{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcos_df = pd.read_csv(\"PCOS_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sl. No</th>\n",
       "      <th>Patient File No.</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>Age (yrs)</th>\n",
       "      <th>Weight (Kg)</th>\n",
       "      <th>Height(Cm)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood Group</th>\n",
       "      <th>Pulse rate(bpm)</th>\n",
       "      <th>RR (breaths/min)</th>\n",
       "      <th>...</th>\n",
       "      <th>Fast food (Y/N)</th>\n",
       "      <th>Reg.Exercise(Y/N)</th>\n",
       "      <th>BP _Systolic (mmHg)</th>\n",
       "      <th>BP _Diastolic (mmHg)</th>\n",
       "      <th>Follicle No. (L)</th>\n",
       "      <th>Follicle No. (R)</th>\n",
       "      <th>Avg. F size (L) (mm)</th>\n",
       "      <th>Avg. F size (R) (mm)</th>\n",
       "      <th>Endometrium (mm)</th>\n",
       "      <th>Column1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.6</td>\n",
       "      <td>152.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>15</td>\n",
       "      <td>78</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>65.0</td>\n",
       "      <td>161.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>15</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>68.8</td>\n",
       "      <td>165.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>65.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sl. No  Patient File No.  PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)   \\\n",
       "0       1                 1           0          28         44.6        152.0   \n",
       "1       2                 2           0          36         65.0        161.5   \n",
       "2       3                 3           1          33         68.8        165.0   \n",
       "3       4                 4           0          37         65.0        148.0   \n",
       "4       5                 5           0          25         52.0        161.0   \n",
       "\n",
       "    BMI  Blood Group  Pulse rate(bpm)   RR (breaths/min)  ...  \\\n",
       "0  19.3           15                78                22  ...   \n",
       "1  24.9           15                74                20  ...   \n",
       "2  25.3           11                72                18  ...   \n",
       "3  29.7           13                72                20  ...   \n",
       "4  20.1           11                72                18  ...   \n",
       "\n",
       "   Fast food (Y/N)  Reg.Exercise(Y/N)  BP _Systolic (mmHg)  \\\n",
       "0              1.0                  0                  110   \n",
       "1              0.0                  0                  120   \n",
       "2              1.0                  0                  120   \n",
       "3              0.0                  0                  120   \n",
       "4              0.0                  0                  120   \n",
       "\n",
       "   BP _Diastolic (mmHg)  Follicle No. (L)  Follicle No. (R)  \\\n",
       "0                    80                 3                 3   \n",
       "1                    70                 3                 5   \n",
       "2                    80                13                15   \n",
       "3                    70                 2                 2   \n",
       "4                    80                 3                 4   \n",
       "\n",
       "   Avg. F size (L) (mm) Avg. F size (R) (mm)  Endometrium (mm)  Column1  \n",
       "0                  18.0                 18.0               8.5      NaN  \n",
       "1                  15.0                 14.0               3.7      NaN  \n",
       "2                  18.0                 20.0              10.0      NaN  \n",
       "3                  15.0                 14.0               7.5      NaN  \n",
       "4                  16.0                 14.0               7.0      NaN  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sl. No</th>\n",
       "      <th>Patient File No.</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>Age (yrs)</th>\n",
       "      <th>Weight (Kg)</th>\n",
       "      <th>Height(Cm)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood Group</th>\n",
       "      <th>Pulse rate(bpm)</th>\n",
       "      <th>RR (breaths/min)</th>\n",
       "      <th>...</th>\n",
       "      <th>Pimples(Y/N)</th>\n",
       "      <th>Fast food (Y/N)</th>\n",
