Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72c7d116-57cb-4a53-86db-6beb597be6db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  spaceship-titanic.zip\n",
      "replace sample_submission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
     ]
    }
   ],
   "source": [
    "!unzip spaceship-titanic.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dff27d6-2ff8-43ea-9907-6e4beeec3eb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import sklearn \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e813fb94-57b1-488a-8081-7bd3d2d59928",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2de4c516-829a-4b33-a86f-8aba565b3c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ac7886c-453e-4d16-8d7d-b5acab17d303",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bdd65a3-c2e1-4fcf-b492-ebc7031db003",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b377569-677d-4e08-9caf-9cf6eb0c8271",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbb3263e-7fa6-47c3-86fd-add5cad542fa",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </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",
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       "      <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",
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       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
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       "    <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87a9f806-b8bc-4e0d-b9ee-21e5d6ecb219",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae36d69d-23d0-4faf-912f-3532893a79b4",
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7a08754-7197-4d25-a6ea-7c2083e9cf0f",
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7115c942-703d-4c83-bcb9-a144c737879a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2238d2c3-2b51-41c9-b3d4-34b3ad8d5713",
   "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>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>7</th>\n",
       "      <td>0006_02</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>28.0</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>NaN</td>\n",
       "      <td>Candra Jacostaffey</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0008_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</td>\n",
       "      <td>B/1/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>34.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Altardr Flatic</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0012_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>31.0</td>\n",
       "      <td>False</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Justie Pooles</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0014_01</td>\n",
       "      <td>Mars</td>\n",
       "      <td>False</td>\n",
       "      <td>F/3/P</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flats Eccle</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0020_03</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>E/0/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>29.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mollen Mcfaddennon</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "7      0006_02      Earth      True  G/0/S  TRAPPIST-1e  28.0  False   \n",
       "10     0008_02     Europa      True  B/1/P  TRAPPIST-1e  34.0  False   \n",
       "15     0012_01      Earth     False    NaN  TRAPPIST-1e  31.0  False   \n",
       "16     0014_01       Mars     False  F/3/P  55 Cancri e  27.0  False   \n",
       "23     0020_03      Earth      True  E/0/S  55 Cancri e  29.0  False   \n",
       "\n",
       "    RoomService  FoodCourt  ShoppingMall  Spa  VRDeck                Name  \\\n",
       "7           0.0        0.0           0.0  0.0     NaN  Candra Jacostaffey   \n",
       "10          0.0        0.0           NaN  0.0     0.0      Altardr Flatic   \n",
       "15         32.0        0.0         876.0  0.0     0.0       Justie Pooles   \n",
       "16       1286.0      122.0           NaN  0.0     0.0         Flats Eccle   \n",
       "23          0.0        0.0           NaN  0.0     0.0  Mollen Mcfaddennon   \n",
       "\n",
       "    Transported  \n",
       "7          True  \n",
       "10         True  \n",
       "15        False  \n",
       "16        False  \n",
       "23        False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1610e5a-9a05-4f26-a669-9519c972ad5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a preprocessing function to transform our dataset\n",
    "def preprocessing(df): \n",
    "  # Fill missing values in homeplanet with missing \n",
    "  df['HomePlanet'].fillna('Missing', inplace=True)\n",
    "  # Cryosleep - highly correlated - drop na rows\n",
    "  df['CryoSleep'].fillna('Missing', inplace=True)\n",
    "   \n",
    "  \n",
    "  \n",
    "    \n",
    "  # Destination \n",
    "  df['Destination'].fillna('Missing', inplace=True)\n",
    "  # Age \n",
    "  df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
    "  # VIP - drop na rows\n",
    "  df['VIP'].fillna('Missing', inplace=True)\n",
    "  # Monetary spending columns \n",
    "  df['RoomService'].fillna(0, inplace=True)\n",
    "  df['FoodCourt'].fillna(0, inplace=True) \n",
    "  df['ShoppingMall'].fillna(0, inplace=True)\n",
    "  df['Spa'].fillna(0, inplace=True)\n",
    "  df['VRDeck'].fillna(0, inplace=True)\n",
    "  # Drop name due to high cardinality\n",
    "  df.drop('Name', axis=1, inplace=True)\n",
    "  # Drop remaining rows\n",
    "  #df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d6bc58f5-32eb-4471-8184-e6c8ac9ec535",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7223d29c-6a15-4c15-ad90-f3ccc611a00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "abt = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "68c77d0c-f127-4053-ba12-9b8c78259469",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing(abt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "944c164f-05aa-40bc-a2e4-9a42be0587e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature columns\n",
    "# Drop identifier column\n",
    "X = abt.drop(['Transported', 'PassengerId'], axis=1)\n",
    "# One hot encode\n",
    "X = pd.get_dummies(X)\n",
    "# Create target columns\n",
    "y = abt['Transported']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28fedb43-5348-4232-b303-c1a715ce616b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and testing partitions\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9b204be-1a6a-44b1-8420-3f5749d95c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "pipelines = {\n",
    "    'rf': make_pipeline(StandardScaler(), RandomForestClassifier(random_state=1234)),\n",
    "    'gb': make_pipeline(StandardScaler(), GradientBoostingClassifier(random_state=1234))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a27f3197-65b4-44c8-9468-99aa3f71a927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'deviance',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_iter_no_change': None,\n",
       " 'random_state': None,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GradientBoostingClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d6ffd35-51e8-4df0-9daf-fe387c879198",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = {\n",
    "    'rf': {\n",
    "        'randomforestclassifier__n_estimators':[100,200,300]\n",
    "    },\n",
    "    'gb':{\n",
    "        'gradientboostingclassifier__n_estimators':[100,200,300]\n",
    "    } \n",
    "}\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3441731f-7ded-4702-91d6-ac78c44a0cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('rf', Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('randomforestclassifier',\n",
       "                 RandomForestClassifier(random_state=1234))])), ('gb', Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('gradientboostingclassifier',\n",
       "                 GradientBoostingClassifier(random_state=1234))]))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelines.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9245d562-3c57-4a52-966d-b33f3fcdd516",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fa4ae0e-59ce-4ed9-a7ef-b0e2e48f324a",
   "metadata": {},
   "outputs": [],
   "source": [
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4e86f67-ad53-4815-89de-48b7f7c5e130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the rf model.\n",
      "Training the gb model.\n"
     ]
    }
   ],
   "source": [
    "# Create a blank dictionary to hold the models \n",
    "fit_models = {}\n",
    "# Loop through all the algos \n",
    "for algo, pipeline in pipelines.items():\n",
    "  print(f'Training the {algo} model.')\n",
    "  # Create new Grid Search CV Cclass \n",
    "  model = GridSearchCV(pipeline, grid[algo], n_jobs=-1, cv=10)\n",
    "  # Train the model \n",
    "  model.fit(X_train, y_train)\n",
    "  # Store results inside of the dictionary\n",
    "  fit_models[algo] = model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba30de61-05ab-4877-8a03-11a54df2daa6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "402332fc-ef16-4439-8e14-b10b7dfb9188",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for algo, model in fit_models.items(): \n",
    "  yhat = model.predict(X_test)\n",
    "  accuracy = accuracy_score(y_test, yhat)\n",
    "  precision = precision_score(y_test, yhat)\n",
    "  recall = recall_score(y_test, yhat)\n",
    "  print(f'Metrics for {algo}: accuracy- {accuracy}, recall- {recall}, precision- {precision}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60a9a814-7e60-4380-af8b-61a671c3e43e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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