ckd_prediction.ipynb

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      "metadata": {
        "id": "RZlpHAGne9nT"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Loading Data"
      ],
      "metadata": {
        "id": "zwrROEK6e-il"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jP2mIKVXY3hD",
        "outputId": "13b28b43-f6aa-4f26-a10d-17d0b6793c46"
      },
      "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": [
        "%cd '/content/drive/MyDrive/ckd/'"
      ],
      "metadata": {
        "id": "axWwlRz4fGYN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a19182b8-c663-4a4f-db26-9b2757e96e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/ckd\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing required Libraries"
      ],
      "metadata": {
        "id": "x1VFpCJIfMCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import r2_score\n",
        "import scipy.stats as stats\n",
        "import missingno as msno\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "aHmdixg6n_Eg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "jCJbW14JezTf"
      }
    },
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      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "7Ayn4RxxeyNy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Set03.csv')\n",
        "df2 = pd.read_csv('kidney_disease.csv')"
      ],
      "metadata": {
        "id": "h8Vt0WvAoA6S"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "   Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0   48              70             1.005        4      0          normal   \n",
              "1   53              90             1.020        2      0        abnormal   \n",
              "2   63              70             1.010        3      0        abnormal   \n",
              "3   68              80             1.010        3      2          normal   \n",
              "4   61              80             1.015        2      0        abnormal   \n",
              "\n",
              "   Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0  abnormal         present  notpresent                   117  ...   \n",
              "1  abnormal         present  notpresent                    70  ...   \n",
              "2  abnormal         present  notpresent                   380  ...   \n",
              "3  abnormal         present     present                   157  ...   \n",
              "4  abnormal      notpresent  notpresent                   173  ...   \n",
              "\n",
              "   Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                  32                    6700                   3.9   \n",
              "1                  29                   12100                   3.7   \n",
              "2                  32                    4500                   3.8   \n",
              "3                  16                   11000                   2.6   \n",
              "4                  24                    9200                   3.2   \n",
              "\n",
              "   Hypertension  Diabetes Mellitus  Coronary Artery Disease  Appetite  \\\n",
              "0           yes                 no                       no      poor   \n",
              "1           yes                yes                       no      poor   \n",
              "2           yes                yes                       no      poor   \n",
              "3           yes                yes                      yes      poor   \n",
              "4           yes                yes                      yes      poor   \n",
              "\n",
              "   Pedal Edema Anemia Class  \n",
              "0          yes    yes     1  \n",
              "1           no    yes     1  \n",
              "2          yes     no     1  \n",
              "3          yes     no     1  \n",
              "4          yes    yes     1  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th>Coronary Artery Disease</th>\n",
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              "      <td>1.010</td>\n",
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              "      <td>16</td>\n",
              "      <td>11000</td>\n",
              "      <td>2.6</td>\n",
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              "      <td>9200</td>\n",
              "      <td>3.2</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
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              "      <td>yes</td>\n",
              "      <td>1</td>\n",
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            "text/plain": [
              "    Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0  48.0            80.0             1.020      1.0    0.0             NaN   \n",
              "1   7.0            50.0             1.020      4.0    0.0             NaN   \n",
              "2  62.0            80.0             1.010      2.0    3.0          normal   \n",
              "3  48.0            70.0             1.005      4.0    0.0          normal   \n",
              "4  51.0            80.0             1.010      2.0    0.0          normal   \n",
              "\n",
              "   Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0    normal      notpresent  notpresent                 121.0  ...   \n",
              "1    normal      notpresent  notpresent                   NaN  ...   \n",
              "2    normal      notpresent  notpresent                 423.0  ...   \n",
              "3  abnormal         present  notpresent                 117.0  ...   \n",
              "4    normal      notpresent  notpresent                 106.0  ...   \n",
              "\n",
              "   Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                  44                    7800                   5.2   \n",
              "1                  38                    6000                   NaN   \n",
              "2                  31                    7500                   NaN   \n",
              "3                  32                    6700                   3.9   \n",
              "4                  35                    7300                   4.6   \n",
              "\n",
              "   Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0           yes                yes                      no     good   \n",
              "1            no                 no                      no     good   \n",
              "2            no                yes                      no     poor   \n",
              "3           yes                 no                      no     poor   \n",
              "4            no                 no                      no     good   \n",
              "\n",
              "  Pedal Edema Anemia Class  \n",
              "0          no     no   ckd  \n",
              "1          no     no   ckd  \n",
              "2          no    yes   ckd  \n",
              "3         yes    yes   ckd  \n",
              "4          no     no   ckd  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th>Age</th>\n",
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              "      <th>Coronary Artery Disease</th>\n",
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              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
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              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423.0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>106.0</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6882898-b995-431b-87e4-3ed015f971da')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6882898-b995-431b-87e4-3ed015f971da 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-c6882898-b995-431b-87e4-3ed015f971da');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mg9SY_01fbZj",
        "outputId": "534666eb-996c-4bf8-ba21-c2b4a168d15d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 158 entries, 0 to 157\n",
            "Data columns (total 25 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Age                      158 non-null    int64  \n",
            " 1   Blood Pressure           158 non-null    int64  \n",
            " 2   Specific Gravity         158 non-null    float64\n",
            " 3   Albumin                  158 non-null    int64  \n",
            " 4   Sugar                    158 non-null    int64  \n",
            " 5   Red Blood Cells          158 non-null    object \n",
            " 6   Pus Cell                 158 non-null    object \n",
            " 7   Pus Cell clumps          158 non-null    object \n",
            " 8   Bacteria                 158 non-null    object \n",
            " 9   Blood Glucose Random     158 non-null    int64  \n",
            " 10  Blood Urea               158 non-null    int64  \n",
            " 11  Serum Creatinine         158 non-null    float64\n",
            " 12  Sodium                   158 non-null    int64  \n",
            " 13  Potassium                158 non-null    float64\n",
            " 14  Hemoglobin               158 non-null    float64\n",
            " 15  Packed Cell Volume       158 non-null    int64  \n",
            " 16  White Blood Cell Count   158 non-null    int64  \n",
            " 17  Red Blood Cell Count     158 non-null    float64\n",
            " 18  Hypertension             158 non-null    object \n",
            " 19  Diabetes Mellitus        158 non-null    object \n",
            " 20  Coronary Artery Disease  158 non-null    object \n",
            " 21  Appetite                 158 non-null    object \n",
            " 22  Pedal Edema              158 non-null    object \n",
            " 23  Anemia                   158 non-null    object \n",
            " 24  Class                    158 non-null    int64  \n",
            "dtypes: float64(5), int64(10), object(10)\n",
            "memory usage: 31.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PhJN-QKfbkg",
        "outputId": "48356f44-976b-46c8-c82e-68713ee0ed83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id              400 non-null    int64  \n",
            " 1   age             391 non-null    float64\n",
            " 2   bp              388 non-null    float64\n",
            " 3   sg              353 non-null    float64\n",
            " 4   al              354 non-null    float64\n",
            " 5   su              351 non-null    float64\n",
            " 6   rbc             248 non-null    object \n",
            " 7   pc              335 non-null    object \n",
            " 8   pcc             396 non-null    object \n",
            " 9   ba              396 non-null    object \n",
            " 10  bgr             356 non-null    float64\n",
            " 11  bu              381 non-null    float64\n",
            " 12  sc              383 non-null    float64\n",
            " 13  sod             313 non-null    float64\n",
            " 14  pot             312 non-null    float64\n",
            " 15  hemo            348 non-null    float64\n",
            " 16  pcv             330 non-null    object \n",
            " 17  wc              295 non-null    object \n",
            " 18  rc              270 non-null    object \n",
            " 19  htn             398 non-null    object \n",
            " 20  dm              398 non-null    object \n",
            " 21  cad             398 non-null    object \n",
            " 22  appet           399 non-null    object \n",
            " 23  pe              399 non-null    object \n",
            " 24  ane             399 non-null    object \n",
            " 25  classification  400 non-null    object \n",
            "dtypes: float64(11), int64(1), object(14)\n",
            "memory usage: 81.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "names = []\n",
        "for col in df.columns:\n",
        "  names.append(col) \n",
        "names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04fEyBBQoNPr",
        "outputId": "a69e817d-7344-4a13-84bd-b595dab6ef4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'Blood Pressure',\n",
              " 'Specific Gravity',\n",
              " 'Albumin',\n",
              " 'Sugar',\n",
              " 'Red Blood Cells',\n",
              " 'Pus Cell',\n",
              " 'Pus Cell clumps',\n",
              " 'Bacteria',\n",
              " 'Blood Glucose Random',\n",
              " 'Blood Urea',\n",
              " 'Serum Creatinine',\n",
              " 'Sodium',\n",
              " 'Potassium',\n",
              " 'Hemoglobin',\n",
              " 'Packed Cell Volume',\n",
              " 'White Blood Cell Count',\n",
              " 'Red Blood Cell Count',\n",
              " 'Hypertension',\n",
              " 'Diabetes Mellitus',\n",
