Type_sin_valor</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Type_Travel_sin_valor</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94379 rows &times; 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience  Seat_Comfort  Arrival_Time_Convenient  Catering  \\\n",
       "0                       0           2.0                      5.0       5.0   \n",
       "1                       0           1.0                      5.0       1.0   \n",
       "2                       1           2.0                      2.0       2.0   \n",
       "3                       0           3.0                      2.0       NaN   \n",
       "4                       1           3.0                      3.0       3.0   \n",
       "...                   ...           ...                      ...       ...   \n",
       "94374                   0           1.0                      4.0       4.0   \n",
       "94375                   1           4.0                      4.0       4.0   \n",
       "94376                   1           2.0                      2.0       2.0   \n",
       "94377                   0           2.0                      NaN       2.0   \n",
       "94378                   0           3.0                      1.0       3.0   \n",
       "\n",
       "       Platform_Location  Onboard_Wifi_Service  Onboard_Entertainment  \\\n",
       "0                    5.0                   4.0                    2.0   \n",
       "1                    2.0                   4.0                    1.0   \n",
       "2                    2.0                   2.0                    4.0   \n",
       "3                    2.0                   3.0                    2.0   \n",
       "4                    3.0                   2.0                    4.0   \n",
       "...                  ...                   ...                    ...   \n",
       "94374                4.0                   1.0                    1.0   \n",
       "94375                4.0                   2.0                    5.0   \n",
       "94376                2.0                   4.0                    5.0   \n",
       "94377                4.0                   4.0                    2.0   \n",
       "94378                3.0                   3.0                    3.0   \n",
       "\n",
       "       Online_Support  Ease_of_Online_Booking  Onboard_Service  ...  \\\n",
       "0                 3.0                     2.0              2.0  ...   \n",
       "1                 4.0                     4.0              5.0  ...   \n",
       "2                 5.0                     5.0              5.0  ...   \n",
       "3                 3.0                     3.0              3.0  ...   \n",
       "4                 5.0                     4.0              4.0  ...   \n",
       "...               ...                     ...              ...  ...   \n",
       "94374             1.0                     1.0              4.0  ...   \n",
       "94375             5.0                     3.0              3.0  ...   \n",
       "94376             4.0                     4.0              4.0  ...   \n",
       "94377             4.0                     4.0              3.0  ...   \n",
       "94378             3.0                     3.0              1.0  ...   \n",
       "\n",
       "       Gender_Male  Gender_sin_valor  Customer_Type_Disloyal Customer  \\\n",
       "0                0                 0                                0   \n",
       "1                1                 0                                0   \n",
       "2                0                 0                                0   \n",
       "3                0                 0                                0   \n",
       "4                0                 0                                0   \n",
       "...            ...               ...                              ...   \n",
       "94374            1                 0                                0   \n",
       "94375            1                 0                                0   \n",
       "94376            1                 0                                0   \n",
       "94377            1                 0                                0   \n",
       "94378            1                 0                                0   \n",
       "\n",
       "       Customer_Type_Loyal Customer  Customer_Type_sin_valor  \\\n",
       "0                                 1                        0   \n",
       "1                                 1                        0   \n",
       "2                                 1                        0   \n",
       "3                                 1                        0   \n",
       "4                                 1                        0   \n",
       "...                             ...                      ...   \n",
       "94374                             1                        0   \n",
       "94375                             1                        0   \n",
       "94376                             0                        1   \n",
       "94377                             1                        0   \n",
       "94378                             1                        0   \n",
       "\n",
       "       Type_Travel_Business Travel  Type_Travel_Personal Travel  \\\n",
       "0                                0                            0   \n",
       "1                                0                            1   \n",
       "2                                1                            0   \n",
       "3                                1                            0   \n",
       "4                                1                            0   \n",
       "...                            ...                          ...   \n",
       "94374                            1                            0   \n",
       "94375                            1                            0   \n",
       "94376                            1                            0   \n",
       "94377                            0                            1   \n",
       "94378                            0                            0   \n",
       "\n",
       "       Type_Travel_sin_valor  Travel_Class_Business  Travel_Class_Eco  \n",
       "0                          1                      1                 0  \n",
       "1                          0                      0                 1  \n",
       "2                          0                      1                 0  \n",
       "3                          0                      1                 0  \n",
       "4                          0                      1                 0  \n",
       "...                      ...                    ...               ...  \n",
       "94374                      0                      1                 0  \n",
       "94375                      0                      1                 0  \n",
       "94376                      0                      1                 0  \n",
       "94377                      0                      0                 1  \n",
       "94378                      1                      0                 1  \n",
       "\n",
       "[94379 rows x 32 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "299c4798",
   "metadata": {},
   "source": [
    "### **Printing value count of columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc67aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_count(col_names,dataset):\n",
    "    for col_name in col_names:\n",
    "        print('')\n",
    "        print(dataset[col_name].value_counts(normalize=False).sort_index())\n",
    "        print(col_name, 'NAN',dataset[col_name].isna().sum())\n",
    "print_count(cols_list,data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2125bc2e",
   "metadata": {},
   "source": [
    "### **Filling null values in the dataset with means**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f1579d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mean(data,col_names):\n",
    "    for col_name in col_names:\n",
    "        if col_name in ['Age','Travel_Distance' ,'Departure_Delay_in_Mins','Arrival_Delay_in_Mins']:\n",
    "            data[col_name].fillna(int(data[col_name].mean()), inplace=True)#data[col_name].mean()\n",
    "        else:\n",
    "            data[col_name].fillna(int(data[col_name].mode()), inplace=True)#data[col_name].mean()\n",
    "    return data\n",
    "cols_list = data.select_dtypes(include=np.number).columns.tolist()\n",
    "cols_list.remove('Overall_Experience')\n",
    "data=fill_mean(data,cols_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4df860d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "38ba5dd3",
   "metadata": {},
   "source": [
    "### Function to clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "db212056",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(data):\n",
    "    cols_list_1 = data.columns.tolist()\n",
    "    if 'Overall_Experience' in cols_list_1:\n",
    "        Overall_Experience=data['Overall_Experience']    \n",
    "    categorical_columns = ['Seat_Class', 'Gender', 'Customer_Type', 'Type_Travel', 'Travel_Class']\n",
    "    data = one_hot_encode(categorical_columns,data)\n",
    "    data=encode_cols_list_similar(cols_list_similar,data)\n",
    "    data['Platform_Location'].replace(['Very Inconvenient', 'Inconvenient', 'Needs Improvement', 'Manageable', 'Convenient', 'Very Convenient'],\n",
    "                             [0, 1, 2, 3, 4, 5], inplace=True)\n",
    "    data['SurveyPos'] = data.apply(lambda row: ((#row.Platform_Location +\n",
    "                                             row.Seat_Comfort +\n",
    "                                             #row.Arrival_Time_Convenient +\n",
    "                                             #row.Catering +\n",
    "                                             #row.Onboard_Wifi_Service +\n",
    "                                             row.Onboard_Entertainment \n",
    "                                             #row.Online_Support +\n",
    "                                             #row.Ease_of_Online_Booking \n",
    "                                             #row.Onboard_Service +\n",
    "                                             #row.Legroom +\n",
    "                                             #row.Baggage_Handling +\n",
    "                                             #row.CheckIn_Service +\n",
    "                                             #row.Cleanliness +\n",
    "                                             #row.Online_Boarding\n",
    "                                             )/2), axis=1)\n",
    "    data['SurveyNeg']=data['SurveyPos']\n",
    "    \n",
    "    #DataSurveyPos[DataSurveyPos>=3]=1\n",
    "    #DataSurveyPos[DataSurveyPos<3]=0\n",
    "    #data['SurveyPos']=DataSurveyPos\n",
    "    data['SurveyPos'].values[data['SurveyPos'].values <= 3.0] = 0\n",
    "    data['SurveyPos'].values[data['SurveyPos'].values > 3.0] = 1\n",
    "    data['SurveyNeg'].values[data['SurveyNeg'].values <= 2.0] = 0\n",
    "    data['SurveyNeg'].values[data['SurveyNeg'].values > 2.0] = 1    \n",
    "    cols_list = data.select_dtypes(include=np.number).columns.tolist()\n",
    "    #cols_list.remove('Overall_Experience')\n",
    "    data=norm_cols(cols_list,data)\n",
    "    data=fill_mean(data,cols_list)\n",
    "    if 'Overall_Experience' in cols_list_1:\n",
    "        data['Overall_Experience'] = Overall_Experience    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "54425260",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(survey_data, travel_data, how='inner', on = 'ID')\n",
    "data = data.drop([\"ID\"], axis=1)\n",
    "data=clean_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "24cb2476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.570033    71173\n",
       "-1.754267    23127\n",
       " 0.000000       79\n",
       "Name: SurveyNeg, dtype: int64"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SurveyNeg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "1117522c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Seat_Comfort',\n",
       " 'Arrival_Time_Convenient',\n",
       " 'Catering',\n",
       " 'Platform_Location',\n",
       " 'Onboard_Wifi_Service',\n",
       " 'Onboard_Entertainment',\n",
       " 'Online_Support',\n",
       " 'Ease_of_Online_Booking',\n",
       " 'Onboard_Service',\n",
       " 'Legroom',\n",
       " 'Baggage_Handling',\n",
       " 'CheckIn_Service',\n",
       " 'Cleanliness',\n",
       " 'Online_Boarding',\n",
       " 'Age',\n",
       " 'Travel_Distance',\n",
       " 'Departure_Delay_in_Mins',\n",
       " 'Arrival_Delay_in_Mins',\n",
       " 'Seat_Class_Green Car',\n",
       " 'Seat_Class_Ordinary',\n",
       " 'Gender_Female',\n",
       " 'Gender_Male',\n",
       " 'Gender_sin_valor',\n",
       " 'Customer_Type_Disloyal Customer',\n",
       " 'Customer_Type_Loyal Customer',\n",
