{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4776892",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loading the dataset\n",
    "df_user = pd.read_csv('diabetes_012_health_indicators_BRFSS2015.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ded40660",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
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       "      <th>PhysActivity</th>\n",
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       "      <th>...</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
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       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Diabetes_012  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0           0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
       "1           0.0     0.0       0.0        0.0  25.0     1.0     0.0   \n",
       "2           0.0     1.0       1.0        1.0  28.0     0.0     0.0   \n",
       "3           0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
       "4           0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
       "\n",
       "   HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
       "0                   0.0           0.0     0.0  ...            1.0   \n",
       "1                   0.0           1.0     0.0  ...            0.0   \n",
       "2                   0.0           0.0     1.0  ...            1.0   \n",
       "3                   0.0           1.0     1.0  ...            1.0   \n",
       "4                   0.0           1.0     1.0  ...            1.0   \n",
       "\n",
       "   NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  \\\n",
       "0          0.0      5.0      18.0      15.0       1.0  0.0   9.0        4.0   \n",
       "1          1.0      3.0       0.0       0.0       0.0  0.0   7.0        6.0   \n",
       "2          1.0      5.0      30.0      30.0       1.0  0.0   9.0        4.0   \n",
       "3          0.0      2.0       0.0       0.0       0.0  0.0  11.0        3.0   \n",
       "4          0.0      2.0       3.0       0.0       0.0  0.0  11.0        5.0   \n",
       "\n",
       "   Income  \n",
       "0     3.0  \n",
       "1     1.0  \n",
       "2     8.0  \n",
       "3     6.0  \n",
       "4     4.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the date (First 5 rows)\n",
    "df_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c63f7d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253680 entries, 0 to 253679\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   Diabetes_012          253680 non-null  float64\n",
      " 1   HighBP                253680 non-null  float64\n",
      " 2   HighChol              253680 non-null  float64\n",
      " 3   CholCheck             253680 non-null  float64\n",
      " 4   BMI                   253680 non-null  float64\n",
      " 5   Smoker                253680 non-null  float64\n",
      " 6   Stroke                253680 non-null  float64\n",
      " 7   HeartDiseaseorAttack  253680 non-null  float64\n",
      " 8   PhysActivity          253680 non-null  float64\n",
      " 9   Fruits                253680 non-null  float64\n",
      " 10  Veggies               253680 non-null  float64\n",
      " 11  HvyAlcoholConsump     253680 non-null  float64\n",
      " 12  AnyHealthcare         253680 non-null  float64\n",
      " 13  NoDocbcCost           253680 non-null  float64\n",
      " 14  GenHlth               253680 non-null  float64\n",
      " 15  MentHlth              253680 non-null  float64\n",
      " 16  PhysHlth              253680 non-null  float64\n",
      " 17  DiffWalk              253680 non-null  float64\n",
      " 18  Sex                   253680 non-null  float64\n",
      " 19  Age                   253680 non-null  float64\n",
      " 20  Education             253680 non-null  float64\n",
      " 21  Income                253680 non-null  float64\n",
      "dtypes: float64(22)\n",
      "memory usage: 42.6 MB\n"
     ]
    }
   ],
   "source": [
    "# informations about dataset\n",
    "df_user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38e7acc3",
   "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>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>...</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "      <td>253680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.296921</td>\n",
       "      <td>0.429001</td>\n",
       "      <td>0.424121</td>\n",
       "      <td>0.962670</td>\n",
       "      <td>28.382364</td>\n",
       "      <td>0.443169</td>\n",
       "      <td>0.040571</td>\n",
       "      <td>0.094186</td>\n",
       "      <td>0.756544</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951053</td>\n",
       "      <td>0.084177</td>\n",
       "      <td>2.511392</td>\n",
       "      <td>3.184772</td>\n",
       "      <td>4.242081</td>\n",
       "      <td>0.168224</td>\n",
       "      <td>0.440342</td>\n",
       "      <td>8.032119</td>\n",
       "      <td>5.050434</td>\n",
       "      <td>6.053875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.698160</td>\n",
       "      <td>0.494934</td>\n",
