Spaceship_Titanic_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Spaceship Titanic**"
      ],
      "metadata": {
        "id": "W-mgbViyBNFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#------Importing libraries------->\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "PJtdW-kmBTpU"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#------Importing Data------->\n",
        "train=pd.read_csv(\"/content/drive/MyDrive/Spaceship titanic/train.csv\")\n",
        "test = pd.read_csv(\"/content/drive/MyDrive/Spaceship titanic/test.csv\")\n",
        "sample=pd.read_csv(\"/content/drive/MyDrive/Spaceship titanic/sample_submission.csv\")"
      ],
      "metadata": {
        "id": "ypopp8GSGDSt"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#------Checking Data------->\n",
        "print(train.head(),\"\\n\")\n",
        "print(train.isna().sum(),\"\\n\\n\")\n",
        "print(test.head(),\"\\n\")\n",
        "print(test.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5ICysNrHE3I",
        "outputId": "3b007528-5e13-4db0-b96e-7a55a9439d95"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
            "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
            "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
            "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
            "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
            "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
            "\n",
            "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
            "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
            "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
            "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
            "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
            "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
            "\n",
            "   Transported  \n",
            "0        False  \n",
            "1         True  \n",
            "2        False  \n",
            "3        False  \n",
            "4         True   \n",
            "\n",
            "PassengerId       0\n",
            "HomePlanet      201\n",
            "CryoSleep       217\n",
            "Cabin           199\n",
            "Destination     182\n",
            "Age             179\n",
            "VIP             203\n",
            "RoomService     181\n",
            "FoodCourt       183\n",
            "ShoppingMall    208\n",
            "Spa             183\n",
            "VRDeck          188\n",
            "Name            200\n",
            "Transported       0\n",
            "dtype: int64 \n",
            "\n",
            "\n",
            "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
            "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
            "1     0018_01      Earth     False  F/4/S  TRAPPIST-1e  19.0  False   \n",
            "2     0019_01     Europa      True  C/0/S  55 Cancri e  31.0  False   \n",
            "3     0021_01     Europa     False  C/1/S  TRAPPIST-1e  38.0  False   \n",
            "4     0023_01      Earth     False  F/5/S  TRAPPIST-1e  20.0  False   \n",
            "\n",
            "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
            "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
            "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
            "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
            "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
            "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez   \n",
            "\n",
            "PassengerId       0\n",
            "HomePlanet       87\n",
            "CryoSleep        93\n",
            "Cabin           100\n",
            "Destination      92\n",
            "Age              91\n",
            "VIP              93\n",
            "RoomService      82\n",
            "FoodCourt       106\n",
            "ShoppingMall     98\n",
            "Spa             101\n",
            "VRDeck           80\n",
            "Name             94\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.columns,\"\\n\")\n",
        "print(test.columns,\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIvf5pueH1-R",
        "outputId": "cce32275-d3cf-43d3-9812-f40a0594e0e6"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',\n",
            "       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',\n",
            "       'Name', 'Transported'],\n",
            "      dtype='object') \n",
            "\n",
            "Index(['PassengerId', 'HomePlanet', 'CryoSleep', 'Cabin', 'Destination', 'Age',\n",
            "       'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck',\n",
            "       'Name'],\n",
            "      dtype='object') \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#------Data Cleaning------->\n",
        "def clean(data):\n",
        "  data = data.drop(['PassengerId','Cabin','Name','RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck'],axis=1)\n",
        "  data['VIP'] = np.where(data['VIP'] == False , 0 ,1)\n",
        "  data['CryoSleep'] = np.where(data['CryoSleep'] == False , 0 ,1)\n",
        "  data['Age'].fillna(data['Age'].median() , inplace=True)\n",
        "  data = data.dropna(subset=['HomePlanet','Destination'])\n",
        "\n",
        "  return data\n",
        "\n",
        "df = clean(train)\n",
        "df['Transported'] = np.where(df['Transported'] == False , 0 , 1)\n",
        "df1 = clean(test)\n",
        "\n",
        "print(df.groupby(\"Destination\")[\"HomePlanet\"].value_counts())\n",