       " 'Customer_Type_sin_valor',\n",
       " 'Type_Travel_Business Travel',\n",
       " 'Type_Travel_Personal Travel',\n",
       " 'Type_Travel_sin_valor',\n",
       " 'Travel_Class_Business',\n",
       " 'Travel_Class_Eco',\n",
       " 'SurveyPos',\n",
       " 'SurveyNeg']"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "1004a964",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.merge(survey_data_test, travel_data_test, how='inner', on = 'ID')\n",
    "data_test = data_test.drop([\"ID\"], axis=1)\n",
    "data_test=clean_data(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "df22690c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_list = data.select_dtypes(include=np.number).columns.tolist()\n",
    "cols_list.remove('Overall_Experience')\n",
    "data=fill_mean(data,cols_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "5defd425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-2.035662    1329\n",
      "-1.318033    5755\n",
      "-0.600404    7831\n",
      " 0.000000      22\n",
      " 0.117225    8003\n",
      " 0.834854    7808\n",
      " 1.552483    4854\n",
      "Name: Seat_Comfort, dtype: int64\n",
      "Seat_Comfort NAN 0\n",
      "\n",
      "-1.960563    1668\n",
      "-1.304256    5131\n",
      "-0.647950    5684\n",
      " 0.000000    3325\n",
      " 0.008357    5844\n",
      " 0.664664    7361\n",
      " 1.320971    6589\n",
      "Name: Arrival_Time_Convenient, dtype: int64\n",
      "Arrival_Time_Convenient NAN 0\n",
      "\n",
      "-1.976522    1507\n",
      "-1.283079    5178\n",
      "-0.589636    6701\n",
      " 0.000000    3357\n",
      " 0.103807    7133\n",
      " 0.797250    6701\n",
      " 1.490693    5025\n",
      "Name: Catering, dtype: int64\n",
      "Catering NAN 0\n",
      "\n",
      "-1.529023    6134\n",
      "-0.760506    6703\n",
      " 0.000000      12\n",
      " 0.008011    9364\n",
      " 0.776529    8178\n",
      " 1.545046    5211\n",
      "Name: Platform_Location, dtype: int64\n",
      "Platform_Location NAN 0\n",
      "\n",
      "-2.468804      41\n",
      "-1.709543    3980\n",
      "-0.950282    7456\n",
      "-0.191021    7495\n",
      " 0.000000      12\n",
      " 0.568240    8743\n",
      " 1.327502    7875\n",
      "Name: Onboard_Wifi_Service, dtype: int64\n",
      "Onboard_Wifi_Service NAN 0\n",
      "\n",
      "-2.517132      833\n",
      "-1.773693     3167\n",
      "-1.030254     5279\n",
      "-0.286815     6675\n",
      " 0.000000        8\n",
      " 0.456624    11436\n",
      " 1.200062     8204\n",
      "Name: Onboard_Entertainment, dtype: int64\n",
      "Onboard_Entertainment NAN 0\n",
      "\n",
      "-1.935878     3767\n",
      "-1.167917     4755\n",
      "-0.399955     5902\n",
      " 0.000000       26\n",
      " 0.368007    11487\n",
      " 1.135969     9665\n",
      "Name: Online_Support, dtype: int64\n",
      "Online_Support NAN 0\n",
      "\n",
      "-2.662832        2\n",
      "-1.896888     3674\n",
      "-1.130943     5474\n",
      "-0.364999     6029\n",
      " 0.000000       18\n",
      " 0.400946    11025\n",
      " 1.166890     9380\n",
      "Name: Ease_of_Online_Booking, dtype: int64\n",
      "Ease_of_Online_Booking NAN 0\n",
      "\n",
      "-1.924918     3412\n",
      "-1.140865     4385\n",
      "-0.356812     6770\n",
      " 0.000000     2872\n",
      " 0.427241    10222\n",
      " 1.211294     7941\n",
      "Name: Onboard_Service, dtype: int64\n",
      "Onboard_Service NAN 0\n",
      "\n",
      "-2.705232      103\n",
      "-1.930907     3025\n",
      "-1.156581     5992\n",
      "-0.382255     6079\n",
      " 0.000000       25\n",
      " 0.392071    10842\n",
      " 1.166397     9536\n",
      "Name: Legroom, dtype: int64\n",
      "Legroom NAN 0\n",
      "\n",
      "-2.330624     2200\n",
      "-1.465598     3659\n",
      "-0.600571     6683\n",
      " 0.000000       40\n",
      " 0.264456    13291\n",
      " 1.129482     9729\n",
      "Name: Baggage_Handling, dtype: int64\n",
      "Baggage_Handling NAN 0\n",
      "\n",
      "-1.854341    4221\n",
      "-1.061229    4262\n",
      "-0.268116    9740\n",
      " 0.000000      22\n",
      " 0.524997    9982\n",
      " 1.318110    7375\n",
      "Name: CheckIn_Service, dtype: int64\n",
      "CheckIn_Service NAN 0\n",
      "\n",
      "-2.356380     2138\n",
      "-1.487425     3607\n",
      "-0.618471     6534\n",
      " 0.000000        2\n",
      " 0.250483    13421\n",
      " 1.119438     9900\n",
      "Name: Cleanliness, dtype: int64\n",
      "Cleanliness NAN 0\n",
      "\n",
      "-2.579450       2\n",
      "-1.810484    4211\n",
      "-1.041517    5134\n",
      "-0.272551    8323\n",
      " 0.000000       2\n",
      " 0.496415    9680\n",
      " 1.265382    8250\n",
      "Name: Online_Boarding, dtype: int64\n",
      "Online_Boarding NAN 0\n",
      "\n",
      "-2.143477    182\n",
      "-2.077416    225\n",
      "-2.011355    220\n",
      "-1.945294    215\n",
      "-1.879233    228\n",
      "            ... \n",
      " 2.480784     24\n",
      " 2.546845     14\n",
      " 2.612906     13\n",
      " 2.678967     22\n",
      " 3.009271      7\n",
      "Name: Age, Length: 76, dtype: int64\n",
      "Age NAN 0\n",
      "\n",
      "-1.891179    10\n",
      "-1.890203     7\n",
      "-1.889227     3\n",
      "-1.888251     7\n",
      "-1.887274     8\n",
      "             ..\n",
      " 4.634196     1\n",
      " 4.711321     1\n",
      " 4.725965     1\n",
      " 4.734752     1\n",
      " 4.765016     1\n",
      "Name: Travel_Distance, Length: 4644, dtype: int64\n",
      "Travel_Distance NAN 0\n",
      "\n",
      "-0.392678     20028\n",
      "-0.366289       946\n",
      "-0.339901       765\n",
      "-0.313512       697\n",
      "-0.287124       667\n",
      "              ...  \n",
      " 15.176472        1\n",
      " 18.844459        1\n",
      " 19.556945        1\n",
      " 23.911030        1\n",
      " 25.415168        1\n",
      "Name: Departure_Delay_in_Mins, Length: 351, dtype: int64\n",
      "Departure_Delay_in_Mins NAN 0\n",
      "\n",
      "-0.397308     19848\n",
      "-0.371355       750\n",
      "-0.345402       686\n",
      "-0.319450       688\n",
      "-0.293497       662\n",
      "              ...  \n",
      " 15.797321        1\n",
      " 18.210943        1\n",
      " 19.015484        1\n",
      " 23.583200        1\n",
      " 24.777035        1\n",
      "Name: Arrival_Delay_in_Mins, Length: 356, dtype: int64\n",
      "Arrival_Delay_in_Mins NAN 0\n",
      "\n",
      "-0.996677    17860\n",
      " 1.003306    17742\n",
      "Name: Seat_Class_Green Car, dtype: int64\n",
      "Seat_Class_Green Car NAN 0\n",
      "\n",
      "-1.003306    17742\n",
      " 0.996677    17860\n",
      "Name: Seat_Class_Ordinary, dtype: int64\n",
      "Seat_Class_Ordinary NAN 0\n",
      "\n",
      "-1.015156    17533\n",
      " 0.985042    18069\n",
      "Name: Gender_Female, dtype: int64\n",
      "Gender_Female NAN 0\n",
      "\n",
      "-0.983383    18099\n",
      " 1.016869    17503\n",
      "Name: Gender_Male, dtype: int64\n",
      "Gender_Male NAN 0\n",
      "\n",
      "-0.029040     35572\n",
      " 34.433995       30\n",
      "Name: Gender_sin_valor, dtype: int64\n",
      "Gender_sin_valor NAN 0\n",
      "\n",
      "-0.444325    29732\n",
      " 2.250541     5870\n",
      "Name: Customer_Type_Disloyal Customer, dtype: int64\n",
      "Customer_Type_Disloyal Customer NAN 0\n",
      "\n",
      "-1.687465     9253\n",
      " 0.592588    26349\n",
      "Name: Customer_Type_Loyal Customer, dtype: int64\n",
      "Customer_Type_Loyal Customer NAN 0\n",
      "\n",
      "-0.324033    32219\n",
      " 3.086021     3383\n",
      "Name: Customer_Type_sin_valor, dtype: int64\n",
      "Customer_Type_sin_valor NAN 0\n",
      "\n",
      "-1.295766    13289\n",
      " 0.771722    22313\n",
      "Name: Type_Travel_Business Travel, dtype: int64\n",
      "Type_Travel_Business Travel NAN 0\n",
      "\n",
      "-0.618062    25761\n",
      " 1.617915     9841\n",
      "Name: Type_Travel_Personal Travel, dtype: int64\n",
      "Type_Travel_Personal Travel NAN 0\n",
      "\n",
      "-0.327461    32154\n",
      " 3.053710     3448\n",
      "Name: Type_Travel_sin_valor, dtype: int64\n",
      "Type_Travel_sin_valor NAN 0\n",
      "\n",
      "-0.962922    18473\n",
      " 1.038476    17129\n",
      "Name: Travel_Class_Business, dtype: int64\n",
      "Travel_Class_Business NAN 0\n",
      "\n",
      "-1.038476    17129\n",
      " 0.962922    18473\n",
      "Name: Travel_Class_Eco, dtype: int64\n",
      "Travel_Class_Eco NAN 0\n",
      "\n",
      "-0.911199    19435\n",
      " 0.000000       30\n",
      " 1.097425    16137\n",
      "Name: SurveyPos, dtype: int64\n",
      "SurveyPos NAN 0\n",
      "\n",
      "-1.758525     8692\n",
      " 0.000000       30\n",
      " 0.568642    26880\n",
      "Name: SurveyNeg, dtype: int64\n",
      "SurveyNeg NAN 0\n"
     ]
    }
   ],
   "source": [
    "print_count(cols_list,data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "6533c0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seat_Comfort                       0\n",
       "Arrival_Time_Convenient            0\n",
       "Catering                           0\n",
       "Platform_Location                  0\n",
       "Onboard_Wifi_Service               0\n",
       "Onboard_Entertainment              0\n",
       "Online_Support                     0\n",
       "Ease_of_Online_Booking             0\n",
       "Onboard_Service                    0\n",
       "Legroom                            0\n",
       "Baggage_Handling                   0\n",
       "CheckIn_Service                    0\n",
       "Cleanliness                        0\n",
       "Online_Boarding                    0\n",
       "Age                                0\n",
       "Travel_Distance                    0\n",
       "Departure_Delay_in_Mins            0\n",
       "Arrival_Delay_in_Mins              0\n",
       "Seat_Class_Green Car               0\n",
       "Seat_Class_Ordinary                0\n",
       "Gender_Female                      0\n",
       "Gender_Male                        0\n",
       "Gender_sin_valor                   0\n",
       "Customer_Type_Disloyal Customer    0\n",
       "Customer_Type_Loyal Customer       0\n",
       "Customer_Type_sin_valor            0\n",
       "Type_Travel_Business Travel        0\n",
       "Type_Travel_Personal Travel        0\n",
       "Type_Travel_sin_valor              0\n",
       "Travel_Class_Business              0\n",
       "Travel_Class_Eco                   0\n",
       "SurveyPos                          0\n",
       "SurveyNeg                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8ae12f9",
   "metadata": {},
   "source": [
    "### **Summary statistics of the dataset and write your observations (2 Marks)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "ddf94550",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall_Experience</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>5.466576e-01</td>\n",
       "      <td>0.497821</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.091950e-15</td>\n",
       "      <td>0.999677</td>\n",
       "      <td>-2.038872</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>0.833606</td>\n",
       "      <td>1.551726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-5.476735e-16</td>\n",
       "      <td>0.951515</td>\n",
       "      <td>-1.962281</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.658469</td>\n",
       "      <td>1.313657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catering</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.679480e-16</td>\n",
       "      <td>0.952567</td>\n",
       "      <td>-1.976191</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.793997</td>\n",
       "      <td>1.486544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform_Location</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>4.646341e-16</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>-2.286186</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>1.535763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.639449e-16</td>\n",
       "      <td>0.999841</td>\n",
       "      <td>-2.461673</td>\n",
       "      <td>-0.945970</td>\n",
       "      <td>-0.188119</td>\n",
       "      <td>0.569732</td>\n",
       "      <td>1.327583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.644205e-15</td>\n",
       "      <td>0.999905</td>\n",
       "      <td>-2.512655</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.201532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_Support</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>6.904289e-16</td>\n",
       "      <td>0.999518</td>\n",
       "      <td>-2.690201</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>1.131922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>8.197222e-16</td>\n",
