08. Logistic_Regression.ipynb

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        "id": "3eba4acc-d13e-408e-85b4-b42d8439c42e"
      },
      "source": [
        "# Logistic regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "473959e9-38b8-4540-9ab9-8d028ddfc167",
      "metadata": {
        "id": "473959e9-38b8-4540-9ab9-8d028ddfc167"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5117ca17-bac5-4eda-aec6-a5df8dc0e628",
      "metadata": {
        "id": "5117ca17-bac5-4eda-aec6-a5df8dc0e628"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
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      "id": "00f48bce",
      "metadata": {
        "colab": {
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          "height": 273
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        "id": "00f48bce",
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      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <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>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <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>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <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>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>"
            ],
            "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",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33           184.60      2019.0   \n",
              "1  ...         24.99          23.41           158.80      1956.0   \n",
              "2  ...         23.57          25.53           152.50      1709.0   \n",
              "3  ...         14.91          26.50            98.87       567.7   \n",
              "4  ...         22.54          16.67           152.20      1575.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "3          0.6638                  0.17300  \n",
              "4          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('Datasets/classification/breast-cancer-wisconsin.csv')\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <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>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <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>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0         M        17.99         10.38          122.80     1001.0   \n",
              "1         M        20.57         17.77          132.90     1326.0   \n",
              "2         M        19.69         21.25          130.00     1203.0   \n",
              "3         M        11.42         20.38           77.58      386.1   \n",
              "4         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33           184.60   \n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "4         0.1809  ...         22.54          16.67           152.20   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = df.iloc[:, 1:]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29f2d528",
      "metadata": {
        "id": "29f2d528"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b59b6f7",
      "metadata": {
        "id": "7b59b6f7"
      },
      "source": [
        "### 1. Looking for null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "d968328f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d968328f",
        "outputId": "682de2e8-83b9-4ac5-a733-5567aacd4332"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "diagnosis                   object\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "31943f8f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "31943f8f",
        "outputId": "11d2d320-48dc-4fe1-a127-baf7f11550eb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9932fc70",
      "metadata": {
        "id": "9932fc70"
      },
      "source": [
        "### 2. Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4a712b29",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a712b29",
        "outputId": "3b90c464-8d1f-4413-d403-ad7164d5bc08"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['diagnosis'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "cdf01bd1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "cdf01bd1",
        "outputId": "3a35cc3e-82c9-4f41-c87d-27f534d192f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Malignant=1, Benign=0\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <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>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <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>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</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",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</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>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0          1        17.99         10.38          122.80     1001.0   \n",
              "1          1        20.57         17.77          132.90     1326.0   \n",
              "2          1        19.69         21.25          130.00     1203.0   \n",
              "3          1        11.42         20.38           77.58      386.1   \n",
              "4          1        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33           184.60   \n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "4         0.1809  ...         22.54          16.67           152.20   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Replace M with 1 and Begnin with 0 (else 0)\n",
        "print(\"Malignant=1, Benign=0\")\n",
        "df[\"diagnosis\"]= df[\"diagnosis\"].map(lambda row: 1 if row=='M' else 0)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "30599d9d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30599d9d",
