Spaceship titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5f59553",
   "metadata": {},
   "source": [
    "# Getting Data Ready"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f3c6de4",
   "metadata": {},
   "source": [
    "**Problem statement :**\n",
    "Predict which passengers are transported to an alternate dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28c2b5f",
   "metadata": {},
   "source": [
    "**Note :** From the problem statement we will understand the given problem is a classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cc1f575",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the necessary library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4ba5de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"train.csv\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6eb63200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/3/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nelly Carsoning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/4/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lerome Peckers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</td>\n",
       "      <td>C/0/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>31.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sabih Unhearfus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>C/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>38.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>Meratz Caltilter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/5/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brence Harperez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
       "1     0018_01      Earth     False  F/4/S  TRAPPIST-1e  19.0  False   \n",
       "2     0019_01     Europa      True  C/0/S  55 Cancri e  31.0  False   \n",
       "3     0021_01     Europa     False  C/1/S  TRAPPIST-1e  38.0  False   \n",
       "4     0023_01      Earth     False  F/5/S  TRAPPIST-1e  20.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df =  pd.read_csv(\"test.csv\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf951470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>PassengerId</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId  Transported\n",
       "0     0013_01        False\n",
       "1     0018_01        False\n",
       "2     0019_01        False\n",
       "3     0021_01        False\n",
       "4     0023_01        False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv(\"sample_submission.csv\")\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51240e90",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbc877a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8693 entries, 0 to 8692\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   PassengerId   8693 non-null   object \n",
      " 1   HomePlanet    8492 non-null   object \n",
      " 2   CryoSleep     8476 non-null   object \n",
      " 3   Cabin         8494 non-null   object \n",
      " 4   Destination   8511 non-null   object \n",
      " 5   Age           8514 non-null   float64\n",
      " 6   VIP           8490 non-null   object \n",
      " 7   RoomService   8512 non-null   float64\n",
      " 8   FoodCourt     8510 non-null   float64\n",
      " 9   ShoppingMall  8485 non-null   float64\n",
      " 10  Spa           8510 non-null   float64\n",
      " 11  VRDeck        8505 non-null   float64\n",
      " 12  Name          8493 non-null   object \n",
      " 13  Transported   8693 non-null   bool   \n",
      "dtypes: bool(1), float64(6), object(7)\n",
      "memory usage: 891.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14eb489b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4277 entries, 0 to 4276\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   PassengerId   4277 non-null   object \n",
      " 1   HomePlanet    4190 non-null   object \n",
      " 2   CryoSleep     4184 non-null   object \n",
      " 3   Cabin         4177 non-null   object \n",
      " 4   Destination   4185 non-null   object \n",
      " 5   Age           4186 non-null   float64\n",
      " 6   VIP           4184 non-null   object \n",
      " 7   RoomService   4195 non-null   float64\n",
      " 8   FoodCourt     4171 non-null   float64\n",
      " 9   ShoppingMall  4179 non-null   float64\n",
      " 10  Spa           4176 non-null   float64\n",
      " 11  VRDeck        4197 non-null   float64\n",
      " 12  Name          4183 non-null   object \n",
      "dtypes: float64(6), object(7)\n",
      "memory usage: 434.5+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0d7a5a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "HomePlanet      201\n",
       "CryoSleep       217\n",
       "Cabin           199\n",
       "Destination     182\n",
       "Age             179\n",
       "VIP             203\n",
       "RoomService     181\n",
       "FoodCourt       183\n",
       "ShoppingMall    208\n",
       "Spa             183\n",
       "VRDeck          188\n",
       "Name            200\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adaaec07",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8514.000000</td>\n",
       "      <td>8512.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8485.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8505.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.827930</td>\n",
       "      <td>224.687617</td>\n",
       "      <td>458.077203</td>\n",
       "      <td>173.729169</td>\n",
       "      <td>311.138778</td>\n",
       "      <td>304.854791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.489021</td>\n",
       "      <td>666.717663</td>\n",
       "      <td>1611.489240</td>\n",
       "      <td>604.696458</td>\n",
       "      <td>1136.705535</td>\n",
       "      <td>1145.717189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>14327.000000</td>\n",
       "      <td>29813.000000</td>\n",
       "      <td>23492.000000</td>\n",
       "      <td>22408.000000</td>\n",
       "      <td>24133.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
       "count  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \n",
       "mean     28.827930    224.687617    458.077203    173.729169    311.138778   \n",
       "std      14.489021    666.717663   1611.489240    604.696458   1136.705535   \n",
       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%      38.000000     47.000000     76.000000     27.000000     59.000000   \n",
       "max      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
       "\n",
       "             VRDeck  \n",
       "count   8505.000000  \n",
       "mean     304.854791  \n",
       "std     1145.717189  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%       46.000000  \n",
       "max    24133.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4df45f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068723</td>\n",
       "      <td>0.130421</td>\n",
       "      <td>0.033133</td>\n",
       "      <td>0.123970</td>\n",
       "      <td>0.101007</td>\n",
       "      <td>-0.075026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoomService</th>\n",
       "      <td>0.068723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015889</td>\n",
       "      <td>0.054480</td>\n",
       "      <td>0.010080</td>\n",
       "      <td>-0.019581</td>\n",
       "      <td>-0.244611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FoodCourt</th>\n",
       "      <td>0.130421</td>\n",
       "      <td>-0.015889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014228</td>\n",
       "      <td>0.221891</td>\n",
       "      <td>0.227995</td>\n",
       "      <td>0.046566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShoppingMall</th>\n",