       "      <td>0.999613</td>\n",
       "      <td>-2.657974</td>\n",
       "      <td>-1.126048</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>1.171841</td>\n",
       "      <td>1.171841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_Service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>9.561382e-16</td>\n",
       "      <td>0.958886</td>\n",
       "      <td>-2.735985</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>1.205449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legroom</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.744414e-16</td>\n",
       "      <td>0.999523</td>\n",
       "      <td>-2.695273</td>\n",
       "      <td>-1.147597</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>1.173917</td>\n",
       "      <td>1.173917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baggage_Handling</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.625674e-16</td>\n",
       "      <td>0.999247</td>\n",
       "      <td>-2.332054</td>\n",
       "      <td>-0.602548</td>\n",
       "      <td>0.262205</td>\n",
       "      <td>1.126959</td>\n",
       "      <td>1.126959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CheckIn_Service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-3.001469e-16</td>\n",
       "      <td>0.999592</td>\n",
       "      <td>-2.652052</td>\n",
       "      <td>-0.271680</td>\n",
       "      <td>-0.271680</td>\n",
       "      <td>0.521778</td>\n",
       "      <td>1.315235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleanliness</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-8.981926e-17</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>-3.215380</td>\n",
       "      <td>-0.611186</td>\n",
       "      <td>0.256879</td>\n",
       "      <td>1.124944</td>\n",
       "      <td>1.124944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_Boarding</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.451913e-15</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>-2.582237</td>\n",
       "      <td>-1.041477</td>\n",
       "      <td>0.499282</td>\n",
       "      <td>0.499282</td>\n",
       "      <td>1.269661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.940864e-16</td>\n",
       "      <td>0.999825</td>\n",
       "      <td>-2.144634</td>\n",
       "      <td>-0.821588</td>\n",
       "      <td>0.038392</td>\n",
       "      <td>0.766067</td>\n",
       "      <td>3.015245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Distance</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-3.615395e-17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.876422</td>\n",
       "      <td>-0.603027</td>\n",
       "      <td>-0.054368</td>\n",
       "      <td>0.543904</td>\n",
       "      <td>4.836868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure_Delay_in_Mins</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.871095e-16</td>\n",
       "      <td>0.999698</td>\n",
       "      <td>-0.384047</td>\n",
       "      <td>-0.384047</td>\n",
       "      <td>-0.384047</td>\n",
       "      <td>-0.069407</td>\n",
       "      <td>41.358241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.708966e-15</td>\n",
       "      <td>0.998107</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-0.052166</td>\n",
       "      <td>40.817349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_Class_Green Car</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>2.546265e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.005211</td>\n",
       "      <td>-1.005211</td>\n",
       "      <td>0.994806</td>\n",
       "      <td>0.994806</td>\n",
       "      <td>0.994806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_Class_Ordinary</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-2.546265e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.994806</td>\n",
       "      <td>-0.994806</td>\n",
       "      <td>-0.994806</td>\n",
       "      <td>1.005211</td>\n",
       "      <td>1.005211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Female</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-2.252339e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>0.986826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.328762e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>1.014994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_sin_valor</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-2.016942e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028575</td>\n",
       "      <td>-0.028575</td>\n",
       "      <td>-0.028575</td>\n",
       "      <td>-0.028575</td>\n",
       "      <td>34.995547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>9.195621e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.445080</td>\n",
       "      <td>-0.445080</td>\n",
       "      <td>-0.445080</td>\n",
       "      <td>-0.445080</td>\n",
       "      <td>2.246763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Type_Loyal Customer</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>4.888068e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.686235</td>\n",
       "      <td>-1.686235</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>0.593031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Type_sin_valor</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>4.778421e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>3.089314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-5.567549e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>0.781082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.729499e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>1.598941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type_Travel_sin_valor</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-6.009855e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>3.038022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.727597e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>1.046698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-1.727597e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SurveyPos</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>5.610250e-16</td>\n",
       "      <td>0.999581</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>1.097565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SurveyNeg</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>-3.153237e-15</td>\n",
       "      <td>0.999581</td>\n",
       "      <td>-1.754267</td>\n",
       "      <td>0.570033</td>\n",
       "      <td>0.570033</td>\n",
       "      <td>0.570033</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   count          mean       std       min  \\\n",
       "Overall_Experience               94379.0  5.466576e-01  0.497821  0.000000   \n",
       "Seat_Comfort                     94379.0 -1.091950e-15  0.999677 -2.038872   \n",
       "Arrival_Time_Convenient          94379.0 -5.476735e-16  0.951515 -1.962281   \n",
       "Catering                         94379.0 -1.679480e-16  0.952567 -1.976191   \n",
       "Platform_Location                94379.0  4.646341e-16  0.999841 -2.286186   \n",
       "Onboard_Wifi_Service             94379.0  1.639449e-16  0.999841 -2.461673   \n",
       "Onboard_Entertainment            94379.0  1.644205e-15  0.999905 -2.512655   \n",
       "Online_Support                   94379.0  6.904289e-16  0.999518 -2.690201   \n",
       "Ease_of_Online_Booking           94379.0  8.197222e-16  0.999613 -2.657974   \n",
       "Onboard_Service                  94379.0  9.561382e-16  0.958886 -2.735985   \n",
       "Legroom                          94379.0  1.744414e-16  0.999523 -2.695273   \n",
       "Baggage_Handling                 94379.0 -1.625674e-16  0.999247 -2.332054   \n",
       "CheckIn_Service                  94379.0 -3.001469e-16  0.999592 -2.652052   \n",
       "Cleanliness                      94379.0 -8.981926e-17  0.999968 -3.215380   \n",
       "Online_Boarding                  94379.0 -1.451913e-15  0.999968 -2.582237   \n",
       "Age                              94379.0 -1.940864e-16  0.999825 -2.144634   \n",
       "Travel_Distance                  94379.0 -3.615395e-17  1.000000 -1.876422   \n",
       "Departure_Delay_in_Mins          94379.0  1.871095e-16  0.999698 -0.384047   \n",
       "Arrival_Delay_in_Mins            94379.0  1.708966e-15  0.998107 -0.390360   \n",
       "Seat_Class_Green Car             94379.0  2.546265e-15  1.000000 -1.005211   \n",
       "Seat_Class_Ordinary              94379.0 -2.546265e-15  1.000000 -0.994806   \n",
       "Gender_Female                    94379.0 -2.252339e-15  1.000000 -1.013339   \n",
       "Gender_Male                      94379.0  1.328762e-15  1.000000 -0.985217   \n",
       "Gender_sin_valor                 94379.0 -2.016942e-15  1.000000 -0.028575   \n",
       "Customer_Type_Disloyal Customer  94379.0  9.195621e-16  1.000000 -0.445080   \n",
       "Customer_Type_Loyal Customer     94379.0  4.888068e-16  1.000000 -1.686235   \n",
       "Customer_Type_sin_valor          94379.0  4.778421e-16  1.000000 -0.323693   \n",
       "Type_Travel_Business Travel      94379.0 -5.567549e-16  1.000000 -1.280261   \n",
       "Type_Travel_Personal Travel      94379.0 -1.729499e-15  1.000000 -0.625407   \n",
       "Type_Travel_sin_valor            94379.0 -6.009855e-16  1.000000 -0.329158   \n",
       "Travel_Class_Business            94379.0  1.727597e-15  1.000000 -0.955375   \n",
       "Travel_Class_Eco                 94379.0 -1.727597e-15  1.000000 -1.046698   \n",
       "SurveyPos                        94379.0  5.610250e-16  0.999581 -0.911098   \n",
       "SurveyNeg                        94379.0 -3.153237e-15  0.999581 -1.754267   \n",
       "\n",
       "                                      25%       50%       75%        max  \n",
       "Overall_Experience               0.000000  1.000000  1.000000   1.000000  \n",
       "Seat_Comfort                    -0.602633  0.115486  0.833606   1.551726  \n",
       "Arrival_Time_Convenient         -0.651906  0.003282  0.658469   1.313657  \n",
       "Catering                        -0.591097  0.101450  0.793997   1.486544  \n",
       "Platform_Location               -0.757406  0.006984  0.771373   1.535763  \n",
       "Onboard_Wifi_Service            -0.945970 -0.188119  0.569732   1.327583  \n",
       "Onboard_Entertainment           -1.026980  0.458695  0.458695   1.201532  \n",
       "Online_Support                  -0.396927  0.367497  1.131922   1.131922  \n",
       "Ease_of_Online_Booking          -1.126048  0.405878  1.171841   1.171841  \n",
       "Onboard_Service                 -0.371125  0.417162  0.417162   1.205449  \n",
       "Legroom                         -1.147597  0.400079  1.173917   1.173917  \n",
       "Baggage_Handling                -0.602548  0.262205  1.126959   1.126959  \n",
       "CheckIn_Service                 -0.271680 -0.271680  0.521778   1.315235  \n",
       "Cleanliness                     -0.611186  0.256879  1.124944   1.124944  \n",
       "Online_Boarding                 -1.041477  0.499282  0.499282   1.269661  \n",
       "Age                             -0.821588  0.038392  0.766067   3.015245  \n",
       "Travel_Distance                 -0.603027 -0.054368  0.543904   4.836868  \n",
       "Departure_Delay_in_Mins         -0.384047 -0.384047 -0.069407  41.358241  \n",
       "Arrival_Delay_in_Mins           -0.390360 -0.390360 -0.052166  40.817349  \n",
       "Seat_Class_Green Car            -1.005211  0.994806  0.994806   0.994806  \n",
       "Seat_Class_Ordinary             -0.994806 -0.994806  1.005211   1.005211  \n",
       "Gender_Female                   -1.013339  0.986826  0.986826   0.986826  \n",
       "Gender_Male                     -0.985217 -0.985217  1.014994   1.014994  \n",
       "Gender_sin_valor                -0.028575 -0.028575 -0.028575  34.995547  \n",
       "Customer_Type_Disloyal Customer -0.445080 -0.445080 -0.445080   2.246763  \n",
       "Customer_Type_Loyal Customer    -1.686235  0.593031  0.593031   0.593031  \n",
       "Customer_Type_sin_valor         -0.323693 -0.323693 -0.323693   3.089314  \n",
       "Type_Travel_Business Travel     -1.280261  0.781082  0.781082   0.781082  \n",
       "Type_Travel_Personal Travel     -0.625407 -0.625407  1.598941   1.598941  \n",
       "Type_Travel_sin_valor           -0.329158 -0.329158 -0.329158   3.038022  \n",
       "Travel_Class_Business           -0.955375 -0.955375  1.046698   1.046698  \n",
       "Travel_Class_Eco                -1.046698  0.955375  0.955375   0.955375  \n",
       "SurveyPos                       -0.911098 -0.911098  1.097565   1.097565  \n",
       "SurveyNeg                        0.570033  0.570033  0.570033   0.570033  "
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "25091365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       -1.660460\n",
       "1        0.215097\n",
       "2       -0.892921\n",
       "3       -1.166278\n",