       "      <th>Reg.Exercise(Y/N)</th>\n",
       "      <th>BP _Systolic (mmHg)</th>\n",
       "      <th>BP _Diastolic (mmHg)</th>\n",
       "      <th>Follicle No. (L)</th>\n",
       "      <th>Follicle No. (R)</th>\n",
       "      <th>Avg. F size (L) (mm)</th>\n",
       "      <th>Avg. F size (R) (mm)</th>\n",
       "      <th>Endometrium (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>271.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>0.327172</td>\n",
       "      <td>31.430684</td>\n",
       "      <td>59.637153</td>\n",
       "      <td>156.484835</td>\n",
       "      <td>24.307579</td>\n",
       "      <td>13.802218</td>\n",
       "      <td>73.247689</td>\n",
       "      <td>19.243993</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489834</td>\n",
       "      <td>0.514815</td>\n",
       "      <td>0.247689</td>\n",
       "      <td>114.661738</td>\n",
       "      <td>76.927911</td>\n",
       "      <td>6.129390</td>\n",
       "      <td>6.641405</td>\n",
       "      <td>15.018115</td>\n",
       "      <td>15.451701</td>\n",
       "      <td>8.475915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156.317519</td>\n",
       "      <td>156.317519</td>\n",
       "      <td>0.469615</td>\n",
       "      <td>5.411006</td>\n",
       "      <td>11.028287</td>\n",
       "      <td>6.033545</td>\n",
       "      <td>4.055129</td>\n",
       "      <td>1.840812</td>\n",
       "      <td>4.430285</td>\n",
       "      <td>1.688629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500359</td>\n",
       "      <td>0.500244</td>\n",
       "      <td>0.432070</td>\n",
       "      <td>7.384556</td>\n",
       "      <td>5.574112</td>\n",
       "      <td>4.229294</td>\n",
       "      <td>4.436889</td>\n",
       "      <td>3.566839</td>\n",
       "      <td>3.318848</td>\n",
       "      <td>2.165381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>271.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>406.000000</td>\n",
       "      <td>406.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sl. No  Patient File No.  PCOS (Y/N)   Age (yrs)  Weight (Kg)  \\\n",
       "count  541.000000        541.000000  541.000000  541.000000   541.000000   \n",
       "mean   271.000000        271.000000    0.327172   31.430684    59.637153   \n",
       "std    156.317519        156.317519    0.469615    5.411006    11.028287   \n",
       "min      1.000000          1.000000    0.000000   20.000000    31.000000   \n",
       "25%    136.000000        136.000000    0.000000   28.000000    52.000000   \n",
       "50%    271.000000        271.000000    0.000000   31.000000    59.000000   \n",
       "75%    406.000000        406.000000    1.000000   35.000000    65.000000   \n",
       "max    541.000000        541.000000    1.000000   48.000000   108.000000   \n",
       "\n",
       "       Height(Cm)          BMI  Blood Group  Pulse rate(bpm)   \\\n",
       "count   541.000000  541.000000   541.000000        541.000000   \n",
       "mean    156.484835   24.307579    13.802218         73.247689   \n",
       "std       6.033545    4.055129     1.840812          4.430285   \n",
       "min     137.000000   12.400000    11.000000         13.000000   \n",
       "25%     152.000000   21.600000    13.000000         72.000000   \n",
       "50%     156.000000   24.200000    14.000000         72.000000   \n",
       "75%     160.000000   26.600000    15.000000         74.000000   \n",
       "max     180.000000   38.900000    18.000000         82.000000   \n",
       "\n",
       "       RR (breaths/min)  ...  Pimples(Y/N)  Fast food (Y/N)  \\\n",
       "count        541.000000  ...    541.000000       540.000000   \n",
       "mean          19.243993  ...      0.489834         0.514815   \n",
       "std            1.688629  ...      0.500359         0.500244   \n",
       "min           16.000000  ...      0.000000         0.000000   \n",
       "25%           18.000000  ...      0.000000         0.000000   \n",
       "50%           18.000000  ...      0.000000         1.000000   \n",
       "75%           20.000000  ...      1.000000         1.000000   \n",
       "max           28.000000  ...      1.000000         1.000000   \n",
       "\n",
       "       Reg.Exercise(Y/N)  BP _Systolic (mmHg)  BP _Diastolic (mmHg)  \\\n",
       "count         541.000000           541.000000            541.000000   \n",