        "outputId": "d20d118e-900d-48a9-ccdf-1549e4bcbdcd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    357\n",
              "1    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['diagnosis'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "58313a11",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "58313a11",
        "outputId": "6ab1cab4-03ad-471b-8746-f5fc98a4ad89"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.372583</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.483918</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        diagnosis  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count  569.000000   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     0.372583    14.127292     19.289649       91.969033   654.889104   \n",
              "std      0.483918     3.524049      4.301036       24.298981   351.914129   \n",
              "min      0.000000     6.981000      9.710000       43.790000   143.500000   \n",
              "25%      0.000000    11.700000     16.170000       75.170000   420.300000   \n",
              "50%      0.000000    13.370000     18.840000       86.240000   551.100000   \n",
              "75%      1.000000    15.780000     21.800000      104.100000   782.700000   \n",
              "max      1.000000    28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
              "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
              "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
              "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
              "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
              "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
              "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
              "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
              "\n",
              "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "count   569.000000        569.000000         569.000000       569.000000   \n",
              "mean    880.583128          0.132369           0.254265         0.272188   \n",
              "std     569.356993          0.022832           0.157336         0.208624   \n",
              "min     185.200000          0.071170           0.027290         0.000000   \n",
              "25%     515.300000          0.116600           0.147200         0.114500   \n",
              "50%     686.500000          0.131300           0.211900         0.226700   \n",
              "75%    1084.000000          0.146000           0.339100         0.382900   \n",
              "max    4254.000000          0.222600           1.058000         1.252000   \n",
              "\n",
              "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "count            569.000000      569.000000               569.000000  \n",
              "mean               0.114606        0.290076                 0.083946  \n",
              "std                0.065732        0.061867                 0.018061  \n",
              "min                0.000000        0.156500                 0.055040  \n",
              "25%                0.064930        0.250400                 0.071460  \n",
              "50%                0.099930        0.282200                 0.080040  \n",
              "75%                0.161400        0.317900                 0.092080  \n",
              "max                0.291000        0.663800                 0.207500  \n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64157447",
      "metadata": {
        "id": "64157447"
      },
      "source": [
        "### 3. Unique Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "ae81abc2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae81abc2",
        "outputId": "c7e8cf75-b69d-422d-9aeb-f6eb179ce31a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The unique number of data values are\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "diagnosis                    2\n",
              "radius_mean                456\n",
              "texture_mean               479\n",
              "perimeter_mean             522\n",
              "area_mean                  539\n",
              "smoothness_mean            474\n",
              "compactness_mean           537\n",
              "concavity_mean             537\n",
              "concave points_mean        542\n",
              "symmetry_mean              432\n",
              "fractal_dimension_mean     499\n",
              "radius_se                  540\n",
              "texture_se                 519\n",
              "perimeter_se               533\n",
              "area_se                    528\n",
              "smoothness_se              547\n",
              "compactness_se             541\n",
              "concavity_se               533\n",
              "concave points_se          507\n",
              "symmetry_se                498\n",
              "fractal_dimension_se       545\n",
              "radius_worst               457\n",
              "texture_worst              511\n",
              "perimeter_worst            514\n",
              "area_worst                 544\n",
              "smoothness_worst           411\n",
              "compactness_worst          529\n",
              "concavity_worst            539\n",
              "concave points_worst       492\n",
              "symmetry_worst             500\n",
              "fractal_dimension_worst    535\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"The unique number of data values are\")\n",