       "4        0.002054\n",
       "           ...   \n",
       "94374   -0.604973\n",
       "94375   -1.349164\n",
       "94376    0.792940\n",
       "94377    0.744300\n",
       "94378    0.124627\n",
       "Name: Travel_Distance, Length: 94379, dtype: float64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Travel_Distance']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "40319a29",
   "metadata": {},
   "source": [
    "### **The correlation matrix using a heatmap**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "93d2952c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Overall_Experience                 1.000000\n",
       "Onboard_Entertainment              0.524758\n",
       "SurveyPos                          0.452524\n",
       "Ease_of_Online_Booking             0.432050\n",
       "SurveyNeg                          0.395033\n",
       "Online_Support                     0.391770\n",
       "Online_Boarding                    0.339122\n",
       "Onboard_Service                    0.336680\n",
       "Travel_Class_Business              0.310013\n",
       "Legroom                            0.302443\n",
       "CheckIn_Service                    0.262519\n",
       "Baggage_Handling                   0.257597\n",
       "Cleanliness                        0.255581\n",
       "Seat_Comfort                       0.243362\n",
       "Customer_Type_Loyal Customer       0.235756\n",
       "Onboard_Wifi_Service               0.229625\n",
       "Gender_Female                      0.214586\n",
       "Catering                           0.116220\n",
       "Age                                0.116213\n",
       "Type_Travel_Business Travel        0.093564\n",
       "Seat_Class_Green Car               0.002992\n",
       "Type_Travel_sin_valor             -0.000248\n",
       "Seat_Class_Ordinary               -0.002992\n",
       "Gender_sin_valor                  -0.003051\n",
       "Customer_Type_sin_valor           -0.004731\n",
       "Platform_Location                 -0.012331\n",
       "Arrival_Time_Convenient           -0.015569\n",
       "Travel_Distance                   -0.041582\n",
       "Departure_Delay_in_Mins           -0.073799\n",
       "Arrival_Delay_in_Mins             -0.080085\n",
       "Type_Travel_Personal Travel       -0.100799\n",
       "Gender_Male                       -0.214417\n",
       "Customer_Type_Disloyal Customer   -0.274699\n",
       "Travel_Class_Eco                  -0.310013\n",
       "Name: Overall_Experience, dtype: float64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()[\"Overall_Experience\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "a837faf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "sns.heatmap(data[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "d4ecdd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating metric function \n",
    "def metrics_score(actual, predicted):\n",
    "    print(classification_report(actual, predicted))\n",
    "    cm = confusion_matrix(actual, predicted)\n",
    "    plt.figure(figsize=(8,5)) \n",
    "    sns.heatmap(cm, annot=True,  fmt='.2f', xticklabels=['Not Satisfied', 'Satisfied'], yticklabels=['Not Satisfied', 'Satisfied'])\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "69d31e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, plot_confusion_matrix\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import seaborn as sns\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "# To get diferent metric scores\n",
    "from sklearn.metrics import confusion_matrix,classification_report,roc_auc_score,plot_confusion_matrix,precision_recall_curve,roc_curve,make_scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "4d80d856",
   "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>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>...</th>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <th>Customer_Type_Loyal Customer</th>\n",
       "      <th>Customer_Type_sin_valor</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Type_Travel_sin_valor</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "      <th>SurveyPos</th>\n",
       "      <th>SurveyNeg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>1.313657</td>\n",
       "      <td>1.486544</td>\n",
       "      <td>1.535763</td>\n",
       "      <td>0.569732</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-1.126048</td>\n",
       "      <td>-1.159411</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>3.038022</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.320753</td>\n",
       "      <td>1.313657</td>\n",
       "      <td>-1.283644</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>0.569732</td>\n",
       "      <td>-1.769817</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>1.205449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>-0.945970</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>1.171841</td>\n",
       "      <td>1.205449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>-0.188119</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>-0.945970</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.320753</td>\n",
       "      <td>0.658469</td>\n",
       "      <td>0.793997</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>-1.703822</td>\n",
       "      <td>-1.769817</td>\n",
       "      <td>-1.925776</td>\n",
       "      <td>-1.892011</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>1</td>\n",
       "      <td>0.833606</td>\n",
       "      <td>0.658469</td>\n",
       "      <td>0.793997</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>-0.945970</td>\n",
       "      <td>1.201532</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>0.569732</td>\n",
       "      <td>1.201532</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.686235</td>\n",
       "      <td>3.089314</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>0.569732</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>-0.329158</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-1.307093</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>-0.188119</td>\n",
       "      <td>-0.284143</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-1.947698</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.593031</td>\n",
       "      <td>-0.323693</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>3.038022</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94379 rows &times; 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience  Seat_Comfort  Arrival_Time_Convenient  Catering  \\\n",
       "0                       0     -0.602633                 1.313657  1.486544   \n",
       "1                       0     -1.320753                 1.313657 -1.283644   \n",
       "2                       1     -0.602633                -0.651906 -0.591097   \n",
       "3                       0      0.115486                -0.651906  0.000000   \n",
       "4                       1      0.115486                 0.003282  0.101450   \n",
       "...                   ...           ...                      ...       ...   \n",
       "94374                   0     -1.320753                 0.658469  0.793997   \n",
       "94375                   1      0.833606                 0.658469  0.793997   \n",
       "94376                   1     -0.602633                -0.651906 -0.591097   \n",
       "94377                   0     -0.602633                 0.000000 -0.591097   \n",
       "94378                   0      0.115486                -1.307093  0.101450   \n",
       "\n",
       "       Platform_Location  Onboard_Wifi_Service  Onboard_Entertainment  \\\n",
       "0               1.535763              0.569732              -1.026980   \n",
       "1              -0.757406              0.569732              -1.769817   \n",
       "2              -0.757406             -0.945970               0.458695   \n",
       "3              -0.757406             -0.188119              -1.026980   \n",
       "4               0.006984             -0.945970               0.458695   \n",
       "...                  ...                   ...                    ...   \n",
       "94374           0.771373             -1.703822              -1.769817   \n",
       "94375           0.771373             -0.945970               1.201532   \n",
       "94376          -0.757406              0.569732               1.201532   \n",
       "94377           0.771373              0.569732              -1.026980   \n",
       "94378           0.006984             -0.188119              -0.284143   \n",
       "\n",
       "       Online_Support  Ease_of_Online_Booking  Onboard_Service  ...  \\\n",
       "0           -0.396927               -1.126048        -1.159411  ...   \n",
       "1            0.367497                0.405878         1.205449  ...   \n",
       "2            1.131922                1.171841         1.205449  ...   \n",
       "3           -0.396927               -0.360085        -0.371125  ...   \n",
       "4            1.131922                0.405878         0.417162  ...   \n",
       "...               ...                     ...              ...  ...   \n",
       "94374       -1.925776               -1.892011         0.417162  ...   \n",
       "94375        1.131922               -0.360085        -0.371125  ...   \n",
       "94376        0.367497                0.405878         0.417162  ...   \n",
       "94377        0.367497                0.405878        -0.371125  ...   \n",
       "94378       -0.396927               -0.360085        -1.947698  ...   \n",
       "\n",
       "       Customer_Type_Disloyal Customer  Customer_Type_Loyal Customer  \\\n",
       "0                             -0.44508                      0.593031   \n",
       "1                             -0.44508                      0.593031   \n",
       "2                             -0.44508                      0.593031   \n",
       "3                             -0.44508                      0.593031   \n",
       "4                             -0.44508                      0.593031   \n",
       "...                                ...                           ...   \n",
       "94374                         -0.44508                      0.593031   \n",
       "94375                         -0.44508                      0.593031   \n",
       "94376                         -0.44508                     -1.686235   \n",
       "94377                         -0.44508                      0.593031   \n",
       "94378                         -0.44508                      0.593031   \n",
       "\n",
       "       Customer_Type_sin_valor  Type_Travel_Business Travel  \\\n",
       "0                    -0.323693                    -1.280261   \n",
       "1                    -0.323693                    -1.280261   \n",
       "2                    -0.323693                     0.781082   \n",
       "3                    -0.323693                     0.781082   \n",
       "4                    -0.323693                     0.781082   \n",
       "...                        ...                          ...   \n",
       "94374                -0.323693                     0.781082   \n",
       "94375                -0.323693                     0.781082   \n",
       "94376                 3.089314                     0.781082   \n",
       "94377                -0.323693                    -1.280261   \n",
       "94378                -0.323693                    -1.280261   \n",
       "\n",
       "       Type_Travel_Personal Travel  Type_Travel_sin_valor  \\\n",
       "0                        -0.625407               3.038022   \n",
       "1                         1.598941              -0.329158   \n",
       "2                        -0.625407              -0.329158   \n",
       "3                        -0.625407              -0.329158   \n",
       "4                        -0.625407              -0.329158   \n",
       "...                            ...                    ...   \n",
       "94374                    -0.625407              -0.329158   \n",
       "94375                    -0.625407              -0.329158   \n",
       "94376                    -0.625407              -0.329158   \n",
       "94377                     1.598941              -0.329158   \n",
       "94378                    -0.625407               3.038022   \n",
       "\n",
       "       Travel_Class_Business  Travel_Class_Eco  SurveyPos  SurveyNeg  \n",
       "0                   1.046698         -1.046698  -0.911098  -1.754267  \n",
       "1                  -0.955375          0.955375  -0.911098  -1.754267  \n",
       "2                   1.046698         -1.046698  -0.911098   0.570033  \n",
       "3                   1.046698         -1.046698  -0.911098   0.570033  \n",
       "4                   1.046698         -1.046698   1.097565   0.570033  \n",
       "...                      ...               ...        ...        ...  \n",
