{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "7ce53ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.figure_factory as ff\n",
    "import seaborn as sns\n",
    "sns.set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "73edef6d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Air Pollution</th>\n",
       "      <th>Alcohol use</th>\n",
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       "      <th>Dry Cough</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P1</td>\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>...</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>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P10</td>\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",
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       "      <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>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P100</td>\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",
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       "      <td>7</td>\n",
       "      <td>9</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P1000</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
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       "      <th>4</th>\n",
       "      <td>P101</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>High</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "index                                                                     \n",
       "0             P1   33       1              2            4             5   \n",
       "1            P10   17       1              3            1             5   \n",
       "2           P100   35       1              4            5             6   \n",
       "3          P1000   37       1              7            7             7   \n",
       "4           P101   46       1              6            8             7   \n",
       "\n",
       "       OccuPational Hazards  Genetic Risk  chronic Lung Disease  \\\n",
       "index                                                             \n",
       "0                         4             3                     2   \n",
       "1                         3             4                     2   \n",
       "2                         5             5                     4   \n",
       "3                         7             6                     7   \n",
       "4                         7             7                     6   \n",
       "\n",
       "       Balanced Diet  ...  Fatigue  Weight Loss  Shortness of Breath  \\\n",
       "index                 ...                                              \n",
       "0                  2  ...        3            4                    2   \n",
       "1                  2  ...        1            3                    7   \n",
       "2                  6  ...        8            7                    9   \n",
       "3                  7  ...        4            2                    3   \n",
       "4                  7  ...        3            2                    4   \n",
       "\n",
       "       Wheezing  Swallowing Difficulty  Clubbing of Finger Nails  \\\n",
       "index                                                              \n",
       "0             2                      3                         1   \n",
       "1             8                      6                         2   \n",
       "2             2                      1                         4   \n",
       "3             1                      4                         5   \n",
       "4             1                      4                         2   \n",
       "\n",
       "       Frequent Cold  Dry Cough  Snoring   Level  \n",
       "index                                             \n",
       "0                  2          3        4     Low  \n",
       "1                  1          7        2  Medium  \n",
       "2                  6          7        2    High  \n",
       "3                  6          7        5    High  \n",
       "4                  4          2        3    High  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane = pd.read_csv(r'C:\\Users\\HP\\Desktop\\podyplomówka\\cancer_patient_data_sets.csv', index_col = 0)\n",
    "dane.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "1831fdd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1000 entries, 0 to 999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Patient Id                1000 non-null   object\n",
      " 1   Age                       1000 non-null   int64 \n",
      " 2   Gender                    1000 non-null   int64 \n",
      " 3   Air Pollution             1000 non-null   int64 \n",
      " 4   Alcohol use               1000 non-null   int64 \n",
      " 5   Dust Allergy              1000 non-null   int64 \n",
      " 6   OccuPational Hazards      1000 non-null   int64 \n",
      " 7   Genetic Risk              1000 non-null   int64 \n",
      " 8   chronic Lung Disease      1000 non-null   int64 \n",
      " 9   Balanced Diet             1000 non-null   int64 \n",
      " 10  Obesity                   1000 non-null   int64 \n",
      " 11  Smoking                   1000 non-null   int64 \n",
      " 12  Passive Smoker            1000 non-null   int64 \n",
      " 13  Chest Pain                1000 non-null   int64 \n",
      " 14  Coughing of Blood         1000 non-null   int64 \n",
      " 15  Fatigue                   1000 non-null   int64 \n",
      " 16  Weight Loss               1000 non-null   int64 \n",
      " 17  Shortness of Breath       1000 non-null   int64 \n",
      " 18  Wheezing                  1000 non-null   int64 \n",
      " 19  Swallowing Difficulty     1000 non-null   int64 \n",
      " 20  Clubbing of Finger Nails  1000 non-null   int64 \n",
      " 21  Frequent Cold             1000 non-null   int64 \n",
      " 22  Dry Cough                 1000 non-null   int64 \n",
      " 23  Snoring                   1000 non-null   int64 \n",
      " 24  Level                     1000 non-null   object\n",
      "dtypes: int64(23), object(2)\n",
