Breast_cancer.ipynb

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      "metadata": {
        "id": "F446J_Ha2DPC"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cancer=datasets.load_breast_cancer()\n",
        "cancer"
      ],
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              " 'frame': None,\n",
              " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
              " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
              " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " 'filename': 'breast_cancer.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
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        "x=cancer.data\n",
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      "execution_count": 13,
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      "execution_count": 15,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "clf=svm.SVC(kernel='rbf',C=1,gamma='scale')"
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      "metadata": {
        "id": "Iwk7_Nk04XGY"
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      "execution_count": 16,
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            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=np.random.rand(5,x.shape[1])\n",
        "df=scaler.transform(df)\n",
        "prediction=clf.predict(df)\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EkasohuN4kHe",
        "outputId": "d1747234-50c5-4a9c-9182-3a84e832f644"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=clf.predict(x_test)\n",
        "accuracy=accuracy_score(y_test,y_pred)\n",
        "confusion_matrixes=confusion_matrix(y_test,y_pred)"
      ],
      "metadata": {
        "id": "Kw_IYLtA5Rg7"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\",accuracy)\n",
        "print(\"Confusion Matrix:\\n\",confusion_matrixes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-D77TCWg5we8",
        "outputId": "406381d0-c6bb-461a-f30a-c9d863e5eebc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9824561403508771\n",
            "Confusion Matrix:\n",
            " [[41  2]\n",
            " [ 0 71]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_decision_boundary(X, y, model, title):\n",
        "    h = .02  # Step size in the mesh\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
        "\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "\n",
        "    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors='k', marker='o')\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "\n",
        "# For visualization, consider only the first two features\n",
        "X_train_2d = X_train[:, :2]\n",
        "X_test_2d = X_test[:, :2]\n",
        "plot_decision_boundary(X_train_2d, y_train, clf, \"SVM Decision Boundary - Training Set\")\n",
        "plot_decision_boundary(X_test_2d, y_test, clf, \"SVM Decision Boundary - Test Set\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "47gqxf_NCQRM",
        "outputId": "8e9b1743-ad8e-4746-9e39-7a3107df9089"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}