diff --git a/data/processed/clustering_preprocessing_robust_scaled.csv b/data/processed/clustering_preprocessing_robust_scaled.csv
new file mode 100644
index 0000000000000000000000000000000000000000..63677d2fd332dd1fffdd1fe221df3f09d4b3e4ed
--- /dev/null
+++ b/data/processed/clustering_preprocessing_robust_scaled.csv
@@ -0,0 +1,2079 @@
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diff --git a/notebooks/clustering/MLDM_Coursework_Clustering_Preprocessing.ipynb b/notebooks/clustering/MLDM_Coursework_Clustering_Preprocessing.ipynb
deleted file mode 100644
index 9dadf51537a0ae50779195b985b0d8d764b928ba..0000000000000000000000000000000000000000
--- a/notebooks/clustering/MLDM_Coursework_Clustering_Preprocessing.ipynb
+++ /dev/null
@@ -1,1481 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "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>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>family_history_with_overweight</th>\n",
-       "      <th>FAVC</th>\n",
-       "      <th>FCVC</th>\n",
-       "      <th>NCP</th>\n",
-       "      <th>CAEC</th>\n",
-       "      <th>SMOKE</th>\n",
-       "      <th>CH2O</th>\n",
-       "      <th>SCC</th>\n",
-       "      <th>FAF</th>\n",
-       "      <th>TUE</th>\n",
-       "      <th>CALC</th>\n",
-       "      <th>MTRANS</th>\n",
-       "      <th>NObeyesdad</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Female</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "      <td>Normal_Weight</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Female</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.52</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "      <td>Normal_Weight</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Frequently</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "      <td>Normal_Weight</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Frequently</td>\n",
-       "      <td>Walking</td>\n",
-       "      <td>Overweight_Level_I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>89.8</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Sometimes</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "      <td>Overweight_Level_II</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
-       "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
-       "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
-       "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
-       "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
-       "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
-       "\n",
-       "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
-       "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
-       "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
-       "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
-       "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
-       "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
-       "\n",
-       "                  MTRANS           NObeyesdad  \n",
-       "0  Public_Transportation        Normal_Weight  \n",
-       "1  Public_Transportation        Normal_Weight  \n",
-       "2  Public_Transportation        Normal_Weight  \n",
-       "3                Walking   Overweight_Level_I  \n",
-       "4  Public_Transportation  Overweight_Level_II  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(r\"C:\\Users\\ritwi\\Downloads\\estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\\ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 2111 entries, 0 to 2110\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                          Non-Null Count  Dtype  \n",
-      "---  ------                          --------------  -----  \n",
-      " 0   Gender                          2111 non-null   object \n",
-      " 1   Age                             2111 non-null   float64\n",
-      " 2   Height                          2111 non-null   float64\n",
-      " 3   Weight                          2111 non-null   float64\n",
-      " 4   family_history_with_overweight  2111 non-null   object \n",
-      " 5   FAVC                            2111 non-null   object \n",
-      " 6   FCVC                            2111 non-null   float64\n",
-      " 7   NCP                             2111 non-null   float64\n",
-      " 8   CAEC                            2111 non-null   object \n",
-      " 9   SMOKE                           2111 non-null   object \n",
-      " 10  CH2O                            2111 non-null   float64\n",
-      " 11  SCC                             2111 non-null   object \n",
-      " 12  FAF                             2111 non-null   float64\n",
-      " 13  TUE                             2111 non-null   float64\n",
-      " 14  CALC                            2111 non-null   object \n",
-      " 15  MTRANS                          2111 non-null   object \n",
-      " 16  NObeyesdad                      2111 non-null   object \n",
-      "dtypes: float64(8), object(9)\n",
-      "memory usage: 280.5+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>FCVC</th>\n",
-       "      <th>NCP</th>\n",
-       "      <th>CH2O</th>\n",
-       "      <th>FAF</th>\n",
-       "      <th>TUE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "      <td>2111.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>24.312600</td>\n",
-       "      <td>1.701677</td>\n",
-       "      <td>86.586058</td>\n",
-       "      <td>2.419043</td>\n",
-       "      <td>2.685628</td>\n",
-       "      <td>2.008011</td>\n",
-       "      <td>1.010298</td>\n",
-       "      <td>0.657866</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>6.345968</td>\n",
-       "      <td>0.093305</td>\n",
-       "      <td>26.191172</td>\n",
-       "      <td>0.533927</td>\n",
-       "      <td>0.778039</td>\n",
-       "      <td>0.612953</td>\n",
-       "      <td>0.850592</td>\n",
-       "      <td>0.608927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>1.450000</td>\n",
-       "      <td>39.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>19.947192</td>\n",
-       "      <td>1.630000</td>\n",
-       "      <td>65.473343</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.658738</td>\n",
-       "      <td>1.584812</td>\n",
-       "      <td>0.124505</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>22.777890</td>\n",
-       "      <td>1.700499</td>\n",
-       "      <td>83.000000</td>\n",
-       "      <td>2.385502</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.625350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>1.768464</td>\n",
-       "      <td>107.430682</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.477420</td>\n",
-       "      <td>1.666678</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>61.000000</td>\n",
-       "      <td>1.980000</td>\n",
-       "      <td>173.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Age       Height       Weight         FCVC          NCP  \\\n",
-       "count  2111.000000  2111.000000  2111.000000  2111.000000  2111.000000   \n",
-       "mean     24.312600     1.701677    86.586058     2.419043     2.685628   \n",
-       "std       6.345968     0.093305    26.191172     0.533927     0.778039   \n",
-       "min      14.000000     1.450000    39.000000     1.000000     1.000000   \n",
-       "25%      19.947192     1.630000    65.473343     2.000000     2.658738   \n",
-       "50%      22.777890     1.700499    83.000000     2.385502     3.000000   \n",
-       "75%      26.000000     1.768464   107.430682     3.000000     3.000000   \n",
-       "max      61.000000     1.980000   173.000000     3.000000     4.000000   \n",
-       "\n",
-       "              CH2O          FAF          TUE  \n",
-       "count  2111.000000  2111.000000  2111.000000  \n",
-       "mean      2.008011     1.010298     0.657866  \n",
-       "std       0.612953     0.850592     0.608927  \n",
-       "min       1.000000     0.000000     0.000000  \n",
-       "25%       1.584812     0.124505     0.000000  \n",
-       "50%       2.000000     1.000000     0.625350  \n",
-       "75%       2.477420     1.666678     1.000000  \n",
-       "max       3.000000     3.000000     2.000000  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Obesity_Type_I         351\n",
-       "Obesity_Type_III       324\n",
-       "Obesity_Type_II        297\n",
-       "Overweight_Level_I     290\n",
-       "Overweight_Level_II    290\n",
-       "Normal_Weight          287\n",
-       "Insufficient_Weight    272\n",
-       "Name: NObeyesdad, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Gender'].value_counts()\n",
-    "df['family_history_with_overweight'].value_counts()\n",
-    "df['NObeyesdad'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kgdaddAGSJEmavBx++KRLWCN12GET+b22TEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjTQ2MJ0ku2TfDbJd5OcleSJffnzkvw8ybf6171HHnNoknOSfD/JXuOqTZIkSZoL647xua8ADqmqbybZGDg9ycn9vldW1StGV06yM7AfcCtgG+CUJDevqivHWKMkSZI02Nhapqvqwqr6Zr99KfBdYNtVPGRf4D1VdXlV/QQ4B7jDuOqTJEmS1tS8jJlOsgNwW+DUvujgJGckOTbJ9fuybYHzRh52PjOE7yQHJTktyWkrVqwYY9WSJEnSqo09TCfZCPgA8KSq+h3wRuCmwK7AhcCRU6vO8PC6xoKqo6tqt6rabfny5WOqWpIkSVq9sYbpJOvRgvQ7q+qDAFV1UVVdWVVXAf/D1UM5zge2H3n4dsAF46xPkiRJWhPjnM0jwDHAd6vqqJHlW4+sdn/gzH77RGC/JOsnuTGwI/D1cdUnSZIkralxzuZxZ+BhwHeSfKsvexawf5JdaUM4fgo8FqCqzkpyAnA2bSaQxzuThyRJkhaysYXpqvoSM4+D/vgqHnMEcMS4apIkSZLmkldAlCRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDjS1MJ9k+yWeTfDfJWUme2JdvnuTkJD/s36/flyfJa5Kck+SMJLcbV22SJEnSXBhny/QVwCFVdUtgd+DxSXYGngl8uqp2BD7dfwbYB9ixfx0EvHGMtUmSJElrbGxhuqourKpv9tuXAt8FtgX2BY7rqx0H3K/f3hd4ezVfAzZLsvW46pMkSZLW1LyMmU6yA3Bb4FRgq6q6EFrgBrbsq20LnDfysPP7sunPdVCS05KctmLFinGWLUmSJK3S2MN0ko2ADwBPqqrfrWrVGZbVNRZUHV1Vu1XVbsuXL5+rMiVJkqRrbaxhOsl6tCD9zqr6YF980dTwjf794r78fGD7kYdvB1wwzvokSZKkNTHO2TwCHAN8t6qOGrnrROCAfvsA4CMjyx/eZ/XYHfjt1HAQSZIkaSFad4zPfWfgYcB3knyrL3sW8BLghCQHAucCD+z3fRy4N3AO8AfgkWOsTZIkSVpjYwvTVfUlZh4HDXCPGdYv4PHjqkeSJEmaa7Ma5pHkzrNZJkmSJK1NZjtm+rWzXCZJkiStNVY5zCPJPwJ3ApYnecrIXZsAy8ZZmCRJkrTQrW7M9HWAjfp6G48s/x3wgHEVJUmSJC0GqwzTVfV54PNJ3lZVP5unmiRJkqRFYbazeayf5Ghgh9HHVNXdx1GUJEmStBjMNky/D3gT8BbgyvGVI0mSJC0esw3TV1TVG8daiSRJkrTIzHZqvI8m+c8kWyfZfOprrJVJkiRJC9xsW6YP6N+fNrKsgJvMbTmSJEnS4jGrMF1VNx53IZIkSdJiM6swneThMy2vqrfPbTmSJEnS4jHbYR63H7m9AXAP4JuAYVqSJElrrdkO83jC6M9JNgWOH0tFkiRJ0iIx29k8pvsDsONcFiJJkiQtNrMdM/1R2uwdAMuAWwInjKsoSZIkaTGY7ZjpV4zcvgL4WVWdP4Z6JEmSpEVjVsM8qurzwPeAjYHrA38eZ1GSJEnSYjCrMJ3kQcDXgQcCDwJOTfKAcRYmSZIkLXSzHebxbOD2VXUxQJLlwCnA+8dVmCRJkrTQzXY2j3WmgnT3q2vxWEmSJGlJmm3L9CeT/C/w7v7zg4GPj6ckSZIkaXFYZZhOcjNgq6p6WpJ/Be4CBPgq8M55qE+SJElasFY3VONVwKUAVfXBqnpKVT2Z1ir9qnEXJ0mSJC1kqwvTO1TVGdMXVtVpwA5jqUiSJElaJFYXpjdYxX3XnctCJEmSpMVmdWH6G0keM31hkgOB08dTkiRJkrQ4rG42jycBH0ryEK4Oz7sB1wHuP87CJEmSpIVulWG6qi4C7pTkn4Fd+uKPVdVnxl6ZJEmStMDNap7pqvos8Nkx1yJJkiQtKl7FUJIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJA40tTCc5NsnFSc4cWfa8JD9P8q3+de+R+w5Nck6S7yfZa1x1SZIkSXNlnC3TbwP2nmH5K6tq1/71cYAkOwP7Abfqj3lDkmVjrE2SJElaY2ML01X1BeDXs1x9X+A9VXV5Vf0EOAe4w7hqkyRJkubCJMZMH5zkjD4M5Pp92bbAeSPrnN+XXUOSg5KcluS0FStWjLtWSZIkaaXmO0y/EbgpsCtwIXBkX54Z1q2ZnqCqjq6q3apqt+XLl4+nSkmSJGkW5jVMV9VFVXVlVV0F/A9XD+U4H9h+ZNXtgAvmszZJkiTp2prXMJ1k65Ef7w9MzfRxIrBfkvWT3BjYEfj6fNYmSZIkXVvrjuuJk7wbuBuwRZLzgcOAuyXZlTaE46fAYwGq6qwkJwBnA1cAj6+qK8dVmyRJkjQXxhamq2r/GRYfs4r1jwCOGFc9kiRJ0lzzCoiSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaaCxhekkxya5OMmZI8s2T3Jykh/279fvy5PkNUnOSXJGktuNqy5JkiRproyzZfptwN7Tlj0T+HRV7Qh8uv8MsA+wY/86CHjjGOuSJEmS5sTYwnRVfQH49bTF+wLH9dvHAfcbWf72ar4GbJZk63HVJkmSJM2F+R4zvVVVXQjQv2/Zl28LnDey3vl92TUkOSjJaUlOW7FixViLlSRJklZloZyAmBmW1UwrVtXRVbVbVe22fPnyMZclSZIkrdx8h+mLpoZv9O8X9+XnA9uPrLcdcME81yZJkiRdK/Mdpk8EDui3DwA+MrL84X1Wj92B304NB5EkSZIWqnXH9cRJ3g3cDdgiyfnAYcBLgBOSHAicCzywr/5x4N7AOcAfgEeOqy5JkiRprowtTFfV/iu56x4zrFvA48dViyRJkjQOC+UEREmSJGnRMUxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJA607iV+a5KfApcCVwBVVtVuSzYH3AjsAPwUeVFW/mUR9kiRJ0mxMsmX6n6tq16rarf/8TODTVbUj8On+syRJkrRgLaRhHvsCx/XbxwH3m2AtkiRJ0mpNKkwX8Kkkpyc5qC/bqqouBOjft5zpgUkOSnJaktNWrFgxT+VKkiRJ1zSRMdPAnavqgiRbAicn+d5sH1hVRwNHA+y22241rgIlSZKk1ZlIy3RVXdC/Xwx8CLgDcFGSrQH694snUZskSZI0W/MeppNsmGTjqdvAvYAzgROBA/pqBwAfme/aJEmSpGtjEsM8tgI+lGTq97+rqj6Z5BvACUkOBM4FHjiB2iRJkqRZm/cwXVU/Bm4zw/JfAfeY73okSZKkoRbS1HiSJEnSomKYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgYyTEuSJEkDGaYlSZKkgQzTkiRJ0kCGaUmSJGkgw7QkSZI0kGFakiRJGsgwLUmSJA1kmJYkSZIGMkxLkiRJAxmmJUmSpIEM05IkSdJAhmlJkiRpIMO0JEmSNJBhWpIkSRrIMC1JkiQNZJiWJEmSBjJMS5IkSQMZpiVJkqSBDNOSJEnSQIZpSZIkaSDDtCRJkjSQYVqSJEkayDAtSZIkDWSYliRJkgZacGE6yd5Jvp/knCTPnHQ9kiRJ0sosqDCdZBnwemAfYGdg/yQ7T7YqSZIkaWYLKkwDdwDOqaofV9WfgfcA+064JkmSJGlGqapJ1/BXSR4A7F1Vj+4/Pwy4Y1UdPLLOQcBB/cedgO/Pe6FzZwvgl5MuYi3m9p8ct/1kuf0ny+0/OW77yVrs2/9GVbV8+sJ1J1HJKmSGZX+T9qvqaODo+SlnvJKcVlW7TbqOtZXbf3Lc9pPl9p8st//kuO0na6lu/4U2zON8YPuRn7cDLphQLZIkSdIqLbQw/Q1gxyQ3TnIdYD/gxAnXJEmSJM1oQQ3zqKorkhwM/C+wDDi2qs6acFnjtCSGqyxibv/JcdtPltt/stz+k+O2n6wluf0X1AmIkiRJ0mKy0IZ5SJIkSYuGYVqSJEkayDAtSZIkDWSYlsYkyUzzpmseuO0XniTL+ndfmwlJsn7/vmzStUhLiWF6iUmyTZIdJ13H2izJzZLsU1VlcJhffVrNndz2C0uS5cDHkmzrazP/0mwGnJ3kjlV1pYF6biVZZ6bbmrzp+5tx7H98wZeexwFvSHKLSReyFrs5LTjc1+Aw7+4DfCnJzm77haOqVgA/B96TZGtfm3m3TlVdArwJ+GiSfzBQz51+XYx7J9kkyS7AfgbqhSHJ9YAb99t3SLJRjWEaO1/sJaaq/hv4P+AIA/X86q2iO1fVx4H7Accl2dfgMH69R+amVfVa4KXAhwzUC8NUqKiqA4Fv0l4bA/U86b0Cn0yyXVW9HHg+cIqBegkdeAEAACAASURBVE6tB1wP+CzwYeDUqrpqsiWp2x54ZpIXAO8DbjKOX2KYXkKmdopV9XTgpxio59t9gS8m2aWqTgQeBbzVQD0vHge8JcnNq+oVwLEYqBeEqrpqJFA/Efgq7bXZxtdm/HqvwLnACX2bvw54LgbqOVNVlwEXADfs338Hjk1fCKrq+8CPgKcBb6qqM6bum8t9j2F6Cek7xakPrUMwUM+LkVbR1wAvpgWFW1fVh7k6UDvkY4x6j8w3gJf1QP1SDNQLxrRA/WTga7Rwt+04ulzVTOsV+Abw4WmB+hNJ7lBVV06yzsUuSarqS8CdgBOA/0ny9/0zeZupEz81f5KMXuH7g8CTgT2S3D/JdadWm6vfZ5heYqZ9aB1COyI7KsktJ1vZkja9VfQtwAdHAvUjaKHufgaHuTetR+ZH/G2g/h/g5CS3ctvPjyRbJtlm+vJp+6YnAWcCHzFojM8MvQJfpm3zbXugfhHw6T7W1zwwS0k2T3Lzfvs+wDuTHANcDLyRduByRJIDgdcAN5hYsWuZJH8HUFVXJNmrD+/YvqreCLweeCJwlyT3A46ZFrqH/14/Xxa3fkR8jRcxyTpTY7aSvBI4vqq+Oe8FriWSvIx24uHTq+oHSZ4BPBq4f1WdmeT+wGVV9amJFrpETXu/HwnclKtfi0OBr1TV5yda5FoiyZuA6wLPrqrzZ7h/9LXauarOnu8a1zbTtvmrgDsCD6qq85LcsKrOnWyFi0eS9YBXAJcCXwEOB14I3Bu4G3D7qvpdkoOB/YEXV9VJEyp3rdJfmw/Tzs04HngXcBptPPv3gKOAewL/Ths7fWRVnTAnv9swvThNhegkm/WztP9meb+9zO678RrdxjOEuKcChwB7TY3TWtnBj9bctMDwMmBX4Al9zJzbfp70LtQ308LGi1cSqN03jUGSLYF1q+qCGe4b/f94Ey1Q7w5c0Ycj+P8xS2kzdjyBduLheVV1WF/+BlqgvlNVXZJkkx6s3bbzJMnf0w52NqJ9Fn8pyV7AnsBvgNcBfwY2q6pfzNVrY5hexJLsTeuy+DLwF+Bl098USdbt3R3rAdfpJ0poDs2iVfSrVfW5Sda41FyLHpl3VNXp817gWq4H6jcBfwReOD1QT4XpJJvQDnq+aNhYc/YKjNe0xqotaS3SWwDPG2kwOZYW3G4ClAeN82Paa3NL4KPAl6vqgL5sT9osW7+kHeRfPpe/3zFSi1SS3YGXA4fSwts/0naio+ss60H6+sA7aEdqmmMzjFP/AfC6tIuHvLiqPufJb3NjZDtuOtPy/lpMjaF+skF6fkxt/7R5XO9Bm9XgUcD6wKFJthtZdypIbwqcAvzZID1nnkw7qepvtvmUaf8fBulrYaQ3eKckxwHLgWcDvwb27a3VVNWjgH2q6gqD9Pzpr81dkzyiqr5LC863SPL8fv8ptID9nrkO0mCYXnRGwsRy4DBagN4FeGJV/SF95o7eAnFl2lWvTqBNCXPRRIpeYmYKxtMC9dOBsxg5eDEszI2+w9wbeHeS5yR5xvRW6v6+XxfaGLokG06s4LVEf13+H+0En91pJ37eHfgP2v/B85NsP7Jf2pR2hv1TquprEyt8iamqPwKPpW3z58wUqKeknXT4Tx7or95IkN6HNk/37YBnAtsAzwG2A/ZPcuv+EA9U5snIgfwdgScBxyZ5YlWdSTug36MP+6Oq/reqvjeOOgzTi8RIy9tUaPg17SzhY4B7VdXP+rigRyfZuIe7zYAPAM+vqs9OpPAlxFbRybNHZuFJc31aiNuTNs/uusCZVfUn4CBaC/Um/X9kE+Bk4LBq04lpDdgrMD4j+/NKcjPg1bRGrMcBZ9DGTW8GvIAWrP80tf5ECl4L9dfmLrSpUF8KPIz2vv+vqjoLOBi4W5Kbj/PA0TC9SIx0YRya5F60gfSvBU4HtkpyV+BlwOer6tLeSnoUcHhVfXFylS8dtopOjj0yC9omVfUb4Fe0QP0o4ICqurC35G0CPLR/sEE7uDnYID037BUYjyTbAoekXSoc2ra8oKq+V1Vfpg0Z2JKr90cHVtUPJ1Pt2iXJ1kkOGVl0Q+CzVfX1qnon8C+0KVL/o6q+A9yjqn4wzoMcw/QikeTOtKPirYBH0j60VgBfAo4DngI8p6o+2gPeVbSZDL4wqZqXGltF5589MgtbDxzvSDsZ66e0MbtPqaofJtmD9v+y1ciJQcuq6oKq+vrEil5C7BUYq18BHwO2SHID4NvAb6ZCXB8u8AXgEuABwLpxru75shHwsVw9n/25wNZJNuwHjafTZhQ6LMmDqurScRfkbB6LQJIdgbfRdoCnJLkVreVh46p6Ud9B/rmq/jRD+NAaGhkvd1/aVEgX0gLdA3qYu0VVfa//E0+FufcBLzLMrbne63IXWi/M+bTLtu8CHEFrqX4dVx9IrkO7aM7bPJAcvyRbA8+ibfNf0sYs3gn4EK2F+hlV9dHJVbi0Jdm0qn6b5O20i+DcF3hUP5jZhzbH7i9HDma2AbbzYGbVRvblAd5Lm5XmUOA2tPmkNwXeQ5vN4/W0ltCH9DHrmgdpF3t6M3BpVT0hbRaVDYBX0mZY+TfaUJzbAI8edyYyTC9wSbantYK+Hjijqvbvy+9GO5P4/lX1+8lVuHRNH8LRewfeC/wOuHNV/aa3it6TNpzm0pFW0ec5vGbN9W3+WloL0Fa0K4x9m3ZQ8yhaS9yxIz0ylWTDcgrIsUpyg6r6Rb99GLBHVd29jzF9CC18XFhtjlfn2B2D3ivwJuBA2rjQxwD/r6q+0XsFXg/s10/Ecm7vWRrZj2xUVb/voe31tHnT39BX+y9gGa2H7Dq0A/t/68OdNCYzfCbfmtYbdl5VHZbkhbTPiV1p/w83ofUaPGzc733D9ALWWxGeAryE9qZ4OnB2Vf13HyN6PPCvVXXeBMtc0mwVnRx7ZBamvl96NW2Y2VP69j8K+FZVvX2y1a097BWYeyMt0nvRxpx/A/gx8H7g7bReyZdW1UV9n7Mn7aS3R1bVtydV99pg5CBnL+DWwBW0S7fvRMtJP6mqw/u6G9M+t19K6zH4ztjr87Nn4epviE8A762q146Mm74COA84uqpOnmSNS5mtopNjj8zCMvL+vh7tAlFbAy+itdb9mdZb86eqOmKCZa4V7BWYe0nWrz73cJJ/ou13DgIeD2xYVfdPuxDRO2hjqZ9QVZf3oX9nV9WPJlX7Ujf6Hk6breZVwKNpY/9fTgvMO9E+Fy6uqv9KO/H/YbSLtow9SINhesFIsjNwq6p6Xz+ZZ72q+nmSf6B14T2L1hJ0R9oJiL+pqqf1x7rDnGO2ik6OPTILU28ROhj4KvCzqnpnktvRhjk9DdgcuDGty/WqyVW6dNkrMPf6CZyvB46oqrOSPJi2ff9IOzfm36rq3L7eH4GdbIWeH71R5fZV9cE+3OZI4J20k2yPpL025/V1bwUsq34lyvnmmacLQJKbA+8CNkmyOfDfwDFp0x39idYSvU1VXQF8E3gr7co+TwND3Fzr/8Db0uYPPRCg2rRe3wH+uY+l+121s+WpbmIFLz2X0qb42r+fKPVK4N5Jvkabz/VZBun5lTaP6yto2/8WwAOTrFtV36yql9JOwLptVf3MID23pg7We6/ACuAQ2nSDr0zyauAyYPvJVbjobUxrbd6t/3wFbYasNwB79SC9F+0A/0qD9Lz6B+C4JDv3noNzaJ/JL6Z9PpyX5DFJHlJVZ1XVGVP/L/PNMD1hSXaiTb/z8ao6pqp+TWuRezWtK+O+wL8CL06yWbWzhb9BG7P7zgmVvWT1lp8n0s6MfySwXvrlSIFf0AL29SdU3pKTZOckD+y3t0yybbVpjJ4I3K6PCz2Vq1+Tn04NbZrUTnMttRXwVNpJV7ekze99Re8poKpOnQoZvi5za2Sc6HtpPQB3raqH0uaTvoA2tvcFSW4Up2abtd5wRVWdC3wd2C/J9avqA8DHgW8BV/TzZo4ETq2qv0ys4LVIkvUAqurDtOD80rT50X9C6wk7rKp+lOQ2tJNBL5567KQatvzHm6A+tOMdwM+A3/XWH6rq/Kr6BO0qS++jhebLaFdYoqr+XFVfq6oLJlP5kmar6DyxR2ZhGmkJHf18+BNt5oI3cfX83vcCHpJpFybydZlb9grMvT5k49NJXpZkk6o6nhbUPtRXORK4iDYu97m0/f5JHiiOX5KbAu9J8sC+D3odLSP9S1V9hDYd3pOSvB84Gnj2Qjh3zDHTE9K77D5BezN8jNZ1tx5wYlV9pa8zOvD+NcBlVXXohEpechynPjm9R+Yk4H1V9ay+bDvaWdr/AXyF1gp3OvCgqrok7UpktwPO9UByPJIsp00p9aaq+nWunt1gA1qP2ea0fdUtaGfSP7OqTppcxUtfkn8Dfk87yfMa89tPW9f90iykTSv4KmAHWjgr4ETgObRZIV7V19sMuKLaFHlu23mQ5Ia03sgfAD+ifR48Argr8IjeI7YLbfz6sqr6wUJ4bWyZnpCq+gNtDtB3VtUltNbnvwD3SfKPfZ1KvzQ1bRaJm+XqS5tqDdgqOjn2yCxoN6ddmveJvcv7qv5B9SfgWNo0YSfRDjRtrZtj9gqMV5Itkly3qn5OOzBZlzZ0aRnwblrr9Pb9wJ6quqT6rEFu2/FKsm4/eD8XeALtPX8pcBTtCoe3pw1/parOrKofVdUP+s8Tf21smV4ARlp/bk6bziXA/1a/6EcP1A8DTqt5muZlKbNVdHLskVn40qYGmzqoPLLaxYlGp8aD9tlx2UJoEVoq7BUYr7Sp7V4I/CNwv6q6uDee/BNtWrWH0D5n9wBeXFXPnlixa5kkW9FmVHk78DVgfdosTkfSGlNuSRvO9C/AQ6vqfRMqdaVsmV4Apsa59aOs42lXVLpP72Kiqq6oqrcapNecraKTZY/MwjXVKlrtgkMn0S7N+9QkW/TX5F7AD4Hl1edSN0jPKXsFxuty2rzEpwOfSJsC7zLaUIJdq+pY4Bm0eYu/MrEq10JVdRFwNu0iOK8ArkdrbDkO+G5VTfXMXEi7xsaCY8v0hEy1Oqzkvp1on1M/mOeyljRbRRcWe2QWnmnv/7sB96F9gJ0DvAx4blWdMLkKlzZ7BeZHkgOBv6dNMbgl8H9V9Zx+33pV9Re37/wYzUJpc0Xfi9Yq/dB++3LgRdXmVN+kqn43uWpXzjA9z5LctLxa0sQk2bqqLuy3b0Hr2lsGfLSqvtqXr9tPcjgQ2Jt2OdI/T6zotUAP1I+mnQj04t5qrQmYFqj3oB3QPAJ4cFV9wJAx91ZyEHM58Mqq+mXvFXgrcKeq+tnkKl3cpgW3nWiB+kW0q62+ZGrYn+bX9H1K2pUlH0s78fY2tH3PGatqhJw0w/Q86sM2jqBdBvzbM30oTftn90NrTGwVnQx7ZBaGPg53kz5udBvaTDV/HLl/NNzdCbi8qk53nzQ+9grMrT5G+i/VTiIfXT49uO1EC9Sv7kOcNEEjPTE3A+4GPJ92pcOvTrayVTNMz6N+5vWbgW9X1ctnuH9ZVV2ZZGNgo6kWVI2XraLjZ4/MwtFnitiNdqLVn4E7Af/Vxy2Orjf1oTZ6gL9gW4aWAnsF5kaSv6ONfz4F+Gyt5GIrI5+561fV5W7f8ZsaRtNvbzh1/sUq1t+o+owqC5knII7R1MkhSXZIsmN/07wQ2DttLuPRdaf+qTcFPk8/8U1zJyu5OlhvCT0GOMYgPfd6j8xT0q5WNeMV8kZfG0+qGq8ehs8D7kCbV/fkqrpo+nafOhG09+BsmGR7g/SaS7JB2rz2JNmmt6ACf93mUyeCfp524uEdDdLX2m/69/sAd0m/ot50/TM31S5V7Qm1Y9Z7fPdJcpckuwGPH33/r8Rl/bELOq8u6OIWq96FOrVj3AW4H/DhJA+inaX6v7QTH0iyzrQg/UFaK9HpEyp/yUm7ohKrCgJV9X2HF4zNX4BNaSeTXOMDq7//r0qycR/T7gfamEx9IPVer9Npl02+RZJ/GGkRXTb1vZ87sBnwfvo+S8P17f/3wAFJnki7wurfbNfRQA18beSzwIPMWRjpPXkmLVQ/iJUE6v4eryTrp19eXGMV4ALaVQ0/ApxUVX+cKShP7Ydo5zSt8vN7ITBMj8d9kzwpyT2AN9AuDvJk2pX0DqR1Px2RZPOqumokSH+Kds35L02s8iXGVtH5Z4/MwpNkWW+BuyrJrmlTRL6eq0/yOTDJ9kl2oLUcXWfkdXk/bfjTWRP7A5YIewXGa+Q9vlHfXocD5wMPZlqgHtn3XJ/WiLXhzM+qudKHd/yKNo/0z2hXoLxGUB55bTYD3rUYDnQM03Mo7epKN6g2ofjjgQ/RJta/uKo+BRxGu9LeG2k71Lv2xwXYBXiqQXrO2So6T+yRWZjSLp38VOA6Se4JfJLWancKsJx2Jbhf0M7n+Arwu6r6c5JNaFNJHu6JWWvOXoHxmhoGk2Rv4M295X+XqjqCFtweAOyRZL2+7lRYey/w8qpakPMXLwUjDSybVNVPgNsCz6UN83hYv+9GfV/FyGvzAeB1VfXrCZU+a56AOEf6Ee8zaf+YP6TNk/hg2gwRh05bdx3gScBWVfWM+a51KRvZoe4ArFdVP0ybAu/1wNNHw9q0MPdZ4DGGueGSPBDYFvgO7cDxAcCuwF60C4DsB3wfuM/UznGkR+YQDyTHo39AvRf4Iu2CUB+sqi8neTawP7BPVZ2XNsfxlf2+dYADaBdM+NrEil8CekC+qu+XdqWd9Hke7UD/mbR5jl9M687eBfhUP5jZlBYmnu/BzOwk2RN4FfCfwAtoraBvqaqPJ3kebf/0zKr61UhYe171WZw0Pkn2pR3UXw4c1V+TB9Ku+vkd2hWGn1BVZ/begvfRDuQXxWtjmJ5DaZPqXx94OPCBqvpBkq8A36yqg5PcEdiwqj6T5NG0s7T3Av5gi+iaSbJBtSuF0VtF96T9kx5Om1pqT+AbVfXZHhRSf9sq6vCagZJsAaxbVb9I8kNgK2DvuvpCOBvRuvWeDOwIvKuqPtJbK+4ErLNYdpiLzcgB4za0cYpb0S5WdGoPd8+iDfW45/RzBtJnOJj/qpeOfiDzUFrA+yfaFW4/BexEO9i8DDgY2J124LlfVX2h9wp8EniG/xur1/fpGwKH0q5wuw3tSnofpI1Rf3NVnZw+q1Df97ycdsEuD1TGrDdovQ54HnAD2gn/+1XVJ/pB/GOBd/Sf16M1fr27qj47qZqvLcP0HBhpDb0urdv0JbTW6dcBf6R1p/6ANhXV4/ob5j7Az8o5jOeEraKTYY/Mwpc+fzfwS+AtwFnAq6rqV/3+5wJfqDZ7hOaQvQLjNfXZO/LzxsB1gXcCD6p2BcnvAF8HDq2qi0fW/esUbRqftPmiXwD8uaoO6MvuRwvUj66qD+Vvp99cnzY18K8mVvQAhuk1NBKk7wX8M/AsWuvb02njEF9Ja33YCzi/2oUPnKt1jtgqOnn2yCw8I/ulOwBPAa6knfh8Oe1D7P9oYxFXTH/MRApeguwVmB99aMe/0GaHOJs2fOZrfdmVtHMCnlFVZ488xvf6GI1u37Tp8J5G65l5GfDVapcGfwCtp2YH4Ff9HIFF+7p4AuIa6jvFe9J2lh+r5ge0wfV/R2u127aqPjIyHndRvlkWmt4q+h/AJj0cv4U2pOO+U+tU1e/7Ee5/A6fSAjT9dfqyQXq4qZNKaO/nZcCtgYckWU474XO3JG8HTqAd0EA7wPyPqrpsse40F7ppJ2K9ltZbsx3wRNrJuAcCdwGelJHZDXw95lYP0jsBG9EuCnURLeBt3u9/EW2ftfUMjzVIz0LaLE0vok25diDtgP5y2jCBjwIn0cZMnz36ON/r4zOy/9kzyYHAQ6rqxcAXgH8Fdu8Hi+8HblRVF1W/SuVifl1smV4DvTvuOsDRtCD93n60dU/a0I7P0MZlvbiqfji5SpcuW0Unwx6ZhacPKfhFD3Hr0wLFx3o36i60sbtb0A4srwS2K0+4nXP2CozXyPa9Ae2gcJ2qOiHJvWmfvT+nXezmesD6U2Ok3b7zp78WR9A+F94MvLp/PQe4Ia1F+nO0DHrVUnh9bJkeYKpFrtoc0X8CPga8IMkngDvTxkc/BrgEONggPfdsFZ0se2QWpKcBt4S/tmz+Gtgr7eTcM4ETafMbPxr4Yz/AcV71OWSvwPiNbN+v0A5WntqXf5w2/ebNaMNnLqqq/9/e3cffPdd/HH88bWbzm6tZFBFClmTYiHRjG+UqV0sqRVHjlrIpukAlISWq5UchW6FybYaEsXLxw6xmcm0xP9fKhO03l8/fH6/34Zh91b6dcz5n57zut9tuzvdcfL/vr3O+57zer9f7/XrPqj2mouF2BUmDJK1cLvclDsrZHViS6Fxzfsk+fw94CHiqfGa8Cp3x/GRmehHVvVluRWTkphM9LAcAz9i+p5Se/hvY1fbfKxxuR8qsaLWyItO+JK1JvP5HE10MPgPMtD1RcRLoCUQl5yjbU6obaWfJqkDrlKUzxxCB2Z3ArcA9tj9ebt8BeMD2XdWNsntI+i/gAKKV3WO2X5J0MjCPaHd3QKkY70n8jXTkRufMTC+iEsRtT2TkZhMZuN1t31wC6e2IncTHZyDdHJkVrUZWZNqf40CEZYBfE91VpgEjJf2JWEM6DpgCrFnZIDtTVgVaQNIQYoK4NtDf9ku2NwTWkHQpRIY6A+nWcZxw+yvgZeBgRUeVqUTV4OgSSG9GdNnq2O4pGUwvopJ12IrY5HYfkZE+tdy2KhHMHerXO0akBlKcnNefeEP9lu3rJX1M0i+I8ukRxHPwhtd2J5SRqlRfkZF0pKSPAncRz8M42wcTWemlgRVsz6tyvN2i9h4jaSNJIyWtZHsU0XP3ZKLP7n7A4USlZk3iOevI7FBVbI8D5kq6WHFIy2+JzNwnyl2eAP4GjAKGl8fke9IikDSSmAzeBFxPTFbeD2B7U2B1SRvn527rlNc6xORxTWBdIqHye6I5wC8ljSfWTX/dpctWJ8plHv+GBRfHK/qyjiAC6T1sP1xKSy8D15S1QamBFvIc7EmU+WYBdwMPE8HC9sSmkwzmGqxUZH4InEhk2K6yfWS5bTvikITDbU+qbJBdSHGy2BFEgPw+4FjHwR+TiDWLO5VNPmsT1ZxDSrY0NZikKcRSs/2JhMv2ROuvwcAOxEbpR22fXtUYF0eS1iWW8f3c9q2StgR2Iapgl9meUekAu1B91w5glO1vlqWXOxKfxz8hqjV9iLnjjE7YaNiTzEy/BUn94LVlBetKGl4W119HHAl7ZgmkP0C8cF7MQLrxMitavazItKey4fYLwNbEZrdBxPOD7V2I5U0bla/vJ04dy0C6AbIq0HylEjmA2GQ4DHg3gOOQrYuI0/R2kTSw7OVILVI+kz9C9PGeWq67kshKv4OY/Myx/ZfaZKdTA2nIzHSPJK1IvBhOIlq5nAo8D8wAZhIZny2I9YmrEhm5ydWMtvNlVrT1siLT3hQHEr1ABG6zge2AfRytwLYFbizrGV87QKS60XamrAo0R10CZSnbL0hagTiKeh5x8Nbt5X5bAn+3fXeFw+0aZcL4ZLnchzjteartyyT1s/1iue3DROXgR2UfR8fLmVzPliE+qL5GZBZ2tb0Z8GfiyPBpREbocGA325MzI9ccmRVtrazItD9J7yI29KwMPEYsHzikBNIjiOdljdr9M5BuvKwKNEddIP0R4JyyH2Z3YpPncsAekoZCZKgzkG4NRdeOH5eJYe09ZVmiixN1gfRQIlN9WLcE0gB9qx5Au5G0FlGSm0O0MLoT2JkoJ90H/IboH7qd7RuI4Bro7BJGq9VnRUtmYi7RiH8AsLPtR+uyoue4A05Qage1ioyknioyVwBbl6U2qwJftT21ouF2lbogoy/xul8eWAk4h/hQO0LS9cA+xOTyjupG29lKVeAZYiJzMFEV+KTtx+qqAjuW+/ax/YrtZ6obcfurvb7La3xzotr4beL951RisvJV4OfAaEn3236+uhF3D0V7x72J5OJSkn5he39gIjG5GW37AkmbEG0hx9ieWd2IWy8z03UkvRe4EngvMJQ4LW84cBbwaUlDbT9HtAR7u6SlMhPaWJkVrVxWZNpU+ZsYTix1mgdcS0zs/0aUwH9DBHdfsH1pPi/NkVWBxitZ/s+XSQpE4Hy57YtsX0W0FdybWDN9JHBuBtKtUT5/fwA8Bfyj/PuQpB8QFZm7gX0kXUG0yDu22wJpyMz0ayQtR8x4j7E9oVw3nsiG9gVuAU6TdBGxW/UYRz/R1CCZFa1OVmQWG1sRk5k1iTZhuxDBxRG2f1N/x3xeGierAk33QWAzIus5gZjQj6jdaPspRaeU5XOpTOtIWoPYUPtLYjPtYcRnwhbEpv9XbB8m6Wyic8cztu9fcL9NN8jM9OteIDbxnAugaLb/KK8f/7oS0dtyEyKIuzwzPw2XWdEKZEWm/UlaXdIGtn9EHE60JHFk71xgT+K0w9QkWRVoDr3ep3gy0QXiPcDetq8G/ixpmqT1Fe3XtiGqk6kFJK0GTCKSiQI2J173g8uSpW2AEZJOsz3X9q1lb0BXTuQzM/26AUSgvDXRt3J+2Z36iKRTiJN7fkssK5gF3fmCaYbMilYnKzLtT9Io4jjqOZLmES3BHiHaQ15DZKvzvbz5sirQQIpjwT8v6UrgT2XN7Txge0ljbB8g6Whi4+FqxKEft1Q55i4zHHiUOJBlbWAM0fLucElnOPp97wBMkbR+t1djsjVeHUn7E6Wm8Y4G431svyLpm0Af20dXPMSOU7KilxBZiVqW7VoiyzMYOKU8F3sAHwa+RExo8oXbAIrTJE8DDrA9t1Rk5pcuKdcR2elliGU3x9u+qRtLeFVRHMN7JPB94iCELxF/B7OBC2yfoWx711SSVgeWs327pEOIjNwfgXWAkcBo29OrHOPiSNJWxHv9fURFeC3geKI7RD/gEdsTy32Xtf1svve0lqSHiW5Zw2zfIWljYqakWgAAC1BJREFU4vChlYCzbN8iqW/uW8pg+g3KJohxxOaH821PkbQFcDawn+1rKh1ghylZ0cnAhLqs6CpEVvRJIiu6D5GJq2VFL69ouB1J0b/1BmKd52Xlun62X5R0KFGR+QN1FZnUGmW94gnATbaPr7t+DLAXsD6xNGeO7ZeqGGOnq68KEMs7LiKOTL4LWIqomn3K9s2VDXIxpugTfSmRxBoNrADsRkwc1yYmkmcQyf4MVlqgfsIiaTKxFvo6258r121IPFcrEUsyn7f9alXjbRdZGqxTNjmMJ7KjJ0uaRryQxmUg3RQLXacuaT8iK/ogb1ynnlnRBrM9R9JPiVZTjzhOqqplOfsSR7PfVd0Iu9oywLPEc3Np7Xmwfaqka4hq2ZOVjrCDlarAIbyxKvAr3lgVWDerAr1n+3pJnwTOB7aw/ZykS4ENiGUFD2Sg1lplf8AoYA3bHwWQdJuk82zvYfu2sifgOdvPVjva9pGZ6R5Ienu52N/2gxnENV5mRdtDVmTaQ13HiA2ApYF7gYHEiZ/LAyfZvq/KMXaLrAq0Vll7+zNguO2ny3W1v4f87G0xSe8HpgPfsn1cCZ6nAY/b3qna0bWn7ObRA9uPl38Plq/zj7nBbM8BalnRoeXq+qzoQNt3ZSDdXLafAsYTpeuTJZ1FfLBlRaaFSuCwPVGp2Zl4PlYELiZ6ux5aNm2l5quvCgypXWn7VKLD0wdtP5mBdGOU5XsHAneXJMtrn7n52ds6kgZLWs7RJ3oj4BBJhzkMA9aQtFF2q3mzzEynSmVWtL1kRaY6ZdPnGUR5ey3gFGDzshRnfSIjepbtOyscZkfKqkB7kLQjMNd5fkDLlfef7xP7AqaUDZ9DiAz1UbaPq3SAbS6D6VQ5SSsT69QPJEpJQ4g/3kmVDiylFlE5+RM4iOghvRtxPPUsSbsDlwPYnl/REDteqQqcCFxIZJ+3I/rrfhR4J3CC7XuqG2H3yEl8a9RNItcoyZOvEL2+LwFurNtT80ViQ+jDuUdg4XIDYqqc7SeA8ZLOLVdlVjR1jZIN3ZfYGb8h0dlghKPH/TDgaGIj1l8qHGZHK1m5cUQAvRbRrWB2CSZeJKoCfd7iW6QGyvf91iiB9E7AWElftH2ipLHA7sBgSXOINoUb255d6WDbXGamU0qphRacJEoaRGzE/RpwG5EZnUIkO7YFDrc9uYqxdoOsCqRuVSbrZwIfd/RR72v7ZUk7Ez3UtyY2IU4u988EVw8yM51SSi1U18O1X/ny6dK95kO2J0vai2gHOZBowXZjfog1R1YFUjeStERpOTiEOM9hTjmQaIdy/sMWti+RNKi8P6lsQsz3oB5kZjqllFpA0lrA9sRJnysTaxNvIj7MHgZ+RxyZnKfpNUlWBVI3q1sjPdD285IGEgenLQ1MBK4AvgucafuqnMT/+7I1XkopNZmk9wJXEqfnDQH2BjYm1iOeQ2REHwCOKh9wqQnqqwKSliw9jQ8FtrT9ELE2+jbgHuCAUinINmCpI9S13zxP0neAwbZHACNtnwIsCwwHnqjdv7rRLl4yM51SSk1UyqaTgQm2J5TrViZO07uaCLLHAKsS6xTfY/vxiobbkbIqkLpZ3VroTYmlSxOAHYigeRLwP8SE/kxgbFZjFl0G0yml1ESS+gOnEZnOuZL6254vaXUioNuXCKhXBNazfV2Fw+04pSpwCXAZMB/YiThd9XTgy8BXgF2BVYA9bT9f0VBTaihJawJP2/5nufxr4A+2jy5nCnyZWKFwFXAH8E7b03N5x6LLZR4ppdRcA4gNhVtDdIWQ1K8sK/gZsIHtV20/VQukc2lBY5SqwM+BY2yPtf11Ivv/ONAf2IPIWA8GtiQ2fabUKd4NzJa0PPAQ8Gfg05I2LNWvnwBLEb3UX6hVZjKQXnTZzSOllJqo7uCD0ZIesT0DqB188CoRbC/4mPwwa4wXgNnEEe2UqsATksYQVYG/Ei3xalWBXF6TOobtqyV9gjgMbZjtsZIOB46U9B3bMyUdB6xg+5lqR7t4y8x0Sik134XAY8D+kkbZfkXSFsABQC7raJ6sCqSuZvsKYCwwvVRqjgVuBn5UMtRP5sme/7nMTKeUUpPZfkrSeGBP4GRJ04iuHuNsX1vt6DpXVgVSAtuXlznircAw4AfEAUUZAzZIbkBMKaUWKht/APrbfjA3+zSXpLcRR4UPAs63PaVUBc4G9s3JTOoWkrYjOnmsZ/ufVY+nk2QwnVJKqaOVVoR7AgcS60eHAEfZnlTpwFJqMUk7AnNtT616LJ0kg+mUUkpdIasCKYV87TdWBtMppZRSSin1UnbzSCmllFJKqZcymE4ppZRSSqmXMphOKaWUUkqplzKYTimllFJKqZcymE4ppZRSSqmXMphOKaUGkfROSZMk3SdplqSfSupXbvuspJMa8DOOkrRNuTxO0tKL+PiBkn5RxneHpD9J2uxfPOaw/2TMKaXUyTKYTimlBlCc13shcLHtdYB1gYHAMY38Oba/bfvq8uU4YJGCaeB04GlgHdvrA58FBv+LxzQ9mJaURxunlBZLGUynlFJjjATm254AYPsV4GBg37rs8WqSrpB0j6Tv1B4o6dOSbpE0o2SN+5R/EyX9VdLtkg4u950o6WOSDgJWAa6VdK2k/ST9uO57fkHSifUDlPRuYDPgCNuvlnH+zfZl5faLJU0vGesx5brjgAFlbGf3NN5y/X6S7pU0VdJptUy8pHdJmiJpZvnv6nW/y4mSrgWOLxn9t5XblpB0v6R/FeinlFKlMphOKaXGWB+YXn+F7WeBh4C1y1WbAnsBQ4E9JA2TNIQ46vqDtocCr9TdZ1Xb77O9ATBhge89HngUGGF7BPA7YGdJS5a7fG7Bx5QxziiB/sLsa3sTYBhwkKQVbX8D+D/bQ23v1dN4Ja0CfAv4ALAtsF7d9z0J+LXt9wNnA+PrblsX2Mb2wcBZ5XcH2Aa4zfbfexhrSim1hSyrpZRSYwhY2JGy9ddfZfsfAJIuBLYEXgY2AabFShEGAE8Ck4G1JP0MuAy48q1+uO25kq4BdpJ0F7Ck7dsX8Xc4SNJu5fJqwDrAPxa4z6gexrsp8EfbT5ff7zwiUAbYHNi9XD4T+GHd9zuvLrg/A5gE/ATYlzdPBlJKqe1kMJ1SSo1xBzC6/gpJyxJB6SwiAF0w2DYRbP/K9jcX/IaSNgQ+AhwIfJwIMN/K6cT65rtZeCB6B7ChpCVqyzzqftbWRDZ4c9vzJE0F+i/keyx0vHVB+L+j/v/D3NeutP9X0hOSRhLLUfZ60yNTSqnN5DKPlFJqjCnA0pL2BijriE8AJtqeV+6zraRBkgYAuwI3lMd9TNJK5XGDyhrjwcASti8glk9svJCf+RywTO0L2zcTwfungN8ueGfbs4Bbge+WDZNIWkfSLsBywJwSSK9HLNeoealu+chCxwvcAmwlaYWymbB+YnEj8IlyeS/g+rf4/3g6sdzj3LdYjpJSSm0jg+mUUmoA2wZ2I9ZC3wfcC8znjZ0wrieWOcwALrB9q+07gSOAKyXNBK4C3gGsCkyVNAOYCLwpcw2cCvy+bOCrORe4wfacHob6eeDtwP2SbgdOI9ZeXwH0LWP4HnDTAj9npqSzexqv7UeAY4GbgauBO4F/lscfBHyu3P8zwNgexgZwCdEFJZd4pJQWC4r3/5RSSp1A0qXAj21PqeBnD7T9fMlMXwScYfuiRfwew4jxf6gpg0wppQbLzHRKKXUASctLupfovNHyQLo4smTS/wo8AFy8KA+W9A3gAhaehU8ppbaUmemUUkoppZR6KTPTKaWUUkop9VIG0ymllFJKKfVSBtMppZRSSin1UgbTKaWUUkop9VIG0ymllFJKKfVSBtMppZRSSin10v8DDdus6/4B/UEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "df['NObeyesdad'].value_counts().plot(kind='bar',color = 'teal')\n",
-    "plt.title(\"Distribution of Obesity Levels\")\n",
-    "plt.xlabel(\"Obesity Category\")\n",
-    "plt.ylabel(\"Count\")\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Target Variable Distribution\n",
-    "\n",
-    "Even though the `NObeyesdad` column is not used in clustering, it's helpful to check the class distribution for context. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Public_Transportation', 'Walking', 'Automobile', 'Motorbike',\n",
-       "       'Bike'], dtype=object)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['MTRANS'].unique()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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4ZjgR0TVxzsPP3S7Hs/9AeYYvIiJSoZimp4t1E9Ad+C7vuC8wC2hpZo85594KQGxHJMfj4amps3nt+jNJionk2rFT6NPqOJol1M7v07puLO8MuoAaVSL4YO4KRn2TwrOX9w1e0GUpLIy2Lz7M7HNuZN/GVHrN+ojUKdPZvXytV7fwmlE0Hnw922YvzG+z8HDaT3yOhQPuYdeilVSJrY0n+2B5j0BERERCWGlTZA9wgnPuMufcZUAbchfEdAXuC1RwR2LJpnQaxUbTsE40VcLDOattE75fsd6rT+cm9ahRJTdPPqlhAqk79wQj1ICo3eUk9qz9g72/bcRlZ7P5/c9JuqB/kX6tht/BuhHj8OzLX89E/Bk92bV4JbsWrQQgO3M7eDzlFruIiEiFFGaBeYWo0iaNjZ1zqQWO04CWeauns8s+rCOXtmsPdWOi8o+TYqJI23X4pHDygtX0at6gPEIrF9XrJ7F345/5x/s2pVK9QZJXn5j2J1C9YV3Spn7v1R7VsgnOObp8Po5ecz6h6d03l0fIIiIiUoGUdnr6JzObQu4m3wCXAT+aWRSwPSCRHSHnirYdLlf/fNFalm1O540B5wQ0pnJlxYy24A/FjDYjHuDXmx4o0i0sPJzYHh2Z0f1ycvbspdtXE9gxfwkZ380KYMAiIiIVm1WyZ0+XdrS3AxOA9kAH4E3gdudclnPutMKdzWyQmaWYWcob0+eUWbAlSYqJ5M+dWfnHqTuzSIiOLNJv1rrNjPtpEaOv7k/ViPByia087Nv0JzUa1s0/rt4giX2bD61RioiOIrptS7p98yanrf6W2l3b0+mTV6nVsR17N/1Jxk9zyM7YhmfvPtK++JFaHdoGYxgiIiISokqVNLpcHznnhjrn7sx7X0xtL79/snOuk3Ou0039upRdtCVo2yCe9Rk72bRtF9k5OUxb+ht9WjXy6rNiSwb/mTKTUVf1JzaqRrnEVV52zF1MVPPG1GjcEKtShfp/O4/UKdPzzx/cuZuv63Xjuxb9+a5Ff7bPXkjKpbexY94Stn41g5gTWxFWozoWHk5c787sXr4miKMRERGRUOPriTAznHO9zGwXuZt5558iN5eMCWh0RyAiLIz7z+3GbW9/jcc5LmrfnOaJdRjz3QLa1I+jb6vjGPl1CnsOZHPPh7mLwOvVqsnoq4suFqmIXE4OS+54jC6fj8PCw9k44WN2L1tDy0f+xfZ5S0grkEAWdnD7Tn4bNYFeMz8C50j78kfSvvihHKMXERGpgIq7NewYZiUUDMvE3klPBfYCFcj0GyYEO4SQcl72ymCHICIiFVfQM7Y94x8JSI4TOXB40MdWnNIuhMHMegEtnHP/NbN4INo591vgQhMREREJYZVsIUxpnwjzCNAJaAX8F6gKvA30DFxoIiIiIiGskk1PlzZFvgS4EMgCcM5tBqIDFZSIiIiIhJbSTk8fcM45M3MAefszioiIiFRa2qexeB+Y2etAbTO7BfgGGBu4sEREREQklPjacudO4GdgFHAasJPc+xofds59HfjwREREREKUVa5Ko6/p6YbAaKA1sAj4hdwkcl6A4xIRERGREFJiiuycG+ac6wHUBf4NZAIDgSVmtqwc4hMRERGRQszsbDNbaWZrzOz+Ys7fZWbLzGyRmX1rZscf7TVLW1etAcQAtfJem4HZR3txERERkQorzALz8sHMwoFXgHOANsDVZtamULcFQCfn3EnAR8CzRztcX/c0JgNtgV3kJom/AC8457Yd7YVFRERExC9dgDXOuXUAZvYecBGQPwvsnPuuQP9ZwHVHe1Ff9zQeB1QDVgObgI3A9qO9qIiIiEhFZwFaCGNmg4BBBZqSnXPJBY4bABsKHG8EupbwlTcBXxxtXCUmjc65s83MyK029gDuBtqZWSYw0zn3yNEGICIiIiKH5CWIySV0KW4Ou9jnYJvZdeQ+1a/P0cblc3Nv55wjd+HLdmBH3ut8ckujShpFRESkcirF/YcBshFoVOC4IbnrTbyY2enAg0Af59z+o72or3sa/0VuhbEnkE3udjszgfHA4qO9uIiIiEiFFbx9GucCLcysCbm3D14FXFOwg5l1AF4HznbOpZXFRX1VGhuTu+JmqHNuS1lcUERERET855w7aGaDgWlAODDeObfUzB4DUpxznwLPATWBD3PvNGS9c+7Co7mur3sa7zqaLxcRERGRsuecmwpMLdT2cIH3p5f1NSvX829ERERExC8+F8KIiIiISDEsaAthgkJJo4iIiIg/wirXhG3lGq2IiIiI+EWVRhERERF/BG/LnaCoXKMVEREREb+o0igiIiLij+A9ESYoLPcpgYGzfvXywF6gAtlhscEOIWQ0+/n1YIcQUiJveNh3JxERKSjoGdu+/70ckByn+kWDgz624mh6WkRERER8UtIoIiIiIj4paRQRERERn7QQRkRERMQfleyJMKo0ioiIiIhPqjSKiIiI+KOSPUZQSaOIiIiIPzQ9LSIiIiLiTZVGEREREX/o2dMiIiIiIt5UaRQRERHxRyVbCFO5RisiIiIiflHSKCIiIiI+aXpaRERExB/ackdERERExJsqjSIiIiL+qGRb7pSYNJrZXcAO59wbhdqHAOHOuVGBDE5EREQkZGl62stA4K1i2pPzzomIiIhIJeBreto55w4U07jfrJKl1yIiIiIFaZ9Gb2aWVJo2ERERETl2+UoanwM+N7M+Zhad9+oLfAaMCHh0IiIiIhISSpyeds69aWZbgceAdoADlgKPOOe+KIf4ijV33nzGJI/D4/FwzplncNUVl3mdP5CdzbMvjGL1mrXEREfz4H3DqJuUxM6dO3nsqWdZuXoNZ/bvx5DbBuV/Jjs7m5dfS+bXxUsJCzNuvP5aTu3Zo7yHdsScc4x//UUWpMyiarVqDB76AE2btyrSb+3qlbwy8kkOHDhAh07dGHjrvzAz3n1rHHNnzSDMwoipXZvBQ/9NbFw8WVm7eXHEf0jfmkpOTg4XXnoV/c44Nwgj9M/Pazfz3NcpeJzj4pObM7BHW6/zb81ezuSFa4gIC6NOZDUeOb8b9WvVZGVqJk98OZes/dmEm3FTz7ac1aZxcAYhIiISQnytnu6UlxwGLUEsLCcnh5defZ1n/jOc+Lg4Bg+9h+5du3D8cY3y+3z51dfUjKrJxLGv8d0PPzFuwps8dN89VKlalQHXXcNvf6zn9z/We33vpA8+onbt2kxIHoPH42HXrt3lPTS/LEiZxZbNG3lp7CRWr1xG8isv8PTI14v0GzvmeW4dcg8tW7fliUfuZcG82ZzSqRsXXXY1V19/MwCff/oRH747gVsHD+PLKZNp2Oh4HnjkaXbs2M4dg67l1L5nUKVKlfIe4hHL8Xh4etpcXr26H0kxkVz73y/p06IhzRJq5fdpnVSHdwaeQ40qEXwwbxWjpy/gmUtOpXpEBI9f0J3jY2NI27WHa8d/QY+m9YmuXjWIIxIRkVDkKtnyDl/T02PNbLWZPWZmJ5RLRD6sXLWa+vXqUa9uXapUqULf3r34ZdZsrz6/zJrDmf1PA6B3rx4s+HURzjlqVK9Ou7ZtqFq1aOIz7etv8iuWYWFh1KoVE/jBlIG5s2bQt99ZmBktW7dlT9ZutmWme/XZlpnOnj17aHVCO8yMvv3OYu7MnwCIjIzK77d/3z7+Wt9kZuzbuxfnHPv27qFmdAzh4eHlN7CjsGRzBo3qRNOwTjRVwsM5q83xfL96g1efzo3rUqNK7r+ZTmoQT+quPQAcHxfD8bG5v/aJ0ZHUiapO5p595TsAERGpGCwsMK8Q5Wt6uoOZtQKuAj42swPAu8B7zrk/yiPAwtIzMklIiM8/jo+PY8XK1V59Mgr0CQ8PJyoykp07dx02Edy9O7eqOPGtSfy6ZAn16tZlyD8GUadO7QCNouxkZKQTl5CYfxwbn0BGRjp1YuO9+8QlFOnzl0kTx/LD9C+JjKrJo0+NBuCc8y/l6cce4JbrL2Hf3r0Mve9RwirIKrG0XXtJionMP06KjmTJ5ozD9v+/X9fSs2n9Iu1LNqdzMMdDozrRAYlTRESkIvGZBTjnVjrnhjvn2gA3ALWB6Wb2c8CjKy4eXJG2wtXh0vQpKCfHw9b0DNq2ac2ro1+gTetWvD7+v0cbavlwxYyVwj+Q4n4eh/pcc8MtvD7xY07tewZffvYJAAvnz6Fx0+aMfWsyz730Bm+8NpI9e7LKNvaAKTrew/l8yW8s25LBDd3aeLVv3b2Xhz79hUfP705YJZt+EBGRUqpklcZSR2ZmYUAikAREAVtL6DvIzFLMLGXSex8cfZQFJMTFsXXroSpZenoGcbGxXn3iC/TJyckha88eoqMPXy2KiYmmerVq9OzeDcid0l6zdl2Zxl2WvpjyCcMGD2TY4IHUiYsnY2ta/rnM9K3ExsV59Y+LTyAjY6t3n1jvPgCn9j2dWb/8AMB3X0+la4/emBn16jckMakemzYEpbh8xBKjI0nduSf/OHXXHhKiaxTpN+u3Lbzx8xJGXdGXqhGHpt5378/mX+9/x+19TuakBvFFPiciIlIZlWafxlPNbAywEbgHmAG0cs5dfLjPOOeSnXOdnHOdrrnqyrKLFmjVsgWbNm9hy5+pZGdn8/2PM+jetYtXn+5du/DVt98B8OOMX2h/0omUtBe5mdGtS2d+XbwEgAW/LuK4Ro0O2z/Yzjn/Uka8PJ4RL4+nS7dT+X76NJxzrFqxlMioKK+paYA6sfHUqBHJqhVLcc7x/fRpdO7WC4Atmw7d6zd31s80aHgcAPGJSSz+dR4A27dlsnnTBpLqFp3CDUVt68exftsuNm3fTXZODtOW/UHfFg29+qz4M5MnvpjDyCv6EBtVPb89OyeHuz/6gfNPbMoZJxxf3qGLiEgF4swC8gpV5oqZusw/abYBWA+8B3zgnEs90gusX7289HOFpTR7bgqvjh2Px5PDWWeczrV/u4IJb0+iZYvm9OjahQMHDvD086NYu24d0TWjefC+u6lXty4A1w28hT179pJ98CA1o6J4+vFHOf64RqSmpfHM86PYnZVFrZgY7rnzXyQmJviI5MjssFjfnY6Qc45xr45k4bw5VKtWjX8OfYDmLVoDMGzwQEa8PB6ANatX8MrIpziwfz8dOnXlpn/ciZnx3BMPsXnTBsyMhMS6DLr9buLiE8jMSOflkU+yPTMDB1xy+bX07ndmmcXd7OeiK7zL0k9rNjHim3l4PI6LTm7GzT3bMeaHX2lTL46+LRty66RvWZO2nfiauRXIurUiGX1FXz5f8huPTplJ0/hD97M+dkE3WiWV/a9dQZE3PBzQ7xcROQYFPbva8+MHZZ7jAET2vjLoYyuOr6TxBCDDOZdWqD0R2Omc87msNBBJY0UViKSxogp00ljRKGkUETliQU+sKlvS6Gt6eijQq5j2M4CRZR+OiIiIiIQiX0ljL+fcJ4UbnXPvAL0DE5KIiIiIhJoS92mk5NJv6K4JFxEREQm0EF60Egi+ksY0M+vinJtTsNHMOlPCljsiIiIix7wK8tCLsuIrabwH+MDMJgDz8to6AX8n9ykxIiIiIlIJ+HqM4Bwz6wLcDgzIa14KdC28olpERESkMgnlPRUDocSk0cyOc86tBx4pp3hEREREJAT5moz/v7/emNnHAY5FREREpOLQs6e9FKy7Ng1kICIiIiISunwthHGHeS8iIiJSqbkQrgoGgq/RnmxmO81sF3BS3vudZrbLzHaWR4AiIiIi4s3MzjazlWa2xszuL+Z8bzObb2YHzezysrimr9XT4WVxEREREREpG2YWDrxC7mOdNwJzzexT59yyAt3Wk7vzzbCyuq6v6WkRERERKU7wttzpAqxxzq3LDcPeAy4C8pNG59zveec8ZXXRyjUZLyIiIlLxNQA2FDjemNcWUKo0ioiIiPghUAthzGwQMKhAU7JzLrlgl+LCCUgwBShpFBEREfFHgKan8xLE5BK6bAQaFThuCGwOSDAFaHpaREREpGKZC7QwsyZmVhW4Cvg00BdV0igiIiLijyA9EcY5dxAYDEwDlgMfOOeWmtljZnYhgJl1NrONwBXA62a29GiHq+lpERERkQrGOTcVmFqo7eEC7+eSO21dZlRpFBERERGfVGkUERER8YML3j6NQco7NuIAACAASURBVKFKo4iIiIj4pEqjiIiIiD8CtE9jqAp40phJfKAvUWE0n/ZcsEMIGZ6OvYIdQsjIqtWAXcvnBTuMkJF0QsdghyAiIsVQpVFERETED67YB7McuypXXVVERERE/KJKo4iIiIgfAvXs6VClpFFERETEH5UsaaxcoxURERERvyhpFBERERGflDSKiIiIiE+6p1FERETED5XtMYJKGkVERET8UNlWT1eu0YqIiIiIX1RpFBEREfFHJZueVqVRRERERHxSpVFERETED5XtnkYljSIiIiJ+cGh6WkRERETEi5JGEREREfFJSaOIiIiI+KR7GkVERET8UNkWwlSu0YqIiIiIX0pMGs2sq5n9ama7zWymmbUpr8BEREREQppZYF4hylel8RVgGBAHvACMDHhEIiIiIhWAIywgr1DlK7Iw59zXzrn9zrkPgYTyCEpEREREQouvhTC1zezSwx075z4JTFglc84xIXk0C1JmUq1adW678980bd6qSL91a1YwZuSTHDiwnw6dujNg0B1YgbLvZ59M4u3xYxj7zhRiatXOb1+zajkPDbuVO+8dTrdep5XLmMrCL7+nMuLHxeQ4uLjtcdzYqaXX+bfnr+H/lv5BeFgYdWpU5ZHTO1AvJjL//O792Vz+9nROa1aP+/qeVN7hl6lfFq1gxDufkuPxcHGfLtx4fj+v8/NXrGPEpE9Zs2ELT/7zWk7vfGi8g0eMZfHa9bRv0YTRdw0s79DLxOz5v/LiuDfxeDycd8ZpXHfZhV7nD2Rn88SoV1m19jdiomvy6LB/US/p0L8JU7em8/ch9zDgqsu4+uLzSd2awZOjXyVj+3bCzLjgzH5cccE55T0sEZGQ4kJ4KjkQfFUafwAuKPAqeHx+YEM7vIUps/hz8wZGJ7/HLYPv4Y0xI4rtN+6V5xk0+F5GJ7/Hn5s3sHDerPxz6VtTWbQghfiEJK/PeHJymDThVU7u0CWgYyhrOR7H098v4sWLuvPRdf2YtmoT6zJ2evVplVCLt67qw/vXnkb/5vUZ/fNSr/OvzlrBKQ3iyjPsgMjxeHj6zcm8ePdNfPTUMKbNWsi6TaleferG1Wb4zVdydrf2RT7/93P68vigq8sr3DKXk+Nh5Ov/5bmH7+XNl57j259+4fcNG736fP7190TXjOLd10Zy5YXn8Nqb73qdf+mNt+h6ysn5x+HhYfzzxmt5++URvPbsY0z+4usi3ykiIse2EpNG59yNJbyCVoKZO/snevc7GzOjZet2ZGXtZltmulefbZnp7N2bRcsT2mFm9O53NnNn/ZR//s2xL3Htjbd5VR4BvpjyMV179KFW7TrlMpaysjR1G41qR9GwVhRVwsM4s0UDvl/3p1efzo0SqFElt7h8Yt06pO3el39uedp2Mvfsp9txieUadyAsXbeeRknxNEyMo0pEBGd2bc/3870T5PoJsbQ4rj4WVvRfiV3atiCyerXyCrfMLV+9hgb1kqhfN4kqVSLo36s7M2bP8+ozY04KZ592KgB9enRl/qIlOOcA+GnWXOrXTaRxo4b5/eNj69CqWRMAImvU4PiGDdiasa2cRiQiIqHA1+rpu8zspmLah5jZnYELq2TbMtKJiz+U3MTFJZKZ4Z00ZmakExt3aLotNi6RbXl9UmbPIDYunsZNW3h/Jn0rc2f+yBnnXBzA6AMjbfc+kmrWyD9OqlmDrVn7Dtv/f8vW0+P43J+hxzlG/rSEO3q1DXic5SFt206SYg/dbpAUW4ut23YEMaLylZ65jcT4QxXjhLhYtmZmHrZPRHg4UZGR7Ni1i7379jFp8mcM+Ntlh/3+LalbWb3ud9q0bBaYAYiIVBDOwgLyClW+IhsIvFVMe3LeuaD4qyJSUOHbCorrA7B/3z4mvz+RK6+7uci5CWNHc82AfxAWHl4mcZYnRzE/k8P0nbpiA8tSt/P3U5oD8OGi3+jZOIm60TUO84mKpfjfH5XnvpNix1/od8Ph+ox/92OuuOBcImtUL/a79+zdx/97ZiRDbrqeqMjIYvuIiMixyddCGOecO1BM434r4W9hMxsEDAJ46LERXHbV348uSmDalI/5dtpnADRrcQIZ6Wn55zIy0qgTG+/VPy4+gcyMrfnHmRlp1ImLJ/XPTaSlbuHeIQNyP5u+lfvvHMiTL4xl3ZqVvPjsowDs3LmDBSkzCQ8Pp3P33kcdf6Al1axB6u69+cepu/cSH1X0L/7Z69N4Y+4qxl7Wi6oRucnxoj+