Cancer_Detection_using_ANN.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "id": "b13c9e14",
      "metadata": {
        "id": "b13c9e14"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler  \n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O763-vrJBMZr",
        "outputId": "18709c48-4d9d-44b2-ea97-36c40978bb6d"
      },
      "id": "O763-vrJBMZr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path='/content/drive/My Drive/Cancer(M or B)'"
      ],
      "metadata": {
        "id": "ozmzba1PBwis"
      },
      "id": "ozmzba1PBwis",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "If-p1WSsB855"
      },
      "id": "If-p1WSsB855",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(os.listdir(path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F67HKhfoB6zE",
        "outputId": "f944201a-9ab3-42c7-8dde-bcb0ae953389"
      },
      "id": "F67HKhfoB6zE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['cancer_data.csv', 'Students data.csv', 'Untitled.ipynb', 'Student_data.ipynb', 'load buston.ipynb', 'Cancer using ANN.ipynb', '.ipynb_checkpoints', 'file.ipynb']\n"
          ]
        }
      ]
    },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
            "0         M        17.99         10.38          122.80     1001.0   \n",
            "1         M        20.57         17.77          132.90     1326.0   \n",
            "2         M        19.69         21.25          130.00     1203.0   \n",
            "3         M        11.42         20.38           77.58      386.1   \n",
            "4         M        20.29         14.34          135.10     1297.0   \n",
            "\n",
            "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
            "0          0.11840           0.27760          0.3001              0.14710   \n",
            "1          0.08474           0.07864          0.0869              0.07017   \n",
            "2          0.10960           0.15990          0.1974              0.12790   \n",
            "3          0.14250           0.28390          0.2414              0.10520   \n",
            "4          0.10030           0.13280          0.1980              0.10430   \n",
            "\n",
            "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
            "0         0.2419  ...         25.38          17.33           184.60   \n",
            "1         0.1812  ...         24.99          23.41           158.80   \n",
            "2         0.2069  ...         23.57          25.53           152.50   \n",
            "3         0.2597  ...         14.91          26.50            98.87   \n",
            "4         0.1809  ...         22.54          16.67           152.20   \n",
            "\n",
            "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
            "0      2019.0            0.1622             0.6656           0.7119   \n",
            "1      1956.0            0.1238             0.1866           0.2416   \n",
            "2      1709.0            0.1444             0.4245           0.4504   \n",
            "3       567.7            0.2098             0.8663           0.6869   \n",
            "4      1575.0            0.1374             0.2050           0.4000   \n",
            "\n",
            "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
            "0                0.2654          0.4601                  0.11890  \n",
            "1                0.1860          0.2750                  0.08902  \n",
            "2                0.2430          0.3613                  0.08758  \n",
            "3                0.2575          0.6638                  0.17300  \n",
            "4                0.1625          0.2364                  0.07678  \n",
            "\n",
            "[5 rows x 31 columns]\n"
          ]
        }
      ],
      "source": [
        "df=pd.read_csv('/content/drive/My Drive/Cancer(M or B)/cancer_data.csv')\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c3d141b",
      "metadata": {
        "colab": {
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        "id": "9c3d141b",
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          "data": {
            "text/plain": [
              "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0         M        17.99         10.38           122.8     1001.0   \n",
              "1         M        20.57         17.77           132.9     1326.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33            184.6   \n",
              "1         0.1812  ...         24.99          23.41            158.8   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "\n",
              "[2 rows x 31 columns]"
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              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</td>\n",
              "      <td>1326.0</td>\n",
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        "df.head(2)"
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            "text/plain": [
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
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      "source": [
        "df.isnull().sum()"
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      "cell_type": "code",
      "execution_count": null,
      "id": "c504aad5",
      "metadata": {
        "id": "c504aad5"
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      "outputs": [],
      "source": [
        "df['diagnosis'].replace(['M','B'],[0,1],inplace=True)"
      ]
    },
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      "execution_count": null,
      "id": "b4b4cdfd",
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          "data": {
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0          0        17.99         10.38           122.8     1001.0   \n",
              "1          0        20.57         17.77           132.9     1326.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33            184.6   \n",
              "1         0.1812  ...         24.99          23.41            158.8   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "\n",
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              "      <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",
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              "      <th>concavity_mean</th>\n",
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              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
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              "      <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>0</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.9</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.8</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae723be7-8090-404f-863d-4c901428215d')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-ae723be7-8090-404f-863d-4c901428215d 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-ae723be7-8090-404f-863d-4c901428215d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6d8740a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "6d8740a2",
