{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task to predict whether person has ckd or notckd??\n",
    "\n",
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
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     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df = pd.read_csv(r'C:/Users/ganes/projects/chronicproj/djangoMLDeployment/src/lung.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 24)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Features:\n",
    "    age - age\n",
    "    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",
    "    classification - class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### rename column names to make it more user-friendly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''columns=pd.read_csv('C:/Users/ganes/projects/chronic/drive-download-20230312T132507Z-001/data_description.txt',sep='-')\n",
    "columns=columns.reset_index()\n",
    "columns.columns=['cols','abb_col_names']'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "0   33       1              2            4             5   \n",
       "1   17       1              3            1             5   \n",
       "2   35       1              4            5             6   \n",
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       "4   46       1              6            8             7   \n",
       "\n",
       "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
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       "   Swal0ing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
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       "2                    1                         4              6          7   \n",
       "3                    4                         5              6          7   \n",
       "4                    4                         2              4          2   \n",
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       "3        5      2  \n",
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     "execution_count": 12,
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.columns=columns['abb_col_names'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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>995</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "0     33       1              2            4             5   \n",
       "1     17       1              3            1             5   \n",
       "2     35       1              4            5             6   \n",
       "3     37       1              7            7             7   \n",
       "4     46       1              6            8             7   \n",
       "..   ...     ...            ...          ...           ...   \n",
       "995   44       1              6            7             7   \n",
       "996   37       2              6            8             7   \n",
       "997   25       2              4            5             6   \n",
       "998   18       2              6            8             7   \n",
       "999   47       1              6            5             6   \n",
       "\n",
       "     OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
       "0                       4             3                     2              2   \n",
       "1                       3             4                     2              2   \n",
       "2                       5             5                     4              6   \n",
       "3                       7             6                     7              7   \n",
       "4                       7             7                     6              7   \n",
       "..                    ...           ...                   ...            ...   \n",
       "995                     7             7                     6              7   \n",
       "996                     7             7                     6              7   \n",
       "997                     5             5                     4              6   \n",
       "998                     7             7                     6              7   \n",
       "999                     5             5                     4              6   \n",
       "\n",
       "     Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
       "0          4  ...        3            4                    2         2   \n",
       "1          2  ...        1            3                    7         8   \n",
       "2          7  ...        8            7                    9         2   \n",
       "3          7  ...        4            2                    3         1   \n",
       "4          7  ...        3            2                    4         1   \n",
       "..       ...  ...      ...          ...                  ...       ...   \n",
       "995        7  ...        5            3                    2         7   \n",
       "996        7  ...        9            6                    5         7   \n",
       "997        7  ...        8            7                    9         2   \n",
       "998        7  ...        3            2                    4         1   \n",
       "999        7  ...        8            7                    9         2   \n",
       "\n",
       "     Swal0ing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
       "0                      3                         1              2          3   \n",
       "1                      6                         2              1          7   \n",
       "2                      1                         4              6          7   \n",
       "3                      4                         5              6          7   \n",
       "4                      4                         2              4          2   \n",
       "..                   ...                       ...            ...        ...   \n",
       "995                    8                         2              4          5   \n",
       "996                    2                         4              3          1   \n",
       "997                    1                         4              6          7   \n",
       "998                    4                         2              4          2   \n",
       "999                    1                         4              6          7   \n",
       "\n",
       "     Snoring  Level  \n",
       "0          4      0  \n",
       "1          2      1  \n",
       "2          2      2  \n",
       "3          5      2  \n",
       "4          3      2  \n",
       "..       ...    ...  \n",
       "995        3      2  \n",
       "996        4      2  \n",
       "997        2      2  \n",
       "998        3      2  \n",
       "999        2      2  \n",
       "\n",
       "[1000 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         int64\n",
       "Gender                      int64\n",
       "Air Pollution               int64\n",
       "Alcohol use                 int64\n",
       "Dust Allergy                int64\n",
       "OccuPational Hazards        int64\n",
       "Genetic Risk                int64\n",
       "chronic Lung Disease        int64\n",
       "Balanced Diet               int64\n",
       "Obesity                     int64\n",
       "Smoking                     int64\n",
       "Passive Smoker              int64\n",
       "Chest Pain                  int64\n",
       "Coughing of Blood           int64\n",
       "Fatigue                     int64\n",
       "Weight Loss                 int64\n",
       "Shortness of Breath         int64\n",
       "Wheezing                    int64\n",
       "Swal0ing Difficulty         int64\n",
       "Clubbing of Finger Nails    int64\n",
       "Frequent Cold               int64\n",
       "Dry Cough                   int64\n",
       "Snoring                     int64\n",
       "Level                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    As it can be seen, red_blood_cell_count, packed_cell_volume and white_blood_cell_count are object type. We need to \n",
    "    change to numerical dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features=['red blood cell count','packed cell volume','white blood cell count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"def convert_dtype(df,feature):\\n    df[feature] = pd.to_numeric(df[feature], errors='coerce')\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def convert_dtype(df,feature):\n",
    "    df[feature] = pd.to_numeric(df[feature], errors='coerce')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'for feature in features:\\n    convert_dtype(df,feature)'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''for feature in features:\n",
    "    convert_dtype(df,feature)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         int64\n",
       "Gender                      int64\n",
       "Air Pollution               int64\n",
       "Alcohol use                 int64\n",
       "Dust Allergy                int64\n",
       "OccuPational Hazards        int64\n",
       "Genetic Risk                int64\n",