              " 'Coronary Artery Disease',\n",
              " 'Appetite',\n",
              " 'Pedal Edema',\n",
              " 'Anemia',\n",
              " 'Class']"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()\n",
        "for col in df2.columns:\n",
        "  print(col)"
      ],
      "metadata": {
        "id": "SmFz585mxoiM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fc7a82c-b9fb-40ee-8abe-cb37f745083c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id              400 non-null    int64  \n",
            " 1   age             391 non-null    float64\n",
            " 2   bp              388 non-null    float64\n",
            " 3   sg              353 non-null    float64\n",
            " 4   al              354 non-null    float64\n",
            " 5   su              351 non-null    float64\n",
            " 6   rbc             248 non-null    object \n",
            " 7   pc              335 non-null    object \n",
            " 8   pcc             396 non-null    object \n",
            " 9   ba              396 non-null    object \n",
            " 10  bgr             356 non-null    float64\n",
            " 11  bu              381 non-null    float64\n",
            " 12  sc              383 non-null    float64\n",
            " 13  sod             313 non-null    float64\n",
            " 14  pot             312 non-null    float64\n",
            " 15  hemo            348 non-null    float64\n",
            " 16  pcv             330 non-null    object \n",
            " 17  wc              295 non-null    object \n",
            " 18  rc              270 non-null    object \n",
            " 19  htn             398 non-null    object \n",
            " 20  dm              398 non-null    object \n",
            " 21  cad             398 non-null    object \n",
            " 22  appet           399 non-null    object \n",
            " 23  pe              399 non-null    object \n",
            " 24  ane             399 non-null    object \n",
            " 25  classification  400 non-null    object \n",
            "dtypes: float64(11), int64(1), object(14)\n",
            "memory usage: 81.4+ KB\n",
            "id\n",
            "age\n",
            "bp\n",
            "sg\n",
            "al\n",
            "su\n",
            "rbc\n",
            "pc\n",
            "pcc\n",
            "ba\n",
            "bgr\n",
            "bu\n",
            "sc\n",
            "sod\n",
            "pot\n",
            "hemo\n",
            "pcv\n",
            "wc\n",
            "rc\n",
            "htn\n",
            "dm\n",
            "cad\n",
            "appet\n",
            "pe\n",
            "ane\n",
            "classification\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df2.drop(['id'],axis=1,inplace=True)\n",
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKS-a_GJaRaB",
        "outputId": "36596566-ed10-4db4-d0aa-99df38057610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 25 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             391 non-null    float64\n",
            " 1   bp              388 non-null    float64\n",
            " 2   sg              353 non-null    float64\n",
            " 3   al              354 non-null    float64\n",
            " 4   su              351 non-null    float64\n",
            " 5   rbc             248 non-null    object \n",
            " 6   pc              335 non-null    object \n",
            " 7   pcc             396 non-null    object \n",
            " 8   ba              396 non-null    object \n",
            " 9   bgr             356 non-null    float64\n",
            " 10  bu              381 non-null    float64\n",
            " 11  sc              383 non-null    float64\n",
            " 12  sod             313 non-null    float64\n",
            " 13  pot             312 non-null    float64\n",
            " 14  hemo            348 non-null    float64\n",
            " 15  pcv             330 non-null    object \n",
            " 16  wc              295 non-null    object \n",
            " 17  rc              270 non-null    object \n",
            " 18  htn             398 non-null    object \n",
            " 19  dm              398 non-null    object \n",
            " 20  cad             398 non-null    object \n",
            " 21  appet           399 non-null    object \n",
            " 22  pe              399 non-null    object \n",
            " 23  ane             399 non-null    object \n",
            " 24  classification  400 non-null    object \n",
            "dtypes: float64(11), object(14)\n",
            "memory usage: 78.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.columns = names\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "z2xHY94Tipw5",
        "outputId": "3e427244-6ae4-4ebc-9c2c-4f8334898c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0    48.0            80.0             1.020      1.0    0.0             NaN   \n",
              "1     7.0            50.0             1.020      4.0    0.0             NaN   \n",
              "2    62.0            80.0             1.010      2.0    3.0          normal   \n",
              "3    48.0            70.0             1.005      4.0    0.0          normal   \n",
              "4    51.0            80.0             1.010      2.0    0.0          normal   \n",
              "..    ...             ...               ...      ...    ...             ...   \n",
              "395  55.0            80.0             1.020      0.0    0.0          normal   \n",
              "396  42.0            70.0             1.025      0.0    0.0          normal   \n",
              "397  12.0            80.0             1.020      0.0    0.0          normal   \n",
              "398  17.0            60.0             1.025      0.0    0.0          normal   \n",
              "399  58.0            80.0             1.025      0.0    0.0          normal   \n",
              "\n",
              "     Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0      normal      notpresent  notpresent                 121.0  ...   \n",
              "1      normal      notpresent  notpresent                   NaN  ...   \n",
              "2      normal      notpresent  notpresent                 423.0  ...   \n",
              "3    abnormal         present  notpresent                 117.0  ...   \n",
              "4      normal      notpresent  notpresent                 106.0  ...   \n",
              "..        ...             ...         ...                   ...  ...   \n",
              "395    normal      notpresent  notpresent                 140.0  ...   \n",
              "396    normal      notpresent  notpresent                  75.0  ...   \n",
              "397    normal      notpresent  notpresent                 100.0  ...   \n",
              "398    normal      notpresent  notpresent                 114.0  ...   \n",
              "399    normal      notpresent  notpresent                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    44                    7800                   5.2   \n",
              "1                    38                    6000                   NaN   \n",
              "2                    31                    7500                   NaN   \n",
              "3                    32                    6700                   3.9   \n",
              "4                    35                    7300                   4.6   \n",
              "..                  ...                     ...                   ...   \n",
              "395                  47                    6700                   4.9   \n",
              "396                  54                    7800                   6.2   \n",
              "397                  49                    6600                   5.4   \n",
              "398                  51                    7200                   5.9   \n",
              "399                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0             yes                yes                      no     good   \n",
              "1              no                 no                      no     good   \n",
              "2              no                yes                      no     poor   \n",
              "3             yes                 no                      no     poor   \n",
              "4              no                 no                      no     good   \n",
              "..            ...                ...                     ...      ...   \n",
              "395            no                 no                      no     good   \n",
              "396            no                 no                      no     good   \n",
              "397            no                 no                      no     good   \n",
              "398            no                 no                      no     good   \n",
              "399            no                 no                      no     good   \n",
              "\n",
              "    Pedal Edema Anemia   Class  \n",
              "0            no     no     ckd  \n",
              "1            no     no     ckd  \n",
              "2            no    yes     ckd  \n",
              "3           yes    yes     ckd  \n",
              "4            no     no     ckd  \n",
              "..          ...    ...     ...  \n",
              "395          no     no  notckd  \n",
              "396          no     no  notckd  \n",
              "397          no     no  notckd  \n",
              "398          no     no  notckd  \n",
              "399          no     no  notckd  \n",
              "\n",
              "[400 rows x 25 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>Red Blood Cell Count</th>\n",
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              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>7500</td>\n",
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              "      <td>no</td>\n",
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              "      <td>ckd</td>\n",
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              "      <th>3</th>\n",
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              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>117.0</td>\n",
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              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
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              "      <td>ckd</td>\n",
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              "      <th>4</th>\n",
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              "      <td>1.010</td>\n",
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              "      <td>notpresent</td>\n",
              "      <td>106.0</td>\n",
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              "      <td>35</td>\n",
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              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>114.0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>7200</td>\n",
              "      <td>5.9</td>\n",
              "      <td>no</td>\n",
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              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
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              "    <tr>\n",
              "      <th>399</th>\n",
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              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>131.0</td>\n",
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              "      <td>6800</td>\n",
              "      <td>6.1</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 25 columns</p>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Class'] = df2['Class'].map({'ckd': 1, 'notckd': 0})\n"
      ],
      "metadata": {
        "id": "0NgmOvZMfqDz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "PHPIv8_jkeJS",
        "outputId": "5892bdbc-0eee-4abb-b0d4-fa2ef14163e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0    48.0            80.0             1.020      1.0    0.0             NaN   \n",
              "1     7.0            50.0             1.020      4.0    0.0             NaN   \n",
              "2    62.0            80.0             1.010      2.0    3.0          normal   \n",
              "3    48.0            70.0             1.005      4.0    0.0          normal   \n",
              "4    51.0            80.0             1.010      2.0    0.0          normal   \n",
              "..    ...             ...               ...      ...    ...             ...   \n",
              "395  55.0            80.0             1.020      0.0    0.0          normal   \n",
