{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Kidney Condition Detection"
      ],
      "metadata": {
        "id": "mWqGedZ2IHlm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "L96SNQ8HVI7m"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using drive to load our dataset\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ea3adROCVORJ",
        "outputId": "9fcec8fc-24df-4307-8efe-ad0bf7967226"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/dataset/kidney_disease.csv\")\n",
        "del df['id'],df['sg'],df['al'],df['su'],df['rbc'],df['pc'],df['pcc'],df['pcv'],df['ba'],df['sc'],df['dm'],df['cad'],df['pe']\n",
        "df"
      ],
      "metadata": {
        "id": "puQFhXRM_inf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "8f6b8886-f61d-4b10-8eea-e1a38ecf8cc4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age    bp    bgr    bu    sod  pot  hemo    wc   rc  htn appet  ane  \\\n",
              "0    48.0  80.0  121.0  36.0    NaN  NaN  15.4  7800  5.2  yes  good   no   \n",
              "1     7.0  50.0    NaN  18.0    NaN  NaN  11.3  6000  NaN   no  good   no   \n",
              "2    62.0  80.0  423.0  53.0    NaN  NaN   9.6  7500  NaN   no  poor  yes   \n",
              "3    48.0  70.0  117.0  56.0  111.0  2.5  11.2  6700  3.9  yes  poor  yes   \n",
              "4    51.0  80.0  106.0  26.0    NaN  NaN  11.6  7300  4.6   no  good   no   \n",
              "..    ...   ...    ...   ...    ...  ...   ...   ...  ...  ...   ...  ...   \n",
              "395  55.0  80.0  140.0  49.0  150.0  4.9  15.7  6700  4.9   no  good   no   \n",
              "396  42.0  70.0   75.0  31.0  141.0  3.5  16.5  7800  6.2   no  good   no   \n",
              "397  12.0  80.0  100.0  26.0  137.0  4.4  15.8  6600  5.4   no  good   no   \n",
              "398  17.0  60.0  114.0  50.0  135.0  4.9  14.2  7200  5.9   no  good   no   \n",
              "399  58.0  80.0  131.0  18.0  141.0  3.5  15.8  6800  6.1   no  good   no   \n",
              "\n",
              "    classification  \n",
              "0              ckd  \n",
              "1              ckd  \n",
              "2              ckd  \n",
              "3              ckd  \n",
              "4              ckd  \n",
              "..             ...  \n",
              "395         notckd  \n",
              "396         notckd  \n",
              "397         notckd  \n",
              "398         notckd  \n",
              "399         notckd  \n",
              "\n",
              "[400 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cb82063-36b1-440a-b39d-b75134a51920\" 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>bp</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
              "      <th>hemo</th>\n",
              "      <th>wc</th>\n",
              "      <th>rc</th>\n",
              "      <th>htn</th>\n",
              "      <th>appet</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</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>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.4</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>good</td>\n",
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              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.3</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>good</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>423.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.6</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</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>117.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>poor</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>106.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.6</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>55.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>15.7</td>\n",
              "      <td>6700</td>\n",
              "      <td>4.9</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>42.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>16.5</td>\n",
              "      <td>7800</td>\n",
              "      <td>6.2</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>12.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>15.8</td>\n",
              "      <td>6600</td>\n",
              "      <td>5.4</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>14.2</td>\n",
              "      <td>7200</td>\n",
              "      <td>5.9</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>58.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>15.8</td>\n",
              "      <td>6800</td>\n",
              "      <td>6.1</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>notckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 13 columns</p>\n",
              "</div>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "      .colab-df-generate {\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.16971408926224,\n        \"min\": 2.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          60.0,\n          64.0,\n          63.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.68363749352526,\n        \"min\": 50.0,\n        \"max\": 180.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          180.0,\n          50.0,\n          60.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bgr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.28171423511773,\n        \"min\": 22.0,\n        \"max\": 490.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          146.0,\n          273.0,\n          140.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.50300584922251,\n        \"min\": 1.5,\n        \"max\": 391.0,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          48.0,\n          76.0,\n          26.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sod\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.40875205179878,\n        \"min\": 4.5,\n        \"max\": 163.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          133.0,\n          137.0,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.193904176556695,\n        \"min\": 2.5,\n        \"max\": 47.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          6.6,\n          5.9,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hemo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9125866088267642,\n        \"min\": 3.1,\n        \"max\": 17.8,\n        \"num_unique_values\": 115,\n        \"samples\": [\n          6.3,\n          11.6,\n          4.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 92,\n        \"samples\": [\n          \"12500\",\n          \"6400\",\n          \"5000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"3.6\",\n          \"6.4\",\n          \"5.9\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"htn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"appet\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"poor\",\n          \"good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ane\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"classification\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ckd\",\n          \"ckd\\t\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['htn']=(df['htn']=='yes').astype(int)\n",