       "mean            0.247689           114.661738             76.927911   \n",
       "std             0.432070             7.384556              5.574112   \n",
       "min             0.000000            12.000000              8.000000   \n",
       "25%             0.000000           110.000000             70.000000   \n",
       "50%             0.000000           110.000000             80.000000   \n",
       "75%             0.000000           120.000000             80.000000   \n",
       "max             1.000000           140.000000            100.000000   \n",
       "\n",
       "       Follicle No. (L)  Follicle No. (R)  Avg. F size (L) (mm)  \\\n",
       "count        541.000000        541.000000            541.000000   \n",
       "mean           6.129390          6.641405             15.018115   \n",
       "std            4.229294          4.436889              3.566839   \n",
       "min            0.000000          0.000000              0.000000   \n",
       "25%            3.000000          3.000000             13.000000   \n",
       "50%            5.000000          6.000000             15.000000   \n",
       "75%            9.000000         10.000000             18.000000   \n",
       "max           22.000000         20.000000             24.000000   \n",
       "\n",
       "       Avg. F size (R) (mm)  Endometrium (mm)  \n",
       "count            541.000000        541.000000  \n",
       "mean              15.451701          8.475915  \n",
       "std                3.318848          2.165381  \n",
       "min                0.000000          0.000000  \n",
       "25%               13.000000          7.000000  \n",
       "50%               16.000000          8.500000  \n",
       "75%               18.000000          9.800000  \n",
       "max               24.000000         18.000000  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541 entries, 0 to 540\n",
      "Data columns (total 45 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Sl. No                  541 non-null    int64  \n",
      " 1   Patient File No.        541 non-null    int64  \n",
      " 2   PCOS (Y/N)              541 non-null    int64  \n",
      " 3    Age (yrs)              541 non-null    int64  \n",
      " 4   Weight (Kg)             541 non-null    float64\n",
      " 5   Height(Cm)              541 non-null    float64\n",
      " 6   BMI                     541 non-null    float64\n",
      " 7   Blood Group             541 non-null    int64  \n",
      " 8   Pulse rate(bpm)         541 non-null    int64  \n",
      " 9   RR (breaths/min)        541 non-null    int64  \n",
      " 10  Hb(g/dl)                541 non-null    float64\n",
      " 11  Cycle(R/I)              541 non-null    int64  \n",
      " 12  Cycle length(days)      541 non-null    int64  \n",
      " 13  Marraige Status (Yrs)   540 non-null    float64\n",
      " 14  Pregnant(Y/N)           541 non-null    int64  \n",
      " 15  No. of abortions        541 non-null    int64  \n",
      " 16    I   beta-HCG(mIU/mL)  541 non-null    float64\n",
      " 17  II    beta-HCG(mIU/mL)  541 non-null    object \n",
      " 18  FSH(mIU/mL)             541 non-null    float64\n",
      " 19  LH(mIU/mL)              541 non-null    float64\n",
      " 20  FSH/LH                  541 non-null    float64\n",
      " 21  Hip(inch)               541 non-null    int64  \n",
      " 22  Waist(inch)             541 non-null    int64  \n",
      " 23  Waist:Hip Ratio         541 non-null    float64\n",
      " 24  TSH (mIU/L)             541 non-null    float64\n",
      " 25  AMH(ng/mL)              541 non-null    object \n",
      " 26  PRL(ng/mL)              541 non-null    float64\n",
      " 27  Vit D3 (ng/mL)          541 non-null    float64\n",
      " 28  PRG(ng/mL)              541 non-null    float64\n",
      " 29  RBS(mg/dl)              541 non-null    float64\n",
      " 30  Weight gain(Y/N)        541 non-null    int64  \n",
      " 31  hair growth(Y/N)        541 non-null    int64  \n",
      " 32  Skin darkening (Y/N)    541 non-null    int64  \n",
      " 33  Hair loss(Y/N)          541 non-null    int64  \n",
      " 34  Pimples(Y/N)            541 non-null    int64  \n",
      " 35  Fast food (Y/N)         540 non-null    float64\n",
      " 36  Reg.Exercise(Y/N)       541 non-null    int64  \n",
      " 37  BP _Systolic (mmHg)     541 non-null    int64  \n",
