Titan.ipynb

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        "<a href=\"https://colab.research.google.com/github/anasimos/Elpa_Form/blob/main/Titan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "agZAiUikdQM0"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n"
      ]
    },
    {
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      "source": [
        "titanic_data= pd.read_csv(\"train.csv\")"
      ],
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      "execution_count": 66,
      "outputs": []
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      "source": [
        "titanic_data.describe()"
      ],
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              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>count</th>\n",
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              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
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              "      <td>891.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.486592</td>\n",
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              "      <td>14.526497</td>\n",
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              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.420000</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>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(titanic_data.corr(), cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
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        "id": "NBUurbmSfP39",
        "outputId": "d618d106-fc6c-4805-89d6-37af7a824e71"
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      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-68-e920751304af>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(titanic_data.corr(), cmap=\"YlGnBu\")\n"
          ]
        },
        {
          "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.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split= StratifiedShuffleSplit(n_splits=1,test_size=0.2)\n",
        "for train_indices, test_indices in split.split(titanic_data, titanic_data[[\"Survived\",\"Pclass\",\"Sex\"]]):\n",
        "  strat_train_set = titanic_data.loc[train_indices]\n",
        "  strat_test_set = titanic_data.loc[test_indices]\n"
      ],
      "metadata": {
        "id": "M5pYjeZ_hB51"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,2,1)\n",
        "strat_train_set['Survived'].hist()\n",
        "strat_train_set['Pclass'].hist()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "strat_test_set['Survived'].hist()\n",
        "strat_test_set['Pclass'].hist()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "lk6awIlibIRZ",
        "outputId": "38093ec9-30e5-4980-d91f-d06cf74e1529"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "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": [
        "strat_train_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsbV-GwYbVtW",
        "outputId": "9f298f56-68d1-4d03-b486-83f9eee8903c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 712 entries, 565 to 276\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  712 non-null    int64  \n",
            " 1   Survived     712 non-null    int64  \n",
            " 2   Pclass       712 non-null    int64  \n",
            " 3   Name         712 non-null    object \n",
            " 4   Sex          712 non-null    object \n",
            " 5   Age          559 non-null    float64\n",
            " 6   SibSp        712 non-null    int64  \n",
            " 7   Parch        712 non-null    int64  \n",
            " 8   Ticket       712 non-null    object \n",
            " 9   Fare         712 non-null    float64\n",
            " 10  Cabin        161 non-null    object \n",
            " 11  Embarked     711 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 72.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "class AgeImputer (BaseEstimator, TransformerMixin):\n",
        "\n",
        "  def fit(self, X):\n",
        "    return self\n",
        "  def transform (self,X):\n",
        "    imputer = SimpleImputer(strategy='mean')\n",
        "    X['Age'] = imputer.fit_transform(X[['Age']])\n",
        "    return X"
      ],
      "metadata": {
        "id": "JWP83ZVKcBAM"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "class FeatureEncoder(BaseEstimator, TransformerMixin):\n",
        "  def fit(self,X,y=None):\n",
        "      return self\n",
        "  def transform(self,X):\n",
        "    encoder= OneHotEncoder()\n",
        "    matrix= encoder.fit_transform(X[['Embarked']]).toarray()\n",
        "\n",
        "    column_names = [\"C\",\"S\",\"Q\",\"N\"]\n",
        "\n",
        "    for i in range(len(matrix.T)):\n",
        "        X[column_names[i]]=matrix.T[i]\n",
        "\n",
        "    matrix= encoder.fit_transform(X[['Sex']]).toarray()\n",
        "\n",
        "    column_names = [\"Female\",\"Male\"]\n",
        "\n",
        "    for i in range(len(matrix.T)):\n",
        "        X[column_names[i]] = matrix.T[i]\n",
        "\n",
        "    return X"
      ],
      "metadata": {
        "id": "YptBoLwac-qF"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.projections.geo import Transform\n",
        "class FeatureDropper(BaseEstimator,TransformerMixin):\n",
        "    def fit(self,X,y=None):\n",
        "      return self\n",
        "    def transform(self,X):\n",
        "      return X.drop([\"Embarked\",\"Name\",\"Ticket\",\"Cabin\",\"Sex\",\"N\"],axis=1,errors='ignore')"
      ],
      "metadata": {
        "id": "DKnMT_DueHJl"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "pipeline = Pipeline([(\"ageimputer\",AgeImputer()),\n",
        "                      (\"featureencoder\",FeatureEncoder()),\n",
        "                      (\"featuredropper\", FeatureDropper())])"
      ],
      "metadata": {
        "id": "YX6EGzxxfLe4"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set = pipeline.fit_transform(strat_train_set)"
      ],
      "metadata": {