        "outputId": "4f193939-9f3e-4821-bbe9-87996208e1ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/DyUHEUkEUNUy16IzEWlTVTOLCjNITXS1GPCEKc1Po3cgwLbDx70OxZiIMtYE8Z0Q294OZyBmcugbCLD10HHOmcT3P4w0Jy8NAV7fZ8vWGjPcqAlCRKaKyHIgWUTOEpGznZ9LgZQJidBElB21wQJ9K2ZN/vGHIcl+H8VZybxe0eh1KMZElJP1EXyc4MB0MfCjYdvbgX9yKSYTwTZVOTfIRVELAqA0P51X99XT2t1PZrJNzDMGTpIgVPUR4BER+ZSqPjVBMZkItulgM7NzU8lNi66V2Erz03h5bz1vH2hk5ZJCr8MxJiKMmiBE5LOq+hgwU0S+MXK/qv4oxGkmSgUCwQJ9KxdP9TqUsJuenUKq38fr+y1BGDPkZIPUqc7vNCA9xM+oRGSliOwVkQoRuTPE/kQRecLZv1FEZjrbE0TkERHZISJ7ROQfx3FNxiX76ttp7e7nnFnR1b0EwbIb583J4fX9Ng5hzJCTdTHd7/z+l/G+sIj4gPuAjwE1wCYRWauqu4cddhvQoqqlIrIa+B7waeAGIFFVzxCRFGC3iPxaVavGG4cJn00Hg+MPK6Js/GHIRXPzeHFPPdVNnczIST35CcZEubEW6/u+iGQ4f9n/SUQaROSzJzltBVChqpWq2gesAVaNOGYV8Ijz+LfAR0VEAAVSRSQeSAb6gLYxXpNxyTtVLRRkJFKSnex1KK64cG4ugLUijHGM9T6IK1W1DbiGYC2mUuAfTnJOEXB42PMaZ1vIY1R1AGgFcggmi07gKHAI+KGqNo98AxG5XUTKRaS8ocHmsLtJVdl0sJlzZmYTzOHRZ3ZuKtMyk3jDEoQxwNgTxFBX1NXAb1S11aV4hqwABoFpwCzg70Vk9siDVPUBVS1T1bK8vDyXQ4ptNS3dHGvrYUUUjj8MEREumpvHWwcaGRgMeB2OMZ4ba4L4HxF5D1gO/ElE8oCek5xTC5QMe17sbAt5jNOdlAk0ESwt/kdV7VfVeuBNwEp6eOgdZ/whmu6gDuXCubm09Qywvdbtv4GMiXxjLfd9J3A+UKaq/QS7f0aOJ4y0CZgrIrNExA+sBtaOOGYtcIvz+HrgJQ2u3HIIuBxARFKBjwDvjSVW445NVc2kJ8UzfxIvEDQWF5TmIoJ1MxnD+JYcXQB8WkQ+R/DL/MrRDnbGFO4A1gN7gCdVdZeI3CMi1zqHPQTkiEgF8A1gaCrsfUCaiOwimGh+rqrbxxGrCbMNlU2smJlN3CRfIOhkslP9LJmWyev7bUzLmLGW+34UmANsIzg2AMGZRr8c7TxVXQesG7HtrmGPewhOaR15Xkeo7cYbR453U9XUxV+dN9PrUCbEhXNzefC1Stp7+klPsrIbJnaNtV5zGbBIbeHemPT2gSYAzpud43EkE+Oiubn89JUDbKhs5mOLCrwOxxjPjLWLaScQffUVzJi8daCJrJQEFkyN7vGHIctnZJGc4OPVffVeh2KMp8bagsgleDfzO0Dv0EZVvfbEp5hooKq8faCR8+bkRP34w5DEeB8XlObyyt4GVDVq7/sw5mTGmiDudjMIE7kONXdxpLWHL8VI99KQyxbk8eKeOg40dFKan+Z1OMZ4YqzTXF8leAd1gvN4E7DFxbhMhHhraPxhTq7HkUysS+fnA/DKXutmMrFrrLWY/ppg+Yv7nU1FwLNuBWUix1sHmshPT2ROXmwVryuaksy8guAaEcbEqrEOUn8FuACnYJ6q7gfy3QrKRIbg+EMT583Jicl++Mvm5/POwWY6ege8DsUYT4w1QfQ6FVmB98ti2JTXKFdR30FjRy/nz4mt8Ychl87Pp39QedPWqjYxaqwJ4lUR+ScgWUQ+BvwGeM69sEwkGBp/OD/Gxh+GlM3MIi0x3sYhTMwaa4K4E2gAdgBfJHh39LfdCspEhrcONFI0JZmS7BSvQ/FEgi+Oi+bm8vJ7wemuxsSasc5iChAclP6yql6vqg/aXdXRrX8wwFsVTVw0NzZbD0Mum5/PsbYe3jvW7nUoxky4UROEBN0tIo3AXmCvs5rcXaOdZya/bYeP0947wCXzYnudjUvmB6/fZjOZWHSyFsTXCc5eOkdVs1U1GzgXuEBEvu56dMYzr+1rwBcnnF8a2y2IgowklhZn8vyuOq9DMWbCnSxB/BVwk6oeHNqgqpXAZ4HPuRmY8dar+xo4q2QKmclWzfTji6ey7fBxjrWebI0sY6LLyUptJKjqh+b4qWqDiNg3R5Rq6uhlR20r37hiHo9vPOR1OJ77+OICfrB+L8/vPsbnYqTkuTFw8hZE3ynuM5PYGxWNqMLFMT7+MKQ0P53Zeams33XM61CMmVAnSxDLRKQtxE87cMZEBGgm3qv7GshO9XNGUabXoUSMjy+eyobKZo532d9FJnaMmiBU1aeqGSF+0lXVupiiUCCgvLavkQtLc2OmvPdYfHzxVAYDyp/22GwmEzvGsya1iQF7jrXR2NEb89NbR1palMnUjCTrZjIxxdUEISIrRWSviFSIyJ0h9ieKyBPO/o0iMnPYvqUi8raI7BKRHSKS5GasJujVfQ0AXDQvtqe3jhQXJ1y5uIBX9zXQ1WfF+0xscC1BiIgPuA+4ClgE3CQii0YcdhvQoqqlwH8C33POjQceA/5GVRcDlwL9bsVq/uzVvQ0sKswgP93y8UgrF0+ldyDAa04SNSbajXVFuVOxAqhw7ptARNYAq4Ddw45ZxZ9Xq/stcK8E60pfCWxX1XcBVLXJxThjzommrnb3DbKpqpmL5+XF5PTWk13zYEBJTvDxk1cOsHJJ4QRFZYx33OxiKgIOD3te42wLeYyqDgCtQA4wD1ARWS8iW0Tk/w31BiJyu4iUi0h5Q4P9VXe69ta1E1BYODXD61Aiki9OWFKUyZ6jbdbNZGJCpA5SxwMXAp9xfn9SRD468iBVfUBVy1S1LC/PBlVP156jbaQlxlOUlex1KBFrWUkm/YPKC7ut9IaJfm4miFqgZNjzYmdbyGOccYdMoIlga+M1VW1U1S6C5cXPdjHWmDcQCLC/vp0FU9OJi8HV48ZqZk4qmckJPPfuEa9DMcZ1biaITcBcEZklIn5gNbB2xDFrgVucx9cDLzllxNcDZ4hIipM4LuGDYxcmzKoau+jpD7Cw0LqXRhMnwtLiTF7Z20BLp900Z6KbawnCGVO4g+CX/R7gSVXdJSL3iMi1zmEPATkiUgF8g+DCRKhqC/AjgklmG7BFVX/vVqwmeP9DfJwwJy/N61Ai3lklWQwElGe3jWwQGxNd3JzFhKquI9g9NHzbXcMe9wA3nODcxwhOdTUuU1XeO9pGaX4a/vhIHZaKHFMzgyXAn9h0mFvPn4lYl5yJUvZtYKhr76Wlq58FNntpzG5YXsx7x9rZdaTN61CMcY0lCMN7R4NfcgumpnscyeRx7bIi/PFxrNkUe/eLmNhhCcKw52gbRVOSybDFgcYsMyWBa84o5JkttbT32E3+JjpZgohxrd39HG7pZtE0614ar1vOn0ln3yBPba7xOhRjXGEJIsbtPtIKwGJLEOO2rGQKy0qm8Mu3qwkE1OtwjAk7SxAxbueRNvLTE6043yn6/PkzqWzs5JV9tk6EiT6WIGJYR+8AVY2dLJ5mK8edqquXFlI0JZl7X6ogeI+nMdHDEkQM23OkDQWWFFn30qlK8MXxN5fMZsuh42yobPY6HGPCyhJEDNt5pJXsVD9TM6x76XTcUFZCbloiP/7TPmtFmKhiCSJGdfcNcqChg8XTMuxO4NOUlODjby8vZUNlM6/stbLzJnpYgohR7x1rI6CwxMYfwuKmFdOZmZPC//7DHgZtRpOJEpYgYtTOI21kJifY2g9h4o+P41srF7CvroNfvl3ldTjGhIUliBjU0z/I/rp2Fk3LsLUfwmjlkqlcOj+PH6zfS01Ll9fhGHPaLEHEoD1H2xgIKMuKrHspnESE71y3BIA7n9phXU1m0rMEEYO217QyJSWBkuwUr0OJOsVZKfx/1yzijYpG/utP+70Ox5jT4up6ECbyHO/qY399OxeU5trsJZesPqeE8qoW/uul/SyYms5VZxSO6/zHN46vQuzN504f1/HGjJW1IGLMH3ceI6CwtHiK16FEraGuprNKpvDVNVt5ea+V4TCTkyWIGPM/24+Sk+pnWqbdHOemZL+Pn39+BfMK0vnrR8pZ846tG2EmH1cThIisFJG9IlIhIneG2J8oIk84+zeKyMwR+6eLSIeIfNPNOGNFQ3svbx1oZGlxpnUvTYDM5AQe/+uPcH5pLnc+vYOv/norjR29XodlzJi5liBExAfcB1wFLAJuEpFFIw67DWhR1VLgP4Hvjdj/I+APbsUYa/6w86h1L02wzOQEHr6ljL+7Yi5/2HmUS77/Mv/2+91U1Hd4HZoxJ+XmIPUKoEJVKwFEZA2wCtg97JhVwN3O498C94qIqKqKyHXAQaDTxRhjynPvHmF+QToFVntpQsX74vi7K+ZxzdJp/PdL+3n4zSoefP0gs3NTWVqcyZKiTBYWZjA9O4Wp1vVnIoibCaIIODzseQ1w7omOUdUBEWkFckSkB/gW8DHAupfCoKali01VLXzzynlehxKzSvPT+PHqs/jHqxbyh51HeWN/Ixsqm3l225H3j4kTyEhOICvFT1aKn+xUPyXZyUzPSiExwedh9CYWReo017uB/1TVjtH6ykXkduB2gOnTbarfaH7nfAmtOrOI1/c3ehxNbJuamcTnL5jF5y+YBUBjRy/76tqpae7mcEsXr+9vpKWzj4r6dtp6BgAQoHBKEkumZbKsZApZKX4Pr8DECjcTRC1QMux5sbMt1DE1IhIPZAJNBFsa14vI94EpQEBEelT13uEnq+oDwAMAZWVldtvqCagqT2+pYcXMbLs5LgLlpiWSm5YIc4LPCzP/XB+rp3+Qw81dVDd3UVHfwfO763h+dx0zclK4sDSXhYW2lodxj5sJYhMwV0RmEUwEq4GbRxyzFrgFeBu4HnhJgwX1Lxo6QETuBjpGJgczdjtr2zjQ0MltF872OhQzTkkJPuYWpDO3IJ0rFhbQ3NnH9prjlFe38KuNhyjISCQzOYGrzygkLs5mppnwci1BOGMKdwDrAR/wsKruEpF7gHJVXQs8BDwqIhVAM8EkYsLs6a01+H1xXD3OO3pN+Iz37ugTyU71c+n8fC6am8f2muO8sreBv/31Vn7xVhX/9sklLJhqLQoTPq6OQajqOmDdiG13DXvcA9xwkte425XgYsTAYIDn3j3CRxfmk5mS4HU4Jkx8ccJZ07NYVjKFxPg4/n3dHq7+rzf4woWz+Lsr5pHstwFtc/rsTuoo93pFI40dfVx3VpHXoRgXxIlwQ1kJL/39pXzq7CLuf62S6+57k4r6dq9DM1EgUmcxmTB5ZkstU1ISuGx+vtehRJVwdRmFS1aqn+9fv4yrl07j609s4xP//SbfuW4Jn1pe7HVoZhKzFkQUa+/p5/ndx7j6jEL88fZRx4JL5uXxh69dxNLiTP7+N+/yz7/baetSmFNm3xpR7Ll3j9LTH+CGspKTH2yiRkFGEr/6wrl84cJZPPJ2NX/z2Ga6+wa9DstMQpYgotiT5YeZV5DGsmJbOS7WxPvi+PY1i7j7E4t4cU8dqx/cYIUCzbjZGESU2lfXzrbDx/n21QutcmuUG208xB/v4zMrZvBE+SH+4sev84WLZnP7xXY/jBkba0FEqSc2HSbBJ3zSZi/FvEXTMvjceTNp6erjZ69XWkvCjJkliCjUNxDgma21XLGwgJy0RK/DMRFgTl7a+0niZutuMmNkCSIKvbinjubOPm48xwanzZ8NJYlDzV3c+vN36Ogd8DokE+EsQUShJ8sPMzUjiYvn5nkdiokwc/LS+MlnzmbP0Xa+/Kst9A8GvA7JRDBLEFGm9ng3r+1r4PrlxfiseJsJ4fIFBfz7J5fw2r4G/vHpHQTrYxrzYTaLKcr8akM1AKtXWPeSCW1o1tNHF+Tz2801NHf2ccXCgpDH3nyurbMSyyxBRJGe/kHWbDrMRxcWUJxl6z6Y0V2+IJ+Wrn5eeq+eqRlJLCmy+2XMB1kXUxRZt+MozZ193HLeTK9DMZOAiHDdmdMoyUrmN5sPc7S12+uQTISxFkQUGOoy+MkrFeSmJVLd1Mmh5i6PozKTQbwvjqyr55IAABDmSURBVM98ZAY/ebmCRzdU85VLS0lNtK8FE2QtiChR09JFTUs3H5mdbXdOm3HJSErgsx+ZQUfPAL9+55AV9zPvswQRJTZUNuP3xXH29CyvQzGTUHFWCtedWURlYycv7D7mdTgmQliCiAIdvQNsrznOWdOnkJRgK4mZU3P2jCxWzMrmtf2N7Kxt9TocEwEsQUSBtw80MRhQzpud43UoZpK75oxCirOSeWpLDQ3tVo4j1rmaIERkpYjsFZEKEbkzxP5EEXnC2b9RRGY62z8mIptFZIfz+3I345zMOnoH2FDZxMLCDPIzkrwOx0xy8b44bl4xHV+c8KuN1XRaOY6Y5lqCEBEfcB9wFbAIuElEFo047DagRVVLgf8EvudsbwQ+oapnALcAj7oV52S35p1DdPcPcsk8K6thwmNKip/V50ynob2Xbz213e60jmFutiBWABWqWqmqfcAaYNWIY1YBjziPfwt8VEREVbeq6hFn+y4gWUSsLOkIvQODPPh6JbNzUynJthvjTPiU5qdx5aIC/mf7UX7+ZpXX4RiPuJkgioDDw57XONtCHqOqA0ArMLIj/VPAFlX9UIeoiNwuIuUiUt7Q0BC2wCeLZ7fWUtfWa60H44qL5+Vx5aIC/n3dHt452Ox1OMYDET1ILSKLCXY7fTHUflV9QFXLVLUsLy+2viQHA8r9r1aypCiD0vw0r8MxUUhE+OGNyyjJTuErj2+hvq3H65DMBHMzQdQCwyvGFTvbQh4jIvFAJtDkPC8GngE+p6oHXIxzUnru3SNUNnbypUtK7cY445qMpAT+72eX09EzwFcet/LgscbNBLEJmCsis0TED6wG1o44Zi3BQWiA64GXVFVFZArwe+BOVX3TxRgnpd6BQX74/F4WT8vgqiVTvQ7HRLn5U9P57qfOYFNVC9/9w3teh2MmkGsJwhlTuANYD+wBnlTVXSJyj4hc6xz2EJAjIhXAN4ChqbB3AKXAXSKyzfnJdyvWyeaxDYeoaenmzqsWEGdrPpgJsOrMIm49fyYPvXGQ5949cvITTFRwtSqXqq4D1o3Ydtewxz3ADSHO+w7wHTdjm6zaevq596X9XDQ3l4tsxTgzgf7pLxays7aVbz21nflT05lXkO51SMZlET1IbT7s/lcP0NLVz7dWLvA6FBNj/PFx3PeZs0nxx/M3j26mvaff65CMyyxBTCLHWnt46I2DrDpzmi3uYjxRkJHEfTefRXVzF196bAt9AzZoHc2s8Psk8q+/301A4ZtXzvc6FBMjhtYaGem6M4t4aksNN97/NtcvLybOmUlnS5RGF0sQk8Sr+xr4/fajfONj8+yuaeO55TOyaO/p5/nddaQnxXPVkkKvQzIusAQxCXT2DvDtZ3cwOzeVL14y2+twjAHgknl5tHb38/r+RlISfFwy3yYaRhtLEJPAd//wHjUt3Txx+3kkxtt6DyYyiAifWDaN7v5B1u+uYyCg3LSixG7cjCI2SB3hXtvXwKMbqvl/LpjFilnZXodjzAfEiXBjWQlnT8/iT+/V84P1e636axSxFkQEq2/r4etPbGNeQZoNTJuIFSfCX55dhC9O+MkrB2ju7ONfVi221m4UsAQRofoHA3x1zVa6+gZZc/PZJPvtH5uJXHEiXHfmNM6dlc29L1ewr66dn352OQW2iNWkZgkiAj2+8RC/21bLxoPN3LC8mE1VLWyqavE6LGNGJSJ88+PzWTQtg2/+5l2u+e83+PHqMzl/Tq7XoZlTZGMQEeiN/Q1sPNjMxXPzOGt6ltfhGDMuf3FGIc98+QLSEuO5+cGN/MNv3qWls8/rsMwpsAQRYZ4sP8y6ncdYMi2DKxcXeB2OMadk/tR01n31Ir506Rye3lrLFT96lcc2VNPTP+h1aGYcLEFEkDXvHOJbT22nND+NG8tK3r871ZjJKNnv41srF/DcHRcyIyeFbz+7k4u+/zL3v3qANqvjNClItExJKysr0/Lycq/DOCWqyk9eOcAP1u/l0vl5XDY/nwSf5W4z+Zyo1Iaq8taBJn76ygHeqGjE74vj4nl5XLO0kMvm55OZkjDBkZohIrJZVctC7bNBao919Q3wv57ZyTNba7l22TR+eMMyfru5xuuwjAkrEeGC0lwuKM1lZ20rz2ytZd2Oo7y4pw6AuflpLJ+RxZKiTGblpjIrN5WpGUm23onHrAXhoZ21rXxtzVYqGzv5+hXz+NvLg8uHnqhAmjHRJKDKwsJ03j7QxObqFrYcOk5r95+7nuLjhJw0P7lpieSmJdLe00+KP55Uv4+UxHhS/D5S/MHfqYnxJCf48A1LKOMtHDief3fRVJTQWhARpqWzj3tfruDnbx4kNy2Rx247lwtKbSqgiS1xIiyfkc3yGcEKAYGAcqyth6rGTiobOzlyvJvGjl4aO/po7OiluqmLzt4BBgIn/qM2KSGOVCdpvLinjuxUfzDJpCaSk+YnJy2RnNRg0slO9eOPt67c0ViCmECtXf387I1KHn7jIF39g6w+p4Q7Vy60/ldjgLg4YdqUZKZNSeb8EH8wDf2F3zcQoKtvgK6+QTqd3129Q88H399X19bDnqNtNHX00TcYet2KjKR4ctOCyaOzd9Bplfy5ZZLi95E87HGKP/4DrZRoZwnCZYMBZUNlE09vqeWPO4/S2TfI1UsL+dpH59qSjcacAn98HP54P1NOUvV+qBtIVWnvHaCpo48mp0XS1Nn75+edfTS299LU2cvhlkG6+gYZHKWVkhgfx09frWBqRhLFWSmUZCVTnJ1CcVYyJVkpFGYmER8lk0xcTRAishL4MeADfqaq3x2xPxH4JbAcaAI+rapVzr5/BG4DBoGvqup6N2MNl0BAOdTcxTsHm9lQ2cQbFY3Ut/eSnhjPNUun8fkLZ7JgaobXYRoTEdwcbxvttQV5f2xjZJUzVaVvMBBsmQxrkQxvqeRnJHK0tYeNlU0829bD8KHc+DihKCuZGTmpzMhOYUZOCtOzU5iRk8r07JRJVTbHtQQhIj7gPuBjQA2wSUTWquruYYfdBrSoaqmIrAa+B3xaRBYBq4HFwDTgRRGZp6qe3GUTCCj9gQDdfYO09wzQ3jNAR+8Ard39