Spaceship_Titanic.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPeNc1wNLWTtx0teTLZKTeK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Keerthana-singupuram/Spaceship-Titanic-Project-using-Machine-Learning./blob/main/Spaceship_Titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **The Spaceship Titanic**\n",
        "\n",
        "\n",
        "![download.jpg](data:image/jpeg;base64,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)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "The data is provided in three different files in csv format which includes multiple factors regarding the passengers of the spaceship titanic. They are *train.csv, test.csv* and *sample_submission.csv*\n",
        "\n",
        "Where the **objective** is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly.\n",
        "\n",
        "The column details of the dataset are as follows : \n",
        "\n",
        "**1.** ***train.csv*** - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.\n",
        "* **PassengerId** - A unique Id for each passenger. Each Id takes the form *gggg_pp* where *gggg* indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.\n",
        "* **HomePlanet** - The planet the passenger departed from, typically their planet of permanent residence.\n",
        "* **CryoSleep** - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.\n",
        "* **Cabin** - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.\n",
        "* **Destination** - The planet the passenger will be debarking to.\n",
        "* **Age** - The age of the passenger.\n",
        "* **VIP** - Whether the passenger has paid for special VIP service during the voyage.\n",
        "* **RoomService, FoodCourt, ShoppingMall, Spa, VRDeck** - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.\n",
        "* **Name** - The first and last names of the passenger.\n",
        "* **Transported** - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.\n",
        "\n",
        "\n",
        "***2. test.csv*** - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of Transported for the passengers in this set.\n",
        "\n",
        "\n",
        "***3. sample_submission.csv*** - A submission file in the correct format.\n",
        "* **PassengerId** - Id for each passenger in the test set.\n",
        "* **Transported** - The target. For each passenger, predict either True or False.\n",
        "\n",
        "**Tools used** **:**\n",
        "* Python - Numpy, Pandas, Scikit-learn, Matplotlib libraries.\n",
        "\n",
        "\n",
        "**Content** **:** \n",
        "* Imputing null values.\n",
        "* Exploratory data analysis.\n",
        "* Feature engineering.\n",
        "* Model building.\n",
        "* Model tuning."
      ],
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        "id": "O9G-raSV0j7E"
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      "cell_type": "markdown",
      "source": [
        "### **Importing Libraries :**"
      ],
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        "id": "y7kJwXOvgYgO"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zhDmCWsTdcVe"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score"
      ]
    },
    {
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      "source": [],
      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Importing Data :**"
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      }
    },
    {
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      "source": [
        "data1=pd.read_csv('/content/train.csv')"
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              "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
              "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
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              "   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",
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              "\n",
              "   Transported  \n",
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              "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
              "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
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              "    .colab-df-convert:hover {\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "execution_count": 170
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      "cell_type": "code",
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      "metadata": {
        "id": "PUfDsbmIfus-"
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      "execution_count": null,
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      "source": [
        "### **Basic Investigation on data :**"
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        "id": "EafHzqfP6gj9"
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        "id": "8IVMI2dg8vXn",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8693, 14)"
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          "metadata": {},
          "execution_count": 171
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    {
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        "data1.columns"
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        "id": "D2d91ya386PH",
        "outputId": "409ff7e1-0049-4863-be23-a508c4a060f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',\n",
              "       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',\n",
              "       'Name', 'Transported'],\n",
              "      dtype='object')"
