logistic_regression.ipynb

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  "metadata": {
    "colab": {
      "name": "logistic_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMGTUG3x1fQB3AbLX4mu3Af",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SMatusik/heart_disease/blob/main/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jd_IfJragaQO"
      },
      "source": [
        "# import bibliotek: numpy (obliczenia), pandas (manipulacja danymi), sklearn (machine learning), matplotlib (wykresy)\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import sklearn\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mC299Qy9guGE"
      },
      "source": [
        "# odczyt pliku csv \r\n",
        "df_raw = pd.read_csv('data.csv')\r\n",
        "df = df_raw.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpwCM2DEguMe",
        "outputId": "2fc2d6f7-7706-422b-d85f-0b65c7285381"
      },
      "source": [
        "# wyswietlenie informacji o kolumnach pobranych danych\r\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    int64  \n",
            " 1   sex      294 non-null    int64  \n",
            " 2   cp       294 non-null    int64  \n",
            " 3   trestps  294 non-null    object \n",
            " 4   chol     294 non-null    object \n",
            " 5   fbs      294 non-null    object \n",
            " 6   restecg  294 non-null    object \n",
            " 7   thalach  294 non-null    object \n",
            " 8   exang    294 non-null    object \n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  slope    294 non-null    object \n",
            " 11  ca       294 non-null    object \n",
            " 12  thal     294 non-null    object \n",
            " 13  num      294 non-null    int64  \n",
            "dtypes: float64(1), int64(4), object(9)\n",
            "memory usage: 32.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "I0MYQedGg2q0",
        "outputId": "dd81a77c-ae95-4a51-c2cc-1da18d1cbac5"
      },
      "source": [
        "# wyswietlenie pierwszych pięciu wierszy obiektu df. Warto zauwazyć, że brakuje niektórych danych, jednak nie są oznaczone jako null\r\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>28</td>\n",
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              "      <td>140</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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            ],
            "text/plain": [
              "   age  sex  cp trestps chol fbs  ... exang oldpeak slope  ca thal num\n",
              "0   28    1   2     130  132   0  ...     0     0.0     ?   ?    ?   0\n",
              "1   29    1   2     120  243   0  ...     0     0.0     ?   ?    ?   0\n",
              "2   29    1   2     140    ?   0  ...     0     0.0     ?   ?    ?   0\n",
              "3   30    0   1     170  237   0  ...     0     0.0     ?   ?    6   0\n",
              "4   31    0   2     100  219   0  ...     0     0.0     ?   ?    ?   0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtdPChadifwS"
      },
      "source": [
        "# zamiana wartosci '?' na null\r\n",
        "df = df.replace('?', np.nan)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9Z93MyGkXTI",
        "outputId": "9946f1f8-4629-494c-cb6d-6cdf408db2fc"
      },
      "source": [
        "# ponowne wyswietlenie informacji o obiekcie - widac, ze wiele jest wartosci typu null\r\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    int64  \n",
            " 1   sex      294 non-null    int64  \n",
            " 2   cp       294 non-null    int64  \n",
            " 3   trestps  293 non-null    object \n",
            " 4   chol     271 non-null    object \n",
            " 5   fbs      286 non-null    object \n",
            " 6   restecg  293 non-null    object \n",
            " 7   thalach  293 non-null    object \n",
            " 8   exang    293 non-null    object \n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  slope    104 non-null    object \n",
            " 11  ca       3 non-null      object \n",
            " 12  thal     28 non-null     object \n",
            " 13  num      294 non-null    int64  \n",
            "dtypes: float64(1), int64(4), object(9)\n",
            "memory usage: 32.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6KWBFM_wc__"
      },
      "source": [
        "Opis zmiennych (za: http://archive.ics.uci.edu/ml/datasets/Heart+Disease):\r\n",
        "1. age - wiek w latach\r\n",
        "2. sex - płeć (1 - mężczyzna, 0 - kobieta)\r\n",
        "3. cp - typ bólu w klatce piersiowej (1 - typowa dusznica, 2 - nietypowa dusznica, 3 - ból niezwiązany z dusznicą, 4 - brak objawów).\r\n",
        "4. trestps - spoczynkowe ciśnienie krwi (w mmHg)\r\n",
        "5. chol - cholesterol w mg/dl\r\n",
