breast_cancer_classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/aduvvuri007/Breast-Cancer-Classification/blob/main/breast_cancer_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Breast Cancer Tumor Classification"
      ],
      "metadata": {
        "id": "BBkiJhHUNb9O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "-oSRDfPONiH_"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "5F3TGoPqPYqj"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "df['target'] = data.target\n",
        "df.head()"
      ],
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          "height": 270
        },
        "id": "S38OQJiPPZh1",
        "outputId": "e5ace63b-1989-4040-e627-082b48b30e88"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\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",
              "   worst symmetry  worst fractal dimension  target  \n",
              "0          0.4601                  0.11890       0  \n",
              "1          0.2750                  0.08902       0  \n",
              "2          0.3613                  0.08758       0  \n",
              "3          0.6638                  0.17300       0  \n",
              "4          0.2364                  0.07678       0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
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              "      <th>0</th>\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>0.07871</td>\n",
              "      <td>...</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",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\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>0.05667</td>\n",
              "      <td>...</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",
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              "      <th>2</th>\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>0.05999</td>\n",
              "      <td>...</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>0.09744</td>\n",
              "      <td>...</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",
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              "    <tr>\n",
              "      <th>4</th>\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>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
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              "      <td>1575.0</td>\n",
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              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Store the feature data\n",
        "X = data.data\n",
        "# store the target data\n",
        "y = data.target\n",
        "# split the data using Scikit-Learn's train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "metadata": {
        "id": "xwknxYBjPnkK"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Determining the optimal number of neighbors for the model"
      ],
      "metadata": {
        "id": "hvLVJvMxjcnP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n_samples = X.shape[0]\n",
        "\n",
        "# Define the range of k values to test\n",
        "k_values = range(1, min(31, n_samples))  # Test up to 30 or the number of samples, whichever is smaller\n",
        "cv_scores = []\n",
        "\n",
        "# Perform 10-fold cross-validation for each k\n",
        "for k in k_values:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
        "    cv_scores.append(scores.mean())\n",
        "\n",
        "# Find the optimal k (the one with the highest mean cross-validation score)\n",
        "optimal_k = k_values[np.argmax(cv_scores)]\n",
        "print(f\"The optimal number of neighbors is {optimal_k}\")\n",
        "\n",
        "# Plot the validation scores\n",
        "plt.plot(k_values, cv_scores)\n",
        "plt.xlabel('Number of Neighbors K')\n",
        "plt.ylabel('Cross-Validated Accuracy')\n",
        "plt.title('Validation Accuracy vs. Number of Neighbors')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "wFr5EYNjcyOo",
        "outputId": "4227e71f-b442-476a-a527-64f6d9df4ce6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The optimal number of neighbors is 10\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": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "logreg = KNeighborsClassifier(n_neighbors=10)\n",
        "logreg.fit(X_train, y_train)\n",
        "logreg.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "9PAIxJ0KQeX4",
        "outputId": "7496e05e-7347-4004-b7d3-b82e77045241",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9440559440559441"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rerunning the model with weighted features"
      ],
      "metadata": {
        "id": "uzb9inYcjU3D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit a Random Forest model to identify important features\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=42, class_weight=\"balanced\")\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "# Get feature importances\n",
        "importances = rf.feature_importances_\n",
        "feature_names = df.columns.tolist()\n",
        "\n",
        "# Create weights based on feature importances (normalizing for practicality)\n",
        "weights = importances / np.max(importances)\n",
        "\n",
        "# Print feature importances for reference\n",
        "for feature_name, importance in zip(feature_names, importances):\n",
        "    print(f\"{feature_name}: {importance}\")\n",
        "\n",
        "# Weighted features for training set\n",
        "X_train_weighted = X_train * weights\n",
        "\n",
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train_weighted)\n",