        "\n",
        "def code_embark(row):\n",
        "    if row == \" Europa \" or row== \"55 Cancri e\":\n",
        "        return 0\n",
        "    elif row == \"Earth\"or row== \"PSO J318.5-22\":\n",
        "        return 1\n",
        "    else:\n",
        "        return 2\n",
        "\n",
        "df['HomePlanet']  = df['HomePlanet'].apply(code_embark)\n",
        "df1['HomePlanet']  = df1['HomePlanet'].apply(code_embark)\n",
        "df['Destination']  = df['Destination'].apply(code_embark)\n",
        "df1['Destination']  = df1['Destination'].apply(code_embark)\n",
        "\n",
        "print(df.isna().sum(),\"\\n\")\n",
        "print(\"Train Data Shape -->\",df.shape,\"\\nTest Data Shape -->\",df1.shape,\"\\n\\n\")\n",
        "print(df.head(),\"\\n\\n\\n\",df1.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOk5XyEfIFv8",
        "outputId": "7394297b-e4c5-4358-afaf-27cc7fca760c"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Destination    HomePlanet\n",
            "55 Cancri e    Europa         886\n",
            "               Earth          690\n",
            "               Mars           193\n",
            "PSO J318.5-22  Earth          712\n",
            "               Mars            49\n",
            "               Europa          19\n",
            "TRAPPIST-1e    Earth         3101\n",
            "               Mars          1475\n",
            "               Europa        1189\n",
            "Name: HomePlanet, dtype: int64\n",
            "HomePlanet     0\n",
            "CryoSleep      0\n",
            "Destination    0\n",
            "Age            0\n",
            "VIP            0\n",
            "Transported    0\n",
            "dtype: int64 \n",
            "\n",
            "Train Data Shape --> (8314, 6) \n",
            "Test Data Shape --> (4100, 5) \n",
            "\n",
            "\n",
            "   HomePlanet  CryoSleep  Destination   Age  VIP  Transported\n",
            "0           2          0            2  39.0    0            0\n",
            "1           1          0            2  24.0    0            1\n",
            "2           2          0            2  58.0    1            0\n",
            "3           2          0            2  33.0    0            0\n",
            "4           1          0            2  16.0    0            1 \n",
            "\n",
            "\n",
            "    HomePlanet  CryoSleep  Destination   Age  VIP\n",
            "0           1          1            2  27.0    0\n",
            "1           1          0            2  19.0    0\n",
            "2           2          1            0  31.0    0\n",
            "3           2          0            2  38.0    0\n",
            "4           1          0            2  20.0    0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#---------------Visualization---------->\n",
        "def count_plot(feature):\n",
        "    sns.countplot(x=feature, data=df)\n",
        "    plt.show()\n",
        "    print(\"\\n\\n\")\n",
        "columns = ['HomePlanet', 'CryoSleep', 'Destination', 'Age', 'VIP', 'Transported']\n",
        "for i in columns:\n",
        "    count_plot(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JckfWcHjOwib",
        "outputId": "5cfdc8cb-1202-431d-cea2-489654e8ccb0"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.corr())\n",
        "sns.heatmap(df.corr(), annot=True, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "jj8T3E07SBYh",
        "outputId": "d96797ed-406d-48a5-8e50-1616e52c8947"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             HomePlanet  CryoSleep  Destination       Age       VIP  \\\n",
            "HomePlanet     1.000000   0.104938    -0.072476  0.204089  0.112806   \n",
            "CryoSleep      0.104938   1.000000    -0.090964 -0.077012 -0.049640   \n",
            "Destination   -0.072476  -0.090964     1.000000 -0.006870 -0.032555   \n",
            "Age            0.204089  -0.077012    -0.006870  1.000000  0.057571   \n",
            "VIP            0.112806  -0.049640    -0.032555  0.057571  1.000000   \n",
            "Transported    0.172199   0.447989    -0.108751 -0.076967 -0.023705   \n",
            "\n",
            "             Transported  \n",
            "HomePlanet      0.172199  \n",
            "CryoSleep       0.447989  \n",
            "Destination    -0.108751  \n",
            "Age            -0.076967  \n",
            "VIP            -0.023705  \n",
            "Transported     1.000000  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop(\"Transported\", axis=1)\n",
        "Y = df[\"Transported\"]"
      ],
      "metadata": {
        "id": "wWHH6H96cnNH"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-------------------------Model  Random Forest-----------------------\")\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import metrics\n",
        "forest = RandomForestClassifier()\n",
        "forest.fit(X, Y)\n",
        "print(\"Score : \",forest.score(X, Y)*100 , \"%\")\n",
        "expected = Y\n",
        "predicted = forest.predict(X)\n",
        "\n",
        "print(metrics.classification_report(expected, predicted))\n",
        "print(metrics.confusion_matrix(expected, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buoCjXqncrWB",
        "outputId": "53795a7e-9b30-40c0-bdcb-405579829abe"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------Model  Random Forest-----------------------\n",
            "Score :  75.8479672840991 %\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.85      0.78      4129\n",
