PROJET_SMA_IAD_k_mediods.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Projet IAD - SMA ( K-Medoids )**"
      ],
      "metadata": {
        "id": "rlVhd_-xtZTl"
      },
      "id": "rlVhd_-xtZTl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Clustering K-Medoids :**\n",
        "\n",
        "![K-medoids.png](data:image/png;base64,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)\n",
        "\n",
        "* L'algorithme K-Medoids (également appelé Partitioning Around Medoid) a été proposé en 1987 par Kaufman et Rousseeuw. Un médoïde peut être défini comme le point du cluster, dont les dissemblances avec tous les autres points du cluster sont minimales.\n",
        "\n",
        "* La dissemblance de medoid(Ci) et object(Pi) est calculée en utilisantE = |Pi - Ci|\n",
        "\n",
        "* Le coût dans l'algorithme K-Medoids est donné comme : \n",
        "\n",
        "![Kmediods.PNG](data:image/png;base64,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)\n",
        "\n",
        "**Algorithme:**\n",
        "1. Initialiser : sélectionnez k points aléatoires parmi les n points de données comme médoïdes.\n",
        "2. Associez chaque point de données au médoïde le plus proche en utilisant n'importe quelle méthode métrique de distance commune.\n",
        "3. Pendant que le coût diminue :\n",
        "        Pour chaque médoïde m, pour chaque point de donnée o qui n'est pas un médoïde :\n",
        "                1. Permuter m et o, associer chaque point de donnée au médoïde le plus proche, recalculer le coût.\n",
        "                2. Si le coût total est supérieur à celui de l'étape précédente, annulez l'échange."
      ],
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        "id": "kchpTAB7ztkz"
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      "id": "kchpTAB7ztkz"
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    {
      "cell_type": "code",
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      "id": "ee053870",
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          "shell.execute_reply": "2021-11-17T05:17:50.124152Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "ee053870"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f215264b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:50.173149Z",
          "iopub.status.busy": "2021-11-17T05:17:50.172403Z",
          "iopub.status.idle": "2021-11-17T05:17:50.212324Z",
          "shell.execute_reply": "2021-11-17T05:17:50.212919Z",
          "shell.execute_reply.started": "2021-11-17T05:16:48.659188Z"
        },
        "papermill": {
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          "end_time": "2021-11-17T05:17:50.213101",
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          "start_time": "2021-11-17T05:17:50.143549",
          "status": "completed"
        },
        "tags": [],
        "id": "f215264b"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('breast-cancer-wisconsin.data')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "Us9Osp1Kt-IU",
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      },
      "id": "Us9Osp1Kt-IU",
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **rename our columns first :** "
      ],
      "metadata": {
        "id": "b3UpX7AUt_1u"
      },
      "id": "b3UpX7AUt_1u"
    },
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        }
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      "id": "2FdPx6HzsqTl",
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              "        0  1  2    3    4  5    6  7    8    9    10\n",
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              "\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-0ab305ae-528c-4ab1-aa44-6a2cc2d2a868 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-0ab305ae-528c-4ab1-aa44-6a2cc2d2a868');\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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9641443",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:50.263983Z",
          "iopub.status.busy": "2021-11-17T05:17:50.259349Z",
          "iopub.status.idle": "2021-11-17T05:17:50.290552Z",
          "shell.execute_reply": "2021-11-17T05:17:50.289943Z",
          "shell.execute_reply.started": "2021-11-17T05:16:48.696934Z"
        },
        "papermill": {
          "duration": 0.056216,
          "end_time": "2021-11-17T05:17:50.290755",
          "exception": false,
          "start_time": "2021-11-17T05:17:50.234539",
          "status": "completed"
        },
        "tags": [],
        "id": "c9641443"
      },
      "outputs": [],
      "source": [
        "dummy = pd.DataFrame(df.columns).T\n",
        "for col in dummy.columns:\n",
        "    dummy[col]=dummy[col].astype(float)\n",
        "    dummy[col]=dummy[col].astype(int)\n",
        "\n",
        "df.columns=range(0,df.shape[1])\n",
        "df[6] = df[6].str.extract('(\\d+)').astype(float)\n",
        "for col in df.columns:\n",
        "    df[col]=df[col].astype(float)\n",
        "df = pd.concat([dummy, df],axis=0)\n",
        "df.reset_index(inplace=True, drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b07befa",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:50.332559Z",
          "iopub.status.busy": "2021-11-17T05:17:50.331525Z",
          "iopub.status.idle": "2021-11-17T05:17:50.340689Z",
          "shell.execute_reply": "2021-11-17T05:17:50.341351Z",
          "shell.execute_reply.started": "2021-11-17T05:16:48.724588Z"
        },
        "papermill": {
          "duration": 0.032652,
          "end_time": "2021-11-17T05:17:50.341528",
          "exception": false,