      " 38  BP _Diastolic (mmHg)    541 non-null    int64  \n",
      " 39  Follicle No. (L)        541 non-null    int64  \n",
      " 40  Follicle No. (R)        541 non-null    int64  \n",
      " 41  Avg. F size (L) (mm)    541 non-null    float64\n",
      " 42  Avg. F size (R) (mm)    541 non-null    float64\n",
      " 43  Endometrium (mm)        541 non-null    float64\n",
      " 44  Column1                 2 non-null      object \n",
      "dtypes: float64(19), int64(23), object(3)\n",
      "memory usage: 190.3+ KB\n"
     ]
    }
   ],
   "source": [
    "pcos_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sl. No                      0\n",
       "Patient File No.            0\n",
       "PCOS (Y/N)                  0\n",
       " Age (yrs)                  0\n",
       "Weight (Kg)                 0\n",
       "Height(Cm)                  0\n",
       "BMI                         0\n",
       "Blood Group                 0\n",
       "Pulse rate(bpm)             0\n",
       "RR (breaths/min)            0\n",
       "Hb(g/dl)                    0\n",
       "Cycle(R/I)                  0\n",
       "Cycle length(days)          0\n",
       "Marraige Status (Yrs)       1\n",
       "Pregnant(Y/N)               0\n",
       "No. of abortions            0\n",
       "  I   beta-HCG(mIU/mL)      0\n",
       "II    beta-HCG(mIU/mL)      0\n",
       "FSH(mIU/mL)                 0\n",
       "LH(mIU/mL)                  0\n",
       "FSH/LH                      0\n",
       "Hip(inch)                   0\n",
       "Waist(inch)                 0\n",
       "Waist:Hip Ratio             0\n",
       "TSH (mIU/L)                 0\n",
       "AMH(ng/mL)                  0\n",
       "PRL(ng/mL)                  0\n",
       "Vit D3 (ng/mL)              0\n",
       "PRG(ng/mL)                  0\n",
       "RBS(mg/dl)                  0\n",
       "Weight gain(Y/N)            0\n",
       "hair growth(Y/N)            0\n",
       "Skin darkening (Y/N)        0\n",
       "Hair loss(Y/N)              0\n",
       "Pimples(Y/N)                0\n",
       "Fast food (Y/N)             1\n",
       "Reg.Exercise(Y/N)           0\n",
       "BP _Systolic (mmHg)         0\n",
       "BP _Diastolic (mmHg)        0\n",
       "Follicle No. (L)            0\n",
       "Follicle No. (R)            0\n",
       "Avg. F size (L) (mm)        0\n",
       "Avg. F size (R) (mm)        0\n",
       "Endometrium (mm)            0\n",
       "Column1                   539\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\1931395523.py:4: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(pcos_df['Marraige Status (Yrs)'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marraige Status (Yrs)', ylabel='Marraige Status (Yrs)'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "#from above code we see null value exists in the marriage status\n",
    "#filling the null value\n",
    "sns.boxplot(pcos_df['Marraige Status (Yrs)'])\n",
    "sns.distplot(pcos_df['Marraige Status (Yrs)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\997129956.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df[\"Cycle(R/I)\"].replace({5: 4}, inplace=True)\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\997129956.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df[\"Cycle(R/I)\"].replace({2: 0, 4: 1}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# pcos_df[pcos_df[\"Cycle(R/I)\"] == 5] \n",
    "pcos_df[\"Cycle(R/I)\"].replace({5: 4}, inplace=True)\n",
    "pcos_df[\"Cycle(R/I)\"].replace({2: 0, 4: 1}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\1769027688.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(pcos_df['Fast food (Y/N)'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fast food (Y/N)', ylabel='Fast food (Y/N)'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "sns.boxplot(pcos_df['Fast food (Y/N)'])\n",
    "#\n",
    "# Distribution plot\n",
    "#\n",
    "sns.distplot(pcos_df['Fast food (Y/N)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\1109871252.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Marraige Status (Yrs)'].fillna((pcos_df['Marraige Status (Yrs)'].median()), inplace=True)\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\1109871252.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Fast food (Y/N)'].fillna(pcos_df['Fast food (Y/N)'].mode(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pcos_df['Marraige Status (Yrs)'].fillna((pcos_df['Marraige Status (Yrs)'].median()), inplace=True)\n",