       "chronic Lung Disease        int64\n",
       "Balanced Diet               int64\n",
       "Obesity                     int64\n",
       "Smoking                     int64\n",
       "Passive Smoker              int64\n",
       "Chest Pain                  int64\n",
       "Coughing of Blood           int64\n",
       "Fatigue                     int64\n",
       "Weight Loss                 int64\n",
       "Shortness of Breath         int64\n",
       "Wheezing                    int64\n",
       "Swal0ing Difficulty         int64\n",
       "Clubbing of Finger Nails    int64\n",
       "Frequent Cold               int64\n",
       "Dry Cough                   int64\n",
       "Snoring                     int64\n",
       "Level                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Let's drop the id column. id column is seems to be an unique identifier for each row so we are dropping that it won't \n",
    "    help us to find any insights from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop([\"id\"],axis=1,inplace=True) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Numerical & Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_cat_num(df):\n",
    "    cat_col=[col for col in df.columns if df[col].dtype=='object']\n",
    "    num_col=[col for col in df.columns if df[col].dtype!='object']\n",
    "    return cat_col,num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col,num_col=extract_cat_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Gender',\n",
       " 'Air Pollution',\n",
       " 'Alcohol use',\n",
       " 'Dust Allergy',\n",
       " 'OccuPational Hazards',\n",
       " 'Genetic Risk',\n",
       " 'chronic Lung Disease',\n",
       " 'Balanced Diet',\n",
       " 'Obesity',\n",
       " 'Smoking',\n",
       " 'Passive Smoker',\n",
       " 'Chest Pain',\n",
       " 'Coughing of Blood',\n",
       " 'Fatigue',\n",
       " 'Weight Loss',\n",
       " 'Shortness of Breath',\n",
       " 'Wheezing',\n",
       " 'Swal0ing Difficulty',\n",
       " 'Clubbing of Finger Nails',\n",
       " 'Frequent Cold',\n",
       " 'Dry Cough',\n",
       " 'Snoring',\n",
       " 'Level']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "### total unique categories in our categorical features to check if any dirtiness in data or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} values '.format(col,df[col].unique()))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    So we need to correct 2 features and the target variable which contain certain discrepancy in some values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndf['diabetes mellitus'].replace(to_replace = {'\\tno':'no','\\tyes':'yes',' yes':'yes'},inplace=True)\\n\\ndf['coronary artery disease'] = df['coronary artery disease'].replace(to_replace = '\\tno', value='no')\\n\\ndf['class'] = df['class'].replace(to_replace = 'ckd\\t', value = 'ckd')\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace incorrect values\n",
    "'''\n",
    "df['diabetes mellitus'].replace(to_replace = {'\\tno':'no','\\tyes':'yes',' yes':'yes'},inplace=True)\n",
    "\n",
    "df['coronary artery disease'] = df['coronary artery disease'].replace(to_replace = '\\tno', value='no')\n",
    "\n",
    "df['class'] = df['class'].replace(to_replace = 'ckd\\t', value = 'ckd')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "##df['diabetes mellitus'] = [float(str(i).replace(\",\", \"\")) for i in df['diabetes mellitus']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} values  '.format(col, df[col].unique()))\n",
    "    print('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Looks good now apart from the NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Checking features distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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qyWw264477tCrr74qwzDs6plMJg0ZMkQrVqxQw4YNbWse69ats6v3yy+/6Omnn9Ydd9whX19fVapUSY899tgN3db8RiUmJqpDhw4KDAyU2WzWe++9p8cff9xuTaVRo0Z2t3u//O/ydaJXX31Vd955pypVqiRfX181b95cS5cuzddmXv8kJSWpQYMGMpvNtr7Zv3+/OnToIF9fX4WGhmrKlCnKzc3Nt4/du3crOjpat912m3x9fRUeHq4BAwY4rJ+K0/XWRFC8rlzPQPF44YUX1KNHD9WtW1deXl5q1qyZhg8frl69ejk7tALd2DdmcBvx8fHat28f96N1QXfccYf27NmjU6dOaenSpYqLi9OWLVucHRb+f4sWLVJOTo4++ugjnTp1Su+8847KlSt33edduHBBH3zwgXJzczVo0CDVrVtXv//+u5YuXapz587J29tbGRkZuvPOO3Xu3Dk988wzqlSpkhYtWqSHHnpIS5cu1SOPPGK3z5deekkeHh567rnndOrUKc2YMUO9evXSjh07bHUWLlyo/v37q2XLlpo+fboyMjL0xhtv6Msvv9TXX3+dL9mhuLz77rt65pln9Oijj2rYsGE6f/68vv32W+3YsUOPP/64JCkjI0Nt2rSxfVCuXLmy1q5dq4EDByorK0vDhw+/4fYvz2hu3LixWrdurbCwMP3nP/+Rr6/vzR4eikFubq5atGihadOmSZKaNWumffv2af78+YqLi3NydCjIggUL1KlTJ4WEhDg7FADAdSQnJ2vixIn65JNPFBgY6OxwUMxycnL0+OOPa+LEibr99ttval99+vTRiy++qA0bNuiJJ54o0nNTUlLUs2dP3XfffXr55ZclSd99952+/PJLDRs2TPfcc4+eeeYZvfnmm3rxxRdVr149SbL992qys7P18ccf69lnn5Uk9ezZU/3791d6erqCg4OLfIxTp07V2LFj1a1bN/3zn//UH3/8odmzZ+uee+7JNyc6ceKEOnXqpB49eqh3794KCgpSbm6uOnfurJ07d2rw4MGqW7euPvnkk3yfWR999FHFx8crKSlJzZo1s9uWlJSk9u3bq2rVqkWOX7r02dlkMikpKUn+/v6SpNdee02PPvqo5s6dyxznFnTgwAENGzZM48aNU3R0tI4dO6aRI0fqqaee0oIFC5wdHgqJ718BuCLemxznams7rp44tWrVKtWoUUN33313gdvvuece1ahRw+7H3UePHlWrVq2UmZl51fWOM2fO6O6779Z3332nAQMG6B//+If+/PNPrVy5Ur/99ptuu+22Qsf466+/aufOnTKbzfLx8bHb9tNPP0mSKlWqpJ9//lkrVqzQY489pvDwcGVkZOjtt99Wu3btdODAAYWEhKhevXqaNGmSxo0bp0GDBtmO+8477yywbcMw9NBDD+nzzz/XwIED1bRpU61fv14jR47U77//rtdff92u/hdffKFly5bp6aefVvny5fXmm28qNjZWR44cUaVKlSRJu3bt0rZt29SjRw+Fhobq8OHDmjdvntq3b68DBw7Iz8+v0H1zuVOnTtkS0i535d1RJGnevHlq0KCBHnroIZUpU0arVq3Sv//9bzVt2lTx8fFq3bq1QkNDNXDgQLsf1aSlpWnWrFl23zW88cYbeuihh9SrVy9duHBBixcv1mOPPabVq1crJibGrt3PPvtM//nPfzRkyBDddtttqlGjhtLT03Xvvffq4sWLeuGFF1S2bFm98847+eY5x48fV1RUlCpXrqwXXnhBFSpU0OHDh512ZeKiYk2kZLGe4Rj/+c9/lJSUpOTkZDVo0EB79uzR8OHDFRIS4prnsZNvDwgXFh8fb4SGhho///yzXfmmTZsMScZff/1lV169enXjtddeK8EIcbn77rvPGDRoEOPjAnbv3m1IMiQZnp6ehoeHh+2xyWQyPD09jY0bN9rKxo8fb3tu2bJlDZPJVOA9pfPupTt8+HBDkvHf//7Xtu306dNGeHi4UaNGDSMnJ8cwjP93H+t69eoZ2dnZtrpvvPGGIcnYu3evYRiX7r0dGBhoNGzY0Pj7779t9VavXm1IMsaNG2crGz9+vFGYfzrCwsKMuLi4fOXt2rUz2rVrZ3v88MMPX/ce2QMHDjSqVKli/Pnnn3blPXr0MPz9/Y1z585dN56iaNGihfHCCy/wWnIR1atXNwYOHGhXNnfuXCMkJMQwDMP46aefDEnG119/bVfnnnvuMZ555pmSChP/v8OHDxseHh7GihUrbGW8lgDnkWQsX778mnXuvvtuY9iwYXZl7733nmGxWBwXGBymMGOe56OPPjJ8fX2N1atXOzYoOMz1xvuvv/6yzUny/kwmk61s06ZNtrqJiYmGpALnIXn8/f2NZs2a2R5f+dk+T1xcnBEWFmZ7PGzYMMNisRgXL1686r6XLFliSDI+//zzq9a50tKlSw1Jxg8//GAYhmFkZWUZPj4+xuuvv25X79ChQ4YkIzEx0VZ25bzm8OHDhqenpzF16lS75+7du9coU6aMXXm7du0MScb8+fPt6n788ceGJGPWrFm2spycHKNDhw752u/Zs6cREhJim7sZhmF89dVX+epdrjCv7759+xq1atWyKztw4IAhyfjf//53zefC9RRmzHv37m08+uijdmX//e9/DUnG0aNHHRgdisvVvn8tSLVq1fK9x40bN85o3Lixg6IDXFdRPvei6Iry3oSbl7e248oyMzMNScbDDz98zXoPPfSQIcnIysoyDOPS51MPD49rrneMGzfOkGQsW7bsqnXy5iuHDh2y2563BpI3j1i+fLlt3cXDw8NubUaS7d/M8+fP230WN4xL8waz2WxMmjTJVrZr166rfka/ct6zYsUKQ5IxZcoUu3qPPvqoYTKZjB9//NFWJsnw9va2K/vmm28MScbs2bNtZQWtfaSmphqSjPfff/+q/XA1ef14rb8r12sKiiE6OtqoWbOm7XHemkqeP/74w6hevbrRqFEj48yZM1fd14ULF4yGDRsaHTp0sCvPG7/9+/fbleetje3YscNWdvz4ccPf39/u/Mg7D641v3Vl11sTQfEpaD0DxSM0NNR466237MomT55s3HHHHU6K6Nq4PR/yMQxDQ4YM0fLly/XZZ58pPDzcbnvz5s3l5eWlTZs22coOHjyoI0eOlIr7Lt+qcnNzlZ2dzfi4gKSkJAUGBmrPnj3as2ePvvnmG8XFxalMmTLq2bOn9uzZoxYtWsjLy8vued99953Onj2ru+++Wy1atMi337x76a5Zs0atWrXSXXfdZdtWrlw5DRo0SIcPH9aBAwfsnte/f3+7WwHm/SIi797iu3fv1vHjx/X000/b/foiJiZGdevWvept/4pDhQoV9Ntvv2nXrl0FbjcMQx9//LE6d+4swzD0559/2v6io6N16tQpffXVV8UWz5kzZ/TTTz+pSpUqvJZcRNu2bXXw4EG7sv/9738KCwuTJIWHhys4ONhunLKysrRjxw7GyQkSExMVGBho98sgXkuAa4uIiLB7fUqXrgrD6/PW9tFHH6l///766KOP8v2aE7cOi8WivXv32uYle/bs0VNPPWX7ZXvr1q2LtL9y5crp9OnTRY6jQoUKOnv2rFJSUor83GtJSkpSixYtVLt2bUlS+fLlFRMTc0O36Fu2bJlyc3PVrVs3uzlHcHCw6tSpo88//9yuvtlsVv/+/e3K1q1bJy8vL7srcXl4eCg+Pj5fe3379tXRo0ft9puUlCRfX1/FxsYWOf48bdu21dGjR3XmzBlb2f/+9z95eHgoNDT0hvcL13Xu3Dl5eNh/vevp6SlJ+W4DA9dyve9fC8LnNgCOdiPvTbh5eWs7rixvHlC+fPlr1svbnpWVpdzcXK1YsUKdO3e+5nrHxx9/rCZNmuS7g8bldQrrvvvu00MPPSTpUr9eftu2pk2bavny5ZIufZ7P+wyVk5OjEydOqFy5crrjjjtueL1hzZo18vT01DPPPGNX/uyzz8owDK1du9auPDIyUrVq1bI9bty4sSwWi23dRpLdFZSsVqtOnDih2rVrq0KFCje1LjJnzhylpKTk+2vcuHG+upfHkHeFqnbt2unnn3/WqVOn7NZUpEv92bNnT50+fVrLly9X2bJlC9zXX3/9pVOnTunuu+8u8FjatWuX7+pra9asUZs2bdSqVStbWeXKlfPd7ivvKsGrV68u8OpZru56ayIoPgWtZ6B4XG2uWtDtNF0Bt+dDPvHx8UpOTtYnn3yi8uXLKz09XZLk7+8vX19f+fv7a+DAgUpISFBAQIAsFouGDh2qiIgItWnTxsnRu4fRo0erU6dOql69uk6fPq3k5GRt3rxZ69evZ3ycLCcnR4sXL1aHDh3sPgzGxMRo0aJFOnfunO0e8AMHDtT8+fN16NAhpaWl6cknn5Skq17eNs8vv/xS4AJH3u0rfvnlF7v7zFevXt2uXsWKFSVd+lCaV1+6dFngK9WtW9ehl2AeNWqUNm7cqFatWql27dqKiorS448/rrZt20qS/vjjD2VmZuqdd97RO++8U+A+jh8/fsPtP/fcc+rcubPCwsJ09OhRjR8/Xp6enurZsyevJRcxYsQI3XnnnZo2bZq6deumnTt32p0PJpNJw4cP15QpU1SnTh2Fh4dr7NixCgkJUZcuXZwbvJvJzc1VYmKiLUk0D68loGSdOXNGP/74o+3xoUOHtGfPHgUEBKh69eoaPXq0fv/9d73//vuSpKeeekpvvfWWnn/+eQ0YMMB2+XNHJk2jeBV1zJOTkxUXF6c33nhDrVu3ts338uZ6cG1FGW8PDw+7eYEkBQYGysfHJ195Ydu+kds4Pv300/rPf/6jTp06qWrVqoqKilK3bt10//33F3lfeTIzM7VmzRoNGTLErj/atm2rjz/+WP/73/+KdEvCH374QYZhqE6dOgVuv/IHL1WrVrX7YYp0aV5VpUqVfLfIyEvqulzHjh1VpUoVJSUl6b777lNubq4++ugjPfzww3YLUUV9fT/++OOaPHmy+vfvr4kTJ+rPP//UyJEjNWDAAG7NV0oUdcw7d+6sJ554QvPmzbPdnm/48OFq1aoVt5dwcdf7/lW6lGBZtWpVTZ8+XZI0bNgwtWvXTjNnzlRMTIwWL16s3bt3X/X7EuBWc733SNy8wrw34eZca23HleV9Rr3ejyguT676448/lJWVdd25x08//XRTPxy4Ms6KFSvKx8dHq1atknRpDaB58