          "start_time": "2021-11-17T05:17:50.308876",
          "status": "completed"
        },
        "tags": [],
        "id": "3b07befa"
      },
      "outputs": [],
      "source": [
        "column_names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', \n",
        "                'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromati', \n",
        "                'Normal Nucleoli', 'Mitoses', 'Class']\n",
        "\n",
        "df.columns=column_names\n",
        "df['Class'] = df['Class'].replace(to_replace={2:1, 4:0})"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **EDA** :"
      ],
      "metadata": {
        "id": "K9vgUMaPtjAA"
      },
      "id": "K9vgUMaPtjAA"
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "u4-8oa5IuG4w",
        "outputId": "4581cc1b-07f6-42b7-dcbe-65d80d0652c3"
      },
      "id": "u4-8oa5IuG4w",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
              "0            1000025.0              5.0                      1.0   \n",
              "1            1002945.0              5.0                      4.0   \n",
              "2            1015425.0              3.0                      1.0   \n",
              "3            1016277.0              6.0                      8.0   \n",
              "4            1017023.0              4.0                      1.0   \n",
              "5            1017122.0              8.0                     10.0   \n",
              "6            1018099.0              1.0                      1.0   \n",
              "7            1018561.0              2.0                      1.0   \n",
              "8            1033078.0              2.0                      1.0   \n",
              "9            1033078.0              4.0                      2.0   \n",
              "10           1035283.0              1.0                      1.0   \n",
              "11           1036172.0              2.0                      1.0   \n",
              "12           1041801.0              5.0                      3.0   \n",
              "13           1043999.0              1.0                      1.0   \n",
              "14           1044572.0              8.0                      7.0   \n",
              "\n",
              "    Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
              "0                        1.0                1.0                          2.0   \n",
              "1                        4.0                5.0                          7.0   \n",
              "2                        1.0                1.0                          2.0   \n",
              "3                        8.0                1.0                          3.0   \n",
              "4                        1.0                3.0                          2.0   \n",
              "5                       10.0                8.0                          7.0   \n",
              "6                        1.0                1.0                          2.0   \n",
              "7                        2.0                1.0                          2.0   \n",
              "8                        1.0                1.0                          2.0   \n",
              "9                        1.0                1.0                          2.0   \n",
              "10                       1.0                1.0                          1.0   \n",
              "11                       1.0                1.0                          2.0   \n",
              "12                       3.0                3.0                          2.0   \n",
              "13                       1.0                1.0                          2.0   \n",
              "14                       5.0               10.0                          7.0   \n",
              "\n",
              "    Bare Nuclei  Bland Chromati  Normal Nucleoli  Mitoses  Class  \n",
              "0           1.0             3.0              1.0      1.0    1.0  \n",
              "1          10.0             3.0              2.0      1.0    1.0  \n",
              "2           2.0             3.0              1.0      1.0    1.0  \n",
              "3           4.0             3.0              7.0      1.0    1.0  \n",
              "4           1.0             3.0              1.0      1.0    1.0  \n",
              "5          10.0             9.0              7.0      1.0    0.0  \n",
              "6          10.0             3.0              1.0      1.0    1.0  \n",
              "7           1.0             3.0              1.0      1.0    1.0  \n",
              "8           1.0             1.0              1.0      5.0    1.0  \n",
              "9           1.0             2.0              1.0      1.0    1.0  \n",
              "10          1.0             3.0              1.0      1.0    1.0  \n",
              "11          1.0             2.0              1.0      1.0    1.0  \n",
              "12          3.0             4.0              4.0      1.0    0.0  \n",
              "13          3.0             3.0              1.0      1.0    1.0  \n",
              "14          9.0             5.0              5.0      4.0    0.0  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample code number</th>\n",
              "      <th>Clump Thickness</th>\n",
              "      <th>Uniformity of Cell Size</th>\n",
              "      <th>Uniformity of Cell Shape</th>\n",
              "      <th>Marginal Adhesion</th>\n",
              "      <th>Single Epithelial Cell Size</th>\n",
              "      <th>Bare Nuclei</th>\n",
              "      <th>Bland Chromati</th>\n",
              "      <th>Normal Nucleoli</th>\n",