      "memory usage: 203.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dane.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f1b9c9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "422c8e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       Air Pollution  Smoking  Passive Smoker\n",
       "index                                        \n",
       "2                  4        2               3\n",
       "3                  7        7               7\n",
       "4                  6        8               7\n",
       "5                  4        2               3\n",
       "10                 6        7               8\n",
       "...              ...      ...             ...\n",
       "995                6        7               8\n",
       "996                6        7               8\n",
       "997                4        2               3\n",
       "998                6        8               7\n",
       "999                6        2               3\n",
       "\n",
       "[365 rows x 3 columns]"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane0 = dane[dane['Level'] == 'High'][['Air Pollution', 'Smoking', 'Passive Smoker']]\n",
    "dane0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "af7da17c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>12.005493</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>73.0</td>\n",
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       "      <th>Gender</th>\n",
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       "      <th>Air Pollution</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>Alcohol use</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Dust Allergy</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>5.165</td>\n",
       "      <td>1.980833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>OccuPational Hazards</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.840</td>\n",
       "      <td>2.107805</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Genetic Risk</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.580</td>\n",
       "      <td>2.126999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic Lung Disease</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.380</td>\n",
       "      <td>1.848518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>Balanced Diet</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.491</td>\n",
       "      <td>2.135528</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>Obesity</th>\n",
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       "      <td>4.465</td>\n",
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       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.948</td>\n",
       "      <td>2.495902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passive Smoker</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.195</td>\n",
       "      <td>2.311778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chest Pain</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.438</td>\n",
       "      <td>2.280209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coughing of Blood</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.859</td>\n",
       "      <td>2.427965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatigue</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.856</td>\n",
       "      <td>2.244616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight Loss</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.855</td>\n",
       "      <td>2.206546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.240</td>\n",
       "      <td>2.285087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wheezing</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.777</td>\n",
       "      <td>2.041921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swallowing Difficulty</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.746</td>\n",
       "      <td>2.270383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clubbing of Finger Nails</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.923</td>\n",
       "      <td>2.388048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequent Cold</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.536</td>\n",
       "      <td>1.832502</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry Cough</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.853</td>\n",
       "      <td>2.039007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snoring</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.926</td>\n",
       "      <td>1.474686</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count    mean        std   min    25%   50%   75%  \\\n",
       "Age                       1000.0  37.174  12.005493  14.0  27.75  36.0  45.0   \n",
       "Gender                    1000.0   1.402   0.490547   1.0   1.00   1.0   2.0   \n",
       "Air Pollution             1000.0   3.840   2.030400   1.0   2.00   3.0   6.0   \n",
       "Alcohol use               1000.0   4.563   2.620477   1.0   2.00   5.0   7.0   \n",
       "Dust Allergy              1000.0   5.165   1.980833   1.0   4.00   6.0   7.0   \n",
       "OccuPational Hazards      1000.0   4.840   2.107805   1.0   3.00   5.0   7.0   \n",
       "Genetic Risk              1000.0   4.580   2.126999   1.0   2.00   5.0   7.0   \n",
       "chronic Lung Disease      1000.0   4.380   1.848518   1.0   3.00   4.0   6.0   \n",
       "Balanced Diet             1000.0   4.491   2.135528   1.0   2.00   4.0   7.0   \n",
       "Obesity                   1000.0   4.465   2.124921   1.0   3.00   4.0   7.0   \n",
       "Smoking                   1000.0   3.948   2.495902   1.0   2.00   3.0   7.0   \n",
       "Passive Smoker            1000.0   4.195   2.311778   1.0   2.00   4.0   7.0   \n",
       "Chest Pain                1000.0   4.438   2.280209   1.0   2.00   4.0   7.0   \n",
       "Coughing of Blood         1000.0   4.859   2.427965   1.0   3.00   4.0   7.0   \n",
       "Fatigue                   1000.0   3.856   2.244616   1.0   2.00   3.0   5.0   \n",
       "Weight Loss               1000.0   3.855   2.206546   1.0   2.00   3.0   6.0   \n",
       "Shortness of Breath       1000.0   4.240   2.285087   1.0   2.00   4.0   6.0   \n",
