code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ajiteshshree/Spaceship-Titanic/main/spaceship-titanic/train.csv\")\n",
    "df_test = pd.read_csv(\"https://raw.githubusercontent.com/ajiteshshree/Spaceship-Titanic/main/spaceship-titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pid = df_test['PassengerId'].tolist()\n",
    "def slice_ids(df):\n",
    "    pid = df['PassengerId'].tolist()\n",
    "    df['PassengerId'] = [int(x[0:4]) for x in pid]\n",
    "    df['PersonNo'] = [int(x[5:]) for x in pid]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = slice_ids(df)\n",
    "df_test = slice_ids(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Name',axis=1,inplace=True)\n",
    "df_test.drop('Name',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.shape :  (8693, 14)\n",
      "df_test.shape :  (4277, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"df.shape : \", df.shape)\n",
    "print(\"df_test.shape : \", df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling NaN values\n",
    "def preprocess(df):\n",
    "    li = ['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck', 'Age']\n",
    "    for col in li:\n",
    "        df[col].fillna(df[col].mean(),inplace=True)\n",
    "    df[\"HomePlanet\"].fillna(df[\"HomePlanet\"].mode()[0],inplace=True)\n",
    "    df[\"CryoSleep\"].fillna(df[\"CryoSleep\"].mode()[0],inplace=True)\n",
    "    df[\"Destination\"].fillna(df[\"Destination\"].mode()[0],inplace=True)\n",
    "    df[\"VIP\"].fillna(df[\"VIP\"].mode()[0],inplace=True)\n",
    "    df[\"Cabin\"].fillna(\"\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess(df)\n",
    "preprocess(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nulls in cabin :  0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    \n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of nulls in cabin : \",df['Cabin'].isnull().sum())\n",
    "df['Cabin'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = df['Transported']\n",
    "df.drop(['Transported'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[],[]\n"
     ]
    }
   ],
   "source": [
    "print([x for x in list(df.isnull().sum()) if x != 0], end=\",\")\n",
    "print([x for x in list(df_test.isnull().sum()) if x != 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def slice_cabin(df):\n",
    "    b=0\n",
    "    cabin_deck =[]\n",
    "    cabin_side = []\n",
    "    while b < df.shape[0]:\n",
    "        if df.iloc[b,3] != \"\":\n",
    "            cabin_deck.append(df.iloc[b,3][0])\n",
    "            cabin_side.append(df.iloc[b,3][-1])\n",
    "        else:\n",
    "            cabin_deck.append(\"\")\n",
    "            cabin_side.append(\"\")\n",
    "        b += 1\n",
    "    df['cabin_deck'] = cabin_deck\n",
    "    df['cabin_side'] = cabin_side\n",
    "    df.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_cabin(df)\n",
    "slice_cabin(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</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>PersonNo</th>\n",
       "      <th>cabin_deck</th>\n",
       "      <th>cabin_side</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</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>1</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId HomePlanet  CryoSleep  Destination   Age    VIP  RoomService  \\\n",
       "0           13      Earth       True  TRAPPIST-1e  27.0  False          0.0   \n",
       "1           18      Earth      False  TRAPPIST-1e  19.0  False          0.0   \n",
       "2           19     Europa       True  55 Cancri e  31.0  False          0.0   \n",
       "3           21     Europa      False  TRAPPIST-1e  38.0  False          0.0   \n",
       "4           23      Earth      False  TRAPPIST-1e  20.0  False         10.0   \n",
       "\n",
       "   FoodCourt  ShoppingMall     Spa  VRDeck  PersonNo cabin_deck cabin_side  \n",
       "0        0.0           0.0     0.0     0.0         1          G          S  \n",
       "1        9.0           0.0  2823.0     0.0         1          F          S  \n",
       "2        0.0           0.0     0.0     0.0         1          C          S  \n",
       "3     6652.0           0.0   181.0   585.0         1          C          S  \n",
       "4        0.0         635.0     0.0     0.0         1          F          S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols = df.columns\n",
    "# cols2 = df_test.columns\n",
    "# for x in cols:\n",
    "#         print(x,end = ' : ')\n",
    "#         print(df[x].unique())\n",
    "# print()\n",
    "# for t in cols2:\n",
    "#     print(t,end = ' : ')\n",
    "#     print(df_test[t].unique())       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_int_cols = [k for k, v in dict(df.dtypes).items() if v == 'O' or v == 'bool']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'cabin_deck', 'cabin_side']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_int_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(df):\n",
    "#     le = LabelEncoder()    \n",
    "#     for x in non_int_cols:\n",
    "#         df[x] = le.fit_transform(df[x])\n",
    "    df = pd.get_dummies(df, columns=non_int_cols)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = encode(df)\n",
    "df_test = encode(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>PassengerId</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>PersonNo</th>\n",
       "      <th>HomePlanet_Earth</th>\n",