              "      <th>Mitoses</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000025.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002945.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1015425.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1016277.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1017023.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1017122.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1018099.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1018561.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1033078.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1033078.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1035283.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1036172.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1041801.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1043999.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1044572.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4bd837d-50eb-4c60-b077-7eb622709540');\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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "X74pYhLtuRWy",
        "outputId": "a64c312f-0ed3-452e-c365-94f5eaf35204"
      },
      "id": "X74pYhLtuRWy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
              "684            466906.0              1.0                      1.0   \n",
              "685            534555.0              1.0                      1.0   \n",
              "686            536708.0              1.0                      1.0   \n",
              "687            566346.0              3.0                      1.0   \n",
              "688            603148.0              4.0                      1.0   \n",
              "689            654546.0              1.0                      1.0   \n",
              "690            654546.0              1.0                      1.0   \n",
              "691            695091.0              5.0                     10.0   \n",
              "692            714039.0              3.0                      1.0   \n",
              "693            763235.0              3.0                      1.0   \n",
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              "695            841769.0              2.0                      1.0   \n",
              "696            888820.0              5.0                     10.0   \n",
              "697            897471.0              4.0                      8.0   \n",
              "698            897471.0              4.0                      8.0   \n",
              "\n",
              "     Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
              "684                       1.0                1.0                          2.0   \n",
              "685                       1.0                1.0                          2.0   \n",
              "686                       1.0                1.0                          2.0   \n",
              "687                       1.0                1.0                          2.0   \n",
              "688                       1.0                1.0                          2.0   \n",
              "689                       1.0                1.0                          2.0   \n",
              "690                       1.0                3.0                          2.0   \n",
              "691                      10.0                5.0                          4.0   \n",
              "692                       1.0                1.0                          2.0   \n",
              "693                       1.0                1.0                          2.0   \n",
              "694                       1.0                1.0                          3.0   \n",
              "695                       1.0                1.0                          2.0   \n",
              "696                      10.0                3.0                          7.0   \n",
              "697                       6.0                4.0                          3.0   \n",
              "698                       8.0                5.0                          4.0   \n",
              "\n",
              "     Bare Nuclei  Bland Chromati  Normal Nucleoli  Mitoses  Class  \n",
              "684          1.0             1.0              1.0      1.0    1.0  \n",
              "685          1.0             1.0              1.0      1.0    1.0  \n",
              "686          1.0             1.0              1.0      1.0    1.0  \n",
              "687          1.0             2.0              3.0      1.0    1.0  \n",
              "688          1.0             1.0              1.0      1.0    1.0  \n",
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              "692          1.0             1.0              1.0      1.0    1.0  \n",
              "693          1.0             2.0              1.0      2.0    1.0  \n",
              "694          2.0             1.0              1.0      1.0    1.0  \n",
              "695          1.0             1.0              1.0      1.0    1.0  \n",
              "696          3.0             8.0             10.0      2.0    0.0  \n",
              "697          4.0            10.0              6.0      1.0    0.0  \n",
              "698          5.0            10.0              4.0      1.0    0.0  "
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              "      <th></th>\n",
              "      <th>Sample code number</th>\n",
              "      <th>Clump Thickness</th>\n",
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              "      <th>Uniformity of Cell Shape</th>\n",