        "\n",
        "# Train k-NN with weighted features\n",
        "knn = KNeighborsClassifier(n_neighbors=10)\n",
        "scores = cross_val_score(knn, X_train_scaled, y_train, cv=10, scoring='accuracy')\n",
        "print(f\"Cross-validated accuracy on training set: {np.mean(scores)}\")\n",
        "\n",
        "# Apply the same transformations to the test set\n",
        "X_test_weighted = X_test * weights\n",
        "X_test_scaled = scaler.transform(X_test_weighted)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "knn.fit(X_train_scaled, y_train)\n",
        "test_accuracy = knn.score(X_test_scaled, y_test)\n",
        "print(f\"Accuracy on test set: {test_accuracy}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkT8IiQJgFcU",
        "outputId": "93408510-02d2-4e22-eca9-868bb57b3974"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean radius: 0.05045620562451859\n",
            "mean texture: 0.01885857796060735\n",
            "mean perimeter: 0.04656409335585341\n",
            "mean area: 0.04880438605899249\n",
            "mean smoothness: 0.00765112066009995\n",
            "mean compactness: 0.013222813675229723\n",
            "mean concavity: 0.07246913865285808\n",
            "mean concave points: 0.11846152472981437\n",
            "mean symmetry: 0.004620362008828828\n",
            "mean fractal dimension: 0.004448151718483312\n",
            "radius error: 0.016153590650197835\n",
            "texture error: 0.004895757316820979\n",
            "perimeter error: 0.009491761301186083\n",
            "area error: 0.02541915893689145\n",
            "smoothness error: 0.006288668644323994\n",
            "compactness error: 0.006224886610812863\n",
            "concavity error: 0.007981852781704804\n",
            "concave points error: 0.004212181521452393\n",
            "symmetry error: 0.004458515302494301\n",
            "fractal dimension error: 0.004711087333231643\n",
            "worst radius: 0.0771259387583946\n",
            "worst texture: 0.02187919925777765\n",
            "worst perimeter: 0.09237844935866851\n",
            "worst area: 0.1390650397388717\n",
            "worst smoothness: 0.010034466844900435\n",
            "worst compactness: 0.01649519775527079\n",
            "worst concavity: 0.04029974191600943\n",
            "worst concave points: 0.11440542004052581\n",
            "worst symmetry: 0.007730673445904379\n",
            "worst fractal dimension: 0.005192038039274339\n",
            "Cross-validated accuracy on training set: 0.9647826086956522\n",
            "Accuracy on test set: 0.956140350877193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Create a SelectFromModel object\n",
        "selector = SelectFromModel(RandomForestClassifier(n_estimators=100))\n",
        "\n",
        "# Fit the selector\n",
        "selector.fit(X_train, y_train)\n",
        "\n",
        "# Transform the data\n",
        "X_train_selected = selector.transform(X_train)\n",
        "X_test_selected = selector.transform(X_test)\n",
        "\n",
        "# Train a kNN model on the selected features\n",
        "knn_selected = KNeighborsClassifier(n_neighbors=10)\n",
        "knn_selected.fit(X_train_selected, y_train)\n",
        "\n",
        "# Evaluate the kNN model on the selected features\n",
        "y_pred_selected = knn_selected.predict(X_test_selected)\n",
        "accuracy_selected = accuracy_score(y_test, y_pred_selected)\n",
        "print(f'Accuracy after Feature Selection: {accuracy_selected:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdGHiv2aNRp1",
        "outputId": "205a17ec-a654-4ba2-f5f8-ff35ac3a69f7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy after Feature Selection: 0.9737\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_indices = selector.get_support()\n",
        "\n",
        "# Get the names of the selected features\n",
        "selected_features = [data.feature_names[i] for i, selected in enumerate(selected_indices) if selected]\n",
        "\n",
        "# Print the selected feature names\n",
        "print(\"Selected Features:\")\n",
        "for feature in selected_features:\n",
        "    print(feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQz0hHTBQUhq",
        "outputId": "966dcf01-7e1c-47c1-8d7a-243d65214f41"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features:\n",
            "mean perimeter\n",
            "mean area\n",
            "mean concavity\n",
            "mean concave points\n",
            "worst radius\n",
            "worst perimeter\n",
            "worst area\n",
            "worst concavity\n",
            "worst concave points\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.calibration import CalibratedClassifierCV\n",
        "from sklearn.metrics import accuracy_score, log_loss\n",
        "\n",
        "calibrated_knn = CalibratedClassifierCV(knn, method='sigmoid', cv=5)\n",
        "calibrated_knn.fit(X_train_selected, y_train)\n",
        "\n",
        "# Predict probabilities\n",
        "y_prob = calibrated_knn.predict_proba(X_test_selected)\n",
        "\n",
        "# Evaluate the accuracy\n",
        "y_pred = calibrated_knn.predict(X_test_selected)\n",
        "accuracy = accuracy_score(y_test, y_pred_selected)\n",
        "logloss = log_loss(y_test, y_prob)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print(f'Log Loss: {logloss:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1c-jhA0O3Gc",
        "outputId": "eb97d6d2-ff91-435b-bd21-6180ca22ee2b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9737\n",
            "Log Loss: 0.1189\n"
          ]
        }
      ]
    }
  ]
}