breast_cancer_log_reg.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "breast_cancer_log_reg.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPj7otKOoqaOvMcDxA+TUwq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KsenyaZ-8/breast_cancer_ML_alg/blob/main/breast_cancer_log_reg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_0L2ku-uYPi"
      },
      "source": [
        "#импортируем специальную библиотеку для работы с линейной алгеброй\n",
        "import numpy as np \n",
        "  \n",
        "#импортируем библиотеку для работы с данными\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "NpEi0yv1uatX",
        "outputId": "1caf06d7-9099-41bb-89d8-3b313691d892"
      },
      "source": [
        "breast_cancer_df = pd.read_csv(\"/content/data.csv\")#подгружаем \"data.csv\" (скачать https://www.kaggle.com/uciml/breast-cancer-wisconsin-data) в \"Файлы\" в поле слева\n",
        "breast_cancer_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>radius_se</th>\n",
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              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
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              "      <th>Unnamed: 32</th>\n",
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              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
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              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
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              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
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              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
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              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
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              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
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              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
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              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
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              "      <td>0.12400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
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              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0B5zmIGueRR",
        "outputId": "3a95f16c-1088-442c-8ffe-fe6a50810dce"
      },
      "source": [
        "breast_cancer_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYv4ajTgLvN7",
        "outputId": "1587b8d7-96ce-4ce3-b1f9-0179d94f118f"
      },
      "source": [
        "breast_cancer_df.isnull().sum() #проверка на пропущенные значения"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "diagnosis                    0\n",
              "radius_mean                  0\n",
              "texture_mean                 0\n",
              "perimeter_mean               0\n",
              "area_mean                    0\n",
              "smoothness_mean              0\n",
              "compactness_mean             0\n",
              "concavity_mean               0\n",
              "concave points_mean          0\n",
              "symmetry_mean                0\n",
              "fractal_dimension_mean       0\n",
              "radius_se                    0\n",
              "texture_se                   0\n",
              "perimeter_se                 0\n",
              "area_se                      0\n",
              "smoothness_se                0\n",
              "compactness_se               0\n",
              "concavity_se                 0\n",
              "concave points_se            0\n",
              "symmetry_se                  0\n",
              "fractal_dimension_se         0\n",
              "radius_worst                 0\n",
              "texture_worst                0\n",
              "perimeter_worst              0\n",
              "area_worst                   0\n",
              "smoothness_worst             0\n",
              "compactness_worst            0\n",
              "concavity_worst              0\n",
              "concave points_worst         0\n",
              "symmetry_worst               0\n",
              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KHAWvfd1JNr"
      },
      "source": [
        "breast_cancer_df.drop(['Unnamed: 32', 'id'], axis = 1, inplace = True) #удалили ненужные столбцы"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_FeRjS9Lr1W"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder #преобразует категориальные данные в числа\n",
        "le=LabelEncoder()\n",
        "breast_cancer_df['diagnosis']=le.fit_transform(breast_cancer_df['diagnosis'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxsRO-7D5aiK"
      },
      "source": [
        "#обозначаем зависимую и независимую переменные\n",
        "y = breast_cancer_df.diagnosis.values\n",
        "x = breast_cancer_df.drop(['diagnosis'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl4nYBa65gVV"
      },
      "source": [
        "#разделяем датасет на данные для обучения и тренировки модели\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    x, y, test_size = 0.15, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-YztHS6NzB2"
      },
      "source": [
        "#для стандартизации данных\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.fit_transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oI788_1RLNHW",
        "outputId": "cdcfba43-a3c5-4a1a-9b58-ece8ffeb8e75"
      },
      "source": [
        "#используем логистическую регрессию\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression(max_iter = 10000)\n",
        "lr.fit(x_train,y_train)\n",
        "\n",
        "#получаем confusion matrix - матрицу ошибок, которая отражает производительность алгоритма\n",
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = lr.predict(x_test)\n",
        "cm = confusion_matrix(y_test,y_pred)\n",
        "print('confusion matrix:\\n',cm)\n",
        "\n",
        "#проверяем accuracy - точность наших выводов\n",
        "from sklearn.metrics import accuracy_score\n",
        "lra = accuracy_score(y_test,y_pred)\n",
        "print('accuracy score = ',lra)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "confusion matrix:\n",
            " [[54  0]\n",
            " [ 2 30]]\n",
            "accuracy score =  0.9767441860465116\n"
          ]
        }
      ]
    }
  ]
}