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              "      <th>687</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.0</td>\n",
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              "      <th>692</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d3ae54a-b08a-4375-a084-2ad233472aba')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7d3ae54a-b08a-4375-a084-2ad233472aba');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "zpNFtIS7uHp9",
        "outputId": "5fdb4aa7-84cb-4a52-ff71-ebb7f4036ff3"
      },
      "id": "zpNFtIS7uHp9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
              "count        6.990000e+02       699.000000               699.000000   \n",
              "mean         1.071704e+06         4.417740                 3.134478   \n",
              "std          6.170957e+05         2.815741                 3.051459   \n",
              "min          6.163400e+04         1.000000                 1.000000   \n",
              "25%          8.706885e+05         2.000000                 1.000000   \n",
              "50%          1.171710e+06         4.000000                 1.000000   \n",
              "75%          1.238298e+06         6.000000                 5.000000   \n",
              "max          1.345435e+07        10.000000                10.000000   \n",
              "\n",
              "       Uniformity of Cell Shape  Marginal Adhesion  \\\n",
              "count                699.000000         699.000000   \n",
              "mean                   3.207439           2.806867   \n",
              "std                    2.971913           2.855379   \n",
              "min                    1.000000           1.000000   \n",
              "25%                    1.000000           1.000000   \n",
              "50%                    1.000000           1.000000   \n",
              "75%                    5.000000           4.000000   \n",
              "max                   10.000000          10.000000   \n",
              "\n",
              "       Single Epithelial Cell Size  Bare Nuclei  Bland Chromati  \\\n",
              "count                   699.000000   683.000000      699.000000   \n",
              "mean                      3.216023     3.544656        3.437768   \n",
              "std                       2.214300     3.643857        2.438364   \n",
              "min                       1.000000     1.000000        1.000000   \n",
              "25%                       2.000000     1.000000        2.000000   \n",
              "50%                       2.000000     1.000000        3.000000   \n",
              "75%                       4.000000     6.000000        5.000000   \n",
              "max                      10.000000    10.000000       10.000000   \n",
              "\n",
              "       Normal Nucleoli     Mitoses       Class  \n",
              "count       699.000000  699.000000  699.000000  \n",
              "mean          2.866953    1.589413    0.655222  \n",
              "std           3.053634    1.715078    0.475636  \n",
              "min           1.000000    1.000000    0.000000  \n",
              "25%           1.000000    1.000000    0.000000  \n",
              "50%           1.000000    1.000000    1.000000  \n",
              "75%           4.000000    1.000000    1.000000  \n",
              "max          10.000000   10.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36b72480-df00-45b5-92fe-23f99149f6fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample code number</th>\n",
              "      <th>Clump Thickness</th>\n",
              "      <th>Uniformity of Cell Size</th>\n",
              "      <th>Uniformity of Cell Shape</th>\n",
              "      <th>Marginal Adhesion</th>\n",
              "      <th>Single Epithelial Cell Size</th>\n",
              "      <th>Bare Nuclei</th>\n",
              "      <th>Bland Chromati</th>\n",
              "      <th>Normal Nucleoli</th>\n",
              "      <th>Mitoses</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>683.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.071704e+06</td>\n",
              "      <td>4.417740</td>\n",
              "      <td>3.134478</td>\n",
              "      <td>3.207439</td>\n",
              "      <td>2.806867</td>\n",
              "      <td>3.216023</td>\n",
              "      <td>3.544656</td>\n",
              "      <td>3.437768</td>\n",
              "      <td>2.866953</td>\n",
              "      <td>1.589413</td>\n",
              "      <td>0.655222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.170957e+05</td>\n",
              "      <td>2.815741</td>\n",
              "      <td>3.051459</td>\n",
              "      <td>2.971913</td>\n",
              "      <td>2.855379</td>\n",
              "      <td>2.214300</td>\n",
              "      <td>3.643857</td>\n",
              "      <td>2.438364</td>\n",
              "      <td>3.053634</td>\n",
              "      <td>1.715078</td>\n",
              "      <td>0.475636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.163400e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.706885e+05</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.171710e+06</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.238298e+06</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.345435e+07</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNvDmAyouLMW",
        "outputId": "d5f6b262-fe59-4bac-e341-bf1cf99e83f7"
      },
      "id": "LNvDmAyouLMW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 699 entries, 0 to 698\n",
