Breast_Cancer_Wisconsin_(Diagnostic)_ 2nd_try.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ma_eo7RQZkfU"
      },
      "outputs": [],
      "source": [
        "!pip install opendatasets --upgrade --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets as od"
      ],
      "metadata": {
        "id": "G_idGP5LZvC2"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_url = 'https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data'"
      ],
      "metadata": {
        "id": "TLuC3-8uaFtL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "od.download(dataset_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jKeQX9KaIN4",
        "outputId": "578985ec-a47e-4df3-b86c-72441ed8a97d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: infinityaspirations\n",
            "Your Kaggle Key: ··········\n",
            "Downloading breast-cancer-wisconsin-data.zip to ./breast-cancer-wisconsin-data\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 48.6k/48.6k [00:00<00:00, 16.0MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "Zi7H5BZSadXj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = './breast-cancer-wisconsin-data'"
      ],
      "metadata": {
        "id": "8KjBq69WakfF"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.listdir(data_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZF8xIIoas-M",
        "outputId": "cf05a10e-834c-4a59-c46c-81476f3f664b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['data.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv = data_dir + '/data.csv'"
      ],
      "metadata": {
        "id": "D_JelzbEazX5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas --quiet"
      ],
      "metadata": {
        "id": "378GoXy6a-4H"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "2G1OZ_1kbCgg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.read_csv(train_csv)"
      ],
      "metadata": {
        "id": "-JTZNUtabLza"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "gOOmjyS0bUL2",
        "outputId": "8e28d296-a1ad-4e81-fb50-98fc47eb7f47"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0    ...          17.33           184.60      2019.0           0.16220   \n",
              "1    ...          23.41           158.80      1956.0           0.12380   \n",
              "2    ...          25.53           152.50      1709.0           0.14440   \n",
              "3    ...          26.50            98.87       567.7           0.20980   \n",
              "4    ...          16.67           152.20      1575.0           0.13740   \n",
              "..   ...            ...              ...         ...               ...   \n",
              "564  ...          26.40           166.10      2027.0           0.14100   \n",
              "565  ...          38.25           155.00      1731.0           0.11660   \n",
              "566  ...          34.12           126.70      1124.0           0.11390   \n",
              "567  ...          39.42           184.60      1821.0           0.16500   \n",
              "568  ...          30.37            59.16       268.6           0.08996   \n",
              "\n",
              "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0              0.66560           0.7119                0.2654          0.4601   \n",
              "1              0.18660           0.2416                0.1860          0.2750   \n",
              "2              0.42450           0.4504                0.2430          0.3613   \n",
              "3              0.86630           0.6869                0.2575          0.6638   \n",
              "4              0.20500           0.4000                0.1625          0.2364   \n",
              "..                 ...              ...                   ...             ...   \n",
              "564            0.21130           0.4107                0.2216          0.2060   \n",
              "565            0.19220           0.3215                0.1628          0.2572   \n",
              "566            0.30940           0.3403                0.1418          0.2218   \n",
              "567            0.86810           0.9387                0.2650          0.4087   \n",
              "568            0.06444           0.0000                0.0000          0.2871   \n",
              "\n",
              "     fractal_dimension_worst  Unnamed: 32  \n",
              "0                    0.11890          NaN  \n",
              "1                    0.08902          NaN  \n",
              "2                    0.08758          NaN  \n",
              "3                    0.17300          NaN  \n",
              "4                    0.07678          NaN  \n",
              "..                       ...          ...  \n",
              "564                  0.07115          NaN  \n",
              "565                  0.06637          NaN  \n",
              "566                  0.07820          NaN  \n",
              "567                  0.12400          NaN  \n",
              "568                  0.07039          NaN  \n",
              "\n",
              "[569 rows x 33 columns]"
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              "      <th></th>\n",
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              "      <th>radius_mean</th>\n",
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              "      <td>1001.0</td>\n",
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              "      <td>17.77</td>\n",
              "      <td>132.90</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>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
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              "      <td>0.42450</td>\n",
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              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
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              "      <td>386.1</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhKeMt2Pb1xS",
        "outputId": "2e8829d8-301f-4fac-d643-aa41800d7df3"
      },
      "execution_count": 13,
      "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",
      "source": [
        "!pip install plotly matplotlib seaborn --quiet"
      ],
      "metadata": {
        "id": "vwFiafdldK_4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
      ],
      "metadata": {
        "id": "sYdu2zVZdcxk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter(raw_df,\n",
        "           title='Radius Mean vs Area Mean',\n",
        "           x='radius_mean',\n",
        "           y='area_mean',\n",
        "           color='diagnosis',\n",
        "           color_discrete_map = {'M':'red','B':'blue'}\n",
        "           )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "RD79ea1hdxS_",
        "outputId": "74ce4601-d4a7-4cb7-e6bf-5b0d034deb46"
      },
      "execution_count": 16,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "px.scatter(raw_df,\n",
        "           title='Concavity Mean vs Concave Points Mean',\n",
        "           x='concavity_mean',\n",
        "           y='concave points_mean',\n",
        "           color='diagnosis',\n",
        "           color_discrete_map = {'M':'red','B':'blue'}\n",