       "Wheezing                  1000.0   3.777   2.041921   1.0   2.00   4.0   5.0   \n",
       "Swallowing Difficulty     1000.0   3.746   2.270383   1.0   2.00   4.0   5.0   \n",
       "Clubbing of Finger Nails  1000.0   3.923   2.388048   1.0   2.00   4.0   5.0   \n",
       "Frequent Cold             1000.0   3.536   1.832502   1.0   2.00   3.0   5.0   \n",
       "Dry Cough                 1000.0   3.853   2.039007   1.0   2.00   4.0   6.0   \n",
       "Snoring                   1000.0   2.926   1.474686   1.0   2.00   3.0   4.0   \n",
       "\n",
       "                           max  \n",
       "Age                       73.0  \n",
       "Gender                     2.0  \n",
       "Air Pollution              8.0  \n",
       "Alcohol use                8.0  \n",
       "Dust Allergy               8.0  \n",
       "OccuPational Hazards       8.0  \n",
       "Genetic Risk               7.0  \n",
       "chronic Lung Disease       7.0  \n",
       "Balanced Diet              7.0  \n",
       "Obesity                    7.0  \n",
       "Smoking                    8.0  \n",
       "Passive Smoker             8.0  \n",
       "Chest Pain                 9.0  \n",
       "Coughing of Blood          9.0  \n",
       "Fatigue                    9.0  \n",
       "Weight Loss                8.0  \n",
       "Shortness of Breath        9.0  \n",
       "Wheezing                   8.0  \n",
       "Swallowing Difficulty      8.0  \n",
       "Clubbing of Finger Nails   9.0  \n",
       "Frequent Cold              7.0  \n",
       "Dry Cough                  7.0  \n",
       "Snoring                    7.0  "
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "c6867768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genetic Risk    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane[['Genetic Risk']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "a043ec73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient Id', 'Age', 'Gender', 'Air Pollution', 'Alcohol use',\n",
       "       'Dust Allergy', 'OccuPational Hazards', 'Genetic Risk',\n",
       "       'chronic Lung Disease', 'Balanced Diet', 'Obesity', 'Smoking',\n",
       "       'Passive Smoker', 'Chest Pain', 'Coughing of Blood', 'Fatigue',\n",
       "       'Weight Loss', 'Shortness of Breath', 'Wheezing',\n",
       "       'Swallowing Difficulty', 'Clubbing of Finger Nails', 'Frequent Cold',\n",
       "       'Dry Cough', 'Snoring', 'Level'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "e6cad188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "1    598\n",
       "2    402\n",
       "dtype: int64"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane2 = dane.groupby('Gender').size()\n",
    "dane2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "966e57b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "_ = dane['Gender'].value_counts().plot(kind = 'bar')\n",
    "_ = plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "8d81604c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Smoking\n",
       " 1    181\n",
       " 2    222\n",
       " 3    172\n",
       " 4     59\n",
       " 5     10\n",
       " 6     60\n",
       " 7    207\n",
       " 8     89\n",
       " dtype: int64]"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane3 = [dane.groupby('Smoking').size()]\n",
    "dane3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "d85261ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dane['Smoking'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "86122d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Passive Smoker\n",
       " 1     60\n",
       " 2    284\n",
       " 3    140\n",
       " 4    161\n",
       " 5     30\n",
       " 6     30\n",
       " 7    187\n",
       " 8    108\n",
       " dtype: int64]"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane4 = [dane.groupby('Passive Smoker').size()]\n",
    "dane4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "c78bbd4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dane['Passive Smoker'].value_counts().plot(kind = 'pie')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "6385071c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Smoking  Gender\n",
       "1        1         102\n",
       "         2          79\n",
       "2        1         102\n",
       "         2         120\n",
       "3        1          79\n",
       "         2          93\n",
       "4        1          49\n",
       "         2          10\n",
       "5        1          10\n",
       "6        1          28\n",
       "         2          32\n",
       "7        1         167\n",
       "         2          40\n",
       "8        1          61\n",
       "         2          28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane.groupby(['Smoking','Gender']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "af3dd196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dane6 = dane.groupby(['Smoking','Gender'])\n",
    "_ = dane6[['Smoking', 'Gender']].value_counts().plot(kind = 'bar')\n",
    "_ = plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "18002f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Smoking    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane[['Smoking']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "f21f91ec",
   "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>Genetic Risk</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Alcohol use</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Genetic Risk  Smoking  Alcohol use\n",
       "index                                    \n",
       "725               1        4            3\n",
       "59                1        4            3\n",
       "727               1        3            3\n",
       "940               1        4            2\n",
       "819               1        1            2\n",
       "...             ...      ...          ...\n",
       "537               7        8            8\n",
       "538               7        7            7\n",
       "755               7        4            7\n",
       "533               7        4            7\n",