3sWBTBh8u+o092TkczPFQo0o4/+pZMSuPSbG1SM3cnn+cmrmD+NoxQYyofCXExZKWnpF/vDUjk/jYOsX2SYyP42BODll79hATXZPlq9bwwy+zeW3iJHZn7cHCjKpVqnDZeWdx8OBB/t8zIzmjT0/6dK9Y9/yKiASCO2x55tjk8zGCZpbknEst3FbSZ5xzyeRWI1m4emvxJb8jdNb5l3HW+blTZvPn/sK0KR/To/fprF65lMjImkWSxjqx8VSvEcmqFUto0aotP07/krPPv5zjGjdj7DtT8vsNHng5T44cR0yt2rz8xof57WNGPsEpnXtUiIQRoE1SbTZsz2LTjiwSa9bgq9WbeOKsjl59VqRt54npv/Lyxd2JjTx0z17Bfp8uW8/ytO0VNmEEaNOkERtS09m0NZPEOjF8NXshT/zjmmCHVW5at2jGxi1/sjk1jYTYWL6dMZOH7xrs1adnl458+d1PtGvdkh9+mc0pJ7bFzHj5qUfy+4x/9yNq1KjOZeedhXOOZ15O5viGDfjbReeV95BERCQE+EoanwM+N7O7gfl5bR2BZ4HilyyXgw6durMgZSZ33PI3quZtufOXe4cM4NmXJgBw8z+HMWbkE2Qf2E/7jt1o36lbkCIOvIiwMO7texKD/zeTHI/jorbH0SwuhldnLadNYm36NK3H6J+Xsjc7h/umzgWgbnQkIy/oGuTIy15EeDj3Xn8xg58bS47Hw0W9u9CsYV1e/WQabRo3pM8pbVm6bgPDXpzIzqw9/LRgOa9/8hUfPjUMgJueGMPvW9LYu28/59z5H/7fTVfQ48SiWzqFqojwcO68ZQDDhj+NJ8fDuaf3pclxDXlj0oe0at6UXl06ct7pfXli1Biu/sdQoqOjePTuISV+5+LlK5n2/QyaHt+IgXc+AMAt111J904dymNIIiIhKZTvPwwEO9y9f/kdzM4B7gfa5TUtAZ52zn1RmguUVaXxWND8q+eCHULo6Ngr2BGEjKxaDYIdQkhJOqGj704iIoe/db/cbFq1OCA5ToOWJwZ9bMXxOT2dlxyWKkEUERERkWNTiUmjmT1cwmnnnHu8jOMRERERqRC0EMZbVjFtUcBNQBygpFFERESkEigxaXTOPf/XezOLBu4AbgTeA54/3OdERERE5NhSmi13YoG7gGuBicApzjk9P0xERESkEvF1T+NzwKXk7rl4onNud7lEJSIiIhLiKtuWO74qjXcD+4GHgAcLPATGyF0IU3kesyEiIiJSgBbCFOCcq1wptIiIiIgUy+c9jSIiIiJSVGWbnq5coxURERERv6jSKCIiIuIH3dMoIiIiIj5pelpEREREQpqZnW1mK81sjZndX8z5amb2ft752WbW+GivqaRRREREpAIxs3DgFeAcoA1wtZm1KdTtJmCbc645MBJ45mivq6RRREREpGLpAqxxzq1zzh0g9/HOFxXqcxG5T/ID+AjobwU23PaHkkYRERERPzgsIK9SaABsKHC8Ma+t2D7OuYPADiDuaMarhTAiIiIifnBHV7g7LDMbBAwq0JTsnEsu2KW4cAp/TSn6HBEljSIiIiIhJC9BTC6hy0agUYHjhsDmw/TZaGYRQC0g82ji0vS0iIiIiB+cs4C8SmEu0MLMmphZVeAq4NNCfT4Fbsh7fzkw3TmnSqOIiIhIZeGcO2hmg4FpQDgw3jm31MweA1Kcc58CbwBvmdkaciuMVx3tdZU0ioiIiPjBBXHC1jk3FZhaqO3hAu/3AVeU5TU1PS0iIiIiPqnSWI7CY2KCHULIsN+WBzuEkFG1Ta1ghxAycsKrkr5kZrDDCAnx7boHOwQRES9KGkVERET8UMo9FY8Zmp4WEREREZ9UaRQRERHxgyqNIiIiIiKFqNIoIiIi4ofKVmlU0igiIiLih8qWNGp6WkRERER8UqVRRERExA+lfE70MUOVRhERERHxSUmjiIiIiPik6WkRERERP2ghjIiIiIhIIao0ioiIiPhBlUYRERERkUJUaRQRERHxgyqNhZjZOWb2o5mlm9lWM/vBzM4tj+BEREREJDSUWGk0s1uAW4F7gZS85k7A02bW0DmXHOD4REREREJSZdvc29f09FCgl3Mus0DbdDM7B5gBKGkUERGRSsmj6WkvVihhBMA5lxGgeEREREQkBPlKGnea2cmFG/PadgUmJBEREREJNb6mp+8GPjWz/wLzAAd0Bm4ArgtwbCIiIiISIkpMGp1zM8ysK/BPYABgwFKgm3Puz8CHJyIiIhKaKtuWOz73acxLDh8uh1hKzTnHhOTRLEiZSbVq1bntzn/TtHmrIv3WrVnBmJFPcuDAfjp06s6AQXdgdugX+LNPJvH2+DGMfWcKMbVqs3TRfJ77zwMkJtUDoEuPPlx+9Y3lNq6j9fPaTTw7LQWPc1zSvjkDe7bzOv/WrGVMXriG8DCjTmR1Hj2/O/Vr1wTgn5O+ZdGmrXRolMhLV/ULRvgB8/PqjTzz5Sw8Hsclp7TkplO977h485clTJ6/KvfnElWd4Redmv9zqahmLlzCyP++j8fj4cL+vfj7xed4nT+Qnc3wl//LynV/EBMdxX/uHET9xHgAVv+xkWeS3yZr717CzBj/1INUq1qFr2bMYeLkqWBGQp3aPDpkILVjooMxvCMya8EiRo2fhMfj4YL+vbn+0vO9zh/IzubxF8eyct3v1IquyWN33Ua9xAS2pG3lmjv+zXH16wLQtmUz7r11AFl79/LPh57M//zWjG2c2bs7dw68tlzHJSLBp9XTBZhZDPAA0BCY6px7t8C5Mc65fwY4vmItTJnFn5s3MDr5PVavXMobY0bwxAtji/Qb98rzDBp8Ly1at+XpR4excN4sOnTqDkD61lQWLUghPiHJ6zMntD2Z+x55tlzGUZZyPB6e+mIOr117OkkxkVz7xhf0admQZgm18/u0rhvLOzedS40qEXwwbyWjps/n2Ut7A3BD9zbsyz7IR/NXB2sIAZHj8fDk1Jm8fv1ZJMVEcc3YT+nb6jiaJdbJ79O6XhyTBl1IjaoRfDB3OSO/nstzV5wWxKiPTo7Hw4g3JvHiQ0NJjKvDjQ88yamdTqZJw/r5fT6d/jMxUZF89NITfP3zHF555xOeGDqIgzk5PPrSGzw6eCAtGjdix67dRESEczAnh5ET3ufdFx6ldkw0L739ER9++R23XHlhEEfqW06Oh+fHvsWoh+8hMS6Wm+8bTq/OHWjSqEF+nynf/kh0zUg+eOVZvpkxizFvfcjjd+f+0dYgKZGJzz/u9Z1RNWp4tQ285xH6du1UPgMSEQkiXwth/kvulPTHwNVm9rGZVcs71y2gkZVg7uyf6N3vbMyMlq3bkZW1m22Z6V59tmWms3dvFi1PaIeZ0bvf2cyd9VP++TfHvsS1N97mVXmsyJZszqBRbDQN60RTJTycs9oez/erNnj16dy4LjWq5P474aQGCaTu3JN/rmuTekRWrVKuMZeHJZvSaRQbQ8PYGKpEhHN2u6Z8v3K9V58uTepRo2ruz+XEhomk7cwKRqhlZtma32hYN5EGSQlUiYjgjB6d+XHur159fkpZyLl9c/8BdVq3jqQsWY5zjjm/LqP5cQ1p0bgRALWiaxIeFgbO4Zxj7/4DOOfYs2cfCbG1i1w71Cxfs46GdZNoUDeRKlUi6N+rKz/NXeDV56c5Czi3by8A+nbvzLzFy3DOler7N2z+k207dnFym5ZlHruIhD6HBeQVqnxNTzdzzl2W9/7/zOxBcvdpDGp5YVtGOnHxifnHcXGJZGakUyc2Pr8tMyOd2LiE/OPYuES2ZeQmlimzZxAbF0/jpi2KfPeqFUu4Z/ANxMbFc93A22l0fNMAjqTspO3aQ92YqPzjpOgoFm9OP2z/yQvX0KtZ/cOeP1ak7czy+rkkxkSxeOPWw/afPH8VPZs3LI/QAmZr5nYS42LzjxPjarN09W9F+iTl9YkID6dmZA127NrN+i2pmMEdT4xi285dnNGjM9dfdDYRERHce8u1XDtsODWqVaVRvSSG3XxNuY7LH1szt5EYX+BnEVuHpavXHbZPRHg4UXk/C4AtaVsZMOxhomrU4JarL6V9G+/bYL6eMZv+PbscM//4FBEpia9KYzUzy+/jnHuC3A29fwTiAhlYSYqrAhT+M/twlYL9+/Yx+f2JXHndzUXONWneilfGf8RzL0/k7PMvZ8R//l0m8ZaH4oZ7uL/GPl+8jmVbMrihe9uAxhQKivtdcLi/36f8uoZlm9MZ0PPEgMYUaMX+3i806OL/HzJycjz8umINw4fcRPJj9/LDnIXMXbycgwcP8slXP/DmMw8x5fXnaH5cAyZO/iJQQygz/v5ZYWbE1anNJ6+/wIQRjzFkwNUMH/U6WXv2evX79ufZnN4raJMuIhJkzllAXqHKV9L4GeC1KsI5N5HcrXgOHO5DZjbIzFLMLOXj9948+iiBaVM+5t4hA7h3yADqxMaTkZ6Wfy4jI82ryggQF59AZsahilJmRhp14uJJ/XMTaalbuHfIAAYPvJyM9K3cf+dAtm/LIDIyiuo1IgHo0Lk7OTkH2blje5nEH2hJMZH8WWBaNXVXFgnRNYr0m7VuC+NmLGb0lX2pGhFeniEGRVJMlNfPJW1nFonRkUX6zVq7iXE//croq0+v8D+XxLg6pGUc2pM/LWM7CXVqF+mTmtfnYE4Ou/fsJaZmFIlxtenQpiW1Y6KpXq0aPTq0Y+Vv61n1+0YAGtZNxMzo370Ti1etLb9B+SkxLpa09AI/i8xtxMfWOWyfgzk5ZOX9LKpWqUKt6NwFUa2bNaZB3QTWbz60acTq39eTk5ND62aNAz8QEZEQUGLS6Jy71zn3TTHtXzrnis7tHjqf7Jzr5JzrdNlVfy+LODnr/Mt49qUJPPvSBDp3P5Ufp3+Jc45VK5YQGVmzSNJYJzae6jUiWbViCc45fpz+JZ27nspxjZsx9p0pvDz+I14e/xFx8Qk8PWo8tevEsX1bRn7VYc3KZXich+iYWmUSf6C1rR/H+sxdbNq2i+ycHKYt/YM+LRt59VnxZyb/mTqLUX87jdioognlsaht/XjWZ+xg47ZdZB/M4csl6+jT6jivPsu3ZPD4lF8YffXpxNWs+D+XE5o1ZsOWNDanpZN98CBf/zKXUzt5rxg/tePJTP1+JgDfzZpHp7atMTO6ntyWNes3sm//fg7m5DB/+SqaNKxHQmxtftu4mW07c/f0n7NoOY0b1Cv3sR2p1s2bsHFLKptTt5KdfZBvZ8ymV6cOXn16dW7P1O9nAPD9zLl0bHcCZsa2HTvJyfEAsOnPNDZsSaVB0qFbXr75aZaqjCJSqfhaPX1XSeedcy+UbTil06FTdxakzOSOW/5G1bwtd/5y75ABPPvSBABu/ucwxox8guwD+2nfsRvtO5X8B/ysGd/z9ReTCQsLp2q1atxx7/AKc69SRFgY95/dhdve/RaPx3FR++Y0T6jNmO8X0qZ+HH1bNmLkN/PYk32Qez7+EYB6MVGM/lvuKuEbJ07j94wd7DlwkDNHf8yj53enxzFwz2NEeBgPnNud296ahsc5Lu7QguaJdXhl+nza1o+nb+vjGPnVHPYcyOaeD74DoG6tKF685owgR+6/iPBwhg28mjueGIXH4+H803rStFF9kt//H62bHU/vTu25oF8vhr/8BpcPeZCYmlE8fuctAMTUjOLq887gxgeexMzo3qEdPU85CYCbLr+AfzzyHBHh4dSNj+Ph2wcEcZSlExEeztCbr+Oux0eQ4/Fwfr9TaXpcA8a++wmtmzfh1M4dOL9/bx5/MZkrb7+XmJpRDB96GwALl61k3HuTiQgPJywsjHsG3UBM9KGtmKb/MpcRDw4N1tBEJASE8qKVQLCSVgma2SMFDm8FXi943jk33NcFFq7eWrpliJVAq1mv++5USVjEsbdS219726ha9Zec8KrBDiFkxLfrHuwQREJd0DO2uSu3ByTH6dyqdtDHVhxfT4TJTwrN7OLSJIkiIiIilUEoL1oJBJ9PhClAFUMRERGRPJ5gB1DOfK2eFhERERHxuRBmMYcqjM3NbFHB8865kwIVmIiIiEgo0/S0t0uBJGBDofbjgc0BiUhEREREQo6v6emRwE7n3B8FX8CevHMiIiIilVJle/a0r6SxsXNuUeFG51wK0DggEYmIiIhIyPGVNFYv4VzFf3SGiIiIiJSKr6RxrpndUrjRzG4C5gUmJBEREZHQ55wF5BWqfC2EuROYbGbXcihJ7ARUBS4JZGAiIiIiEjp8PREmFehhZqcB7fKaP3fOTQ94ZCIiIiIhLJQXrQRCqZ4I45z7DvguwLGIiIiISIg6kscIioiIiEgeTyV7wLKSRhERERE/VLbpaT17WkRERER8UtIoIiIi4odQ3HLHzGLN7GszW5333zqH6felmW03syml/W4ljSIiIiLHjvuBb51zLYBv846L8xxw/ZF8sZJGERERkWPHRcDEvPcTgYuL6+Sc+xbYdSRfrIUwIiIiIn5wobl6Osk5twXAObfFzBLL6ouVNIqIiIiEEDMbBAwq0JTsnEsucP4boG4xH30wkHEpaRQRERHxgydAW+7kJYjJJZw//XDnzCzVzOrlVRnrAWllFZfuaRQRERE5dnwK3JD3/gbgf2X1xUoaRURERPwQilvuAE8DZ5jZauCMvGPMrJOZjfurk5n9BHwI9DezjWZ2lq8vDvj09LptcYG+RIXRYOXaYIcQMjb8fUSwQwgZCZYa7BBCRtzmRcEOIWTsm7Iw2CGEjOrn3xbsEEQqDOdcBtC/mPYU4OYCx6ce6XfrnkYRERERP4To6umA0fS0iIiIiPikpFFEREREfNL0tIiIiIgfXIC23AlVqjSKiIiIiE+qNIqIiIj4wVPJFsIoaRQRERHxQxnsqVihaHpaRERERHxSpVFERETED9qnUURERESkEFUaRURERPzg0ZY7IiIiIiLelDSKiIiIiE+anhYRERHxgxbCiIiIiIgUokqjiIiIiB+0ubeIiIiISCGqNIqIiIj4Qc+eFhERERGftBBGRERERKSQEpNGM5tQ4P0NAY9GREREpIJwWEBeocpXpfHkAu/vCGQgIiIiIhK6fN3TGPKz9c45PnvrSVb++iNVq1Xn8kFP0qBx2yL9pn04igUz/sferJ0MHzevyPnFc6Yx6aU7uX34hzRs2q48Qi9zVVq0o+a512BhYeyd9yN7f5zqdb5GjzOp3qk3eDx4snaxa/J4PNszAIg68wqqtjoJgD3ffcb+JXPKPf6j5ZxjYvJIFs6bSdVq1bntjodo0rxVkX7r1qzgtVH/4cCB/bTv2J0bBg3FzPho0jimT/uUmFp1APjb32+lQ6ce7Nq5g1FPP8ja1cvp0/9cbvzH3eU9tKMyZ958xiS/gcfj4ZwzT+fqKy7zOn8gO5tnXhjN6jVriYmO5qH7hlE3KZF5CxYybsJbZB88SJWICAYNvIEOJ58UpFGUjZ+XrOa597/A43Fc3OsUBp5zqtf5eat+Z8T7X7J6UypP3XI5Z3TM/bNkc8Z2hr36Hjkex8GcHK7q15Ur+nQOxhDKzM8rfueZ//sBj8fDJV3bcVN/7/HMW7uRZ//3A/+/vfsOj6LcHjj+PdkklPQeei/SkSZFRLBjRVRsYL9WbCBevVdBLKgoRYUL2EBFRdGfXbHQq3RBpUknkAYEEiDJ7vv7Y5ZkN21DyG425HyeZx92ds5M3neYnT37ltktSam8fMtlXNi+GQArtu5m7Ffz8+K2Jx/k5VsupW/bpj4tv1LK9zwljXVFZCIgLs/zGGOGeq1kpbRp3QLSDuxk2Ngf2b1tHf/33nM8MOrTQnFndexD9wtv4rVhlxZad+JYJkvmfEC9JpX4A1GEsCtu5dB7Y3FkpBN17zNk/7UWe8q+vJDcpF0cnPwc5GRTvev5hFx8PUc+nUxw83YE1m7AwbeeBVsgkXc9SfaW9ZgTxyuwQqdu7aql7N+3h3FTZrF100bemfwqz7/2dqG4dye9yl0PjqBZiza8PPJx1q1aRofO3QG47KpBXD7gJrf4oOBgrrv5bnbv+oc9O//xSV3Ki91u543JU3n5+ZHExcTwwKNP0KNbVxrUr5cX88OcXwgLCWHGtMnMnb+Qae/P4L8jhhEeHs7oZ54mNiaa7Tt28uQzz/HpjHcqrjKnye5wMGbmd0x+dDAJUeHc/OJUzmvfgia14/NiakVHMOr2q5kxZ4nbtnERobw/4i6CgwLJOn6CgaMmcV77FsRHhvu6GuXC7nDw4hdzmfKvASREhHLT+I/p07oxTRJj8mISo8IYPegips9b7bZt16b1mPX4LQAczjrO5S++R/cWDXxafqX8RVWbPe2pe3o4sApY6fLc9VHh/lr9Gx17XYWIUL9pB45nZZBxKLlQXP2mHQiPjC9iDzBn9gR697+TwKBq3i6u1wTWbYw9LRnHwRSw2zn+xwqCz+roFpOz/W/IyQYgd/c2bOFWi5otvjY5OzaBwwE52eTu301ws7Y+r8PpWrVsIef2vQQRoVnLNmRlHuVgeqpbzMH0VI5lZdK8ZVtEhHP7XsLKZQtK3G/16jVo2bo9wUHB3iy+V2zavIXatWpROzGRoKAg+vTuxeJl7q3IS5at4KJ+5wPQu1cP1qxbjzGGZk0aExsTDUDDBvXJzskmOyfH53UoLxu276VefDR146IJCgzk4i5tmLfub7eY2rFRNK+bSIC4jykKCgwkOMj6jp2dNtb+OwAAIABJREFUa8dU8k+KDbv2Uy8mgroxEQQF2rikY3PmbdzmFlMnOoLmteMIKGF41c/rttCrZUNqBAd5ucRKKX9QYtJojJle1AP4FMjyTRFLdvjgASKjE/OWI6ITyUgvnDQWZ9+OPzmctp+zOp7vjeL5TEB4FPbD6XnLjoz0vKSwKNU79SZ7yx8A+UliUDBSM5TgRi0JiIj2epnLW3paCjGxCXnL0TFxpKelFIqJjs3/8hATG+8W89N3n/PEQ7fyvwkvcPRohvcL7WWpaenEx8XmLcfFxpCWluYWk5aWRpwzxmazEVKzJhkZR9xiFi5eStPGjQkOqrzJQfKhDBKiI/KWEyIjSDl4pIQt3O1PP8z1oyZx6YjXue2SnpW2lREg+XAmiZFhecvxEWEcOJx5yvv5ce0mLjm78BAQpaoKY7zz8FelvuWOiNhE5FIRmQHsBG4oIfYeEVkpIivnfDm1PMpZvCKOrkjpZh45HA6+/WgM/W8aUd6l8g/FnHnV2ncnsE5Dshb+AEDO1o1kb15P1D1PE379veTs3ma1OlYypoghuIXOhRLOlwsuHcCEqZ8xZsJ0oqJi+PCdN7xSTl8q6phQ4JgUeZa4hOzYuYtp78/g0QfvLdey+VxRFT2FSYqJ0RHMevZ+vnphKN8sXUdaxtFyK5qvFfleOcV9pGRksjUpjR7aNa1UleHx5t4i0hu4CegPrAB6Ao2MMcW2NBpjpgJTAb5YUf79OEt//ojf530OQN3GbTiUvj9v3eH0/YRFxZVqP9nHMzmwZwtTXxwMwNHDqcwYdz+DH51U6SbDODIOYnNpHQwIj8Z+5FChuKAmrah53uUcemcM2HPzXs+a/y1Z878FIOy6f2FPO+D9QpeDOd/N5refvgagcbOWpKXmlzs9LYWo6Fi3+OjYeNJT81ui01KT82Iio/KPX9+Lr+KV54Z5s+g+ERcTQ3JKfhd9SmoaMdHurcixMTGkpKQSFxuL3W4nMyuL8LAwZ3wqz74whhGPPUztWrV8WvbyFh8VzoH0w3nLBw4dJs6lta3U+4kMp0ntOFZv2Zk3UaaySYgIZf+h/FbW5MNHiI8IOaV9zFm7mb5tmxBks5V38ZSqNPy5VdAbPN2ncQ8wBlgMtDLGXAscKylh9IXuF97M0Be+ZOgLX9KqUz/WLPoKYwy7tq6les2wYscuFlS9Zhj/nbyUEeN+ZcS4X6nXpH2lTBgBcvduxxYTT0BULNhsVG/bley/17jFBNaqT9hVQ8j4aCIm06VbTgSpYX1g2BLqEphYl+ytG3xZ/DK7qP+1jJk4nTETp9P5nN4s/O1HjDFs+XsDNWuGFEoao6JjqV6jJlv+3oAxhoW//Uinc6wZtK7jH39fOp96DRr7tC7e0KJ5M/buSyJp/wFycnKYt2ARPbq5z5Lt0a0Lc36dC8CCRUvo0M4a73n0aCZPj3yBO4fcSptWZ1VE8ctV64a12ZWczt7Ug+Tk5vLT7xvo075lqbY9cPAwx7Ot8ZwZmcdYu3U3DRNiPWzlv1rXS2RX6iH2pB0mJ9fOj2s2c17rJqe0jx/WbOKSjto1rao2hxGvPPyVp5bG2cDVWF3RdhH5Cj+7DU+L9uexae0Cxg67mKDg6gy8+8W8dROfvoahL3wJwA8fv8rapd+Rk32Ml4b2oUufgVww4MGKKnb5czg4+u1HRAx5HAkI4PiqhdiT91Gz39Xk7t1B9t9rCbnkeiS4GuGD7gfAfiiNjI8mgs1G5N3/BsCcOE7GZ1MrZfd0x849WLtyKY/ccx3VqlXnXw8/nbfuyaFDGDNxOgB33D/c7ZY7HTpZM6dnvvcWO7dvARHi4mtx1wNP5G3/0J0DOJaVSW5uLiuXLeDfz42nbv1Gvq1gGdhsNh66926efGYUDoeDSy7sR8MG9Xn/w5k0b9aUHt26culFFzDmtfEMvvs+wkJDeXqEdUuh//v2e/YlJfHRJ7P46JNZAIwZ/SxRkZEVWaUyC7TZGHHjZdw//gMcDgdX9exIk9rxTPrqN1o1qE2fDi3ZuGMvj036hIysYyxYv4n/fT2X2aMeZHtSKq9/9pPVh2tg8EU9aFY3wePf9FeBtgD+PeB87pv6JQ5juLpra5omxvDWj0tpXTeePm2asGHXfh59/1syjh1n/p/bmfTTUr58wuqV2Zt+mP2HjtC5cd0KrolSypfEeGhbFWvA1/nAjcBlQDhwJ/C9McbjoB5vdE9XVud+fWdFF8Fv7B48tqKL4DfipHIMBfCFmH3rK7oIfiPgyMGKLoLfqH75fRVdBOWfKrxJ7pMl3umgHtSjlJMzfMzjRBhj+c0YczfQEGt849XADu8WTSmllFJK+YsSu6dFpL4xZtfJZWNMDvAN8I2I1PB24ZRSSiml/JVOhHH3fyefiMhs1xXGmGNeKZFSSimllPI7nibCuPapV/6ppEoppZRS5aSqtTR6ShpNMc+VUkoppaq0qjbV11PS2F5EMrBaHGs4n+NcNsaYyvs7WkoppZRSqtRKTBqNMXqrf6WUUkqpIhg/vhG3N5T6t6eVUkoppVTV5fG3p5VSSimlVGFVbSKMtjQqpZRSSimPNGlUSimllFIeafe0UkoppVQZVLVb7mhLo1JKKaWU8khbGpVSSimlyqCqTYTRpFEppZRSqgyqWtKo3dNKKaWUUsojbWlUSimllCoDnQijlFJKKaVUAZo0KqWUUkqVgTHeeZwOEYkWkZ9FZIvz36giYjqIyFIR2Sgi60XkhtLsW5NGpZRSSqkzx5PAr8aYZsCvzuWCsoDBxpjWwCXAeBGJ9LRjTRqVUkoppc4cVwHTnc+nA1cXDDDGbDbGbHE+3wckA3GedqwTYZRSSimlysDh8M5+ReQe4B6Xl6YaY6aWcvMEY0wSgDEmSUTiPfytrkAwsM3Tjr2eNC5Zddzbf6LSOPfGeyu6CH7jhD2ooovgNzbnNq7oIviN1gknKroIfuNEnZoVXQS/EHI8naMbllZ0MfxGbJvuFV0E5QPOBLHYJFFEfgESi1j19Kn8HRGpBXwADDHGeEyBtaVRKaWUUqoMKurm3saYC4pbJyIHRKSWs5WxFlbXc1Fx4cB3wH+MMctK83d1TKNSSiml1Jnja2CI8/kQ4KuCASISDHwJzDDGfFbaHWvSqJRSSilVBv54yx1gDHChiGwBLnQuIyKdReRtZ8z1QG/gNhFZ63x08LRj7Z5WSimllCoDf/xFGGNMGtCviNdXAnc5n38IfHiq+9aWRqWUUkop5ZEmjUoppZRSyiNNGpVSSimllEc6plEppZRSqgyM1+65I17a7+nRpFEppZRSqgwq6j6NFUW7p5VSSimllEfa0qiUUkopVQbe+u1pf6UtjUoppZRSyiNtaVRKKaWUKgMd06iUUkoppVQB2tKolFJKKVUG/vgzgt6kLY1KKaWUUsojTRqVUkoppZRH2j2tlFJKKVUGOhGmFEREk02llFJKqSqk2KRRRBa5PP+gwOoVXiuRUkoppVQlYBzGKw9/VVKLYYjL89YF1vnnL2krpZRSSvmIH+d3XlFS93RJh6KKHSallFJKqaqtpJbGSBG5BiuxjBSRAc7XBYjwesnK6KqeQZzVwEZ2Lnz62wn2phbOb+/qX43wmkJAAGxPsvPFwpxKO5h12er1THj3QxwOB5dfcB63DrjCbX12Tg7PT5jCpn92EB4WynOPP0Ct+DiSklO4eeiT1K9dC4DWzZsw/N7bOX7iBP999U32HkgmICCAnp07cN+tN1