       "      <td>0.494210</td>\n",
       "      <td>0.189571</td>\n",
       "      <td>6.608694</td>\n",
       "      <td>0.496761</td>\n",
       "      <td>0.197294</td>\n",
       "      <td>0.292087</td>\n",
       "      <td>0.429169</td>\n",
       "      <td>0.481639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215759</td>\n",
       "      <td>0.277654</td>\n",
       "      <td>1.068477</td>\n",
       "      <td>7.412847</td>\n",
       "      <td>8.717951</td>\n",
       "      <td>0.374066</td>\n",
       "      <td>0.496429</td>\n",
       "      <td>3.054220</td>\n",
       "      <td>0.985774</td>\n",
       "      <td>2.071148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</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",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Diabetes_012         HighBP       HighChol      CholCheck  \\\n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000   \n",
       "mean        0.296921       0.429001       0.424121       0.962670   \n",
       "std         0.698160       0.494934       0.494210       0.189571   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       1.000000   \n",
       "50%         0.000000       0.000000       0.000000       1.000000   \n",
       "75%         0.000000       1.000000       1.000000       1.000000   \n",
       "max         2.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                 BMI         Smoker         Stroke  HeartDiseaseorAttack  \\\n",
       "count  253680.000000  253680.000000  253680.000000         253680.000000   \n",
       "mean       28.382364       0.443169       0.040571              0.094186   \n",
       "std         6.608694       0.496761       0.197294              0.292087   \n",
       "min        12.000000       0.000000       0.000000              0.000000   \n",
       "25%        24.000000       0.000000       0.000000              0.000000   \n",
       "50%        27.000000       0.000000       0.000000              0.000000   \n",
       "75%        31.000000       1.000000       0.000000              0.000000   \n",
       "max        98.000000       1.000000       1.000000              1.000000   \n",
       "\n",
       "        PhysActivity         Fruits  ...  AnyHealthcare    NoDocbcCost  \\\n",
       "count  253680.000000  253680.000000  ...  253680.000000  253680.000000   \n",
       "mean        0.756544       0.634256  ...       0.951053       0.084177   \n",
       "std         0.429169       0.481639  ...       0.215759       0.277654   \n",
       "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "25%         1.000000       0.000000  ...       1.000000       0.000000   \n",
       "50%         1.000000       1.000000  ...       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",
       "             GenHlth       MentHlth       PhysHlth       DiffWalk  \\\n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000   \n",
       "mean        2.511392       3.184772       4.242081       0.168224   \n",
       "std         1.068477       7.412847       8.717951       0.374066   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%         2.000000       0.000000       0.000000       0.000000   \n",
       "50%         2.000000       0.000000       0.000000       0.000000   \n",
       "75%         3.000000       2.000000       3.000000       0.000000   \n",
       "max         5.000000      30.000000      30.000000       1.000000   \n",
       "\n",
       "                 Sex            Age      Education         Income  \n",
       "count  253680.000000  253680.000000  253680.000000  253680.000000  \n",
       "mean        0.440342       8.032119       5.050434       6.053875  \n",
       "std         0.496429       3.054220       0.985774       2.071148  \n",
       "min         0.000000       1.000000       1.000000       1.000000  \n",
       "25%         0.000000       6.000000       4.000000       5.000000  \n",
       "50%         0.000000       8.000000       5.000000       7.000000  \n",
       "75%         1.000000      10.000000       6.000000       8.000000  \n",
       "max         1.000000      13.000000       6.000000       8.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary Statistics\n",
    "df_user.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a8a8ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the continuous columns\n",
    "continuous_columns = ['BMI', 'MentHlth', 'PhysHlth', 'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84c83da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No missing values detected.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Step 1: Handling Missing Values (if any)\n",
    "if df_user.isnull().sum().sum() > 0:\n",
    "    for column in continuous_columns:\n",
    "        if df_user[column].isnull().sum() > 0:\n",
    "            df_user[column].fillna(df_user[column].median(), inplace=True)\n",
    "else:\n",
    "    print(\"No missing values detected.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43da8165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Handling Outliers using IQR method\n",
