Breast_Cancer_prediction_.ipynb

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      "name": "Breast Cancer prediction .ipynb",
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      "source": [
        "# importing libraries\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reading data from the file\n",
        "df=pd.read_csv(\"data.csv\")"
      ],
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        "id": "5MELuuywcQXh"
      },
      "execution_count": null,
      "outputs": []
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        "df.head()"
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              "         id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0    842302         M  ...                  0.11890          NaN\n",
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          "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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      "source": [
        "# return all the columns with null values count\n",
        "df.isna().sum()"
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              "id                           0\n",
              "diagnosis                    0\n",
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              "area_mean                    0\n",
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              "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",
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              "perimeter_worst              0\n",
              "area_worst                   0\n",
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              "concavity_worst              0\n",
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              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
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      "source": [
        "# return the size of dataset\n",
        "df.shape"
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        "outputId": "cd49bb1d-3c3a-4f66-85d7-0644ae3cdb89"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the column\n",
        "df=df.dropna(axis=1)"
      ],
      "metadata": {
        "id": "OdYLUKLxdmhS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# shape of dataset after removing the null column\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Xi7oWKrdqBo",
        "outputId": "352aa6ac-b354-4a30-8314-d41fdcb20024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# describe the dataset\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "d3LLpkQDdvm7",
        "outputId": "5394ef37-d018-45e3-8ca3-6bc24b9ec075"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5791926d-b7ce-494c-911a-9763247653b3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>id</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>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</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>5.690000e+02</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",
              "      <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>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>3.037183e+07</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>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</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>1.250206e+08</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>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</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>8.670000e+03</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>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</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>8.692180e+05</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>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</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>9.060240e+05</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>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</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>8.813129e+06</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>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</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>9.113205e+08</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>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5791926d-b7ce-494c-911a-9763247653b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "          const element = document.querySelector('#df-5791926d-b7ce-494c-911a-9763247653b3');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          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",
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              "  "
            ],
            "text/plain": [
              "                 id  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "count  5.690000e+02   569.000000  ...      569.000000               569.000000\n",
              "mean   3.037183e+07    14.127292  ...        0.290076                 0.083946\n",
              "std    1.250206e+08     3.524049  ...        0.061867                 0.018061\n",
              "min    8.670000e+03     6.981000  ...        0.156500                 0.055040\n",
              "25%    8.692180e+05    11.700000  ...        0.250400                 0.071460\n",
              "50%    9.060240e+05    13.370000  ...        0.282200                 0.080040\n",
              "75%    8.813129e+06    15.780000  ...        0.317900                 0.092080\n",
              "max    9.113205e+08    28.110000  ...        0.663800                 0.207500\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the count of malignant<M> and Benign<B> cells\n",