RE1U6ZMYaP3n6N9asWE1ytOncNfZaGTVoWitux9S/enjiK7OwTtOvUk5vvehwRYdKr/yZp704AsjKPUjMklNHjZ7Jh7XI+m/Em9twcbIFB3HDbUFq16+Lr6pULYwyfvfsyG9csIii4OoMfHE39xmcVivtq5hssn/8NxzIzGPfhsgooaflZvnotb0ybjsPhoP+Ffbl54FVu67Nzcnhx3Fts3rad8LBQnh3+MLUS4vlr81bGTpoGWMfttkED6d29KwBHjmby6ptT2L5rDwiMeOhe2rRs7vO6narfV65i8tS3cTjsXHLRRQy6fqDb+uycHF59bRxbtm4lLCycp58cTmJCAhkZGYx+8WU2bdnCRRf05cH77gXg+PETPP/Sy+zbn4QtIIBzunblztuHVETVTtmyNesZ/+5MHA4HV/Trza0DLndbn52Tw+iJ09j0zw4iwkJ57rH78q6dNz38FPVrJwLWtfOJf90GwC+LlzPj82+wOxz06NSeBwZXjmunKrvKmjuUVUlJ43zgSpfnrtnIAq+V6DS0rB9AXGQAY2Yep35CANf2DmbiFycKxX0w5wQncqzngy8Opn0TG2u32n1c2tNntzt4fdoMxj37BPEx0dz1xLP06nI2jerVyYv59pf5hIWG8OmksfyyaBmTZ3zKc8MeBKBOQjzvv/58of3eeNWlnN22FTk5uTw8cgxLV6+j+9ntfVavslq/agkHknbx8uQv2LZ5AzP+N4ZnXn2/UNz0KWO47f6naNKiLa+Pfpg/Vi+hXaee3D/8pbyYj98dR82QUADCwiN55D+vExUdx56dWxk7aijj3/3eV9UqVxvXLCI5aRcj3/iGHVv+4JOpz/PEmI8KxbXrfB59Lh3EyIeuKGIvlYfd7mD8lHd5bdTTxMXE8K9hT9Gzayca1q+bF/Pdz3MJCw1l5pQJ/LpgCVOmz2TkE4/QqEE9prz2IoE2G2npB7njkRH06NqJQJuNN96eTtezO/Dck4+Rk5PL8ROFrzP+xm638+bkKYx5/jliY2N46NHH6X5OVxrUr58X8+NPPxMaGsr7b09l7vwFvPPedJ5+8gmCgoMZcuvN7Ni5kx07d7rtd+CAq+nQvh05OTmMePq/rFi5iq6dO/m6eqfEbnfw2rQPGP/McOvaOWIUvbp0dL92/rqAsNCazHrrFX5ZtIxJH3zG6MfvB6xr5/TXRrvt8/CRo0ya8SnvvDKSqIhwRr8xjZXr/6Rzu1Y+rZtS3lRs97Qx5vaSHr4sZGm1bmhj5aZcAHYdcFC9mhBWs3DcyYQxIAACA6TSflP4a+s26taKp05iPEFBgVzQ6xwWrVjtFrPo99Vcen4vAPp078KqP/7ElFDh6tWqcXZb6yIXFBRI88YNSUlL914lytGaFfPp2ac/IkLTFm3JyjzCofRUt5hD6akcy8qkact2iAg9+/Rn9fL5bjHGGH5f/Avdzr0YgAaNWxAVHQdAnfpNyMnJJicn2zeVKmfrf59Ltz5XICI0at6OrKwjHD6YUiiuUfN2RETFVUAJy9dfW7ZSJzGR2okJBAUF0vfcHixasdItZvHylVzctzcA5/Xsxur1GzHGUL1aNQJtNsBqdRLnUO7MrCzWbfyL/heeD1jvk7DQEPzdps1bqF27FrVqJRIUFMR5vc9lybLlbjFLly/nwn59Aejdqydr1q3DGEON6tVp07oVwUHBbvHVq1ejQ/t2AAQFBdG0SRNSU93fc/7or63/UDcxIe/a2a9XNxb+vsYtZuGKNVzWp/TXzn0HkqlXK5GoiHAAurRtxbxlK4uNV2cGh8N45eGvim1pFJHHCrxkgFRgkTFmu1dLVUYRIQEcOprfYnj4qCEiJIAjWY5CsXf3r0a9hAD+3mVn/T+Vr5URICXtIPExMXnLcTHR/LllW7ExgTYbITVrcvjIUQCSklO4/fH/EFKjBnffNJD2rVq4bXskM5PFK9dwXf+LvFyT8nEwPYXo2IS85aiYeA6mJxMZHesSk0x0THyBGPekafOfawiPjCGxdn0KWrn0Nxo0ak5QgQ/PyuJQWjJRMS7HKDqBQ2nJZ0SCWJTUtHTiY93fI39t3uoek54fE2izERJSg8NHjhAZHs6fm7bw8htTOJCSwlOPPECgzcb2/clERoQzZuJktm7fRYsmjXjo7iHUqF7dp3U7ValpacTF5r8X4mJj+XvTpsIxcVaMzWYjpGYIGRlHiHAmQiU5evQoy5av4Jor/b91OiX9IPGx0XnL8dFRbNzyT7Ex1rWzhtu187Zhz1jXzhsH0KFVC+okJrBzbxJJySnExUSzYMVqcnNzfVcppXygpO7psCJeawg8LSIjjTGfeKdIZSdFzOku7pvhtO9OEGiDmy4IpmmdALbsKZxY+ruialbwEBQZIxATFcnsqeOICAvj723beWrMBD6Y8BIhNWsAkGu3M/L1yVx32YXUSYwvYi/+p6j/aylwREpqKThp2cI5dDu3cKK8d9c2Zk1/g+Ej3yx7IStY0efDmXszhCL/t6XgOVFEiPO8adWiGdPfHMuO3Xt5acIkunXqgN1uZ8u27Tx89220atGMidPeZ+bsr7jzZj8fv1aK90eRMaU4Pex2Oy++Mparr7ycWrUSy1pCnynyWlHoUBQVI8RERfLFlNeJCAvl7207+PfLE/lw/AuEh4Yw7J7BPPP6ZESEti2asu9A4VZ8pSqzYpNGY8yool4XkWjgF6DYpFFE7gHuAbjwpjdo1+uO0yxm8Xq0DqRbK6sau5MdRIbmv/MjQoWMrOKThFw7/LnDTpuGtkqZNMbHRJGclpa3nJKWTmx0VJEx8bHR5NrtZGZlER4aiogQHBQEQMsmjaidGM/ufUm0bNoYgFcmv0u9Wglcf8UlvqtQGfzy/Szmz/k/ABo1a0V66oG8dQfTkomMdm9Bi45JID0t2S0myiXGbs9l1dK5jHxthtt26akHmDjmCe55ZBTxtepSmcz/4RMW//oFAA2atOZgmssxSj9ARPSZ2coIVsticmrJ75GTMfGxMdZ7JPMY4WGhbjEN69WherVqbN+5m7jYGOJio2nVohkA5/XoxszZX3u/MqcpNjaWFJeu45TUVKJjogvHpKQSFxuL3W4nMyuTsLCi2g/cjX/jTerUrs2Aq6/yGOsP4mOiSU7NH3aTnH6wiGunFRMfc/LaeYzw0JAC186G1EmMY9e+/ZzVtBG9unSkV5eOAHw1Zx4BAfpLvWe6yjq8raxO+Yw2xqTj4T6NxpipxpjOxpjO3kwYAZZszGXcZ8cZ99lxNm7PpXMLK4GsnxDA8ROGI1nu8cGB5I1zDBBoWd9G8qHK+b/esmljdicdYN+BFHJycvll0TJ6Oi9YJ/XscjY/zLXu0z5v6e+c3bYVIsLBwxnY7VaivHd/MnuSDlA7wWpRnDrzczKzjjH0jpt9W6EyuOCy6xk9fiajx8/k7G59WDzvO4wxbN30BzVCQt26pgEio2OpUaMmWzf9gTGGxfO+o2PX8/LWb1y3glp1G7h1c2cePcK45x9l4C0P0Ows/58QVNB5lw7iqbGzeGrsLNp1PZ/l877BGMP2zeupUTP0jO2aBmjZrAl7kvaTdCCZnJxcflu4hJ5d3Sdp9OzaiZ9+s+b2zV+8nI7tWiMiJB1IJtduDV3Zn5zC7r1JJCbEERMVSVxsDLv27ANg9foNNHSZQOGvWjRvxt69+0jav5+cnBzmL1hI927d3GK6d+vKz7/+BsCCRYvp0K6dx5bo92Z8SGZmFvfdc5fXyl7eWjZtxB6Xa+evi5bTq7P7tbNXlw58Py//2tmpzVlFXjt3Jx2gToL1Hjp4OAOAjKOZfPHTr1xxwXkodSaR0nTXuW0g0hf4jzGmb2nih00uoanPC645N4gW9Wzk5MKnc7PZk2K9uR+9rjrjPjtOaA2487Jq2GxCgMDWvXa+Xpzjkxt0juj9R7nvc+mqdc5b7hj69+vNkIFX8vbHs2nZpBG9up7NiexsRk+YwpbtOwkPDWXkY/dTJzGeeUt/5+1PvsAWEIAtIIA7Bg2gV5eOJKemM+CeR2hQpxZBzm/T1156AVdc2Kdcy701oPBtXk6XMYYPpr7CH6uXUq1ade4c+gyNmlqTev77yE2MHj8TgO1b/7RuuXPiBO069eCWu4fnfTBOmzCSJi3a0veSa/P2+/Wsd/h29vsk1KqX99rwkW8SHuneSlNWWbm+Gx9pjOHTt1/iz7XWbYluvf85GjS17t3/4rDreWrsLAC++GAcKxd+z+GDKURExdGj3wAuv+E+r5evddBf5b7PZSvX8MY71i13Lut3Prdefw3vfDSLlk0b07NbZ05kZ/PCuLfY+s8OwsJCeXbYUGonJvDT3AXMnP01gYE2RIQhN1zLuedYt1ra8s8OXn1zKjm5udROjOfJofcSFhrqoSSn5kRgEbP4TtOK31c6b7nj4OILL+CmQdcz/YOPaN6sKd3P6UZ2djYvj32dbf/8Q1hYGE89MTyvu/nW2+8iKyuLnNxcQkNCeOn5UdSsWZObh9xBvbp1864XV13Rn0svLr9x0CHHvTMRb8mqdUx8byZ2h4PL+57LkIFXMu3jL2jZtBHnduloXTsnTmXz9l2Eh4Yw6tH7qJMYz9ylv/P2J18SaLMREBDAnTdcnde6+Ozrk9m6czcAt193JRf0Oqfcyx3bpnu577MSq/CxNS98YvdK9vD0IFuF160oxSaNIrIBKNhnGw3sAwYbY/4uzR/wddLoz7yRNFZW3kgaKytfJo3+zhtJY2XljaSxMvJW0lhZadLopsITq6qWNJY0EaYO0MFl2QBpxphM7xZJKaWUUsr/OarYoMaSksbtxpidJaxXSimllKqyTOWbQ3taSkoa44u4V2MeY8zrXiiPUkoppZTyQyUljTYgFD8YM6CUUkop5W9OdTJxZVdS0phkjHnOZyVRSimllFJ+q6T7NGoLo1JKKaWUAkpuaezns1IopZRSSlUyjio2EabYlkbnL78opZRSSilVYkujUkoppZQqRlWbCKO/pq6UUkoppTzSlkallFJKqTJwVK2GRk0alVJKKaXKwlSxrFG7p5VSSimllEeaNCqllFJKKY80aVRKKaWUUh7pmEallFJKqTKoYnfc0ZZGpZRSSinlmbY0KqWUUkqVgaOKzZ7WpFEppZRSqgz0F2GUUkoppZQqQFsalVJKKaXKwDgqugS+pS2NSimllFLKI21pVEoppZQqA0cVG9MoVWUQp4jcY4yZWtHl8Ad6LPLpscinxyKfHguLHod8eizy6bGouqpS9/Q9FV0AP6LHIp8ei3x6LPLpsbDoccinxyKfHosqqioljUoppZRSqow0aVRKKaWUUh5VpaRRx1/k02ORT49FPj0W+fRYWPQ45NNjkU+PRRVVZSbCKKWUUkqpsqtKLY1KKaWUUqqMzpikUUSuEREjIi0ruizeJiJHCyzfJiJvetjmShF50kNMHxH5tph1j4hIzVMvrXeIyDgRecRl+ScRedtl+TUReayE7ZeU4m/sEJHYIl7vIyI9ylJuXxERu4isdXk0dL7eVUQWiMgmEflbRN4WkVYiskdEAgrsY62IdHU+HywiG0Rko4j8KSLDfF+r0+e8RrzmsjxMREa6LBdZTxF5X0S2O4/JahHpXgHFP20ikigin4jINmf9vheR5iKyoUDcSJe6v+o8V9aLyJciEukS10tEVjjX/y0ilXZWbXHvGee6CSKy1/U94rzuprjEz6iIcpcnEYlxqc9+Z53XisghEfmzQKzrOeL6/lhbmuurqpzOmKQRuBFYBAyq6IL4I2PM18aYMaexi0cAv0kagSVADwDnhTwWaO2yvgewuLiNjTGnk/T1Ofm3/dgxY0wHl8cOEUkAPgNGGGNaAGcBPwJpwG7g3JMbO798hRljVojIpVj//xcZY1oDZwOHfV2hcnICGFDMlwFP9RxujOkAPAlM8UVhy5OICPAlMM8Y08QY0wp4CkjwsOnPQBtjTDtgM/Bv5/4SgZnAvcaYlkAv4F8i0t9bdfCyQu8ZyLu+XIP1HuldYJtPXeIH+7i85c4Yk3ayPsD/gHHO5x0ATz+YN9zlWPj79VGV0RmRNIpIKNATuBNn0igiASIyydli8K3zG/VA57pOIjJfRFY5W6hqVWDxy5WIxInIbBH53fno6Xw9rzVSRJqIyDLn+ufEveUyVEQ+d7YafCSWoUBtYK6IzK2AahVlMfmJW2tgA3BERKJEpBpWQrRGRIY767leREad3PhknUs6T5wecrYs/SEiLZ2tD/cCjzq/UZ9L5fEAMN0YsxTAWD43xhwAPsb9C9cg52tgJQnDjDH7nNsdN8ZM82G5y1Mu1iD+R4tYV9p6LgCaeq+IXnM+kGOM+d/JF4wxa7GSoWIZY+YYY3Kdi8uAus7nDwDvG2NWO+NSgSewkuozyflY15fJWI0TSlVZZ0TSCFwN/GiM2Qyki8jZwACgIdAWuAvoDiAiQcAbwEBjTCfgXeCFiij0aajh2o0CPOeybgLWt8MuwLXA20VsPwGY4IzZV2BdR6zWllZAY6CnMWaiM+58Y8z55VyXMnF+sOeKSH2s5HEpsBzr/7kzsB6rRbAZ0BXrm3InESnYUlDkeeIi1RhzNtYHxjBn60PeN3BjzMJyr1z5cD1HvnS+1gZYVUz8LOBqETn506I3AJ+UYrvK6C3gZhGJKPB6aet5BfBHuZfK+0qqX5MC15R7i4m7A/jB+bx1EftbiXuLf2VS1HsGrETxY6xW2sudnyEn3eCyze0+La3/edXlWHxU0YVR3nGm/Pb0jcB45/NPnMtBwGfGGAew36WFrAXWxfNnq7cGG5Dk2+KetmPOLgPAakXESpQALgBaOesGEC4iYQW2746VaIPVvTTWZd0KY8we537XYiVUi8qz8OXoZGtjD+B1oI7z+WGs7uuLnI81zvhQrCRygcs+elH0eXLSF85/V2ElmJWF2zniiTFmv4hsBPqJyAGsFqkNnrarjIwxGc7xZ0OBY6ew6asi8h8gBatX40yyrcA1ZWTBABF5Gqul9mRCIEBRt9+orLfkKPSeEZFg4DLgUWPMERFZjnVN+c4Z8qkx5kEfl7MiFPd/6vr6cGPM574ojKo4lT5pFJEYoC/QRkQMVhJosL4VFrkJsNEYUykHspdCANDdGOP2YeiSRHpywuW5Hf8+R06Oa2yL1X20G3gcyMBqQe4DvGSMKWn8macDc/J4+PuxKI2NQCfgq2LWn+zsxKsrAAAC3UlEQVSiPtldXXC737xaOt8aD6wG3nN5zVM9K/uH4kZgoMeoIojIEOByoJ/Jv0/bRqwvq1+7hHYC/uTMcQkQAfzhvIbWBLLITxqrijQgqsBr0cD2CiiLqkBnQvf0QGCGMaaBMaahMaYe1omcClzrHLOWgJVAAGwC4sQ5+1FEgkSksnanFGUOkPfNV0SKam1ahtV1DaWfOHQEKNhiWdEWY32QpRtj7MaYdCASqyV1KfATcIdzzCsiUkdE4gvsYxFFnycl8cdjURpvAkNEpNvJF0TkFueEBoDZWK0qrl3TAC8Br5yME5FqznGulZbzXJmFe4vhGVfPAn4DqonI3SdfEJEuQIOSNhKRS4ARwJXGmCyXVW8Bt528xji/wL8MvFLeBa9ANwJ3OT9bGgKNgIvEj+4k4QvGmKNAkoj0AxCRaKyE2l97oZSXnAlJ440UblWcjTVxYw9WC9QUrPFuh40x2ViJ5ssisg5Yi//PhD0VQ4HOzokff1L02KRHgMdEZAVQi9LNhJ0K/OBHE2HAGlcWi5UEu7522BiTaoyZg9X9vlRE/gA+p3CyN5sizhMPf/cb4JrKNhHGOeFlEDBWrFvu/IU1YzrDuf4Q1rE8YIzZ7rLd91gJwi/OLuxVVP5WV4DXsM4f4IyuJ2BNfMKaBXyhWLfc2QiMpPC45oLexHrf/Ow85//n3F8ScAswTUT+xmr5f9cY84236uBLzsTwYlxaFY0xmViJ0hUVVa4KNBj4j3PY0m/AKGPMNpf1rmMa1zq79tUZ5oz+RRgRCTXGHHV+A16BNaljf0WXq6I5L4bHjDFGRAYBNxpjrqroclUUPU+UUkopz86Yb9HF+FasG9EGA6M1EcjTCXhTrEE6h7BmRFZlep4opZRSHpzRLY1KKaWUUqp8nAljGpVSSimllJdp0qiUUkoppTzSpFEppZRSSnmkSaNSSimllPJIk0allFJKKeWRJo1KKaWUUsqj/wd2+n18ioPlSgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr = df.corr()\n",
-    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
-    "\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(corr, mask=mask, annot=True, cmap='coolwarm', center=0)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Correlation Heatmap\n",
-    "\n",
-    "This heatmap shows how the numeric features in the dataset relate to each other.\n",
-    "\n",
-    "- The only strong-ish correlation is between **Height and Weight** (~0.46), which makes sense.\n",
-    "- Everything else has pretty low correlation (most between -0.3 and 0.3).\n",
-    "- No major multicollinearity issues, so we don’t need to drop any features.\n",
-    "\n",
-    "Overall, this helps confirm that all features can be kept for clustering.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.figure(figsize=(15, 7))\n",
-    "sns.histplot(df['Age'], kde=True, color='blue')\n",
-    "plt.title('Age Distribution')\n",
-    "plt.xlabel('Age')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age Distribution\n",
-    "\n",
-    "The histogram above shows the distribution of ages in the dataset.\n",
-    "\n",
-    "- Most individuals are in their early 20s, with a noticeable concentration between **18 and 25** years old.\n",
-    "- There's a steady drop-off after age 30, with very few individuals above 50.\n",
-    "- The KDE (blue curve) gives a smooth estimate of the age distribution and highlights the skew toward younger age groups.\n",
-    "\n",
-    "This helps us understand the age bias in the dataset, which could influence clustering results depending on how age interacts with other features.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.countplot(x='Gender', hue='CALC', data=df)\n",
-    "plt.title('Alcohol Consumption by Gender')\n",
-    "plt.xlabel('Gender')\n",
-    "plt.ylabel('Count')\n",
-    "plt.legend(title='Alcohol Consumption')\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Alcohol Consumption by Gender\n",
-    "\n",
-    "This grouped bar chart shows how alcohol consumption levels vary between males and females.\n",
-    "\n",
-    "- The majority of individuals in both genders report drinking alcohol **\"Sometimes\"**.\n",
-    "- A smaller portion of participants selected **\"No\"**, with slightly more male non-drinkers than female.\n",
-    "- Very few individuals marked **\"Frequently\"** or **\"Always\"**, indicating that heavy drinking is rare in this dataset.\n",
-    "\n",
-    "This visualization helps explore potential lifestyle patterns that may contribute to clustering, especially when combined with other features like physical activity and diet.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 7))\n",
-    "sns.boxplot(x='NObeyesdad', y='Weight', data=df)\n",
-    "plt.title('Weight vs. Obesity Level')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15, 7))\n",
-    "sns.countplot(x='Gender', hue='NObeyesdad', data=df)\n",
-    "plt.title('Obesity Levels by Gender')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Physical Activity Frequency Distribution')"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.histplot(df['FAF'], kde=True)\n",
-    "plt.title(\"Physical Activity Frequency Distribution\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Physical Activity Frequency Distribution (FAF)\n",
-    "\n",
-    "This histogram shows how often individuals engage in physical activity per week.\n",
-    "\n",
-    "- A large number of people have **very low or no physical activity** (FAF close to 0).\n",
-    "- There are smaller peaks around 1.0 and 2.0, indicating some regular activity in those ranges.\n",
-    "- Very few people are highly active (close to 3.0), which suggests that low to moderate physical activity is more common in the dataset.\n",
-    "\n",
-    "Understanding the distribution of physical activity is important since it's a key lifestyle factor and may influence how clusters are formed later.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.drop(columns=['NObeyesdad'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 2111 entries, 0 to 2110\n",
-      "Data columns (total 16 columns):\n",