            "Data columns (total 11 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   Sample code number           699 non-null    float64\n",
            " 1   Clump Thickness              699 non-null    float64\n",
            " 2   Uniformity of Cell Size      699 non-null    float64\n",
            " 3   Uniformity of Cell Shape     699 non-null    float64\n",
            " 4   Marginal Adhesion            699 non-null    float64\n",
            " 5   Single Epithelial Cell Size  699 non-null    float64\n",
            " 6   Bare Nuclei                  683 non-null    float64\n",
            " 7   Bland Chromati               699 non-null    float64\n",
            " 8   Normal Nucleoli              699 non-null    float64\n",
            " 9   Mitoses                      699 non-null    float64\n",
            " 10  Class                        699 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 60.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66ca2077",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample code number              0\n",
              "Clump Thickness                 0\n",
              "Uniformity of Cell Size         0\n",
              "Uniformity of Cell Shape        0\n",
              "Marginal Adhesion               0\n",
              "Single Epithelial Cell Size     0\n",
              "Bare Nuclei                    16\n",
              "Bland Chromati                  0\n",
              "Normal Nucleoli                 0\n",
              "Mitoses                         0\n",
              "Class                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ab979f6",
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        "execution": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(699, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **dropping the NaN values :**"
      ],
      "metadata": {
        "id": "8mRRSduGuXyu"
      },
      "id": "8mRRSduGuXyu"
    },
    {
      "cell_type": "code",
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      "id": "ee84cac0",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample code number             0\n",
              "Clump Thickness                0\n",
              "Uniformity of Cell Size        0\n",
              "Uniformity of Cell Shape       0\n",
              "Marginal Adhesion              0\n",
              "Single Epithelial Cell Size    0\n",
              "Bare Nuclei                    0\n",
              "Bland Chromati                 0\n",
              "Normal Nucleoli                0\n",
              "Mitoses                        0\n",
              "Class                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "df = df.dropna(axis=0)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9f177d78",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(683, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df.shape"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "807cf3cd",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Sample code number  Clump Thickness  Uniformity of Cell Size  \\\n",
              "0             1000025.0              5.0                      1.0   \n",
              "1             1002945.0              5.0                      4.0   \n",
              "2             1015425.0              3.0                      1.0   \n",
              "3             1016277.0              6.0                      8.0   \n",
              "4             1017023.0              4.0                      1.0   \n",
              "..                  ...              ...                      ...   \n",
              "694            776715.0              3.0                      1.0   \n",
              "695            841769.0              2.0                      1.0   \n",
              "696            888820.0              5.0                     10.0   \n",
              "697            897471.0              4.0                      8.0   \n",
              "698            897471.0              4.0                      8.0   \n",
              "\n",
              "     Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
              "0                         1.0                1.0                          2.0   \n",
              "1                         4.0                5.0                          7.0   \n",
              "2                         1.0                1.0                          2.0   \n",
              "3                         8.0                1.0                          3.0   \n",
              "4                         1.0                3.0                          2.0   \n",
              "..                        ...                ...                          ...   \n",
              "694                       1.0                1.0                          3.0   \n",
              "695                       1.0                1.0                          2.0   \n",
              "696                      10.0                3.0                          7.0   \n",
              "697                       6.0                4.0                          3.0   \n",
              "698                       8.0                5.0                          4.0   \n",
              "\n",
              "     Bare Nuclei  Bland Chromati  Normal Nucleoli  Mitoses  \n",
              "0            1.0             3.0              1.0      1.0  \n",
              "1           10.0             3.0              2.0      1.0  \n",
              "2            2.0             3.0              1.0      1.0  \n",
              "3            4.0             3.0              7.0      1.0  \n",