              "396  42.0            70.0             1.025      0.0    0.0          normal   \n",
              "397  12.0            80.0             1.020      0.0    0.0          normal   \n",
              "398  17.0            60.0             1.025      0.0    0.0          normal   \n",
              "399  58.0            80.0             1.025      0.0    0.0          normal   \n",
              "\n",
              "     Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0      normal      notpresent  notpresent                 121.0  ...   \n",
              "1      normal      notpresent  notpresent                   NaN  ...   \n",
              "2      normal      notpresent  notpresent                 423.0  ...   \n",
              "3    abnormal         present  notpresent                 117.0  ...   \n",
              "4      normal      notpresent  notpresent                 106.0  ...   \n",
              "..        ...             ...         ...                   ...  ...   \n",
              "395    normal      notpresent  notpresent                 140.0  ...   \n",
              "396    normal      notpresent  notpresent                  75.0  ...   \n",
              "397    normal      notpresent  notpresent                 100.0  ...   \n",
              "398    normal      notpresent  notpresent                 114.0  ...   \n",
              "399    normal      notpresent  notpresent                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    44                    7800                   5.2   \n",
              "1                    38                    6000                   NaN   \n",
              "2                    31                    7500                   NaN   \n",
              "3                    32                    6700                   3.9   \n",
              "4                    35                    7300                   4.6   \n",
              "..                  ...                     ...                   ...   \n",
              "395                  47                    6700                   4.9   \n",
              "396                  54                    7800                   6.2   \n",
              "397                  49                    6600                   5.4   \n",
              "398                  51                    7200                   5.9   \n",
              "399                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0             yes                yes                      no     good   \n",
              "1              no                 no                      no     good   \n",
              "2              no                yes                      no     poor   \n",
              "3             yes                 no                      no     poor   \n",
              "4              no                 no                      no     good   \n",
              "..            ...                ...                     ...      ...   \n",
              "395            no                 no                      no     good   \n",
              "396            no                 no                      no     good   \n",
              "397            no                 no                      no     good   \n",
              "398            no                 no                      no     good   \n",
              "399            no                 no                      no     good   \n",
              "\n",
              "    Pedal Edema Anemia Class  \n",
              "0            no     no   1.0  \n",
              "1            no     no   1.0  \n",
              "2            no    yes   1.0  \n",
              "3           yes    yes   1.0  \n",
              "4            no     no   1.0  \n",
              "..          ...    ...   ...  \n",
              "395          no     no   0.0  \n",
              "396          no     no   0.0  \n",
              "397          no     no   0.0  \n",
              "398          no     no   0.0  \n",
              "399          no     no   0.0  \n",
              "\n",
              "[400 rows x 25 columns]"
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              "      <th>White Blood Cell Count</th>\n",
              "      <th>Red Blood Cell Count</th>\n",
              "      <th>Hypertension</th>\n",
              "      <th>Diabetes Mellitus</th>\n",
              "      <th>Coronary Artery Disease</th>\n",
              "      <th>Appetite</th>\n",
              "      <th>Pedal Edema</th>\n",
              "      <th>Anemia</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>121.0</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423.0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>106.0</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>140.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>6700</td>\n",
              "      <td>4.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>42.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>75.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54</td>\n",
              "      <td>7800</td>\n",
              "      <td>6.2</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>12.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>100.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49</td>\n",
              "      <td>6600</td>\n",
              "      <td>5.4</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>114.0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>7200</td>\n",
              "      <td>5.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>58.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>131.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>6800</td>\n",
              "      <td>6.1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23d2cf20-749a-4147-9e28-4377411bdf64')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged = df.append(df2, ignore_index=True)"
      ],
      "metadata": {
        "id": "qaNUdQnWb1Fb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
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        "id": "u_2kWdPqeRQH",
        "outputId": "08ee9d20-3443-4242-a180-78ef01e1b734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0    48.0            70.0             1.005      4.0    0.0          normal   \n",
              "1    53.0            90.0             1.020      2.0    0.0        abnormal   \n",
              "2    63.0            70.0             1.010      3.0    0.0        abnormal   \n",
              "3    68.0            80.0             1.010      3.0    2.0          normal   \n",
              "4    61.0            80.0             1.015      2.0    0.0        abnormal   \n",
              "..    ...             ...               ...      ...    ...             ...   \n",
              "553  55.0            80.0             1.020      0.0    0.0          normal   \n",
              "554  42.0            70.0             1.025      0.0    0.0          normal   \n",
              "555  12.0            80.0             1.020      0.0    0.0          normal   \n",
              "556  17.0            60.0             1.025      0.0    0.0          normal   \n",
              "557  58.0            80.0             1.025      0.0    0.0          normal   \n",
              "\n",
              "     Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0    abnormal         present  notpresent                 117.0  ...   \n",
              "1    abnormal         present  notpresent                  70.0  ...   \n",
              "2    abnormal         present  notpresent                 380.0  ...   \n",
              "3    abnormal         present     present                 157.0  ...   \n",
              "4    abnormal      notpresent  notpresent                 173.0  ...   \n",
              "..        ...             ...         ...                   ...  ...   \n",
              "553    normal      notpresent  notpresent                 140.0  ...   \n",
              "554    normal      notpresent  notpresent                  75.0  ...   \n",
              "555    normal      notpresent  notpresent                 100.0  ...   \n",
              "556    normal      notpresent  notpresent                 114.0  ...   \n",
              "557    normal      notpresent  notpresent                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    32                    6700                   3.9   \n",
              "1                    29                   12100                   3.7   \n",
              "2                    32                    4500                   3.8   \n",
              "3                    16                   11000                   2.6   \n",
              "4                    24                    9200                   3.2   \n",
              "..                  ...                     ...                   ...   \n",
              "553                  47                    6700                   4.9   \n",
              "554                  54                    7800                   6.2   \n",
              "555                  49                    6600                   5.4   \n",
              "556                  51                    7200                   5.9   \n",
              "557                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0             yes                 no                      no     poor   \n",
              "1             yes                yes                      no     poor   \n",
              "2             yes                yes                      no     poor   \n",
              "3             yes                yes                     yes     poor   \n",
              "4             yes                yes                     yes     poor   \n",
              "..            ...                ...                     ...      ...   \n",
              "553            no                 no                      no     good   \n",
              "554            no                 no                      no     good   \n",
              "555            no                 no                      no     good   \n",
              "556            no                 no                      no     good   \n",
              "557            no                 no                      no     good   \n",
              "\n",
              "    Pedal Edema Anemia Class  \n",
              "0           yes    yes   1.0  \n",
              "1            no    yes   1.0  \n",
              "2           yes     no   1.0  \n",
              "3           yes     no   1.0  \n",
              "4           yes    yes   1.0  \n",
              "..          ...    ...   ...  \n",
              "553          no     no   0.0  \n",
              "554          no     no   0.0  \n",
              "555          no     no   0.0  \n",
              "556          no     no   0.0  \n",
              "557          no     no   0.0  \n",
              "\n",
              "[558 rows x 25 columns]"
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              "      <th>Age</th>\n",
              "      <th>Blood Pressure</th>\n",
              "      <th>Specific Gravity</th>\n",
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              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>12100</td>\n",
              "      <td>3.7</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>4500</td>\n",
              "      <td>3.8</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>present</td>\n",
              "      <td>157.0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>11000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>173.0</td>\n",
              "      <td>...</td>\n",
              "      <td>24</td>\n",
              "      <td>9200</td>\n",
              "      <td>3.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>140.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>6700</td>\n",
              "      <td>4.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>554</th>\n",
              "      <td>42.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>75.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54</td>\n",
              "      <td>7800</td>\n",
              "      <td>6.2</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>12.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>100.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49</td>\n",
              "      <td>6600</td>\n",
              "      <td>5.4</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>114.0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>7200</td>\n",
              "      <td>5.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>58.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>131.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>6800</td>\n",