HGvroa61hyOt3VTUd1BR30FXXzDM3DQ/587O4aolU7liYQFJCZPnfwxjYpWIkBjvIzHeR9YJWinDB6n7BgIcbe2mpqWbw81dHGruorq5i0NNXWw91EJ7z8AHzs1PT6Q4K5n89CTyMxLJS0skPyORzOQE0hITSE30kZ4UT2piPGmJ8STG+0jwiSdl1N1sQawAKlS1EkBE1gCrgOEJYhVwt/P4t8C9EvyvsApYo6q9wEERqXBe7+1wB9nc2ccn/vsNBgPKQCDAQEAZHFQGhj0/2USvOAmu1TsnL41Pn1PCvIJ0zpmZxZy8NKuNb0yU88fHBVsLOakf2qeqHO/qp7q5i+qmTg41BZNHbUs3FQ0dvF3Z9IGZW6PxxQnxQz++OBJ8QnxcHHECi4syefBzIScinRY3E0QRcHjY8xrg3BMdo6oDItIK5DjbN4w4t2jkG4jI7cDtztMOEdkbntDHLBdoBDjIBwOOIu9fYxSza4wOE3aNn5mINwkt5DW+DfzsllN+zRkn2jGpB6lV9QHgAa/eX0TKTzR/OFrYNUYHu8boMNHX6OZQey1QMux5sbMt5DEiEg9kEhysHsu5xhhjXORmgtgEzBWRWSLiJzjovHbEMWuBoYbR9cBLGry1ey2wWkQSRWQWMBd4x8VYjTHGjOBaF5MzpnAHsJ7gNNeHVXWXiNwDlKvqWuAh4FFnELqZYBLBOe5JggPaA8BXvJrBdBKedW9NILvG6GDXGB0m9BqjphaTMcaY8IqO2/2MMcaEnSUIY4wxIVmCOAUiUiUiO0Rkm4hMrhrjoxCRh0WkXkR2DtuWLSIviMh+5/ekXiT7BNd4t4jUOp/nNhH5Cy9jPB0iUiIiL4vIbhHZJSJfc7ZHzec4yjVG0+eYJCLviMi7zjX+i7N9lohsFJEKEXnCmQDkXhw2BjF+IlIFlKlqVN14JCIXAx3AL1V1ibPt+0Czqn5XRO4EslT1W17GeTpOcI13Ax2q+kMvYwsHESkEClV1i4ikA5uB64BbiZLPcZRrvJHo+RwFSFXVDhFJAN4AvgZ8A3haVdeIyP8F3lXVn7oVh7UgzPtU9TWCs8mGWwU84jx+hOA/xEnrBNcYNVT1qKpucR63A3sIViGIms9xlGuMGhrU4TxNcH4UuJxgWSKYgM/REsSpUeB5EdnslPuIZgWqetR5fAwo8DIYF90hItudLqhJ2/0ynIjMBM4CNhKln+OIa4Qo+hxFxCci24B64AXgAHBcVYeq/4UsQRROliBOzYWqejZwFfAVp9si6jk3MUZjn+RPgTnAmcBR4D+8Def0iUga8BTwd6raNnxftHyOIa4xqj5HVR1U1TMJVpJYASyY6BgsQZwCVa11ftcDzxD88KJVndPnO9T3W+9xPGGnqnXOP8YA8CCT/PN0+qyfAn6lqk87m6Pqcwx1jdH2OQ5R1ePAy8B5wBSnLBFMQAkiSxDjJCKpzsAYIpIKXAnsHP2sSW14OZRbgN95GIsrhr44HZ9kEn+ezuDmQ8AeVf3RsF1R8zme6Bqj7HPME5EpzuNkguvq7CGYKK53DnP9c7RZTOMkIrMJthogWKrkcVX9Nw9DChsR+TVwKcGSwnXAPwPPAk8C04Fq4EZVnbSDvCe4xksJdksoUAV8cVh//aQiIhcCrwM7gKGFmP+JYB99VHyOo1zjTUTP57iU4CC0j+Af8k+q6j3O988aIBvYCnzWWTfHnTgsQRhjjAnFupiMMcaEZAnCGGNMSJYgjDHGhGQJwhhjTEiWIIwxxoRkCcIYY0xIliCMGYWIXCoi/+M8vtaphGpMTHBtTWpjIplzN644ZRnGxFlHfa17URkTWawFYWKGiMwUkb0i8kuCZRgeEpHy4QuyOMetFJH3RGQL8JfDtt8qIvc6j38hItcP29fh/C4UkdecBWt2ishFo8TTISI/cN7/RRFZISKviEiliFzrHONzjtnkVCn9orM9TUT+JCJbJLh41aph17hHRB50Xvd5p1SDMeNmCcLEmrnAT1R1MfD3qloGLAUuEZGlIpJEsNDbJ4DlwNRxvv7NwHqnCucyYNsox6YCLzmxtAPfIVhz55PAPc4xtwGtqnoOcA7w1yIyC+gBPulUFb4M+A+nVTR0jfc5r3sc+NQ4r8EYwLqYTOypVtUNzuMbnfU84oFCYBHBP5oOqup+ABF5DBjPmh+bgIedaqPPqupoCaIP+KPzeAfQq6r9IrIDmOlsvxJYOqy1kkkwAdQA/+6Umg8QXBdgaI2Hg8Ped/Ow1zJmXCxBmFjTCcG1fYFvAueoaouI/AJIGsfrDOC0wEUkDvBDcMU650v7auAXIvIjVf3lCV6jX/9cDC0A9DqvERhW0lmAv1XV9cNPFJFbgTxguZNUqobFP7x42yBgXUzmlFgXk4lVGQSTRauIFBBc/AngPWCmiMxxnt90gvOrCHZBAVxLcElIRGQGUKeqDwI/A84+zTjXA19yWiSIyDynzHwmUO8kh8uAGaf5PsZ8iLUgTExS1XdFZCvBhHAYeNPZ3uN0O/1eRLoIlpVOD/ESDwK/E5F3CXYTdTrbLwX+QUT6gQ7gc6cZ6s8IdhFtccYYGgiuQ/wr4DmnO6rcuQ5jwsrKfRtjjAnJupiMMcaEZF1MxrhMRDYCiSM2/5Wq7vAiHmPGyrqYjDHGhGRdTMYYY0KyBGGMMSYkSxDGGGNCsgRhjDEmpP8f6PiNEx/qS7kAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['radius_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6290a97a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6290a97a",
        "outputId": "4e299c09-0bcf-44fc-d919-201246a29901"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.029136363636363"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ],
      "source": [
        "df['radius_mean'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a699fb6a",
      "metadata": {
        "id": "a699fb6a"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"radius_mean\"] > 25:\n",
        "    df.loc[x, \"radius_mean\"] = df['radius_mean'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8766129",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "b8766129",
        "outputId": "6a42bcbb-45f4-4d0f-913d-7fb5becf7e60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0          0        17.99         10.38          122.80     1001.0   \n",
              "1          0        20.57         17.77          132.90     1326.0   \n",
              "2          0        19.69         21.25          130.00     1203.0   \n",
              "3          0        11.42         20.38           77.58      386.1   \n",
              "4          0        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33           184.60   \n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "4         0.1809  ...         22.54          16.67           152.20   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d0514c4f-31c9-48f5-a433-cfbd47c1feef\">\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>...</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>0</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0514c4f-31c9-48f5-a433-cfbd47c1feef')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-d0514c4f-31c9-48f5-a433-cfbd47c1feef 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-d0514c4f-31c9-48f5-a433-cfbd47c1feef');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9676bb61",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "9676bb61",
        "outputId": "24be16ba-cde9-44b6-b42d-1d6dcf61a96c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['radius_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c92f82e8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "c92f82e8",
        "outputId": "f0ed58ef-48c4-4c56-ffcb-75768bfa05f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['texture_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ccd3914",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ccd3914",
        "outputId": "ccd6fe70-1516-45f2-c5a1-bc410848d394"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19.307344497607655\n",
            "9.71\n",
            "30.72\n"
          ]
        }
      ],
      "source": [
        "print(df['texture_mean'].mean())\n",
        "print(df['texture_mean'].min())\n",
        "print(df['texture_mean'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b485e739",
      "metadata": {
        "id": "b485e739"
      },
      "outputs": [],
      "source": [
        "# for x in df.index:\n",
        "#   if df.loc[x, \"texture_mean\"] > 19:\n",
        "#     df.loc[x, \"texture_mean\"] = 19"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af5e266b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "af5e266b",
        "outputId": "5bda0117-93df-4ca7-e2e8-3ad5993a1292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['perimeter_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f893829",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f893829",
        "outputId": "36439775-ef80-4462-d847-118359dfe512"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "91.39521531100479\n",
            "43.79\n",
            "186.9\n"
          ]
        }
      ],
      "source": [
        "print(df['perimeter_mean'].mean())\n",
        "print(df['perimeter_mean'].min())\n",
        "print(df['perimeter_mean'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e46cabd",
      "metadata": {
        "id": "1e46cabd"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"perimeter_mean\"] > 155:\n",
        "    df.loc[x, \"perimeter_mean\"] = df['perimeter_mean'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "216cb403",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "216cb403",
        "outputId": "a5df79e4-7b94-4426-a4ef-8a4c174246c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['perimeter_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2572ce3f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "2572ce3f",
        "outputId": "1ea9f54d-5cfb-4299-a63d-7c07983c4741"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['area_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c69062d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c69062d4",
        "outputId": "33b9e83a-3635-4670-eb60-da1635b4ac47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "644.9239234449759\n",
            "143.5\n",
            "2501.0\n"
          ]
        }
      ],
      "source": [
        "print(df['area_mean'].mean())\n",
        "print(df['area_mean'].min())\n",
        "print(df['area_mean'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9cf8c89d",
      "metadata": {
        "id": "9cf8c89d"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a67830d3",
      "metadata": {
        "id": "a67830d3"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"area_mean\"] > 1500:\n",
