StrokePrediction.ipynb

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    "colab": {
      "name": "StrokePrediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNmmFl6aBeeaVVIxQzaAc8h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oktaviacitra/classification/blob/main/StrokePrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read File CSV"
      ],
      "metadata": {
        "id": "ZHsdIpVlOOqg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "md_6-Xwo5i0s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2006e070-8adf-4636-9a02-407f89adfac1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "origin_path = \"/content/drive\"\n",
        "drive.mount(origin_path)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "folder_path = origin_path + \"/MyDrive/Learning Journey/Stroke Data\"\n",
        "dataset_path = folder_path + \"/full_data.csv\"\n",
        "dataframe = pd.read_csv(dataset_path)\n",
        "dataframe.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "NzVSYG3t9Kzj",
        "outputId": "df2de981-a65d-414e-a4e0-66e6f621147d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender   age  hypertension  heart_disease ever_married work_type  \\\n",
              "0    Male  67.0             0              1          Yes   Private   \n",
              "1    Male  80.0             0              1          Yes   Private   \n",
              "2  Female  49.0             0              0          Yes   Private   \n",
              "\n",
              "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "0          Urban             228.69  36.6  formerly smoked       1  \n",
              "1          Rural             105.92  32.5     never smoked       1  \n",
              "2          Urban             171.23  34.4           smokes       1  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
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              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "      \n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing Data"
      ],
      "metadata": {
        "id": "zCH2WoPMOTOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = ['gender', 'ever_married', 'Residence_type',\n",
        "                       'work_type', 'smoking_status']\n",
        "for column in categorical_columns:\n",
        "  dataframe[column] = pd.factorize(dataframe[column])[0]"
      ],
      "metadata": {
        "id": "awIeEGVLKyQn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "value_columns = ['age', 'avg_glucose_level', 'bmi']\n",
        "dataframe[value_columns] = StandardScaler().fit_transform(dataframe[value_columns])\n",
        "dataframe.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "eIp6_79jL4Ni",
        "outputId": "e9a9cf5a-3881-4e55-f69b-c7395cb33f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender       age  hypertension  heart_disease  ever_married  work_type  \\\n",
              "0       0  1.040584             0              1             0          0   \n",
              "1       0  1.614270             0              1             0          0   \n",
              "2       1  0.246250             0              0             0          0   \n",
              "\n",
              "   Residence_type  avg_glucose_level       bmi  smoking_status  stroke  \n",
              "0               0           2.723411  1.193238               0       1  \n",
              "1               1          -0.000523  0.589390               1       1  \n",
              "2               0           1.448529  0.869222               2       1  "
            ],
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              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ec70203-a8eb-479e-98da-112cb651022f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Change 'Unknown' with specific value in column 'smoking_status'"
      ],
      "metadata": {
        "id": "zF3U9uvT7mcl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ss means smoking_status\n",
        "unknown_ss_indices = dataframe.index[dataframe['smoking_status'] == 3].tolist()\n",
        "unknown_ss_dataframe = dataframe.iloc[unknown_ss_indices]\n",
        "known_ss_dataframe = dataframe.copy()\n",
        "known_ss_dataframe.drop(unknown_ss_indices, inplace=True)\n",
        "unknown_ss_dataframe.shape, known_ss_dataframe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPMqY3DoyJZC",
        "outputId": "5497ab1a-3cce-4836-f95b-e5f1a29b2bb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1500, 11), (3481, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unknown_ss_dataframe.drop(['smoking_status'], axis=1)\n",
        "X_unknown = unknown_ss_dataframe.to_numpy()\n",
        "\n",
        "y_known = known_ss_dataframe['smoking_status']\n",
        "known_ss_dataframe.drop(['smoking_status'], axis=1)\n",
        "X_known = known_ss_dataframe.to_numpy()\n",
        "\n",
        "X_known.shape, y_known.shape, X_unknown.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYJHBKwD44cR",
        "outputId": "f2bf8056-5a84-4595-8d6e-690dc28f798d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3481, 11), (3481,), (1500, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import numpy as np\n",
        "\n",
        "classifier = DecisionTreeClassifier().fit(X_known, y_known)\n",
        "y_unknown = classifier.predict(X_unknown)\n",
        "\n",
        "dataframe.loc[unknown_ss_indices,['smoking_status']] = y_unknown"
      ],
      "metadata": {
        "id": "fHiHiJjB4qzH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split data"
      ],
      "metadata": {
        "id": "yutEwof6B1MM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataframe.copy()\n",
        "X.drop(['stroke'], axis=1)\n",
        "\n",
        "y = dataframe['stroke']\n",
        "\n",
        "X, y = X.to_numpy(), y.to_numpy()\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fj7Et2NrD9IY",
        "outputId": "ccf18162-5108-45f5-a73c-91cf168d640b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4981, 11), (4981,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDmaEcalCTWC",
        "outputId": "3397702b-0965-40d1-c7e5-eacf89d9724d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3486, 11), (1495, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict Stroke"
      ],
      "metadata": {
        "id": "12CuVHg8Si04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        " \n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'kernel': ['rbf']}\n",
        " \n",
        "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)\n",
        "grid.fit(X_train, y_train)\n",
        "grid.best_params_, grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4KPtOQISlcF",
        "outputId": "81b29dc1-9df6-4187-abd3-2cdfec997feb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.951 total time=   0.5s\n",
            "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.951 total time=   0.5s\n",
            "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.951 total time=   0.6s\n",
            "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.951 total time=   0.5s\n",
            "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.951 total time=   0.3s\n",
            "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.1s\n",
            "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.1s\n",
            "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.1s\n",
            "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.986 total time=   0.1s\n",
            "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.986 total time=   0.1s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.981 total time=   0.3s\n",
            "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.978 total time=   0.3s\n",
            "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.981 total time=   0.3s\n",
            "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.983 total time=   0.3s\n",
            "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.974 total time=   0.3s\n",
            "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.983 total time=   0.3s\n",
            "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.981 total time=   0.3s\n",
            "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.976 total time=   0.3s\n",
            "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.951 total time=   0.1s\n",
            "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.983 total time=   0.3s\n",
            "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.981 total time=   0.3s\n",
            "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.976 total time=   0.3s\n",
            "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.983 total time=   0.3s\n",
            "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.981 total time=   0.3s\n",
            "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.986 total time=   0.3s\n",
            "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.976 total time=   0.3s\n",
            "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s\n",
            "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.1s\n",
            "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n",
            "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.2s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}, SVC(C=1, gamma=0.1))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "Xr6gNlF0eVGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_pred = grid.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxLDOxF9SpfL",
        "outputId": "0957f228-353f-4f43-cdcf-6efc27a5da08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1417\n",
            "           1       1.00      0.97      0.99        78\n",
            "\n",
            "    accuracy                           1.00      1495\n",
            "   macro avg       1.00      0.99      0.99      1495\n",
            "weighted avg       1.00      1.00      1.00      1495\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred, labels=grid.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=grid.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "eCP8YwqA4siC",
        "outputId": "037b98ec-ec75-4f81-9c38-955b0322dd26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fde77d8cc10>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}