+Z2ty7Ozc3VG2+8oblz5+rQoUPKycmxbcu7NV5R/fLLLwoJCcmXWHb5us3lCnrfqlixom3dRpL+/vtvTZ8+XYmJifr999/tkuJPnTp1Q3FKUqtWrQpMZKtYsWK+2/Z9+eWXGj9+vFJTU3Xu3Dm7bZs2bdK8efNsayqSNGbMGH322Wf69NNP7ZLCpEtJTFOmTNGePXvsEgULSo4rKGnzamtjV65ttWvXTrGxsZo4caJef/11tW/fXl26dNHjjz8us9mc7/mu5nprIigeV1vPQPHo3Lmzpk6dqurVq6tBgwb6+uuv9dprr2nAgAHODq1AnAHIZ968eZJkd+9Z6VK2Zb9+/SRJr7/+ujw8PBQbG6vs7GxFR0dr7ty5JRyp+zp+/Lj69u2rY8eOyd/fX40bN9b69evVsWNHSYyPM3322Wc6duyYFi9erMWLF+fbfnl2+Ouvv6758+frP//5j5YuXap27do5JKa8X5leyZG/Or3aL0BycnLs4qlXr54OHjyo1atXa926dfr44481d+5cjRs3ThMnTrRlHPfu3fuq97gt6NcPhfXbb7+pZ8+eOnHihCpXrqy77rpL27dvV+XKlSXxWnIFLVu21PLlyzV69GhNmjRJ4eHhmjVrlt2vR55//nmdPXtWgwYNUmZmpu666y6tW7cu333r4VgbN27UkSNHCvzQy2sJKDm7d+/Wvffea3uckJAgSYqLi9PChQt17NgxHTlyxLY9PDxcn376qUaMGKE33nhDoaGh+ve//63o6OgSjx03pqhj/s477+jixYuKj4+3uwJOXn24tqKOd3H57bffdOrUKbsEIJPJVOCc4vIFB0m2q/CuX79ea9eu1dq1a5WYmKi+fftq0aJFNxTPkiVLlJ2drZkzZ2rmzJn5ticlJWnixImF3l9ubq5MJpPWrl1b4PypXLlydo9vdtHQ09NTjz/+uN59913NnTtXX375pY4eParevXvb1SvqeJcrV04pKSkaOnSoWrRooUqVKqlbt26aMmXKTcWLklPUMe/Xr59Onz6tt956S88++6wqVKigDh066OWXXy7x2FE0hfn+9ciRI3a/zr7zzjuVnJysMWPG6MUXX1SdOnW0YsWKG0qEBUqj671H4uYV5r0JN+d6azuuyt/fX1WqVNG33357zXrffvutqlatKovFor///rvY2r/WekNBPD09FRkZKenSVYeuTGSaNm2axo4dqwEDBmjy5MkKCAiQh4eHhg8fXmJXQSnMus3QoUOVmJio4cOHKyIiQv7+/jKZTOrRo0eJxPnTTz/pvvvuU926dfXaa6+pWrVqmjFjhnbv3q2zZ8/q6aefVvv27W1rKitWrNDLL7+syZMn5/uRzH//+1899NBDuueeezR37lxVqVJFXl5eSkxMVHJycr62b2bOZTKZtHTpUm3fvl2rVq3S+vXrNWDAAM2cOVPbt2/PN79zNYVZE8HNu9Z6Bm7e7NmzNXbsWD399NM6fvy4QkJC9OSTT2rcuHHODq1AJE0hn8IkUvj4+GjOnDmaM2dOCUSEK12eEV8Qxsd58m7NV1DfL1u2TKtXr9bff/8tX19fW0LHqFGjNGHCBOXm5qpixYrat2/fNdsICwvLd2lOSfr+++9t24sir/7BgwfVoUMHu20HDx68oUt+VqxYUZmZmfnKf/nlF9WsWdOurGzZsurevbu6d++uCxcuqGvXrpo6dapGjx6typUrq3z58srJybFNcopTQYltl+O15BoefPBBPfjgg1fdbjKZNGnSJE2aNKkEo8KVoqKirvoZgtcSUHLat29/zc/zBS0mtG/fXl9//bUDo4IjFXXMN2/e7NiA4FA38hq/3IQJEzRhwoQit/vBBx9Ikl1CZcWKFe1uHZHnyl9QS5K3t7c6d+6szp07Kzc3V08//bTefvttjR07VrVr1y7ybTeSkpLUsGFDjR8/Pt+2t99+W8nJyUVKmqpVq5YMw1B4eHiRrlB1ubCwMH3++ec6d+6c3dWmLr8ixuX69u2rmTNnatWqVVq7dq0qV66cL2H1Rsa7bt26xX4rRJScGxnzoUOHaujQoQ6MCo5QmO9fC/o3+7HHHtNjjz3mgIgA13e990jcPPrX8a63tuPKHnzwQb377rv64osvdNddd+Xb/t///leHDx+23VGjcuXKslgs113vqFWr1nXr5N0948o1h4LmHlcq6N/TpUuX6t577803HpmZmbrttttsj4syTwkLC9PGjRt1+vRpuyStG123yYszLi7O7oci58+fL3DtxRFWrVql7OxsrVy50nZlrAceeED/+te/NG3aNG3fvl01atSQdOn2cXFxcerSpYtefPHFfPv6+OOP5ePjo/Xr19td7SkxMbHQ8YSFhemHH37IV17QepkktWnTRm3atNHUqVOVnJysXr16afHixfrnP/9Z6Dad5XprIrh511rPwM0rX768Zs2apVmzZjk7lELxuH4VAEBh/P3331q2bJkefPBBPfroo/n+hgwZotOnT2vlypUFPt/Dw0NdunTRqlWrtHv37nzb8/7xfuCBB7Rz506lpqbatp09e1bvvPOOatSoke8+z9fTokULBQYGav78+XaXRF27dq2+++67G7qXb61atbR9+3ZduHDBVrZ69Wr9+uuvdvVOnDhh99jb21v169eXYRiyWq3y9PRUbGysPv744wInTn/88UeRYwMAAABQeJ999pkmT56s8PBwu1+21qpVS99//73dZ/JvvvlGX375pd3zr/zM7+HhYbtabN78I+/W5oX58v/XX3/V1q1b1a1btwLnXf3799ePP/6oHTt2FPoYu3btKk9PT02cODHfl6aGYeQ7hoJER0fLarXq3XfftZXl5uZeNWm8cePGaty4sf7973/r448/Vo8ePbglAAAAAFzWyJEj5evrqyeffDLf5+OTJ0/qqaeekp+fn0aOHCmp8OsdsbGx+uabb7R8+fKr1sm7zdvWrVtt23Jycm74dmWenp75PvcvWbJEv//+u11ZUeYpDzzwgHJycvTWW2/Zlb/++usymUzq1KlTscQ5e/bsq15hq7jlXQ3rytsCXpnodObMGT3yyCOqWrWqFi1aVGCymaenp0wmk13shw8f1ooVKwodzwMPPKDt27dr586dtrI//vhDSUlJdvX++uuvfP3WtGlTSbJbAwOAPHwbAwDFZOXKlTp9+rQeeuihAre3adNGlStXVlJSkrp3715gnWnTpmnDhg1q166dBg0apHr16unYsWNasmSJvvjiC1WoUEEvvPCCPvroI3Xq1EnPPPOMAgICtGjRIh06dEgff/yx3aXbC8PLy0svv/yy+vfvr3bt2qlnz57KyMjQG2+8oRo1amjEiBFF7ot//vOfWrp0qe6//35169ZNP/30kz788MN897COiopScHCw2rZtq6CgIH333Xd66623FBMTY/s1xksvvaTPP/9crVu31hNPPKH69evr5MmT+uqrr7Rx40adPHmyyPEBAAAAyG/t2rX6/vvvdfHiRWVkZOizzz5TSkqKwsLCtHLlSrvbHw8YMECvvfaaoqOjNXDgQB0/flzz589XgwYNlJWVZav3z3/+UydPnlSHDh0UGhqqX375RbNnz1bTpk1Vr149SZe+wPb09NTLL7+sU6dOyWw2q0OHDgoMDMwXY3JysgzDuOq864EHHlCZMmWUlJSk1q1bF+q4a9WqpSlTpmj06NE6fPiwunTpovLly+vQoUNavny5Bg0apOeee+6a++jSpYtatWqlZ599Vj/++KPq1q2rlStX2uYrBS0c9O3b17bfK2/NBwAAALiSOnXqaNGiRerVq5caNWqkgQMHKjw8XIcPH9aCBQv0559/6qOPPrJbAyjMesfIkSO1dOlSPfbYYxowYICaN2+ukydPauXKlZo/f76aNGmiBg0aqE2bNho9erROnjypgIAALV68WBcvXryhY3nwwQc1adIk9e/fX3feeaf27t2rpKSkfHfJqFWrlipUqKD58+erfPnyKlu2rFq3bq3w8PB8++zcubPuvfde/etf/9Lhw4fVpEkTbdiwQZ988omGDx+eb22ksHF+8MEH8vf3V/369ZWamqqNGzeqUqVKN3TcRRUVFWW7avCTTz6pM2fO6N1331VgYKCOHTtmqzdx4kQdOHBAY8aM0SeffGK3j1q1aikiIkIxMTF67bXXdP/99+vxxx/X8ePHNWfOHNWuXfu6t33M8/zzz+uDDz7Q/fffr2HDhqls2bJ65513FBYWZrePRYsWae7cuXrkkUdUq1YtnT59Wu+++64sFoseeOCB4ukcALcWAwBQLDp37mz4+PgYZ8+evWqdfv36GV5eXsaff/5pGIZhSDLGjx9vV+eXX34x+vbta1SuXNkwm81GzZo1jfj4eCM7O9tW56effjIeffRRo0KFCoaPj4/RqlUrY/Xq1Xb7+fzzzw1JxpIlS+zKDx06ZEgyEhMT7cr/7//+z2jWrJlhNpuNgIAAo1evXsZvv/1mV2f8+PFGYf/pmDlzplG1alXDbDYbbdu2NXbv3m20a9fOaNeuna3O22+/bdxzzz1GpUqVDLPZbNSqVcsYOXKkcerUKbt9ZWRkGPHx8Ua1atUMLy8vIzg42LjvvvuMd955p1CxAAAAALi6xMREQ5Ltz9vb2wgODjY6duxovPHGG0ZWVlaBz/vwww+NmjVrGt7e3kbTpk2N9evXG3FxcUZYWJitztKlS42oqCgjMDDQ8Pb2NqpXr248+eSTxrFjx+z29e677xo1a9Y0PD09DUnG559/XmCbjRo1MqpXr37N42nfvr0RGBh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      "text/plain": [
       "<Figure size 3000x2000 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "for i,feature in enumerate(num_col):\n",
    "    plt.subplot(8,3,i+1)\n",
    "    df[feature].hist()\n",
    "    plt.title(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Observations:\n",
    "        1.age looks a bit left skewed\n",
    "        2.Blood gluscose random is right skewed\n",
    "        3.Blood Urea is also a bit right skewed\n",
    "        4.Rest of the features are lightly skewed"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's check the label distribution for categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i,feature in enumerate(cat_col):\n",
    "    plt.subplot(4,3,i+1)\n",
    "    df[feature].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    A few features have imbalanced categories. Stratified folds will be necessary while cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sns.countplot(x=\\'class\\',data=df)\\nplt.xlabel(\"classification\")\\nplt.ylabel(\"Count\")\\nplt.title(\"target Class\")'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sns.countplot(x='class',data=df)\n",
    "plt.xlabel(\"classification\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"target Class\")'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ckd-chronic kidney disease\n",
    "## notckd-->> not crornic kidney disease"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  7. Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "corr_df = df.corr()\n",
    "sns.heatmap(corr_df,annot=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Positive Correlation:\n",
    "    \n",
    "    Specific gravity -> Red blood cell count, Packed cell volume and Hemoglobin\n",
    "    Sugar -> Blood glucose random\n",
    "    Blood Urea -> Serum creatinine\n",
    "    Hemoglobin -> Red Blood cell count <- packed cell volume\n",
    "    \n",
    "    \n",
    "    Negative Correlation:\n",
    "    Albumin, Blood urea -> Red blood cell count, packed cell volume, Hemoglobin\n",
    "    Serum creatinine -> Sodium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.groupby(['red blood cells','class'])['red blood cell count'].agg(['count','mean','median','min','max'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check for Positive correlation and its impact on classes¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#px.violin(df,y='red blood cell count',x=\"class\", color=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#px.scatter(df,'haemoglobin','packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'grid=sns.FacetGrid(df, hue=\"class\",aspect=2)\\ngrid.map(sns.kdeplot, \\'red blood cell count\\')\\ngrid.add_legend()'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### analysing distribution of 'red_blood_cell_count' in both Labels \n",
    "\n",
    "'''grid=sns.FacetGrid(df, hue=\"class\",aspect=2)\n",
    "grid.map(sns.kdeplot, 'red blood cell count')\n",
    "grid.add_legend()'''"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Both distributions are quite different, distribution CKD is quite normal and evenly distributed but not CKD distribution is a little bit left-skewed but quite close to a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining violin and scatter plot & kde_plot functions\n",
    "'''def violin(col):\n",
    "    fig = px.violin(df, y=col, x=\"class\", color=\"class\", box=True)\n",
    "    return fig.show()\n",
    "\n",
    "def scatters(col1,col2):\n",
    "    fig = px.scatter(df, x=col1, y=col2, color=\"class\")\n",
    "    return fig.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def kde_plot(feature):\\n    grid = sns.FacetGrid(df, hue=\"class\",aspect=2)\\n    grid.map(sns.kdeplot, feature)\\n    grid.add_legend()'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def kde_plot(feature):\n",
    "    grid = sns.FacetGrid(df, hue=\"class\",aspect=2)\n",
    "    grid.map(sns.kdeplot, feature)\n",