       "94374               1.046698         -1.046698  -0.911098  -1.754267  \n",
       "94375               1.046698         -1.046698   1.097565   0.570033  \n",
       "94376               1.046698         -1.046698   1.097565   0.570033  \n",
       "94377              -0.955375          0.955375  -0.911098  -1.754267  \n",
       "94378              -0.955375          0.955375  -0.911098   0.570033  \n",
       "\n",
       "[94379 rows x 34 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "462c24b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(survey_data, travel_data, how='inner', on = 'ID')\n",
    "data = data.drop([\"ID\"], axis=1)\n",
    "data=clean_data(data)\n",
    "\n",
    "cols_to_drop=['Gender_sin_valor','Type_Travel_sin_valor','Customer_Type_sin_valor','Seat_Class_Ordinary',\n",
    "              'Seat_Class_Green Car','Onboard_Wifi_Service','Departure_Delay_in_Mins','Customer_Type_Loyal Customer']\n",
    "data=data.drop(columns=cols_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "ab9e8a43",
   "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>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>Legroom</th>\n",
       "      <th>...</th>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "      <th>SurveyPos</th>\n",
       "      <th>SurveyNeg</th>\n",
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       "      <td>-0.602633</td>\n",
       "      <td>1.313657</td>\n",
       "      <td>1.486544</td>\n",
       "      <td>1.535763</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-1.126048</td>\n",
       "      <td>-1.159411</td>\n",
       "      <td>-0.373759</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.260286</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.280261</td>\n",
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       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.320753</td>\n",
       "      <td>1.313657</td>\n",
       "      <td>-1.283644</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>-1.769817</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>1.205449</td>\n",
       "      <td>-1.147597</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>1.171841</td>\n",
       "      <td>1.205449</td>\n",
       "      <td>1.173917</td>\n",
       "      <td>...</td>\n",
       "      <td>2.705421</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>-0.373759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077909</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.320753</td>\n",
       "      <td>0.658469</td>\n",
       "      <td>0.793997</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>-1.769817</td>\n",
       "      <td>-1.925776</td>\n",
       "      <td>-1.892011</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>2.861511</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>1</td>\n",
       "      <td>0.833606</td>\n",
       "      <td>0.658469</td>\n",
       "      <td>0.793997</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>1.201532</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>-0.373759</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.104196</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>-0.651906</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>1.201532</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.602633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.591097</td>\n",
       "      <td>0.771373</td>\n",
       "      <td>-1.026980</td>\n",
       "      <td>0.367497</td>\n",
       "      <td>0.405878</td>\n",
       "      <td>-0.371125</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-1.307093</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>-0.284143</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-1.947698</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.338059</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94379 rows &times; 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience  Seat_Comfort  Arrival_Time_Convenient  Catering  \\\n",
       "0                       0     -0.602633                 1.313657  1.486544   \n",
       "1                       0     -1.320753                 1.313657 -1.283644   \n",
       "2                       1     -0.602633                -0.651906 -0.591097   \n",
       "3                       0      0.115486                -0.651906  0.000000   \n",
       "4                       1      0.115486                 0.003282  0.101450   \n",
       "...                   ...           ...                      ...       ...   \n",
       "94374                   0     -1.320753                 0.658469  0.793997   \n",
       "94375                   1      0.833606                 0.658469  0.793997   \n",
       "94376                   1     -0.602633                -0.651906 -0.591097   \n",
       "94377                   0     -0.602633                 0.000000 -0.591097   \n",
       "94378                   0      0.115486                -1.307093  0.101450   \n",
       "\n",
       "       Platform_Location  Onboard_Entertainment  Online_Support  \\\n",
       "0               1.535763              -1.026980       -0.396927   \n",
       "1              -0.757406              -1.769817        0.367497   \n",
       "2              -0.757406               0.458695        1.131922   \n",
       "3              -0.757406              -1.026980       -0.396927   \n",
       "4               0.006984               0.458695        1.131922   \n",
       "...                  ...                    ...             ...   \n",
       "94374           0.771373              -1.769817       -1.925776   \n",
       "94375           0.771373               1.201532        1.131922   \n",
       "94376          -0.757406               1.201532        0.367497   \n",
       "94377           0.771373              -1.026980        0.367497   \n",
       "94378           0.006984              -0.284143       -0.396927   \n",
       "\n",
       "       Ease_of_Online_Booking  Onboard_Service   Legroom  ...  \\\n",
       "0                   -1.126048        -1.159411 -0.373759  ...   \n",
       "1                    0.405878         1.205449 -1.147597  ...   \n",
       "2                    1.171841         1.205449  1.173917  ...   \n",
       "3                   -0.360085        -0.371125 -0.373759  ...   \n",
       "4                    0.405878         0.417162  0.400079  ...   \n",
       "...                       ...              ...       ...  ...   \n",
       "94374               -1.892011         0.417162  0.400079  ...   \n",
       "94375               -0.360085        -0.371125 -0.373759  ...   \n",
       "94376                0.405878         0.417162  0.400079  ...   \n",
       "94377                0.405878        -0.371125  0.400079  ...   \n",
       "94378               -0.360085        -1.947698  0.400079  ...   \n",
       "\n",
       "       Arrival_Delay_in_Mins  Gender_Female  Gender_Male  \\\n",
       "0                  -0.260286       0.986826    -0.985217   \n",
       "1                  -0.390360      -1.013339     1.014994   \n",
       "2                   2.705421       0.986826    -0.985217   \n",
       "3                   0.077909       0.986826    -0.985217   \n",
       "4                  -0.390360       0.986826    -0.985217   \n",
       "...                      ...            ...          ...   \n",
       "94374               2.861511      -1.013339     1.014994   \n",
       "94375              -0.104196      -1.013339     1.014994   \n",
       "94376              -0.390360      -1.013339     1.014994   \n",
       "94377              -0.390360      -1.013339     1.014994   \n",
       "94378               0.338059      -1.013339     1.014994   \n",
       "\n",
       "       Customer_Type_Disloyal Customer  Type_Travel_Business Travel  \\\n",
       "0                             -0.44508                    -1.280261   \n",
       "1                             -0.44508                    -1.280261   \n",
       "2                             -0.44508                     0.781082   \n",
       "3                             -0.44508                     0.781082   \n",
       "4                             -0.44508                     0.781082   \n",
       "...                                ...                          ...   \n",
       "94374                         -0.44508                     0.781082   \n",
       "94375                         -0.44508                     0.781082   \n",
       "94376                         -0.44508                     0.781082   \n",
       "94377                         -0.44508                    -1.280261   \n",
       "94378                         -0.44508                    -1.280261   \n",
       "\n",
       "       Type_Travel_Personal Travel  Travel_Class_Business  Travel_Class_Eco  \\\n",
       "0                        -0.625407               1.046698         -1.046698   \n",
       "1                         1.598941              -0.955375          0.955375   \n",
       "2                        -0.625407               1.046698         -1.046698   \n",
       "3                        -0.625407               1.046698         -1.046698   \n",
       "4                        -0.625407               1.046698         -1.046698   \n",
       "...                            ...                    ...               ...   \n",
       "94374                    -0.625407               1.046698         -1.046698   \n",
       "94375                    -0.625407               1.046698         -1.046698   \n",
       "94376                    -0.625407               1.046698         -1.046698   \n",
       "94377                     1.598941              -0.955375          0.955375   \n",
       "94378                    -0.625407              -0.955375          0.955375   \n",
       "\n",
       "       SurveyPos  SurveyNeg  \n",
       "0      -0.911098  -1.754267  \n",
       "1      -0.911098  -1.754267  \n",
       "2      -0.911098   0.570033  \n",
       "3      -0.911098   0.570033  \n",
       "4       1.097565   0.570033  \n",
       "...          ...        ...  \n",
       "94374  -0.911098  -1.754267  \n",
       "94375   1.097565   0.570033  \n",
       "94376   1.097565   0.570033  \n",
       "94377  -0.911098  -1.754267  \n",
       "94378  -0.911098   0.570033  \n",
       "\n",
       "[94379 rows x 26 columns]"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "111b78a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'Overall_Experience'\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(columns=target_col), data[target_col], test_size=0.1, stratify=data[target_col], random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "f6d4d83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "5b40f210",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = { \n",
    "    'n_estimators': [200,250, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,8,54,100],\n",
    "    'criterion' :['entropy'],\n",
    "    'bootstrap' :[False, True]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "ef10146e",
   "metadata": {},
   "outputs": [],
   "source": [
    "w=0.6\n",
    "rf=RandomForestClassifier(#random_state=45,\n",
    "                          max_features=10,#'auto',10\n",
    "                          n_estimators= 2000,#54\n",
    "                          max_depth=100,\n",
    "                          criterion='entropy',#entropy#\n",
    "                          bootstrap=False,\n",
    "                          #class_weight={0: w,1: 1-w},\n",
    "                          #ccp_alpha=0.1,\n",
    "                          n_jobs = -1,\n",
    "                          #warm_start=True,\n",
    "                          #max_samples =0.999,\n",
    "                          #min_weight_fraction_leaf=0.1,\n",
    "                          #ccp_alpha=0.000001,\n",
    "                          #max_leaf_nodes=4000,\n",
    "                          min_samples_leaf=1,\n",
    "                          #min_weight_fraction_leaf=0.00002,\n",
    "                          verbose=0,\n",
    "                          min_samples_split=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "137093d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.82538673447765 test\n",
      "CPU times: total: 16min 12s\n",
      "Wall time: 2min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf.fit(X_train, y_train)\n",
    "#print (accuracy_score(y_train, rf.predict(X_train))*100, 'train')\n",
    "print (accuracy_score(y_test, rf.predict(X_test))*100, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "e35d970f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_rf=rf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "54f824ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     38507\n",
      "           1       1.00      1.00      1.00     46434\n",
      "\n",
      "    accuracy                           1.00     84941\n",
      "   macro avg       1.00      1.00      1.00     84941\n",