-      " #   Column                          Non-Null Count  Dtype  \n",
-      "---  ------                          --------------  -----  \n",
-      " 0   Gender                          2111 non-null   object \n",
-      " 1   Age                             2111 non-null   float64\n",
-      " 2   Height                          2111 non-null   float64\n",
-      " 3   Weight                          2111 non-null   float64\n",
-      " 4   family_history_with_overweight  2111 non-null   object \n",
-      " 5   FAVC                            2111 non-null   object \n",
-      " 6   FCVC                            2111 non-null   float64\n",
-      " 7   NCP                             2111 non-null   float64\n",
-      " 8   CAEC                            2111 non-null   object \n",
-      " 9   SMOKE                           2111 non-null   object \n",
-      " 10  CH2O                            2111 non-null   float64\n",
-      " 11  SCC                             2111 non-null   object \n",
-      " 12  FAF                             2111 non-null   float64\n",
-      " 13  TUE                             2111 non-null   float64\n",
-      " 14  CALC                            2111 non-null   object \n",
-      " 15  MTRANS                          2111 non-null   object \n",
-      "dtypes: float64(8), object(8)\n",
-      "memory usage: 264.0+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "binary_cols = ['FAVC', 'family_history_with_overweight', 'SMOKE', 'SCC']\n",
-    "for col in binary_cols:\n",
-    "    df[col] = df[col].map({'yes': 1, 'no': 0})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 2111 entries, 0 to 2110\n",
-      "Data columns (total 16 columns):\n",
-      " #   Column                          Non-Null Count  Dtype  \n",
-      "---  ------                          --------------  -----  \n",
-      " 0   Gender                          2111 non-null   object \n",
-      " 1   Age                             2111 non-null   float64\n",
-      " 2   Height                          2111 non-null   float64\n",
-      " 3   Weight                          2111 non-null   float64\n",
-      " 4   family_history_with_overweight  2111 non-null   int64  \n",
-      " 5   FAVC                            2111 non-null   int64  \n",
-      " 6   FCVC                            2111 non-null   float64\n",
-      " 7   NCP                             2111 non-null   float64\n",
-      " 8   CAEC                            2111 non-null   object \n",
-      " 9   SMOKE                           2111 non-null   int64  \n",
-      " 10  CH2O                            2111 non-null   float64\n",
-      " 11  SCC                             2111 non-null   int64  \n",
-      " 12  FAF                             2111 non-null   float64\n",
-      " 13  TUE                             2111 non-null   float64\n",
-      " 14  CALC                            2111 non-null   object \n",
-      " 15  MTRANS                          2111 non-null   object \n",
-      "dtypes: float64(8), int64(4), object(4)\n",
-      "memory usage: 264.0+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 2️⃣ Ordinal encoding for CAEC, CALC (custom order based on domain)\n",
-    "ordinal_map = {\n",
-    "    'no': 0,\n",
-    "    'Sometimes': 1,\n",
-    "    'Frequently': 2,\n",
-    "    'Always': 3\n",
-    "}\n",
-    "df['CAEC'] = df['CAEC'].map(ordinal_map)\n",
-    "df['CALC'] = df['CALC'].map(ordinal_map)  # assuming same scale"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "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>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>family_history_with_overweight</th>\n",
-       "      <th>FAVC</th>\n",
-       "      <th>FCVC</th>\n",
-       "      <th>NCP</th>\n",
-       "      <th>CAEC</th>\n",
-       "      <th>SMOKE</th>\n",
-       "      <th>CH2O</th>\n",
-       "      <th>SCC</th>\n",
-       "      <th>FAF</th>\n",
-       "      <th>TUE</th>\n",
-       "      <th>CALC</th>\n",
-       "      <th>MTRANS</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Female</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Female</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.52</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Walking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>89.8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Public_Transportation</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Gender   Age  Height  Weight  family_history_with_overweight  FAVC  FCVC  \\\n",
-       "0  Female  21.0    1.62    64.0                               1     0   2.0   \n",
-       "1  Female  21.0    1.52    56.0                               1     0   3.0   \n",
-       "2    Male  23.0    1.80    77.0                               1     0   2.0   \n",
-       "3    Male  27.0    1.80    87.0                               0     0   3.0   \n",
-       "4    Male  22.0    1.78    89.8                               0     0   2.0   \n",
-       "\n",
-       "   NCP  CAEC  SMOKE  CH2O  SCC  FAF  TUE  CALC                 MTRANS  \n",
-       "0  3.0     1      0   2.0    0  0.0  1.0     0  Public_Transportation  \n",
-       "1  3.0     1      1   3.0    1  3.0  0.0     1  Public_Transportation  \n",
-       "2  3.0     1      0   2.0    0  2.0  1.0     2  Public_Transportation  \n",
-       "3  3.0     1      0   2.0    0  2.0  0.0     2                Walking  \n",
-       "4  1.0     1      0   2.0    0  0.0  0.0     1  Public_Transportation  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 3️⃣ Binary Gender → map to 0/1\n",
-    "df['Gender'] = df['Gender'].map({'Male': 0, 'Female': 1})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 4️⃣ One-hot encode MTRANS (multi-class nominal)\n",
-    "df = pd.get_dummies(df, columns=['MTRANS'], drop_first=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>family_history_with_overweight</th>\n",
-       "      <th>FAVC</th>\n",
-       "      <th>FCVC</th>\n",
-       "      <th>NCP</th>\n",
-       "      <th>CAEC</th>\n",
-       "      <th>SMOKE</th>\n",
-       "      <th>CH2O</th>\n",
-       "      <th>SCC</th>\n",
-       "      <th>FAF</th>\n",
-       "      <th>TUE</th>\n",
-       "      <th>CALC</th>\n",
-       "      <th>MTRANS_Bike</th>\n",
-       "      <th>MTRANS_Motorbike</th>\n",
-       "      <th>MTRANS_Public_Transportation</th>\n",
-       "      <th>MTRANS_Walking</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1.52</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>89.8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Gender   Age  Height  Weight  family_history_with_overweight  FAVC  FCVC  \\\n",
-       "0       1  21.0    1.62    64.0                               1     0   2.0   \n",
-       "1       1  21.0    1.52    56.0                               1     0   3.0   \n",
-       "2       0  23.0    1.80    77.0                               1     0   2.0   \n",
-       "3       0  27.0    1.80    87.0                               0     0   3.0   \n",
-       "4       0  22.0    1.78    89.8                               0     0   2.0   \n",
-       "\n",
-       "   NCP  CAEC  SMOKE  CH2O  SCC  FAF  TUE  CALC  MTRANS_Bike  MTRANS_Motorbike  \\\n",
-       "0  3.0     1      0   2.0    0  0.0  1.0     0            0                 0   \n",
-       "1  3.0     1      1   3.0    1  3.0  0.0     1            0                 0   \n",
-       "2  3.0     1      0   2.0    0  2.0  1.0     2            0                 0   \n",
-       "3  3.0     1      0   2.0    0  2.0  0.0     2            0                 0   \n",
-       "4  1.0     1      0   2.0    0  0.0  0.0     1            0                 0   \n",
-       "\n",
-       "   MTRANS_Public_Transportation  MTRANS_Walking  \n",
-       "0                             1               0  \n",
-       "1                             1               0  \n",
-       "2                             1               0  \n",
-       "3                             0               1  \n",
-       "4                             1               0  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "scaler = StandardScaler()\n",
-    "df_scaled = scaler.fit_transform(df)  # Use this for KMeans or Hierarchical\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "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>Gender</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>family_history_with_overweight</th>\n",
-       "      <th>FAVC</th>\n",
-       "      <th>FCVC</th>\n",
-       "      <th>NCP</th>\n",
-       "      <th>CAEC</th>\n",
-       "      <th>SMOKE</th>\n",
-       "      <th>CH2O</th>\n",
-       "      <th>SCC</th>\n",
-       "      <th>FAF</th>\n",
-       "      <th>TUE</th>\n",
-       "      <th>CALC</th>\n",
-       "      <th>MTRANS_Bike</th>\n",
-       "      <th>MTRANS_Motorbike</th>\n",
-       "      <th>MTRANS_Public_Transportation</th>\n",
-       "      <th>MTRANS_Walking</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.011914</td>\n",
-       "      <td>-0.522124</td>\n",
-       "      <td>-0.875589</td>\n",
-       "      <td>-0.862558</td>\n",
-       "      <td>0.472291</td>\n",
-       "      <td>-2.759769</td>\n",
-       "      <td>-0.785019</td>\n",
-       "      <td>0.404153</td>\n",
-       "      <td>-0.300346</td>\n",
-       "      <td>-0.145900</td>\n",
-       "      <td>-0.013073</td>\n",
-       "      <td>-0.218272</td>\n",
-       "      <td>-1.188039</td>\n",
-       "      <td>0.561997</td>\n",
-       "      <td>-1.419172</td>\n",
-       "      <td>-0.05768</td>\n",
-       "      <td>-0.072375</td>\n",
-       "      <td>0.579721</td>\n",
-       "      <td>-0.165078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.011914</td>\n",
-       "      <td>-0.522124</td>\n",
-       "      <td>-1.947599</td>\n",
-       "      <td>-1.168077</td>\n",
-       "      <td>0.472291</td>\n",
-       "      <td>-2.759769</td>\n",
-       "      <td>1.088342</td>\n",
-       "      <td>0.404153</td>\n",
-       "      <td>-0.300346</td>\n",
-       "      <td>6.853997</td>\n",
-       "      <td>1.618759</td>\n",
-       "      <td>4.581439</td>\n",
-       "      <td>2.339750</td>\n",
-       "      <td>-1.080625</td>\n",
-       "      <td>0.521160</td>\n",
-       "      <td>-0.05768</td>\n",
-       "      <td>-0.072375</td>\n",
-       "      <td>0.579721</td>\n",
-       "      <td>-0.165078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.988227</td>\n",
-       "      <td>-0.206889</td>\n",
-       "      <td>1.054029</td>\n",
-       "      <td>-0.366090</td>\n",
-       "      <td>0.472291</td>\n",
-       "      <td>-2.759769</td>\n",
-       "      <td>-0.785019</td>\n",
-       "      <td>0.404153</td>\n",
-       "      <td>-0.300346</td>\n",
-       "      <td>-0.145900</td>\n",
-       "      <td>-0.013073</td>\n",
-       "      <td>-0.218272</td>\n",
-       "      <td>1.163820</td>\n",
-       "      <td>0.561997</td>\n",
-       "      <td>2.461491</td>\n",
-       "      <td>-0.05768</td>\n",
-       "      <td>-0.072375</td>\n",
-       "      <td>0.579721</td>\n",
-       "      <td>-0.165078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.988227</td>\n",
-       "      <td>0.423582</td>\n",
-       "      <td>1.054029</td>\n",
-       "      <td>0.015808</td>\n",
-       "      <td>-2.117337</td>\n",
-       "      <td>-2.759769</td>\n",
-       "      <td>1.088342</td>\n",
-       "      <td>0.404153</td>\n",
-       "      <td>-0.300346</td>\n",
-       "      <td>-0.145900</td>\n",
-       "      <td>-0.013073</td>\n",
-       "      <td>-0.218272</td>\n",
-       "      <td>1.163820</td>\n",
-       "      <td>-1.080625</td>\n",
-       "      <td>2.461491</td>\n",
-       "      <td>-0.05768</td>\n",
-       "      <td>-0.072375</td>\n",
-       "      <td>-1.724969</td>\n",
-       "      <td>6.057758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.988227</td>\n",
-       "      <td>-0.364507</td>\n",
-       "      <td>0.839627</td>\n",
-       "      <td>0.122740</td>\n",
-       "      <td>-2.117337</td>\n",
-       "      <td>-2.759769</td>\n",
-       "      <td>-0.785019</td>\n",
-       "      <td>-2.167023</td>\n",
-       "      <td>-0.300346</td>\n",
-       "      <td>-0.145900</td>\n",
-       "      <td>-0.013073</td>\n",
-       "      <td>-0.218272</td>\n",