              "      <td>6.1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>558 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9ddfa88-96d7-42c6-8de5-b6fa1b0e3b76')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df_merged.to_csv(\"kidney_merged.csv\",  encoding='utf-8') "
      ],
      "metadata": {
        "id": "nZT6De2UeW8K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Run this cell\n",
        "ckd_df = pd.read_csv('kidney_merged.csv')\n",
        "ckd_df.drop(['Unnamed: 0'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "QJgCz78v832L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Run this cell\n",
        "kd_df = pd.concat([ckd_df]*2)\n",
        "kd_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "39q24tUl9De5",
        "outputId": "e5c10176-0444-4456-e254-2664ef19b4c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0    48.0            70.0             1.005      4.0    0.0          normal   \n",
              "1    53.0            90.0             1.020      2.0    0.0        abnormal   \n",
              "2    63.0            70.0             1.010      3.0    0.0        abnormal   \n",
              "3    68.0            80.0             1.010      3.0    2.0          normal   \n",
              "4    61.0            80.0             1.015      2.0    0.0        abnormal   \n",
              "..    ...             ...               ...      ...    ...             ...   \n",
              "553  55.0            80.0             1.020      0.0    0.0          normal   \n",
              "554  42.0            70.0             1.025      0.0    0.0          normal   \n",
              "555  12.0            80.0             1.020      0.0    0.0          normal   \n",
              "556  17.0            60.0             1.025      0.0    0.0          normal   \n",
              "557  58.0            80.0             1.025      0.0    0.0          normal   \n",
              "\n",
              "     Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0    abnormal         present  notpresent                 117.0  ...   \n",
              "1    abnormal         present  notpresent                  70.0  ...   \n",
              "2    abnormal         present  notpresent                 380.0  ...   \n",
              "3    abnormal         present     present                 157.0  ...   \n",
              "4    abnormal      notpresent  notpresent                 173.0  ...   \n",
              "..        ...             ...         ...                   ...  ...   \n",
              "553    normal      notpresent  notpresent                 140.0  ...   \n",
              "554    normal      notpresent  notpresent                  75.0  ...   \n",
              "555    normal      notpresent  notpresent                 100.0  ...   \n",
              "556    normal      notpresent  notpresent                 114.0  ...   \n",
              "557    normal      notpresent  notpresent                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    32                    6700                   3.9   \n",
              "1                    29                   12100                   3.7   \n",
              "2                    32                    4500                   3.8   \n",
              "3                    16                   11000                   2.6   \n",
              "4                    24                    9200                   3.2   \n",
              "..                  ...                     ...                   ...   \n",
              "553                  47                    6700                   4.9   \n",
              "554                  54                    7800                   6.2   \n",
              "555                  49                    6600                   5.4   \n",
              "556                  51                    7200                   5.9   \n",
              "557                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0             yes                 no                      no     poor   \n",
              "1             yes                yes                      no     poor   \n",
              "2             yes                yes                      no     poor   \n",
              "3             yes                yes                     yes     poor   \n",
              "4             yes                yes                     yes     poor   \n",
              "..            ...                ...                     ...      ...   \n",
              "553            no                 no                      no     good   \n",
              "554            no                 no                      no     good   \n",
              "555            no                 no                      no     good   \n",
              "556            no                 no                      no     good   \n",
              "557            no                 no                      no     good   \n",
              "\n",
              "    Pedal Edema Anemia Class  \n",
              "0           yes    yes   1.0  \n",
              "1            no    yes   1.0  \n",
              "2           yes     no   1.0  \n",
              "3           yes     no   1.0  \n",
              "4           yes    yes   1.0  \n",
              "..          ...    ...   ...  \n",
              "553          no     no   0.0  \n",
              "554          no     no   0.0  \n",
              "555          no     no   0.0  \n",
              "556          no     no   0.0  \n",
              "557          no     no   0.0  \n",
              "\n",
              "[1116 rows x 25 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Blood Pressure</th>\n",
              "      <th>Specific Gravity</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Sugar</th>\n",
              "      <th>Red Blood Cells</th>\n",
              "      <th>Pus Cell</th>\n",
              "      <th>Pus Cell clumps</th>\n",
              "      <th>Bacteria</th>\n",
              "      <th>Blood Glucose Random</th>\n",
              "      <th>...</th>\n",
              "      <th>Packed Cell Volume</th>\n",
              "      <th>White Blood Cell Count</th>\n",
              "      <th>Red Blood Cell Count</th>\n",
              "      <th>Hypertension</th>\n",
              "      <th>Diabetes Mellitus</th>\n",
              "      <th>Coronary Artery Disease</th>\n",
              "      <th>Appetite</th>\n",
              "      <th>Pedal Edema</th>\n",
              "      <th>Anemia</th>\n",
              "      <th>Class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
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              "      <td>no</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>70.0</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>12100</td>\n",
              "      <td>3.7</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>4500</td>\n",
              "      <td>3.8</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>present</td>\n",
              "      <td>157.0</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>11000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>173.0</td>\n",
              "      <td>...</td>\n",
              "      <td>24</td>\n",
              "      <td>9200</td>\n",
              "      <td>3.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>140.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>6700</td>\n",
              "      <td>4.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>554</th>\n",
              "      <td>42.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>75.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54</td>\n",
              "      <td>7800</td>\n",
              "      <td>6.2</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>12.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>100.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49</td>\n",
              "      <td>6600</td>\n",
              "      <td>5.4</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>114.0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>7200</td>\n",
              "      <td>5.9</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>58.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>131.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53</td>\n",
              "      <td>6800</td>\n",
              "      <td>6.1</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1116 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77893e35-caaa-428f-b40e-b7bb05b14283')\"\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",
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              "      </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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
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              "          document.querySelector('#df-77893e35-caaa-428f-b40e-b7bb05b14283 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-77893e35-caaa-428f-b40e-b7bb05b14283');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kd_df.to_csv(\"kidney_final2.csv\",  encoding='utf-8') "
      ],
      "metadata": {
        "id": "jHdvxc0N9J5c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pickle to csv**"
      ],
      "metadata": {
        "id": "ymVavfQMgMWZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle as pkl\n",
        "# import pandas as pd\n",
        "# with open(\"diag_dict.pkl\", \"rb\") as f:\n",
        "#     object = pkl.load(f)\n",
        "    \n",
        "# df = pd.DataFrame([object])\n",
        "# df.to_csv(r'file1.csv')"
      ],
      "metadata": {
        "id": "rsjbiZvHgHxN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "\n",
        "\n",
        "# with open('12m_test_ids.pkl', 'rb') as f:\n",
        "#     data = pickle.load(f)\n",
        "\n",
        "# print(data)"
      ],
      "metadata": {
        "id": "WygVwiacgJuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "bMGqNi9wf7Ry"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "lxuN9pE8f5ld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kd_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ue3eP1lbwIvM",
        "outputId": "a8fcfa82-6937-4092-b703-fbbc1db36f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1116 entries, 0 to 557\n",
            "Data columns (total 25 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Age                      1098 non-null   float64\n",
            " 1   Blood Pressure           1092 non-null   float64\n",
            " 2   Specific Gravity         1022 non-null   float64\n",
            " 3   Albumin                  1024 non-null   float64\n",
            " 4   Sugar                    1018 non-null   float64\n",
            " 5   Red Blood Cells          812 non-null    object \n",
            " 6   Pus Cell                 986 non-null    object \n",
            " 7   Pus Cell clumps          1108 non-null   object \n",
            " 8   Bacteria                 1108 non-null   object \n",
            " 9   Blood Glucose Random     1028 non-null   float64\n",
            " 10  Blood Urea               1078 non-null   float64\n",
            " 11  Serum Creatinine         1082 non-null   float64\n",
            " 12  Sodium                   942 non-null    float64\n",
            " 13  Potassium                940 non-null    float64\n",
            " 14  Hemoglobin               1012 non-null   float64\n",
            " 15  Packed Cell Volume       976 non-null    object \n",
            " 16  White Blood Cell Count   906 non-null    object \n",
            " 17  Red Blood Cell Count     856 non-null    object \n",
            " 18  Hypertension             1112 non-null   object \n",
            " 19  Diabetes Mellitus        1112 non-null   object \n",
            " 20  Coronary Artery Disease  1112 non-null   object \n",
            " 21  Appetite                 1114 non-null   object \n",
            " 22  Pedal Edema              1114 non-null   object \n",
            " 23  Anemia                   1114 non-null   object \n",
            " 24  Class                    1112 non-null   float64\n",
            "dtypes: float64(12), object(13)\n",
            "memory usage: 226.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "kd_df['Class'].value_counts().plot(kind='bar',color=['salmon','lightblue'],title=\"Count of Diagnosis of kidney disease\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "xHE4seyXhk7b",
        "outputId": "ceff2051-f040-4a4f-b60c-f0e3d5aa2b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking how many instances of each class are present**"
      ],
