Kidney_EDA.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Kidney EDA.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOMbQoOwKjsSXIESfX2EvrM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Devansharma/Health-App/blob/main/Kidney_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FibVOTfeaxjp"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIOU4j2Jbnsa"
      },
      "source": [
        "df = pd.read_csv(\"kidney.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "VPzGVNrKb5wm",
        "outputId": "6ae9c858-9fb1-48c3-db2e-1a6bb194fc7b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>rbc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pcc</th>\n",
              "      <th>ba</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
              "      <th>hemo</th>\n",
              "      <th>pcv</th>\n",
              "      <th>wc</th>\n",
              "      <th>rc</th>\n",
              "      <th>htn</th>\n",
              "      <th>dm</th>\n",
              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>notpresent</td>\n",
              "      <td>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.4</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>ckd</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\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>18.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.3</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>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\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>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.6</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>3</td>\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>56.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>11.2</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>4</td>\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>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.6</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",
              "</div>"
            ],
            "text/plain": [
              "   id   age    bp     sg   al   su  ...   dm cad appet   pe  ane  classification\n",
              "0   0  48.0  80.0  1.020  1.0  0.0  ...  yes  no  good   no   no             ckd\n",
              "1   1   7.0  50.0  1.020  4.0  0.0  ...   no  no  good   no   no             ckd\n",
              "2   2  62.0  80.0  1.010  2.0  3.0  ...  yes  no  poor   no  yes             ckd\n",
              "3   3  48.0  70.0  1.005  4.0  0.0  ...   no  no  poor  yes  yes             ckd\n",
              "4   4  51.0  80.0  1.010  2.0  0.0  ...   no  no  good   no   no             ckd\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_elZM7E1bwCc",
        "outputId": "dce0d1a3-9b5b-40e2-e2e9-84bd884e319f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xn38TKHrb3rJ",
        "outputId": "76d07906-9127-4d77-bd0e-49d50152113b"
      },
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rbc               152\n",
              "rc                130\n",
              "wc                105\n",
              "pot                88\n",
              "sod                87\n",
              "pcv                70\n",
              "pc                 65\n",
              "hemo               52\n",
              "su                 49\n",
              "sg                 47\n",
              "al                 46\n",
              "bgr                44\n",
              "bu                 19\n",
              "sc                 17\n",
              "bp                 12\n",
              "age                 9\n",
              "ba                  4\n",
              "pcc                 4\n",
              "htn                 2\n",
              "dm                  2\n",
              "cad                 2\n",
              "ane                 1\n",
              "appet               1\n",
              "pe                  1\n",
              "classification      0\n",
              "id                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCwW1o11edEd"
      },
      "source": [
        "cols_names={\"bp\":\"blood_pressure\",\n",
        "          \"sg\":\"specific_gravity\",\n",
        "          \"al\":\"albumin\",\n",
        "          \"su\":\"sugar\",\n",
        "          \"rbc\":\"red_blood_cells\",\n",
        "          \"pc\":\"pus_cell\",\n",
        "          \"pcc\":\"pus_cell_clumps\",\n",
        "          \"ba\":\"bacteria\",\n",
        "          \"bgr\":\"blood_glucose_random\",\n",
        "          \"bu\":\"blood_urea\",\n",
        "          \"sc\":\"serum_creatinine\",\n",
        "          \"sod\":\"sodium\",\n",
        "          \"pot\":\"potassium\",\n",
        "          \"hemo\":\"haemoglobin\",\n",
        "          \"pcv\":\"packed_cell_volume\",\n",
        "          \"wc\":\"white_blood_cell_count\",\n",
        "          \"rc\":\"red_blood_cell_count\",\n",
        "          \"htn\":\"hypertension\",\n",
        "          \"dm\":\"diabetes_mellitus\",\n",
        "          \"cad\":\"coronary_artery_disease\",\n",
        "          \"appet\":\"appetite\",\n",
        "          \"pe\":\"pedal_edema\",\n",
        "          \"ane\":\"anemia\"}\n",
        "\n",
        "df.rename(columns=cols_names, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmtFq9jDd4em"
      },
      "source": [
        "numerical_features = []\n",
        "categorical_features = []\n",
        "\n",
        "for i in df.columns:\n",
        "    if df[i].nunique()>7:\n",
        "        numerical_features.append(i)\n",
        "    else:\n",
        "        categorical_features.append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Gi9kuE4eOo7",
        "outputId": "f6d2b0d7-1e96-4253-e46e-a61fd1772d88"
      },
      "source": [
        "for feats in categorical_features:\n",
        "    print(f'{feats} has {df[feats].unique()} categories.\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "specific_gravity has [1.02  1.01  1.005 1.015   nan 1.025] categories.\n",
            "\n",
            "albumin has [ 1.  4.  2.  3.  0. nan  5.] categories.\n",
            "\n",
            "sugar has [ 0.  3.  4.  1. nan  2.  5.] categories.\n",
            "\n",