              "4            1.0             3.0              1.0      1.0  \n",
              "..           ...             ...              ...      ...  \n",
              "694          2.0             1.0              1.0      1.0  \n",
              "695          1.0             1.0              1.0      1.0  \n",
              "696          3.0             8.0             10.0      2.0  \n",
              "697          4.0            10.0              6.0      1.0  \n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>694</th>\n",
              "      <td>776715.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>697</th>\n",
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              "      <td>4.0</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>10.0</td>\n",
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              "      <th>698</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "df.iloc[:,:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04b1ddf2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:50.793366Z",
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          "status": "completed"
        },
        "tags": [],
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        },
        "id": "04b1ddf2",
        "outputId": "d46cc727-20a2-41e5-b7a4-ee22279fa799"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1.0\n",
              "1      1.0\n",
              "2      1.0\n",
              "3      1.0\n",
              "4      1.0\n",
              "      ... \n",
              "694    1.0\n",
              "695    1.0\n",
              "696    0.0\n",
              "697    0.0\n",
              "698    0.0\n",
              "Name: Class, Length: 683, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df.iloc[:,-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**let's verify NaN values :**"
      ],
      "metadata": {
        "id": "V9-r0rmKutZY"
      },
      "id": "V9-r0rmKutZY"
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96hFfc_xupem",
        "outputId": "5e9fead0-6787-4122-8d8e-9b77d68dee48"
      },
      "id": "96hFfc_xupem",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 683 entries, 0 to 698\n",
            "Data columns (total 11 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   Sample code number           683 non-null    float64\n",
            " 1   Clump Thickness              683 non-null    float64\n",
            " 2   Uniformity of Cell Size      683 non-null    float64\n",
            " 3   Uniformity of Cell Shape     683 non-null    float64\n",
            " 4   Marginal Adhesion            683 non-null    float64\n",
            " 5   Single Epithelial Cell Size  683 non-null    float64\n",
            " 6   Bare Nuclei                  683 non-null    float64\n",
            " 7   Bland Chromati               683 non-null    float64\n",
            " 8   Normal Nucleoli              683 non-null    float64\n",
            " 9   Mitoses                      683 non-null    float64\n",
            " 10  Class                        683 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 64.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92NfA7JduxrU",
        "outputId": "01b45a62-1f1d-4fb2-f795-224c809e5ded"
      },
      "id": "92NfA7JduxrU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sample code number             0\n",
              "Clump Thickness                0\n",
              "Uniformity of Cell Size        0\n",
              "Uniformity of Cell Shape       0\n",
              "Marginal Adhesion              0\n",
              "Single Epithelial Cell Size    0\n",
              "Bare Nuclei                    0\n",
              "Bland Chromati                 0\n",
              "Normal Nucleoli                0\n",
              "Mitoses                        0\n",
              "Class                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split data to train / test data sets :**"
      ],
      "metadata": {
        "id": "NfBU0NaPu1yw"
      },
      "id": "NfBU0NaPu1yw"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1c6d24a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:50.864303Z",
          "iopub.status.busy": "2021-11-17T05:17:50.859859Z",
          "iopub.status.idle": "2021-11-17T05:17:51.885054Z",
          "shell.execute_reply": "2021-11-17T05:17:51.884421Z",
          "shell.execute_reply.started": "2021-11-17T05:16:48.864486Z"
        },
        "papermill": {
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          "exception": false,
          "start_time": "2021-11-17T05:17:50.827209",
          "status": "completed"
        },
        "tags": [],
        "id": "f1c6d24a"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split    \n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "X = df.iloc[:,:-1].values\n",
        "y = df.iloc[:,-1].values\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=101)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Scalling our values to help our model on training :**"
      ],
      "metadata": {
        "id": "4DJImT0KvW-u"
      },
      "id": "4DJImT0KvW-u"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7652fb2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:51.935553Z",
          "iopub.status.busy": "2021-11-17T05:17:51.934870Z",
          "iopub.status.idle": "2021-11-17T05:17:51.938338Z",
          "shell.execute_reply": "2021-11-17T05:17:51.938904Z",
          "shell.execute_reply.started": "2021-11-17T05:16:49.199839Z"
        },
        "papermill": {
          "duration": 0.031505,