       "812               7        7            7\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = dane[['Genetic Risk', 'Smoking','Alcohol use']]\n",
    "x.sort_values('Genetic Risk')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "15eebd5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Air Pollution\n",
       "8     19\n",
       "5     20\n",
       "7     30\n",
       "4     90\n",
       "1    141\n",
       "3    173\n",
       "2    201\n",
       "6    326\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane7 = dane['Air Pollution'].value_counts()\n",
    "dane7.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "c0b501b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dane7 = dane['Air Pollution'].value_counts().plot(kind = 'bar')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "00915ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "5024be32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genetic Risk\n",
       "4     40\n",
       "1     40\n",
       "5    100\n",
       "6    108\n",
       "3    173\n",
       "2    212\n",
       "7    327\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = dane8 = dane['Genetic Risk'].value_counts()\n",
    "dane8.sort_values()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "8388706b",
   "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>Patient Id</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>...</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <th>Wheezing</th>\n",
       "      <th>Swallowing 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",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P1</td>\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>...</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>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P10</td>\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>...</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>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P100</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P1000</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P101</td>\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>...</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>High</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>P995</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>P996</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>P997</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>P998</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>P999</td>\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>...</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>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "index                                                                     \n",
       "0             P1   33       1              2            4             5   \n",
       "1            P10   17       1              3            1             5   \n",
       "2           P100   35       1              4            5             6   \n",
       "3          P1000   37       1              7            7             7   \n",
       "4           P101   46       1              6            8             7   \n",
       "...          ...  ...     ...            ...          ...           ...   \n",
       "995         P995   44       1              6            7             7   \n",
       "996         P996   37       2              6            8             7   \n",
       "997         P997   25       2              4            5             6   \n",
       "998         P998   18       2              6            8             7   \n",
       "999         P999   47       1              6            5             6   \n",
       "\n",
       "       OccuPational Hazards  Genetic Risk  chronic Lung Disease  \\\n",
       "index                                                             \n",
       "0                         4             3                     2   \n",
       "1                         3             4                     2   \n",
       "2                         5             5                     4   \n",
       "3                         7             6                     7   \n",
       "4                         7             7                     6   \n",
       "...                     ...           ...                   ...   \n",
       "995                       7             7                     6   \n",
       "996                       7             7                     6   \n",
       "997                       5             5                     4   \n",
       "998                       7             7                     6   \n",
       "999                       5             5                     4   \n",
       "\n",
       "       Balanced Diet  ...  Fatigue  Weight Loss  Shortness of Breath  \\\n",
       "index                 ...                                              \n",
       "0                  2  ...        3            4                    2   \n",
       "1                  2  ...        1            3                    7   \n",
       "2                  6  ...        8            7                    9   \n",
       "3                  7  ...        4            2                    3   \n",
       "4                  7  ...        3            2                    4   \n",
       "...              ...  ...      ...          ...                  ...   \n",
       "995                7  ...        5            3                    2   \n",
       "996                7  ...        9            6                    5   \n",
       "997                6  ...        8            7                    9   \n",
       "998                7  ...        3            2                    4   \n",
       "999                6  ...        8            7                    9   \n",
       "\n",
       "       Wheezing  Swallowing Difficulty  Clubbing of Finger Nails  \\\n",
       "index                                                              \n",
       "0             2                      3                         1   \n",
       "1             8                      6                         2   \n",
