binary_classification_model.ipynb

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        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score,roc_auc_score,precision_score, recall_score, f1_score"
      ],
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        "id": "LRSpzy3JGR9V"
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      "execution_count": null,
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        "df = pd.read_csv('/content/data.csv')"
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              "         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",
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              "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",
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              "\n",
              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0  ...          17.33           184.60      2019.0            0.1622   \n",
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              "4  ...          16.67           152.20      1575.0            0.1374   \n",
              "\n",
              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0             0.6656           0.7119                0.2654          0.4601   \n",
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              "4             0.2050           0.4000                0.1625          0.2364   \n",
              "\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fd958173-7fef-406d-aab9-13694bc561b2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd958173-7fef-406d-aab9-13694bc561b2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fd958173-7fef-406d-aab9-13694bc561b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYOxLiBO5yQd",
        "outputId": "b29e8e67-8f3d-4271-f4c2-e7193102aabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "FFe5vz5bVXSq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Class Distribution:\")\n",
        "print(df['diagnosis'].value_counts())\n",
        "sns.countplot(x=df['diagnosis'])\n",
        "plt.xticks(ticks=[0,1], labels=['Malignant', 'Benign'])\n",
        "plt.title(\"Class Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "tILsD2KzUtRD",
        "outputId": "2ae7cd14-eed0-4e62-c2f0-c95b2c1e182e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Distribution:\n",
            "diagnosis\n",
            "B    357\n",
            "M    212\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.heatmap(df.corr(numeric_only=True), cmap=\"magma\", annot=False)\n",
        "plt.title(\"Feature Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "IViEdtxGgi26",
        "outputId": "672fb1c1-44a7-4b82-ed79-982e80189327"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['id','Unnamed: 32'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "0A3YmQRtQUjE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "qFXKg1fXQ4Bq",
        "outputId": "d2d3c31c-3d89-47c0-be38-ba417a428b4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a88049f9-850c-47c0-b7a8-ec97f7b51383\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <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",
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              "                                                    [key], {});\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "df['diagnosis'] = le.fit_transform(df['diagnosis'])\n"
      ],
      "metadata": {
        "id": "ZS0SPYun5_px"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(\"diagnosis\", axis=1)\n",
        "y = df[\"diagnosis\"]"
      ],
      "metadata": {
        "id": "E9rMQ8FXGeYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "-P0ohT_aHacM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "DeF7GU10Hf0t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(max_iter=200),\n",
        "    \"Decision Tree\": DecisionTreeClassifier(),\n",
        "    \"Random Forest\": RandomForestClassifier(),\n",
        "    \"SVM\": SVC(probability=True),\n",
        "    \"k-NN\": KNeighborsClassifier(),\n",
        "\n",
        "}\n"
      ],
      "metadata": {
        "id": "Mf8v0jlZHipu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracies[name] = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print(\"Model Accuracies: \",accuracies)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eplg2fryX-hd",
        "outputId": "ac787213-c765-43f8-c2ec-7c75f738408b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracies:  {'Logistic Regression': 0.9736842105263158, 'Decision Tree': 0.9385964912280702, 'Random Forest': 0.9649122807017544, 'SVM': 0.9736842105263158, 'k-NN': 0.9473684210526315}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precisions = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    precisions[name] = precision_score(y_test, y_pred)\n",
        "\n",
        "print(\"Model Precisions:\", precisions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7RpKvggaqB9",
        "outputId": "c05494d9-87b6-4651-d949-eaa6ad204e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Precisions: {'Logistic Regression': 0.9761904761904762, 'Decision Tree': 0.9090909090909091, 'Random Forest': 0.975609756097561, 'SVM': 0.9761904761904762, 'k-NN': 0.9302325581395349}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recalls = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    recalls[name] = recall_score(y_test, y_pred)\n",
        "\n",
        "print(\"Model Recalls:\", recalls)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JW5TO6vmatRZ",
        "outputId": "f3a8d3d8-fd05-46d0-ad4d-e36756b570ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Recalls: {'Logistic Regression': 0.9534883720930233, 'Decision Tree': 0.9069767441860465, 'Random Forest': 0.9302325581395349, 'SVM': 0.9534883720930233, 'k-NN': 0.9302325581395349}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_scores = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    f1_scores[name] = f1_score(y_test, y_pred)\n",
        "\n",
        "print(\"Model F1-Scores:\", f1_scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3VOZmGmawFg",
        "outputId": "b49ef7ed-40d9-47a0-aeae-0f46941a2133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model F1-Scores: {'Logistic Regression': 0.9647058823529412, 'Decision Tree': 0.9176470588235294, 'Random Forest': 0.9523809523809523, 'SVM': 0.9647058823529412, 'k-NN': 0.9302325581395349}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "roc_auc_scores = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred_prob = model.predict_proba(X_test)[:, 1]\n",
        "    roc_auc_scores[name] = roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "print(\"Model ROC-AUC Scores:\", roc_auc_scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgswZCsZbCyy",
        "outputId": "14e3b267-e488-459f-c61c-190d99df887d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model ROC-AUC Scores: {'Logistic Regression': 0.99737962659679, 'Decision Tree': 0.9299050114641335, 'Random Forest': 0.9950867998689813, 'SVM': 0.99737962659679, 'k-NN': 0.9816573861775302}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = max(accuracies, key=accuracies.get)\n",
        "print(f\"Best Model: {best_model} with accuracy {accuracies[best_model]:.3f}\")"
      ],
      "metadata": {
        "id": "F5xLoqUHbn-t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b19af140-7863-446f-db49-f86943771240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: Logistic Regression with accuracy 0.974\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_precision_model = max(precisions, key=precisions.get)\n",
        "print(f\"Best Model : {best_precision_model} with Precision {precisions[best_precision_model]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qX2Z4HT_b5Bh",
        "outputId": "0a7abf81-71e4-4cbe-c6b1-71be834da4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model : Logistic Regression with Precision 0.976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_recall_model = max(recalls, key=recalls.get)\n",
        "print(f\"Best Model : {best_recall_model} with recall {recalls[best_recall_model]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDkmr4CCb0p6",
        "outputId": "602d6218-6dd3-403f-d4ab-e40325896707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model : Logistic Regression with recall 0.953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_f1_model = max(f1_scores, key=f1_scores.get)\n",
        "print(f\"Best Model : {best_f1_model} with f1_score {f1_scores[best_f1_model]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQJc5sj3bwhD",
        "outputId": "e5eca73b-cff6-4972-9f15-05c26cbee2a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model : Logistic Regression with f1_score 0.965\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_roc_auc_model = max(roc_auc_scores, key=roc_auc_scores.get)\n",
        "print(f\"Best Model : {best_roc_auc_model} with roc_auc_score {roc_auc_scores[best_roc_auc_model]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sv1QCOvLbtPJ",
        "outputId": "24f00cc7-570e-4fb0-912c-bc0bac7d8ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model : Logistic Regression with roc_auc_score 0.997\n"
          ]
        }
      ]
    }
  ]
}