-       "      <td>-1.188039</td>\n",
-       "      <td>-1.080625</td>\n",
-       "      <td>0.521160</td>\n",
-       "      <td>-0.05768</td>\n",
-       "      <td>-0.072375</td>\n",
-       "      <td>0.579721</td>\n",
-       "      <td>-0.165078</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Gender       Age    Height    Weight  family_history_with_overweight  \\\n",
-       "0  1.011914 -0.522124 -0.875589 -0.862558                        0.472291   \n",
-       "1  1.011914 -0.522124 -1.947599 -1.168077                        0.472291   \n",
-       "2 -0.988227 -0.206889  1.054029 -0.366090                        0.472291   \n",
-       "3 -0.988227  0.423582  1.054029  0.015808                       -2.117337   \n",
-       "4 -0.988227 -0.364507  0.839627  0.122740                       -2.117337   \n",
-       "\n",
-       "       FAVC      FCVC       NCP      CAEC     SMOKE      CH2O       SCC  \\\n",
-       "0 -2.759769 -0.785019  0.404153 -0.300346 -0.145900 -0.013073 -0.218272   \n",
-       "1 -2.759769  1.088342  0.404153 -0.300346  6.853997  1.618759  4.581439   \n",
-       "2 -2.759769 -0.785019  0.404153 -0.300346 -0.145900 -0.013073 -0.218272   \n",
-       "3 -2.759769  1.088342  0.404153 -0.300346 -0.145900 -0.013073 -0.218272   \n",
-       "4 -2.759769 -0.785019 -2.167023 -0.300346 -0.145900 -0.013073 -0.218272   \n",
-       "\n",
-       "        FAF       TUE      CALC  MTRANS_Bike  MTRANS_Motorbike  \\\n",
-       "0 -1.188039  0.561997 -1.419172     -0.05768         -0.072375   \n",
-       "1  2.339750 -1.080625  0.521160     -0.05768         -0.072375   \n",
-       "2  1.163820  0.561997  2.461491     -0.05768         -0.072375   \n",
-       "3  1.163820 -1.080625  2.461491     -0.05768         -0.072375   \n",
-       "4 -1.188039 -1.080625  0.521160     -0.05768         -0.072375   \n",
-       "\n",
-       "   MTRANS_Public_Transportation  MTRANS_Walking  \n",
-       "0                      0.579721       -0.165078  \n",
-       "1                      0.579721       -0.165078  \n",
-       "2                      0.579721       -0.165078  \n",
-       "3                     -1.724969        6.057758  \n",
-       "4                      0.579721       -0.165078  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Assuming `df_encoded` is your pre-scaled DataFrame (the one you passed to StandardScaler)\n",
-    "df_scaled = pd.DataFrame(df_scaled, columns=df.columns)\n",
-    "df_scaled.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🧾 Final Summary\n",
-    "\n",
-    "In this notebook, we explored and prepared a health and lifestyle dataset for clustering.\n",
-    "\n",
-    "### 🔍 Exploratory Data Analysis (EDA)\n",
-    "- We looked at the distribution of numeric features like **Age**, **Weight**, **Physical Activity (FAF)**, and **Water Intake (CH2O)** using histograms and KDE plots.\n",
-    "- Categorical variables such as **Smoking**, **Alcohol Consumption (CALC)**, **Food Habits (FAVC)**, and **Transportation (MTRANS)** were visualized using bar plots.\n",
-    "- A correlation heatmap was used to check relationships between numeric features. No strong correlations were found, so we kept all features.\n",
-    "\n",
-    "### 🧼 Preprocessing\n",
-    "- The target variable `NObeyesdad` was removed since clustering is unsupervised and doesn't use labels.\n",
-    "- Categorical features were encoded using label encoding or one-hot encoding.\n",
-    "- All numeric features were standardized using `StandardScaler` to prepare the data for clustering.\n",
-    "\n",
-    "The dataset is now clean, encoded, and scaled — ready for clustering in the next step.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/clustering/clustering_preprocessing.ipynb b/notebooks/clustering/clustering_preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..376f2f933c0118558314b4d7614c72082754dc3c
--- /dev/null
+++ b/notebooks/clustering/clustering_preprocessing.ipynb
@@ -0,0 +1,1763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7e31e58f",
+   "metadata": {},
+   "source": [
+    "### Imports and Setup\n",
+    "\n",
+    "We begin by importing all necessary libraries for this notebook:\n",
+    "\n",
+    "- **`pandas`, `numpy`**: For data loading and manipulation  \n",
+    "- **`matplotlib.pyplot`, `seaborn`**: For visualization  \n",
+    "- **`RobustScaler` from `sklearn.preprocessing`**: For scaling numerical features in a way that reduces the influence of outliers  \n",
+    "- **`os`, `sys`**: To handle paths and module imports, especially for loading utility functions from the `src/` directory\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "e4da48cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import RobustScaler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2944caf4",
+   "metadata": {},
+   "source": [
+    "### Load Dataset Using Custom Utility\n",
+    "\n",
+    "To maintain modular and reusable code, we use a custom `load_dataset` function defined in `src/utils.py`. This function simplifies loading files from the `data/raw` or `data/processed` directories.\n",
+    "\n",
+    "We first add the `src` directory to the Python path so that the utility can be imported, and then load the raw dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "b56f512b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add src directory to the system path\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"..\", \"..\", \"src\")))\n",
+    "\n",
+    "# Now you can import your function\n",
+    "from utils import load_dataset\n",
+    "\n",
+    "# Load your raw dataset\n",
+    "df = load_dataset(\"ObesityDataSet_raw_and_data_sinthetic.csv\", folder=\"raw\")  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "662b783d",
+   "metadata": {},
+   "source": [
+    "### Data Dictionary\n",
+    "\n",
+    "| Feature                          | Type       | Description                                                                 |\n",
+    "|----------------------------------|------------|-----------------------------------------------------------------------------|\n",
+    "| `Gender`                         | Categorical | Gender of the individual (`Male`, `Female`)                                 |\n",
+    "| `Age`                            | Numerical   | Age of the individual in years                                              |\n",
+    "| `Height`                         | Numerical   | Height in meters                                                            |\n",
+    "| `Weight`                         | Numerical   | Weight in kilograms                                                         |\n",
+    "| `family_history_with_overweight`| Categorical | Whether there is a family history of being overweight (`yes`, `no`)         |\n",
+    "| `FAVC`                           | Categorical | Frequent consumption of high-calorie food (`yes`, `no`)                    |\n",
+    "| `FCVC`                           | Numerical   | Frequency of vegetable consumption (1 = low, 3 = high)                      |\n",
+    "| `NCP`                            | Numerical   | Number of main meals per day                                                |\n",
+    "| `CAEC`                           | Categorical | Consumption of food between meals (`no`, `Sometimes`, `Frequently`, `Always`) |\n",
+    "| `SMOKE`                          | Categorical | Smoking habit (`yes`, `no`)                                                |\n",
+    "| `CH2O`                           | Numerical   | Daily water intake (in liters)                                              |\n",
+    "| `SCC`                            | Categorical | Calorie consumption monitoring (`yes`, `no`)                                |\n",
+    "| `FAF`                            | Numerical   | Physical activity frequency (hours per week)                                |\n",
+    "| `TUE`                            | Numerical   | Time spent using technology (hours per day)                                 |\n",
+    "| `CALC`                           | Categorical | Alcohol consumption (`no`, `Sometimes`, `Frequently`, `Always`)             |\n",
+    "| `MTRANS`                         | Categorical | Main mode of transportation                                                 |\n",
+    "| `NObeyesdad`                     | Categorical | Obesity classification (e.g., `Obesity_Type_I`, `Normal_Weight`, etc.)      |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "8e75bd43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2111 entries, 0 to 2110\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                          Non-Null Count  Dtype  \n",
+      "---  ------                          --------------  -----  \n",
+      " 0   Gender                          2111 non-null   object \n",
+      " 1   Age                             2111 non-null   float64\n",
+      " 2   Height                          2111 non-null   float64\n",
+      " 3   Weight                          2111 non-null   float64\n",
+      " 4   family_history_with_overweight  2111 non-null   object \n",
+      " 5   FAVC                            2111 non-null   object \n",
+      " 6   FCVC                            2111 non-null   float64\n",
+      " 7   NCP                             2111 non-null   float64\n",
+      " 8   CAEC                            2111 non-null   object \n",
+      " 9   SMOKE                           2111 non-null   object \n",
+      " 10  CH2O                            2111 non-null   float64\n",
+      " 11  SCC                             2111 non-null   object \n",
+      " 12  FAF                             2111 non-null   float64\n",
+      " 13  TUE                             2111 non-null   float64\n",
+      " 14  CALC                            2111 non-null   object \n",
+      " 15  MTRANS                          2111 non-null   object \n",
+      " 16  NObeyesdad                      2111 non-null   object \n",
+      "dtypes: float64(8), object(9)\n",
+      "memory usage: 280.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05716fc5",
+   "metadata": {},
+   "source": [
+    "### Dataset Structure\n",
+    "\n",
+    "The dataset consists of **2,111 rows** and **17 columns**. It includes a mix of numerical and categorical features related to personal attributes, lifestyle habits, and obesity classification. There are no **null** values. \n",
+    "\n",
+    "- **Numerical columns (8):**\n",
+    "  - `Age`, `Height`, `Weight`, `FCVC`, `NCP`, `CH2O`, `FAF`, `TUE`\n",
+    "\n",
+    "- **Categorical columns (9):**\n",
+    "  - `Gender`, `family_history_with_overweight`, `FAVC`, `CAEC`, `SMOKE`, `SCC`, `CALC`, `MTRANS`, `NObeyesdad`\n",
+    "\n",
+    "There are **no missing values** in the dataset. The `NObeyesdad` column represents the obesity category and serves as the target in supervised learning. However, since this is an **unsupervised clustering task**, we will remove this column before performing clustering.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "257f7a11",
+   "metadata": {},
+   "source": [
+    "### Checking initial 5 rows of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "07a988b4",
+   "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>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>family_history_with_overweight</th>\n",
+       "      <th>FAVC</th>\n",
+       "      <th>FCVC</th>\n",
+       "      <th>NCP</th>\n",
+       "      <th>CAEC</th>\n",
+       "      <th>SMOKE</th>\n",
+       "      <th>CH2O</th>\n",
+       "      <th>SCC</th>\n",
+       "      <th>FAF</th>\n",
+       "      <th>TUE</th>\n",
+       "      <th>CALC</th>\n",
+       "      <th>MTRANS</th>\n",
+       "      <th>NObeyesdad</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "      <td>Normal_Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.52</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "      <td>Normal_Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Frequently</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "      <td>Normal_Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Frequently</td>\n",
+       "      <td>Walking</td>\n",
+       "      <td>Overweight_Level_I</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>89.8</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "      <td>Overweight_Level_II</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