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          "execution_count": 172
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "jjfDy_ydgG8M"
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      "execution_count": null,
      "outputs": []
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        "data1.describe()"
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        "id": "l2nM1Ppy8yEB",
        "outputId": "94b02e62-8031-47d3-ad50-4bd9baaaaf60"
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      "execution_count": null,
      "outputs": [
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            "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",
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              "mean     304.854791  \n",
              "std     1145.717189  \n",
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              "      <th>mean</th>\n",
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              "    <tr>\n",
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              "      <td>79.000000</td>\n",
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              "      <td>29813.000000</td>\n",
              "      <td>23492.000000</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "<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",
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            " 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"
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              "Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',\n",
              "       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',\n",
              "       'Name'],\n",
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      "execution_count": null,
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              "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
              "count  4186.000000   4195.000000   4171.000000   4179.000000   4176.000000   \n",
              "mean     28.658146    219.266269    439.484296    177.295525    303.052443   \n",
              "std      14.179072    607.011289   1527.663045    560.821123   1117.186015   \n",
              "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
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              "max      79.000000  11567.000000  25273.000000   8292.000000  19844.000000   \n",
              "\n",
              "             VRDeck  \n",
              "count   4197.000000  \n",
              "mean     310.710031  \n",
              "std     1246.994742  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%       36.000000  \n",
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              "      <td>4179.000000</td>\n",
              "      <td>4176.000000</td>\n",
              "      <td>4197.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28.658146</td>\n",
              "      <td>219.266269</td>\n",
              "      <td>439.484296</td>\n",
              "      <td>177.295525</td>\n",
              "      <td>303.052443</td>\n",
              "      <td>310.710031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.179072</td>\n",
              "      <td>607.011289</td>\n",
              "      <td>1527.663045</td>\n",
              "      <td>560.821123</td>\n",
              "      <td>1117.186015</td>\n",
              "      <td>1246.994742</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>26.000000</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>37.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>79.000000</td>\n",
              "      <td>11567.000000</td>\n",
              "      <td>25273.000000</td>\n",
              "      <td>8292.000000</td>\n",
              "      <td>19844.000000</td>\n",
              "      <td>22272.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3333058-0b0c-46b2-806d-4fe88c8cfd9d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3333058-0b0c-46b2-806d-4fe88c8cfd9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3333058-0b0c-46b2-806d-4fe88c8cfd9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oA3DdNdSfk3L",
        "outputId": "4847a5a2-d576-41ac-d9a1-ca3714dded61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data1.iloc[:,[2,3,5,6,7,10,11,13]]\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QT8TdX2QfpK7",
        "outputId": "96926b4f-d66a-43a1-cfba-bb18a755811c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CryoSleep     Cabin   Age    VIP  RoomService     Spa  VRDeck  \\\n",
              "0        False     B/0/P  39.0  False          0.0     0.0     0.0   \n",
              "1        False     F/0/S  24.0  False        109.0   549.0    44.0   \n",
              "2        False     A/0/S  58.0   True         43.0  6715.0    49.0   \n",
              "3        False     A/0/S  33.0  False          0.0  3329.0   193.0   \n",
              "4        False     F/1/S  16.0  False        303.0   565.0     2.0   \n",
              "...        ...       ...   ...    ...          ...     ...     ...   \n",
              "8688     False    A/98/P  41.0   True          0.0  1643.0    74.0   \n",
              "8689      True  G/1499/S  18.0  False          0.0     0.0     0.0   \n",
              "8690     False  G/1500/S  26.0  False          0.0     1.0     0.0   \n",
              "8691     False   E/608/S  32.0  False          0.0   353.0  3235.0   \n",
              "8692     False   E/608/S  44.0  False        126.0     0.0    12.0   \n",
              "\n",
              "      Transported  \n",