        "           )"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "enz5GUZgfnsc",
        "outputId": "f6bc16e5-0600-455f-a5d4-6a3c455f65a3"
      },
      "execution_count": 17,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "px.scatter(raw_df,\n",
        "           title='Radius Mean vs Smoothness Mean',\n",
        "           x='radius_mean',\n",
        "           y='smoothness_mean',\n",
        "           color='diagnosis',\n",
        "           color_discrete_map = {'M':'red','B':'blue'}\n",
        "           )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "B_-9YNKAhg-n",
        "outputId": "856527b2-baf2-4c98-a70a-b9fa8a413cc7"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "px.scatter(raw_df,\n",
        "           title='Texture Mean vs Smoothness Mean',\n",
        "           x='texture_mean',\n",
        "           y='smoothness_mean',\n",
        "           color='diagnosis',\n",
        "           color_discrete_map = {'M':'red','B':'blue'}\n",
        "           )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "U8DHp9G-h3Nv",
        "outputId": "ee552adf-08bd-4351-8f1b-91a3980e73c3"
      },
      "execution_count": 19,
      "outputs": [
        {
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "sM_MEiM1kVMm"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df, test_df = train_test_split(raw_df, test_size = 0.2)\n",
        "train_df, val_df = train_test_split(train_val_df, test_size = 0.25)"
      ],
      "metadata": {
        "id": "13tU9G7Ukzx_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_cols = list(train_df.columns)[2:12]\n",
        "target_col = 'diagnosis'"
      ],
      "metadata": {
        "id": "i_titlVumkxP"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZ5hFQG_m4dx",
        "outputId": "e622f555-37da-42aa-9888-252886defbcb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EjoRBeYfm-Pl",
        "outputId": "b5f58092-9c24-4bd4-8192-002670c0b8e1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'diagnosis'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_inputs = train_df[input_cols].copy()\n",
        "train_targets = train_df[target_col].copy()"
      ],
      "metadata": {
        "id": "s7y2ATCOnQZI"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_inputs = val_df[input_cols].copy()\n",
        "val_targets = val_df[target_col].copy()"
      ],
      "metadata": {
        "id": "2kb9DkmKndpF"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_inputs = test_df[input_cols].copy()\n",
        "test_targets = test_df[target_col].copy()"
      ],
      "metadata": {
        "id": "KPUOfJt2niOf"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "qtCpgR7mnx5J"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df[input_cols].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efoLUVJ0n_s3",
        "outputId": "477660a7-83ef-4784-a415-0d831b35a4b5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "stcHN_k4obeh"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "m0x0IS5tooR3"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(raw_df[input_cols])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "IPQIwSEporw3",
        "outputId": "883eebee-1c4c-4baa-a1f7-f681857fdf22"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(scaler.data_min_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dO97PSHpALq",
        "outputId": "cab0ccd9-b52d-4d0d-89aa-3f7656359ea7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6.981, 9.71, 43.79, 143.5, 0.05263, 0.01938, 0.0, 0.0, 0.106, 0.04996]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(scaler.data_max_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4YIjio7pEDE",
        "outputId": "3202ceb3-e4ca-466a-c5d2-1ad37fd41c22"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[28.11, 39.28, 188.5, 2501.0, 0.1634, 0.3454, 0.4268, 0.2012, 0.304, 0.09744]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_inputs = scaler.transform(train_inputs)\n",
        "val_inputs = scaler.transform(val_inputs)\n",
        "test_inputs = scaler.transform(test_inputs)"
      ],
      "metadata": {
        "id": "r5T819lRpMIf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(train_inputs, train_targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "KMejqbKxqbmM",
        "outputId": "10d12dae-00f5-472e-ac30-2a8c7501372f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_preds = model.predict(train_inputs)\n",
        "\n",
        "print(\"Prediction on training set:\", train_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEG0S1tHrsce",
        "outputId": "eb395d48-33a3-4eb2-c800-3513eca91d90"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction on training set: ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M'\n",
            " 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M'\n",
            " 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B'\n",
            " 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'B']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on training set:\", model.score(train_inputs, train_targets))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "raYFBZTur4DS",
        "outputId": "11973a54-debb-4271-a59e-1a1ae99271ea"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training set: 0.9413489736070382\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_preds = model.predict(val_inputs)\n",
        "\n",
        "print(\"Prediction on validation set:\", val_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SJz_DLOs7wX",
        "outputId": "4e83edf3-765f-4e47-bc62-ec34f267e3f7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction on validation set: ['M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M'\n",
            " 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B'\n",
            " 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'B']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on validation set:\", model.score(val_inputs, val_targets))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vS7Ha24-tW_A",
        "outputId": "7436a739-f46a-45b2-aa7d-2e9a27f4327a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on validation set: 0.868421052631579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_preds = model.predict(test_inputs)\n",
        "\n",
        "print(\"Prediction on test set:\", test_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQXWMp8WtZ6S",
        "outputId": "8b446117-1201-43c9-8a10-3451931deeb5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction on test set: ['M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B'\n",
            " 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B'\n",
            " 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'B'\n",
            " 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'M' 'B' 'M' 'B' 'M' 'B']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on test set:\", model.score(test_inputs, test_targets))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XsDQ-PLtgAY",
        "outputId": "506ac97c-78d2-4049-f3d2-fd28626c16ec"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.9473684210526315\n"
          ]
        }
      ]
    }
  ]
}