    "pcos_df['Fast food (Y/N)'].fillna(pcos_df['Fast food (Y/N)'].mode(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_delete = [\"Sl. No\", \"Patient File No.\", \"Column1\"]\n",
    "for col in columns_to_delete:\n",
    "    if col in pcos_df.columns:\n",
    "        del pcos_df[col]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541 entries, 0 to 540\n",
      "Data columns (total 42 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   PCOS (Y/N)              541 non-null    int64  \n",
      " 1    Age (yrs)              541 non-null    int64  \n",
      " 2   Weight (Kg)             541 non-null    float64\n",
      " 3   Height(Cm)              541 non-null    float64\n",
      " 4   BMI                     541 non-null    float64\n",
      " 5   Blood Group             541 non-null    int64  \n",
      " 6   Pulse rate(bpm)         541 non-null    int64  \n",
      " 7   RR (breaths/min)        541 non-null    int64  \n",
      " 8   Hb(g/dl)                541 non-null    float64\n",
      " 9   Cycle(R/I)              541 non-null    int64  \n",
      " 10  Cycle length(days)      541 non-null    int64  \n",
      " 11  Marraige Status (Yrs)   541 non-null    float64\n",
      " 12  Pregnant(Y/N)           541 non-null    int64  \n",
      " 13  No. of abortions        541 non-null    int64  \n",
      " 14    I   beta-HCG(mIU/mL)  541 non-null    float64\n",
      " 15  II    beta-HCG(mIU/mL)  541 non-null    object \n",
      " 16  FSH(mIU/mL)             541 non-null    float64\n",
      " 17  LH(mIU/mL)              541 non-null    float64\n",
      " 18  FSH/LH                  541 non-null    float64\n",
      " 19  Hip(inch)               541 non-null    int64  \n",
      " 20  Waist(inch)             541 non-null    int64  \n",
      " 21  Waist:Hip Ratio         541 non-null    float64\n",
      " 22  TSH (mIU/L)             541 non-null    float64\n",
      " 23  AMH(ng/mL)              541 non-null    object \n",
      " 24  PRL(ng/mL)              541 non-null    float64\n",
      " 25  Vit D3 (ng/mL)          541 non-null    float64\n",
      " 26  PRG(ng/mL)              541 non-null    float64\n",
      " 27  RBS(mg/dl)              541 non-null    float64\n",
      " 28  Weight gain(Y/N)        541 non-null    int64  \n",
      " 29  hair growth(Y/N)        541 non-null    int64  \n",
      " 30  Skin darkening (Y/N)    541 non-null    int64  \n",
      " 31  Hair loss(Y/N)          541 non-null    int64  \n",
      " 32  Pimples(Y/N)            541 non-null    int64  \n",
      " 33  Fast food (Y/N)         540 non-null    float64\n",
      " 34  Reg.Exercise(Y/N)       541 non-null    int64  \n",
      " 35  BP _Systolic (mmHg)     541 non-null    int64  \n",
      " 36  BP _Diastolic (mmHg)    541 non-null    int64  \n",
      " 37  Follicle No. (L)        541 non-null    int64  \n",
      " 38  Follicle No. (R)        541 non-null    int64  \n",
      " 39  Avg. F size (L) (mm)    541 non-null    float64\n",
      " 40  Avg. F size (R) (mm)    541 non-null    float64\n",
      " 41  Endometrium (mm)        541 non-null    float64\n",
      "dtypes: float64(19), int64(21), object(2)\n",
      "memory usage: 177.6+ KB\n"
     ]
    }
   ],
   "source": [
    "pcos_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pcos_df[\"II    beta-HCG(mIU/mL)\"] = pd.to_numeric(pcos_df[\"II    beta-HCG(mIU/mL)\"], errors='coerce')\n",
    "pcos_df[\"AMH(ng/mL)\"] = pd.to_numeric(pcos_df[\"AMH(ng/mL)\"], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\691906672.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df[\"II    beta-HCG(mIU/mL)\"].fillna(pcos_df[\"II    beta-HCG(mIU/mL)\"].median(),inplace=True)\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\691906672.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df[\"AMH(ng/mL)\"].fillna(pcos_df[\"AMH(ng/mL)\"].median(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "pcos_df[\"II    beta-HCG(mIU/mL)\"].fillna(pcos_df[\"II    beta-HCG(mIU/mL)\"].median(),inplace=True)\n",
    "pcos_df[\"AMH(ng/mL)\"].fillna(pcos_df[\"AMH(ng/mL)\"].median(),inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCOS (Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      " Age (yrs) \n",
      "min :  20 , max: 48 \n",
      "\n",
      "Weight (Kg) \n",
      "min :  31.0 , max: 108.0 \n",
      "\n",
      "Height(Cm)  \n",
      "min :  137.0 , max: 180.0 \n",