        "    df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e34cb322",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "e34cb322",
        "outputId": "03a65642-979a-441c-ff03-3276c265264c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['area_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48456eba",
      "metadata": {
        "id": "48456eba"
      },
      "outputs": [],
      "source": [
        "# for x in df.index:\n",
        "#   if df.loc[x, \"area_mean\"] < 300:\n",
        "#     df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "febb92e5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "febb92e5",
        "outputId": "ccafacd9-0a2d-461e-814f-515374445548"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0          0        17.99         10.38          122.80     1001.0   \n",
              "1          0        20.57         17.77          132.90     1326.0   \n",
              "2          0        19.69         21.25          130.00     1203.0   \n",
              "3          0        11.42         20.38           77.58      386.1   \n",
              "4          0        20.29         14.34          135.10     1297.0   \n",
              "5          0        12.45         15.70           82.57      477.1   \n",
              "6          0        18.25         19.98          119.60     1040.0   \n",
              "7          0        13.71         20.83           90.20      577.9   \n",
              "8          0        13.00         21.82           87.50      519.8   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760         0.30010              0.14710   \n",
              "1          0.08474           0.07864         0.08690              0.07017   \n",
              "2          0.10960           0.15990         0.19740              0.12790   \n",
              "3          0.14250           0.28390         0.24140              0.10520   \n",
              "4          0.10030           0.13280         0.19800              0.10430   \n",
              "5          0.12780           0.17000         0.15780              0.08089   \n",
              "6          0.09463           0.10900         0.11270              0.07400   \n",
              "7          0.11890           0.16450         0.09366              0.05985   \n",
              "8          0.12730           0.19320         0.18590              0.09353   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33           184.60   \n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "4         0.1809  ...         22.54          16.67           152.20   \n",
              "5         0.2087  ...         15.47          23.75           103.40   \n",
              "6         0.1794  ...         22.88          27.66           153.20   \n",
              "7         0.2196  ...         17.06          28.14           110.60   \n",
              "8         0.2350  ...         15.49          30.73           106.20   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "5       741.6            0.1791             0.5249           0.5355   \n",
              "6      1606.0            0.1442             0.2576           0.3784   \n",
              "7       897.0            0.1654             0.3682           0.2678   \n",
              "8       739.3            0.1703             0.5401           0.5390   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "5                0.1741          0.3985                  0.12440  \n",
              "6                0.1932          0.3063                  0.08368  \n",
              "7                0.1556          0.3196                  0.11510  \n",
              "8                0.2060          0.4378                  0.10720  \n",
              "\n",
              "[9 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>1001.0</td>\n",
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              "      <td>2019.0</td>\n",
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              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</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>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</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>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</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>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</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>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.15780</td>\n",
              "      <td>0.08089</td>\n",
              "      <td>0.2087</td>\n",
              "      <td>...</td>\n",
              "      <td>15.47</td>\n",
              "      <td>23.75</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.5249</td>\n",
              "      <td>0.5355</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
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              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.11270</td>\n",
              "      <td>0.07400</td>\n",
              "      <td>0.1794</td>\n",
              "      <td>...</td>\n",
              "      <td>22.88</td>\n",
              "      <td>27.66</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.1442</td>\n",
              "      <td>0.2576</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
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              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.05985</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>...</td>\n",
              "      <td>17.06</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.2678</td>\n",
              "      <td>0.1556</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
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              "      <th>8</th>\n",
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              "      <td>13.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>0.09353</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>...</td>\n",
              "      <td>15.49</td>\n",
              "      <td>30.73</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.1703</td>\n",
              "      <td>0.5401</td>\n",
              "      <td>0.5390</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
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          },
          "metadata": {},
          "execution_count": 157
        }
      ],
      "source": [
        "df.head(9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc6a5e8c",
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        "id": "dc6a5e8c",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['concavity_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "679f32fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "679f32fb",
        "outputId": "8e813e2e-d55e-46c5-eff9-408f569e9ce3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.08629843137254903\n",
            "0.0\n",
            "0.4108\n"
          ]
        }
      ],
      "source": [
        "print(df['concavity_mean'].mean())\n",
        "print(df['concavity_mean'].min())\n",
        "print(df['concavity_mean'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5133bb2b",
      "metadata": {
        "id": "5133bb2b"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"concavity_mean\"] > 0.34:\n",
        "    df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bb4e6d59",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "bb4e6d59",
        "outputId": "1abe9b82-c823-49d2-9170-1bcb80473e74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['concavity_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a92d7dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "8a92d7dd",
        "outputId": "3b85ddc1-6da7-4a63-b28c-ae90838537b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0          0        17.99         10.38          122.80     1001.0   \n",
              "1          0        20.57         17.77          132.90     1326.0   \n",
              "2          0        19.69         21.25          130.00     1203.0   \n",
              "3          0        11.42         20.38           77.58      386.1   \n",
              "4          0        20.29         14.34          135.10     1297.0   \n",