        "6. fbs - wartość cukru we krwi na czczo (1 - większy niż 120mg/dl, 0 - mniejszy)\r\n",
        "7. restecg - wyniki elektrokardiografu (0 - OK, 1 - anomalie fali ST oraz T, 2 - zwężenie lewej komory serca)\r\n",
        "8. thalach - maksymalny puls \r\n",
        "9. exang -  czy ćwiczenia spodowoały dusznicę (1 - tak, 0 - nie)\r\n",
        "10. oldpeak - obniżenie odcinka ST (1 - nie OK, 0 - OK)\r\n",
        "11. slope - nachylenie odcinka ST (1 - narastające, 2 - płaskie, 3 - spadające)\r\n",
        "12. ca - ilosc większych naczyń krwionośnycych pokolorowanych przez fluoroskopię. (0-3)\r\n",
        "13. thal - niedokrwistość tarcznikowata (3 - normalna, 6 - stały defekt, 7 - uleczalny defekt).\r\n",
        "14. num - diagnoza choroby serca (0 - < 50% zwężenia średnicy, 1 - > 50%).\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8UGQUI60r5N"
      },
      "source": [
        "# wyrzucennie kolumn, które nie zawierają informacji dla >50% pacjentów.\r\n",
        "df = df.drop(['slope', 'ca', 'thal'], axis=1)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FP4KAjB21C5V",
        "outputId": "559ebd39-9011-426f-c72a-7622919e9a9c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    int64  \n",
            " 1   sex      294 non-null    int64  \n",
            " 2   cp       294 non-null    int64  \n",
            " 3   trestps  293 non-null    object \n",
            " 4   chol     271 non-null    object \n",
            " 5   fbs      286 non-null    object \n",
            " 6   restecg  293 non-null    object \n",
            " 7   thalach  293 non-null    object \n",
            " 8   exang    293 non-null    object \n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  num      294 non-null    int64  \n",
            "dtypes: float64(1), int64(4), object(6)\n",
            "memory usage: 25.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDhtvKOd1gli",
        "outputId": "5599bc02-052a-4f4a-c2a7-106de2bb497d"
      },
      "source": [
        "# zamiana danych z typu object na typ float64\r\n",
        "object_cols = [col for col in df.columns if df[col].dtype == 'object']\r\n",
        "for col in object_cols:\r\n",
        "  df[col] = df[col].astype('float')\r\n",
        "\r\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    int64  \n",
            " 1   sex      294 non-null    int64  \n",
            " 2   cp       294 non-null    int64  \n",
            " 3   trestps  293 non-null    float64\n",
            " 4   chol     271 non-null    float64\n",
            " 5   fbs      286 non-null    float64\n",
            " 6   restecg  293 non-null    float64\n",
            " 7   thalach  293 non-null    float64\n",
            " 8   exang    293 non-null    float64\n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  num      294 non-null    int64  \n",
            "dtypes: float64(7), int64(4)\n",
            "memory usage: 25.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2bAhivr1C8O"
      },
      "source": [
        "# uzupełnienie brakujących danych (w przypadku danych 0-1 uzupełnimy najczęściej uzupełnianą wartością, w przypadku danych liczbowych - wartością średnią).\r\n",
        "trestps_mean = int(df['trestps'].mean())\r\n",
        "chol_mean = int(df['chol'].mean())\r\n",
        "thalach_mean = int(df['thalach'].mean())\r\n",
        "\r\n",
        "\r\n",
        "fbs_mode = int(df['fbs'].mode())\r\n",
        "restecg_mode = int(df['restecg'].mode())\r\n",
        "exang_mode = int(df['exang'].mode())\r\n",
        "\r\n",
        "df['trestps'].fillna(trestps_mean, inplace=True)\r\n",
        "df['chol'].fillna(chol_mean, inplace=True)\r\n",
        "df['thalach'].fillna(thalach_mean, inplace=True)\r\n",
        "\r\n",
        "df['fbs'].fillna(fbs_mode, inplace=True)\r\n",
        "df['restecg'].fillna(restecg_mode, inplace=True)\r\n",
        "df['exang'].fillna(exang_mode, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJ3IKDpy_Y0U",
        "outputId": "41307c90-1181-4167-f1c8-41cd643cf36c"
      },
      "source": [
        "# sprawdzenie czy wszystkie dane zostały uzupelnione\r\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    int64  \n",
            " 1   sex      294 non-null    int64  \n",
            " 2   cp       294 non-null    int64  \n",
            " 3   trestps  294 non-null    float64\n",
            " 4   chol     294 non-null    float64\n",
            " 5   fbs      294 non-null    float64\n",
            " 6   restecg  294 non-null    float64\n",
            " 7   thalach  294 non-null    float64\n",
            " 8   exang    294 non-null    float64\n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  num      294 non-null    int64  \n",
            "dtypes: float64(7), int64(4)\n",
            "memory usage: 25.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4Ndnswt-s_I"