              "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",
              "\n",
              "[8693 rows x 8 columns]"
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              "\n",
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              "      <th>CryoSleep</th>\n",
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              "      <th>2</th>\n",
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              "      <td>16.0</td>\n",
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              "      <td>True</td>\n",
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              "      <td>0.0</td>\n",
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              "  </tbody>\n",
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              "<p>8693 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14438585-8164-483e-bb97-7076f3e5b890')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14438585-8164-483e-bb97-7076f3e5b890 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14438585-8164-483e-bb97-7076f3e5b890');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.mode.chained_assignment=None"
      ],
      "metadata": {
        "id": "_kf_1OoSgJk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Time to encode the label :**"
      ],
      "metadata": {
        "id": "Xs6jpqtXgPH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "l1=LabelEncoder()\n",
        "data['CryoSleep']=l1.fit_transform(data['CryoSleep'])\n",
        "data['Cabin']=l1.fit_transform(data['Cabin'])\n",
        "data['VIP']=l1.fit_transform(data['VIP'])"
      ],
      "metadata": {
        "id": "F0kAIiMVgMFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtQezhvpg17L",
        "outputId": "3a8aebaa-77ad-4d3d-b451-f87be5dba777"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8693 entries, 0 to 8692\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   CryoSleep    8693 non-null   int64  \n",
            " 1   Cabin        8693 non-null   int64  \n",
            " 2   Age          8514 non-null   float64\n",
            " 3   VIP          8693 non-null   int64  \n",
            " 4   RoomService  8512 non-null   float64\n",
            " 5   Spa          8510 non-null   float64\n",
            " 6   VRDeck       8505 non-null   float64\n",
            " 7   Transported  8693 non-null   bool   \n",
            "dtypes: bool(1), float64(4), int64(3)\n",
            "memory usage: 484.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JI6I6Rn9gwM9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VlkOAx8DgwA4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dealing with null values :**"
      ],
      "metadata": {
        "id": "ZSTK8xTQhAtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLWPqSi4hFGq",
        "outputId": "e9c8ad48-cc06-47d6-ccc8-5551f1aea61d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep      False\n",
              "Cabin          False\n",
              "Age             True\n",
              "VIP            False\n",
              "RoomService     True\n",
              "Spa             True\n",
              "VRDeck          True\n",
              "Transported    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BY6_FxsbhL6R",
        "outputId": "c9941f32-c35e-47c9-de62-199e51cfec3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep        0\n",
              "Cabin            0\n",
              "Age            179\n",
              "VIP              0\n",
              "RoomService    181\n",
              "Spa            183\n",
              "VRDeck         188\n",
              "Transported      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZtHjP8vFhWZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Age'].fillna(data['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "8haTzeoQhdVj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['RoomService'].fillna(data['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "JQ1JcRpnheyk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Spa'].fillna(data['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "j_r05hP0jpcK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['VRDeck'].fillna(data['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "2TdE4inBjtFS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UgH6OQQRhXAt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsDk4nWnj5ZU",
        "outputId": "3ab3c087-44da-427e-9cb6-41a734354a47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8693 entries, 0 to 8692\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   CryoSleep    8693 non-null   int64  \n",
            " 1   Cabin        8693 non-null   int64  \n",
            " 2   Age          8693 non-null   float64\n",
            " 3   VIP          8693 non-null   int64  \n",
            " 4   RoomService  8693 non-null   float64\n",
            " 5   Spa          8693 non-null   float64\n",
            " 6   VRDeck       8693 non-null   float64\n",
            " 7   Transported  8693 non-null   bool   \n",
            "dtypes: bool(1), float64(4), int64(3)\n",
            "memory usage: 484.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Perfect! No more nulls, lets check the behaviour of the data set.*"
      ],
      "metadata": {
        "id": "STIOhZy7kLWd"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c1orANEFhYQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N0a_n9nMdzwa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "alm0wSvbtFmM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Exploratory Data Analysis** "