    "    grid.add_legend()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"kde_plot('red blood cell count')\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''kde_plot('red blood cell count')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kde_plot('haemoglobin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count', 'packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count', 'haemoglobin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('haemoglobin','packed cell volume')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1.RBC count range ~2 to <4.5 and Hemoglobin between 3 to <13 are mostly classified as positive for chronic kidney  \n",
    "    disease(i.e ckd).\n",
    "    2.RBC count range >4.5 to ~6.1 and Hemoglobin between >13 to 17.8 are classified as negative for chronic kidney \n",
    "    disease(i.e nockd)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#violin('red blood cell count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#violin('packed cell volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Now let's check for negative correlation and its impact on classes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Albumin, Blood urea -> Red blood cell count, packed cell volume, Haemoglobin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('red blood cell count','albumin')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Clearly, albumin levels of above 0 affect ckd largely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatters('packed cell volume','blood urea')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Packed cell volume >= 40 largely affects to be non ckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fig = px.bar(df, x=\"specific gravity\", y=\"packed cell volume\",\\n             color=\\'class\\', barmode=\\'group\\',\\n             height=400)\\nfig.show()'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''fig = px.bar(df, x=\"specific gravity\", y=\"packed cell volume\",\n",
    "             color='class', barmode='group',\n",
    "             height=400)\n",
    "fig.show()'''"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Clearly, specific gravity >=1.02 affects non ckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Air Pollution</th>\n",
       "      <th>Alcohol use</th>\n",
       "      <th>Dust Allergy</th>\n",
       "      <th>OccuPational Hazards</th>\n",
       "      <th>Genetic Risk</th>\n",
       "      <th>chronic Lung Disease</th>\n",
       "      <th>Balanced Diet</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>...</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <th>Wheezing</th>\n",
       "      <th>Swal0ing Difficulty</th>\n",
       "      <th>Clubbing of Finger Nails</th>\n",
       "      <th>Frequent Cold</th>\n",
       "      <th>Dry Cough</th>\n",
       "      <th>Snoring</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
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       "      <td>3</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "0   33       1              2            4             5   \n",
       "1   17       1              3            1             5   \n",
       "2   35       1              4            5             6   \n",
       "3   37       1              7            7             7   \n",
       "4   46       1              6            8             7   \n",
       "\n",
       "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
       "0                     4             3                     2              2   \n",
       "1                     3             4                     2              2   \n",
       "2                     5             5                     4              6   \n",
       "3                     7             6                     7              7   \n",
       "4                     7             7                     6              7   \n",
       "\n",
       "   Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
       "0        4  ...        3            4                    2         2   \n",
       "1        2  ...        1            3                    7         8   \n",
       "2        7  ...        8            7                    9         2   \n",
       "3        7  ...        4            2                    3         1   \n",
       "4        7  ...        3            2                    4         1   \n",
       "\n",
       "   Swal0ing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
       "0                    3                         1              2          3   \n",
       "1                    6                         2              1          7   \n",
       "2                    1                         4              6          7   \n",
       "3                    4                         5              6          7   \n",
       "4                    4                         2              4          2   \n",
       "\n",
       "   Snoring  Level  \n",
       "0        4      0  \n",
       "1        2      1  \n",
       "2        2      2  \n",
       "3        5      2  \n",
       "4        3      2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Gender                      0\n",
       "Snoring                     0\n",
       "Dry Cough                   0\n",
       "Frequent Cold               0\n",
       "Clubbing of Finger Nails    0\n",
       "Swal0ing Difficulty         0\n",
       "Wheezing                    0\n",
       "Shortness of Breath         0\n",
       "Weight Loss                 0\n",
       "Fatigue                     0\n",
       "Coughing of Blood           0\n",
       "Chest Pain                  0\n",
       "Passive Smoker              0\n",
       "Smoking                     0\n",
       "Obesity                     0\n",
       "Balanced Diet               0\n",
       "chronic Lung Disease        0\n",
       "Genetic Risk                0\n",
       "OccuPational Hazards        0\n",
       "Dust Allergy                0\n",
       "Alcohol use                 0\n",
       "Air Pollution               0\n",
       "Level                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['red blood cells'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "### filling missing with Random value"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].dropna().sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample=data['red blood cells'].dropna().sample(data['red blood cells'].isnull().sum())\n",
    "#random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[data['red blood cells'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index=data[data['red blood cells'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.loc[data['red blood cells'].isnull(),'red blood cells']=random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "0   33       1              2            4             5   \n",
       "1   17       1              3            1             5   \n",
       "2   35       1              4            5             6   \n",
       "3   37       1              7            7             7   \n",
       "4   46       1              6            8             7   \n",
       "\n",
       "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
       "0                     4             3                     2              2   \n",
       "1                     3             4                     2              2   \n",
       "2                     5             5                     4              6   \n",
       "3                     7             6                     7              7   \n",
       "4                     7             7                     6              7   \n",
       "\n",
       "   Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
       "0        4  ...        3            4                    2         2   \n",
       "1        2  ...        1            3                    7         8   \n",
       "2        7  ...        8            7                    9         2   \n",
       "3        7  ...        4            2                    3         1   \n",
       "4        7  ...        3            2                    4         1   \n",
       "\n",
       "   Swal0ing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
       "0                    3                         1              2          3   \n",
       "1                    6                         2              1          7   \n",
       "2                    1                         4              6          7   \n",
       "3                    4                         5              6          7   \n",
       "4                    4                         2              4          2   \n",
       "\n",
       "   Snoring  Level  \n",
       "0        4      0  \n",
       "1        2      1  \n",
       "2        2      2  \n",
       "3        5      2  \n",
       "4        3      2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['red blood cells'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "### lets create a function so that I can easily do it for all features\n",
    "def Random_value_imputation(feature):\n",
    "    random_sample=data[feature].dropna().sample(data[feature].isnull().sum())               \n",