        "df.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "781c8d0d",
      "metadata": {
        "id": "781c8d0d"
      },
      "source": [
        "### 4. Data Spread between the two types (Malignant, Benign)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "f485b981",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f485b981",
        "outputId": "e860ccf7-1e93-4e04-d449-3df2400a9bba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "diagnosis                    int64\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "ecf2fc1d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "ecf2fc1d",
        "outputId": "3264dcfe-95bb-4162-f20d-d74c5f77f885"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    357\n",
            "1    212\n",
            "Name: diagnosis, dtype: int64\n",
            "0.37258347978910367\n",
            "Total number of data points = 569\n",
            "Malignant (diagnosis = 1) = 37.26%\n",
            "Benign (diagnosis = 0) = 62.74%\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "diagnosis_counts = df[\"diagnosis\"].value_counts()\n",
        "mean_diagnosis = df[\"diagnosis\"].mean()\n",
        "total_data_points = len(df)\n",
        "\n",
        "print(diagnosis_counts)\n",
        "print(mean_diagnosis)\n",
        "\n",
        "print(\"Total number of data points =\", total_data_points)\n",
        "print(\"Malignant (diagnosis = 1) = {:.2f}%\".format(mean_diagnosis * 100))\n",
        "print(\"Benign (diagnosis = 0) = {:.2f}%\".format((1 - mean_diagnosis) * 100))\n",
        "\n",
        "sns.countplot(data=df, x=\"diagnosis\")\n",
        "plt.ylabel(\"Number of data points\")\n",
        "plt.title(\"Malignant (1) vs Benign Data(0) points\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbf9838a",
      "metadata": {
        "id": "cbf9838a"
      },
      "source": [
        "### 5. Feature selection (Correlation Matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "0698f6ff",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0698f6ff",
        "outputId": "e8c8a5ae-0588-4a56-b00c-893e9bd3cf15"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>0.793566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>0.782914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>0.776454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>0.733825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>0.659610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>0.590998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>0.567134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>0.556141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>0.548236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>0.456903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>0.421465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>0.416294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>0.408042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>0.323872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>0.292999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>0.253730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>0.077972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>0.067016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>0.012838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>0.008303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>0.006522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         diagnosis\n",
              "diagnosis                 1.000000\n",
              "concave points_worst      0.793566\n",
              "perimeter_worst           0.782914\n",
              "concave points_mean       0.776614\n",
              "radius_worst              0.776454\n",
              "perimeter_mean            0.742636\n",
              "area_worst                0.733825\n",
              "radius_mean               0.730029\n",
              "area_mean                 0.708984\n",
              "concavity_mean            0.696360\n",
              "concavity_worst           0.659610\n",
              "compactness_mean          0.596534\n",
              "compactness_worst         0.590998\n",
              "radius_se                 0.567134\n",
              "perimeter_se              0.556141\n",
              "area_se                   0.548236\n",
              "texture_worst             0.456903\n",
              "smoothness_worst          0.421465\n",
              "symmetry_worst            0.416294\n",
              "texture_mean              0.415185\n",
              "concave points_se         0.408042\n",
              "smoothness_mean           0.358560\n",
              "symmetry_mean             0.330499\n",
              "fractal_dimension_worst   0.323872\n",
              "compactness_se            0.292999\n",
              "concavity_se              0.253730\n",
              "fractal_dimension_se      0.077972\n",
              "smoothness_se             0.067016\n",
              "fractal_dimension_mean    0.012838\n",
              "texture_se                0.008303\n",
              "symmetry_se               0.006522"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corr = df.corr()\n",
        "corr[['diagnosis']].abs().sort_values(by='diagnosis', ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9947c0cd",
      "metadata": {
        "id": "9947c0cd"
      },
      "source": [
        "#### Feature Selection and Correlation Matrix Visualization for Breast Cancer Diagnosis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "dc7351a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "dc7351a6",