    "for column in continuous_columns:\n",
    "    Q1 = df_user[column].quantile(0.25)\n",
    "    Q3 = df_user[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    df_user[column] = np.where(df_user[column] > upper_bound, upper_bound, \n",
    "                               np.where(df_user[column] < lower_bound, lower_bound, df_user[column]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29b80eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3: Data Visualization\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
    "fig.suptitle('Continuous Variable Distributions After Outlier Handling')\n",
    "\n",
    "for i, column in enumerate(continuous_columns):\n",
    "    row, col = divmod(i, 2)\n",
    "    axs[row, col].hist(df_user[column], bins=20, color='green', alpha=0.7)\n",
    "    axs[row, col].set_title(f'{column} Distribution')\n",
    "    axs[row, col].set_xlabel(column)\n",
    "    axs[row, col].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fb09b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create box plots for each continuous variable\n",
    "continuous_columns = ['BMI', 'MentHlth', 'PhysHlth', 'Age']\n",
    "df_user[continuous_columns].plot(kind='box', figsize=(10, 6))\n",
    "plt.title('Box Plot of Continuous Variables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "804f11cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZ8vT01PBwcG6/PLLNXz4cE2YMME+rZ+zJ598ss72p556Ss8///x51/fEE08oPDxcf//737V48WL7HNbTpk1zmP6tvu6++2773y0Wi+Li4jRz5kxNnDjxV+8bQMMwGae7VRgAoPT0dHXo0EHPPPPMaYMYAODiQ0gGgJ9t2bJFCxcuVK9evRQcHKzdu3frxRdfVFFRkbZv316v6cQAAE0Twy0A4GcBAQHavHmz5s6dq4KCAlksFvXr108vvPACARkALjFcSQYAAACcMAUcAAAA4ISQDAAAADghJAMAAABOuHGvAdlsNh05ckRBQUENtkwpAAAAGo5hGCouLlZ0dPQZF4oiJDegI0eOKCYmxtVlAAAA4CwyMzPPuCInIbkBBQUFSar5otd3uVYAAABcOEVFRYqJibHnttMhJDegU0MsgoODCckAAABu7GxDY7lxDwAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwIlLQ/L06dN11VVXKSgoSOHh4br55pu1e/duhz7jxo2TyWRyePTs2dOhj9Vq1YMPPqiwsDAFBARoxIgRysrKcuiTn5+vMWPGyGKxyGKxaMyYMSooKHDok5GRoeHDhysgIEBhYWF66KGHVFFRcUHeOwAAANyXS0Py6tWrdf/992v9+vVatmyZqqqqNGjQIJWWljr0GzJkiLKzs+2Pr7/+2mH7I488ok8//VSLFi3SmjVrVFJSomHDhqm6utreZ/To0UpLS9OSJUu0ZMkSpaWlacyYMfbt1dXVuvHGG1VaWqo1a9Zo0aJF+vjjjzV58uQL+0UAAACA2zEZhmG4uohTjh07pvDwcK1evVp9+/aVVHMluaCgQJ999lmdryksLFSLFi30/vvv67bbbpMkHTlyRDExMfr66681ePBg7dq1SwkJCVq/fr169OghSVq/fr2Sk5P1008/qX379vrmm280bNgwZWZmKjo6WpK0aNEijRs3Trm5uee0zHRRUZEsFosKCwtZlhoAAMANnWtec6sxyYWFhZKkkJAQh/bvvvtO4eHhio+P14QJE5Sbm2vflpKSosrKSg0aNMjeFh0drcTERK1du1aStG7dOlksFntAlqSePXvKYrE49ElMTLQHZEkaPHiwrFarUlJS6qzXarWqqKjI4QEAAICmz21CsmEYmjRpkvr06aPExER7+9ChQ/XBBx9o5cqVevnll7Vp0yZdd911slqtkqScnBz5+PioefPmDvuLiIhQTk6OvU94eHitY4aHhzv0iYiIcNjevHlz+fj42Ps4mz59un2Ms8ViUUxMTP2/AAAAAHAbXq4u4JQHHnhAW7du1Zo1axzaTw2hkKTExER1795dsbGx+uqrrzRq1KjT7s8wDJlMJvvzX/791/T5pSeeeEKTJk2yPy8qKiIoAwAAXATc4krygw8+qC+++EKrVq1Sy5Ytz9g3KipKsbGx2rt3ryQpMjJSFRUVys/Pd+iXm5trvzIcGRmpo0eP1trXsWPHHPo4XzHOz89XZWVlrSvMp5jNZgUHBzs8AAAA0PS5NCQbhqEHHnhAn3zyiVauXKm4uLizvubEiRPKzMxUVFSUJKlbt27y9vbWsmXL7H2ys7O1fft29erVS5KUnJyswsJCbdy40d5nw4YNKiwsdOizfft2ZWdn2/ssXbpUZrNZ3bp1a5D3CwAAgKbBpbNbTJw4UR9++KE+//xztW/f3t5usVjk5+enkpISTZ06VbfccouioqJ08OBBPfnkk8rIyNCuXbsUFBQkSfrjH/+oL7/8UvPnz1dISIimTJmiEydOKCUlRZ6enpJqxjYfOXJEb775piTpvvvuU2xsrP7zn/9IqpkC7sorr1RERIRmzJihvLw8jRs3TjfffLNmz559Tu+H2S0AAADc27nmNZeG5NON9Z03b57GjRunsrIy3Xzzzfrxxx9VUFCgqKgo9e/fX88995zD2N/y8nL96U9/0ocffqiysjINGDBAr732mkOfvLw8PfTQQ/riiy8kSSNGjNCcOXPUrFkze5+MjAxNnDhRK1eulJ+fn0aPHq2XXnpJZrP5nN4PIRkAAMC9NYmQfLEhJANoCqxWq1JTUx3akpKSzvmCAAA0Zeea19xmdgsAQONITU3VrMXLFRUXL0nKTt+jR1VzbwYAoAYhGQAuQVFx8Wqd0NXVZQCA23KLKeAAAAAAd0JIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcOLl6gIAALVZrValpqbWak9KSpLZbHZBRQBwaSEkA4AbSk1N1azFyxUVF29vy07fo0clJScnu64wALhEEJIBwE1FxcWrdUJXV5cBAJckxiQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4cWlInj59uq666ioFBQUpPDxcN998s3bv3u3QxzAMTZ06VdHR0fLz81O/fv20Y8cOhz5Wq1UPPvigwsLCFBAQoBEjRigrK8uhT35+vsaMGSOLxSKLxaIxY8aooKDAoU9GRoaGDx+ugIAAhYWF6aGHHlJFRcUFee8AAABwXy4NyatXr9b999+v9evXa9myZaqqqtKgQYNUWlpq7/Piiy9q5syZmjNnjjZt2qTIyEhdf/31Ki4utvd55JFH9Omnn2rRokVas2aNSkpKNGzYMFVXV9v7jB49WmlpaVqyZImWLFmitLQ0jRkzxr69urpaN954o0pLS7VmzRotWrRIH3/8sSZPntw4XwwAAAC4DZdOAbdkyRKH5/PmzVN4eLhSUlLUt29fGYahV155RU899ZRGjRolSXr33XcVERGhDz/8UL///e9VWFiouXPn6v3339fAgQMlSQsWLFBMTIyWL1+uwYMHa9euXVqyZInWr1+vHj16SJLeeustJScna/fu3Wrfvr2WLl2qnTt3KjMzU9HR0ZKkl19+WePGjdMLL7yg4ODgRvzKAAAAwJXcakxyYWGhJCkkJESSlJ6erpycHA0aNMjex2w269prr9XatWslSSkpKaqsrHToEx0drcTERHufdevWyWKx2AOyJPXs2VMWi8WhT2Jioj0gS9LgwYNltVqVkpJSZ71Wq1VFRUUODwAAADR9bhOSDcPQpEmT1KdPHyUmJkqScnJyJEkREREOfSMiIuzbcnJy5OPjo+bNm5+xT3h4eK1jhoeHO/RxPk7z5s3l4+Nj7+Ns+vTp9jHOFotFMTEx5/u2AQAA4IbcJiQ/8MAD2rp1qxYuXFhrm8lkcnhuGEatNmfOferqX58+v/TEE0+osLDQ/sjMzDxjTQAAAGga3CIkP/jgg/riiy+0atUqtWzZ0t4eGRkpSbWu5Obm5tqv+kZGRqqiokL5+fln7HP06NFaxz127JhDH+fj5Ofnq7KystYV5lPMZrOCg4MdHgAAAGj6XBqSDcPQAw88oE8++UQrV65UXFycw/a4uDhFRkZq2bJl9raKigqtXr1avXr1kiR169ZN3t7eDn2ys7O1fft2e5/k5GQVFhZq48aN9j4bNmxQYWGhQ5/t27crOzvb3mfp0qUym83q1q1bw795AAAAuC2Xzm5x//3368MPP9Tnn3+uoKAg+5Vci8UiPz8/mUwmPfLII5o2bZratWundu3aadq0afL399fo0aPtfcePH6/JkycrNDRUISEhmjJlijp16mSf7aJjx44aMmSIJkyYoDfffFOSdN9992nYsGFq3769JGnQoEFKSEjQmDFjNGPGDOXl5WnKlCmaMGECV4gBAAAuMS4Nya+//rokqV+/fg7t8+bN07hx