      ],
      "metadata": {
        "id": "NX2mWnhekqpI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lC5upacj_Wow",
        "outputId": "d9594bff-ec9d-493f-e02d-e1c302c99dfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         CryoSleep        Cabin          Age          VIP   RoomService  \\\n",
              "count  8693.000000  8693.000000  8693.000000  8693.000000   8693.000000   \n",
              "mean      0.399287  3227.857702    28.827930     0.069596    220.609554   \n",
              "std       0.538345  2018.301775    14.339054     0.333871    660.331898   \n",
              "min       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
              "25%       0.000000  1341.000000    20.000000     0.000000      0.000000   \n",
              "50%       0.000000  3218.000000    27.000000     0.000000      0.000000   \n",
              "75%       1.000000  5018.000000    37.000000     0.000000     41.000000   \n",
              "max       2.000000  6560.000000    79.000000     2.000000  14327.000000   \n",
              "\n",
              "                Spa        VRDeck  \n",
              "count   8693.000000   8693.000000  \n",
              "mean     305.195733    298.885270  \n",
              "std     1125.405913   1133.970181  \n",
              "min        0.000000      0.000000  \n",
              "25%        0.000000      0.000000  \n",
              "50%        0.000000      0.000000  \n",
              "75%       53.000000     40.000000  \n",
              "max    22408.000000  24133.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09aa79b5-7a98-430e-bfdc-2a533cecfa89\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CryoSleep</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Age</th>\n",
              "      <th>VIP</th>\n",
              "      <th>RoomService</th>\n",
              "      <th>Spa</th>\n",
              "      <th>VRDeck</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "      <td>8693.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.399287</td>\n",
              "      <td>3227.857702</td>\n",
              "      <td>28.827930</td>\n",
              "      <td>0.069596</td>\n",
              "      <td>220.609554</td>\n",
              "      <td>305.195733</td>\n",
              "      <td>298.885270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.538345</td>\n",
              "      <td>2018.301775</td>\n",
              "      <td>14.339054</td>\n",
              "      <td>0.333871</td>\n",
              "      <td>660.331898</td>\n",
              "      <td>1125.405913</td>\n",
              "      <td>1133.970181</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",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1341.000000</td>\n",
              "      <td>20.000000</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>0.000000</td>\n",
              "      <td>3218.000000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>5018.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6560.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14327.000000</td>\n",
              "      <td>22408.000000</td>\n",
              "      <td>24133.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09aa79b5-7a98-430e-bfdc-2a533cecfa89')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-09aa79b5-7a98-430e-bfdc-2a533cecfa89 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09aa79b5-7a98-430e-bfdc-2a533cecfa89');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "64aL-k7tfwxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(data['Spa'],vert=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "oVqb9rfoBsUG",
        "outputId": "a4921438-0d07-48b1-c2dc-ad30d164d654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f0888ea3d00>,\n",
              "  <matplotlib.lines.Line2D at 0x7f0888ea3fd0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f0888eb12e0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f0888eb15b0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f0888ea3b50>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f0888eb1880>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f0888eb1b50>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NHx15ERkfxd8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i2wzKYeTgCL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(data[\"CryoSleep\"],color = 'red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "IbB77bn4foeC",
        "outputId": "8002c6e8-8269-459b-a60b-1ec68bd90833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='CryoSleep', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 240
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4Gio1NcJnIfD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=data['CryoSleep']\n",
        "y=data['Cabin']\n",
        "sns.jointplot(x,y,kind='hex',color='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "ru9GDE75FDZP",
        "outputId": "6210e6ca-6be3-464c-d224-ef561c141254"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f0888ec4220>"
            ]
          },
          "metadata": {},
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qEqovv2Gn9er"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in data.iloc[:,:-1].columns:\n",
        "    if data.iloc[:,:-1][col].dtypes != 'object':\n",
        "        q1 , q3 =data.iloc[:,:-1][col].quantile(0.25),data.iloc[:,:-1][col].quantile(0.75)\n",
        "        iqr = q3 - q1\n",
        "        ll = q1-1.5*iqr\n",
        "        ul = q3 + 1.5*iqr\n",
        "\n",