              "5          0        12.45         15.70           82.57      477.1   \n",
              "6          0        18.25         19.98          119.60     1040.0   \n",
              "7          0        13.71         20.83           90.20      577.9   \n",
              "8          0        13.00         21.82           87.50      519.8   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760         0.30010              0.14710   \n",
              "1          0.08474           0.07864         0.08690              0.07017   \n",
              "2          0.10960           0.15990         0.19740              0.12790   \n",
              "3          0.14250           0.28390         0.24140              0.10520   \n",
              "4          0.10030           0.13280         0.19800              0.10430   \n",
              "5          0.12780           0.17000         0.15780              0.08089   \n",
              "6          0.09463           0.10900         0.11270              0.07400   \n",
              "7          0.11890           0.16450         0.09366              0.05985   \n",
              "8          0.12730           0.19320         0.18590              0.09353   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.2419  ...         25.38          17.33           184.60   \n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "4         0.1809  ...         22.54          16.67           152.20   \n",
              "5         0.2087  ...         15.47          23.75           103.40   \n",
              "6         0.1794  ...         22.88          27.66           153.20   \n",
              "7         0.2196  ...         17.06          28.14           110.60   \n",
              "8         0.2350  ...         15.49          30.73           106.20   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "5       741.6            0.1791             0.5249           0.5355   \n",
              "6      1606.0            0.1442             0.2576           0.3784   \n",
              "7       897.0            0.1654             0.3682           0.2678   \n",
              "8       739.3            0.1703             0.5401           0.5390   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
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              "5                0.1741          0.3985                  0.12440  \n",
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              "7                0.1556          0.3196                  0.11510  \n",
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              "      <th>diagnosis</th>\n",
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              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
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              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</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>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</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>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "      <td>0</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>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "      <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>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>12.45</td>\n",
              "      <td>15.70</td>\n",
              "      <td>82.57</td>\n",
              "      <td>477.1</td>\n",
              "      <td>0.12780</td>\n",
              "      <td>0.17000</td>\n",
              "      <td>0.15780</td>\n",
              "      <td>0.08089</td>\n",
              "      <td>0.2087</td>\n",
              "      <td>...</td>\n",
              "      <td>15.47</td>\n",
              "      <td>23.75</td>\n",
              "      <td>103.40</td>\n",
              "      <td>741.6</td>\n",
              "      <td>0.1791</td>\n",
              "      <td>0.5249</td>\n",
              "      <td>0.5355</td>\n",
              "      <td>0.1741</td>\n",
              "      <td>0.3985</td>\n",
              "      <td>0.12440</td>\n",
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              "      <td>18.25</td>\n",
              "      <td>19.98</td>\n",
              "      <td>119.60</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>0.09463</td>\n",
              "      <td>0.10900</td>\n",
              "      <td>0.11270</td>\n",
              "      <td>0.07400</td>\n",
              "      <td>0.1794</td>\n",
              "      <td>...</td>\n",
              "      <td>22.88</td>\n",
              "      <td>27.66</td>\n",
              "      <td>153.20</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>0.1442</td>\n",
              "      <td>0.2576</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>0.3063</td>\n",
              "      <td>0.08368</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>13.71</td>\n",
              "      <td>20.83</td>\n",
              "      <td>90.20</td>\n",
              "      <td>577.9</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.16450</td>\n",
              "      <td>0.09366</td>\n",
              "      <td>0.05985</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>...</td>\n",
              "      <td>17.06</td>\n",
              "      <td>28.14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>897.0</td>\n",
              "      <td>0.1654</td>\n",
              "      <td>0.3682</td>\n",
              "      <td>0.2678</td>\n",
              "      <td>0.1556</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.11510</td>\n",
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              "      <th>8</th>\n",
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              "      <td>13.00</td>\n",
              "      <td>21.82</td>\n",
              "      <td>87.50</td>\n",
              "      <td>519.8</td>\n",
              "      <td>0.12730</td>\n",
              "      <td>0.19320</td>\n",
              "      <td>0.18590</td>\n",
              "      <td>0.09353</td>\n",
              "      <td>0.2350</td>\n",
              "      <td>...</td>\n",
              "      <td>15.49</td>\n",
              "      <td>30.73</td>\n",
              "      <td>106.20</td>\n",
              "      <td>739.3</td>\n",
              "      <td>0.1703</td>\n",
              "      <td>0.5401</td>\n",
              "      <td>0.5390</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>0.10720</td>\n",
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              "<p>9 rows × 31 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 162
        }
      ],
      "source": [
        "df.head(9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d39064b3",
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          "height": 335
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        "id": "d39064b3",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['symmetry_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1003806",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1003806",
        "outputId": "a7a06abf-a879-40a4-b5c7-9dc997cc0a86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.08412879012345678\n",
            "0.0\n",
            "0.3189\n"
          ]
        }
      ],
      "source": [
        "print(df['concavity_mean'].mean())\n",
        "print(df['concavity_mean'].min())\n",
        "print(df['concavity_mean'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "74a234e4",
      "metadata": {
        "id": "74a234e4"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"symmetry_mean\"] > 0.29:\n",
        "    df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f659b79e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "f659b79e",
        "outputId": "093927a9-a66e-4388-f27a-80dd1dc60e7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['radius_se'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d4f1b63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3d4f1b63",
        "outputId": "36fae128-0041-444c-a8ec-6c2be5168b8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.37843787128712875\n",
            "0.1115\n",