        "outputId": "64650ae8-6888-44b5-e1fd-12bef0c542cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected Features:\n",
            "Index(['concave points_mean', 'perimeter_worst', 'concave points_worst'], dtype='object')\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import SelectKBest, f_regression, r_regression\n",
        "import numpy as np\n",
        "\n",
        "# Separate the features (X) and the target variable (y)\n",
        "X = df.drop('diagnosis', axis=1)\n",
        "y = df['diagnosis']\n",
        "\n",
        "# Perform feature selection\n",
        "selector = SelectKBest(score_func=f_regression, k=3)\n",
        "selected_features = selector.fit_transform(X, y)\n",
        "\n",
        "# Get the indices of the selected features\n",
        "selected_indices = selector.get_support(indices=True)\n",
        "\n",
        "# Get the names of the selected features\n",
        "selected_feature_names = X.columns[selected_indices]\n",
        "\n",
        "# Print the selected feature names\n",
        "print(\"Selected Features:\")\n",
        "print(selected_feature_names)\n",
        "\n",
        "# Create a DataFrame with the selected features\n",
        "selected_df = X[selected_feature_names]\n",
        "\n",
        "# Calculate the correlation matrix between selected features and target\n",
        "correlation_matrix = selected_df.join(y).corr()\n",
        "new_df = selected_df.join(y)\n",
        "\n",
        "# Create a triangular mask\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "\n",
        "# Create a heatmap to visualize the correlation matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', mask=mask)\n",
        "plt.title('Correlation Matrix between Selected Features and Target')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "f9c351de",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9c351de",
        "outputId": "05c7466e-f93c-4a84-ab88-66c660782321"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ True  True  True  True]\n",
            " [False  True  True  True]\n",
            " [False False  True  True]\n",
            " [False False False  True]]\n"
          ]
        }
      ],
      "source": [
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "print(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "8fa775c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8fa775c1",
        "outputId": "b2eac15c-83f6-4dfb-fa99-1273b57eae76"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.14710</td>\n",
              "      <td>184.60</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.07017</td>\n",
              "      <td>158.80</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.12790</td>\n",
              "      <td>152.50</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.10520</td>\n",
              "      <td>98.87</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.10430</td>\n",
              "      <td>152.20</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   concave points_mean  perimeter_worst  concave points_worst  diagnosis\n",
              "0              0.14710           184.60                0.2654          1\n",
              "1              0.07017           158.80                0.1860          1\n",
              "2              0.12790           152.50                0.2430          1\n",
              "3              0.10520            98.87                0.2575          1\n",
              "4              0.10430           152.20                0.1625          1"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbc039ad",
      "metadata": {
        "id": "cbc039ad"
      },
      "source": [
        "### 6. Dimension Reduction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "d11f63f1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "d11f63f1",
        "outputId": "3cfd57ee-a9d6-4b1b-8505-85e7bf76e867"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'PCA projection of Breast Cancer Dataset')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "X = df.drop(['diagnosis'], axis=1)\n",
        "y = df['diagnosis']\n",
        "\n",
        "X_scaled = StandardScaler().fit_transform(X)\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "X_pca_scaled = pca.fit_transform(X_scaled)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(X_pca_scaled[:, 0], X_pca_scaled[:, 1], c=df['diagnosis'], alpha=0.7, s=30, cmap='plasma')\n",
        "plt.xlabel('PCA component 1')\n",
        "plt.ylabel('PCA component 2')\n",
        "plt.title('PCA projection of Breast Cancer Dataset')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36330ea4-5cf4-47c8-b042-c3d5813eb97f",
      "metadata": {
        "id": "36330ea4-5cf4-47c8-b042-c3d5813eb97f"
      },
      "source": [
        "## Train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "2541cb14",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2541cb14",
        "outputId": "91d133da-dbd4-499d-e3a6-91bd997dd685"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>184.60</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>158.80</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>152.50</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>98.87</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>152.20</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   perimeter_worst  concave points_worst  diagnosis\n",
              "0           184.60                0.2654          1\n",
              "1           158.80                0.1860          1\n",
              "2           152.50                0.2430          1\n",
              "3            98.87                0.2575          1\n",