        "        data[\"Spa\"] = np.where(data[\"Spa\"]>ul,data[\"Spa\"].mean(),np.where(data[\"Spa\"]<ll,data[\"Spa\"].mean(),data[\"Spa\"]))\n",
        "\n",
        "        data[\"Age\"] = np.where(data[\"Age\"]>ul,data[\"Age\"].mean(),np.where(data[\"Age\"]<ll,data[\"Age\"].mean(),data[\"Age\"]))  \n",
        "\n",
        "        data[\"Cabin\"] = np.where(data[\"Cabin\"]>ul,data[\"Cabin\"].mean(),np.where(data[\"Cabin\"]<ll,data[\"Cabin\"].mean(),data[\"Cabin\"]))\n",
        "\n",
        "        data[\"VIP\"] = np.where(data[\"VIP\"]>ul,data[\"VIP\"].mean(),np.where(data[\"VIP\"]<ll,data[\"VIP\"].mean(),data[\"VIP\"]))  \n",
        "\n",
        "        data[\"RoomService\"] = np.where(data[\"RoomService\"]>ul,data[\"RoomService\"].mean(),np.where(data[\"RoomService\"]<ll,data[\"RoomService\"].mean(),data[\"RoomService\"]))\n",
        "\n",
        "        data[\"VRDeck\"] = np.where(data[\"VRDeck\"]>ul,data[\"VRDeck\"].mean(),np.where(data[\"VRDeck\"]<ll,data[\"VRDeck\"].mean(),data[\"VRDeck\"]))"
      ],
      "metadata": {
        "id": "bmnKU586lP6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q1=data.iloc[:,:-1].quantile(0.25)\n",
        "Q3=data.iloc[:,:-1].quantile(0.75)\n",
        "IQR=Q3-Q1\n",
        "IQR\n",
        "print(\"The count of outliers of the all factors is : \")\n",
        "((data.iloc[:,:-1] < (Q1 - 1.5 * IQR)) | (data.iloc[:,:-1]> (Q3 + 1.5 * IQR))).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hibekqQ8mInW",
        "outputId": "9ede5e34-99a1-4099-a853-b8b6ffcd3b06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The count of outliers of the all factors is : \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep      0\n",
              "Cabin          0\n",
              "Age            0\n",
              "VIP            0\n",
              "RoomService    0\n",
              "Spa            0\n",
              "VRDeck         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.iloc[:,:-1].values\n",
        "y=data.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "_zddXRTqmdBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bar=data['Transported'].value_counts()\n",
        "bar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fp0lhunmmf8",
        "outputId": "5256c495-ad1f-4b5e-9afe-1465bf9c3134"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     4378\n",
              "False    4315\n",
              "Name: Transported, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Generation of equal ratio of classes :**"
      ],
      "metadata": {
        "id": "2rUsFeH2ohys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import NearMiss\n",
        "nm=NearMiss()\n",
        "x_data,y_data=nm.fit_resample(x,y)"
      ],
      "metadata": {
        "id": "lbpWLECaon9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Count of hashable objects :**"
      ],
      "metadata": {
        "id": "6QlNw_IUpH7R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(Counter(y_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaWNoh6QowrK",
        "outputId": "ae552059-e7c1-4259-b2a8-404655e6a0dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({False: 4315, True: 4315})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data Normalization :**"
      ],
      "metadata": {
        "id": "2wPd9EOXpS0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ssd=StandardScaler()\n",
        "x_scaled=ssd.fit_transform(x_data)\n",
        "x_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkRHbNALprbA",
        "outputId": "a3274f0e-fb11-4387-d6df-4f5d00949155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-7.39890343e-01, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11],\n",
              "       [-7.39890343e-01, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11],\n",
              "       [-7.39890343e-01, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11],\n",
              "       ...,\n",
              "       [ 1.12061480e+00, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11],\n",
              "       [-7.39890343e-01, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11],\n",
              "       [ 1.12061480e+00, -2.15095497e-10,  3.73745479e-12, ...,\n",
              "        -5.96855898e-12, -6.66489086e-12,  1.86446414e-11]])"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x_scaled,y_data,test_size=0.2,random_state=20)"
      ],
      "metadata": {
        "id": "GFHu2E9jpvJf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Machine Learning Algorithms :**"
      ],
      "metadata": {
        "id": "L4vgEDSdp7eJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***1.*** ***Naive Bayes***"
      ],
      "metadata": {
        "id": "PDrxna2Zqud2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb=GaussianNB()\n",
        "gnb.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SsW44xwQq7zk",
        "outputId": "edd236e6-a11c-451d-b055-1942f91591fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gnb_pred=gnb.predict(x_test)\n",
        "gnb_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWUgMyearKyD",
        "outputId": "31005b39-8c73-4c48-b8c3-bf226d5f9612"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False, False, ..., False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,gnb_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvoqBnCgrSv-",
        "outputId": "e2ee5745-26c1-4bf8-df6b-e9056ede64df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71.32097334878331"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X5arylZxr1cm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***2.*** ***Random Forest*** \n",