            "1.37\n"
          ]
        }
      ],
      "source": [
        "print(df['radius_se'].mean())\n",
        "print(df['radius_se'].min())\n",
        "print(df['radius_se'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d191f861",
      "metadata": {
        "id": "d191f861"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"radius_se\"] > 1:\n",
        "    df.drop(x, inplace = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db04088b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "db04088b",
        "outputId": "64292c90-9523-48b6-b35f-051309af6c1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['radius_se'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "936d7238",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "936d7238",
        "outputId": "b5a3e32d-1532-4dd6-d315-c2ba0a49f6ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['perimeter_se'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5e90aa6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5e90aa6",
        "outputId": "8a634dfa-b0a7-489c-8a82-6ae9210bb4ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.583450629722922\n",
            "0.7714\n",
            "11.07\n"
          ]
        }
      ],
      "source": [
        "print(df['perimeter_se'].mean())\n",
        "print(df['perimeter_se'].min())\n",
        "print(df['perimeter_se'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3803b5af",
      "metadata": {
        "id": "3803b5af"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"perimeter_se\"] > 6:\n",
        "    df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b5cbe9b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "b5cbe9b6",
        "outputId": "4f92824b-b87d-4934-b199-bab7366bdaad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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xjHHcmGsPiMgMIBuIEZFlwNDIYyKD3UTGD5XUtrFwZgJhLhsoHs2KvFSuX5PLfVuOsnHJLJbMTnY6kjGOOV2L4FLgv4Ac4Hbgp96PW4Bv+DaamQyPR9lf287ibOsWOp1bL19AZkI0//zYO3ZvgQlpYxYCVb1fVS8APq2qFwz72KiqT0xTRjMBx5q76XIP2IyhcUiMjuCHVy3m0IkO7n6pzOk4xjjmdF1DN6jqH4B8Ebnl1OdV9XafJTOTYgPFE7NhYRZXLp3F3S+VcflZM1gwwwqoCT2n6xoaGm2MBxJG+DB+pqS2nXCXUJRls3rH6zsfXkRSTAT//Ng79A9YF5EJPWO2CFT1F94/vzc9ccyZKqltpygrgajwMKejBIzUuEi+d+Uibn5wF79+/Shf+OAcpyMZM63Ge0PZT0QkUUQiRGSziDSIyA2+DmcmRlXZX9tm4wOTcMVZM7mkOIs7XjhMeUOn03GMmVbjvY/gElVtBz7E4FpDc4F/8lUoMzn1HSdp7HRbIZgEEeEHH1lMVLiLWx9/B49HnY5kzLQZbyEY6kK6AnhUVdt8lMecARsoPjOZidF860PFbK9o4Q/bjjkdx5hpM95C8BcROQisADaLSAZgyzf6mX01g3sQ2BpDk/d/VuRwXlE6P3ruIFXN3U7HMWZajGtXc1W9TUR+ArSp6oCIdAFX+jaamaiS2jYK0uOID+HN6sfy4LbKcZ13TmEa2442c+Nvt3PjufnvLuV93ZpcX8YzxjETecdYwOD9BMO/5ndTnMecgZLadlsqYQqkxEZy6aIZ/HlPLW9XtrAiL9XpSMb41HhnDf2ewaUmPgCs8n7YqqN+pK27j+qWHhsoniJrClLJT4vlmb11tPf0OR3HGJ8ab4tgJVCsqjaVwk+V1NlA8VRyiXD1shx+9mIpf9pTyw3WLWSC2HgHi/cBM3wZxJyZ/d7N6q1FMHXSE6K4aGEWB+ra2VtjE+VM8BpvIUgH9ovI8yLy9NCHL4OZiSmpbScrMYr0+CinowSVdXPTyU6O4c97amnqPOl0HGN8YrxdQ9/1ZQhz5kpq26xbyAfCXMJHl+dw90tlfO/P+/nZtcucjmTMlBtXi0BVX2HwjuII7+fbgbd9mMtMQI97gLL6ThZbt5BPzEiK5vz5GTy9p5ZN+22fYxN8xjtr6HPAY8AvvIeygad8FcpMzMHj7XgUiq1F4DMfnJ/BghkJfPOpvbTZLCITZMbbNfQlYDWwDUBVS0Uk02epzISU2ECxz4W7XFy4IJN7Xj7CF/6wkw+fPWtS17Gb0ow/Gu9g8UlVdQ898N5UdtqppCJymYgcEpEyEblthOdvEZH9IvKOd1XTvPFHN0NKattJiokgJyXG6ShBLSclllUFqWwrb+J4u62wYoLHeAvBKyLyDQY3sb8YeBT481hfICJhwN3A5UAxcK2IFJ9y2i5gpaqezWDX008mEt4MKqltY3F24rtLIRjfuWRhFlHhYfxlTy12W40JFuMtBLcBDcBe4PPAs8C3TvM1q4EyVS33tiYe5pT1iVT1JVUdWtlrK5Az3uBmUN+Ah4N1HSy28YFpERsVzsXFWZQ3drHP2yVnTKAb76JzHhF5CnhKVRvGee1soGrY42pgzRjnfxZ4bqQnROQm4CaA3FzrYx2u9EQn7gEPi7KtEEyXVfmpvHW0mef21jE/K4HI8PH+PmWMfxrzJ1gGfVdEGoFDwCHv7mTfnsoQ3t3OVgL/OdLzqvpLVV2pqiszMjKm8qUD3j7vHgQ2dXT6hLmEDy+ZRWtPH6+Vjff3ImP81+l+lfk6sA5YpaqpqprK4G/160Tk66f52hpg9rDHOd5j7yEiFwHfBDaqqt26OUElNW3ERYaRnxbndJSQUpAex6JZibx2uJGOXptOagLb6QrBJ4BrVfXo0AFVLQduAD55mq/dDhSJSIGIRAIfB96zLIWILGPw3oSNqlo/0fAG9tW2s2hWEi6XDRRPt0sXzaDf42HzQfvRNYHtdIUgQlUbTz3oHSeIGOsLVbUfuBl4HjgAPKKqJSLyfRHZ6D3tP4F44FER2W3rF03MgEfZX9vOomzrFnJCenwUqwvS2FHRTH2HTSc1get0g8XuST4HgKo+y+AMo+HHvj3s84tOdw0zuqONnfT0DdiMIQdduCCTXZUtPL/vOJ9Ym+90HGMm5XQtgiUi0j7CRwdw1nQENKMb2qN4sc0Yckx8VDgfnJfBgeMdHG3scjqOMZMyZiFQ1TBVTRzhI0FVx+waMr63r6aNqHAXczJsoNhJ585JJzE6nL+VHLebzExAsgnQAWxfbRsLZyYSHmb/jE6KDHdxwYJMjjV3U1rf6XQcYybM3kEClMejlNS0s9gGiv3CirwUUmIjeGH/CWsVmIBjhSBAVbV003Gy3waK/US4y8WGBVnUtPawv86WnjCBxQpBgLKBYv+zZHYy6fFRvLD/BB5rFZgAYoUgQO2rbSMiTCjKinc6ivEKcwkXLcykvuMk71TbZvcmcFghCFD7atqYl5VAVHiY01HMMIuzk5iZFM3mAycY8FirwAQGKwQBSHXwjmIbH/A/LhEuWphFU5ebXZUtTscxZlysEASg4+29NHW5bWkJP7VgRgKzU2LYfLCe/gGP03GMOS0rBAFoaKB4kbUI/JKIcHHxDNp6+thxzFoFxv9ZIQhA+2racAksnJngdBQzijkZceSnxfLyoXr6rFVg/JwVggBUUtvGnIx4YiPHtcGccYB4xwrae/t562iz03GMGZMVggC0r6bd7h8IAIUZ8RSmx/HK4Qbc/dYqMP7LCkGAOdHey/H2XisEAeKihVl0nuxn29Emp6MYMyorBAFmT1UrAEtnJzucxIxHfnocRZnxvHq4gZP9A07HMWZEVggCzJ7qVsJdwiLbrD5gbFiYRZd7gK1HrFVg/JMVggCzu6qVBTMTiI6wO4oDRW5qLPOzEni11Da6N/7JCkEA8XiUd6rarFsoAG1YmElP3wC/3VLhdBRj3scKQQApb+yi42Q/S3KsEASanJRYFs5I4FevldPWY60C41+sEAQQGygObBu89xX85vWjTkcx5j2sEASQ3VWtxEeFU5hhS08HolnJMVy+eAb3vn6U1m6303GMeZcVggCyp7qVs3OSCHOJ01HMJH3tonl0ufv51WvlTkcx5l1WCAJEb98AB+raWWLdQgFt/owErjhrJvdtqaCp86TTcYwBwBar8WMPbqt89/Oq5m76BpT2nr73HDeB52sXFfHM3jp+8Wo53/i7hU7HMcZaBIGiqqUbGJx9YgLb3MwErlqazW/fqKCmtcfpOMZYIQgU1S09JEaHkxQT4XQUMwVuuWQeAD99/pDDSYyxQhAwqpq7rTUQRHJSYrlxXT5P7q5hX41tdG+cZYUgAHSf7Kepy83slBino5gp9MXz55IUE8GPnjuIqm10b5xjhSAAHGseHB/IS4tzOImZSkkxEXz5wiJeL2vklcMNTscxIcwKQQCoaOwizCVkW4sg6HzinDxyU2P54TMHbEtL4xgrBAGgoqmLnJQYIsLsnyvYRIa7+NcPFVNa38l9W2zpCeMMe2fxc+5+DzWtPeRbt1DQurg4iw0LMrlzUyl1bTad1Ew/KwR+rqqlG49CfprNGApm3924iAGP8oO/HHA6iglBVgj83LGmLgTITbUWQTCbnRrLly6YyzN763jVBo7NNLNC4OcqmrrJSowmJtJ2JAt2N60vJD8tlm89tY+uk/1OxzEhxAqBHxvwKJXN3eRZt1BIiI4I48cfPZuqlm5+8Mx+p+OYEGKFwI8db+vF3e8hP926hULFmsI0blpfyENvVbFp/wmn45gQ4dNCICKXicghESkTkdtGeH69iLwtIv0ico0vswSiiqYuAJsxFGJuuXgeC2cmctsT79BoS1WbaeCzQiAiYcDdwOVAMXCtiBSfclol8GngQV/lCGQVTV2kxEbYQnMhJio8jDs/tpT23n5ufewdPB5bfsL4li9bBKuBMlUtV1U38DBw5fATVLVCVd8B7JbKU6gqFU3dtqxEiJo/I4FvXL6AzQfrufulMqfjmCDny0KQDVQNe1ztPWbGobyxi66T/dYtFMI+dW4+Vy3L5vZNh/lbyXGn45ggFhCDxSJyk4jsEJEdDQ2hMcf6jbJGAOZkWCEIVSLCf1x9FmdnJ/GVh3fxdmWL05FMkPJlIagBZg97nOM9NmGq+ktVXamqKzMyMqYknL/bUtZEckwEqXGRTkcxDoqOCOM3n15FZkI0n/ntdvbXtjsdyQQhXxaC7UCRiBSISCTwceBpH75e0BjwKG+WNzEnMx4RcTqOcVh6fBS//+xqYiLCuP7XWymptY1szNTyWSFQ1X7gZuB54ADwiKqWiMj3RWQjgIisEpFq4P8AvxCREl/lCSQltW209fQxJyPe6SjGT+SlxfHQ584hOiKMj/1iK6+VhkYXqZke4b68uKo+Czx7yrFvD/t8O4NdRmaYLWVNgI0PBKMHt1We0dc/8cVzufG+7Xz6vu3cetl8PndeobUazRkLiMHiUPN6WQPzsxJIiLb7B8x7zUyK4dEvrOWS4iz+/dmDfOq+7dS02tLV5sxYIfAzXSf72X60hfPnh8aguJm4hOgIfn79cv7tykXsqGjm4ttf4a5NpbZQnZk0KwR+5o0jTbgHPHxwnhUCMzoR4RNr83n+a+v54LwM7th0mHN/9CI/+etByuo7nI5nAoxPxwjMxL18qJ64yDBW5qdS0dTtdBzj52anxnLPDSvYVdnC/75yhHteOcLPXz7CghkJXL54JqvyUzh7djLxUfZf3YzOfjr8iKry8qEGzp2bTmS4NdbM+C3LTeEXn1jJifZent1bxzPv1HHHpsMAiMCcjHjy02LJSYklJyWGtPhIkmMiSYqNIDkmguTYSJJiIghz2cBzKLJC4EfK6jupae3hixfMcTqK8VPjmXUUFR7G1ctzuGzRDKpbe6hq6aa2pYd9Ne28WtqIu3/0pb2SYiJIi4skJS6S1LhI0uOjyE2NJS8t9t0/bRJD8LFC4Ef+5l1/fsOCLIeTmGAQGxXOvKwE5mUlvHtMVfnQ2bNo7nbT2u2mtaePtu4+WrrdtHT30drtprlr8KOquZudx1po7nK/57qzkqJZnJ3E2TlJLM9NYXleCtERtoNeILNC4Ef+tv8ES3KSmJEU7XQUE6REhKTYCJJiI4Dx3afS0dvHsaZuKpu7qWjq4kBdB/tq2t79xSUy3MWK3BTWz8vgkkVZdiNkALJC4CeOt/Wyp6qVf7p0vtNRTJA7k5vakmMiWVuYxtrCNHrcAxxr6qK8sYsjDZ28+dcmfvzXg2TER1E8K5FFsxLJTo4Z8Ya369bknslfwUwxKwR+4oUDg79dXVJs3UImMMREhrFgZiILZiYC0Nrt5kBdOyV17bxW2sArhxtIi4tkWW4Ky3KTSYm1BRT9lRUCP/H8vuMUpMcxN9Oa1SYwJcdGsnZOOmvnpNN9sp/9de28XdnKpgMn2HTgBIXpcSzPTWFRdqLTUc0prBD4gYaOk7xxpJEvnj/X1o0xQSE2KpyV+amszE+lucvNrqoWdlW28tjb1Ty9x8WBunauW5PHkpwk+5n3A1YI/MBz++rwKGxcOsvpKMZMudS4SDYsyOLC+ZlUemci/XlPHY/sqKZ4ZiLXrcnlI8uy7aY3B9ldS37g6d21zD9lmp8xwUZEyEuL4+rlObz1zQ3820cWo8C3ntrH6h9u4l+eeIe91bbXghOsBDusprWHHcda+MdL5jkdxZhp8+c9dYSJcMOaXKpaenjraDOP7azmobeqyE6O4dw5aZyVnUR42Oi/q9rMo6ljhcBhj++sBuDKpdkOJzFm+okIuamDdy1fcdZMdle1sLW8mUd3VvPcvuOsKUxlTUGadRv5mH13HeTxKI/urOLcOWnMTo11Oo4xjoqJDGPtnHTOKUyjrL6TN440sflAPS8famDZ7GQ+OC+DtPgop2MGJSsEDtpa3kRVcw//eIndRGbMEBGhKCuBoqwEGjtOsuVIIzuPtbDzWAtLZidz/rwMMhPt7vupZIXAQX/cUUVCdDiXLprhdBRj/FJ6QhRXLs3mwgWZvF7ayLajzeypamVRdhLnFKZSaMtZTAmbNeSQ+o7B5YKvXpZtC3YZc73cZyUAAAyiSURBVBoJ0RFcftZM/unS+XxwfgalJzq45I5X+e7TJe9bFM9MnLUIHPLA1kr6BpRPnZvvdBRjAkZcVDiXFM/g3DnpbD5wgt+9WcHD2ys5f14ma+ekETHGLKOR2MyjQdYicMDJ/gEe2HaMC+ZnWNPWmEmIjwrnyqXZfPnCIvJS4/hryXHu2lxKqW3TOSlWCBzw1K4aGjvd3LiuwOkoxgS0rMRoPnVuPp9ZV4AA922p4JEdVXSe