        "id": "oxqVKW74gC8K"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFQSOq_0gSLD",
        "outputId": "1747a245-3c0d-4bd7-d83c-25e2283ce9f3"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 712 entries, 565 to 276\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  712 non-null    int64  \n",
            " 1   Survived     712 non-null    int64  \n",
            " 2   Pclass       712 non-null    int64  \n",
            " 3   Age          712 non-null    float64\n",
            " 4   SibSp        712 non-null    int64  \n",
            " 5   Parch        712 non-null    int64  \n",
            " 6   Fare         712 non-null    float64\n",
            " 7   C            712 non-null    float64\n",
            " 8   S            712 non-null    float64\n",
            " 9   Q            712 non-null    float64\n",
            " 10  Female       712 non-null    float64\n",
            " 11  Male         712 non-null    float64\n",
            "dtypes: float64(7), int64(5)\n",
            "memory usage: 72.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X = strat_train_set.drop(['Survived'], axis=1)\n",
        "y = strat_train_set['Survived']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_data = scaler.fit_transform(X)\n",
        "y_data = y.to_numpy()"
      ],
      "metadata": {
        "id": "56FfqokPgpBA"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "clf =RandomForestClassifier()\n",
        "\n",
        "param_grid = [\n",
        "    {\"n_estimators\":[10,100,200,500], \"max_depth\": [None,5,10], \"min_samples_split\":[2,3,4]}\n",
        "              ]\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=3, scoring=\"accuracy\", return_train_score=True)\n",
        "grid_search.fit(X_data, y_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "pAOA8fZayyX3",
        "outputId": "77920e33-f6ec-4184-cba0-df4b850ea99f"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
              "             param_grid=[{'max_depth': [None, 5, 10],\n",
              "                          'min_samples_split': [2, 3, 4],\n",
              "                          'n_estimators': [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
              "             param_grid=[{&#x27;max_depth&#x27;: [None, 5, 10],\n",
              "                          &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
              "                          &#x27;n_estimators&#x27;: [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
              "             param_grid=[{&#x27;max_depth&#x27;: [None, 5, 10],\n",
              "                          &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
              "                          &#x27;n_estimators&#x27;: [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf = grid_search.best_estimator_"
      ],
      "metadata": {
        "id": "XH934r2209a0"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "O9NFsqUy2t0U",
        "outputId": "a3b46036-47a3-4ccc-e40f-b5e178e8dfc8"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strat_test_set = pipeline.fit_transform(strat_test_set)"
      ],
      "metadata": {
        "id": "dqwvfNN32w5a"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = strat_test_set.drop(['Survived'], axis=1)\n",
        "y_test = strat_test_set['Survived']\n",
        "\n",
        "scaler= StandardScaler()\n",
        "\n",
        "X_data_test = scaler.fit_transform(X_test)\n",
        "y_data_test = y_test.to_numpy()\n"
      ],
      "metadata": {
        "id": "QWqjs0512_b_"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf.score(X_data_test,y_data_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGp0PqqB3tRn",
        "outputId": "0f8e34ef-86ab-44b5-f523-7ae997a2bb77"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8212290502793296"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data = pipeline.fit_transform(titanic_data)"
      ],
      "metadata": {
        "id": "nCdCRqTM3_HC"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "m6SUiM4G4NwL",
        "outputId": "2a59bc61-cf3b-43d7-c0ed-3e0afd0b3d43"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass        Age  SibSp  Parch     Fare    C  \\\n",
              "0              1         0       3  22.000000      1      0   7.2500  0.0   \n",
              "1              2         1       1  38.000000      1      0  71.2833  1.0   \n",
              "2              3         1       3  26.000000      0      0   7.9250  0.0   \n",
              "3              4         1       1  35.000000      1      0  53.1000  0.0   \n",
              "4              5         0       3  35.000000      0      0   8.0500  0.0   \n",
              "..           ...       ...     ...        ...    ...    ...      ...  ...   \n",
              "886          887         0       2  27.000000      0      0  13.0000  0.0   \n",
              "887          888         1       1  19.000000      0      0  30.0000  0.0   \n",
              "888          889         0       3  29.699118      1      2  23.4500  0.0   \n",
              "889          890         1       1  26.000000      0      0  30.0000  1.0   \n",
              "890          891         0       3  32.000000      0      0   7.7500  0.0   \n",
              "\n",
              "       S    Q  Female  Male  \n",
              "0    0.0  1.0     0.0   1.0  \n",
              "1    0.0  0.0     1.0   0.0  \n",
              "2    0.0  1.0     1.0   0.0  \n",
              "3    0.0  1.0     1.0   0.0  \n",
              "4    0.0  1.0     0.0   1.0  \n",
              "..   ...  ...     ...   ...  \n",
              "886  0.0  1.0     0.0   1.0  \n",
              "887  0.0  1.0     1.0   0.0  \n",
              "888  0.0  1.0     1.0   0.0  \n",
              "889  0.0  0.0     0.0   1.0  \n",
              "890  1.0  0.0     0.0   1.0  \n",
              "\n",
              "[891 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f70113a-18af-48a2-b1eb-e21ead4ca45b\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>C</th>\n",
