Breast_Cancer_Wisconsin.ipynb

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      "name": "Breast Cancer Wisconsin ",
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        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "\n",
        "data=pd.read_csv('data.csv')\n",
        "data.head()"
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              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
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      "execution_count": 1,
      "metadata": {
        "id": "56PSOibX2gtN"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import  keras\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
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        "outputId": "822d7a08-ef21-443c-d976-696392470b8b"
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      "execution_count": 6,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
              "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
              "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
              "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
              "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
              "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
              "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
              "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
              "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
              "      dtype='object')"
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          "execution_count": 6
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        "outputId": "d3528742-8ae7-4c19-fd34-8e7745be6ce1"
      },
      "execution_count": 7,
      "outputs": [
        {
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          "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"
          ]
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    {
      "cell_type": "code",
      "source": [
        "data.isna ().sum()"
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        "outputId": "8d4ce5c4-ca01-4cd5-b680-986fd53bcc06"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "diagnosis                    0\n",
              "radius_mean                  0\n",
              "texture_mean                 0\n",
              "perimeter_mean               0\n",
              "area_mean                    0\n",
              "smoothness_mean              0\n",
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              "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",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.drop ('Unnamed: 32',axis=1)"
      ],
      "metadata": {
        "id": "kMsYa9_x7IuD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['diagnosis'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnxGXizt7gdH",
        "outputId": "94f87a95-a0c1-476b-90a8-ef99386710f9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'B'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l=list(data.columns)\n",
        "print(l)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_ADF5jR7tsY",
        "outputId": "bf4934c3-85ab-4281-b9b9-56f72e1895f5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data['diagnosis'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "iXsl7bdS8KgN",
        "outputId": "f53ef937-fec8-46f2-a263-1b514d78e7c7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b3e5d9e10>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr=data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(corr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "G-CzpWIk8ndN",
        "outputId": "6cb907ef-af49-422c-9386-f5ce7fdaf172"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b3e07cad0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['diagnosis']=data['diagnosis'].map({'M':1,'B':0})\n",
        "data['diagnosis'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0g30xvP9PLS",
        "outputId": "a514997f-b03d-4447-8309-7607d9589dea"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop('diagnosis',axis=1)\n",
        "y=data['diagnosis']\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split (x,y,test_size=0.3)"
      ],
      "metadata": {
        "id": "coBg7fED9PSa"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msrJZdxL-yag",
        "outputId": "70efa02b-44fc-48f2-cf61-5ba46552cd0b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(398, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EvwHB_Hj-3UY",
        "outputId": "16c72351-77b4-4539-f5e2-8b4a60dadd9e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(171, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using support vector classifer"
      ],
      "metadata": {
        "id": "TC0t5B3s_YWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "svc = svm.SVC ()\n",
        "#Loading the training data in the model\n",
        "svc.fit (x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Lqd5ftL_Ac4",
        "outputId": "a915caef-bc8c-4d3b-ad24-cdb5158f13ba"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting output with test data\n",
        "y_pred = svc.predict (x_test)\n",
        "y_pred\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1rpuWhW_eT6",
        "outputId": "f9f209e0-bbf7-4e55-ef1f-3e5e568f3a26"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Accuracy Score of Support vector classifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(' Accuracy Score of Support vector classifier:')\n",
        "print (accuracy_score (y_test,y_pred ))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUBg_Nvx_p5T",
        "outputId": "0cfe4a6e-7036-40bd-8e14-fdf3b6ace673"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Accuracy Score of Support vector classifier:\n",
            "0.6491228070175439\n"
          ]
        }
      ]
    }
  ]
}