        "df['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3EUS2Z9d3hE",
        "outputId": "116efeda-1c86-4520-a148-4c6f48586221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encode the categorical data values\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "labelencoder_Y.fit_transform(df.iloc[:,1].values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHFBajKyXLMs",
        "outputId": "11769ad0-c4d7-4efb-af7c-54e8ef43873e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 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, 1, 1, 1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['diagnosis'],label=\"count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "7HtY8ci5d6vD",
        "outputId": "c02d4096-61c7-4735-9513-2203b592a31f"
      },
      "execution_count": null,
      "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 0x7f344f371250>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a+O60i1gOctHmaZegH+fv5rz35fk4ys/sr2O5hcIzqqqtwNZp13EoSrKtqmanXYe0kL+bS2e5nT7aA6wf217X2iRJS2C5hcLXgeOTvDzJi4GzgeumXJMkrRjL6vRRVe1N8m7gfwKrgMur6o4pl7WSeFpOy5W/m0skVTXtGiRJy8RyO30kSZoiQ0GS1BkKK1ySSvJfxrYPSzKX5PPTrEsCSLIvyfYk30hyW5JfmHZNh7pldaFZU/E3wKuSHFlVfwu8EW8D1vLxt1W1ESDJm4D/CPzSdEs6tDlTEMD1wK+09XOAT0yxFml/fgJ4ZNpFHOoMBcHoGVNnJzkC+Dng5inXI807sp0++ibwn4F/P+2CDnWePhJVtSPJBkazhOunW430Y8ZPH/08cHWSV5X30g/GmYLmXQdchKeOtExV1VcZPRhvZtq1HMqcKWje5cCjVXV7klOnXYy0UJITGD3p4OFp13IoMxQEQFXtBj4y7TqkBY5Msr2tB9hcVfumWdChzsdcSJI6rylIkjpDQZLUGQqSpM5QkCR1hoIkqfOWVKlJ8n7gCUbP2PlyVf3VFGv5wLRr0MpkKEgLVNV7rUErlaePtKIl+bdJ/neSrwD/qLVdmeSstv7eJF9PsjPJ1iRp7a9LsqM9rO0Pk+xs7e9I8tkkX0jyrST/aey9zklyezvWh1rbqvZ+O1vfby9SwweT3Nne76Il/Q+kFceZglasJK8FzgY2Mvq3cBtw64Jhf1JVH2jjPwb8c+AvgCuAf1VVX03ywQX7bAROAp4C7k7yUWAf8CHgtYwe//yXSc4E7gPWVtWr2nscvaDGnwLeApxQVbWwX3q+OVPQSvZPgM9V1ZNV9X1GDwVc6JeT3JzkduA04JXtD/NR7QFtAH++YJ8bq+qxqvoBcCfwM8DrgC9W1VxV7QU+DvxT4NvA30/y0SSnA99fcKzHgB8AlyV5K/Dkc/6ppQMwFKT9aN8v8WfAWVX1s8ClwBET7PrU2Po+DjAjr6pHgFcDXwR+k9F3Boz37wVOBj7NaJbyhcl/AungGQpayb4MnJnkyCRHAb+6oH8+AL6b5KXAWQBV9SjweJLXt/6zJ3ivW4BfSrI6ySpG313xpSSrgRdV1WeAfwe8Znyn9r4/WVXXA7/NKECkwXhNQStWVd2W5FPAN4CHgK8v6H80yaXATuCBBf3nApcm+SHwJUaneQ70XvcnOR+4idHTPv97VV2b5NXAFUnm/wftggW7HgVc22YtAf7Ns/hRpYn5lFTpWUjy0qp6oq2fD6ypqvdMuSzpOXOmID07v5LkAkb/hu4F3jHdcqTnhzMFSVLnhWZJUmcoSJI6Q0GS1BkKkqTOUJAkdf8f0rm+gk1Pwo0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# label encoding(convert the value of M and B into 1 and 0)\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "df.iloc[:,1]=labelencoder_Y.fit_transform(df.iloc[:,1].values)"
      ],
      "metadata": {
        "id": "2iQ2S3MOd-Zz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a pair plot\n",
        "sns.pairplot(df.iloc[:,1:5], hue=\"diagnosis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "66wqFcLDYfkS",
        "outputId": "56d1baed-1179-4fdf-aed6-c451fcfa782b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f344db61a10>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 595.5x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "AA0GPoEdeB72",
        "outputId": "f7ee1efb-dd6d-4705-f135-86d7fa81a83b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e54a1a00-e4ad-4fe5-82b6-c93d6ff895d9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</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>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>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</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>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>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</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>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>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</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>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>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</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>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>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e54a1a00-e4ad-4fe5-82b6-c93d6ff895d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e54a1a00-e4ad-4fe5-82b6-c93d6ff895d9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e54a1a00-e4ad-4fe5-82b6-c93d6ff895d9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         id  diagnosis  ...  symmetry_worst  fractal_dimension_worst\n",
              "0    842302          1  ...          0.4601                  0.11890\n",
              "1    842517          1  ...          0.2750                  0.08902\n",
              "2  84300903          1  ...          0.3613                  0.08758\n",
              "3  84348301          1  ...          0.6638                  0.17300\n",
              "4  84358402          1  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df.iloc[:,1:5],hue=\"diagnosis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "yqHLRjemeGGy",
        "outputId": "1991ae36-4b9c-4b2d-97f8-20c4c3deaab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f344297cc10>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 595.5x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the correlation\n",
        "df.iloc[:,1:32].corr()"
      ],
      "metadata": {
        "id": "5KvNUC3GeKwS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "572a8259-80a5-4f5b-a821-e918bf212780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5a7b47fe-9da7-46e9-b240-9a460d49d9fd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "      <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>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</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>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730029</td>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.330499</td>\n",
              "      <td>-0.012838</td>\n",
              "      <td>0.567134</td>\n",
              "      <td>-0.008303</td>\n",
              "      <td>0.556141</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>-0.067016</td>\n",
              "      <td>0.292999</td>\n",
              "      <td>0.253730</td>\n",
              "      <td>0.408042</td>\n",
              "      <td>-0.006522</td>\n",
              "      <td>0.077972</td>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.323872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>0.679090</td>\n",
              "      <td>-0.097317</td>\n",
              "      <td>0.674172</td>\n",
              "      <td>0.735864</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>0.206000</td>\n",
              "      <td>0.194204</td>\n",
              "      <td>0.376169</td>\n",
              "      <td>-0.104321</td>\n",
              "      <td>-0.042641</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.007066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>0.275869</td>\n",
              "      <td>0.386358</td>\n",
              "      <td>0.281673</td>\n",
              "      <td>0.259845</td>\n",
              "      <td>0.006614</td>\n",
              "      <td>0.191975</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.163851</td>\n",
              "      <td>0.009127</td>\n",
              "      <td>0.054458</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.119205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>-0.086761</td>\n",
              "      <td>0.693135</td>\n",
              "      <td>0.744983</td>\n",
              "      <td>-0.202694</td>\n",
              "      <td>0.250744</td>\n",
              "      <td>0.228082</td>\n",
              "      <td>0.407217</td>\n",
              "      <td>-0.081629</td>\n",
              "      <td>-0.005523</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.051019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.732562</td>\n",
              "      <td>-0.066280</td>\n",
              "      <td>0.726628</td>\n",
              "      <td>0.800086</td>\n",
              "      <td>-0.166777</td>\n",
              "      <td>0.212583</td>\n",
              "      <td>0.207660</td>\n",
              "      <td>0.372320</td>\n",
              "      <td>-0.072497</td>\n",
              "      <td>-0.019887</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.003738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.301467</td>\n",
              "      <td>0.068406</td>\n",
              "      <td>0.296092</td>\n",
              "      <td>0.246552</td>\n",
              "      <td>0.332375</td>\n",
              "      <td>0.318943</td>\n",
              "      <td>0.248396</td>\n",
              "      <td>0.380676</td>\n",
              "      <td>0.200774</td>\n",
              "      <td>0.283607</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.499316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.497473</td>\n",
              "      <td>0.046205</td>\n",
              "      <td>0.548905</td>\n",
              "      <td>0.455653</td>\n",
              "      <td>0.135299</td>\n",
              "      <td>0.738722</td>\n",
              "      <td>0.570517</td>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.229977</td>\n",
              "      <td>0.507318</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.687382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.631925</td>\n",
              "      <td>0.076218</td>\n",
              "      <td>0.660391</td>\n",
              "      <td>0.617427</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.670279</td>\n",
              "      <td>0.691270</td>\n",
              "      <td>0.683260</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.449301</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.514930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.698050</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.710650</td>\n",
              "      <td>0.690299</td>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.490424</td>\n",
              "      <td>0.439167</td>\n",
              "      <td>0.615634</td>\n",
              "      <td>0.095351</td>\n",
              "      <td>0.257584</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.368661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>0.303379</td>\n",
              "      <td>0.128053</td>\n",
              "      <td>0.313893</td>\n",
              "      <td>0.223970</td>\n",
              "      <td>0.187321</td>\n",
              "      <td>0.421659</td>\n",
              "      <td>0.342627</td>\n",
              "      <td>0.393298</td>\n",
              "      <td>0.449137</td>\n",
              "      <td>0.331786</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>0.438413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>-0.012838</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.164174</td>\n",
              "      <td>0.039830</td>\n",
              "      <td>-0.090170</td>\n",
              "      <td>0.401964</td>\n",
              "      <td>0.559837</td>\n",
              "      <td>0.446630</td>\n",
              "      <td>0.341198</td>\n",
              "      <td>0.345007</td>\n",
              "      <td>0.688132</td>\n",