              "      <th>S</th>\n",
              "      <th>Q</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f70113a-18af-48a2-b1eb-e21ead4ca45b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3f70113a-18af-48a2-b1eb-e21ead4ca45b 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-3f70113a-18af-48a2-b1eb-e21ead4ca45b');\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",
              "\n",
              "\n",
              "<div id=\"df-5bdd95fc-0fb7-434d-943f-51704eddd246\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5bdd95fc-0fb7-434d-943f-51704eddd246')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5bdd95fc-0fb7-434d-943f-51704eddd246 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_final =final_data.drop(['Survived'], axis=1)\n",
        "y_final=final_data[\"Survived\"]\n",
        "scaler = StandardScaler()\n",
        "X_data_final = scaler.fit_transform(X_final)\n",
        "y_data_final= y_final.to_numpy()"
      ],
      "metadata": {
        "id": "KSuAA78-4RSF"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prod_clf =RandomForestClassifier()\n",
        "\n",
        "param_grid = [\n",
        "    {\"n_estimators\":[10,100,200,500], \"max_depth\": [None,5,10], \"min_samples_split\":[2,3,4]}\n",
        "              ]\n",
        "grid_search = GridSearchCV(prod_clf, param_grid, cv=3, scoring=\"accuracy\", return_train_score=True)\n",
        "grid_search.fit(X_data_final, y_data_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "XAukhXBe5Pae",
        "outputId": "1ce09aa6-e518-47bf-f82b-ede95a3b0bcb"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
              "             param_grid=[{'max_depth': [None, 5, 10],\n",
              "                          'min_samples_split': [2, 3, 4],\n",
              "                          'n_estimators': [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring='accuracy')"
            ],
            "text/html": [
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              "             param_grid=[{&#x27;max_depth&#x27;: [None, 5, 10],\n",
              "                          &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
              "                          &#x27;n_estimators&#x27;: [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
              "             param_grid=[{&#x27;max_depth&#x27;: [None, 5, 10],\n",
              "                          &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
              "                          &#x27;n_estimators&#x27;: [10, 100, 200, 500]}],\n",
              "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></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-10\" type=\"checkbox\" ><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></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prod_final_clf =grid_search.best_estimator_"
      ],
      "metadata": {
        "id": "q68BOd2-5lmw"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prod_final_clf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Fdzq0_BO6CIh",
        "outputId": "af3a60e5-9284-4cf6-c15e-b308da77ccbe"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, n_estimators=200)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data =pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "UuDV-7x66HeS"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_test_data= pipeline.fit_transform(test_data)"
      ],
      "metadata": {
        "id": "ug6lzPM76bTF"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_test_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo6mYEBl620T",
        "outputId": "7dc7586f-eb4c-47df-d11b-90b7ed228de4"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Age          418 non-null    float64\n",
            " 3   SibSp        418 non-null    int64  \n",
            " 4   Parch        418 non-null    int64  \n",
            " 5   Fare         417 non-null    float64\n",
            " 6   C            418 non-null    float64\n",
            " 7   S            418 non-null    float64\n",
            " 8   Q            418 non-null    float64\n",
            " 9   Female       418 non-null    float64\n",
            " 10  Male         418 non-null    float64\n",
            "dtypes: float64(7), int64(4)\n",
            "memory usage: 36.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_final_test = final_test_data\n",
        "X_final_test = X_final_test.fillna(method='ffill')\n",
        "\n",
        "scaler=StandardScaler()\n",
        "X_data_final_test=scaler.fit_transform(X_final_test)"
      ],
      "metadata": {
        "id": "Nxi1g2Og643W"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = prod_final_clf.predict(X_data_final_test)"
      ],
      "metadata": {
        "id": "H-sJpOPP7uP2"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df=pd.DataFrame(test_data['PassengerId'])\n",
        "final_df['Survived']=prediction\n",
        "final_df.to_csv('pred.csv',index=False)\n"
      ],
      "metadata": {
        "id": "qEsaiTED75Em"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-dCUgHKa9AnT",
        "outputId": "464d4605-b207-40fe-e8e7-85bba889480d"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         0\n",
              "2            894         0\n",
              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
              "413         1305         0\n",
              "414         1306         1\n",
              "415         1307         0\n",
              "416         1308         0\n",
              "417         1309         1\n",
              "\n",
              "[418 rows x 2 columns]"
            ],
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              "</table>\n",
              "<p>418 rows × 2 columns</p>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3b4dc2c9-86cc-473b-9061-28437b2b70a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xs9fl1AQ9Uc_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}