      "weighted avg       1.00      1.00      1.00     84941\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_score(y_train, y_pred_train_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "6ea04125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95      4279\n",
      "           1       0.97      0.95      0.96      5159\n",
      "\n",
      "    accuracy                           0.96      9438\n",
      "   macro avg       0.96      0.96      0.96      9438\n",
      "weighted avg       0.96      0.96      0.96      9438\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_test_rf = rf.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "1e79b937",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict probabilities\n",
    "lr_probs = rf.predict_proba(X_test)\n",
    "# keep probabilities for the positive outcome only\n",
    "lr_probs = lr_probs[:, 1]\n",
    "# calculate scores\n",
    "lr_auc = roc_auc_score(y_test, lr_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "a076fd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_test_rf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "ab52deb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Importance'>"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rf.feature_importances_\n",
    "\n",
    "columns = X_test.columns\n",
    "\n",
    "importance_df = pd.DataFrame(importances, index = columns, columns = ['Importance']).sort_values(by = 'Importance', ascending = False)\n",
    "\n",
    "plt.figure(figsize = (13, 13))\n",
    "\n",
    "sns.barplot(x=importance_df.Importance, y=importance_df.index)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2568e063",
   "metadata": {},
   "source": [
    "### Complete dataset train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "ca7d939c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.merge(survey_data_test, travel_data_test, how='inner', on = 'ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "c89a240a",
   "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>ID</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Wifi_Service</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>...</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_Boarding</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type_Travel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>Departure_Delay_in_Mins</th>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99900001</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99900002</td>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Female</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1425</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99900003</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>...</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99900004</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Very Convenient</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99900005</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Male</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1610</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>99935598</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>99935599</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>99935600</td>\n",
       "      <td>Good</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>Extremely Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Needs Improvement</td>\n",
       "      <td>Male</td>\n",
       "      <td>Disloyal Customer</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Business Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>99935601</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Inconvenient</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Good</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>420</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>99935602</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>Good</td>\n",
       "      <td>Manageable</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Poor</td>\n",
       "      <td>...</td>\n",
       "      <td>Good</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35602 rows &times; 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID       Seat_Comfort Seat_Class Arrival_Time_Convenient  \\\n",
       "0      99900001         Acceptable  Green Car              Acceptable   \n",
       "1      99900002     Extremely Poor   Ordinary                    Good   \n",
       "2      99900003          Excellent   Ordinary               Excellent   \n",
       "3      99900004         Acceptable  Green Car               Excellent   \n",
       "4      99900005          Excellent   Ordinary          Extremely Poor   \n",
       "...         ...                ...        ...                     ...   \n",
       "35597  99935598  Needs Improvement  Green Car               Excellent   \n",
       "35598  99935599  Needs Improvement   Ordinary       Needs Improvement   \n",
       "35599  99935600               Good  Green Car          Extremely Poor   \n",
       "35600  99935601          Excellent   Ordinary               Excellent   \n",
       "35601  99935602               Good   Ordinary              Acceptable   \n",
       "\n",
       "                Catering  Platform_Location Onboard_Wifi_Service  \\\n",
       "0             Acceptable         Manageable    Needs Improvement   \n",
       "1                   Poor         Manageable           Acceptable   \n",
       "2              Excellent    Very Convenient            Excellent   \n",
       "3             Acceptable    Very Convenient                 Poor   \n",
       "4              Excellent  Needs Improvement            Excellent   \n",
       "...                  ...                ...                  ...   \n",
       "35597  Needs Improvement         Manageable           Acceptable   \n",
       "35598               Good  Needs Improvement           Acceptable   \n",
       "35599               Good  Needs Improvement    Needs Improvement   \n",
       "35600          Excellent       Inconvenient           Acceptable   \n",
       "35601               Good         Manageable                 Poor   \n",
       "\n",
       "      Onboard_Entertainment Online_Support Ease_of_Online_Booking  ...  \\\n",
       "0                 Excellent           Good              Excellent  ...   \n",
       "1                      Poor     Acceptable             Acceptable  ...   \n",
       "2                 Excellent      Excellent      Needs Improvement  ...   \n",
       "3                Acceptable      Excellent                   Poor  ...   \n",
       "4                 Excellent      Excellent              Excellent  ...   \n",
       "...                     ...            ...                    ...  ...   \n",
       "35597     Needs Improvement     Acceptable             Acceptable  ...   \n",
       "35598             Excellent      Excellent                   Good  ...   \n",
       "35599                  Good           Poor      Needs Improvement  ...   \n",
       "35600             Excellent           Good              Excellent  ...   \n",
       "35601                  Good           Poor                   Poor  ...   \n",
       "\n",
       "             Cleanliness    Online_Boarding  Gender      Customer_Type   Age  \\\n",
       "0              Excellent               Poor  Female                NaN  36.0   \n",
       "1              Excellent         Acceptable  Female  Disloyal Customer  21.0   \n",
       "2      Needs Improvement          Excellent    Male     Loyal Customer  60.0   \n",
       "3              Excellent               Poor  Female     Loyal Customer  29.0   \n",
       "4              Excellent          Excellent    Male  Disloyal Customer  18.0   \n",
       "...                  ...                ...     ...                ...   ...   \n",
       "35597               Good         Acceptable    Male     Loyal Customer   8.0   \n",
       "35598               Good               Good  Female     Loyal Customer  53.0   \n",
       "35599          Excellent  Needs Improvement    Male  Disloyal Customer  22.0   \n",
       "35600          Excellent               Good  Female     Loyal Customer  67.0   \n",
       "35601               Good               Poor    Male                NaN  20.0   \n",
       "\n",
       "           Type_Travel Travel_Class Travel_Distance  Departure_Delay_in_Mins  \\\n",
       "0      Business Travel     Business             532                      0.0   \n",
       "1      Business Travel     Business            1425                      9.0   \n",
       "2      Business Travel     Business            2832                      0.0   \n",
       "3      Personal Travel          Eco            1352                      0.0   \n",
       "4      Business Travel     Business            1610                     17.0   \n",
       "...                ...          ...             ...                      ...   \n",
       "35597  Personal Travel          Eco            1334                      0.0   \n",
       "35598  Business Travel     Business            1772                      0.0   \n",
       "35599  Business Travel          Eco            1180                      0.0   \n",
       "35600  Personal Travel          Eco             420                     23.0   \n",
       "35601  Personal Travel          Eco            1680                      0.0   \n",
       "\n",
       "      Arrival_Delay_in_Mins  \n",
       "0                       0.0  \n",
       "1                      28.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "...                     ...  \n",
       "35597                   0.0  \n",
       "35598                   0.0  \n",
       "35599                   0.0  \n",
       "35600                  16.0  \n",
       "35601                   0.0  \n",
       "\n",
       "[35602 rows x 24 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "9176f9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.merge(survey_data_test, travel_data_test, how='inner', on = 'ID')\n",
    "data_test = data_test.drop([\"ID\"], axis=1)\n",
    "data_test=clean_data(data_test)\n",
    "#cols_to_drop=['Gender_sin_valor','Type_Travel_sin_valor','Customer_Type_sin_valor','Seat_Class_Ordinary',\n",
    "#              'Seat_Class_Green Car']\n",
    "data_test=data_test.drop(columns=cols_to_drop)\n",
    "X_train = data.drop([\"Overall_Experience\"], axis=1)\n",
    "y_train = data[\"Overall_Experience\"]\n",
    "X_test_2 = data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "97364abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall_Experience</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
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       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Travel_Class_Business</th>\n",
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       "      <td>-1.283644</td>\n",
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       "      <td>-0.591097</td>\n",
       "      <td>-0.757406</td>\n",
       "      <td>0.458695</td>\n",
       "      <td>1.131922</td>\n",
       "      <td>1.171841</td>\n",
       "      <td>1.205449</td>\n",
       "      <td>1.173917</td>\n",
       "      <td>...</td>\n",
       "      <td>2.705421</td>\n",
       "      <td>0.986826</td>\n",
       "      <td>-0.985217</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
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       "      <td>-0.651906</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-1.026980</td>\n",
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       "      <td>-1.925776</td>\n",
       "      <td>-1.892011</td>\n",
       "      <td>0.417162</td>\n",
       "      <td>0.400079</td>\n",
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       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
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       "      <td>0.781082</td>\n",
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       "      <td>-1.046698</td>\n",
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       "      <th>94375</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.658469</td>\n",
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       "      <td>0.771373</td>\n",
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       "      <td>-0.373759</td>\n",
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       "      <td>-1.046698</td>\n",
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       "      <td>0.417162</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.390360</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>0.781082</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>1.046698</td>\n",
       "      <td>-1.046698</td>\n",
       "      <td>1.097565</td>\n",
       "      <td>0.570033</td>\n",
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       "    <tr>\n",
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       "      <td>-1.280261</td>\n",