    "    random_sample.index=data[data[feature].isnull()].index\n",
    "    data.loc[data[feature].isnull(),feature]=random_sample\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "### as rest of the features has less missing values,so I can fill it using mode concept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_mode(feature):\n",
    "    mode=data[feature].mode()[0]\n",
    "    data[feature]=data[feature].fillna(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    impute_mode(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Gender                      0\n",
       "Air Pollution               0\n",
       "Alcohol use                 0\n",
       "Dust Allergy                0\n",
       "OccuPational Hazards        0\n",
       "Genetic Risk                0\n",
       "chronic Lung Disease        0\n",
       "Balanced Diet               0\n",
       "Obesity                     0\n",
       "Smoking                     0\n",
       "Passive Smoker              0\n",
       "Chest Pain                  0\n",
       "Coughing of Blood           0\n",
       "Fatigue                     0\n",
       "Weight Loss                 0\n",
       "Shortness of Breath         0\n",
       "Wheezing                    0\n",
       "Swal0ing Difficulty         0\n",
       "Clubbing of Finger Nails    0\n",
       "Frequent Cold               0\n",
       "Dry Cough                   0\n",
       "Snoring                     0\n",
       "Level                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "### lets fill missing values in Numerical features using Random value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in num_col:\n",
    "    Random_value_imputation(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Gender                      0\n",
       "Air Pollution               0\n",
       "Alcohol use                 0\n",
       "Dust Allergy                0\n",
       "OccuPational Hazards        0\n",
       "Genetic Risk                0\n",
       "chronic Lung Disease        0\n",
       "Balanced Diet               0\n",
       "Obesity                     0\n",
       "Smoking                     0\n",
       "Passive Smoker              0\n",
       "Chest Pain                  0\n",
       "Coughing of Blood           0\n",
       "Fatigue                     0\n",
       "Weight Loss                 0\n",
       "Shortness of Breath         0\n",
       "Wheezing                    0\n",
       "Swal0ing Difficulty         0\n",
       "Clubbing of Finger Nails    0\n",
       "Frequent Cold               0\n",
       "Dry Cough                   0\n",
       "Snoring                     0\n",
       "Level                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_col].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    print('{} has {} categories'.format(col, data[col].nunique()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### as we have just 2 categories in each feature then we can consider Label Encoder as it will not cause Curse of Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    data[col]=le.fit_transform(data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Air Pollution</th>\n",
       "      <th>Alcohol use</th>\n",
       "      <th>Dust Allergy</th>\n",
       "      <th>OccuPational Hazards</th>\n",
       "      <th>Genetic Risk</th>\n",
       "      <th>chronic Lung Disease</th>\n",
       "      <th>Balanced Diet</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>...</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <th>Wheezing</th>\n",
       "      <th>Swal0ing Difficulty</th>\n",
       "      <th>Clubbing of Finger Nails</th>\n",
       "      <th>Frequent Cold</th>\n",
       "      <th>Dry Cough</th>\n",
       "      <th>Snoring</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "0   33       1              2            4             5   \n",
       "1   17       1              3            1             5   \n",
       "2   35       1              4            5             6   \n",
       "3   37       1              7            7             7   \n",
       "4   46       1              6            8             7   \n",
       "\n",
       "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
       "0                     4             3                     2              2   \n",
       "1                     3             4                     2              2   \n",
       "2                     5             5                     4              6   \n",
       "3                     7             6                     7              7   \n",
       "4                     7             7                     6              7   \n",
       "\n",
       "   Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
       "0        4  ...        3            4                    2         2   \n",
       "1        2  ...        1            3                    7         8   \n",
       "2        7  ...        8            7                    9         2   \n",
       "3        7  ...        4            2                    3         1   \n",
       "4        7  ...        3            2                    4         1   \n",
       "\n",
       "   Swal0ing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
       "0                    3                         1              2          3   \n",
       "1                    6                         2              1          7   \n",
       "2                    1                         4              6          7   \n",
       "3                    4                         5              6          7   \n",
       "4                    4                         2              4          2   \n",
       "\n",
       "   Snoring  Level  \n",
       "0        4      0  \n",
       "1        2      1  \n",
       "2        2      2  \n",
       "3        5      2  \n",
       "4        3      2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#SelectKBest-to select k best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#chi2-Internally this class is going to check that whether p-value is less than 0.05 or not\n",
    "#based on that,it will actually order all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest#Also known as Information Gain\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_col=[col for col in data.columns if col!='Level']\n",
    "dep_col='Level'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[ind_col]\n",
    "y=data[dep_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ordered_rank_features=SelectKBest(score_func=chi2,k=13)\n",
    "ordered_feature=ordered_rank_features.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>SelectKBest(k=13, score_func=&lt;function chi2 at 0x0000010E7875DCF0&gt;)</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\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=13, score_func=&lt;function chi2 at 0x0000010E7875DCF0&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k=13, score_func=<function chi2 at 0x0000010E7875DCF0>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44.1872019 ,   4.66816478, 518.63153298, 781.90984132,\n",
       "       401.04088307, 415.68565443, 488.6497259 , 302.39615669,\n",
       "       588.9337429 , 712.08756223, 671.00625311, 752.95979136,\n",
       "       524.48952111, 818.66888369, 518.90044626, 206.66656281,\n",
       "       330.88070877, 201.4261893 , 113.07424926, 257.90767922,\n",
       "       192.71327574, 152.02954698,  91.7481552 ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get scores(rank) of feature,what we can do we can use scores function\n",
    "ordered_feature.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>44.187202</td>\n",
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       "      <th>2</th>\n",
       "      <td>518.631533</td>\n",
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       "      <th>3</th>\n",
       "      <td>781.909841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>401.040883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>415.685654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>488.649726</td>\n",
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       "      <th>7</th>\n",
       "      <td>302.396157</td>\n",
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       "      <th>8</th>\n",
       "      <td>588.933743</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>712.087562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>671.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>752.959791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>524.489521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>818.668884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>518.900446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>206.666563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>330.880709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>201.426189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>113.074249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>257.907679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>192.713276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>152.029547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>91.748155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Score\n",