4yRJjz32mMrKyjRx4kTl5+erR48eWrp0qYKCguz9Z82aJS8vL916660qKyvTgAEDNH/+fHl6etr7fPDBB3rooYfss2CMGDFCc+bMsW/39PTUV199pYkTJ6p3797y8/PT6NGj9dJLL12gdw8AAAB35dKQbBjGWfuYTCZNnTpVU6dOPW0fX19fzZ49W7Nnzz5tn5CQEC1YsOCMx2rVqpW+/PLLs9YEAACAi5tLQzIAuIrValVqamqt9qSkJJnNZhdUBABwJ4RkAJek1NRUzVq8XFFx8fa27PQ9elQ1N/ICAC5thGQAl6youHi1Tujq6jIAAG7ILeZJBgAAANwJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACderi4AAC42VqtVqampDm1JSUkym80uqggAcL4IyQDQwFJTUzVr8XJFxcVLkrLT9+hRScnJya4tDABwzgjJAHABRMXFq3VCV1eXAQCoJ8YkAwAAAE4IyQAAAIATQjIAAADghJAMAAAAOCEkAwAAAE4IyQAAAIATQjIAAADghJAMAAAAOGExEQDAeatr6W2J5bcBXDwIyQCA8+a89LbE8tsALi6EZABAvbD0NoCLGWOSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMCJl6sLAIDzZbValZqa6tCWlJQks9nsoooAABcbQjKAJic1NVWzFi9XVFy8JCk7fY8elZScnOzawgAAFw1CMoAmKSouXq0Turq6DADARYoxyQAAAIATQjIAAADghJAMAAAAOCEkAwAAAE4IyQAAAIATl4bk77//XsOHD1d0dLRMJpM+++wzh+3jxo2TyWRyePTs2dOhj9Vq1YMPPqiwsDAFBARoxIgRysrKcuiTn5+vMWPGyGKxyGKxaMyYMSooKHDok5GRoeHDhysgIEBhYWF66KGHVFFRcSHeNgAAANycS0NyaWmpunTpojlz5py2z5AhQ5SdnW1/fP311w7bH3nkEX366adatGiR1qxZo5KSEg0bNkzV1dX2PqNHj1ZaWpqWLFmiJUuWKC0tTWPGjLFvr66u1o033qjS0lKtWbNGixYt0scff6zJkyc3/JsGAACA23PpPMlDhw7V0KFDz9jHbDYrMjKyzm2FhYWaO3eu3n//fQ0cOFCStGDBAsXExGj58uUaPHiwdu3apSVLlmj9+vXq0aOHJOmtt95ScnKydu/erfbt22vp0qXauXOnMjMzFR0dLUl6+eWXNW7cOL3wwgsKDg6u8/hWq1VWq9X+vKio6Ly/BgAAAHA/bj8m+bvvvlN4eLji4+M1YcIE5ebm2relpKSosrJSgwYNsrdFR0crMTFRa9eulSStW7dOFovFHpAlqWfPnrJYLA59EhMT7QFZkgYPHiyr1aqUlJTT1jZ9+nT7EA6LxaKYmJgGe98AAABwHbdecW/o0KH67W9/q9jYWKWnp+vpp5/Wddddp5SUFJnNZuXk5MjHx0fNmzd3eF1ERIRycnIkSTk5OQoPD6+17/DwcIc+ERERDtubN28uHx8fe5+6PPHEE5o0aZL9eVFREUEZAH4lq9Wq1NRUh7akpCSZzWYXVQTgUuTWIfm2226z/z0xMVHdu3dXbGysvvrqK40aNeq0rzMMQyaTyf78l3//NX2cmc1m/tEGgAaWmpqqWYuXKyouXpKUnb5Hj0pKTk52bWEALiluP9zil6KiohQbG6u9e/dKkiIjI1VRUaH8/HyHfrm5ufYrw5GRkTp69GitfR07dsyhj/MV4/z8fFVWVta6wgwAuPCi4uLVOqGrWid0tYdlAGhMTSoknzhxQpmZmYqKipIkdevWTd7e3lq2bJm9T3Z2trZv365evXpJqrnyUFhYqI0bN9r7bNiwQYWFhQ59tm/fruzsbHufpUuXymw2q1u3bo3x1gAAAOBG6jXcIj09XXFxcb/64CUlJdq3b5/DftPS0hQSEqKQkBBNnTpVt9xyi6KionTw4EE9+eSTCgsL08iRIyVJFotF48eP1+TJkxUaGqqQkBBNmTJFnTp1ss920bFjRw0ZMkQTJkzQm2++KUm67777NGzYMLVv316SNGjQICUkJGjMmDGaMWOG8vLyNGXKFE2YMOG0M1sAAADg4lWvK8lt27ZV//79tWDBApWXl9f74Js3b1bXrl3VtWtXSdKkSZPUtWtX/d///Z88PT21bds23XTTTYqPj9fYsWMVHx+vdevWKSgoyL6PWbNm6eabb9att96q3r17y9/fX//5z3/k6elp7/PBBx+oU6dOGjRokAYNGqTOnTvr/ffft2/39PTUV199JV9fX/Xu3Vu33nqrbr75Zr300kv1fm8AAABouup1JXnLli165513NHnyZD3wwAO67bbbNH78eF199dXntZ9+/frJMIzTbv/222/Pug9fX1/Nnj1bs2fPPm2fkJAQLViw4Iz7adWqlb788suzHg8A4P7qmiFDYpYMAOeuXleSExMTNXPmTB0+fFjz5s1TTk6O+vTpoyuuuEIzZ87UsWPHGrpOAADO2akZMhZtzLA/Zi1eXmdwBoC6/Kob97y8vDRy5Ej961//0t/+9jft379fU6ZMUcuWLXXXXXc53AgHAEBj+uUMGcySAeB8/aqQvHnzZk2cOFFRUVGaOXOmpkyZov3792vlypU6fPiwbrrppoaqEwAAAGg09RqTPHPmTM2bN0+7d+/WDTfcoPfee0833HCDPDxqMndcXJzefPNNdejQoUGLBQAAABpDvULy66+/rnvuuUd33323IiMj6+zTqlUrzZ0791cVBwAAALhCvULyqRXvzsTHx0djx46tz+4BAAAAl6rXmOR58+bpo48+qtX+0Ucf6d133/3VRQEAAACuVK+Q/Ne//lVhYWG12sPDwzVt2rRfXRQAAADgSvUKyYcOHapzWerY2FhlZGT86qIAAAAAV6pXSA4PD9fWrVtrtW/ZskWhoaG/uigAAADAleoVkm+//XY99NBDWrVqlaqrq1VdXa2VK1fq4Ycf1u23397QNQIAAACNql6zWzz//PM6dOiQBgwYIC+vml3YbDbdddddjEkGAABAk1evkOzj46PFixfrueee05YtW+Tn56dOnTopNja2oesDAAAAGl29QvIp8fHxio+Pb6haAAAAALdQr5BcXV2t+fPna8WKFcrNzZXNZnPYvnLlygYpDgAAAHCFeoXkhx9+WPPnz9eNN96oxMREmUymhq4LAAAAcJl6heRFixbpX//6l2644YaGrgfARcJqtSo1NbVWe1JSksxmswsqAgDg3NX7xr22bds2dC0ALiKpqamatXi5ouL+d99CdvoePSopOTnZdYUBAHAO6hWSJ0+erFdffVVz5sxhqAWA04qKi1frhK6uLgMAgPNWr5C8Zs0arVq1St98842uuOIKeXt7O2z/5JNPGqQ4AAAAwBXqFZKbNWumkSNHNnQtAAAAgFuoV0ieN29eQ9cBAAAAuA2P+r6wqqpKy5cv15tvvqni4mJJ0pEjR1RSUtJgxQEAAACuUK8ryYcOHdKQIUOUkZEhq9Wq66+/XkFBQXrxxRdVXl6uN954o6HrBAAAABpNva4kP/zww+revbvy8/Pl5+dnbx85cqRWrFjRYMUBAAAArlDv2S1++OEH+fj4OLTHxsbq8OHDDVIYAAAA4Cr1upJss9lUXV1dqz0rK0tBQUG/uigAAADAleoVkq+//nq98sor9ucmk0klJSV65plnWKoaAAAATV69hlvMmjVL/fv3V0JCgsrLyzV69Gjt3btXYWFhWrhwYUPXCAAAADSqeoXk6OhopaWlaeHChUpNTZXNZtP48eP1u9/9zuFGPgAAAKApqldIliQ/Pz/dc889uueeexqyHgAAAMDl6hWS33vvvTNuv+uuu+pVDAAAAOAO6hWSH374YYfnlZWVOnnypHx8fOTv709IBgAAQJNWr9kt8vPzHR4lJSXavXu3+vTpw417AAAAaPLqFZLr0q5dO/31r3+tdZUZAAAAaGoaLCRLkqenp44cOdKQuwQAAAAaXb3GJH/xxRcOzw3DUHZ2tubMmaPevXs3SGEAAACAq9QrJN98880Oz00mk1q0aKHrrrtOL7/8ckPUBQAAALhMvUKyzWZr6DoAAAAAt9GgY5IBAACAi0G9riRPmjTpnPvOnDmzPocAAMDtWK1Wpaam1mpPSkqS2Wx2QUUALpR6heQff/xRqampqqqqUvv27SVJe/bskaenp5KSkuz9TCZTw1QJAIAbSE1N1azFyxUVF29vy07fo0clJScnu64wAA2uXiF5+PDhCgoK0rvvvqvmzZtLqllg5O6779Y111yjyZMnN2iRAAC4i6i4eLVO6OrqMgBcYPUak/zyyy9r+vTp9oAsSc2bN9fzzz/P7BYAAABo8uoVkouKinT06NFa7bm5uSouLv7VRQEAAACuVK+QPHLkSN19993697//raysLGVlZenf//63xo8fr1GjRjV0jQAAAECjqteY5DfeeENTpkzRnXfeqcrKypodeXlp/PjxmjFjRoMWCAAAADS2eoVkf39/vfbaa5oxY4b2798vwzDUtm1bBQQENHR9AAAAQKP7VYuJZGdnKzs7W/Hx8QoICJBhGA1VFwAAAOAy9QrJJ06c0IABAxQfH68bbrhB2dnZkqR7772X6d8AAADQ5NUrJD/66KPy9vZWRkaG/P397e233XablixZ0mDFAQAAAK5QrzHJS5cu1bfffquWLVs6tLdr106HDh1qkMIAAAAAV6nXleTS0lKHK8inHD9+nLXrAQAA0OTVKyT37dtX7733nv25yWSSzWbTjBkz1L9//wYrDgAAAHCFeg23mDFjhvr166fNmzeroqJCjz32mHbs2KG8vDz98MMPDV0jAAAA0KjqdSU5ISFBW7du1dVXX63rr79epaWlGjVqlH788Ue1adOmoWsEAAAAGtV5X0murKzUoEGD9Oabb+rZZ5+9EDUBAAAALnXeV5K9vb21fft2mUymC1EPAAAA4HL1Gm5x1113ae7cuQ1dCwAAAOAW6nXjXkVFhd5++20tW7ZM3bt3V0BAgMP2mTNnNkhxAAAAgCucV0g+cOCAWrdure3btyspKUmStGfPHoc+DMMAAABAU3deIbldu3bKzs7WqlWrJNUsQ/33v/9dERERF6Q4AAAAwBXOa0yyYRgOz7/55huVlpY2aEEAAACAq9Xrxr1TnEMzAAAAcDE4r5BsMplqjTlmDDIAAAAuNuc1JtkwDI0bN05ms1mSVF5erj/84Q+1Zrf45JNPGq5CAAAAoJGdV0geO3asw/M777yzQYsBAAAA3MF5heR58+ZdqDoAAAAAt/GrbtwDAAAALkaEZAAAAMAJIRkAAABwQkgGAAAAnJzXjXsAAODXs1qtSk1NrdWelJRkn2YVgGsRkgEAaGSpqamatXi5ouLi7W3Z6Xv0qKTk5GTXFQbAjpAMAIALRMXFq3VCV1eXAeA0GJMMAAAAOCEkAwAAAE4IyQAAAIATl4bk77//XsOHD1d0dLRMJpM+++wzh+2GYWjq1KmKjo6Wn5+f+vXrpx07djj0sVqtevDBBxUWFqaAgACNGDFCWVlZDn3y8/M1ZswYWSwWWSwWjRkzRgUFBQ59MjIyNHz4cAUEBCgsLEwPPfSQKioqLsTbBgAAgJtzaUguLS1Vly5dNGfOnDq3v/jii5o5c6bmzJmjTZs2KTIyUtdff72Ki4vtfR555BF9+umnWrRokdasWaOSkhINGzZM1dXV9j6jR49WWlqalixZoiVLligtLU1jxoyxb6+urtaNN96o0tJSrVmzRosWLdLHH3+syZMnX7g3DwAAALfl0tkthg4dqqFDh9a5zTAMvfLKK3rqqac0atQoSdK7776riIgIffjhh/r973+vwsJCzZ07V++//74GDhwoSVqwYIFiYmK0fPlyDR48WLt27dKSJUu0fv169ejRQ5L01ltvKTk5Wbt371b79u21dOlS7dy5U5mZmYqOjpYkvfzyyxo3bpxeeOEFBQcHN8JXAwAAAO7Cbcckp6enKycnR4MGDbK3mc1mXXvttVq7dq0kKSUlRZWVlQ59oqOjlZiYaO+zbt06WSwWe0CWpJ49e8pisTj0SUxMtAdkSRo8eLCsVqtSUlJOW6PValVRUZHDAwAAAE2f24bknJwcSVJERIRDe0REhH1bTk6OfHx81Lx58zP2CQ8Pr7X/8PBwhz7Ox2nevLl8fHzsfeoyffp0+zhni8WimJiY83yXAAAAcEduv5iIyWRyeG4YRq02Z8596upfnz7OnnjiCU2aNMn+vKioiKCMi0JdS+ayXC4A4FLitiE5MjJSUs1V3qioKHt7bm6u/apvZGSkKioqlJ+f73A1OTc3V7169bL3OXr0aK39Hzt2zGE/GzZscNien5+vysrKWleYf8lsNhMacFFyXjKX5XIBAA2hKV2EcdvhFnFxcYqMjNSyZcvsbRUVFVq9erU9AHfr1k3e3t4OfbKzs7V9+3Z7n+TkZBUWFmrjxo32Phs2bFBhYaFDn+3btys7O9veZ+nSpTKbzerWrdsFfZ+Auzq1ZG7rhK72sAwAwK9x6iLMoo0ZWrQxQ7MWL68Vmt2FS68kl5SUaN++ffbn6enpSktLU0hIiFq1aqVHHnlE06ZNU7t27dSuXTtNmzZN/v7+Gj16tCTJYrFo/Pjxmjx5skJDQxUSEqIpU6aoU6dO9tkuOnbsqCFDhmjChAl68803JUn33Xefhg0bpvbt20uSBg0apISEBI0ZM0YzZsxQXl6epkyZogkTJjCzBQAAQAM6dRHG3bk0JG/evFn9+/e3Pz81vnfs2LGaP3++HnvsMZWVlWnixInKz89Xjx49tHTpUgUFBdlfM2vWLHl5eenWW29VWVmZBgwYoPnz58vT09Pe54MPPtBDDz1knwVjxIgRDnMze3p66quvvtLEiRPVu3dv+fn5afTo0XrppZcu9JcAAAAAbsilIblfv34yDOO0200mk6ZOnaqpU6eeto+vr69mz56t2bNnn7ZPSEiIFixYcMZaWrVqpS+//PKsNQMAAODi57ZjkgEAAABXISQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4ISQDAAAATgjJAAAAgBNCMgAAAOCEkAwAAAA4ISQDAAAATgjJAAAAgBMvVxcAAAAahtVqVWpqaq32pKQkmc1mF1QENF2EZAAALhKpqamatXi5ouLi7W3Z6Xv0qKTk5GTXFQY0QYRkAAAuIlFx8Wqd0NXVZQBNHmOSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnBCSAQAAACeEZAAAAMAJIRkAAABwQkgGAAAAnHi5ugAAAOBerFarUlNTa7UnJSXJbDa7oCKg8RGSAQCAg9TUVM1avFxRcfH2tuz0PXpUUnJysusKAxoRIRkAANQSFRev1gldXV0G4DKMSQYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACceLm6AAD1Z7ValZqa6tCWlJQks9nsoooAALg4EJKBJiw1NVWzFi9XVFy8JCk7fY8elZScnOzawgAAaOIIyUATFxUXr9YJXV1dBgAAFxXGJAMAAABOCMkAAACAE0IyAAAA4ISQDAAAADghJAMAAABOCMkAAACAE0IyAAAA4ISQDAAAADghJAMAAABOCMkAAACAE5alBgAAjcJqtSo1NbVWe1JSksxmswsqAk6PkAwAABpFamqqZi1erqi4eHtbdvoePSopOTnZdYUBdSAkAwCARhMVF6/WCV1dXQZwVoxJBoBLjM0wVFJlUpXN5upSAMBtcSUZAC4BhmHoo81Z+mZ7tjbsL9DJqgBt3ZylkV0vc3VpAOCWCMkAcAl4Y/UB/W3JTw5tx4qt+iQ1S90DXFQUALgxhlsAwEXuP1uO2APy7/terheuDVb/sJPy9/HU8ZIKrT3hpyIrQy8A4JfcOiRPnTpVJpPJ4REZGWnfbhiGpk6dqujoaPn5+alfv37asWOHwz6sVqsefPBBhYWFKSAgQCNGjFBWVpZDn/z8fI0ZM0YWi0UWi0VjxoxRQUFBY7xFALigNh/M0+SPtkiS7ukdpydu6KjLm3kp2Num3yS1VICPp4qqPDV3S6mLKwUA9+LWIVmSrrjiCmVnZ9sf27Zts2978cUXNXPmTM2ZM0ebNm1SZGSkrr/+ehUXF9v7PPLII/r000+1aNEirVmzRiUlJRo2bJiqq6vtfUaPHq20tDQtWbJES5YsUVpamsaMGdOo7xMAGlpuUbl+/36KKqpsGpQQoadu7OiwvXmAj2668jJJhjZmV2r74ULXFAoAbsjtQ7KXl5ciIyPtjxYtWkiquYr8yiuv6KmnntKoUaOUmJiod999VydPntSHH34oSSosLNTcuXP18ssva+DAgeratasWLFigbdu2afny5ZKkXbt2acmSJXr77beVnJys5ORkvfXWW/ryyy+1e/dul71vAPg1bDZDk/61RSdKK9QxKliv3t5Vnh6mWv1aBJnV0rdKkjRz2Z7GLhMA3Jbbh+S9e/cqOjpacXFxuv3223XgwAFJUnp6unJycjRo0CB7X7PZrGuvvVZr166VJKWkpKiystKhT3R0tBITE+191q1bJ4vFoh49etj79OzZUxaLxd7ndKxWq4qKihweAOAO/vnfA1qz77j8vD01+46u8vPxPG3f9kEV8jBJK3/KVcqh/EasEgDcl1uH5B49eui9997Tt99+q7feeks5OTnq1auXTpw4oZycHElSRESEw2siIiLs23JycuTj46PmzZufsU94eHitY4eHh9v7nM706dPt45gtFotiYmLq/V4BoKGkZRbopW9rfhM2dUSC2oYHnrF/oJeha1vVLAn88lJ+gwYAkpuH5KFDh+qWW25Rp06dNHDgQH311VeSpHfffdfex2Ry/PWhYRi12pw596mr/7ns54knnlBhYaH9kZmZedb3BAAX0k85Rbr33c2qshm6sXOUbu1+bv95HxXvKx9PD63df0Jr9x+/wFUCgPtz65DsLCAgQJ06ddLevXvts1w4X+3Nzc21X12OjIxURUWF8vPzz9jn6NGjtY517NixWlepnZnNZgUHBzs8AMBVtmQW6LY31+t4iVUdo4I1bWSns/5n/5Qwf0/dfnVNoH79u/0XskwAaBKaVEi2Wq3atWuXoqKiFBcXp8jISC1btsy+vaKiQqtXr1avXr0kSd26dZO3t7dDn+zsbG3fvt3eJzk5WYWFhdq4caO9z4YNG1RYWGjvAwDuLCv/pOauSdfv3t6gwrJKdW3VTIsm9JTFz/u89jPhmsvlYZL+u/e4dmVzjwWAS5tbr7g3ZcoUDR8+XK1atVJubq6ef/55FRUVaezYsTKZTHrkkUc0bdo0tWvXTu3atdO0adPk7++v0aNHS5IsFovGjx+vyZMnKzQ0VCEhIZoyZYp9+IYkdezYUUOGDNGECRP05ptvSpLuu+8+DRs2TO3bt3fZeweAXyoqq1TGSS/N21qql9LWylpVLU+TSScrqrU3t8TeL/nyUL01trsCzef/z3tMiL9u6BSlL7dm663vD2jmbVc24DsAgKbFrUNyVlaW7rjjDh0/flwtWrRQz549tX79esXGxkqSHnvsMZWVlWnixInKz89Xjx49tHTpUgUFBdn3MWvWLHl5eenWW29VWVmZBgwYoPnz58vT8393en/wwQd66KGH7LNgjBgxQnPmzGncNwsAdTAMQ1sPF+r7PcdkM3ylQqskq0MfD5PUvXWIhiZG6o6rW8nX+/QzWZzNfX0v15dbs/XFliP605D2irL4/cp3AABNk1uH5EWLFp1xu8lk0tSpUzV16tTT9vH19dXs2bM1e/bs0/YJCQnRggUL6lsmAFwQVYa0dOdR/ZRTs0BSM+9q9W4VoEFXdVCwn7dsNkOSdGVMM4UGmhvkmJ1bNlOPuBBtSM/TvB8O6skbOp79RYAbsFqtSk1NdWhLSkqS2dww5wYuPW4dkgHgUlVRbWjtCT/lVxbLJKl32zCFlBzUHYktlHzlZRf02L+/9nJtSM/Thxsy9MB1bRXse35jmwFXSE1N1azFyxUVFy9Jyk7fo0dVc+8RUB9N6sY9ALhUvLf9pPIrPeXr5aGRXS9Tt9jmOseJKn61fvHhahceqBJrld76/kDjHBRoAFFx8Wqd0FWtE7rawzJQX4RkAHAzn/14WCsOWiUZGpIYqZgQ/0Y9voeHSZMH1dy4/NZ/Dyi7sKxRjw8A7oCQDABuZO/RYj3xyTZJUvvASsWGBrikjsFXROiq1s1VXmnTy0v3uKQGAHAlQjKAS5rNZii3qFxbMgu0t8RbRVaby2optVbpjx+kqqyyWolhXmofWOGyWkwmk/2mvY9Ts7TjSKHLagEAVyAkA7hkpZd66Y3v92vhpkx9t+eYdhab9ejyQs1dk67K6sYNy4Zh6KlPt2lfbonCg8y6v1tgo41BPp2urZpreJdoGYY07etdMgzDtQUBQCMiJAO4JH25r0xbi3xVWW3Ix8tDsaH+Cvaq1skqQ899uVM3/v2/jToWd+HGTH2WdkSeHibNGZ2kZr7u8c/zY4Pby8fTQz/sO6FXlu91dTkA0Gjc419hAGhEb6zerw921ATgq1uH6A99L9fNV16mfmFlmnClv0IDfLTnaIlue3O9Dhdc+KC8LatQU/+zQ5L0p8HtdXVcyAU/5rmKCfHX1BFXSJJeXbFX/9qU6eKKAKBxME8ygEvKwo0Z+us3P0mS2gdaldwm1L7NZJKui/XVuCFX6o5/rldG3knd/s91Wjihp1o2vzAzTGxMz9P4dzeposqmgR3Ddd81l1+Q4/wao3u0Ulb+Sb323X498ek2hQebxfIMuBjVtSCJxKIklypCMoBLxvbDhXrm85ortqPa+6q6qKTOfpc189Oi+3rqjrfW69CJk/rtG+v09tjuuiLa0qD1LNt5VA98mCprlU3dY5vr5VuvlIeHiwcin8afBrfXkYIyfZZ2ROPf3awbLzfLxhBlXGScFySRWJTkUkZIBnBJKCyr1B8/SFFFtU0DO0boN+0qtHjT6ftHN/PT4vuS9bu312v/sVL95vV1mnXblRqSGFlnf5vNUEpGvtbvP6GNu0q0/biffthwSEG+3tJJHwXuK1N+YLbCg8zadrhQK3/K1Q/7jstmSAM6hGvO6CT5+XheoHf/65lMJr34my4yJH2edkRf7CtXgKe/fPPLdFlzP1eXBzSYUwuSAIRk4ALj13euZxiG/vTRFmXmlallcz+9/Nsu2rll81lfF2nx1ScTe+uBD1P1373H9YcFKfptt5a6oVOUktuEqqisUjuOFGn9gRP6z5YjOlJY/otXe0qVFTpeUiHJR+k7yvTBjtrfB7d1j9ELIxPl5en+t4j4eHno1du76sZOUXrsX6kqsHro36lZ6nSZRb3bhp59BwDQhBCSgQuMX9+53t+W7NbSnUfl4+mh13/XTRZ/73N+rcXPW/PGXaXnvtypd9cd0kcpWfooJUteHiZVOY03CDR7qX+HcPlX5OtAdp6iYtuouLxSmVmH1aJZoKye/sopLFdsaICu6xCu/h1aqG14UEO/3Qtu0BWR8hhg0XOrjulQmbe2HS5U+vFSdQlw/6APAOeKkAw0An595zpvfX9Ab6zeL0l6YWSiOrU8/3HFXp4eevamRA1OjNTX27K1fGeucorK5WGSLm8RqE6XWTQoIUL9O4TL19tT69at06KCarUOq1ktL7goXbd3D7yo/lMU4O2hK5tZldShtVb+lKvCskqts/qpa5ZVF8+7BHApIyQDuGj9OyVLL3y9S5L0+NAO+m33mF+1v15twtSrTZieu8lQZl6ZWgSZ3XoccWNoFeKv3/VopaU7jmrfsRLNSSmVb+he3d+/rUyuXg0FAH4FfjcG4KJzuKBMDy78UVM+2iJJmnBNnH7ft+GmVjOZTGoV6n/JB+RTvD09dEOnSLUNqFlG+6Wle/TXJT+xQh+AJo0ryQCaNJvNUEmVSak5Fdr+/QHtOVqs/2w9ovJKm0wm6Z7ecXpiaEeual5gJpNJVwRXqPflzfTutpN6c/UBBft66/7+bV1dGgDUCyEZQJNTXGHT7mJv/Xf9IRWcrJDNCNCKYyWSdtn7XB0Xov8blqDEyxp2bmOc2ZDLfRXTKlbPf7VLM77drUCzl8b2au3qsgDgvBGSATQZhWWVmrVsjxZuKJC12iyp5tf7njLUMthLia3D1SYsQF1jm6tffAuuHrvIvddcrqLyKv19xV4988UOGYahcb3jXF0WAJwXQjKAJuHg8VLd8+4mHThWKkmyeFWrZ/toRTfz04n923VHj1ZKTk5ycZU45dGB7VRWUaW3/puuqf/ZqcKyKj00gJv5ADQdhGQAbm/DgRP6/YIUFZysVLTFV2MTvLUjPVtxkcGSpDxyl9sxmUx68oaOCvL11sxlezRr+R7lFpfrscEdTjtPtc1mKO9khQ4VVimvwkMtq21NYpEVABcnQjIAt7Zke7YeXPijKqsNdWlp0Vt3ddf+HT9q50FXV4azMZlMemhAOwX7emnqf3bqgw0Z+jztiO7sGavrOoSrymaTtdKmtMwCrTtwQmmZBaqosv38an+t+/6AWoX4K7jSq9bCLUBTwsqrTRMhGYDb+mLLET26OE3VNkNDEyM167Yr5evtqf2uLgznZVzvOLUK9deLS3brp5xivbF6v32Bl7oE+5hkraqW1eahA8dLJfnqz6sK9UJorvp3CG+8woEGwsqrTRMhGYBb+mhzpv788VbZDOmWpJZ68Ted5enBuIqm6roOEerfPlwrduXq7TUHdLTIKk8Pk7w8TIqPCFLPy0PV4/IQtQrx1+aNG7RwQ4YCWnVU+vFSpR48riMl0t3zN+na+Bb6fzd2VLuIMy/nnVlUpT0l3tq1PVsnSipUZTMUYvJR5+OVutpm8L2ERsfKq00PIRmAWymvrNYLX+3S++sPSZLuuDpGL9zcSR6EmibPZDJpYEKEBiZEnENfKTzIV+FBvgopzZBHUJi+Tbdq9Z5jWrPvuMb0jNUf+7VRRLCv/TUnSmq2L9yYoU0HiySZpeIS+/ZC+ei5H4r1/k/fac7oJKYHBHBGhGQAbsFmM/RjZr6e/GS7dh8tliT9sV8bPTa4PTMiXOK8PaTbr/DX5Jt76oWvdmn5rqOav/ag5q89qIhgs9pHBisr7+TPQzNqeJikcJ8qtWkZodBAswzDUNreDOVV+ejgiZMa9fpaPXfTFbrtqlYufGcA3BkhGcAFZxiGDheUaVuhjwp/LNG/M2uWizZ7e8jXy1P5Jyv0373HdLykZt7jsECzXr61i66Nb+HKsuFm4sIC9PbY7lqz97hmfPuTth4u1NEiq44WHbP3iY8I1LDO0WpjytXSbVlq3TrEvs3jmFXDu7bQonQfLd+Vqz9/vE1pmQV67qZEZtEAUAshGcAFYxhSWmaBtmQVqOBkpSQfHciokDKy6uwf4OOpgQkRenpYgsICueMbdevTLkx92vVRqbVKu7KLtPtosaIsvkpq1VzN/H0kSevWHa/ztQHeHvrnmO56ffV+vbx0txZuzNTRIqv+MTpJfj6ejfk2ALg5QjKAC8JaZWhjvq9ycmqu8nl7mhTlU6FerYPVunWsDEOyVlWrvNImH0+TktuEqVtsc/l4cUUP5ybA7KXurUPU/RdXi8+Fh4dJ9/dvq/YRQbr/w1St/ClXo99er3fGXqXmAT4XqFoATQ0hGUCDO1Fi1fNri5Rj9ZKnh0nXtA1Tx6hgHdmzRTfF+yk5uY2rSwQ0MCFCH9zbQ+Pf3awfMwo09NX/avqoTkwzB0ASIRlAAzteYtWtb6zTgfxqeZsM3dy1paKb+bm6LKBO3VuH6N9/SNZ976co/Xip7p6/SaOSLtP4PnHqGBnMrCpwKyxK0rgIyQAaTHlltSa8t1kHjpcqzM9DnQOLCchwe+0igvT1Q9do5rLdmrsmXZ+kHtYnqYfVzN9bV8Y0k7enh2w2QyaTSWXFJcoqMOv4wTzFRwQp2K/uJbaBC4FFSRoXIRlAg7DZDE3+1xb9mFEgi5+3Hk/21393Fbm6LOCc+Pl46qkbEzS0U5Rmr9irDel5KjhZqe92H6ujt7cO7T+hH/afUHQzX10mbvhD42FRksZDSAbQIF78dre+2pYtb0+T3rizm5S7x9UlAectqVVzzbv7alVW27TtcKF+yq6Zs9vTQ7IZ0s49+7XpYL5KvJspK79MRwrKdUR+enVTiTp0ruDGP+AiQkgG8Ku9/d8DemP1fknSX0d1VnKbUK3LdXFRwK/g7emhpFbNldSquUP7uurDKjpRqdYJLVVcXqmtWYVKOZSn9UcqNOiV7/Xyb7uoL/N7AxcF5loC8Kt8nJKl57/aJUn60+D2uqVbSxdXBDSOIF9v9W4bpmtCyxQd6KFjxVaNnbdRf1+xVzab4eryAPxKhGQA9fbNtmw99vFWSdL4PnGa2I+p3XDpae5j0/R+Ft1xdSsZhjRz2R7d+95m5ZdWuLo0AL8CIRnAeauqtumv3/ykP36QqmqboVFJl+mpGzrKZGK6LFyafDxNmj6qk2b8prPMXh5a+VOu+r30neb/kK7KapurywNQD4xJBnBe9uUW68lPtmvjwTxJ0tjkWP2/YQnMJwtI+m33GHWMCtaUj7bop5xiTf3PTr23/pCuT4hQt1bN1T4ySCaZlF1SreIqk/JKK2QYhgJ9+XEMuBvOSgBnZTOklJwKvTZ3g/6797gkKdDspb/d0lk3do5ycXWAe0m8zKIvH+yjRZsy9fLS3TpwrFRvrj5QR88A6dghSZJJksXbT9p5Ui3alKhteGCj1gygNkIygDoZhnToRKn2HC3R3qMB+k9OiaQSeZikAR0j9NQNHdU6LMDVZQJuycvTQ3f2jNXwLtH6dkeOUg/lK+VQvjLzT8rDZJJhs6mq2iZPr5o5lssrbSqo9NTne8v15Svf646rY/TIwHiFBbKKGuAqhGQADgzDUEpOhVYf91NhzpGfW00K9jHp9p5xGtMzVjEh/i6tEWgqLH7eurV7jG7tHuPQvm7dOi3amGFfFKK4vFI/bv9J8g3Sj0crtWB9hj5NPazJg9prXK/WDGfCBcVy13UjJAOwO1JQpgcX/qiUQyWSPOXtaVLHyGAFleVoYp9o9e7V0dUlAhelIF9vtfKv0u1XB8kUEa9pX+/S1qxC/eXLnVqyPUcv/qYzv7nBBcNy13UjJAOQJG1Mz9PED1J0vKRCZk8pxrdC1yV1kJ+Ppw7uPCwPZq4AGkXPy0P12cTe+mBjhqZ/vUsbD+ZpyKvfa3yfON13TRtZ/L1dXSIuQix3XRtTwAHQoo0ZGv3Weh0vqVDHqGDNuM6iK4Ir5Ofj6erSgEuSh4dJY3rG6ttH+ir58lCVV9r0j1X71efFlXp56W5tOHBCZRXVri4TaBA2w5C1yv2+n7mSDFzi3lt3UP/3+Q5J0rDOUXrxN521JWWTi6sCIEkxIf76cEIPLdt5VDOX7dFPOcWavXKfZq/cJy8PkyItvjKZJJNMMpkka3m5Ssr9FV55RFEWX0U185PB4n9wM1U2aePBPB0rsupovp++/jJfj9syNL5PnKtLc0BIBi5h7/8iIP/+2sv1+JAOLAgCuBmTyaRBV0RqYMcIfbUtW19vy1bKoXzlFluVlV9Wxys8VHK8VAeOl0qSgrz8FHe4Qj1sBjcAwuUqqw1tyPfV8aMnfm6p+Y1l+vES1xV1GoRk4BL1/vpDepqADDQZHh4mDe8SreFdomUYhg4XlCm32PrzleKay8Xbtm3X0p258gyNUXZBuTLyT6q4Snp1c4m+zfqvnh1xhXpcHurS94FLl81m6PUfS3W8wkvenib1jAtV5YlM3d4tUsP6X+Hq8mohJAOXoAXrD+npz7ZLku7rS0AGmhqTyaSWzf3VsrnjdIwVR7z1o7larVs1l1pJ1spqrUrdpYxyX/2UU6zb/rlev+vRSo8P7aAgX24ARON6/qtdWne4QiYZurFTtGJDA3Sw9KAiAjzl5el+t8kRkoEzqGvuyKY+b+QHGw7p//0ckCdcE6cnhhKQgYuV2dtTHYIqNeWaCK08EayFGzP0wYYMrdiVqz8Pba+bulzGEAw0iqU7cvTOD+mSpK7NrIoNdf8pDQnJwBk4zx3ZlOeNNAxDb6w+oL8t+UmSdG+fOD15Q0cCMnAJCPTx0PRRnTS8S5Se+GSbDp04qUcXb9G8Hw7qT4Pbq1ebMHkSlnGBWKuq9cLXuyRJw9v6yqPU/cYf14WQDJzFxTB3ZGFZpaZ8tEXLdh6VJI3vE6enbiQgA5eaXm3C9O0jffXOD+l6bdV+bc0q1Ji5GxUW6KPBV0TqqtYhirL4Kre0WlW2mv9c8+8Efq331h7SoRMn1SLIrJHxfvr8R1dXdG4IycBFzDAMLd+Vq+e+3KmMvJPy8fTQMyMSNPrqVvzgAy5Rvt6emtivrX7bLUazV+7V52lHdLykQh9sqBmK8T+B8jy2X37engoL9JGv1Vvbj1XqapvBVWfUqa4hikVWm15dVSxJ+tPg9vKrzHJFafVCSAYuQlXVNq38KVd/X7lX2w8XSZIua+an1+9MUueWzVxbHAC30CLIrL/clKinhyVo3f4T+nZHjvYfK1F2YbmO5J9UpU2qthkqsVapxFolyawX1hbr/Z++013Jsfpt9xhZ/Lj5D/9T1/LW6zLLVOLVQldEB+s3SS21YQMhGYALFFZ66L1tpXpwxUodL7FKkvx9PDW2V2v9oS/L2QKozdvTQ33jW6hvfAt729q1a7VgQ6Yi2iSqxFql3GKr9mUcUUG1jzLyTur5r3bpleV79cd+bTS+T5x8vVmdEzV+OUQx/2SFcrMPSpL+b1hCk7tJlJAMNHHVhrQzu0hbswp0tMhfOl4TjkMCfHTbVTGacM3lCgnwcXGVAJoSk8kkbw8p2M9bwX7eim7mp2bF6RqZFK5sc0vN+yFde46WaMa3u/XhhgxNGRyvEV0uYxgGHGxIz5NkUlKkd5Ocn5uQDDRRNpuh7zOsWp7rr/KcmhvyTDJ0dbRZ913fWX3jW8jbDeedBNB0mb1MuuPqVrqte4w+33JYLy7ZrcMFZXp08Ra9tmq/Hr0+XkOuiGxyVwzR8PJKK7Qnp2Ys8m/a+7m4mvohJANN0I8Z+Xrmix3amlUqyUOBZi91bmlRUHGGxl0VquSOEa4uEcBFzMPDpJFdW2poYpTmrknXm6v3a29uiSZ+kKpWIf4amhipoZ2ilBgd7JaLRODC25B+QoakSHOV4po1zbjZNKsGLlEVVTa9snyP3li9XzZD8vOS4vys6t+tjbw8PXRw5yFXlwjgEuLr7an7+7fVnT1j9c6adL2zJl0ZeSf15vcH9Ob3B+TtaVJMc3/FhPgrNNBH5YWlSi/xVuXRYjUP8FEzbvy7KJ0osWrP0Zq5kDsEVbi4mvojJANNxK7sIk361xbtyq6ZreLmK6M1OOKklmzN4koNAJey+Hnr0evj9ftrL9d3u4/p623ZWvVTrkorqnXgeKkOHC/9RW+zdm3PkSR5epgU5u2r0PRyxSWUK9Li65o3gAZVMxZZatMiQBavprFwSF0IyYCbq7YZ+uf3BzRr2R5VVNvU3N9b00Z20tBOUVq3bp2rywMAO38fL93QKUo3dIqSzWYop6hcB0+UKjPvpPJPVmrH3oP68XCxKr0DlX+yUhVVNh21eumdrSc1b9sK9W4TplFJl2lIYqT8fYgoTVF+hYf2nqgJxj3iQlWaedTFFdUf34GAG9t5pEj/9/l2bT6UL0ka2DFc00d1Vosgs4srA4Az8/AwKbqZn6Kb+UltatrW+eTKo/S4WickyDAMnSitUOrOvaryCtDe/Cqt2Xdca/Yd1//7bLuGJkbplqTLdFVcCDchNxGGYWhHcc3Pp46RQWoRZFbpWV7jzgjJgBvKyj+pmUv36NO0wzIMKdDspf8bnqDfdmvJSnkALgomk0lhgWbFB1bq9quD1bJ9F32Selif/JilQydO6uPULH2cmiWzl4c6RgXriuhghQb4KNDXS34/z8tsqGbIRpCvtw4frVBRpYeqWRHQZVJyKnWiwlOeHiYlt2l6U745IyQDbuJwQZmW7sjRtztytDE9Tzajpn14l2j9eUh7tWzu79oCAeACignx18MD2+mhAW2VmpGvj1MP66ut2Sosq1RaZoHSMgvOYS/+Wv3dPoUGmOVvMysmy6rLi8oVEcxY5wutqtqmhTtPSpKujGmmIN+mf1MmIRlwoX25xfp2x1F9uyNHW7MKHbb1bhuqPw/pwDLSAC4pJpNJ3WJD1C02RM/flKiDJ0q17XChducUq7i8SqXWKpVVVv/cV6qsNlRcXqnsE4U6XFSlKsOkYyVWSd6ak1KqOSkrdHlYgHpcHqKr40IUFxaoaIuvwgJrhgXYDEM2o+bP8ipDlTbJWlktQ5KPF8M8ztWiTZk6UmKTj8nQVa2bu7qcBkFIBhpZXmmFdhb5aPKKAh35/Ht7u8kkdY9trsFXRGrwFZGKCeHKMYBLm4eHSZe3CNTlLQLP2nfdunVauCFDoZcnKrfYqp8OHJLNy0+Hiv43w8bCjZnncNRA6egBSTVDOQI8/JS7uUQ/2dJ1TbswtWkRyLA3J5l5J/W3b36SJMUHVcjsdXEsU05IBhqBYUj7j5UoLbNAWfllknwk2eTtaVKvNmEakhipgR0juCEPAH4Fk+l/S2l7Ha/Q7VdHKuHK7tp8ME/rD5xQakaBDueXKbe43D6k7UyqbYaKbJ5ad7hC6w7vlCRFBvuqT7swXdMuTL3ahF3y/25XVdv0yOI0FVurFB/ipTifpjvlmzNCMnCBbcmt0HfH/VSUky1JMkmKMFfpN4kW3Te8t4IvgnFbAOCuLH7eGtAxQgN+sRJpZbVNBScr5WGquVpsMpnk6WHSpo0b9XFKplp3vFKSVGKt0q5du9Q6MkRZlf7adDBfOUXl+ndKlv6dkiVJigg2KyEqWG1aBKqZv7cs/j4K8PGUj5eHvD095OPloX3HKnWiwkPNyyoVZPa6qJbtnrNqn1IO5SvI7KX7kwK0ckeBq0tqMIRkNHlWq1Wpqam12pOSkmQ2u+5/+LuyizTt6136794SSZ4ye3mo02UWdWppUd6B7erdMpyADAAu4O3pUecVYF8vkzx/Ds5STcCO9K3WzfF+Sk7uqfLKam1Mz6uZqm7vce3MLtLRIquOFh3Tqt3HznJUf61Ze7Dmarevt/wNX/nvLZPR4rgSW1qa5M+DtfuP6+8r9kqSnh+ZqPCyDBdX1LAIyWjyUlNTNWvxckXFxdvbstP36FFJycnJjV5PdmGZXl66Rx+nZskwJE+T1Nq/QgO7dZDvz9MW5TV6VQCAX8vX21N941uob3wLSVJxeaV25xRrV3aRMvPLVHiyUgVlFTpZUa2KKpsqqm01V62LSpV3slJWm6eqDUOFZZUqlJcW7izTwp0bJNWsTndFtEWtQ/3VKjRA4UFm+fl4ys/bU16eJnmYTDpcXK3iKpPyT1bI02SSv4/rxv4u2Z6jhxf9KJshjex6mW668jKtW0dIBtxOVFy8Wid0dWkNx4qtemP1fi1Yf0jWKpsk6cbOUbo+/KRW7ThsD8gAgItDkK+3urcOUffWIWfst27dOi3amKHYjleq1Fqt/JMV+mnfAQUFBSrrpJcOF5Rp/7FS7T92LktvBEjHDtmf+Xj4a/vqQl2dva3mt5WXWRQfGXhBb557f91B/d8XO2QYNYtcTRvZ6YIdy5UIycCvtC+3RB9uyNCHGw+pvLImHF/VurmevKGjurZqztLRAABJNdPbBfp6KdDXS9WBlbr9qiAlJyfreIlV27IKtftosQ6dOKmMvFLllVaqvLJaJyuqVG2rmaauoqJS1iqbTB41V6SrbYYqbB7aX1Ct/RsztPDn43h7mtQ+MkiJ0RYl/hycW4cFKNjXq94zc1RW2/TN9hy9sybdPmf1HVe30nM3XSGvi3RFREIyUA9HCsq0aneuPvvxsDYdzLe3d4lppknXx6tvuzCmCAIAnJOwQLP6dwhX/w7hZ+x36op064SuMgxD1iqbdu/cri6tw1URGKHthwu17XChCk5WavvhIm0/XCRt+t+0dz5eHmoRaJavt4d8vDzl42mSj5eHTpYU61ixr7ZtOSJPj5qbGE8WmlW6pVQLD/6ojLyTOnCsREXlVTX78fTQwwPbaWK/Nhf1zzpCMnAOyiqrdaSgTDuLfJS6qlCZn6+0b/P0MKl/+3D9rmcr9YtvcVH/gwEAcA8mk0m+3p6yeNvU8zIfJSd3kCQZhqGs/DJ7YN52uFA7jhQpr7RCFVU2HS4oO80evZRr/eVwD29lHLRKOmJvCQs0a0zPWP2uZyv7YiwXM0IyUIfyymptOpinxTtO6r/H/FSYfeDnLT6SquVhkpJaNdeAjhEalXQZS54CANyCyWRSTIi/YkL8NbRTlL29vLJax4qtOl5iVXllzQ2FldU2VVTZtOOn3fph3wk1j2pVM7TDZuj40cNKiLaofZvWahXir5bN/RUfEXRJrUJISIZL1TV9myumbqu2GdpxpNA+rc/mQ/mq+PnmO6nm5ocQfx8Fq1TDOzbTPTf0UvMAn0atEQCA+vL19rSHZ2fNSg4q80iVWl9msbcdLD2o33TwV3Jym8Ys060QkuFSztO3NebUbRknTtaE4n3HtHb/CRWcrHTYHhnsq/YWm4qLi5SU2FEBZi8d3PmjerU0