              "4           152.20                0.1625          1"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df = new_df.drop(columns=['concave points_mean'], axis=1)\n",
        "new_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "8be19c31",
      "metadata": {
        "id": "8be19c31"
      },
      "outputs": [],
      "source": [
        "# X = new_df.drop('diagnosis', axis=1)\n",
        "# y = new_df['diagnosis']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "3c999e22-ea0c-47e3-84b2-800a25f0fff9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c999e22-ea0c-47e3-84b2-800a25f0fff9",
        "outputId": "74cf3627-11d6-482b-b913-2e46a039aa67"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((569, 3), (455, 3), (114, 3))"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df, test_df = train_test_split(new_df, test_size=0.2, random_state=42)\n",
        "new_df.shape, train_df.shape, test_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "726af50d-b38b-41f2-bf5f-55adee71b90f",
      "metadata": {
        "id": "726af50d-b38b-41f2-bf5f-55adee71b90f"
      },
      "source": [
        "## Data Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2def1607",
      "metadata": {
        "id": "2def1607"
      },
      "source": [
        "### 1. Normalization\n",
        "Normalization scales the values of a dataset to a specific range, typically between **0 and 1**, to ensure consistency and improve the performance."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1acfbb3b",
      "metadata": {
        "id": "1acfbb3b"
      },
      "source": [
        "#### Method 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "50fd0eb0",
      "metadata": {
        "id": "50fd0eb0"
      },
      "outputs": [],
      "source": [
        "def normalization(data, train_data):\n",
        "    min_value = np.min(train_data, axis=0)\n",
        "    max_value = np.max(train_data, axis=0)\n",
        "    normalized_data = (data - min_value) / (max_value - min_value)\n",
        "    return normalized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "54708587",
      "metadata": {
        "id": "54708587"
      },
      "outputs": [],
      "source": [
        "# Assuming train_data and test_data are the respective train and test sets\n",
        "normalized_train = normalization(train_df, train_df)\n",
        "normalized_test  = normalization(test_df, train_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "a8f7e47c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a8f7e47c",
        "outputId": "3652afcd-3029-4b75-cec1-3b78459a18cc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.055971</td>\n",
              "      <td>0.601375</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>0.620253</td>\n",
              "      <td>0.997595</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.056428</td>\n",
              "      <td>0.174811</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>0.119414</td>\n",
              "      <td>0.210859</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.078135</td>\n",
              "      <td>0.088625</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     perimeter_worst  concave points_worst  diagnosis\n",
              "68          0.055971              0.601375        0.0\n",
              "181         0.620253              0.997595        1.0\n",
              "63          0.056428              0.174811        0.0\n",
              "248         0.119414              0.210859        0.0\n",
              "60          0.078135              0.088625        0.0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "normalized_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "a40bae1e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a40bae1e",
        "outputId": "d98137cd-0764-4085-99fd-6e23f4939592"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>65.50</td>\n",
              "      <td>0.17500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>176.50</td>\n",
              "      <td>0.29030</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>65.59</td>\n",
              "      <td>0.05087</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>77.98</td>\n",
              "      <td>0.06136</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>69.86</td>\n",
              "      <td>0.02579</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     perimeter_worst  concave points_worst  diagnosis\n",
              "68             65.50               0.17500          0\n",
              "181           176.50               0.29030          1\n",
              "63             65.59               0.05087          0\n",
              "248            77.98               0.06136          0\n",
              "60             69.86               0.02579          0"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "1c031149",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1c031149",
        "outputId": "8581662e-b04b-4a45-c00d-9b8812a05888"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimum values:\n",
            "perimeter_worst         54.49\n",
            "concave points_worst     0.00\n",
            "diagnosis                0.00\n",
            "dtype: float64\n",
            "\n",
            "Maximum values:\n",
            "perimeter_worst         251.200\n",
            "concave points_worst      0.291\n",
            "diagnosis                 1.000\n",
            "dtype: float64\n",
            "\n",
            "Standard deviation:\n",
            "perimeter_worst         33.374664\n",
            "concave points_worst     0.065326\n",