7Hc6WkCxQjDN+gc83P3SEc7KTuK8onSn4xgTFOZmxvOVDUVcMD+TvdVt3PHCYXYea0ZVnY4WEKwQTLM/7a6lsrmbr2wossW2jJlCEWEuLi7O4uYL55KZEMXjb9dw3xsVtHTbYPLpWCGYRu5+D//9YikLZyZy0cJMp+MYE5SyEqP53PpCNi6ZRWVzN3dtLmXb0SZrHYzBCsE0+v3WY1Q0dXPrZfOtNWCMD7lEOKcwja9eWMTslBj+tLuWe7ccpcWmmo7ICsE0aely87PNpayfl8H58601YMx0SImL5DPrCrhy6SyqWnq468XB1oHHWgfvYfcRTJN/f/YAnSf7+ebfLXQ6ijEhRURYU5DGvKwEnny7hj/trmVvTRtXL8txOprfsBbBNHittIFHd1bz+fWFzJ9hew4Y44SU2EhuXJfPVUuzqWnp4WebS/n9mxV4PNY6sELgY63dbm57fC+FGXF8ZUOR03GMCWkiwqqCVL66oYi8tFj+9U8lXPfrrVQ2dTsdzVFWCHzI41H+4ZE9NHSc5I6/X2prChnjJ5JjI/n0ufn8+KNnUVLTzqV3vsr9b4Ru68AKgQ/duekwmw/W880rFrJkdrLTcYwxw4gIH1uVy/NfX8+qglS+83QJ1/5qK8eaupyONu2sEPjIA9uO8bMXy/j7lTl8cm2e03GMMaOYlRzD/Teu4icfPZv9tYOtgzs3HabbHTrLVFgh8IHfbz3GN5/cxwXzM/jhVWfZPQPG+DkR4e9XzeZvt6znwgWZ3LmplAv/6xUe31nNQAh0F1khmEIDHuWnfzvEvz61j4sWZnLPDSsmvCyuMcY5M5Ni+Pn1K3j0C2vJTIziHx7dw8V3vMKTu6rpH/A4Hc9n7F1qihxv6+XG327nv73dQffcsMIGh40JUKvyU3nqi+u4+7rlRIa5+Pof93DR7a/w2y1Hae/tczrelLMbys7Qyf4B/rC1kjtfOEyfx8MPr1rMdatzrTvImADncglXnD2TyxfP4IUDJ7jn5SN898/7+fFfD/GRZbO4alkOK/NScLkC//+6FYJJau5y8+iOKu5/o4Latl7OK0rnBx9ZTF5anNPRjDFTyOUSLl00g0sXzWBvdRt/2HqMJ3fV8NBbVWQlRnH54pmcPz+D1QWpxEYG5luq+HJFPhG5DLgLCAN+rao/OuX5KOB3wAqgCfiYqlaMdc2VK1fqjh07fBN4DAMeZX9tO1vLm3i9rJEtZY30e5RzClP50gVzOa8oY8pf88FtlVN+TWPM/zfZrSo7T/az+cAJnt1bx0uHGnD3e4gIE5bNTmFZbjKLs5NYnJ1EbmosYX7SYhCRnaq6cqTnfFa+RCQMuBu4GKgGtovI06q6f9hpnwVaVHWuiHwc+DHwMV9lGk5V6fco7n4PfQMe3P0e2nv7ae5y09x1kuauPho6TlLR1MWRhk6O1HfS5R4AoCA9js+eV8BVy7JZMCNxOuIaY/zI0FaZVy7Npsc9wPaKZrYcaWTrkSbu21KB2zuwHO4SZiXHMDs1htkpsWQnx5ASF0lybAQpsZEkxUSQEB1OVHgYUeEuoiMG/5zu7iZftmNWA2WqWg4gIg8DVwLDC8GVwHe9nz8G/I+IiPqgmfK7Nyu4a1Mpbu+bvnvAw3heZVZSNIUZ8VyzIofleSmcU5hGVmL0VMczxgSomMgw1s/LYP28wV4Bd7+H0voOSmraqWjqoqqlh6rmbjYdOEFj5/iWwY4IEyLDBgtCmEsIdwkuEW69bAEfXTH1i+X5shBkA1XDHlcDa0Y7R1X7RaQNSAMah58kIjcBN3kfdorIIZ8kfr/0Y9D45jS92BlI55TvmZ8KlJwQOFkDJSf4YdbrRz7sdzmHXPOt9x2aSNZR72wNiJENVf0l8Mvpfl0R2TFan5o/sZxTL1CyBkpOCJysgZITpi6rL+8jqAFmD3uc4z024jkiEg4kMThobIwxZpr4shBsB4pEpEBEIoGPA0+fcs7TwKe8n18DvOiL8QFjjDGj81nXkLfP/2bgeQanj96rqiUi8n1gh6o+DfwG+L2IlAHNDBYLfzLt3VGTZDmnXqBkDZScEDhZAyUnTFFWn95HYIwxxv/ZWkPGGBPirBAYY0yIs0IwAhG5TEQOiUiZiNzmdJ7RiMi9IlIvIvuczjIWEZktIi+JyH4RKRGRrzqdaTQiEi0ib4nIHm/W7zmdaSwiEiYiu0TkL05nGYuIVIjIXhHZLSLTv0bMOIlIsog8JiIHReSAiKx1OtOpRGS+9/s49NEuIl87o2vaGMF7eZfGOMywpTGAa09ZGsMviMh6oBP4naoudjrPaERkJjBTVd8WkQRgJ/ARP/2eChCnqp0iEgG8DnxVVbc6HG1EInILsBJIVNUPOZ1nNCJSAaxUVb+8UWuIiNwPvKaqv/bOdoxV1Vanc43G+35VA6xR1WOTvY61CN7v3aUxVNUNDC2N4XdU9VUGZ1v5NVWtU9W3vZ93AAcYvKvc7+igTu/DCO+HX/62JCI5wBXAr53OEgxEJAlYz+BsRlTV7c9FwGsDcORMigBYIRjJSEtj+OWbViASkXxgGbDN2SSj83a37AbqgRdU1V+z3gn8MxAIW2cp8DcR2eldMsYfFQANwH3e7rZfi4i/ryv/ceChM72IFQIzbUQkHngc+JqqtjudZzSqOqCqSxm8G361iPhdt5uIfAioV9WdTmcZpw+o6nLgcuBL3m5NfxMOLAfuUdVlQBfgz2OEkcBG4NEzvZYVgvcbz9IYZoK8/e2PAw+o6hNO5xkPb7fAS8BlTmcZwTpgo7fv/WHgQhH5g7ORRqeqNd4/64EnGeyC9TfVQPWwFuBjDBYGf3U58LaqnjjTC1kheL/xLI1hJsA7APsb4ICq3u50nrGISIaIJHs/j2Fw0sBBZ1O9n6r+i6rmqGo+gz+jL6rqDQ7HGpGIxHknCeDtarkE8LuZbqp6HKgSkfneQxt477L5/uZapqBbCAJk9dHpNNrSGA7HGpGIPAScD6SLSDXwHVX9jbOpRrQO+ASw19v3DvANVX3WwUyjmQnc752N4QIeUVW/npoZALKAJ737eIcDD6rqX52NNKovAw94fwksB250OM+IvAX1YuDzU3I9mz5qjDGhzbqGjDEmxFkhMMaYEGeFwBhjQpwVAmOMCXFWCIwxJsRZITDGmBBnhcCYYUTkCyLyySm61jem4jrG+JrdR2CMl4iEq2r/FF6vU1XjJ/g1Yao6MFUZjBkPaxGYoCIi+d5NRR7wbizymIjEisgKEXnFu/rl8949EhCRl0XkTu9mKV8Vke+KyD8Oe+4OEdnhvdYqEXlCREpF5AfDXvMG72Y2u0XkF97VS38ExHiPPTDaed7jnSLyUxHZA4y4EYqI/Mi7sc87IvJf3mMZIvK4iGz3fqzz6TfXBC0rBCYYzQd+rqoLgXbgS8B/A9eo6grgXuCHw86PVNWVqvrTEa7lVtWVwP8Cf/JeazHwaRFJE5GFwMeAdd4VSweA61X1NqBHVZeq6vWjned9jThgm6ouUdXXTw0gImnAVcAiVT0bGCpCdwF3qOoq4KPYvgRmkmytIROMqlR1i/fzPwDfYPDN+wXvejdhQN2w8/84xrWGFhzcC5Soah2AiJQzuErtB4AVwHbvtWMY3MfgVBvGOG+AwZVZR9MG9AK/kcEtKYfWProIKPZeDyBRROKHbaxjzLhYITDB6NSBrw4G38RH23+2a4xrnfT+6Rn2+dDjcECA+1X1X06TaazzescaF/AuhLiawWJyDXAzcCGDLfpzVLX3NK9tzJisa8gEo9xhm45fB2wFMoaOiUiEiCyaotfaDFwjIpnea6eKSJ73uT7vPgynO29M3g19kryrtX4dWOJ96m8MrpY5dN7SM/7bmJBkhcAEo0MM7oJ1AEjBOz4A/Ng7ILsbOHcqXkhV9wPfYnAbxneAFxhcyhrgl8A7IvLAac47nQTgL96vex24xXv8K8BK7wDyfuALU/F3MqHHpo+aoOLdE/kvqup320sa46+sRWCMMSHOWgTG+BEReRIoOOXwrar6vBN5TGiwQmCMMSHOuoaMMSbEWSEwxpgQZ4XAGGNCnBUCY4wJcf8PB6iIUE4n4mkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['perimeter_se'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df27aa55",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "df27aa55",
        "outputId": "a339910b-4bff-4746-b339-c5a7088065c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(df['area_se'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "229ee93f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "229ee93f",
        "outputId": "7704509b-9cb6-45e5-d872-be9b0471c731"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31.874384615384617\n",
            "6.802\n",
            "112.4\n"
          ]
        }
      ],
      "source": [
        "print(df['area_se'].mean())\n",
        "print(df['area_se'].min())\n",
        "print(df['area_se'].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60889518",
      "metadata": {
        "id": "60889518"
      },
      "outputs": [],
      "source": [
        "for x in df.index:\n",
        "  if df.loc[x, \"area_se\"] > 100:\n",
        "    df.drop(x, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6df0832e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "6df0832e",
        "outputId": "15a2e6cd-81cb-469f-f8d9-a5da5a242e29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "1          0        20.57         17.77          132.90     1326.0   \n",
              "2          0        19.69         21.25          130.00     1203.0   \n",
              "3          0        11.42         20.38           77.58      386.1   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "1         0.1812  ...         24.99          23.41           158.80   \n",
              "2         0.2069  ...         23.57          25.53           152.50   \n",
              "3         0.2597  ...         14.91          26.50            98.87   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "\n",
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              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
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              "      <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",
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              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
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              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
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              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
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          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "954086bc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "954086bc",
        "outputId": "03cf6dd2-99ad-4e4c-d891-bfe37f6df8cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(383, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a869cf57",
      "metadata": {
        "id": "a869cf57"
      },
      "outputs": [],
      "source": [
        "features=df.drop(['diagnosis'],axis=1)\n",
        "labels=df['diagnosis']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b53f973e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b53f973e",
        "outputId": "7d2cf6fc-f33f-480f-c42c-080fbf1c94d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "383"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ],
      "source": [
        "labels.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b233d4ae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b233d4ae",
        "outputId": "1be22ca4-898f-4c63-be50-68ad9e4ecced"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
              "       'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
              "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
              "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
              "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
              "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
              "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
              "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