       "0    44.187202\n",
       "1     4.668165\n",
       "2   518.631533\n",
       "3   781.909841\n",
       "4   401.040883\n",
       "5   415.685654\n",
       "6   488.649726\n",
       "7   302.396157\n",
       "8   588.933743\n",
       "9   712.087562\n",
       "10  671.006253\n",
       "11  752.959791\n",
       "12  524.489521\n",
       "13  818.668884\n",
       "14  518.900446\n",
       "15  206.666563\n",
       "16  330.880709\n",
       "17  201.426189\n",
       "18  113.074249\n",
       "19  257.907679\n",
       "20  192.713276\n",
       "21  152.029547\n",
       "22   91.748155"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datascores=pd.DataFrame(ordered_feature.scores_,columns=[\"Score\"])\n",
    "datascores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Alcohol use</td>\n",
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       "      <td>Dust Allergy</td>\n",
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       "      <th>5</th>\n",
       "      <td>OccuPational Hazards</td>\n",
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       "      <th>6</th>\n",
       "      <td>Genetic Risk</td>\n",
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       "      <td>chronic Lung Disease</td>\n",
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       "      <td>Obesity</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Smoking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Passive Smoker</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chest Pain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Coughing of Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fatigue</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Weight Loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shortness of Breath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Wheezing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Swal0ing Difficulty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Clubbing of Finger Nails</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Frequent Cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dry Cough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Snoring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0\n",
       "0                        Age\n",
       "1                     Gender\n",
       "2              Air Pollution\n",
       "3                Alcohol use\n",
       "4               Dust Allergy\n",
       "5       OccuPational Hazards\n",
       "6               Genetic Risk\n",
       "7       chronic Lung Disease\n",
       "8              Balanced Diet\n",
       "9                    Obesity\n",
       "10                   Smoking\n",
       "11            Passive Smoker\n",
       "12                Chest Pain\n",
       "13         Coughing of Blood\n",
       "14                   Fatigue\n",
       "15               Weight Loss\n",
       "16       Shortness of Breath\n",
       "17                  Wheezing\n",
       "18       Swal0ing Difficulty\n",
       "19  Clubbing of Finger Nails\n",
       "20             Frequent Cold\n",
       "21                 Dry Cough\n",
       "22                   Snoring"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcolumns=pd.DataFrame(X.columns)\n",
    "dfcolumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_rank=pd.concat([dfcolumns,datascores],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Age</td>\n",
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       "      <th>1</th>\n",
       "      <td>Gender</td>\n",
       "      <td>4.668165</td>\n",
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       "      <th>2</th>\n",
       "      <td>Air Pollution</td>\n",
       "      <td>518.631533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alcohol use</td>\n",
       "      <td>781.909841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dust Allergy</td>\n",
       "      <td>401.040883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OccuPational Hazards</td>\n",
       "      <td>415.685654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Genetic Risk</td>\n",
       "      <td>488.649726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chronic Lung Disease</td>\n",
       "      <td>302.396157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Balanced Diet</td>\n",
       "      <td>588.933743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Obesity</td>\n",
       "      <td>712.087562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Smoking</td>\n",
       "      <td>671.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Passive Smoker</td>\n",
       "      <td>752.959791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chest Pain</td>\n",
       "      <td>524.489521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Coughing of Blood</td>\n",
       "      <td>818.668884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fatigue</td>\n",
       "      <td>518.900446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Weight Loss</td>\n",
       "      <td>206.666563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shortness of Breath</td>\n",
       "      <td>330.880709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Wheezing</td>\n",
       "      <td>201.426189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Swal0ing Difficulty</td>\n",
       "      <td>113.074249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Clubbing of Finger Nails</td>\n",
       "      <td>257.907679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Frequent Cold</td>\n",
       "      <td>192.713276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dry Cough</td>\n",
       "      <td>152.029547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Snoring</td>\n",
       "      <td>91.748155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0       Score\n",
       "0                        Age   44.187202\n",
       "1                     Gender    4.668165\n",
       "2              Air Pollution  518.631533\n",
       "3                Alcohol use  781.909841\n",
       "4               Dust Allergy  401.040883\n",
       "5       OccuPational Hazards  415.685654\n",
       "6               Genetic Risk  488.649726\n",
       "7       chronic Lung Disease  302.396157\n",
       "8              Balanced Diet  588.933743\n",
       "9                    Obesity  712.087562\n",
       "10                   Smoking  671.006253\n",
       "11            Passive Smoker  752.959791\n",
       "12                Chest Pain  524.489521\n",
       "13         Coughing of Blood  818.668884\n",
       "14                   Fatigue  518.900446\n",
       "15               Weight Loss  206.666563\n",
       "16       Shortness of Breath  330.880709\n",
       "17                  Wheezing  201.426189\n",
       "18       Swal0ing Difficulty  113.074249\n",
       "19  Clubbing of Finger Nails  257.907679\n",
       "20             Frequent Cold  192.713276\n",
       "21                 Dry Cough  152.029547\n",
       "22                   Snoring   91.748155"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Higher the score is,more important feature is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>44.187202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gender</td>\n",
       "      <td>4.668165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air Pollution</td>\n",
       "      <td>518.631533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alcohol use</td>\n",
       "      <td>781.909841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dust Allergy</td>\n",
       "      <td>401.040883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OccuPational Hazards</td>\n",
       "      <td>415.685654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Genetic Risk</td>\n",
       "      <td>488.649726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chronic Lung Disease</td>\n",
       "      <td>302.396157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Balanced Diet</td>\n",
       "      <td>588.933743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Obesity</td>\n",
       "      <td>712.087562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Smoking</td>\n",
       "      <td>671.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Passive Smoker</td>\n",
       "      <td>752.959791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chest Pain</td>\n",
       "      <td>524.489521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Coughing of Blood</td>\n",
       "      <td>818.668884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fatigue</td>\n",
       "      <td>518.900446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Weight Loss</td>\n",