        "\n"
      ],
      "metadata": {
        "id": "bXcJTlwbqRCM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc=RandomForestClassifier()\n",
        "rfc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GnS5QdKCpzTU",
        "outputId": "ce825b90-cfbe-4db4-d6a0-0fd523ae4508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_pred=rfc.predict(x_test)\n",
        "rfc_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEi5OzwYqGk8",
        "outputId": "46a8e1ef-b10c-4b3c-8f8d-5fda01edb697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False, False, ..., False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "accuracy_score(y_test,rfc_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foH8icQoqK-T",
        "outputId": "a75ec04c-45f5-4590-8d4d-8e471a410e98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71.32097334878331"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***3.*** ***Decision Tree***"
      ],
      "metadata": {
        "id": "UTtsezNarBhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtc=DecisionTreeClassifier()\n",
        "dtc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "PISUIJwCqoy9",
        "outputId": "bf3dddf5-1232-4c8c-b544-7dd9045ccd7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dtc_pred=dtc.predict(x_test)\n",
        "dtc_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JIjjRRIseMJ",
        "outputId": "06bba2ec-10fe-4365-c6f4-049e41af6ccd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False, False, ..., False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,dtc_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsExEzlBskk0",
        "outputId": "f26e0248-16b7-4bd6-fb24-a97cee736f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71.84241019698725"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x97acruylg_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zc7kCiMQmak9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Optimization**\n",
        "### ***Ensemble Method :***"
      ],
      "metadata": {
        "id": "D5cybLv5tSRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "vcr=VotingClassifier(estimators=[(\"Random_Forest\",rfc),(\"Decision_Tree\",dtc),(\"Naive_Bayes\",gnb)])\n",
        "vcr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "ZYMo-8FitlXp",
        "outputId": "1a1570d6-6e06-4360-c2ce-89435c5648ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('Random_Forest', RandomForestClassifier()),\n",
              "                             ('Decision_Tree', DecisionTreeClassifier()),\n",
              "                             ('Naive_Bayes', GaussianNB())])"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;Random_Forest&#x27;, RandomForestClassifier()),\n",
              "                             (&#x27;Decision_Tree&#x27;, DecisionTreeClassifier()),\n",
              "                             (&#x27;Naive_Bayes&#x27;, GaussianNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;Random_Forest&#x27;, RandomForestClassifier()),\n",
              "                             (&#x27;Decision_Tree&#x27;, DecisionTreeClassifier()),\n",
              "                             (&#x27;Naive_Bayes&#x27;, GaussianNB())])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>Random_Forest</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>Decision_Tree</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>Naive_Bayes</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vcr_pred=vcr.predict(x_test)\n",
        "vcr_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EumC5drhuCst",
        "outputId": "81736503-7878-40ef-fd4d-10946cb6d62e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False, False, ..., False,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,vcr_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ld1I6uTSuHnf",
        "outputId": "2676b5bd-0799-4dc2-c8bd-f182fc5f7b39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71.32097334878331"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LLJRM4cmupYc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Conversion of predicted values to a data frame :***"
      ],
      "metadata": {
        "id": "QjWyPRhquSvl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_result=pd.DataFrame(np.c_[y_test,vcr_pred],columns=[\"Actual\",\"Transported\"])\n",
        "training_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jPWq-twPuPM4",
        "outputId": "eb8803d2-aeca-4055-8342-064d0ba3da5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Actual  Transported\n",
              "0       True         True\n",
              "1       True        False\n",
              "2      False        False\n",
              "3      False        False\n",
              "4       True         True\n",
              "...      ...          ...\n",
              "1721   False         True\n",
              "1722   False        False\n",
              "1723   False        False\n",
              "1724   False         True\n",
              "1725    True         True\n",
              "\n",
              "[1726 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d928d258-05de-4df6-84c5-62d8dbde631d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Actual</th>\n",
              "      <th>Transported</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1721</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1726 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d928d258-05de-4df6-84c5-62d8dbde631d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d928d258-05de-4df6-84c5-62d8dbde631d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d928d258-05de-4df6-84c5-62d8dbde631d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H7JpPQhsj_3c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_result['Transported'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYGe_xhrugMf",