      "\n",
      "BMI \n",
      "min :  12.4 , max: 38.9 \n",
      "\n",
      "Blood Group \n",
      "min :  11 , max: 18 \n",
      "\n",
      "Pulse rate(bpm)  \n",
      "min :  13 , max: 82 \n",
      "\n",
      "RR (breaths/min) \n",
      "min :  16 , max: 28 \n",
      "\n",
      "Hb(g/dl) \n",
      "min :  8.5 , max: 14.8 \n",
      "\n",
      "Cycle(R/I) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "Cycle length(days) \n",
      "min :  0 , max: 12 \n",
      "\n",
      "Marraige Status (Yrs) \n",
      "min :  0.0 , max: 30.0 \n",
      "\n",
      "Pregnant(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "No. of abortions \n",
      "min :  0 , max: 5 \n",
      "\n",
      "  I   beta-HCG(mIU/mL) \n",
      "min :  1.3 , max: 32460.97 \n",
      "\n",
      "II    beta-HCG(mIU/mL) \n",
      "min :  0.99 , max: 25000.0 \n",
      "\n",
      "FSH(mIU/mL) \n",
      "min :  0.21 , max: 5052.0 \n",
      "\n",
      "LH(mIU/mL) \n",
      "min :  0.02 , max: 2018.0 \n",
      "\n",
      "FSH/LH \n",
      "min :  0.0 , max: 1372.83 \n",
      "\n",
      "Hip(inch) \n",
      "min :  26 , max: 48 \n",
      "\n",
      "Waist(inch) \n",
      "min :  24 , max: 47 \n",
      "\n",
      "Waist:Hip Ratio \n",
      "min :  0.76 , max: 0.98 \n",
      "\n",
      "TSH (mIU/L) \n",
      "min :  0.04 , max: 65.0 \n",
      "\n",
      "AMH(ng/mL) \n",
      "min :  0.1 , max: 66.0 \n",
      "\n",
      "PRL(ng/mL) \n",
      "min :  0.4 , max: 128.24 \n",
      "\n",
      "Vit D3 (ng/mL) \n",
      "min :  0.0 , max: 6014.66 \n",
      "\n",
      "PRG(ng/mL) \n",
      "min :  0.047 , max: 85.0 \n",
      "\n",
      "RBS(mg/dl) \n",
      "min :  60.0 , max: 350.0 \n",
      "\n",
      "Weight gain(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "hair growth(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "Skin darkening (Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "Hair loss(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "Pimples(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "Fast food (Y/N) \n",
      "min :  0.0 , max: 1.0 \n",
      "\n",
      "Reg.Exercise(Y/N) \n",
      "min :  0 , max: 1 \n",
      "\n",
      "BP _Systolic (mmHg) \n",
      "min :  12 , max: 140 \n",
      "\n",
      "BP _Diastolic (mmHg) \n",
      "min :  8 , max: 100 \n",
      "\n",
      "Follicle No. (L) \n",
      "min :  0 , max: 22 \n",
      "\n",
      "Follicle No. (R) \n",
      "min :  0 , max: 20 \n",
      "\n",
      "Avg. F size (L) (mm) \n",
      "min :  0.0 , max: 24.0 \n",
      "\n",
      "Avg. F size (R) (mm) \n",
      "min :  0.0 , max: 24.0 \n",
      "\n",
      "Endometrium (mm) \n",
      "min :  0.0 , max: 18.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in pcos_df.columns:\n",
    "    print(col,\"\\nmin : \",min(pcos_df[col]),\", max:\",max(pcos_df[col]),\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>PCOS (Y/N)</th>\n",
       "      <th>Age (yrs)</th>\n",
       "      <th>Weight (Kg)</th>\n",
       "      <th>Height(Cm)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood Group</th>\n",
       "      <th>Pulse rate(bpm)</th>\n",
       "      <th>RR (breaths/min)</th>\n",
       "      <th>Hb(g/dl)</th>\n",
       "      <th>Cycle(R/I)</th>\n",
       "      <th>...</th>\n",
       "      <th>Pimples(Y/N)</th>\n",
       "      <th>Fast food (Y/N)</th>\n",
       "      <th>Reg.Exercise(Y/N)</th>\n",
       "      <th>BP _Systolic (mmHg)</th>\n",
       "      <th>BP _Diastolic (mmHg)</th>\n",
       "      <th>Follicle No. (L)</th>\n",
       "      <th>Follicle No. (R)</th>\n",
       "      <th>Avg. F size (L) (mm)</th>\n",
       "      <th>Avg. F size (R) (mm)</th>\n",
       "      <th>Endometrium (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.327172</td>\n",
       "      <td>31.430684</td>\n",
       "      <td>59.637153</td>\n",
       "      <td>156.484835</td>\n",
       "      <td>24.307579</td>\n",
       "      <td>13.802218</td>\n",
       "      <td>73.247689</td>\n",
       "      <td>19.243993</td>\n",
       "      <td>11.160037</td>\n",
       "      <td>0.279113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489834</td>\n",
       "      <td>0.514815</td>\n",
       "      <td>0.247689</td>\n",
       "      <td>114.661738</td>\n",
       "      <td>76.927911</td>\n",
       "      <td>6.129390</td>\n",
       "      <td>6.641405</td>\n",