       "      <td>0.033133</td>\n",
       "      <td>0.054480</td>\n",
       "      <td>-0.014228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013879</td>\n",
       "      <td>-0.007322</td>\n",
       "      <td>0.010141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spa</th>\n",
       "      <td>0.123970</td>\n",
       "      <td>0.010080</td>\n",
       "      <td>0.221891</td>\n",
       "      <td>0.013879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153821</td>\n",
       "      <td>-0.221131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VRDeck</th>\n",
       "      <td>0.101007</td>\n",
       "      <td>-0.019581</td>\n",
       "      <td>0.227995</td>\n",
       "      <td>-0.007322</td>\n",
       "      <td>0.153821</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.207075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transported</th>\n",
       "      <td>-0.075026</td>\n",
       "      <td>-0.244611</td>\n",
       "      <td>0.046566</td>\n",
       "      <td>0.010141</td>\n",
       "      <td>-0.221131</td>\n",
       "      <td>-0.207075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age  RoomService  FoodCourt  ShoppingMall       Spa  \\\n",
       "Age           1.000000     0.068723   0.130421      0.033133  0.123970   \n",
       "RoomService   0.068723     1.000000  -0.015889      0.054480  0.010080   \n",
       "FoodCourt     0.130421    -0.015889   1.000000     -0.014228  0.221891   \n",
       "ShoppingMall  0.033133     0.054480  -0.014228      1.000000  0.013879   \n",
       "Spa           0.123970     0.010080   0.221891      0.013879  1.000000   \n",
       "VRDeck        0.101007    -0.019581   0.227995     -0.007322  0.153821   \n",
       "Transported  -0.075026    -0.244611   0.046566      0.010141 -0.221131   \n",
       "\n",
       "                VRDeck  Transported  \n",
       "Age           0.101007    -0.075026  \n",
       "RoomService  -0.019581    -0.244611  \n",
       "FoodCourt     0.227995     0.046566  \n",
       "ShoppingMall -0.007322     0.010141  \n",
       "Spa           0.153821    -0.221131  \n",
       "VRDeck        1.000000    -0.207075  \n",
       "Transported  -0.207075     1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c646ec9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      object\n",
       "HomePlanet       object\n",
       "CryoSleep        object\n",
       "Cabin            object\n",
       "Destination      object\n",
       "Age             float64\n",
       "VIP              object\n",
       "RoomService     float64\n",
       "FoodCourt       float64\n",
       "ShoppingMall    float64\n",
       "Spa             float64\n",
       "VRDeck          float64\n",
       "Name             object\n",
       "Transported        bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cf82590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',\n",
       "       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',\n",
       "       'Name', 'Transported'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39392070",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the necessary libary for model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0df80c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the dataset to build a model(training and validation)\n",
    "X = train_df.drop(\"Transported\", axis=1)\n",
    "y = train_df.Transported\n",
    "\n",
    "X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y , test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19d49271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6954, 13), (1739, 13), (6954,), (1739,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#undersatabd the splitting\n",
    "X_train_full.shape, X_valid_full.shape, y_train.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ea2151e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      object\n",
       "HomePlanet       object\n",
       "CryoSleep        object\n",
       "Cabin            object\n",
       "Destination      object\n",
       "Age             float64\n",
       "VIP              object\n",
       "RoomService     float64\n",
       "FoodCourt       float64\n",
       "ShoppingMall    float64\n",
       "Spa             float64\n",
       "VRDeck          float64\n",
       "Name             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f846cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3       False\n",
       "4        True\n",
       "        ...  \n",
       "8688    False\n",
       "8689    False\n",
       "8690     True\n",
       "8691    False\n",
       "8692     True\n",
       "Name: Transported, Length: 8693, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "496d4116",
   "metadata": {},
   "source": [
    "**Creating a numerical and categorical column for better preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e82a315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId',\n",
       " 'HomePlanet',\n",
       " 'CryoSleep',\n",
       " 'Cabin',\n",
       " 'Destination',\n",
       " 'VIP',\n",
       " 'Name']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col = [i for i in X if X[i].dtype==\"O\"]\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "783d5d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col = [i for i in X if X[i].dtype!=\"O\"]\n",
    "num_col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8448a6e7",
   "metadata": {},
   "source": [
    "**Transformimg the variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a26459b",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_transformer = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef955bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "    (\"one_hot\", OneHotEncoder(handle_unknown=\"ignore\"))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "625445e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = ColumnTransformer(transformers=[\n",
    "    (\"num_transformer\", numerical_transformer, num_col),\n",
    "    (\"cat_transformer\", categorical_transformer, cat_col)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f7805f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08743f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                              ('model', model)\n",
    "                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2631807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num_transformer',\n",
       "                                                  SimpleImputer(),\n",
       "                                                  ['Age', 'RoomService',\n",
       "                                                   'FoodCourt', 'ShoppingMall',\n",
       "                                                   'Spa', 'VRDeck']),\n",
       "                                                 ('cat_transformer',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('one_hot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['PassengerId', 'HomePlanet',\n",
       "                                                   'CryoSleep', 'Cabin',\n",
       "                                                   'Destination', 'VIP',\n",
       "                                                   'Name'])])),\n",
       "                ('model', RandomForestClassifier())])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "483bbf08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num_transformer',\n",
       "                                                  SimpleImputer(),\n",
       "                                                  ['Age', 'RoomService',\n",
       "                                                   'FoodCourt', 'ShoppingMall',\n",