              "      <td>-0.253691</td>\n",
              "      <td>-0.051269</td>\n",
              "      <td>-0.205151</td>\n",
              "      <td>-0.231854</td>\n",
              "      <td>0.504942</td>\n",
              "      <td>0.458798</td>\n",
              "      <td>0.346234</td>\n",
              "      <td>0.175325</td>\n",
              "      <td>0.334019</td>\n",
              "      <td>0.767297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>0.567134</td>\n",
              "      <td>0.679090</td>\n",
              "      <td>0.275869</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>0.732562</td>\n",
              "      <td>0.301467</td>\n",
              "      <td>0.497473</td>\n",
              "      <td>0.631925</td>\n",
              "      <td>0.698050</td>\n",
              "      <td>0.303379</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213247</td>\n",
              "      <td>0.972794</td>\n",
              "      <td>0.951830</td>\n",
              "      <td>0.164514</td>\n",
              "      <td>0.356065</td>\n",
              "      <td>0.332358</td>\n",
              "      <td>0.513346</td>\n",
              "      <td>0.240567</td>\n",
              "      <td>0.227754</td>\n",
              "      <td>0.715065</td>\n",
              "      <td>0.194799</td>\n",
              "      <td>0.719684</td>\n",
              "      <td>0.751548</td>\n",
              "      <td>0.141919</td>\n",
              "      <td>0.287103</td>\n",
              "      <td>0.380585</td>\n",
              "      <td>0.531062</td>\n",
              "      <td>0.094543</td>\n",
              "      <td>0.049559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>-0.008303</td>\n",
              "      <td>-0.097317</td>\n",
              "      <td>0.386358</td>\n",
              "      <td>-0.086761</td>\n",
              "      <td>-0.066280</td>\n",
              "      <td>0.068406</td>\n",
              "      <td>0.046205</td>\n",
              "      <td>0.076218</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.128053</td>\n",
              "      <td>0.164174</td>\n",
              "      <td>0.213247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.223171</td>\n",
              "      <td>0.111567</td>\n",
              "      <td>0.397243</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.194998</td>\n",
              "      <td>0.230283</td>\n",
              "      <td>0.411621</td>\n",
              "      <td>0.279723</td>\n",
              "      <td>-0.111690</td>\n",
              "      <td>0.409003</td>\n",
              "      <td>-0.102242</td>\n",
              "      <td>-0.083195</td>\n",
              "      <td>-0.073658</td>\n",
              "      <td>-0.092439</td>\n",
              "      <td>-0.068956</td>\n",
              "      <td>-0.119638</td>\n",
              "      <td>-0.128215</td>\n",
              "      <td>-0.045655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>0.556141</td>\n",
              "      <td>0.674172</td>\n",
              "      <td>0.281673</td>\n",
              "      <td>0.693135</td>\n",
              "      <td>0.726628</td>\n",
              "      <td>0.296092</td>\n",
              "      <td>0.548905</td>\n",
              "      <td>0.660391</td>\n",
              "      <td>0.710650</td>\n",
              "      <td>0.313893</td>\n",
              "      <td>0.039830</td>\n",
              "      <td>0.972794</td>\n",
              "      <td>0.223171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937655</td>\n",
              "      <td>0.151075</td>\n",
              "      <td>0.416322</td>\n",
              "      <td>0.362482</td>\n",
              "      <td>0.556264</td>\n",
              "      <td>0.266487</td>\n",
              "      <td>0.244143</td>\n",
              "      <td>0.697201</td>\n",
              "      <td>0.200371</td>\n",
              "      <td>0.721031</td>\n",
              "      <td>0.730713</td>\n",
              "      <td>0.130054</td>\n",
              "      <td>0.341919</td>\n",
              "      <td>0.418899</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.085433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>0.548236</td>\n",
              "      <td>0.735864</td>\n",
              "      <td>0.259845</td>\n",
              "      <td>0.744983</td>\n",
              "      <td>0.800086</td>\n",
              "      <td>0.246552</td>\n",
              "      <td>0.455653</td>\n",
              "      <td>0.617427</td>\n",
              "      <td>0.690299</td>\n",
              "      <td>0.223970</td>\n",
              "      <td>-0.090170</td>\n",
              "      <td>0.951830</td>\n",
              "      <td>0.111567</td>\n",
              "      <td>0.937655</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.075150</td>\n",
              "      <td>0.284840</td>\n",
              "      <td>0.270895</td>\n",
              "      <td>0.415730</td>\n",
              "      <td>0.134109</td>\n",
              "      <td>0.127071</td>\n",
              "      <td>0.757373</td>\n",
              "      <td>0.196497</td>\n",
              "      <td>0.761213</td>\n",
              "      <td>0.811408</td>\n",
              "      <td>0.125389</td>\n",
              "      <td>0.283257</td>\n",
              "      <td>0.385100</td>\n",
              "      <td>0.538166</td>\n",
              "      <td>0.074126</td>\n",
              "      <td>0.017539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>-0.067016</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>0.006614</td>\n",