       "      <td>15.018115</td>\n",
       "      <td>15.451701</td>\n",
       "      <td>8.475915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.469615</td>\n",
       "      <td>5.411006</td>\n",
       "      <td>11.028287</td>\n",
       "      <td>6.033545</td>\n",
       "      <td>4.055129</td>\n",
       "      <td>1.840812</td>\n",
       "      <td>4.430285</td>\n",
       "      <td>1.688629</td>\n",
       "      <td>0.866904</td>\n",
       "      <td>0.448978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500359</td>\n",
       "      <td>0.500244</td>\n",
       "      <td>0.432070</td>\n",
       "      <td>7.384556</td>\n",
       "      <td>5.574112</td>\n",
       "      <td>4.229294</td>\n",
       "      <td>4.436889</td>\n",
       "      <td>3.566839</td>\n",
       "      <td>3.318848</td>\n",
       "      <td>2.165381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)          BMI  \\\n",
       "count  541.000000  541.000000   541.000000   541.000000  541.000000   \n",
       "mean     0.327172   31.430684    59.637153   156.484835   24.307579   \n",
       "std      0.469615    5.411006    11.028287     6.033545    4.055129   \n",
       "min      0.000000   20.000000    31.000000   137.000000   12.400000   \n",
       "25%      0.000000   28.000000    52.000000   152.000000   21.600000   \n",
       "50%      0.000000   31.000000    59.000000   156.000000   24.200000   \n",
       "75%      1.000000   35.000000    65.000000   160.000000   26.600000   \n",
       "max      1.000000   48.000000   108.000000   180.000000   38.900000   \n",
       "\n",
       "       Blood Group  Pulse rate(bpm)   RR (breaths/min)    Hb(g/dl)  \\\n",
       "count   541.000000        541.000000        541.000000  541.000000   \n",
       "mean     13.802218         73.247689         19.243993   11.160037   \n",
       "std       1.840812          4.430285          1.688629    0.866904   \n",
       "min      11.000000         13.000000         16.000000    8.500000   \n",
       "25%      13.000000         72.000000         18.000000   10.500000   \n",
       "50%      14.000000         72.000000         18.000000   11.000000   \n",
       "75%      15.000000         74.000000         20.000000   11.700000   \n",
       "max      18.000000         82.000000         28.000000   14.800000   \n",
       "\n",
       "       Cycle(R/I)  ...  Pimples(Y/N)  Fast food (Y/N)  Reg.Exercise(Y/N)  \\\n",
       "count  541.000000  ...    541.000000       540.000000         541.000000   \n",
       "mean     0.279113  ...      0.489834         0.514815           0.247689   \n",
       "std      0.448978  ...      0.500359         0.500244           0.432070   \n",
       "min      0.000000  ...      0.000000         0.000000           0.000000   \n",
       "25%      0.000000  ...      0.000000         0.000000           0.000000   \n",
       "50%      0.000000  ...      0.000000         1.000000           0.000000   \n",
       "75%      1.000000  ...      1.000000         1.000000           0.000000   \n",
       "max      1.000000  ...      1.000000         1.000000           1.000000   \n",
       "\n",
       "       BP _Systolic (mmHg)  BP _Diastolic (mmHg)  Follicle No. (L)  \\\n",
       "count           541.000000            541.000000        541.000000   \n",
       "mean            114.661738             76.927911          6.129390   \n",
       "std               7.384556              5.574112          4.229294   \n",
       "min              12.000000              8.000000          0.000000   \n",
       "25%             110.000000             70.000000          3.000000   \n",
       "50%             110.000000             80.000000          5.000000   \n",
       "75%             120.000000             80.000000          9.000000   \n",
       "max             140.000000            100.000000         22.000000   \n",
       "\n",
       "       Follicle No. (R)  Avg. F size (L) (mm)  Avg. F size (R) (mm)  \\\n",
       "count        541.000000            541.000000            541.000000   \n",
       "mean           6.641405             15.018115             15.451701   \n",
       "std            4.436889              3.566839              3.318848   \n",
       "min            0.000000              0.000000              0.000000   \n",
       "25%            3.000000             13.000000             13.000000   \n",
