Titanic.ipynb

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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"
      ]
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    {
      "cell_type": "code",
      "source": [
        "titanic_data =pd.read_csv(\"train.csv\")"
      ],
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      "outputs": []
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        "titanic_data.head(2)"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "\n",
              "   Parch     Ticket     Fare Cabin Embarked  \n",
              "0      0  A/5 21171   7.2500   NaN        S  \n",
              "1      0   PC 17599  71.2833   C85        C  "
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        "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",
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              "\n",
              "            Parch        Fare  \n",
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              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <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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              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
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      "source": [
        "#correlation heatmap for survival\n",
        "#0 means random / each feature has a 1 to 1 correlation with its self\n",
        "#class and survived are neg correlated / survived and fair are pos correlated\n",
        "\n",
        "sns.heatmap(titanic_data.corr(), cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
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      "execution_count": 5,
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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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]"
      ],
      "metadata": {
        "id": "NdlBEB-5uMr1"
      },
      "execution_count": 6,
      "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",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CvVfHsoZv915",
        "outputId": "7febd214-ad59-4aac-b95d-03522eedd713"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Bg22ajjwsWK",
        "outputId": "166acbf9-c228-4566-fe90-8bb0ff061262"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 712 entries, 815 to 162\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          562 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        157 non-null    object \n",
            " 11  Embarked     710 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, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        imputer = SimpleImputer(strategy=\"mean\")\n",
        "        X['Age'] = imputer.fit_transform(X[['Age']])\n",
        "        return X"
      ],
      "metadata": {
        "id": "AqgY1Spcw8pL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "        column_names = [\"C\",\"S\",\"Q\",\"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",
        "        column_names = [\"Female\", \"Male\"]\n",
        "        for i in range(len(matrix.T)):\n",
        "            X[column_names[i]] = matrix.T[i]\n",
        "        return X"
      ],
      "metadata": {
        "id": "4KfW1aWk1a6D"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class FeatureDropper(BaseEstimator, TransformerMixin):\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        return X.drop([\"Embarked\",\"Name\", \"Ticket\", \"Cabin\", \"Sex\", \"N\"], axis=1, errors=\"ignore\")"
      ],
      "metadata": {
        "id": "6iG-aOPi7t8c"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "pipeline = Pipeline([(\"ageimputer\", AgeImputer()),\n",
        "                     (\"featureencoder\", FeatureEncoder()),\n",
        "                     (\"featuredropper\", FeatureDropper())])"
      ],
      "metadata": {
        "id": "3b9Bzv5w8tjf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set = pipeline.fit_transform(strat_train_set)"
      ],
      "metadata": {
        "id": "H8nFfxeWT7bT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "79FRcePiUDJ4",
        "outputId": "360b9b36-108a-4fad-d88c-bf0f31dcbdb6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass        Age  SibSp  Parch   Fare    C    S  \\\n",
              "815          816         0       1  30.046851      0      0   0.00  0.0  0.0   \n",
              "672          673         0       2  70.000000      0      0  10.50  0.0  0.0   \n",
              "264          265         0       3  30.046851      0      0   7.75  0.0  1.0   \n",
              "497          498         0       3  30.046851      0      0  15.10  0.0  0.0   \n",
              "775          776         0       3  18.000000      0      0   7.75  0.0  0.0   \n",
              "\n",
              "       Q  Female  Male  \n",
              "815  1.0     0.0   1.0  \n",
              "672  1.0     0.0   1.0  \n",
              "264  0.0     1.0   0.0  \n",
              "497  1.0     0.0   1.0  \n",
              "775  1.0     0.0   1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14277725-5ca5-423e-a053-8a11de95f980\">\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>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>815</th>\n",
              "      <td>816</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>30.046851</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</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>672</th>\n",
              "      <td>673</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.50</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>264</th>\n",
              "      <td>265</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>30.046851</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>498</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>30.046851</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.10</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>775</th>\n",
              "      <td>776</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.75</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14277725-5ca5-423e-a053-8a11de95f980')\"\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-14277725-5ca5-423e-a053-8a11de95f980 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-14277725-5ca5-423e-a053-8a11de95f980');\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strat_train_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhY5e-Y2U-T0",
        "outputId": "9a412e77-d009-4f64-adca-41741403cf5d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 712 entries, 815 to 162\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": "uhqxyIIhVAXS"
      },
      "execution_count": 16,
      "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/"
        },
        "id": "Fw-Ap88eXbmV",
        "outputId": "71f4edf0-fb5a-4378-c94d-e200b990be1a"
      },
      "execution_count": 17,
      "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')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf = grid_search.best_estimator_"
      ],
      "metadata": {
        "id": "419LiCNKm7z-"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57d5XHnFnDXm",
        "outputId": "6c2cdcc1-3e32-4c4c-cd31-41fc96d3304a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, min_samples_split=3, n_estimators=200)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strat_test_set = pipeline.fit_transform(strat_test_set)"
      ],
      "metadata": {
        "id": "8ubCCZ57nFM2"
      },
      "execution_count": 20,
      "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",
        "X_data_test = scaler.fit_transform(X_test)\n",
        "y_data_test = y_test.to_numpy()"
      ],
      "metadata": {
        "id": "4hUH660Pn6kP"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_clf.score(X_data_test, y_data_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIsiZNO7383h",
        "outputId": "bddb767b-25d4-4aea-965a-274994682cf3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8379888268156425"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data =pipeline.fit_transform(titanic_data)"
      ],
      "metadata": {
        "id": "c4geNHhh7dB3"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_final = final_data.drop(['Survived'], axis=1)\n",
        "y_final = final_data['Survived']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_data_final = scaler.fit_transform(X_final)\n",
        "y_data_final = y_final.to_numpy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "aaCc52zH7wtn",
        "outputId": "0095625a-e4fa-4e55-99c7-bccfd2c11631"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-61a9af4d95c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Survived'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfinal_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Survived'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_data_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_final\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'final_data' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kkflbO85OFeh"
      },
      "execution_count": 23,
      "outputs": []
    }
  ]
}