              "      <td>-0.202694</td>\n",
              "      <td>-0.166777</td>\n",
              "      <td>0.332375</td>\n",
              "      <td>0.135299</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.187321</td>\n",
              "      <td>0.401964</td>\n",
              "      <td>0.164514</td>\n",
              "      <td>0.397243</td>\n",
              "      <td>0.151075</td>\n",
              "      <td>0.075150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.336696</td>\n",
              "      <td>0.268685</td>\n",
              "      <td>0.328429</td>\n",
              "      <td>0.413506</td>\n",
              "      <td>0.427374</td>\n",
              "      <td>-0.230691</td>\n",
              "      <td>-0.074743</td>\n",
              "      <td>-0.217304</td>\n",
              "      <td>-0.182195</td>\n",
              "      <td>0.314457</td>\n",
              "      <td>-0.055558</td>\n",
              "      <td>-0.058298</td>\n",
              "      <td>-0.102007</td>\n",
              "      <td>-0.107342</td>\n",
              "      <td>0.101480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>0.292999</td>\n",
              "      <td>0.206000</td>\n",
              "      <td>0.191975</td>\n",
              "      <td>0.250744</td>\n",
              "      <td>0.212583</td>\n",
              "      <td>0.318943</td>\n",
              "      <td>0.738722</td>\n",
              "      <td>0.670279</td>\n",
              "      <td>0.490424</td>\n",
              "      <td>0.421659</td>\n",
              "      <td>0.559837</td>\n",
              "      <td>0.356065</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.416322</td>\n",
              "      <td>0.284840</td>\n",
              "      <td>0.336696</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.801268</td>\n",
              "      <td>0.744083</td>\n",
              "      <td>0.394713</td>\n",
              "      <td>0.803269</td>\n",
              "      <td>0.204607</td>\n",
              "      <td>0.143003</td>\n",
              "      <td>0.260516</td>\n",
              "      <td>0.199371</td>\n",
              "      <td>0.227394</td>\n",
              "      <td>0.678780</td>\n",
              "      <td>0.639147</td>\n",
              "      <td>0.483208</td>\n",
              "      <td>0.277878</td>\n",
              "      <td>0.590973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>0.253730</td>\n",
              "      <td>0.194204</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.228082</td>\n",
              "      <td>0.207660</td>\n",
              "      <td>0.248396</td>\n",
              "      <td>0.570517</td>\n",
              "      <td>0.691270</td>\n",
              "      <td>0.439167</td>\n",
              "      <td>0.342627</td>\n",
              "      <td>0.446630</td>\n",
              "      <td>0.332358</td>\n",
              "      <td>0.194998</td>\n",
              "      <td>0.362482</td>\n",
              "      <td>0.270895</td>\n",
              "      <td>0.268685</td>\n",
              "      <td>0.801268</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.771804</td>\n",
              "      <td>0.309429</td>\n",
              "      <td>0.727372</td>\n",
              "      <td>0.186904</td>\n",
              "      <td>0.100241</td>\n",
              "      <td>0.226680</td>\n",
              "      <td>0.188353</td>\n",
              "      <td>0.168481</td>\n",
              "      <td>0.484858</td>\n",
              "      <td>0.662564</td>\n",
              "      <td>0.440472</td>\n",
              "      <td>0.197788</td>\n",
              "      <td>0.439329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>0.408042</td>\n",
              "      <td>0.376169</td>\n",
              "      <td>0.163851</td>\n",
              "      <td>0.407217</td>\n",
              "      <td>0.372320</td>\n",
              "      <td>0.380676</td>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.683260</td>\n",
              "      <td>0.615634</td>\n",
              "      <td>0.393298</td>\n",
              "      <td>0.341198</td>\n",
              "      <td>0.513346</td>\n",
              "      <td>0.230283</td>\n",
              "      <td>0.556264</td>\n",
              "      <td>0.415730</td>\n",
              "      <td>0.328429</td>\n",
              "      <td>0.744083</td>\n",
              "      <td>0.771804</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.312780</td>\n",
              "      <td>0.611044</td>\n",
              "      <td>0.358127</td>\n",
              "      <td>0.086741</td>\n",
              "      <td>0.394999</td>\n",
              "      <td>0.342271</td>\n",
              "      <td>0.215351</td>\n",
              "      <td>0.452888</td>\n",
              "      <td>0.549592</td>\n",
              "      <td>0.602450</td>\n",
              "      <td>0.143116</td>\n",
              "      <td>0.310655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>-0.006522</td>\n",
              "      <td>-0.104321</td>\n",
              "      <td>0.009127</td>\n",
              "      <td>-0.081629</td>\n",
              "      <td>-0.072497</td>\n",
              "      <td>0.200774</td>\n",
              "      <td>0.229977</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.095351</td>\n",
              "      <td>0.449137</td>\n",
              "      <td>0.345007</td>\n",
              "      <td>0.240567</td>\n",
              "      <td>0.411621</td>\n",