       "                                                   'Spa', 'VRDeck']),\n",
       "                                                 ('cat_transformer',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('one_hot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['PassengerId', 'HomePlanet',\n",
       "                                                   'CryoSleep', 'Cabin',\n",
       "                                                   'Destination', 'VIP',\n",
       "                                                   'Name'])])),\n",
       "                ('model', RandomForestClassifier())])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_pipeline.fit(X_train_full, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "32fb9b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "y_pred = my_pipeline.predict(X_valid_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9ae29944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, ..., False,  True, False])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41205ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "64b4ba57",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = accuracy_score(y_valid, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "627e333f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7826336975273146"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2312627e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = my_pipeline.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "466cad33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d71f0a1",
   "metadata": {},
   "source": [
    "As of now we did the modeling using Random forest classifier let's check out different model and do our best to update the scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6a44f7",
   "metadata": {},
   "source": [
    "### XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "cbbf112f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "model1 = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e840a3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pipeline1 = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                              ('model', model1)\n",
    "                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7c14a198",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:51:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num_transformer',\n",
       "                                                  SimpleImputer(),\n",
       "                                                  ['Age', 'RoomService',\n",
       "                                                   'FoodCourt', 'ShoppingMall',\n",
       "                                                   'Spa', 'VRDeck']),\n",
       "                                                 ('cat_transformer',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('one_hot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['PassengerId', 'HomePlanet',\n",
       "                                                   'CryoSleep', 'Cabin...\n",
       "                               gamma=0, gpu_id=-1, importance_type=None,\n",
       "                               interaction_constraints='',\n",
       "                               learning_rate=0.300000012, max_delta_step=0,\n",
       "                               max_depth=6, min_child_weight=1, missing=nan,\n",
       "                               monotone_constraints='()', n_estimators=100,\n",
       "                               n_jobs=6, num_parallel_tree=1, predictor='auto',\n",
       "                               random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "                               scale_pos_weight=1, subsample=1,\n",
       "                               tree_method='exact', validate_parameters=1,\n",
       "                               verbosity=None))])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_pipeline1.fit(X_train_full, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b2b146d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = my_pipeline1.predict(X_valid_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0e874354",
   "metadata": {},
   "outputs": [],
   "source": [
    "score1 = accuracy_score(y_valid, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8f354813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7722829212190915"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c23617e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7826336975273146, 0.7722829212190915)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score, score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8f55b93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fe1d0357",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred1 = my_pipeline1.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6e736bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True, False])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8bcfcc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4277 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Transported\n",
       "0        0013_01        False\n",
       "1        0018_01        False\n",
       "2        0019_01        False\n",
       "3        0021_01        False\n",
       "4        0023_01        False\n",
       "...          ...          ...\n",
       "4272     9266_02        False\n",
       "4273     9269_01        False\n",
       "4274     9271_01        False\n",
       "4275     9273_01        False\n",
       "4276     9277_01        False\n",
       "\n",
       "[4277 rows x 2 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cec7b7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission = pd.DataFrame({'PassengerId': test_df.PassengerId, \"Transported\": test_pred})\n",
    "# you could use any filename. We choose submission here\n",
    "my_submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b22d017e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>4277 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Transported\n",
       "0        0013_01         True\n",
       "1        0018_01        False\n",
       "2        0019_01         True\n",
       "3        0021_01         True\n",
       "4        0023_01        False\n",
       "...          ...          ...\n",
       "4272     9266_02         True\n",
       "4273     9269_01        False\n",
       "4274     9271_01         True\n",
       "4275     9273_01         True\n",
       "4276     9277_01         True\n",
       "\n",
       "[4277 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ddcc29ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission1 = pd.DataFrame({\"PassengerId\": test_df.PassengerId,\"Transported\":test_pred1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "57edac13",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "<p>4277 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Transported\n",
       "0        0013_01         True\n",
       "1        0018_01        False\n",
       "2        0019_01         True\n",
       "3        0021_01         True\n",
       "4        0023_01         True\n",
       "...          ...          ...\n",
       "4272     9266_02         True\n",
       "4273     9269_01        False\n",
       "4274     9271_01         True\n",
       "4275     9273_01         True\n",
       "4276     9277_01        False\n",
       "\n",
       "[4277 rows x 2 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_submission1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "10d44332",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission1.to_csv(\"submission1.csv\", index=False)"
   ]
  }
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