       "2             2                      1                         4   \n",
       "3             1                      4                         5   \n",
       "4             1                      4                         2   \n",
       "...         ...                    ...                       ...   \n",
       "995           7                      8                         2   \n",
       "996           7                      2                         4   \n",
       "997           2                      1                         4   \n",
       "998           1                      4                         2   \n",
       "999           2                      1                         4   \n",
       "\n",
       "       Frequent Cold  Dry Cough  Snoring   Level  \n",
       "index                                             \n",
       "0                  2          3        4     Low  \n",
       "1                  1          7        2  Medium  \n",
       "2                  6          7        2    High  \n",
       "3                  6          7        5    High  \n",
       "4                  4          2        3    High  \n",
       "...              ...        ...      ...     ...  \n",
       "995                4          5        3    High  \n",
       "996                3          1        4    High  \n",
       "997                6          7        2    High  \n",
       "998                4          2        3    High  \n",
       "999                6          7        2    High  \n",
       "\n",
       "[1000 rows x 25 columns]"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "f2c57644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Level\n",
       "High      365\n",
       "Medium    332\n",
       "Low       303\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dane['Level'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "f02e1f34",
   "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>Patient Id</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>...</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Weight Loss</th>\n",
       "      <th>Shortness of Breath</th>\n",
       "      <th>Wheezing</th>\n",
       "      <th>Swallowing 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",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P1</td>\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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P10</td>\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>...</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P100</td>\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>...</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P1000</td>\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>...</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P101</td>\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>...</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>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
       "index                                                                     \n",
       "0             P1   33       1              2            4             5   \n",
       "1            P10   17       1              3            1             5   \n",
       "2           P100   35       1              4            5             6   \n",
       "3          P1000   37       1              7            7             7   \n",
       "4           P101   46       1              6            8             7   \n",
       "\n",
       "       OccuPational Hazards  Genetic Risk  chronic Lung Disease  \\\n",
       "index                                                             \n",
       "0                         4             3                     2   \n",
       "1                         3             4                     2   \n",
       "2                         5             5                     4   \n",
       "3                         7             6                     7   \n",
       "4                         7             7                     6   \n",
       "\n",
       "       Balanced Diet  ...  Fatigue  Weight Loss  Shortness of Breath  \\\n",
       "index                 ...                                              \n",
       "0                  2  ...        3            4                    2   \n",
       "1                  2  ...        1            3                    7   \n",
       "2                  6  ...        8            7                    9   \n",
       "3                  7  ...        4            2                    3   \n",
       "4                  7  ...        3            2                    4   \n",
       "\n",
       "       Wheezing  Swallowing Difficulty  Clubbing of Finger Nails  \\\n",
       "index                                                              \n",
       "0             2                      3                         1   \n",
       "1             8                      6                         2   \n",
       "2             2                      1                         4   \n",
       "3             1                      4                         5   \n",
       "4             1                      4                         2   \n",
       "\n",
       "       Frequent Cold  Dry Cough  Snoring  Level  \n",
       "index                                            \n",
       "0                  2          3        4      1  \n",
       "1                  1          7        2      2  \n",
       "2                  6          7        2      3  \n",
       "3                  6          7        5      3  \n",
       "4                  4          2        3      3  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dane.replace({'Level':{'High' : 3, 'Medium' : 2, 'Low' : 1}})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "52632684",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "a47f580a",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "np.set_printoptions(precision=6, suppress=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "7caae544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y shape: (1000,)\n",