       "      <td>206.666563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shortness of Breath</td>\n",
       "      <td>330.880709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Wheezing</td>\n",
       "      <td>201.426189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Swal0ing Difficulty</td>\n",
       "      <td>113.074249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Clubbing of Finger Nails</td>\n",
       "      <td>257.907679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Frequent Cold</td>\n",
       "      <td>192.713276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dry Cough</td>\n",
       "      <td>152.029547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Snoring</td>\n",
       "      <td>91.748155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Features       Score\n",
       "0                        Age   44.187202\n",
       "1                     Gender    4.668165\n",
       "2              Air Pollution  518.631533\n",
       "3                Alcohol use  781.909841\n",
       "4               Dust Allergy  401.040883\n",
       "5       OccuPational Hazards  415.685654\n",
       "6               Genetic Risk  488.649726\n",
       "7       chronic Lung Disease  302.396157\n",
       "8              Balanced Diet  588.933743\n",
       "9                    Obesity  712.087562\n",
       "10                   Smoking  671.006253\n",
       "11            Passive Smoker  752.959791\n",
       "12                Chest Pain  524.489521\n",
       "13         Coughing of Blood  818.668884\n",
       "14                   Fatigue  518.900446\n",
       "15               Weight Loss  206.666563\n",
       "16       Shortness of Breath  330.880709\n",
       "17                  Wheezing  201.426189\n",
       "18       Swal0ing Difficulty  113.074249\n",
       "19  Clubbing of Finger Nails  257.907679\n",
       "20             Frequent Cold  192.713276\n",
       "21                 Dry Cough  152.029547\n",
       "22                   Snoring   91.748155"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank.columns=['Features','Score']\n",
    "features_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Coughing of Blood</td>\n",
       "      <td>818.668884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alcohol use</td>\n",
       "      <td>781.909841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Passive Smoker</td>\n",
       "      <td>752.959791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Obesity</td>\n",
       "      <td>712.087562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Smoking</td>\n",
       "      <td>671.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Balanced Diet</td>\n",
       "      <td>588.933743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chest Pain</td>\n",
       "      <td>524.489521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fatigue</td>\n",
       "      <td>518.900446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air Pollution</td>\n",
       "      <td>518.631533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Genetic Risk</td>\n",
       "      <td>488.649726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Features       Score\n",
       "13  Coughing of Blood  818.668884\n",
       "3         Alcohol use  781.909841\n",
       "11     Passive Smoker  752.959791\n",
       "9             Obesity  712.087562\n",
       "10            Smoking  671.006253\n",
       "8       Balanced Diet  588.933743\n",
       "12         Chest Pain  524.489521\n",
       "14            Fatigue  518.900446\n",
       "2       Air Pollution  518.631533\n",
       "6        Genetic Risk  488.649726"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetch largest 10 values of Score column\n",
    "features_rank.nlargest(10,'Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_new=features_rank.nlargest(10,'Score')['Features'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=np.array(x_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Coughing of Blood  Alcohol use  Passive Smoker  Obesity  Smoking  \\\n",
      "0                    4            4               2        4        3   \n",
      "1                    3            1               4        2        2   \n",
      "2                    8            5               3        7        2   \n",
      "3                    8            7               7        7        7   \n",
      "4                    9            8               7        7        8   \n",
      "..                 ...          ...             ...      ...      ...   \n",
      "995                  7            7               8        7        7   \n",
      "996                  7            8               8        7        7   \n",
      "997                  8            5               3        7        2   \n",
      "998                  9            8               7        7        8   \n",
      "999                  8            5               3        7        2   \n",
      "\n",
      "     Balanced Diet  Chest Pain  Fatigue  Air Pollution  Genetic Risk  \n",
      "0                2           2        3              2             3  \n",
      "1                2           2        1              3             4  \n",
      "2                6           4        8              4             5  \n",
      "3                7           7        4              7             6  \n",
      "4                7           7        3              6             7  \n",
      "..             ...         ...      ...            ...           ...  \n",
      "995              7           7        5              6             7  \n",
      "996              7           7        9              6             7  \n",
      "997              6           4        8              4             5  \n",
      "998              7           7        3              6             7  \n",
      "999              6           4        8              6             5  \n",
      "\n",
      "[1000 rows x 10 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Coughing of Blood    int64\n",
       "Alcohol use          int64\n",
       "Passive Smoker       int64\n",
       "Obesity              int64\n",
       "Smoking              int64\n",
       "Balanced Diet        int64\n",
       "Chest Pain           int64\n",
       "Fatigue              int64\n",
       "Air Pollution        int64\n",
       "Genetic Risk         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns=data[sc]\n",
    "print(selected_columns)\n",
    "selected_columns.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(selected_columns,y,train_size=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750, 10)\n",
      "(250, 10)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coughing of Blood  Alcohol use  Passive Smoker  Obesity  Smoking  Balanced Diet  Chest Pain  Fatigue  Air Pollution  Genetic Risk\n",
       "3                  8            2               4        1        2              4           2        6              7               62\n",
       "                   2            2               3        2        3              3           4        3              3               44\n",
       "8                  5            3               7        2        6              4           8        4              5               43\n",
       "4                  1            4               4        1        2              2           6        2              2               39\n",
       "9                  8            7               7        8        7              7           3        6              7               30\n",
       "4                  2            2               3        3        3              3           4        1              2               29\n",
       "7                  8            8               7        7        7              7           9        6              7               27\n",
       "                   7            8               7        7        7              7           5        6              7               25\n",
       "                   6            4               7        3        7              8           3        1              7               23\n",
       "                   7            7               7        7        6              7           8        6              7               22\n",
       "1                  1            4               3        1        4              3           3        3              2               21\n",
       "2                  6            3               2        3        6              2           3        1              3               19\n",
       "7                  7            7               7        7        7              7           2        6              7               16\n",
       "8                  5            3               7        2        6              4           8        6              5               16\n",