        "outputId": "cbce5577-3743-4773-c649-a53177e99769"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1110\n",
              "True      616\n",
              "Name: Transported, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BPiFSZM6vVuL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data=test_data.iloc[:,[2,3,5,6,7,10,11]]\n",
        "testing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GOglBYeMulaP",
        "outputId": "7e99d2bf-406c-4df8-8e4a-83e5e3c3edb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CryoSleep     Cabin   Age    VIP  RoomService     Spa  VRDeck\n",
              "0         True     G/3/S  27.0  False          0.0     0.0     0.0\n",
              "1        False     F/4/S  19.0  False          0.0  2823.0     0.0\n",
              "2         True     C/0/S  31.0  False          0.0     0.0     0.0\n",
              "3        False     C/1/S  38.0  False          0.0   181.0   585.0\n",
              "4        False     F/5/S  20.0  False         10.0     0.0     0.0\n",
              "...        ...       ...   ...    ...          ...     ...     ...\n",
              "4272      True  G/1496/S  34.0  False          0.0     0.0     0.0\n",
              "4273     False       NaN  42.0  False          0.0    10.0   144.0\n",
              "4274      True   D/296/P   NaN  False          0.0     0.0     0.0\n",
              "4275     False   D/297/P   NaN  False          0.0     0.0   523.0\n",
              "4276      True  G/1498/S  43.0  False          0.0     0.0     0.0\n",
              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RtuUWOabk4VY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WA0Su9WTu9ue",
        "outputId": "15e3e0bc-c420-476e-bffe-903453930e2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4277 entries, 0 to 4276\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   CryoSleep    4184 non-null   object \n",
            " 1   Cabin        4177 non-null   object \n",
            " 2   Age          4186 non-null   float64\n",
            " 3   VIP          4184 non-null   object \n",
            " 4   RoomService  4195 non-null   float64\n",
            " 5   Spa          4176 non-null   float64\n",
            " 6   VRDeck       4197 non-null   float64\n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 234.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Normalization of labels :**"
      ],
      "metadata": {
        "id": "uEV0xl6gvhLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "l1=LabelEncoder()\n",
        "testing_data['CryoSleep']=l1.fit_transform(testing_data['CryoSleep'])\n",
        "testing_data['Cabin']=l1.fit_transform(testing_data['Cabin'])\n",
        "testing_data['VIP']=l1.fit_transform(testing_data['VIP'])"
      ],
      "metadata": {
        "id": "-zxPJAY5vDYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Dealing with nulls in testing data :***"
      ],
      "metadata": {
        "id": "s_XETS7Kv-FL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "C7ZO8IGtvJO7",
        "outputId": "153349c9-9dc8-416e-b873-f6b7467b7390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5f8afcc-0278-472e-8686-0101f23c6335 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5f8afcc-0278-472e-8686-0101f23c6335');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlfcekAHvRSh",
        "outputId": "61a9adbc-250f-40f1-dd53-02717fe9b845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep      False\n",
              "Cabin          False\n",
              "Age             True\n",
              "VIP            False\n",
              "RoomService     True\n",
              "Spa             True\n",
              "VRDeck          True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TE--434zvYQB",
        "outputId": "6e8b7800-92b4-4391-b410-a814787c1415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep        0\n",
              "Cabin            0\n",
              "Age             91\n",
              "VIP              0\n",
              "RoomService     82\n",
              "Spa            101\n",
              "VRDeck          80\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4T3J5xGTwdxS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data['Age'].fillna(testing_data['Age'].mean(),inplace=True)\n",
        "testing_data['Spa'].fillna(testing_data['Spa'].mean(),inplace=True)\n",
        "testing_data['VRDeck'].fillna(testing_data['VRDeck'].mean(),inplace=True)\n",
        "testing_data['RoomService'].fillna(testing_data['RoomService'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "f5h3IGobvdDl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xC57LdAEwwIV",
        "outputId": "0a21289e-4c9d-4c82-99f3-1365a8659bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4277 entries, 0 to 4276\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   CryoSleep    4277 non-null   int64  \n",
            " 1   Cabin        4277 non-null   int64  \n",
            " 2   Age          4277 non-null   float64\n",
            " 3   VIP          4277 non-null   int64  \n",
            " 4   RoomService  4277 non-null   float64\n",
            " 5   Spa          4277 non-null   float64\n",
            " 6   VRDeck       4277 non-null   float64\n",
            "dtypes: float64(4), int64(3)\n",