              "      <td>0.266487</td>\n",
              "      <td>0.134109</td>\n",
              "      <td>0.413506</td>\n",
              "      <td>0.394713</td>\n",
              "      <td>0.309429</td>\n",
              "      <td>0.312780</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.369078</td>\n",
              "      <td>-0.128121</td>\n",
              "      <td>-0.077473</td>\n",
              "      <td>-0.103753</td>\n",
              "      <td>-0.110343</td>\n",
              "      <td>-0.012662</td>\n",
              "      <td>0.060255</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>-0.030413</td>\n",
              "      <td>0.389402</td>\n",
              "      <td>0.078079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>0.077972</td>\n",
              "      <td>-0.042641</td>\n",
              "      <td>0.054458</td>\n",
              "      <td>-0.005523</td>\n",
              "      <td>-0.019887</td>\n",
              "      <td>0.283607</td>\n",
              "      <td>0.507318</td>\n",
              "      <td>0.449301</td>\n",
              "      <td>0.257584</td>\n",
              "      <td>0.331786</td>\n",
              "      <td>0.688132</td>\n",
              "      <td>0.227754</td>\n",
              "      <td>0.279723</td>\n",
              "      <td>0.244143</td>\n",
              "      <td>0.127071</td>\n",
              "      <td>0.427374</td>\n",
              "      <td>0.803269</td>\n",
              "      <td>0.727372</td>\n",
              "      <td>0.611044</td>\n",
              "      <td>0.369078</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.037488</td>\n",
              "      <td>-0.003195</td>\n",
              "      <td>-0.001000</td>\n",
              "      <td>-0.022736</td>\n",
              "      <td>0.170568</td>\n",
              "      <td>0.390159</td>\n",
              "      <td>0.379975</td>\n",
              "      <td>0.215204</td>\n",
              "      <td>0.111094</td>\n",
              "      <td>0.591328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>-0.253691</td>\n",
              "      <td>0.715065</td>\n",
              "      <td>-0.111690</td>\n",
              "      <td>0.697201</td>\n",
              "      <td>0.757373</td>\n",
              "      <td>-0.230691</td>\n",
              "      <td>0.204607</td>\n",
              "      <td>0.186904</td>\n",
              "      <td>0.358127</td>\n",
              "      <td>-0.128121</td>\n",
              "      <td>-0.037488</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.093492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>-0.051269</td>\n",
              "      <td>0.194799</td>\n",
              "      <td>0.409003</td>\n",
              "      <td>0.200371</td>\n",
              "      <td>0.196497</td>\n",
              "      <td>-0.074743</td>\n",
              "      <td>0.143003</td>\n",
              "      <td>0.100241</td>\n",
              "      <td>0.086741</td>\n",
              "      <td>-0.077473</td>\n",
              "      <td>-0.003195</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.219122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>-0.205151</td>\n",
              "      <td>0.719684</td>\n",
              "      <td>-0.102242</td>\n",
              "      <td>0.721031</td>\n",
              "      <td>0.761213</td>\n",
              "      <td>-0.217304</td>\n",
              "      <td>0.260516</td>\n",
              "      <td>0.226680</td>\n",
              "      <td>0.394999</td>\n",
              "      <td>-0.103753</td>\n",
              "      <td>-0.001000</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.138957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>-0.231854</td>\n",
              "      <td>0.751548</td>\n",
              "      <td>-0.083195</td>\n",
              "      <td>0.730713</td>\n",
              "      <td>0.811408</td>\n",
              "      <td>-0.182195</td>\n",
              "      <td>0.199371</td>\n",
              "      <td>0.188353</td>\n",
              "      <td>0.342271</td>\n",
              "      <td>-0.110343</td>\n",
              "      <td>-0.022736</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.079647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>0.504942</td>\n",
              "      <td>0.141919</td>\n",
              "      <td>-0.073658</td>\n",
              "      <td>0.130054</td>\n",
              "      <td>0.125389</td>\n",
              "      <td>0.314457</td>\n",
              "      <td>0.227394</td>\n",
              "      <td>0.168481</td>\n",
              "      <td>0.215351</td>\n",
              "      <td>-0.012662</td>\n",
              "      <td>0.170568</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.617624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.458798</td>\n",
              "      <td>0.287103</td>\n",
              "      <td>-0.092439</td>\n",
              "      <td>0.341919</td>\n",
              "      <td>0.283257</td>\n",
              "      <td>-0.055558</td>\n",
              "      <td>0.678780</td>\n",
              "      <td>0.484858</td>\n",
              "      <td>0.452888</td>\n",
              "      <td>0.060255</td>\n",
              "      <td>0.390159</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.810455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>0.346234</td>\n",
              "      <td>0.380585</td>\n",
              "      <td>-0.068956</td>\n",
              "      <td>0.418899</td>\n",
              "      <td>0.385100</td>\n",