          "end_time": "2021-11-17T05:17:51.939085",
          "exception": false,
          "start_time": "2021-11-17T05:17:51.907580",
          "status": "completed"
        },
        "tags": [],
        "id": "b7652fb2"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test  = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d2f81f3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:17:51.985321Z",
          "iopub.status.busy": "2021-11-17T05:17:51.982553Z",
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          "shell.execute_reply": "2021-11-17T05:18:05.251331Z",
          "shell.execute_reply.started": "2021-11-17T05:16:49.209011Z"
        },
        "papermill": {
          "duration": 13.292117,
          "end_time": "2021-11-17T05:18:05.252213",
          "exception": false,
          "start_time": "2021-11-17T05:17:51.960096",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d2f81f3",
        "outputId": "eb3630d1-086b-4a11-ba58-cf3aa4cd8823"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn-extra in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: scikit-learn>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn-extra) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn-extra) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn-extra) (1.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (1.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-learn-extra"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Building our K-medois Model :**"
      ],
      "metadata": {
        "id": "fgOHdvSqvdns"
      },
      "id": "fgOHdvSqvdns"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f92a567",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:18:05.312942Z",
          "iopub.status.busy": "2021-11-17T05:18:05.311980Z",
          "iopub.status.idle": "2021-11-17T05:18:05.506288Z",
          "shell.execute_reply": "2021-11-17T05:18:05.507315Z",
          "shell.execute_reply.started": "2021-11-17T05:16:57.702164Z"
        },
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          "exception": false,
          "start_time": "2021-11-17T05:18:05.279480",
          "status": "completed"
        },
        "tags": [],
        "id": "1f92a567"
      },
      "outputs": [],
      "source": [
        "from sklearn_extra.cluster import KMedoids\n",
        "\n",
        "kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1263c43c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:18:05.608935Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "1263c43c"
      },
      "outputs": [],
      "source": [
        "y_pred = kmedoids.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f2cd9a82",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:18:05.673659Z",
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          "shell.execute_reply.started": "2021-11-17T05:16:57.781313Z"
        },
        "papermill": {
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          "end_time": "2021-11-17T05:18:05.686394",
          "exception": false,
          "start_time": "2021-11-17T05:18:05.638885",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2cd9a82",
        "outputId": "65e2016a-0e74-43c4-adc3-7510168e6600"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      0.91      0.96        82\n",
            "         1.0       0.95      1.00      0.98       144\n",
            "\n",
            "    accuracy                           0.97       226\n",
            "   macro avg       0.98      0.96      0.97       226\n",
            "weighted avg       0.97      0.97      0.97       226\n",
            "\n",
            "[[ 75   7]\n",
            " [  0 144]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ead39247",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-17T05:18:05.750630Z",
          "iopub.status.busy": "2021-11-17T05:18:05.749898Z",
          "iopub.status.idle": "2021-11-17T05:18:05.758821Z",
          "shell.execute_reply": "2021-11-17T05:18:05.759414Z",
          "shell.execute_reply.started": "2021-11-17T05:16:57.808155Z"
        },
        "papermill": {
          "duration": 0.045571,
          "end_time": "2021-11-17T05:18:05.759591",
          "exception": false,
          "start_time": "2021-11-17T05:18:05.714020",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ead39247",
        "outputId": "42e702ac-ac54-4a8d-c997-6fe0cf09376a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "96.90265486725663\n",
            "95.36423841059603\n",
            "100.0\n",
            "97.6271186440678\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics \n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "acc=(metrics.accuracy_score(y_test, y_pred)*100)\n",
        "print(acc)    \n",
        "\n",
        "from sklearn.metrics import precision_score\n",
        "pre=(precision_score(y_test, y_pred)*100)\n",
        "print(pre)\n",
        "\n",
        "from sklearn.metrics import recall_score\n",
        "# recall_score = (Vrai Positif) divisé par l'ensemble des positifs (Vrai Positif + Faux Négatif)\n",
        "rec=(recall_score(y_test, y_pred)*100)\n",
        "print(rec)\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "f1=(f1_score(y_test, y_pred)*100)\n",
        "print(f1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed99aecb",
      "metadata": {
        "papermill": {
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          "status": "completed"
        },
        "tags": [],
        "id": "ed99aecb"
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      "outputs": [],
      "source": [
        ""
      ]
    }
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