            "memory usage: 234.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Perfect! No more nulls here.*"
      ],
      "metadata": {
        "id": "aZXjaGYExJei"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Outlier Detection :***"
      ],
      "metadata": {
        "id": "qTgFf_eQygz8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(testing_data.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "j1gKQCzUwv4c",
        "outputId": "6a76a9f3-0e28-4a2b-9af1-23aac4a32c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 230
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1=testing_data.quantile(0.25)\n",
        "Q3=testing_data.quantile(0.75)\n",
        "IQR=Q3-Q1\n",
        "IQR\n",
        "print(\"The total count of outliers is : \")\n",
        "((testing_data < (Q1 - 1.5 * IQR)) | (testing_data> (Q3 + 1.5 * IQR))).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNowo0UCwvZE",
        "outputId": "aa6816a6-ab89-417a-c660-89108844b6db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total count of outliers is : \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CryoSleep        0\n",
              "Cabin            0\n",
              "Age             75\n",
              "VIP            167\n",
              "RoomService    908\n",
              "Spa            908\n",
              "VRDeck         907\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nn7w3U2dvr3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Data Standardization :***"
      ],
      "metadata": {
        "id": "_DcSvbsmzSpC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss=StandardScaler()\n",
        "test_scaled=ss.fit_transform(testing_data)\n",
        "test_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_AcVaAjzD_W",
        "outputId": "c570952d-6417-4c84-c705-05f965116286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.11673669e+00,  1.15821732e+00, -1.18221624e-01, ...,\n",
              "        -3.64779540e-01, -2.74557735e-01, -2.51560551e-01],\n",
              "       [-7.58521582e-01,  2.51716199e-01, -6.88601357e-01, ...,\n",
              "        -3.64779540e-01,  2.28300781e+00, -2.51560551e-01],\n",
              "       [ 1.11673669e+00, -1.33985065e+00,  1.66968243e-01, ...,\n",
              "        -3.64779540e-01, -2.74557735e-01, -2.51560551e-01],\n",
              "       ...,\n",
              "       [ 1.11673669e+00, -9.98800607e-01, -2.53299485e-16, ...,\n",
              "        -3.64779540e-01, -2.74557735e-01, -2.51560551e-01],\n",
              "       [-7.58521582e-01, -9.97812057e-01, -2.53299485e-16, ...,\n",
              "        -3.64779540e-01, -2.74557735e-01,  1.71876593e-01],\n",
              "       [ 1.11673669e+00,  1.05540803e+00,  1.02253784e+00, ...,\n",
              "        -3.64779540e-01, -2.74557735e-01, -2.51560551e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(test_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "oPu_-rsGzjJK",
        "outputId": "a9f33d72-5945-4db5-b491-2791550f1dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 233
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vcr_pred_test=vcr.predict(test_scaled)\n",
        "vcr_pred_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1F-DHiEzqyR",
        "outputId": "cd6fba9f-8b59-434a-a202-abc1298e1e74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False,  True, ...,  True, False,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Conversion of predicted values to a dataframe :***"
      ],
      "metadata": {
        "id": "jdv5pVkDz66_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_result=pd.DataFrame(np.c_[test_data['PassengerId'],vcr_pred_test],columns=[\"PassengerId\",\"Transported\"])\n",
        "test_result.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oyxRS8SqzuxH",
        "outputId": "fd4b94bc-6b2b-4048-c5e2-f979837ce9e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  PassengerId Transported\n",
              "0     0013_01        True\n",
              "1     0018_01       False\n",
              "2     0019_01        True\n",
              "3     0021_01       False\n",
              "4     0023_01       False"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f335e78-2f49-41ac-87b4-d44c579ada6b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Transported</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0013_01</td>\n",
              "      <td>True</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>True</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f335e78-2f49-41ac-87b4-d44c579ada6b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f335e78-2f49-41ac-87b4-d44c579ada6b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3f335e78-2f49-41ac-87b4-d44c579ada6b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_result['Transported'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBao9MVY0DKU",
        "outputId": "9aa4c6a0-f0e8-4f6e-cf95-4ef481766db0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([True, False], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_result['Transported'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QghRtYy30H-0",
        "outputId": "7cea4c0b-c262-4a82-efbf-bb52a6202911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    2640\n",
              "True     1637\n",
              "Name: Transported, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_result.to_csv(\"Titanic Spaceship Project.csv\")"
      ],
      "metadata": {
        "id": "LEpf_nQF0MXW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}