       "50%            6.000000             15.000000             16.000000   \n",
       "75%           10.000000             18.000000             18.000000   \n",
       "max           20.000000             24.000000             24.000000   \n",
       "\n",
       "       Endometrium (mm)  \n",
       "count        541.000000  \n",
       "mean           8.475915  \n",
       "std            2.165381  \n",
       "min            0.000000  \n",
       "25%            7.000000  \n",
       "50%            8.500000  \n",
       "75%            9.800000  \n",
       "max           18.000000  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCOS (Y/N)                0\n",
       " Age (yrs)                0\n",
       "Weight (Kg)               0\n",
       "Height(Cm)                0\n",
       "BMI                       0\n",
       "Blood Group               0\n",
       "Pulse rate(bpm)           0\n",
       "RR (breaths/min)          0\n",
       "Hb(g/dl)                  0\n",
       "Cycle(R/I)                0\n",
       "Cycle length(days)        0\n",
       "Marraige Status (Yrs)     0\n",
       "Pregnant(Y/N)             0\n",
       "No. of abortions          0\n",
       "  I   beta-HCG(mIU/mL)    0\n",
       "II    beta-HCG(mIU/mL)    0\n",
       "FSH(mIU/mL)               0\n",
       "LH(mIU/mL)                0\n",
       "FSH/LH                    0\n",
       "Hip(inch)                 0\n",
       "Waist(inch)               0\n",
       "Waist:Hip Ratio           0\n",
       "TSH (mIU/L)               0\n",
       "AMH(ng/mL)                0\n",
       "PRL(ng/mL)                0\n",
       "Vit D3 (ng/mL)            0\n",
       "PRG(ng/mL)                0\n",
       "RBS(mg/dl)                0\n",
       "Weight gain(Y/N)          0\n",
       "hair growth(Y/N)          0\n",
       "Skin darkening (Y/N)      0\n",
       "Hair loss(Y/N)            0\n",
       "Pimples(Y/N)              0\n",
       "Fast food (Y/N)           1\n",
       "Reg.Exercise(Y/N)         0\n",
       "BP _Systolic (mmHg)       0\n",
       "BP _Diastolic (mmHg)      0\n",
       "Follicle No. (L)          0\n",
       "Follicle No. (R)          0\n",
       "Avg. F size (L) (mm)      0\n",
       "Avg. F size (R) (mm)      0\n",
       "Endometrium (mm)          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_16484\\3206681086.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Fast food (Y/N)'].fillna(pcos_df['Fast food (Y/N)'].median(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "pcos_df['Fast food (Y/N)'].fillna(pcos_df['Fast food (Y/N)'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCOS (Y/N)                0\n",
       " Age (yrs)                0\n",
       "Weight (Kg)               0\n",
       "Height(Cm)                0\n",
       "BMI                       0\n",
       "Blood Group               0\n",
       "Pulse rate(bpm)           0\n",
       "RR (breaths/min)          0\n",
       "Hb(g/dl)                  0\n",
       "Cycle(R/I)                0\n",
       "Cycle length(days)        0\n",
       "Marraige Status (Yrs)     0\n",
       "Pregnant(Y/N)             0\n",
       "No. of abortions          0\n",
       "  I   beta-HCG(mIU/mL)    0\n",
       "II    beta-HCG(mIU/mL)    0\n",
       "FSH(mIU/mL)               0\n",
       "LH(mIU/mL)                0\n",
       "FSH/LH                    0\n",
       "Hip(inch)                 0\n",
       "Waist(inch)               0\n",
       "Waist:Hip Ratio           0\n",
       "TSH (mIU/L)               0\n",
       "AMH(ng/mL)                0\n",
       "PRL(ng/mL)                0\n",
       "Vit D3 (ng/mL)            0\n",
       "PRG(ng/mL)                0\n",
       "RBS(mg/dl)                0\n",
       "Weight gain(Y/N)          0\n",
       "hair growth(Y/N)          0\n",
       "Skin darkening (Y/N)      0\n",
       "Hair loss(Y/N)            0\n",
       "Pimples(Y/N)              0\n",
       "Fast food (Y/N)           0\n",
       "Reg.Exercise(Y/N)         0\n",
       "BP _Systolic (mmHg)       0\n",
       "BP _Diastolic (mmHg)      0\n",
       "Follicle No. (L)          0\n",
       "Follicle No. (R)          0\n",
       "Avg. F size (L) (mm)      0\n",
       "Avg. F size (R) (mm)      0\n",
       "Endometrium (mm)          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcos_df.to_csv(\"cleandata.csv\")"
   ]
  }
 ],
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