       "      <td>1.598941</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>-1.754267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>0</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-1.307093</td>\n",
       "      <td>0.101450</td>\n",
       "      <td>0.006984</td>\n",
       "      <td>-0.284143</td>\n",
       "      <td>-0.396927</td>\n",
       "      <td>-0.360085</td>\n",
       "      <td>-1.947698</td>\n",
       "      <td>0.400079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.338059</td>\n",
       "      <td>-1.013339</td>\n",
       "      <td>1.014994</td>\n",
       "      <td>-0.44508</td>\n",
       "      <td>-1.280261</td>\n",
       "      <td>-0.625407</td>\n",
       "      <td>-0.955375</td>\n",
       "      <td>0.955375</td>\n",
       "      <td>-0.911098</td>\n",
       "      <td>0.570033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94379 rows &times; 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall_Experience  Seat_Comfort  Arrival_Time_Convenient  Catering  \\\n",
       "0                       0     -0.602633                 1.313657  1.486544   \n",
       "1                       0     -1.320753                 1.313657 -1.283644   \n",
       "2                       1     -0.602633                -0.651906 -0.591097   \n",
       "3                       0      0.115486                -0.651906  0.000000   \n",
       "4                       1      0.115486                 0.003282  0.101450   \n",
       "...                   ...           ...                      ...       ...   \n",
       "94374                   0     -1.320753                 0.658469  0.793997   \n",
       "94375                   1      0.833606                 0.658469  0.793997   \n",
       "94376                   1     -0.602633                -0.651906 -0.591097   \n",
       "94377                   0     -0.602633                 0.000000 -0.591097   \n",
       "94378                   0      0.115486                -1.307093  0.101450   \n",
       "\n",
       "       Platform_Location  Onboard_Entertainment  Online_Support  \\\n",
       "0               1.535763              -1.026980       -0.396927   \n",
       "1              -0.757406              -1.769817        0.367497   \n",
       "2              -0.757406               0.458695        1.131922   \n",
       "3              -0.757406              -1.026980       -0.396927   \n",
       "4               0.006984               0.458695        1.131922   \n",
       "...                  ...                    ...             ...   \n",
       "94374           0.771373              -1.769817       -1.925776   \n",
       "94375           0.771373               1.201532        1.131922   \n",
       "94376          -0.757406               1.201532        0.367497   \n",
       "94377           0.771373              -1.026980        0.367497   \n",
       "94378           0.006984              -0.284143       -0.396927   \n",
       "\n",
       "       Ease_of_Online_Booking  Onboard_Service   Legroom  ...  \\\n",
       "0                   -1.126048        -1.159411 -0.373759  ...   \n",
       "1                    0.405878         1.205449 -1.147597  ...   \n",
       "2                    1.171841         1.205449  1.173917  ...   \n",
       "3                   -0.360085        -0.371125 -0.373759  ...   \n",
       "4                    0.405878         0.417162  0.400079  ...   \n",
       "...                       ...              ...       ...  ...   \n",
       "94374               -1.892011         0.417162  0.400079  ...   \n",
       "94375               -0.360085        -0.371125 -0.373759  ...   \n",
       "94376                0.405878         0.417162  0.400079  ...   \n",
       "94377                0.405878        -0.371125  0.400079  ...   \n",
       "94378               -0.360085        -1.947698  0.400079  ...   \n",
       "\n",
       "       Arrival_Delay_in_Mins  Gender_Female  Gender_Male  \\\n",
       "0                  -0.260286       0.986826    -0.985217   \n",
       "1                  -0.390360      -1.013339     1.014994   \n",
       "2                   2.705421       0.986826    -0.985217   \n",
       "3                   0.077909       0.986826    -0.985217   \n",
       "4                  -0.390360       0.986826    -0.985217   \n",
       "...                      ...            ...          ...   \n",
       "94374               2.861511      -1.013339     1.014994   \n",
       "94375              -0.104196      -1.013339     1.014994   \n",
       "94376              -0.390360      -1.013339     1.014994   \n",
       "94377              -0.390360      -1.013339     1.014994   \n",
       "94378               0.338059      -1.013339     1.014994   \n",
       "\n",
       "       Customer_Type_Disloyal Customer  Type_Travel_Business Travel  \\\n",
       "0                             -0.44508                    -1.280261   \n",
       "1                             -0.44508                    -1.280261   \n",
       "2                             -0.44508                     0.781082   \n",
       "3                             -0.44508                     0.781082   \n",
       "4                             -0.44508                     0.781082   \n",
       "...                                ...                          ...   \n",
       "94374                         -0.44508                     0.781082   \n",
       "94375                         -0.44508                     0.781082   \n",
       "94376                         -0.44508                     0.781082   \n",
       "94377                         -0.44508                    -1.280261   \n",
       "94378                         -0.44508                    -1.280261   \n",
       "\n",
       "       Type_Travel_Personal Travel  Travel_Class_Business  Travel_Class_Eco  \\\n",
       "0                        -0.625407               1.046698         -1.046698   \n",
       "1                         1.598941              -0.955375          0.955375   \n",
       "2                        -0.625407               1.046698         -1.046698   \n",
       "3                        -0.625407               1.046698         -1.046698   \n",
       "4                        -0.625407               1.046698         -1.046698   \n",
       "...                            ...                    ...               ...   \n",
       "94374                    -0.625407               1.046698         -1.046698   \n",
       "94375                    -0.625407               1.046698         -1.046698   \n",
       "94376                    -0.625407               1.046698         -1.046698   \n",
       "94377                     1.598941              -0.955375          0.955375   \n",
       "94378                    -0.625407              -0.955375          0.955375   \n",
       "\n",
       "       SurveyPos  SurveyNeg  \n",
       "0      -0.911098  -1.754267  \n",
       "1      -0.911098  -1.754267  \n",
       "2      -0.911098   0.570033  \n",
       "3      -0.911098   0.570033  \n",
       "4       1.097565   0.570033  \n",
       "...          ...        ...  \n",
       "94374  -0.911098  -1.754267  \n",
       "94375   1.097565   0.570033  \n",
       "94376   1.097565   0.570033  \n",
       "94377  -0.911098  -1.754267  \n",
       "94378  -0.911098   0.570033  \n",
       "\n",
       "[94379 rows x 26 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "11296359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Arrival_Time_Convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_Location</th>\n",
       "      <th>Onboard_Entertainment</th>\n",
       "      <th>Online_Support</th>\n",
       "      <th>Ease_of_Online_Booking</th>\n",
       "      <th>Onboard_Service</th>\n",
       "      <th>Legroom</th>\n",
       "      <th>Baggage_Handling</th>\n",
       "      <th>...</th>\n",
       "      <th>Arrival_Delay_in_Mins</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Customer_Type_Disloyal Customer</th>\n",
       "      <th>Type_Travel_Business Travel</th>\n",
       "      <th>Type_Travel_Personal Travel</th>\n",
       "      <th>Travel_Class_Business</th>\n",
       "      <th>Travel_Class_Eco</th>\n",
       "      <th>SurveyPos</th>\n",
       "      <th>SurveyNeg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.117225</td>\n",
       "      <td>0.008357</td>\n",
       "      <td>0.103807</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>1.200062</td>\n",
       "      <td>0.368007</td>\n",
       "      <td>1.166890</td>\n",
       "      <td>1.211294</td>\n",
       "      <td>1.166397</td>\n",
       "      <td>1.129482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>0.985042</td>\n",
       "      <td>-0.983383</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>1.038476</td>\n",
       "      <td>-1.038476</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.035662</td>\n",
       "      <td>0.664664</td>\n",
       "      <td>-1.283079</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>-1.773693</td>\n",
       "      <td>-0.399955</td>\n",
       "      <td>-0.364999</td>\n",
       "      <td>1.211294</td>\n",
       "      <td>-0.382255</td>\n",
       "      <td>0.264456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329374</td>\n",
       "      <td>0.985042</td>\n",
       "      <td>-0.983383</td>\n",
       "      <td>2.250541</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>1.038476</td>\n",
       "      <td>-1.038476</td>\n",
       "      <td>-0.911199</td>\n",
       "      <td>-1.758525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.552483</td>\n",
       "      <td>1.320971</td>\n",
       "      <td>1.490693</td>\n",
       "      <td>1.545046</td>\n",
       "      <td>1.200062</td>\n",
       "      <td>1.135969</td>\n",
       "      <td>-1.130943</td>\n",
       "      <td>-1.140865</td>\n",
       "      <td>-1.156581</td>\n",
       "      <td>-1.465598</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>-1.015156</td>\n",
       "      <td>1.016869</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>1.038476</td>\n",
       "      <td>-1.038476</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.117225</td>\n",
       "      <td>1.320971</td>\n",
       "      <td>0.103807</td>\n",
       "      <td>1.545046</td>\n",
       "      <td>-0.286815</td>\n",
       "      <td>1.135969</td>\n",
       "      <td>-1.896888</td>\n",
       "      <td>-0.356812</td>\n",
       "      <td>-1.156581</td>\n",
       "      <td>1.129482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>0.985042</td>\n",
       "      <td>-0.983383</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>-1.295766</td>\n",
       "      <td>1.617915</td>\n",
       "      <td>-0.962922</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>-0.911199</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.552483</td>\n",
       "      <td>-1.960563</td>\n",
       "      <td>1.490693</td>\n",
       "      <td>-0.760506</td>\n",
       "      <td>1.200062</td>\n",
       "      <td>1.135969</td>\n",
       "      <td>1.166890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.382255</td>\n",
       "      <td>1.129482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>-1.015156</td>\n",
       "      <td>1.016869</td>\n",
       "      <td>2.250541</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>1.038476</td>\n",
       "      <td>-1.038476</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>-0.600404</td>\n",
       "      <td>1.320971</td>\n",
       "      <td>-0.589636</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>-1.030254</td>\n",
       "      <td>-0.399955</td>\n",
       "      <td>-0.364999</td>\n",
       "      <td>0.427241</td>\n",
       "      <td>1.166397</td>\n",
       "      <td>0.264456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>-1.015156</td>\n",
       "      <td>1.016869</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>-1.295766</td>\n",
       "      <td>1.617915</td>\n",
       "      <td>-0.962922</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>-0.911199</td>\n",
       "      <td>-1.758525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>-0.600404</td>\n",
       "      <td>-0.647950</td>\n",
       "      <td>0.797250</td>\n",
       "      <td>-0.760506</td>\n",
       "      <td>1.200062</td>\n",
       "      <td>1.135969</td>\n",
       "      <td>0.400946</td>\n",
       "      <td>0.427241</td>\n",
       "      <td>0.392071</td>\n",
       "      <td>0.264456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>0.985042</td>\n",
       "      <td>-0.983383</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>1.038476</td>\n",
       "      <td>-1.038476</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>0.834854</td>\n",
       "      <td>-1.960563</td>\n",
       "      <td>0.797250</td>\n",
       "      <td>-0.760506</td>\n",
       "      <td>0.456624</td>\n",