            "red_blood_cells has [nan 'normal' 'abnormal'] categories.\n",
            "\n",
            "pus_cell has ['normal' 'abnormal' nan] categories.\n",
            "\n",
            "pus_cell_clumps has ['notpresent' 'present' nan] categories.\n",
            "\n",
            "bacteria has ['notpresent' 'present' nan] categories.\n",
            "\n",
            "hypertension has ['yes' 'no' nan] categories.\n",
            "\n",
            "diabetes_mellitus has ['yes' 'no' ' yes' '\\tno' '\\tyes' nan] categories.\n",
            "\n",
            "coronary_artery_disease has ['no' 'yes' '\\tno' nan] categories.\n",
            "\n",
            "appetite has ['good' 'poor' nan] categories.\n",
            "\n",
            "pedal_edema has ['no' 'yes' nan] categories.\n",
            "\n",
            "anemia has ['no' 'yes' nan] categories.\n",
            "\n",
            "classification has ['ckd' 'ckd\\t' 'notckd'] categories.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkUMQANXeP2S"
      },
      "source": [
        "df['diabetes_mellitus'] = df['diabetes_mellitus'].replace(to_replace = {'no':'no','yes':'yes',' yes':'yes'})\n",
        "df['coronary_artery_disease'] = df['coronary_artery_disease'].replace(to_replace = 'no', value='no')\n",
        "df['classification'] = df['classification'].replace(to_replace = 'ckd', value = 'ckd')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wmyzRkTeoFT",
        "outputId": "5dd15368-0e61-45e0-9edc-19cc59109535"
      },
      "source": [
        "for feats in categorical_features:\n",
        "    print(f'{feats} has {df[feats].unique()} categories.\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "specific_gravity has [1.02  1.01  1.005 1.015   nan 1.025] categories.\n",
            "\n",
            "albumin has [ 1.  4.  2.  3.  0. nan  5.] categories.\n",
            "\n",
            "sugar has [ 0.  3.  4.  1. nan  2.  5.] categories.\n",
            "\n",
            "red_blood_cells has [nan 'normal' 'abnormal'] categories.\n",
            "\n",
            "pus_cell has ['normal' 'abnormal' nan] categories.\n",
            "\n",
            "pus_cell_clumps has ['notpresent' 'present' nan] categories.\n",
            "\n",
            "bacteria has ['notpresent' 'present' nan] categories.\n",
            "\n",
            "hypertension has ['yes' 'no' nan] categories.\n",
            "\n",
            "diabetes_mellitus has ['yes' 'no' nan] categories.\n",
            "\n",
            "coronary_artery_disease has ['no' 'yes' nan] categories.\n",
            "\n",
            "appetite has ['good' 'poor' nan] categories.\n",
            "\n",
            "pedal_edema has ['no' 'yes' nan] categories.\n",
            "\n",
            "anemia has ['no' 'yes' nan] categories.\n",
            "\n",
            "classification has ['ckd' 'notckd'] categories.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LD1k_QdqdEH7",
        "outputId": "cf8dd175-5a37-4f03-c079-d7db4ce41669"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15,15))\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "fig.suptitle('Distributions of numerical Features')\n",
        "\n",
        "\n",
        "for ax, feats in zip(axes.flatten(), numerical_features):\n",
        "    sns.distplot(a=df[feats], ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-291e42095285>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeats\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumerical_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[0;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax, x)\u001b[0m\n\u001b[1;32m   2566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2567\u001b[0m     \u001b[0;31m# Make a a 1-d float array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2568\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2569\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2570\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    795\u001b[0m               dtype='datetime64[ns]')\n\u001b[1;32m    796\u001b[0m         \"\"\"\n\u001b[0;32m--> 797\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/arrays/numpy_.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0m_HANDLED_TYPES\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '\\t?'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UeoInN_xd1r_",
        "outputId": "4adb0272-ca18-45fd-d124-2c65a4c57d5d"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15,15))\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "fig.suptitle('Distributions of categorical Features')\n",
        "\n",
        "\n",
        "for ax, feats in zip(axes.flatten(), categorical_features):\n",
        "    sns.countplot(df[feats], ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLxoQhMjlME2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "56ce5a1e-980c-4c35-d3cd-f22eedab6c9d"
      },
      "source": [
        "sns.countplot(x='classification',data=df)\n",
        "plt.xlabel(\"classification\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"target Class\")\n",
        "plt.show()\n",
        "print('Percent of chronic kidney disease sample: ',round(len(df[df['classification']=='ckd'])/len(df['classification'])*100,2),\"%\")\n",
        "print('Percent of not a chronic kidney disease sample: ',round(len(df[df['classification']=='notckd'])/len(df['classification'])*100,2),\"%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Percent of chronic kidney disease sample:  62.5 %\n",
            "Percent of not a chronic kidney disease sample:  37.5 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "WfP1xwnwghrc",
        "outputId": "835ddfc1-60f1-4e00-da0b-6ea648bb44a0"
      },
      "source": [
        "corr_df = df.corr()\n",
        "f,ax=plt.subplots(figsize=(15,15))\n",
        "mask = np.zeros_like(corr_df)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "sns.heatmap(corr_df,annot=True,fmt=\".2f\",ax=ax,linewidths=0.5,linecolor=\"orange\", mask = mask, square=True)\n",
        "plt.xticks(rotation=45)\n",
        "plt.yticks(rotation=45)\n",
        "plt.title('Correlations between different features')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qd7XcAMlhVqC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}