              "      <td>-0.058298</td>\n",
              "      <td>0.639147</td>\n",
              "      <td>0.662564</td>\n",
              "      <td>0.549592</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>0.379975</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.686511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>0.175325</td>\n",
              "      <td>0.531062</td>\n",
              "      <td>-0.119638</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.538166</td>\n",
              "      <td>-0.102007</td>\n",
              "      <td>0.483208</td>\n",
              "      <td>0.440472</td>\n",
              "      <td>0.602450</td>\n",
              "      <td>-0.030413</td>\n",
              "      <td>0.215204</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>0.511114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>0.334019</td>\n",
              "      <td>0.094543</td>\n",
              "      <td>-0.128215</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.074126</td>\n",
              "      <td>-0.107342</td>\n",
              "      <td>0.277878</td>\n",
              "      <td>0.197788</td>\n",
              "      <td>0.143116</td>\n",
              "      <td>0.389402</td>\n",
              "      <td>0.111094</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.537848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>0.323872</td>\n",
              "      <td>0.007066</td>\n",
              "      <td>0.119205</td>\n",
              "      <td>0.051019</td>\n",
              "      <td>0.003738</td>\n",
              "      <td>0.499316</td>\n",
              "      <td>0.687382</td>\n",
              "      <td>0.514930</td>\n",
              "      <td>0.368661</td>\n",
              "      <td>0.438413</td>\n",
              "      <td>0.767297</td>\n",
              "      <td>0.049559</td>\n",
              "      <td>-0.045655</td>\n",
              "      <td>0.085433</td>\n",
              "      <td>0.017539</td>\n",
              "      <td>0.101480</td>\n",
              "      <td>0.590973</td>\n",
              "      <td>0.439329</td>\n",
              "      <td>0.310655</td>\n",
              "      <td>0.078079</td>\n",
              "      <td>0.591328</td>\n",
              "      <td>0.093492</td>\n",
              "      <td>0.219122</td>\n",
              "      <td>0.138957</td>\n",
              "      <td>0.079647</td>\n",
              "      <td>0.617624</td>\n",
              "      <td>0.810455</td>\n",
              "      <td>0.686511</td>\n",
              "      <td>0.511114</td>\n",
              "      <td>0.537848</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a7b47fe-9da7-46e9-b240-9a460d49d9fd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5a7b47fe-9da7-46e9-b240-9a460d49d9fd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5a7b47fe-9da7-46e9-b240-9a460d49d9fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         diagnosis  ...  fractal_dimension_worst\n",
              "diagnosis                 1.000000  ...                 0.323872\n",
              "radius_mean               0.730029  ...                 0.007066\n",
              "texture_mean              0.415185  ...                 0.119205\n",
              "perimeter_mean            0.742636  ...                 0.051019\n",
              "area_mean                 0.708984  ...                 0.003738\n",
              "smoothness_mean           0.358560  ...                 0.499316\n",
              "compactness_mean          0.596534  ...                 0.687382\n",
              "concavity_mean            0.696360  ...                 0.514930\n",
              "concave points_mean       0.776614  ...                 0.368661\n",
              "symmetry_mean             0.330499  ...                 0.438413\n",
              "fractal_dimension_mean   -0.012838  ...                 0.767297\n",
              "radius_se                 0.567134  ...                 0.049559\n",
              "texture_se               -0.008303  ...                -0.045655\n",
              "perimeter_se              0.556141  ...                 0.085433\n",
              "area_se                   0.548236  ...                 0.017539\n",
              "smoothness_se            -0.067016  ...                 0.101480\n",
              "compactness_se            0.292999  ...                 0.590973\n",
              "concavity_se              0.253730  ...                 0.439329\n",
              "concave points_se         0.408042  ...                 0.310655\n",
              "symmetry_se              -0.006522  ...                 0.078079\n",
              "fractal_dimension_se      0.077972  ...                 0.591328\n",
              "radius_worst              0.776454  ...                 0.093492\n",
              "texture_worst             0.456903  ...                 0.219122\n",
              "perimeter_worst           0.782914  ...                 0.138957\n",
              "area_worst                0.733825  ...                 0.079647\n",
              "smoothness_worst          0.421465  ...                 0.617624\n",
              "compactness_worst         0.590998  ...                 0.810455\n",
              "concavity_worst           0.659610  ...                 0.686511\n",
              "concave points_worst      0.793566  ...                 0.511114\n",
              "symmetry_worst            0.416294  ...                 0.537848\n",