      "X shape: (1000, 23)\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(['Level', 'Patient Id'], axis=1)\n",
    "y = data['Level']\n",
    "\n",
    "\n",
    "print(\"Y shape:\", y.shape)\n",
    "print(\"X shape:\", X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "9139408a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split (X, y)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "2f45152a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (750, 23)\n",
      "y_train shape: (750,)\n",
      "X_test shape: (250, 23)\n",
      "y_test shape: (250,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "8ba2674d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "ba0a5bda",
   "metadata": {},
   "outputs": [
    {
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       "       [0.999889, 0.000075, 0.000035],\n",
       "       [0.000308, 0.999692, 0.      ]])"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob = classifier.predict_proba(X_test)\n",
    "y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "08f121e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       2, 3, 3, 2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 1, 3, 1, 3, 2, 3, 3, 3, 2,\n",
       "       1, 3, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 3, 2, 1, 3, 1, 1, 1, 2, 3, 3,\n",
       "       1, 3, 2, 1, 1, 3, 2, 2, 1, 1, 3, 3, 3, 2, 1, 3, 3, 1, 1, 2, 2, 3,\n",
       "       1, 3, 1, 1, 1, 3, 1, 3, 2, 1, 2, 2, 1, 1, 2, 3, 1, 1, 3, 1, 2, 2,\n",
       "       2, 3, 1, 3, 3, 1, 1, 2, 1, 2, 1, 3, 2, 1, 2, 1, 3, 2, 3, 1, 3, 1,\n",
       "       2, 2, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 3, 3, 1, 2, 2,\n",
       "       1, 2, 2, 2, 3, 2, 3, 3, 1, 2, 2, 1, 3, 1, 3, 3, 1, 1, 2, 3, 1, 1,\n",
       "       1, 2, 1, 3, 2, 2, 2, 1, 3, 2, 3, 1, 2, 2, 3, 2, 3, 1, 1, 3, 1, 2,\n",
       "       3, 2, 3, 3, 3, 3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "c876fff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "import seaborn as sns\n",
    "sns.set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "cbb6c719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.992\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plot_confusion_matrix(cm)\n",
    "\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy',':', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "e3c2afe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "colorscale": [
          [
           0,
           "rgb(3, 5, 18)"
          ],
          [
           0.09090909090909091,
           "rgb(25, 25, 51)"
          ],
          [
           0.18181818181818182,
           "rgb(44, 42, 87)"
          ],
          [
           0.2727272727272727,
           "rgb(58, 60, 125)"
          ],
          [
           0.36363636363636365,
           "rgb(62, 83, 160)"
          ],
          [
           0.45454545454545453,
           "rgb(62, 109, 178)"
          ],
          [
           0.5454545454545454,
           "rgb(72, 134, 187)"
          ],
          [
           0.6363636363636364,
           "rgb(89, 159, 196)"
          ],
          [
           0.7272727272727273,
           "rgb(114, 184, 205)"
          ],
          [
           0.8181818181818182,
           "rgb(149, 207, 216)"
          ],
          [
           0.9090909090909091,
           "rgb(192, 229, 232)"
          ],
          [
           1,
           "rgb(234, 252, 253)"
          ]
         ],
         "reversescale": true,
         "showscale": true,
         "type": "heatmap",
         "x": [
          "pred_1",
          "pred_2",
          "pred_3"
         ],
         "y": [
          "true_1",
          "true_2",
          "true_3"
         ],
         "z": [
          [
           0,
           0,
           85
          ],
          [
           2,
           87,
           0
          ],
          [
           76,
           0,
           0
          ]
         ]
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "color": "#000000"
          },
          "showarrow": false,
          "text": "0",
          "x": "pred_1",
          "xref": "x",
          "y": "true_1",
          "yref": "y"
         },
         {
          "font": {
           "color": "#000000"
          },
          "showarrow": false,
          "text": "0",
          "x": "pred_2",
          "xref": "x",
          "y": "true_1",
          "yref": "y"
         },
         {
          "font": {
           "color": "#FFFFFF"
          },
          "showarrow": false,
          "text": "85",
          "x": "pred_3",
          "xref": "x",
          "y": "true_1",
          "yref": "y"
         },
         {
          "font": {
           "color": "#000000"
          },
          "showarrow": false,
          "text": "2",
          "x": "pred_1",
          "xref": "x",
          "y": "true_2",
          "yref": "y"
         },
         {
          "font": {
           "color": "#FFFFFF"
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   "source": [
    "def plot_confusion_matrix(cm):\n",
    "    cm = cm[::-1]\n",
    "    cm = pd.DataFrame(cm, columns=['pred_1', 'pred_2', 'pred_3'], index=['true_1', 'true_2', 'true_3'])\n",
    "    fig = ff.create_annotated_heatmap(z = cm.values, x = list(cm.columns), y = list(cm.index), colorscale = 'ice', showscale = True, reversescale = True)\n",
    "    fig.update_layout(width=400, height=400, title='Confusion Matrix - Accuracy: {:.4f}'.format(acc), font_size=14)\n",
    "    fig.show()\n",
    "plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "a1ffeb65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      pred_1       0.97      1.00      0.99        76\n",
      "      pred_2       1.00      0.98      0.99        89\n",
      "      pred_3       1.00      1.00      1.00        85\n",
      "\n",
      "    accuracy                           0.99       250\n",
      "   macro avg       0.99      0.99      0.99       250\n",
      "weighted avg       0.99      0.99      0.99       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred, target_names=['pred_1', 'pred_2', 'pred_3']))"
   ]
  }
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