      "metadata": {
        "id": "QRP3D2cG-NH_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "kd_df['Age'].plot(kind='hist');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "3QIxQT1cgi_k",
        "outputId": "27cad338-4ddd-4621-d07e-a32b22827d2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking the  age difference with a histogram**"
      ],
      "metadata": {
        "id": "OIn5jHOe-SCV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kd_df['Class'].value_counts()"
      ],
      "metadata": {
        "id": "EqJV1pa0hDHQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4de949c-7499-4a9a-ab3d-3c6914a92375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    582\n",
              "0.0    530\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "p = msno.bar(kd_df)"
      ],
      "metadata": {
        "id": "4oIRGzaNg87h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "9d7fd8ab-8a85-40fa-c7d4-30711fd6dc8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference: Here any features which are not touching the 1116 mark at the top  have some null values.**"
      ],
      "metadata": {
        "id": "LU2c18SgC9DC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(121), sns.histplot(kd_df['Blood Pressure'])\n",
        "plt.subplot(122), kd_df['Blood Pressure'].plot.box(figsize=(16,5))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "-p44xiefCBj8",
        "outputId": "a3e408f3-e8aa-4ec4-c951-23ff317fc94c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference: In the above graph we can see the distribution of blood pressure and  in the subplot it is visible that blood pressure column has some outliers in it.**"
      ],
      "metadata": {
        "id": "vT1s_93BCupo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (19,19))\n",
        "sns.heatmap(kd_df.corr(), annot = True, cmap = 'coolwarm') # looking for strong correlations with \"class\" row "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "73O0phUECy6q",
        "outputId": "ba37c9fd-ca08-4603-e4d3-12593e9522db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f97ce2a1550>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1368x1368 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking for strong correlations with Classes**"
      ],
      "metadata": {
        "id": "D55LVtii-0c3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "DXHcXwhuDziI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = kd_df"
      ],
      "metadata": {
        "id": "ib19BF54MNdO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hITi71trD3eI",
        "outputId": "c621a775-6639-431b-9366-d6dfcd41dcb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1116 entries, 0 to 557\n",
            "Data columns (total 25 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Age                      1098 non-null   float64\n",
            " 1   Blood Pressure           1092 non-null   float64\n",
            " 2   Specific Gravity         1022 non-null   float64\n",
            " 3   Albumin                  1024 non-null   float64\n",
            " 4   Sugar                    1018 non-null   float64\n",
            " 5   Red Blood Cells          812 non-null    object \n",
            " 6   Pus Cell                 986 non-null    object \n",
            " 7   Pus Cell clumps          1108 non-null   object \n",
            " 8   Bacteria                 1108 non-null   object \n",
            " 9   Blood Glucose Random     1028 non-null   float64\n",
            " 10  Blood Urea               1078 non-null   float64\n",
            " 11  Serum Creatinine         1082 non-null   float64\n",
            " 12  Sodium                   942 non-null    float64\n",
            " 13  Potassium                940 non-null    float64\n",
            " 14  Hemoglobin               1012 non-null   float64\n",
            " 15  Packed Cell Volume       976 non-null    object \n",
            " 16  White Blood Cell Count   906 non-null    object \n",
            " 17  Red Blood Cell Count     856 non-null    object \n",
            " 18  Hypertension             1112 non-null   object \n",
            " 19  Diabetes Mellitus        1112 non-null   object \n",
            " 20  Coronary Artery Disease  1112 non-null   object \n",
            " 21  Appetite                 1114 non-null   object \n",
            " 22  Pedal Edema              1114 non-null   object \n",
            " 23  Anemia                   1114 non-null   object \n",
            " 24  Class                    1112 non-null   float64\n",
            "dtypes: float64(12), object(13)\n",
            "memory usage: 226.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "- Check for null values\n",
        "- Scale the values for accurate prediction\n",
        "- If exists, Drop rows with high number of corrup data "
      ],
      "metadata": {
        "id": "iNcmLJy0_FLa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "z5w51HUB_ZRX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01bdd0ea-3407-4a07-85e2-74b69673e1e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1116, 25)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HV1vda_dMSVx",
        "outputId": "c29219b6-f261-4770-e19b-26f6f6078ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Blood Pressure', 'Specific Gravity', 'Albumin', 'Sugar',\n",
              "       'Red Blood Cells', 'Pus Cell', 'Pus Cell clumps', 'Bacteria',\n",
              "       'Blood Glucose Random', 'Blood Urea', 'Serum Creatinine', 'Sodium',\n",
              "       'Potassium', 'Hemoglobin', 'Packed Cell Volume',\n",
              "       'White Blood Cell Count', 'Red Blood Cell Count', 'Hypertension',\n",
              "       'Diabetes Mellitus', 'Coronary Artery Disease', 'Appetite',\n",
              "       'Pedal Edema', 'Anemia', 'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "oGAQuAbHdKWu",
        "outputId": "98ff6d62-323a-4216-c1ba-b232f656c954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Age  Blood Pressure  Specific Gravity  Albumin  Sugar Red Blood Cells  \\\n",
              "0  48.0            70.0             1.005      4.0    0.0          normal   \n",
              "1  53.0            90.0             1.020      2.0    0.0        abnormal   \n",
              "2  63.0            70.0             1.010      3.0    0.0        abnormal   \n",
              "3  68.0            80.0             1.010      3.0    2.0          normal   \n",
              "4  61.0            80.0             1.015      2.0    0.0        abnormal   \n",
              "\n",
              "   Pus Cell Pus Cell clumps    Bacteria  Blood Glucose Random  ...  \\\n",
              "0  abnormal         present  notpresent                 117.0  ...   \n",
              "1  abnormal         present  notpresent                  70.0  ...   \n",
              "2  abnormal         present  notpresent                 380.0  ...   \n",
              "3  abnormal         present     present                 157.0  ...   \n",
              "4  abnormal      notpresent  notpresent                 173.0  ...   \n",
              "\n",
              "   Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                  32                    6700                   3.9   \n",
              "1                  29                   12100                   3.7   \n",
              "2                  32                    4500                   3.8   \n",
              "3                  16                   11000                   2.6   \n",
              "4                  24                    9200                   3.2   \n",
              "\n",
              "   Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0           yes                 no                      no     poor   \n",
              "1           yes                yes                      no     poor   \n",
              "2           yes                yes                      no     poor   \n",
              "3           yes                yes                     yes     poor   \n",
              "4           yes                yes                     yes     poor   \n",
              "\n",
              "  Pedal Edema Anemia Class  \n",
              "0         yes    yes   1.0  \n",
              "1          no    yes   1.0  \n",
              "2         yes     no   1.0  \n",
              "3         yes     no   1.0  \n",
              "4         yes    yes   1.0  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th>Age</th>\n",
              "      <th>Blood Pressure</th>\n",
              "      <th>Specific Gravity</th>\n",
              "      <th>Albumin</th>\n",
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              "      <td>1.005</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
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              "      <td>53.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
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              "      <td>3.7</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>4500</td>\n",
              "      <td>3.8</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
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              "      <td>yes</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
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              "      <td>present</td>\n",
              "      <td>157.0</td>\n",
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              "      <td>16</td>\n",
              "      <td>11000</td>\n",
              "      <td>2.6</td>\n",
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              "      <td>yes</td>\n",
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              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.015</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
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              "<p>5 rows × 25 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Converting text attributes to numerical attributes**"
      ],
      "metadata": {
        "id": "OVrWy0aua65m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Hypertension'] = data['Hypertension'].map({'yes':1,'no':0})\n",
        "data['Diabetes Mellitus'] = data['Diabetes Mellitus'].map({'yes':1,'no':0})\n",
        "data['Coronary Artery Disease'] = data['Coronary Artery Disease'].map({'yes':1,'no':0})\n",
        "data['Appetite'] = data['Appetite'].map({'good':1,'poor':0})\n",
        "data['Anemia'] = data['Anemia'].map({'yes':1,'no':0})\n",
        "data['Pedal Edema'] = data['Pedal Edema'].map({'yes':1,'no':0})\n",
        "data['Bacteria'] = data['Bacteria'].map({'present':1,'notpresent':0})\n",
        "data['Pus Cell clumps'] = data['Pus Cell clumps'].map({'present':1,'notpresent':0})\n",
        "data['Pus Cell'] = data['Pus Cell'].map({'abnormal':1,'normal':0})\n",
        "data['Red Blood Cells'] = data['Red Blood Cells'].map({'abnormal':1,'normal':0})"
      ],
      "metadata": {
        "id": "OM2ZPrRUZc-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "QpOl7RJEDkj1",
        "outputId": "4457f7b1-19a5-4657-98ee-368728fbeae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar  Red Blood Cells  \\\n",
              "0    48.0            70.0             1.005      4.0    0.0                0   \n",
              "1    53.0            90.0             1.020      2.0    0.0                1   \n",
              "2    63.0            70.0             1.010      3.0    0.0                1   \n",
              "3    68.0            80.0             1.010      3.0    2.0                0   \n",
              "4    61.0            80.0             1.015      2.0    0.0                1   \n",
              "..    ...             ...               ...      ...    ...              ...   \n",
              "553  55.0            80.0             1.020      0.0    0.0                0   \n",
              "554  42.0            70.0             1.025      0.0    0.0                0   \n",
              "555  12.0            80.0             1.020      0.0    0.0                0   \n",
              "556  17.0            60.0             1.025      0.0    0.0                0   \n",
              "557  58.0            80.0             1.025      0.0    0.0                0   \n",
              "\n",
              "     Pus Cell  Pus Cell clumps  Bacteria  Blood Glucose Random  ...  \\\n",
              "0           1                1         0                 117.0  ...   \n",
              "1           1                1         0                  70.0  ...   \n",
              "2           1                1         0                 380.0  ...   \n",
              "3           1                1         1                 157.0  ...   \n",