              "fractal_dimension_worst   0.323872  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize the correlation\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.iloc[:,1:10].corr(),annot=True,fmt=\".0%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "pzGOTztJeZ00",
        "outputId": "bd6553ef-8d63-4d1a-d4ee-b4cce8e360f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3440d4dc10>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the dataset into dependent(X) and Independent(Y) datasets\n",
        "X=df.iloc[:,2:31].values\n",
        "Y=df.iloc[:,1].values"
      ],
      "metadata": {
        "id": "QQx6_6MlegO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# spliting the data into trainning and test dateset\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=0.20,random_state=0)\n"
      ],
      "metadata": {
        "id": "imDb5iACei1C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train=StandardScaler().fit_transform(X_train)\n",
        "X_test=StandardScaler().fit_transform(X_test)"
      ],
      "metadata": {
        "id": "AtY5UKJue7ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# models/ Algorithms\n",
        "\n",
        "def models(X_train,Y_train):\n",
        "        #logistic regression\n",
        "        from sklearn.linear_model import LogisticRegression\n",
        "        log=LogisticRegression(random_state=0)\n",
        "        log.fit(X_train,Y_train)\n",
        "        \n",
        "        \n",
        "        #Decision Tree\n",
        "        from sklearn.tree import DecisionTreeClassifier\n",
        "        tree=DecisionTreeClassifier(random_state=0,criterion=\"entropy\")\n",
        "        tree.fit(X_train,Y_train)\n",
        "        \n",
        "        #Random Forest\n",
        "        from sklearn.ensemble import RandomForestClassifier\n",
        "        forest=RandomForestClassifier(random_state=0,criterion=\"entropy\",n_estimators=10)\n",
        "        forest.fit(X_train,Y_train)\n",
        "        \n",
        "        print('[0]logistic regression accuracy:',log.score(X_train,Y_train))\n",
        "        print('[1]Decision tree accuracy:',tree.score(X_train,Y_train))\n",
        "        print('[2]Random forest accuracy:',forest.score(X_train,Y_train))\n",
        "        \n",
        "        return log,tree,forest\n",
        "        "
      ],
      "metadata": {
        "id": "6o5PpvFxeqqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=models(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVCijzefewJN",
        "outputId": "7360b0df-7e72-43ab-8656-529f05219256"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]logistic regression accuracy: 0.9912087912087912\n",
            "[1]Decision tree accuracy: 1.0\n",
            "[2]Random forest accuracy: 0.9978021978021978\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# testing the models/result\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "for i in range(len(model)):\n",
        "    print(\"Model\",i)\n",
        "    print(classification_report(Y_test,model[i].predict(X_test)))\n",
        "    print('Accuracy : ',accuracy_score(Y_test,model[i].predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrDW_VonfC_J",
        "outputId": "6c0ae7e1-3da5-4efa-81f0-a72823270e82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97        67\n",
            "           1       0.98      0.94      0.96        47\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.97      0.96      0.96       114\n",
            "weighted avg       0.97      0.96      0.96       114\n",
            "\n",
            "Accuracy :  0.9649122807017544\n",
            "Model 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95        67\n",
            "           1       0.93      0.91      0.92        47\n",
            "\n",
            "    accuracy                           0.94       114\n",
            "   macro avg       0.94      0.94      0.94       114\n",
            "weighted avg       0.94      0.94      0.94       114\n",
            "\n",
            "Accuracy :  0.9385964912280702\n",
            "Model 2\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98        67\n",
            "           1       1.00      0.94      0.97        47\n",
            "\n",
            "    accuracy                           0.97       114\n",
            "   macro avg       0.98      0.97      0.97       114\n",
            "weighted avg       0.97      0.97      0.97       114\n",
            "\n",
            "Accuracy :  0.9736842105263158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction of random-forest\n",
        "pred=model[2].predict(X_test)\n",
        "print('Predicted values:')\n",
        "print(pred)\n",
        "print('Actual values:')\n",
        "print(Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kej6twKGfK6y",
        "outputId": "ab7eda63-79e0-4130-e2bd-b97142f332b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted values:\n",
            "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0]\n",
            "Actual values:\n",
            "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
            " 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1\n",
            " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
            " 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump\n",
        "dump(model[2],\"Cancer_prediction.joblib\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5vE1EB_fOMC",
        "outputId": "f6d7deab-24a8-4bf7-9177-ba9244f7356a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Cancer_prediction.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}