       "      <td>-1.935878</td>\n",
       "      <td>-1.130943</td>\n",
       "      <td>-1.924918</td>\n",
       "      <td>-0.382255</td>\n",
       "      <td>-2.330624</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>-1.015156</td>\n",
       "      <td>1.016869</td>\n",
       "      <td>2.250541</td>\n",
       "      <td>0.771722</td>\n",
       "      <td>-0.618062</td>\n",
       "      <td>-0.962922</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>1.552483</td>\n",
       "      <td>1.320971</td>\n",
       "      <td>1.490693</td>\n",
       "      <td>-1.529023</td>\n",
       "      <td>1.200062</td>\n",
       "      <td>0.368007</td>\n",
       "      <td>1.166890</td>\n",
       "      <td>1.211294</td>\n",
       "      <td>1.166397</td>\n",
       "      <td>1.129482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017939</td>\n",
       "      <td>0.985042</td>\n",
       "      <td>-0.983383</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>-1.295766</td>\n",
       "      <td>1.617915</td>\n",
       "      <td>-0.962922</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>0.834854</td>\n",
       "      <td>0.008357</td>\n",
       "      <td>0.797250</td>\n",
       "      <td>0.008011</td>\n",
       "      <td>0.456624</td>\n",
       "      <td>-1.935878</td>\n",
       "      <td>-1.896888</td>\n",
       "      <td>-0.356812</td>\n",
       "      <td>0.392071</td>\n",
       "      <td>0.264456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397308</td>\n",
       "      <td>-1.015156</td>\n",
       "      <td>1.016869</td>\n",
       "      <td>-0.444325</td>\n",
       "      <td>-1.295766</td>\n",
       "      <td>1.617915</td>\n",
       "      <td>-0.962922</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>1.097425</td>\n",
       "      <td>0.568642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35602 rows &times; 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Seat_Comfort  Arrival_Time_Convenient  Catering  Platform_Location  \\\n",
       "0          0.117225                 0.008357  0.103807           0.008011   \n",
       "1         -2.035662                 0.664664 -1.283079           0.008011   \n",
       "2          1.552483                 1.320971  1.490693           1.545046   \n",
       "3          0.117225                 1.320971  0.103807           1.545046   \n",
       "4          1.552483                -1.960563  1.490693          -0.760506   \n",
       "...             ...                      ...       ...                ...   \n",
       "35597     -0.600404                 1.320971 -0.589636           0.008011   \n",
       "35598     -0.600404                -0.647950  0.797250          -0.760506   \n",
       "35599      0.834854                -1.960563  0.797250          -0.760506   \n",
       "35600      1.552483                 1.320971  1.490693          -1.529023   \n",
       "35601      0.834854                 0.008357  0.797250           0.008011   \n",
       "\n",
       "       Onboard_Entertainment  Online_Support  Ease_of_Online_Booking  \\\n",
       "0                   1.200062        0.368007                1.166890   \n",
       "1                  -1.773693       -0.399955               -0.364999   \n",
       "2                   1.200062        1.135969               -1.130943   \n",
       "3                  -0.286815        1.135969               -1.896888   \n",
       "4                   1.200062        1.135969                1.166890   \n",
       "...                      ...             ...                     ...   \n",
       "35597              -1.030254       -0.399955               -0.364999   \n",
       "35598               1.200062        1.135969                0.400946   \n",
       "35599               0.456624       -1.935878               -1.130943   \n",
       "35600               1.200062        0.368007                1.166890   \n",
       "35601               0.456624       -1.935878               -1.896888   \n",
       "\n",
       "       Onboard_Service   Legroom  Baggage_Handling  ...  \\\n",
       "0             1.211294  1.166397          1.129482  ...   \n",
       "1             1.211294 -0.382255          0.264456  ...   \n",
       "2            -1.140865 -1.156581         -1.465598  ...   \n",
       "3            -0.356812 -1.156581          1.129482  ...   \n",
       "4             0.000000 -0.382255          1.129482  ...   \n",
       "...                ...       ...               ...  ...   \n",
       "35597         0.427241  1.166397          0.264456  ...   \n",
       "35598         0.427241  0.392071          0.264456  ...   \n",
       "35599        -1.924918 -0.382255         -2.330624  ...   \n",
       "35600         1.211294  1.166397          1.129482  ...   \n",
       "35601        -0.356812  0.392071          0.264456  ...   \n",
       "\n",
       "       Arrival_Delay_in_Mins  Gender_Female  Gender_Male  \\\n",
       "0                  -0.397308       0.985042    -0.983383   \n",
       "1                   0.329374       0.985042    -0.983383   \n",
       "2                  -0.397308      -1.015156     1.016869   \n",
       "3                  -0.397308       0.985042    -0.983383   \n",
       "4                  -0.397308      -1.015156     1.016869   \n",
       "...                      ...            ...          ...   \n",
       "35597              -0.397308      -1.015156     1.016869   \n",
       "35598              -0.397308       0.985042    -0.983383   \n",
       "35599              -0.397308      -1.015156     1.016869   \n",
       "35600               0.017939       0.985042    -0.983383   \n",
       "35601              -0.397308      -1.015156     1.016869   \n",
       "\n",
       "       Customer_Type_Disloyal Customer  Type_Travel_Business Travel  \\\n",
       "0                            -0.444325                     0.771722   \n",
       "1                             2.250541                     0.771722   \n",
       "2                            -0.444325                     0.771722   \n",
       "3                            -0.444325                    -1.295766   \n",
       "4                             2.250541                     0.771722   \n",
       "...                                ...                          ...   \n",
       "35597                        -0.444325                    -1.295766   \n",
       "35598                        -0.444325                     0.771722   \n",
       "35599                         2.250541                     0.771722   \n",
       "35600                        -0.444325                    -1.295766   \n",
       "35601                        -0.444325                    -1.295766   \n",
       "\n",
       "       Type_Travel_Personal Travel  Travel_Class_Business  Travel_Class_Eco  \\\n",
       "0                        -0.618062               1.038476         -1.038476   \n",
       "1                        -0.618062               1.038476         -1.038476   \n",
       "2                        -0.618062               1.038476         -1.038476   \n",
       "3                         1.617915              -0.962922          0.962922   \n",
       "4                        -0.618062               1.038476         -1.038476   \n",
       "...                            ...                    ...               ...   \n",
       "35597                     1.617915              -0.962922          0.962922   \n",
       "35598                    -0.618062               1.038476         -1.038476   \n",
       "35599                    -0.618062              -0.962922          0.962922   \n",
       "35600                     1.617915              -0.962922          0.962922   \n",
       "35601                     1.617915              -0.962922          0.962922   \n",
       "\n",
       "       SurveyPos  SurveyNeg  \n",
       "0       1.097425   0.568642  \n",
       "1      -0.911199  -1.758525  \n",
       "2       1.097425   0.568642  \n",
       "3      -0.911199   0.568642  \n",
       "4       1.097425   0.568642  \n",
       "...          ...        ...  \n",
       "35597  -0.911199  -1.758525  \n",
       "35598   1.097425   0.568642  \n",
       "35599   1.097425   0.568642  \n",
       "35600   1.097425   0.568642  \n",
       "35601   1.097425   0.568642  \n",
       "\n",
       "[35602 rows x 25 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "8f866e2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 train\n",
      "1.0 test\n"
     ]
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)\n",
    "print (accuracy_score(y_train, rf.predict(X_train)), 'train')\n",
    "print (accuracy_score(y_test, rf.predict(X_test)), 'test')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "35b0dcd3",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "fa834c67",
   "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>ID</th>\n",
       "      <th>Overall_Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99900001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99900002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99900003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99900004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99900005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>99935598</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>99935599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>99935600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>99935601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>99935602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35602 rows &times; 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Overall_Experience\n",
       "0      99900001                   1\n",
       "1      99900002                   0\n",
       "2      99900003                   0\n",
       "3      99900004                   0\n",
       "4      99900005                   1\n",
       "...         ...                 ...\n",
       "35597  99935598                   1\n",
       "35598  99935599                   0\n",
       "35599  99935600                   0\n",
       "35600  99935601                   1\n",
       "35601  99935602                   0\n",
       "\n",
       "[35602 rows x 2 columns]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sample_Submission = pd.read_csv(\"Sample_Submission.csv\")\n",
    "Sample_Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "89b2e8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_rf = rf.predict(X_test_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "87959d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Importance'>"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rf.feature_importances_\n",
    "\n",
    "columns = X_test_2.columns\n",
    "\n",
    "importance_df = pd.DataFrame(importances, index = columns, columns = ['Importance']).sort_values(by = 'Importance', ascending = False)\n",
    "\n",
    "plt.figure(figsize = (13, 13))\n",
    "\n",
    "sns.barplot(x=importance_df.Importance, y=importance_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "daad4ab1",
   "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>ID</th>\n",
       "      <th>Overall_Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99900001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99900002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99900003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99900004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99900005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>99935598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>99935599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>99935600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>99935601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>99935602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35602 rows &times; 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Overall_Experience\n",
       "0      99900001                   1\n",
       "1      99900002                   1\n",
       "2      99900003                   1\n",
       "3      99900004                   0\n",
       "4      99900005                   1\n",
       "...         ...                 ...\n",
       "35597  99935598                   0\n",
       "35598  99935599                   1\n",
       "35599  99935600                   0\n",
       "35600  99935601                   1\n",
       "35601  99935602                   0\n",
       "\n",
       "[35602 rows x 2 columns]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submission['Overall_Experience'] = y_pred_test_rf\n",
    "Submission['Overall_Experience'] = Submission['Overall_Experience'].astype('int')\n",
    "Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "6126e01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('OscarRincon_STE.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
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}