              "4           1                0         0                 173.0  ...   \n",
              "..        ...              ...       ...                   ...  ...   \n",
              "553         0                0         0                 140.0  ...   \n",
              "554         0                0         0                  75.0  ...   \n",
              "555         0                0         0                 100.0  ...   \n",
              "556         0                0         0                 114.0  ...   \n",
              "557         0                0         0                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    32                    6700                   3.9   \n",
              "1                    29                   12100                   3.7   \n",
              "2                    32                    4500                   3.8   \n",
              "3                    16                   11000                   2.6   \n",
              "4                    24                    9200                   3.2   \n",
              "..                  ...                     ...                   ...   \n",
              "553                  47                    6700                   4.9   \n",
              "554                  54                    7800                   6.2   \n",
              "555                  49                    6600                   5.4   \n",
              "556                  51                    7200                   5.9   \n",
              "557                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus Coronary Artery Disease Appetite  \\\n",
              "0               1                  0                     0.0        0   \n",
              "1               1                  1                     0.0        0   \n",
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              "    Pedal Edema  Anemia  Class  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dealing with Null Values**"
      ],
      "metadata": {
        "id": "5Q4jZK5gbKmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sA49tGdrMU5Z",
        "outputId": "727c9207-954c-4988-8a57-eb4f796cc6aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                         18\n",
              "Blood Pressure              24\n",
              "Specific Gravity            94\n",
              "Albumin                     92\n",
              "Sugar                       98\n",
              "Red Blood Cells            304\n",
              "Pus Cell                   130\n",
              "Pus Cell clumps              8\n",
              "Bacteria                     8\n",
              "Blood Glucose Random        88\n",
              "Blood Urea                  38\n",
              "Serum Creatinine            34\n",
              "Sodium                     174\n",
              "Potassium                  176\n",
              "Hemoglobin                 104\n",
              "Packed Cell Volume         140\n",
              "White Blood Cell Count     210\n",
              "Red Blood Cell Count       260\n",
              "Hypertension                 4\n",
              "Diabetes Mellitus            4\n",
              "Coronary Artery Disease      4\n",
              "Appetite                     2\n",
              "Pedal Edema                  2\n",
              "Anemia                       2\n",
              "Class                        4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Replcaing column with numerical values with their mean value. Binary classes are kept as null.**"
      ],
      "metadata": {
        "id": "oRHlWX2QdiGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#age\n",
        "mean_age = data['Age'].mean()\n",
        "mean_age\n",
        "data['Age'].fillna(value=mean_age, inplace=True) \n",
        "#bp\n",
        "mean_bp  = data['Blood Pressure'].mean()\n",
        "mean_bp\n",
        "data['Blood Pressure'].fillna(value=mean_bp, inplace=True) \n",
        "#sg\n",
        "mean_sg = data['Specific Gravity'].mean()\n",
        "mean_sg \n",
        "data['Specific Gravity'].fillna(value=mean_sg, inplace=True) \n",
        "#albumin\n",
        "mean_albumin = data['Albumin'].mean()\n",
        "mean_albumin \n",
        "data['Albumin'].fillna(value=mean_albumin, inplace=True) \n",
        "#sugar\n",
        "mean_sugar = data['Sugar'].mean()\n",
        "mean_sugar\n",
        "data['Sugar'].fillna(value=mean_sugar, inplace=True) \n",
        "#bgr\n",
        "mean_bgr =  data['Blood Glucose Random'].mean()\n",
        "mean_bgr\n",
        "data['Blood Glucose Random'].fillna(value=mean_bgr, inplace=True) \n",
        "#bu\n",
        "mean_bu =  data['Blood Urea'].mean()\n",
        "mean_bu\n",
        "data['Blood Urea'].fillna(value=mean_bu, inplace=True) \n",
        "#sc\n",
        "mean_sc =  data['Serum Creatinine'].mean()\n",
        "mean_sc \n",
        "data['Serum Creatinine'].fillna(value=mean_sc, inplace=True) \n",
        "#sodium\n",
        "mean_sodium =  data['Sodium'].mean()\n",
        "mean_sodium\n",
        "data['Sodium'].fillna(value=mean_sodium, inplace=True) \n",
        "#potasium\n",
        "mean_potassium =  data['Potassium'].mean()\n",
        "mean_potassium\n",
        "data['Potassium'].fillna(value=mean_potassium, inplace=True) \n",
        "#hemoglobin\n",
        "mean_hemoglobin =  data['Hemoglobin'].mean()\n",
        "mean_hemoglobin\n",
        "data['Hemoglobin'].fillna(value=mean_hemoglobin, inplace=True) "
      ],
      "metadata": {
        "id": "Wep2t_BjNN7g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "data.isnull().sum()"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Age                          0\n",
              "Blood Pressure               0\n",
              "Specific Gravity             0\n",
              "Albumin                      0\n",
              "Sugar                        0\n",
              "Red Blood Cells            304\n",
              "Pus Cell                   130\n",
              "Pus Cell clumps              8\n",
              "Bacteria                     8\n",
              "Blood Glucose Random         0\n",
              "Blood Urea                   0\n",
              "Serum Creatinine             0\n",
              "Sodium                       0\n",
              "Potassium                    0\n",
              "Hemoglobin                   0\n",
              "Packed Cell Volume         140\n",
              "White Blood Cell Count     210\n",
              "Red Blood Cell Count       260\n",
              "Hypertension                 4\n",
              "Diabetes Mellitus            4\n",
              "Coronary Artery Disease      4\n",
              "Appetite                     2\n",
              "Pedal Edema                  2\n",
              "Anemia                       2\n",
              "Class                        4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping still existing null values\n",
        "data.dropna(subset=['Diabetes Mellitus'], how='all', inplace=True)\n",
        "data.dropna(subset=['Appetite'], how='all', inplace=True)\n",
        "data.dropna(subset=['Pedal Edema'], how='all', inplace=True)\n",
        "data.dropna(subset=['Anemia'], how='all', inplace=True)\n",
        "data.dropna(subset=['Class'], how='all', inplace=True)\n",
        "data.dropna(subset=['Red Blood Cell Count'], how='all', inplace=True)\n",
        "data.dropna(subset=['Packed Cell Volume'], how='all', inplace=True)\n",
        "data.dropna(subset=['White Blood Cell Count'], how='all', inplace=True)\n",
        "data.dropna(subset=['Red Blood Cells'], how='all', inplace=True)\n",
        "data.dropna(subset=['Pus Cell'], how='all', inplace=True)\n",
        "data.dropna(subset=['Pus Cell clumps'], how='all', inplace=True)\n",
        "data.dropna(subset=['Bacteria'], how='all', inplace=True)\n",
        "data.dropna(subset=['Coronary Artery Disease'], how='all', inplace=True)"
      ],
      "metadata": {
        "id": "nwQ0Jk-GFKF4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# #Final dataset after preprocessing\n",
        "# data.to_csv(\"kidney_clean.csv\",  encoding='utf-8') "
      ],
      "metadata": {
        "id": "cK2ybetbFUZK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load Final Dataset\n",
        "# data = pd.read_csv(\"kidney_clean.csv\") "
      ],
      "metadata": {
        "id": "QspVwguxFbdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data.drop(['Unnamed: 0'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "MFc4BVfhFndL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Spliting"
      ],
      "metadata": {
        "id": "hZr8QUb4eFfW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"kidney_clean.csv\") "
      ],
      "metadata": {
        "id": "P8AnfU0FxT12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(['Unnamed: 0'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "B__wbeUNxUH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "W6vIyeyvBvMu",
        "outputId": "6626a4d2-7661-4773-84ef-1839442d87b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar  Red Blood Cells  \\\n",
              "0    48.0            70.0             1.005      4.0    0.0                0   \n",
              "1    53.0            90.0             1.020      2.0    0.0                1   \n",
              "2    63.0            70.0             1.010      3.0    0.0                1   \n",
              "3    68.0            80.0             1.010      3.0    2.0                0   \n",
              "4    61.0            80.0             1.015      2.0    0.0                1   \n",
              "..    ...             ...               ...      ...    ...              ...   \n",
              "677  55.0            80.0             1.020      0.0    0.0                0   \n",
              "678  42.0            70.0             1.025      0.0    0.0                0   \n",
              "679  12.0            80.0             1.020      0.0    0.0                0   \n",
              "680  17.0            60.0             1.025      0.0    0.0                0   \n",
              "681  58.0            80.0             1.025      0.0    0.0                0   \n",
              "\n",
              "     Pus Cell  Pus Cell clumps  Bacteria  Blood Glucose Random  ...  \\\n",
              "0           1                1         0                 117.0  ...   \n",
              "1           1                1         0                  70.0  ...   \n",
              "2           1                1         0                 380.0  ...   \n",
              "3           1                1         1                 157.0  ...   \n",
              "4           1                0         0                 173.0  ...   \n",
              "..        ...              ...       ...                   ...  ...   \n",
              "677         0                0         0                 140.0  ...   \n",
              "678         0                0         0                  75.0  ...   \n",
              "679         0                0         0                 100.0  ...   \n",
              "680         0                0         0                 114.0  ...   \n",
              "681         0                0         0                 131.0  ...   \n",
              "\n",
              "     Packed Cell Volume  White Blood Cell Count  Red Blood Cell Count  \\\n",
              "0                    32                    6700                   3.9   \n",
              "1                    29                   12100                   3.7   \n",
              "2                    32                    4500                   3.8   \n",
              "3                    16                   11000                   2.6   \n",
              "4                    24                    9200                   3.2   \n",
              "..                  ...                     ...                   ...   \n",
              "677                  47                    6700                   4.9   \n",
              "678                  54                    7800                   6.2   \n",
              "679                  49                    6600                   5.4   \n",
              "680                  51                    7200                   5.9   \n",
              "681                  53                    6800                   6.1   \n",
              "\n",
              "     Hypertension  Diabetes Mellitus  Coronary Artery Disease  Appetite  \\\n",
              "0               1                  0                      0.0         0   \n",