            "diagnosis                0.483719\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"Minimum values:\")\n",
        "print(train_df.min())\n",
        "print()\n",
        "\n",
        "print(\"Maximum values:\")\n",
        "print(train_df.max())\n",
        "print()\n",
        "\n",
        "print(\"Standard deviation:\")\n",
        "print(train_df.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "5be7fc56",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5be7fc56",
        "outputId": "4f05de9f-267e-4fb1-be05-f464a4c8a639"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Minimum values:\n",
            "perimeter_worst         0.0\n",
            "concave points_worst    0.0\n",
            "diagnosis               0.0\n",
            "dtype: float64\n",
            "\n",
            "Maximum values:\n",
            "perimeter_worst         1.0\n",
            "concave points_worst    1.0\n",
            "diagnosis               1.0\n",
            "dtype: float64\n",
            "\n",
            "Standard deviation:\n",
            "perimeter_worst         0.169664\n",
            "concave points_worst    0.224488\n",
            "diagnosis               0.483719\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"Minimum values:\")\n",
        "print(normalized_train.min())\n",
        "print()\n",
        "\n",
        "print(\"Maximum values:\")\n",
        "print(normalized_train.max())\n",
        "print()\n",
        "\n",
        "print(\"Standard deviation:\")\n",
        "print(normalized_train.std())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dea6f6da",
      "metadata": {
        "id": "dea6f6da"
      },
      "source": [
        "#### Method 2\n",
        "\n",
        "The output type of the `fit_transform` method of `MinMaxScaler` is a numpy array. When you apply `fit_transform` to your data, it returns a transformed version of the input data, where each feature (column) has been scaled to the specified range."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "83715803",
      "metadata": {
        "id": "83715803"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fit the scaler on the train DataFrame\n",
        "scaler.fit(train_df)\n",
        "\n",
        "# Normalize the train DataFrame\n",
        "normalized_train = scaler.transform(train_df)\n",
        "\n",
        "# Normalize the test DataFrame using the fitted scaler\n",
        "normalized_test = scaler.transform(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "2c4acd85",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c4acd85",
        "outputId": "baf44a81-c39f-4942-e77c-f0f1c8120f56"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 1.0, -0.0207411926185756, 1.0)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "normalized_train.min(), normalized_train.max(), normalized_test.min(), normalized_test.max()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1983d003",
      "metadata": {
        "id": "1983d003"
      },
      "source": [
        "### 2. Standardization"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "005aec25",
      "metadata": {
        "id": "005aec25"
      },
      "source": [
        "#### Method 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "782c494f",
      "metadata": {
        "id": "782c494f"
      },
      "outputs": [],
      "source": [
        "def standardization(data, train_data):\n",
        "    mean_value = np.mean(train_data, axis=0)\n",
        "    std_value  = np.std(train_data, axis=0)\n",
        "    standardized_data = (data - mean_value) / std_value\n",
        "    return standardized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "ba065692",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ba065692",
        "outputId": "2e43691e-75d5-4620-a158-03a4f05041e4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>-1.247920</td>\n",
              "      <td>0.932012</td>\n",
              "      <td>-0.768706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>2.081617</td>\n",
              "      <td>2.698947</td>\n",
              "      <td>1.300887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>-1.245220</td>\n",
              "      <td>-0.970239</td>\n",
              "      <td>-0.768706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>-0.873572</td>\n",
              "      <td>-0.809483</td>\n",
              "      <td>-0.768706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>-1.117138</td>\n",
              "      <td>-1.354582</td>\n",
              "      <td>-0.768706</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     perimeter_worst  concave points_worst  diagnosis\n",
              "68         -1.247920              0.932012  -0.768706\n",
              "181         2.081617              2.698947   1.300887\n",
              "63         -1.245220             -0.970239  -0.768706\n",
              "248        -0.873572             -0.809483  -0.768706\n",
              "60         -1.117138             -1.354582  -0.768706"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "standardized_train = standardization(train_df, train_df)\n",
        "standardized_test  = standardization(test_df, train_df)\n",
        "\n",
        "standardized_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "77b0e255",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77b0e255",
        "outputId": "8e5d948f-ba32-4fde-8998-2160a3ec7987"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(perimeter_worst        -1.578174\n",
              " concave points_worst   -1.749805\n",
              " diagnosis              -0.768706\n",
              " dtype: float64,\n",