        "df['appet']=(df['appet']=='good').astype(int)\n",
        "df['ane']=(df['ane']=='yes').astype(int)\n",
        "df['classification']=(df['classification']=='ckd').astype(int)\n"
      ],
      "metadata": {
        "id": "Rf9xpgMNEG3y"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.fillna(0)"
      ],
      "metadata": {
        "id": "V5Eelp9zFbUV"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop([df.index[76],df.index[133],df.index[162],df.index[185]])"
      ],
      "metadata": {
        "id": "u96jEKrBHH0G"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
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          "height": 423
        },
        "id": "B4SayP5qHgwp",
        "outputId": "8b5af7c5-3834-4bdc-b693-1e7e8f2cb8d9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age    bp    bgr    bu    sod  pot  hemo    wc   rc  htn  appet  ane  \\\n",
              "0    48.0  80.0  121.0  36.0    0.0  0.0  15.4  7800  5.2    1      1    0   \n",
              "1     7.0  50.0    0.0  18.0    0.0  0.0  11.3  6000    0    0      1    0   \n",
              "2    62.0  80.0  423.0  53.0    0.0  0.0   9.6  7500    0    0      0    1   \n",
              "3    48.0  70.0  117.0  56.0  111.0  2.5  11.2  6700  3.9    1      0    1   \n",
              "4    51.0  80.0  106.0  26.0    0.0  0.0  11.6  7300  4.6    0      1    0   \n",
              "..    ...   ...    ...   ...    ...  ...   ...   ...  ...  ...    ...  ...   \n",
              "395  55.0  80.0  140.0  49.0  150.0  4.9  15.7  6700  4.9    0      1    0   \n",
              "396  42.0  70.0   75.0  31.0  141.0  3.5  16.5  7800  6.2    0      1    0   \n",
              "397  12.0  80.0  100.0  26.0  137.0  4.4  15.8  6600  5.4    0      1    0   \n",
              "398  17.0  60.0  114.0  50.0  135.0  4.9  14.2  7200  5.9    0      1    0   \n",
              "399  58.0  80.0  131.0  18.0  141.0  3.5  15.8  6800  6.1    0      1    0   \n",
              "\n",
              "     classification  \n",
              "0                 1  \n",
              "1                 1  \n",
              "2                 1  \n",
              "3                 1  \n",
              "4                 1  \n",
              "..              ...  \n",
              "395               0  \n",
              "396               0  \n",
              "397               0  \n",
              "398               0  \n",
              "399               0  \n",
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              "[392 rows x 13 columns]"
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              "          '<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",
              "\n",
              "\n",
              "<div id=\"df-9a845159-dbce-42c0-a7dc-131b4df84b96\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a845159-dbce-42c0-a7dc-131b4df84b96')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9a845159-dbce-42c0-a7dc-131b4df84b96 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_096f24ae-3154-420d-9754-9770c20dffda\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_096f24ae-3154-420d-9754-9770c20dffda button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 392,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.589049704965028,\n        \"min\": 0.0,\n        \"max\": 90.0,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          60.0,\n          26.0,\n          63.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.513424355349287,\n        \"min\": 0.0,\n        \"max\": 180.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          100.0,\n          80.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bgr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88.27078573562652,\n        \"min\": 0.0,\n        \"max\": 490.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          150.0,\n          424.0,\n          159.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.86006843947563,\n        \"min\": 0.0,\n        \"max\": 391.0,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          85.0,\n          42.0,\n          26.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sod\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.76009035147553,\n        \"min\": 0.0,\n        \"max\": 163.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          122.0,\n          129.0,\n          146.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4365711690381584,\n        \"min\": 0.0,\n        \"max\": 47.0,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          3.6,\n          3.8,\n          6.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hemo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.034697701579665,\n        \"min\": 0.0,\n        \"max\": 17.8,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          3.1,\n          11.6,\n          9.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"5600\",\n          \"7900\",\n          \"16300\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 48,\n        \"samples\": [\n          \"5.6\",\n          \"5.1\",\n          \"2.1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"htn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"appet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ane\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"classification\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = df.drop(['classification'], axis = 1)\n",
        "y = df.classification.values"
      ],
      "metadata": {
        "id": "jvdxSOtN35up"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y, test_size = 0.2, random_state= 0)"
      ],
      "metadata": {
        "id": "dHaFMd8A94Ks"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(n_estimators = 1000, random_state= 1)\n",
        "rf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JEFcVUBLW9Pi",
        "outputId": "dbf5c9f4-d229-4184-abe6-ffa193fb6f85"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=1000, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=1000, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=1000, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=rf.predict(x_test)"
      ],
      "metadata": {
        "id": "M66dC8FOXNEt"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_pred,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L06DnXKhXPzS",
        "outputId": "b454914f-414f-407b-caa7-5599ab136d5a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.92      0.86        26\n",
            "           1       0.96      0.89      0.92        53\n",
            "\n",
            "    accuracy                           0.90        79\n",
            "   macro avg       0.88      0.90      0.89        79\n",
            "weighted avg       0.91      0.90      0.90        79\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('kcd.pkl','wb') as f:\n",
        "    pickle.dump(rf,f)\n",
        "\n",
        "# load\n",
        "with open('kcd.pkl', 'rb') as f:\n",
        "    rf = pickle.load(f)\n",
        "#rf.predict()"
      ],
      "metadata": {
        "id": "4IrkPQCLXhYw"
      },
      "execution_count": 47,
      "outputs": []
    }
  ]
}