              "1               1                  1                      0.0         0   \n",
              "2               1                  1                      0.0         0   \n",
              "3               1                  1                      1.0         0   \n",
              "4               1                  1                      1.0         0   \n",
              "..            ...                ...                      ...       ...   \n",
              "677             0                  0                      0.0         1   \n",
              "678             0                  0                      0.0         1   \n",
              "679             0                  0                      0.0         1   \n",
              "680             0                  0                      0.0         1   \n",
              "681             0                  0                      0.0         1   \n",
              "\n",
              "     Pedal Edema  Anemia  Class  \n",
              "0              1       1    1.0  \n",
              "1              0       1    1.0  \n",
              "2              1       0    1.0  \n",
              "3              1       0    1.0  \n",
              "4              1       1    1.0  \n",
              "..           ...     ...    ...  \n",
              "677            0       0    0.0  \n",
              "678            0       0    0.0  \n",
              "679            0       0    0.0  \n",
              "680            0       0    0.0  \n",
              "681            0       0    0.0  \n",
              "\n",
              "[682 rows x 25 columns]"
            ],
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              "\n",
              "  <div id=\"df-a9ab2c39-c9ac-440a-903e-10d6e01c7cfe\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Blood Pressure</th>\n",
              "      <th>Specific Gravity</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Sugar</th>\n",
              "      <th>Red Blood Cells</th>\n",
              "      <th>Pus Cell</th>\n",
              "      <th>Pus Cell clumps</th>\n",
              "      <th>Bacteria</th>\n",
              "      <th>Blood Glucose Random</th>\n",
              "      <th>...</th>\n",
              "      <th>Packed Cell Volume</th>\n",
              "      <th>White Blood Cell Count</th>\n",
              "      <th>Red Blood Cell Count</th>\n",
              "      <th>Hypertension</th>\n",
              "      <th>Diabetes Mellitus</th>\n",
              "      <th>Coronary Artery Disease</th>\n",
              "      <th>Appetite</th>\n",
              "      <th>Pedal Edema</th>\n",
              "      <th>Anemia</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
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              "      <td>1.005</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53.0</td>\n",
              "      <td>90.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>70.0</td>\n",
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              "      <td>29</td>\n",
              "      <td>12100</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>4500</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>68.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>16</td>\n",
              "      <td>11000</td>\n",
              "      <td>2.6</td>\n",
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              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>80.0</td>\n",
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              "      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-a9ab2c39-c9ac-440a-903e-10d6e01c7cfe 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-a9ab2c39-c9ac-440a-903e-10d6e01c7cfe');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X = data.iloc[:,:-1]\n",
        "y = data['Class']"
      ],
      "metadata": {
        "id": "P4JCUsInePXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, stratify = y, shuffle = True)"
      ],
      "metadata": {
        "id": "ea7VVH1l8x2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "Z4f6USbd8q27",
        "outputId": "8b697d53-487c-47f9-9ee9-7f157a4bede3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      Age  Blood Pressure  Specific Gravity  Albumin  Sugar  Red Blood Cells  \\\n",
              "398  42.0            80.0              1.02      0.0    0.0                0   \n",
              "663  59.0            60.0              1.02      0.0    0.0                0   \n",
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              "1    53.0            90.0              1.02      2.0    0.0                1   \n",
              "..    ...             ...               ...      ...    ...              ...   \n",
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              "660  64.0            70.0              1.02      0.0    0.0                0   \n",
              "547  60.0            90.0              1.01      3.0    5.0                1   \n",
              "132  72.0            60.0              1.02      0.0    0.0                0   \n",
              "269  57.0            60.0              1.02      0.0    0.0                0   \n",
              "\n",
              "     Pus Cell  Pus Cell clumps  Bacteria  Blood Glucose Random  ...  \\\n",
              "398         0                0         0            132.000000  ...   \n",
              "663         0                0         0            113.000000  ...   \n",
              "294         0                0         0             83.000000  ...   \n",
              "366         0                0         0            303.000000  ...   \n",
              "1           1                1         0             70.000000  ...   \n",
              "..        ...              ...       ...                   ...  ...   \n",
              "584         0                0         0            142.904669  ...   \n",
              "660         0                0         0             97.000000  ...   \n",
              "547         0                0         1            490.000000  ...   \n",
              "132         0                0         0            109.000000  ...   \n",
              "269         0                0         0            105.000000  ...   \n",
              "\n",
              "     Hemoglobin  Packed Cell Volume  White Blood Cell Count  \\\n",
              "398        14.4                  50                    5000   \n",
              "663        15.3                  54                    6500   \n",
              "294        17.5                  40                    9900   \n",
              "366        10.4                  35                   10900   \n",
              "1           9.5                  29                   12100   \n",
              "..          ...                 ...                     ...   \n",
              "584        16.4                  43                   10800   \n",
              "660        13.8                  49                    6400   \n",
              "547        11.5                  35                   12000   \n",
              "132        15.0                  52                   10500   \n",
              "269        15.7                  44                   10400   \n",
              "\n",
              "     Red Blood Cell Count  Hypertension  Diabetes Mellitus  \\\n",
              "398                   4.5             0                  0   \n",
              "663                   4.9             0                  0   \n",
              "294                   4.7             0                  0   \n",
              "366                   4.3             0                  1   \n",
              "1                     3.7             1                  1   \n",
              "..                    ...           ...                ...   \n",
              "584                   5.7             0                  0   \n",
              "660                   4.8             0                  0   \n",
              "547                   4.5             1                  1   \n",
              "132                   5.5             0                  0   \n",
              "269                   6.2             0                  0   \n",
              "\n",
              "     Coronary Artery Disease  Appetite  Pedal Edema  Anemia  \n",
              "398                      0.0         1            0       0  \n",
              "663                      0.0         1            0       0  \n",
              "294                      0.0         1            0       0  \n",
              "366                      0.0         0            0       0  \n",
              "1                        0.0         0            0       1  \n",
              "..                       ...       ...          ...     ...  \n",
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              "547                      0.0         1            0       0  \n",
              "132                      0.0         1            0       0  \n",
              "269                      0.0         1            0       0  \n",
              "\n",
              "[511 rows x 24 columns]"
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Blood Pressure</th>\n",
              "      <th>Specific Gravity</th>\n",
              "      <th>Albumin</th>\n",
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              "      <th>398</th>\n",
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              "      <td>109.000000</td>\n",
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              "      <th>269</th>\n",
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              "      <td>60.0</td>\n",
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              "          document.querySelector('#df-916de54b-4a57-4b22-a5e8-5eecf043ef12 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-916de54b-4a57-4b22-a5e8-5eecf043ef12');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "SoKsipCMCW8C",
        "outputId": "4514aa3f-c795-4fbe-b39b-7dd9e20d2192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           Age  Blood Pressure  Specific Gravity  Albumin  Sugar  \\\n",
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              "197  64.000000            60.0             1.010      4.0    1.0   \n",
              "..         ...             ...               ...      ...    ...   \n",
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              "254  44.000000            60.0             1.025      0.0    0.0   \n",
              "\n",
              "     Red Blood Cells  Pus Cell  Pus Cell clumps  Bacteria  \\\n",
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              "\n",
              "     Blood Glucose Random  ...  Hemoglobin  Packed Cell Volume  \\\n",
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              "540                 110.0  ...         9.1                  26   \n",
              "305                  89.0  ...        15.0                  40   \n",
              "197                 239.0  ...         9.5                  29   \n",
              "..                    ...  ...         ...                 ...   \n",
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              "254                  96.0  ...        16.9                  41   \n",
              "\n",
              "     White Blood Cell Count  Red Blood Cell Count  Hypertension  \\\n",
              "139                    6500                   4.9             0   \n",
              "462                    6000                   4.5             0   \n",
              "540                    9200                   3.4             1   \n",
              "305                   10300                   4.8             0   \n",
              "197                    7500                   3.4             1   \n",
              "..                      ...                   ...           ...   \n",
              "375                    8800                   3.4             1   \n",
              "18                     5000                   2.9             1   \n",
              "5                      6900                   3.4             1   \n",
              "477                    9100                   5.2             0   \n",
              "254                    7200                   5.0             0   \n",
              "\n",
              "     Diabetes Mellitus  Coronary Artery Disease  Appetite  Pedal Edema  Anemia  \n",
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              "477                  0                      0.0         1            0       0  \n",
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          "base_uri": "https://localhost:8080/"