              " perimeter_worst         4.322305\n",
              " concave points_worst    2.709674\n",
              " diagnosis               1.300887\n",
              " dtype: float64)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "standardized_train.min(), standardized_train.max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "06e454df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "06e454df",
        "outputId": "45de244f-7975-4497-956b-df56b449a7f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate the minimum and maximum values for each feature\n",
        "min_values = standardized_train.min()\n",
        "max_values = standardized_train.max()\n",
        "\n",
        "# Create a bar plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(min_values.index, min_values, label='Min')\n",
        "plt.bar(max_values.index, max_values, label='Max')\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Normalized Values')\n",
        "plt.title('Minimum and Maximum Values for Each Feature')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bcb7f2ab",
      "metadata": {
        "id": "bcb7f2ab"
      },
      "source": [
        "#### Method 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "0b575829-450f-4743-8a2e-c0fecff8bfda",
      "metadata": {
        "id": "0b575829-450f-4743-8a2e-c0fecff8bfda"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "450032d4-365c-4f17-9a21-aea4c4d3ef31",
      "metadata": {
        "id": "450032d4-365c-4f17-9a21-aea4c4d3ef31"
      },
      "outputs": [],
      "source": [
        "standardized_train = scaler.fit_transform(train_df)\n",
        "standardized_test =  scaler.transform(test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "166ef345-da6e-44a9-a5f7-ea74e42630bf",
      "metadata": {
        "id": "166ef345-da6e-44a9-a5f7-ea74e42630bf"
      },
      "source": [
        "## Model with GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "658bfabf",
      "metadata": {
        "id": "658bfabf"
      },
      "outputs": [],
      "source": [
        "normalized_train = normalization(train_df, train_df)\n",
        "normalized_test  = normalization(test_df, train_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "4170528e",
      "metadata": {
        "id": "4170528e"
      },
      "outputs": [],
      "source": [
        "# Extract features and target variable columns from normalized_train, and normalized_test\n",
        "x_train = normalized_train.drop(columns=['diagnosis']).values\n",
        "y_train = normalized_train['diagnosis'].values\n",
        "\n",
        "x_test = normalized_test.drop(columns=['diagnosis']).values\n",
        "y_test = normalized_test['diagnosis'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "051f8b4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "051f8b4d",
        "outputId": "1c2e827a-60bb-46be-d0b3-faf68f78536e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(455, 2) (455,) (114, 2) (114,)\n"
          ]
        }
      ],
      "source": [
        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "3a3743a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a3743a5",
        "outputId": "88d292af-fe26-4951-8b23-72ecd1c62cf7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.min(), x_train.max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "90fa4490-58af-45a2-9a75-196b286876bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "90fa4490-58af-45a2-9a75-196b286876bb",
        "outputId": "26a4a906-d540-47ba-a8f9-8345aec0f7d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LogisticRegression(C=1)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'C': [0.01, 0.1, 1, 2, 5],\n",
        "    'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']\n",
        "}\n",
        "\n",
        "logistic_regression = LogisticRegression()\n",
        "model = GridSearchCV(logistic_regression, param_grid)\n",
        "model.fit(x_train, y_train)\n",
        "best_clf = model.best_estimator_\n",
        "best_clf"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d785402-5f36-4dcf-8034-489a56f849e8",
      "metadata": {
        "id": "8d785402-5f36-4dcf-8034-489a56f849e8"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "9f2ba573-9a34-497a-bc85-2bb609f918e4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f2ba573-9a34-497a-bc85-2bb609f918e4",
        "outputId": "c66e6be8-7fa0-4395-8842-eef808eec5e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.97      0.95        71\n",
            "         1.0       0.95      0.88      0.92        43\n",
            "\n",
            "    accuracy                           0.94       114\n",
            "   macro avg       0.94      0.93      0.93       114\n",
            "weighted avg       0.94      0.94      0.94       114\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_pred = best_clf.predict(x_test)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f0f9b98-511b-4206-81f5-e9bdba58f4be",
      "metadata": {
        "id": "3f0f9b98-511b-4206-81f5-e9bdba58f4be"
      },
      "source": [
        "## Plot the decision boundary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "17371560-eeae-4d4e-959a-85b59458492c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "17371560-eeae-4d4e-959a-85b59458492c",
        "outputId": "775aee61-fd87-409c-f32a-a4599b9c8578"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "plot_decision_regions(x_train, y_train.astype(np.int_), clf=best_clf)\n",
        "\n",
        "ax.set_xlabel(\"Feature 1\")\n",
        "ax.set_ylabel(\"Feature 2\")\n",
        "ax.set_title(\"Decision Boundary\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}