              "       'symmetry_worst', 'fractal_dimension_worst'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ],
      "source": [
        "features.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "05cc8f6f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "05cc8f6f",
        "outputId": "de2b933b-7116-418f-8b7d-b2168a68d66b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(features['area_mean'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60a3c5b2",
      "metadata": {
        "id": "60a3c5b2"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(features, labels,test_size=0.20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1daae3e2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1daae3e2",
        "outputId": "6dabed41-5c82-46c3-a489-7f243e65c2b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(306, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ],
      "source": [
        "X_train.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68cf3361",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68cf3361",
        "outputId": "ba931d6f-6ff7-4645-b3b2-c07004a954ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(306,)"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7uYoG--UPr1",
        "outputId": "2becd336-91a4-40f9-a03c-7d58f50ffc8f"
      },
      "id": "H7uYoG--UPr1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "358    0\n",
              "192    1\n",
              "20     1\n",
              "228    0\n",
              "54     0\n",
              "      ..\n",
              "268    1\n",
              "191    1\n",
              "336    0\n",
              "125    1\n",
              "58     1\n",
              "Name: diagnosis, Length: 77, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7048b51a",
      "metadata": {
        "id": "7048b51a"
      },
      "outputs": [],
      "source": [
        "st_x= StandardScaler()  \n",
        "st_x.fit_transform(X_train) \n",
        "X_train= st_x.transform(X_train)   \n",
        "X_test= st_x.transform(X_test) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ac7f9c5",
      "metadata": {
        "id": "6ac7f9c5"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc161259",
      "metadata": {
        "id": "fc161259"
      },
      "outputs": [],
      "source": [
        "# define the keras model\n",
        "model = Sequential()\n",
        "model.add(Dense(12, input_shape=(30,), activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(4, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8uDZ6JE9FD3q"
      },
      "id": "8uDZ6JE9FD3q",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51889209",
      "metadata": {
        "id": "51889209"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,epochs=10, batch_size=10,\n",
        "          verbose=1,\n",
        "          validation_data=(X_test, y_test)\n",
        "          )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pERV0qGFEghL",
        "outputId": "5229da7a-e71f-42bc-b3cd-e062b2cf16d3"
      },
      "id": "pERV0qGFEghL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "31/31 [==============================] - 1s 6ms/step - loss: 0.6379 - accuracy: 0.7712 - val_loss: 0.5365 - val_accuracy: 0.8442\n",
            "Epoch 2/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.5375 - accuracy: 0.8366 - val_loss: 0.4427 - val_accuracy: 0.8831\n",
            "Epoch 3/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.4443 - accuracy: 0.8693 - val_loss: 0.3509 - val_accuracy: 0.9221\n",
            "Epoch 4/10\n",
            "31/31 [==============================] - 0s 1ms/step - loss: 0.3619 - accuracy: 0.8987 - val_loss: 0.2797 - val_accuracy: 0.9351\n",
            "Epoch 5/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.3030 - accuracy: 0.9183 - val_loss: 0.2260 - val_accuracy: 0.9481\n",
            "Epoch 6/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.2564 - accuracy: 0.9379 - val_loss: 0.1835 - val_accuracy: 0.9740\n",
            "Epoch 7/10\n",
            "31/31 [==============================] - 0s 3ms/step - loss: 0.2145 - accuracy: 0.9477 - val_loss: 0.1465 - val_accuracy: 0.9740\n",
            "Epoch 8/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.1783 - accuracy: 0.9575 - val_loss: 0.1187 - val_accuracy: 0.9740\n",
            "Epoch 9/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.1486 - accuracy: 0.9641 - val_loss: 0.0963 - val_accuracy: 0.9870\n",
            "Epoch 10/10\n",
            "31/31 [==============================] - 0s 2ms/step - loss: 0.1245 - accuracy: 0.9771 - val_loss: 0.0805 - val_accuracy: 0.9870\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ffab2ef2510>"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(X_test)\n",
        "y_train_pred=model.predict(X_train)"
      ],
      "metadata": {
        "id": "76bxDnT9TgAl"
      },
      "id": "76bxDnT9TgAl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.where(y_pred > 0.5, 1, 0)\n",
        "y_train_pred = np.where(y_train_pred > 0.5, 1, 0)"
      ],
      "metadata": {
        "id": "764r4aiPYSth"
      },
      "id": "764r4aiPYSth",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=accuracy_score(y_test, y_pred)\n",
        "y=accuracy_score(y_train, y_train_pred)"
      ],
      "metadata": {
        "id": "8pq6XMtYT9UW"
      },
      "id": "8pq6XMtYT9UW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=pd.DataFrame([[y],[x]],columns=['Accuracy'],index=['train','test'])"
      ],
      "metadata": {
        "id": "gHXBUNLdZskJ"
      },
      "id": "gHXBUNLdZskJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "S8KRqzJ-ZrgG",
        "outputId": "4f06eb76-7349-4d82-d047-bc0b892fd2f1"
      },
      "id": "S8KRqzJ-ZrgG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Accuracy\n",
              "train  0.977124\n",
              "test   0.987013"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5101067-e59e-476d-aa1a-40932918b6ae\">\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>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>0.977124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>0.987013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5101067-e59e-476d-aa1a-40932918b6ae')\"\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-e5101067-e59e-476d-aa1a-40932918b6ae 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-e5101067-e59e-476d-aa1a-40932918b6ae');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_pred==1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGfEmHFob9SK",
        "outputId": "cde73806-9e07-4388-bfbe-93fa5b01075b"
      },
      "id": "aGfEmHFob9SK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([54])"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "SPAeYlEDM8rM"
      },
      "id": "SPAeYlEDM8rM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJ80H9D4bj4y",
        "outputId": "2c897863-cd02-458a-f9fd-d624b8efa0ff"
      },
      "id": "UJ80H9D4bj4y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[22,  0],\n",
              "       [ 1, 54]])"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix\n",
        " \n",
        "fig, ax = plot_confusion_matrix(conf_mat=cm, figsize=(6, 6), cmap=plt.cm.Greens)\n",
        "plt.xlabel('Predictions', fontsize=18)\n",
        "plt.ylabel('Actuals', fontsize=18)\n",
        "plt.title('Confusion Matrix', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "FHv1T2RdbwYh",
        "outputId": "d3048ec4-faa0-4fe9-9812-f7cece5af091"
      },
      "id": "FHv1T2RdbwYh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6DGK6l9yAjg",
        "outputId": "69520491-3b45-4bd8-ac1d-0502d332256c"
      },
      "id": "-6DGK6l9yAjg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98        22\n",
            "           1       1.00      0.98      0.99        55\n",
            "\n",
            "    accuracy                           0.99        77\n",
            "   macro avg       0.98      0.99      0.98        77\n",
            "weighted avg       0.99      0.99      0.99        77\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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      "provenance": []
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}