       "      <th>HomePlanet_Europa</th>\n",
       "      <th>...</th>\n",
       "      <th>cabin_deck_B</th>\n",
       "      <th>cabin_deck_C</th>\n",
       "      <th>cabin_deck_D</th>\n",
       "      <th>cabin_deck_E</th>\n",
       "      <th>cabin_deck_F</th>\n",
       "      <th>cabin_deck_G</th>\n",
       "      <th>cabin_deck_T</th>\n",
       "      <th>cabin_side_</th>\n",
       "      <th>cabin_side_P</th>\n",
       "      <th>cabin_side_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24.0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>58.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>33.0</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId   Age  RoomService  FoodCourt  ShoppingMall     Spa  VRDeck  \\\n",
       "0            1  39.0          0.0        0.0           0.0     0.0     0.0   \n",
       "1            2  24.0        109.0        9.0          25.0   549.0    44.0   \n",
       "2            3  58.0         43.0     3576.0           0.0  6715.0    49.0   \n",
       "3            3  33.0          0.0     1283.0         371.0  3329.0   193.0   \n",
       "4            4  16.0        303.0       70.0         151.0   565.0     2.0   \n",
       "\n",
       "   PersonNo  HomePlanet_Earth  HomePlanet_Europa  ...  cabin_deck_B  \\\n",
       "0         1                 0                  1  ...             1   \n",
       "1         1                 1                  0  ...             0   \n",
       "2         1                 0                  1  ...             0   \n",
       "3         2                 0                  1  ...             0   \n",
       "4         1                 1                  0  ...             0   \n",
       "\n",
       "   cabin_deck_C  cabin_deck_D  cabin_deck_E  cabin_deck_F  cabin_deck_G  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             1             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             1             0   \n",
       "\n",
       "   cabin_deck_T  cabin_side_  cabin_side_P  cabin_side_S  \n",
       "0             0            0             1             0  \n",
       "1             0            0             0             1  \n",
       "2             0            0             0             1  \n",
       "3             0            0             0             1  \n",
       "4             0            0             0             1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId',\n",
       " 'Age',\n",
       " 'RoomService',\n",
       " 'FoodCourt',\n",
       " 'ShoppingMall',\n",
       " 'Spa',\n",
       " 'VRDeck',\n",
       " 'PersonNo',\n",
       " 'HomePlanet_Earth',\n",
       " 'HomePlanet_Europa',\n",
       " 'HomePlanet_Mars',\n",
       " 'CryoSleep_False',\n",
       " 'CryoSleep_True',\n",
       " 'Destination_55 Cancri e',\n",
       " 'Destination_PSO J318.5-22',\n",
       " 'Destination_TRAPPIST-1e',\n",
       " 'VIP_False',\n",
       " 'VIP_True',\n",
       " 'cabin_deck_',\n",
       " 'cabin_deck_A',\n",
       " 'cabin_deck_B',\n",
       " 'cabin_deck_C',\n",
       " 'cabin_deck_D',\n",
       " 'cabin_deck_E',\n",
       " 'cabin_deck_F',\n",
       " 'cabin_deck_G',\n",
       " 'cabin_deck_T',\n",
       " 'cabin_side_',\n",
       " 'cabin_side_P',\n",
       " 'cabin_side_S']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8693, 30)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4277, 30)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df, df_test = df.align(df_test, join='left', axis=1, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7917864948809387\n"
     ]
    }
   ],
   "source": [
    "logisticRegr = LogisticRegression(max_iter=10000)\n",
    "logisticRegr.fit(df,y)\n",
    "print(logisticRegr.score(df, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = logisticRegr.predict(df_test).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame({'PassengerId' : test_pid,'Transported':res})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Transported</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0013_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0018_01</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0019_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0023_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9266_02</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9269_01</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9271_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9273_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9277_01</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4277 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Transported\n",
       "PassengerId             \n",
       "0013_01             True\n",
       "0018_01            False\n",
       "0019_01             True\n",
       "0021_01             True\n",
       "0023_01             True\n",
       "...                  ...\n",
       "9266_02             True\n",
       "9269_01            False\n",
       "9271_01             True\n",
       "9273_01             True\n",
       "9277_01             True\n",
       "\n",
       "[4277 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions.to_csv('predictions.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"
  },
  "vscode": {
   "interpreter": {
    "hash": "63963b3f4c440940f0b94a3100916033a226cb4f45979123153792d60aa56d6a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}