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        "id": "9I24-Fs6Ca1r",
        "outputId": "f7f8aac5-fe9b-4f3a-c137-f30653c0dc18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "460    0.0\n",
              "499    1.0\n",
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              "28     1.0\n",
              "      ... \n",
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              "Name: Class, Length: 511, dtype: float64"
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          "metadata": {},
          "execution_count": 87
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    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9_ZzGPJCeHT",
        "outputId": "b11770bb-2be5-4a79-cd3d-3d91398fdc49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "325    0.0\n",
              "432    0.0\n",
              "155    0.0\n",
              "183    1.0\n",
              "100    0.0\n",
              "      ... \n",
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              "204    1.0\n",
              "356    1.0\n",
              "Name: Class, Length: 171, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "q-I3AfG6eW4h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing classifiers**"
      ],
      "metadata": {
        "id": "J9Tx2JECbEtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import  BaggingClassifier, GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "metadata": {
        "id": "PLP0F4SLbOA3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Cross Validation and applying Classifier Algorithms**"
      ],
      "metadata": {
        "id": "UtOKP6WNxx_Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "crossvalidation_score=[]\n",
        "mod = []\n",
        "model =[BaggingClassifier(), SVC(),RandomForestClassifier(), GradientBoostingClassifier(),\n",
        "        DecisionTreeClassifier(), ExtraTreeClassifier(), KNeighborsClassifier(),LogisticRegression(),GaussianNB(),]\n",
        "for mdl in model:\n",
        "    crossvalidation_score.append(cross_val_score(mdl, X_train, y_train, scoring='accuracy', cv=5).mean())\n",
        "    mod.append(mdl)\n",
        "model_df= pd.DataFrame(columns=['model','crossvalidation_score'])\n",
        "model_df['model']= mod\n",
        "model_df['crossvalidation_score']= crossvalidation_score\n",
        "model_df.sort_values(by=['crossvalidation_score'], ascending=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ge1-4o3nX6jS",
        "outputId": "1ec7c70a-e613-4ca7-b811-406f0eeb8d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          model  crossvalidation_score\n",
              "1                         SVC()               0.784809\n",
              "6        KNeighborsClassifier()               0.890425\n",
              "7          LogisticRegression()               0.988254\n",
              "0           BaggingClassifier()               0.998039\n",
              "2      RandomForestClassifier()               0.998039\n",
              "3  GradientBoostingClassifier()               0.998039\n",
              "4      DecisionTreeClassifier()               0.998039\n",
              "5         ExtraTreeClassifier()               1.000000\n",
              "8                  GaussianNB()               1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fee12f51-bfbb-4e6b-b09e-f5322289ac5e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>crossvalidation_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SVC()</td>\n",
              "      <td>0.784809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>KNeighborsClassifier()</td>\n",
              "      <td>0.890425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LogisticRegression()</td>\n",
              "      <td>0.988254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BaggingClassifier()</td>\n",
              "      <td>0.998039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForestClassifier()</td>\n",
              "      <td>0.998039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GradientBoostingClassifier()</td>\n",
              "      <td>0.998039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DecisionTreeClassifier()</td>\n",
              "      <td>0.998039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ExtraTreeClassifier()</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GaussianNB()</td>\n",
              "      <td>1.000000</td>\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-fee12f51-bfbb-4e6b-b09e-f5322289ac5e');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**KNeighbors Classifier**"
      ],
      "metadata": {
        "id": "s-Bzf3R34zLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "kmodel = KNeighborsClassifier()\n",
        "kmodel.fit(X_train,y_train)\n",
        "pred = kmodel.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(kmodel.score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "TTFBgECF44T9",
        "outputId": "ba8c2106-5534-44b8-f755-23d79c8f7ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.92      0.98      0.95       121\n",
            "         1.0       0.95      0.78      0.86        50\n",
            "\n",
            "    accuracy                           0.92       171\n",
            "   macro avg       0.93      0.88      0.90       171\n",
            "weighted avg       0.93      0.92      0.92       171\n",
            "\n",
            "<bound method ClassifierMixin.score of KNeighborsClassifier()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression**"
      ],
      "metadata": {
        "id": "YyjqSmqG2X7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "lmodel = LogisticRegression()\n",
        "lmodel.fit(X_train,y_train)\n",
        "pred = lmodel.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(lmodel.score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "xHqTd9ap1z2J",
        "outputId": "e69c9177-43b5-4dc7-b575-f87de63780e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      1.00      0.99       121\n",
            "         1.0       1.00      0.94      0.97        50\n",
            "\n",
            "    accuracy                           0.98       171\n",
            "   macro avg       0.99      0.97      0.98       171\n",
            "weighted avg       0.98      0.98      0.98       171\n",
            "\n",
            "<bound method ClassifierMixin.score of LogisticRegression()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree"
      ],
      "metadata": {
        "id": "EXA3RCy_2l6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "dmodel = DecisionTreeClassifier()\n",
        "dmodel.fit(X_train,y_train)\n",
        "pred = dmodel.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(dmodel.score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "DXd_CLyG10AF",
        "outputId": "0dcfc460-df66-4eec-9975-8685d78df8d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00       121\n",
            "         1.0       1.00      1.00      1.00        50\n",
            "\n",
            "    accuracy                           1.00       171\n",
            "   macro avg       1.00      1.00      1.00       171\n",
            "weighted avg       1.00      1.00      1.00       171\n",
            "\n",
            "<bound method ClassifierMixin.score of DecisionTreeClassifier()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gradient Boosting Classifier"
      ],
      "metadata": {
        "id": "V1mrnkvv3-SD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "gmodel = GradientBoostingClassifier()\n",
        "gmodel.fit(X_train,y_train)\n",
        "pred = gmodel.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(gmodel.score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "SCFdMVCz10I4",
        "outputId": "1b322efb-314d-4dc9-f511-e793562d333f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00       121\n",
            "         1.0       1.00      1.00      1.00        50\n",
            "\n",
            "    accuracy                           1.00       171\n",
            "   macro avg       1.00      1.00      1.00       171\n",
            "weighted avg       1.00      1.00      1.00       171\n",
            "\n",
            "<bound method ClassifierMixin.score of GradientBoostingClassifier()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier**"
      ],
      "metadata": {
        "id": "zJo6mt7N4GsK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "rmodel = RandomForestClassifier()\n",
        "rmodel.fit(X_train,y_train)\n",
        "pred = rmodel.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(rmodel.score)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "f8kGhrtc4J13",
        "outputId": "d4dc185a-e13e-4491-a9f4-e40041879cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00       121\n",
            "         1.0       1.00      1.00      1.00        50\n",
            "\n",
            "    accuracy                           1.00       171\n",
            "   macro avg       1.00      1.00      1.00       171\n",
            "weighted avg       1.00      1.00      1.00       171\n",
            "\n",
            "<bound method ClassifierMixin.score of RandomForestClassifier()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Picking the final Model"
      ],
      "metadata": {
        "id": "BewnJQwdykOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train,y_train)\n",
        "pred = model.predict(X_test)\n",
        "print(classification_report(y_test, pred))\n",
        "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.2f')\n",
        "print(model.score)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "_2ZYIeaXaMSF",
        "outputId": "4f2dd525-fcfd-46ca-bf86-d33bc59515a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      1.00      0.99       121\n",
            "         1.0       1.00      0.94      0.97        50\n",
            "\n",
            "    accuracy                           0.98       171\n",
            "   macro avg       0.99      0.97      0.98       171\n",
            "weighted avg       0.98      0.98      0.98       171\n",
            "\n",
            "<bound method ClassifierMixin.score of LogisticRegression()>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the model"
      ],
      "metadata": {
        "id": "-vLra8Bzd0BS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle  # Pickle is a library that helps us to save model\n",
        "with open('ckd_prediction.pkl', 'wb') as files:\n",
        "    pickle.dump(model, files)"
      ],
      "metadata": {
        "id": "v6KTuvqAf6fd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('ckd_prediction.pkl' , 'rb') as f: # Loading the model\n",
        "    fmodel = pickle.load(f) # fmodel is the loaded model"
      ],
      "metadata": {
        "id": "Z_YV7cTnf9hB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fmodel.predict(X_test) # Running prediction on test set using the loaded model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Obk1IlyYf_qe",
        "outputId": "3ddd5c67-455c-4669-e63e-b2c681bd1f67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
              "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,\n",
              "       0., 1., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.,\n",
              "       0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,\n",
              "       0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 0.,\n",
              "       0., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       1., 0., 0., 0., 1., 1., 0., 1., 1., 0., 0., 1., 0., 0., 1., 1., 1.,\n",
              "       1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 1., 1., 1., 1., 0.,\n",
              "       0.])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ignoring scikit learn warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore') "
      ],
      "metadata": {
        "id": "TPblZa4PkWuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Running prediction on a single patient data using the loaded model\n",
        "fmodel.predict([[48.0,70.0,1.005,4.0,0.0,0,1,1,0,117.0,56.0,3.8,111.0,2.5,11.2,32,6700,3.9,1,0,0.0,0,1,1]]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eooVPDnUegoL",
        "outputId": "c049dcc5-fb2b-4a19-8ed0-55e2f909a805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Running prediction on a single patient data using the loaded model\n",
        "fmodel.predict([[59.0,70.0,1.025,0.0,0.0,0,0,0,0,130.0,39.0,0.7,147.0,4.7,13.5,46,6700,4.5,0,0,0.0,1,0,0]]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmIpCHnVwvrG",
        "outputId": "fc73e07d-94f7-472f-ab4b-4e782914b48f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    }
  ]
}