      },
      "source": [
        "# zamiana danych na typ float\r\n",
        "int_cols = [col for col in df.columns if df[col].dtype == 'int']\r\n",
        "for col in int_cols:\r\n",
        "  df[col] = df[col].astype('float')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9OZ_Ff-87-G",
        "outputId": "de6fe126-3c55-45a4-e8ff-093463ef9bb1"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      294 non-null    float64\n",
            " 1   sex      294 non-null    float64\n",
            " 2   cp       294 non-null    float64\n",
            " 3   trestps  294 non-null    float64\n",
            " 4   chol     294 non-null    float64\n",
            " 5   fbs      294 non-null    float64\n",
            " 6   restecg  294 non-null    float64\n",
            " 7   thalach  294 non-null    float64\n",
            " 8   exang    294 non-null    float64\n",
            " 9   oldpeak  294 non-null    float64\n",
            " 10  num      294 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 25.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7Xl1ZgRA7Wm"
      },
      "source": [
        "# podzielenie na dane opisujące i dane opisywane. Kolumna 'num' bedzie zawierała dane ktore będziemy chcieli przewidzieć\r\n",
        "data = df.copy()\r\n",
        "target = data.pop('num')\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5hMjWSTBJns"
      },
      "source": [
        "# import klasy train_test_split, ktora automatycznie podzieli dane na dane treningowe i testowe\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(data, target, test_size=0.1)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-EF0_ROCKYQ"
      },
      "source": [
        "# zaimportowanie klasy z scikit o nazwie LogisticRegression i utworzenie obiektu tej klasy\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "\r\n",
        "logistic_regressor = LogisticRegression()\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WmkEWEWCwf6"
      },
      "source": [
        "# import klasy StandardScaler, ktora sluzy do przeskalowania danych, tak aby średnia ich rozkłądu była równa 0, a odchylenie standardowe równe 1\r\n",
        "# standaryzacja dopasowywana jest do danych treningowych, a następnie wg obliczonych wspolczynników standaryzujemy dane testowe\r\n",
        "from sklearn.preprocessing import StandardScaler\r\n",
        "\r\n",
        "scaler = StandardScaler()\r\n",
        "\r\n",
        "scaler.fit(X_train)\r\n",
        "\r\n",
        "X_train = scaler.transform(X_train)\r\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0w5FJNECWzC",
        "outputId": "25897164-afb7-4a07-b1b2-e29de969a7c6"
      },
      "source": [
        "# metoda uczaca regresor dla danych treningowych\r\n",
        "logistic_regressor.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3oZOhveFWip"
      },
      "source": [
        "# utworzenie obiektu zawierającego decyzje regresora dla danych testowych\r\n",
        "Y_pred = logistic_regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "a49ifVfPFNkZ",
        "outputId": "f37f3c95-d12f-4772-f1d1-182879d413e5"
      },
      "source": [
        "# import bibliotek z sklearn oraz mlxtend sluzacych do narysowania macierzy pomyłek\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from mlxtend.plotting import plot_confusion_matrix\r\n",
        "\r\n",
        "cm = confusion_matrix(Y_test, Y_pred)\r\n",
        "plot_confusion_matrix(cm)\r\n",
        "\r\n",
        "print(f'Accuracy: {accuracy_score(Y_test, Y_pred)}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "IJbhndblKDxC",
        "outputId": "87a87bb7-ce1d-4f8e-819b-8b256e404695"
      },
      "source": [
        "# narysowanie wykresu danych pozytywnych (choroba serca) i negatywnych (brak choroby) w zależności od wieku\r\n",
        "plt.figure(figsize=(12, 5))\r\n",
        "plt.scatter(x= df['age'], y = df['num'], c= df['num'], cmap='RdYlGn')\r\n",
        "plt.title('Wykres zależności posiadania choroby serca w zależności od wieku')\r\n",
        "plt.xlabel('Wiek')\r\n",
        "plt.ylabel('Posiadanie choroby serca')\r\n",
        "plt.grid()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSYsjnIKPi62",
        "outputId": "67df2e7b-54f4-49e6-9d2b-4d9aa1ac23e7"
      },
      "source": [
        "# wyliczenie metryk klasyfikacji modelu\r\n",
        "from sklearn.metrics import classification_report\r\n",
        "\r\n",
        "print(classification_report(Y_test, Y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      0.88      0.93        24\n",
            "         1.0       0.67      1.00      0.80         6\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.83      0.94      0.87        30\n",
            "weighted avg       0.93      0.90      0.91        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}