       "4                  4            2               4        3        2              2           3        2              3               14\n",
       "8                  7            7               7        7        7              7           4        7              6               14\n",
       "7                  7            8               7        4        7              7           4        6              7               14\n",
       "9                  8            7               7        8        7              7           3        8              7               13\n",
       "2                  1            2               3        2        3              4           2        3              3               13\n",
       "8                  7            7               7        7        7              7           4        6              6               12\n",
       "6                  8            7               3        8        7              9           5        6              6               11\n",
       "2                  3            6               2        7        1              2           2        2              3               10\n",
       "1                  2            2               2        7        1              1           1        3              3               10\n",
       "4                  2            5               2        1        3              1           2        4              1                9\n",
       "6                  6            8               4        6        6              7           5        2              6                9\n",
       "2                  2            2               1        6        5              3           4        3              2                9\n",
       "5                  4            4               6        5        4              6           5        2              5                9\n",
       "                   5            6               5        6        5              6           1        4              6                9\n",
       "3                  2            5               2        2        5              1           1        5              2                9\n",
       "1                  2            2               2        3        3              1           1        3              3                8\n",
       "3                  1            2               1        3        4              4           2        3              2                8\n",
       "6                  5            6               6        6        6              6           5        4              5                8\n",
       "2                  1            1               1        1        2              6           1        2              4                8\n",
       "5                  3            1               1        2        2              2           3        2              2                8\n",
       "2                  1            1               2        6        3              1           3        2              3                8\n",
       "                   2            2               1        3        2              1           1        1              4                8\n",
       "3                  3            1               4        3        3              4           1        2              3                7\n",
       "8                  7            3               2        4        7              7           2        6              6                7\n",
       "1                  2            1               3        2        2              1           1        1              2                7\n",
       "7                  7            7               7        7        7              7           2        7              7                7\n",
       "6                  2            2               1        4        1              3           2        3              1                7\n",
       "2                  2            4               4        6        5              7           3        1              6                7\n",
       "3                  4            7               6        7        4              2           8        2              5                7\n",
       "5                  6            5               5        8        6              5           4        5              6                7\n",
       "4                  3            3               3        2        3              4           1        2              4                7\n",
       "1                  1            4               3        2        2              2           3        2              2                7\n",
       "4                  2            4               4        2        2              2           1        4              2                7\n",
       "                   3            3               1        4        1              2           2        2              1                7\n",
       "2                  2            2               2        1        3              2           2        3              2                6\n",
       "5                  3            2               4        3        2              1           2        2              1                6\n",
       "4                  3            3               2        4        7              7           2        2              7                6\n",
       "                                1               2        2        2              3           4        1              2                6\n",
       "3                  1            4               2        2        2              2           1        3              4                5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    266\n",
       "1    261\n",
       "0    223\n",
       "Name: Level, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check whether dataset is imbalance or not\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Lets find best model using Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      1\n",
      "2      2\n",
      "3      2\n",
      "4      2\n",
      "      ..\n",
      "995    2\n",
      "996    2\n",
      "997    2\n",
      "998    2\n",
      "999    2\n",
      "Name: Level, Length: 1000, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nparams={\\n \"learning_rate\"    : [0.05, 0.20, 0.25 ] ,\\n \"max_depth\"        : [ 5, 8, 10, 12],\\n \"min_child_weight\" : [ 1, 3, 5, 7 ],\\n \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.4 ],\\n \"colsample_bytree\" : [ 0.3, 0.4, 0.7 ]\\n    \\n}'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Hyper Parameter Optimization with respect to XGBoost\n",
    "'''\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.20, 0.25 ] ,\n",
    " \"max_depth\"        : [ 5, 8, 10, 12],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.7 ]\n",
    "    \n",
    "}'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from xgboost import XGBClassifier\\nclassifier=XGBClassifier()'"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from xgboost import XGBClassifier\n",
    "classifier=XGBClassifier()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"classifier=RandomForestClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\\n              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0, gpu_id=-1,\\n              importance_type='gain', interaction_constraints='',\\n              learning_rate=0.25, max_delta_step=0, max_depth=5,\\n              min_child_weight=1, monotone_constraints='()',\\n              n_estimators=100, n_jobs=2, num_parallel_tree=1,\\n              objective='binary:logistic', random_state=0, reg_alpha=0,\\n              reg_lambda=1, scale_pos_weight=1, subsample=1,\\n              tree_method='exact', use_label_encoder=False,\\n              validate_parameters=1, verbosity=None)\\n\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''classifier=RandomForestClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.25, max_delta_step=0, max_depth=5,\n",
    "              min_child_weight=1, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=2, num_parallel_tree=1,\n",
    "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "              tree_method='exact', use_label_encoder=False,\n",
    "              validate_parameters=1, verbosity=None)\n",
    "'''\n",
    "## we have got this model on the basis of cross valudation & hyper-parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[80  0  0]\n",
      " [ 0 71  0]\n",
      " [ 0  0 99]]\n"
     ]
    }
   ],
   "source": [
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10e79ea2c50>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### to make confusion matrix user-friendly\n",
    "plt.imshow(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./../savedModels/model3.joblib']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(model,'./../savedModels/model3.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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