E5ABALjIEZLhchd6+rYSa5UOHi9V+vFS7cst0Y4jhdp+uEg5ReUO/QLNXup5eaj6tA1Vn3Yt1KZFgNavX69FG/MUYOZUAQDgUsJPfjRpVdU2HT9ZrRMVHrLmFKu4vFLF5VXKzfNV2qpCFSxdqsKyyjpf6+VhUtdWzdSnbQv1aReqLi2bXbTT2AAAgPNDSHby2muvacaMGcrOztYVV1yhV155Rddcc42ry3Kpxlz22TCkskpD2YVlKi6vUnF5pYrKqlRUXqmCk5U6UlCmI4XlNX8WlOloUblshiT5SydyfrEnL8laLalakhQa4KPWYQGKCwtQYnSwEi+zqGNUMFeIAQBAnUgIv7B48WI98sgjeu2119S7d2+9+eabGjp0qHbu3KlWrVq5ujyXOdOyzz169FRFtU3WSpus1dU1f1bV3C1bXlVtD7rF5VUqKqt0fF5eqazcIh0p9JN+SJe1yiZrVYC++Dpf+nrl6Qty4mmSzB42NQ8KUJCvl4J8vVSRl60hCWG6rmeSopv5KsjX+wJ8ZQAAwMWKkPwLM2fO1Pjx43XvvfdKkl555RV9++23ev311zV9+nQXV+cop7BcH6xP1+EjR2QzZH+0aBEueXjIZjNUbRiqrKxW7vHjshmGvU+zZs0lk4eqf+5zqm/Nij4//2mTva2wqFjHA7tod4FvzWtshir9umrMf/JU9fnXDfBuPKWqqp//XjPHsJeHScF+3vbQG2T2VjN/b0VZ/BTdzFfRzfxqHhZf7d2eqn9tylTrhPb2PR60ZurKCB+1jwxqgPoAAMClhpD8s4qKCqWkpOjxxx93aB80aJDWrl1b52usVqusVqv9eWFhoSSpqKjowhX6s32HC/Tqku11bMk7h1cX1POo1jNuNUny8pS8PUzy9qj5089L8vM2yc/bJH9P0//+7mWSn5dJJ44e0e7DeWoRGSVvD0P5WenqHx+qLokdf16U49SQiVPHzq/561Hp+FHpuKQdO3Yo46dcWctP2ms5enCfNipHpaWlzmWeF+d912e/7l7fhdqvu79v6qO+hqiN+lxT37ns+2J83xdDfXV9TqVXhDZKdjrl1LEMwzhzRwOGYRjG4cOHDUnGDz/84ND+wgsvGPHx8XW+5plnnjEk8eDBgwcPHjx48Ghij8zMzDNmQ64kO3FeUtgwjNMuM/zEE09o0qRJ9uc2m015eXkKDQ29qJcmLioqUkxMjDIzMxUcHOzqciA+E3fD5+F++EzcD5+Je7mUPg/DMFRcXKzo6Ogz9iMk/ywsLEyenp7KyclxaM/NzVVERESdrzGbzbVmd2jWrNmFKtHtBAcHX/QnUlPDZ+Je+DzcD5+J++EzcS+XyudhsVjO2odJYX/m4+Ojbt26admyZQ7ty5YtU69evVxUFQAAAFyBK8m/MGnSJI0ZM0bdu3dXcnKy/vnPfyojI0N/+MMfXF0aAAAAGhEh+Rduu+02nThxQn/5y1+UnZ2txMREff3114qNjXV1aW7FbDbrmWeeafCFRFB/fCbuhc/D/fCZuB8+E/fC51GbyTDONv8FAAAAcGlhTDIAAADghJAMAAAAOCEkAwAAAE4IyQAAAIATQjJO6/vvv9fw4cMVHR0tk8mkzz77zGH7uHHjZDKZHB49e/Z0TbGXgOnTp+uqq65SUFCQwsPDdfPNN2v37t0OfQzD0NSpUxUdHS0/Pz/169dPO3bscFHFF7dz+Tw4RxrX66+/rs6dO9sXQ0hOTtY333xj38750fjO9plwjrjW9OnTZTKZ9Mgjj9jbOE/+h5CM0yotLVWXLl00Z86c0/YZMmSIsrOz7Y+vv/66ESu8tKxevVr333+/1q9fr2XLlqmqqkqDBg1SaWmpvc+LL76omTNnas6cOdq0aZMiIyN1/fXXq7i42IWVX5zO5fOQOEcaU8uWLfXXv/5Vmzdv1ubNm3Xdddfppptusv+A5/xofGf7TCTOEVfZtGmT/vnPf6pz584O7Zwnv2AA50CS8emnnzq0jR071rjppptcUg8MIzc315BkrF692jAMw7DZbEZkZKTx17/+1d6nvLzcsFgsxhtvvOGqMi8Zzp+HYXCOuIPmzZsbb7/9NueHGzn1mRgG54irFBcXG+3atTOWLVtmXHvttcbDDz9sGAY/R5xxJRm/ynfffafw8HDFx8drwoQJys3NdXVJl4zCwkJJUkhIiCQpPT1dOTk5GjRokL2P2WzWtddeq7Vr17qkxkuJ8+dxCueIa1RXV2vRokUqLS1VcnIy54cbcP5MTuEcaXz333+/brzxRg0cONChnfPEESvuod6GDh2q3/72t4qNjVV6erqefvppXXfddUpJSWHFngvMMAxNmjRJffr0UWJioiQpJydHkhQREeHQNyIiQocOHWr0Gi8ldX0eEueIK2zbtk3JyckqLy9XYGCgPv30UyUkJNh/wHN+NL7TfSYS54grLFq0SCkpKdq8eXOtbfwccURIRr3ddttt9r8nJiaqe/fuio2N1VdffaVRo0a5sLKL3wMPPKCtW7dqzZo1tbaZTCaH54Zh1GpDwzrd58E50vjat2+vtLQ0FRQU6OOPP9bYsWO1evVq+3bOj8Z3us8kISGBc6SRZWZm6uGHH9bSpUvl6+t72n6cJzUYboEGExUVpdjYWO3du9fVpVzUHnzwQX3xxRdatWqVWrZsaW+PjIyU9L8rAafk5ubWuiqAhnO6z6MunCMXno+Pj9q2bavu3btr+vTp6tKli1599VXODxc63WdSF86RCyslJUW5ubnq1q2bvLy85OXlpdWrV+vvf/+7vLy87OcC50kNQjIazIkTJ5SZmamoqChXl3JRMgxDDzzwgD755BOtXLlScXFxDtvj4uIUGRmpZcuW2dsqKiq0evVq9erVq7HLveid7fOoC+dI4zMMQ1arlfPDjZz6TOrCOXJhDRgwQNu2bVNaWpr90b17d/3ud79TWlqaLr/8cs6TX2C4BU6rpKRE+/btsz9PT09XWlqaQkJCFBISoqlTp+qWW25RVFSUDh48qCeffFJhYWEaOXKkC6u+eN1///368MMP9fnnnysoKMj+P32LxSI/Pz/7XJfTpk1Tu3bt1K5dO02bNk3+/v4aPXq0i6u/+Jzt8ygpKeEcaWRPPvmkhg4dqpiYGBUXF2vRokX67rvvtGTJEs4PFznTZ8I50viCgoIc7puQpICAAIWGhtrbOU9+wWXzasDtrVq1ypBU6zF27Fjj5MmTxqBBg4wWLVoY3t7eRqtWrYyxY8caGRkZri77olXXZyHJmDdvnr2PzWYznnnmGSMyMtIwm81G3759jW3btrmu6IvY2T4PzpHGd8899xixsbGGj4+P0aJFC2PAgAHG0qVL7ds5PxrfmT4TzhH38Msp4AyD8+SXTIZhGI2ezAEAAAA3xphkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAAAHBCSAYAAACcEJIBAAAAJ4RkAAAAwAkhGQAuMePGjZPJZLI/QkNDNWTIEG3dutXe59S29evXO7zWarUqNDRUJpNJ3333nUP/zz77rJHeAQBceIRkALgEDRkyRNnZ2crOztaKFSvk5eWlYcOGOfSJiYnRvHnzHNo+/fRTBQYGNmapAOAShGQAuASZzWZFRkYqMjJSV155pf785z8rMzNTx44ds/cZO3asFi1apLKyMnvbO++8o7Fjx7qiZABoVIRkALjElZSU6IMPPlDbtm0VGhpqb+/WrZvi4uL08ccfS5IyMzP1/fffa8yYMa4qFQAaDSEZAC5BX375pQIDAxUYGKigoCB98cUXWrx4sTw8HH8s3H333XrnnXckSfPmzdMNN9ygFi1auKJkAGhUhGQAuAT1799faWlpSktL04YNGzRo0CANHTpUhw4dcuh35513at26dTpw4IDmz5+ve+65x0UVA0DjIiQDwCUoICBAbdu2Vdu2bXX11Vdr7ty5Ki0t1VtvveXQLzQ0VMOGDdP48eNVXl6uoUOHuqhiAGhchGQAgEwmkzw8PBxu0jvlnnvu0Xfffae77rpLnp6eLqgOABqfl6sLAAA0PqvVqpycHElSfn6+5syZo5KSEg0fPrxW3yFDhujYsWMKDg5u7DIBwGUIyQBwCVqyZImioqIkSUFBQerQoYM++ugj9evXr1Zfk8mksLCwRq4QAFzLZBiG4eoiAAAAAHfCmGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJwQkgEAAAAnhGQAAADACSEZAAAAcEJIBgAAAJz8f388YOKrnv5MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Example for 'BMI'\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df_user['BMI'], kde=True)\n",
    "plt.title('Histogram and KDE for BMI')\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf79f4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a heatmap of correlations between continuous variables\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df_user[continuous_columns].corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Heatmap of Correlations Between Continuous Variables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf42e242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the processed DataFrame to a new CSV file\n",
    "df_user.to_csv('processed_diabetes_012_health_indicators.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "238acf3c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}