            "           1       0.82      0.67      0.74      4185\n",
            "\n",
            "    accuracy                           0.76      8314\n",
            "   macro avg       0.77      0.76      0.76      8314\n",
            "weighted avg       0.77      0.76      0.76      8314\n",
            "\n",
            "[[3513  616]\n",
            " [1392 2793]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-------------------------Model  KNN-----------------------\")\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn =KNeighborsClassifier()\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.43)\n",
        "knn.fit(X_train, Y_train)\n",
        "\n",
        "print(\"Accuracy: \", knn.score(X_test, Y_test) * 100)\n",
        "\n",
        "expected = Y\n",
        "predicted = knn.predict(X)\n",
        "print(metrics.classification_report(expected, predicted))\n",
        "print(metrics.confusion_matrix(expected, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhPE8YdLc6qV",
        "outputId": "d69560c8-ec59-4957-eae4-872864ca2ddb"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------Model  KNN-----------------------\n",
            "Accuracy:  68.42841163310962\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.74      0.71      4129\n",
            "           1       0.72      0.68      0.70      4185\n",
            "\n",
            "    accuracy                           0.71      8314\n",
            "   macro avg       0.71      0.71      0.71      8314\n",
            "weighted avg       0.71      0.71      0.71      8314\n",
            "\n",
            "[[3040 1089]\n",
            " [1339 2846]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-------------------------Model SVM-----------------------\")\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import metrics\n",
        "# Split  training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.6)\n",
        "svm = SVC()\n",
        "svm.fit(X_train, y_train)\n",
        "y_pred = svm.predict(X_test)\n",
        "print(\"Score :\", (accuracy_score(y_test, y_pred))*100,\"%\")\n",
        "\n",
        "expected = Y\n",
        "predicted = svm.predict(X)\n",
        "print(metrics.classification_report(expected, predicted))\n",
        "print(metrics.confusion_matrix(expected, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pS-J9uBrdAqE",
        "outputId": "68a427b4-aada-44eb-f012-05006c6db238"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------Model SVM-----------------------\n",
            "Score : 72.27901383042695 %\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.76      0.73      4129\n",
            "           1       0.74      0.67      0.71      4185\n",
            "\n",
            "    accuracy                           0.72      8314\n",
            "   macro avg       0.72      0.72      0.72      8314\n",
            "weighted avg       0.72      0.72      0.72      8314\n",
            "\n",
            "[[3158  971]\n",
            " [1366 2819]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-------------------------Model Naive Gauss-----------------------\")\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn import metrics\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.5)\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)\n",
        "y_pred = nb.predict(X_test)\n",
        "print(\"Score :\", (accuracy_score(y_test, y_pred))*100,\"%\")\n",
        "\n",
        "expected = Y\n",
        "predicted = nb.predict(X)\n",
        "print(metrics.classification_report(expected, predicted))\n",
        "print(metrics.confusion_matrix(expected, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZnqbbi9dMgw",
        "outputId": "74c9775d-f75d-44bd-a388-20439a20f949"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------Model Naive Gauss-----------------------\n",
            "Score : 70.29107529468367 %\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.77      0.72      4129\n",
            "           1       0.74      0.64      0.69      4185\n",
            "\n",
            "    accuracy                           0.70      8314\n",
            "   macro avg       0.71      0.71      0.70      8314\n",
            "weighted avg       0.71      0.70      0.70      8314\n",
            "\n",
            "[[3182  947]\n",
            " [1508 2677]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"-------------------------Model Logistic R-----------------------\")\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.7)\n",
        "lr.fit(X_train, Y_train)\n",
        "\n",
        "print(\"Accuracy: \", lr.score(X_test, Y_test) * 100)\n",
        "\n",
        "expected = Y\n",
        "predicted = lr.predict(X)\n",
        "print(metrics.classification_report(expected, predicted))\n",
        "print(metrics.confusion_matrix(expected, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnUetzb3flgt",
        "outputId": "ec83ab10-1b3a-46c1-c741-a69b3696ac21"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------Model Logistic R-----------------------\n",
            "Accuracy:  71.1512027491409\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.84      0.75      4129\n",
            "           1       0.79      0.60      0.68      4185\n",
            "\n",
            "    accuracy                           0.72      8314\n",
            "   macro avg       0.73      0.72      0.71      8314\n",
            "weighted avg       0.73      0.72      0.71      8314\n",
            "\n",
            "[[3453  676]\n",
            " [1674 2511]]\n"
          ]
        }
      ]
    }
  ]
}