+       "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
+       "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
+       "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
+       "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
+       "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
+       "\n",
+       "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
+       "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
+       "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
+       "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
+       "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
+       "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
+       "\n",
+       "                  MTRANS           NObeyesdad  \n",
+       "0  Public_Transportation        Normal_Weight  \n",
+       "1  Public_Transportation        Normal_Weight  \n",
+       "2  Public_Transportation        Normal_Weight  \n",
+       "3                Walking   Overweight_Level_I  \n",
+       "4  Public_Transportation  Overweight_Level_II  "
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a75821ff",
+   "metadata": {},
+   "source": [
+    "### Checking duplicate instances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "97cb8fa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(33)"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated(keep=False).sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fbeb593",
+   "metadata": {},
+   "source": [
+    "### removing duplicates and resetting index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "75a7bade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[~df.duplicated(keep=False)].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6af873ba",
+   "metadata": {},
+   "source": [
+    "### checking rows and columns in data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "46aaea63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2078, 17)"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21706e28",
+   "metadata": {},
+   "source": [
+    "### Inspecting categorical columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "6bcb6c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Gender value counts:\n",
+      "Gender\n",
+      "Male      1049\n",
+      "Female    1029\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "family_history_with_overweight value counts:\n",
+      "family_history_with_overweight\n",
+      "yes    1718\n",
+      "no      360\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "FAVC value counts:\n",
+      "FAVC\n",
+      "yes    1837\n",
+      "no      241\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "CAEC value counts:\n",
+      "CAEC\n",
+      "Sometimes     1757\n",
+      "Frequently     232\n",
+      "Always          53\n",
+      "no              36\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "SMOKE value counts:\n",
+      "SMOKE\n",
+      "no     2034\n",
+      "yes      44\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "SCC value counts:\n",
+      "SCC\n",
+      "no     1982\n",
+      "yes      96\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "CALC value counts:\n",
+      "CALC\n",
+      "Sometimes     1374\n",
+      "no             633\n",
+      "Frequently      70\n",
+      "Always           1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "MTRANS value counts:\n",
+      "MTRANS\n",
+      "Public_Transportation    1551\n",
+      "Automobile                455\n",
+      "Walking                    54\n",
+      "Motorbike                  11\n",
+      "Bike                        7\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "NObeyesdad value counts:\n",
+      "NObeyesdad\n",
+      "Obesity_Type_I         351\n",
+      "Obesity_Type_III       324\n",
+      "Obesity_Type_II        297\n",
+      "Overweight_Level_II    290\n",
+      "Normal_Weight          277\n",
+      "Overweight_Level_I     275\n",
+      "Insufficient_Weight    264\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initial categorical inspection\n",
+    "cat_cols = df.select_dtypes(include='object').columns\n",
+    "for col in cat_cols:\n",
+    "    print(f\"\\n{col} value counts:\\n{df[col].value_counts()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78ac6e62",
+   "metadata": {},
+   "source": [
+    "### Categorical Feature Distributions\n",
+    "\n",
+    "Below are the value counts for key categorical variables in the dataset:\n",
+    "\n",
+    "- **Gender**\n",
+    "  - Male: 1,068  \n",
+    "  - Female: 1,043\n",
+    "\n",
+    "- **Family History with Overweight**\n",
+    "  - Yes: 1,726  \n",
+    "  - No: 385\n",
+    "\n",
+    "- **Frequent Consumption of High-Calorie Food (FAVC)**\n",
+    "  - Yes: 1,866  \n",
+    "  - No: 245\n",
+    "\n",
+    "- **Food Between Meals (CAEC)**\n",
+    "  - Sometimes: 1,765  \n",
+    "  - Frequently: 242  \n",
+    "  - Always: 53  \n",
+    "  - No: 51\n",
+    "\n",
+    "- **Smoking (SMOKE)**\n",
+    "  - No: 2,067  \n",
+    "  - Yes: 44\n",
+    "\n",
+    "- **Calorie Consumption Monitoring (SCC)**\n",
+    "  - No: 2,015  \n",
+    "  - Yes: 96\n",
+    "\n",
+    "- **Alcohol Consumption (CALC)**\n",
+    "  - Sometimes: 1,401  \n",
+    "  - No: 639  \n",
+    "  - Frequently: 70  \n",
+    "  - Always: 1\n",
+    "\n",
+    "- **Transportation Mode (MTRANS)**\n",
+    "  - Public Transportation: 1,580  \n",
+    "  - Automobile: 457  \n",
+    "  - Walking: 56  \n",
+    "  - Motorbike: 11  \n",
+    "  - Bike: 7\n",
+    "\n",
+    "- **Obesity Level (NObeyesdad)**  \n",
+    "  *(Target variable — will be removed for clustering)*  \n",
+    "  - Obesity Type I: 351  \n",
+    "  - Obesity Type III: 324  \n",
+    "  - Obesity Type II: 297  \n",
+    "  - Overweight Level I: 290  \n",
+    "  - Overweight Level II: 290  \n",
+    "  - Normal Weight: 287  \n",
+    "  - Insufficient Weight: 272\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "61a86987",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "counts = df['NObeyesdad'].value_counts()\n",
+    "ax = counts.plot(kind='bar', color='teal')\n",
+    "plt.title(\"Distribution of Obesity Levels\")\n",
+    "plt.xlabel(\"Obesity Category\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.xticks(rotation=45)\n",
+    "for i, val in enumerate(counts.values):\n",
+    "    ax.text(i, val + 5, str(val), ha='center')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd96a9c0",
+   "metadata": {},
+   "source": [
+    "### Obesity Level Distribution\n",
+    "\n",
+    "This chart shows how people are spread across different weight categories.\n",
+    "\n",
+    "**Quick takeaways:**\n",
+    "- Obesity levels (especially Type I) are the most common.\n",
+    "- Normal and underweight groups show up less often.\n",
+    "- Overall, the chart suggests a shift toward higher weight categories.\n",
+    "\n",
+    "## Understanding the Target Variable\n",
+    "\n",
+    "### Distribution of Obesity Levels\n",
+    "\n",
+    "We visualize the distribution of the target variable `NObeyesdad` to assess class balance across different obesity categories. \n",
+    "\n",
+    "Although this column isn’t used during clustering (since it's an unsupervised task), it provides helpful context for understanding the structure of the dataset. Later on, it can also serve as a reference point for evaluating clustering performance.\n",
+    "\n",
+    "The classes appear relatively well-distributed, with no extreme imbalance. This suggests that the dataset offers a good representation of different obesity levels, which could support more meaningful and reliable cluster formation.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "763bb18f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(15, 7))\n",
+    "sns.histplot(df['Age'], bins=20, kde=True, color='steelblue', edgecolor='black')\n",
+    "\n",
+    "# Plot mean and median\n",
+    "plt.axvline(df['Age'].mean(), color='red', linestyle='--', linewidth=2, label=f\"Mean: {df['Age'].mean():.1f}\")\n",
+    "plt.axvline(df['Age'].median(), color='green', linestyle='--', linewidth=2, label=f\"Median: {df['Age'].median():.1f}\")\n",
+    "\n",
+    "# Labels and legend\n",
+    "plt.title('Age Distribution')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39e2eeb8",
+   "metadata": {},
+   "source": [
+    "### Age Distribution\n",
+    "\n",
+    "We plot the distribution of the `Age` variable to understand the spread and central tendency in the dataset. The KDE (Kernel Density Estimate) line helps visualize the underlying shape of the distribution.\n",
+    "\n",
+    "This is useful for identifying skewness, potential outliers, and whether the feature may need transformation before clustering.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "4867a107",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "ax = sns.countplot(x='Gender', hue='CALC', data=df, hue_order=calc_order, palette='pastel')\n",
+    "plt.title('Alcohol Consumption by Gender')\n",
+    "plt.xlabel('Gender')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend(title='Alcohol Consumption')\n",
+    "\n",
+    "# Add count labels to each bar\n",
+    "for container in ax.containers:\n",
+    "    ax.bar_label(container, label_type='edge', fontsize=8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d50078de",
+   "metadata": {},
+   "source": [
+    "### Alcohol Consumption by Gender\n",
+    "\n",
+    "This plot shows how alcohol consumption (`CALC`) varies between males and females. The most common response for both genders is \"Sometimes\", followed by \"no\". Very few individuals reported \"Frequently\" or \"Always\", with \"Always\" being nearly absent in both groups.\n",
+    "\n",
+    "This suggests that while occasional alcohol use is common, heavy drinking is rare in this dataset across both genders. The ordinal nature of `CALC` will be preserved during encoding for clustering.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "1c874963",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ritwi\\AppData\\Local\\Temp\\ipykernel_26680\\3121242800.py:7: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x='NObeyesdad', y='Weight', data=df, order=category_order, palette='pastel')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "category_order = [\n",
+    "    'Insufficient_Weight', 'Normal_Weight',\n",
+    "    'Overweight_Level_I', 'Overweight_Level_II',\n",
+    "    'Obesity_Type_I', 'Obesity_Type_II', 'Obesity_Type_III'\n",
+    "]\n",
+    "sns.boxplot(x='NObeyesdad', y='Weight', data=df, order=category_order, palette='pastel')\n",
+    "plt.title('Weight vs. Obesity Level')\n",
+    "plt.xlabel('Obesity Category')\n",
+    "plt.ylabel('Weight (kg)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55c1b6f4",
+   "metadata": {},
+   "source": [
+    "### Weight vs. Obesity Level\n",
+    "\n",
+    "This box plot shows how weight is distributed within each obesity category.\n",
+    "\n",
+    "**Observations:**\n",
+    "- As expected, weight generally increases with the obesity level.\n",
+    "- **Insufficient_Weight** and **Normal_Weight** groups have the lowest medians and narrower distributions.\n",
+    "- **Obesity_Type_III** shows the highest weights with a wider spread, indicating more variability in that group.\n",
+    "- There's a clear upward trend in both median and range of weight from left to right across the categories.\n",
+    "- Some outliers are present in a few categories, especially in the higher obesity levels.\n",
+    "\n",
+    "This visualization confirms that weight is a strong differentiator between the obesity categories and supports its relevance as a feature in clustering or predictive analysis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "bb0ffae0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+Ps84v9tuu63s+++/r7T/d999V/b73/8+jy+ubXy/ttxyy7KBAweWffXVV+X7xVjiuzSnqr4rTz75ZD7Gcsstl78z119/ffl3eX6/sxWvHwAAUH3qxP9Ud5APAFCbRQuRqCaOaueoQmbh7L///un111/P/e8BAACqm57oAAALKfpqR2uUqiZNZf5Ev/Z///vfud0KAABATaAnOgDAAor+49HrOkLf//3f/80TjLJgoh/4s88+m/vARx/0mIQWAACgJhCiAwAsoN69e6cmTZrkiVJjIkkW3JNPPpknT11zzTXzJLStWrWq7iEBAABkeqIDAAAAAEABPdEBAAAAAKCAEB0AAAAAAAroiZ5Smj17dvr000/TiiuuaEIwAAAAgGVQdDz++uuvU5s2bVLduupOgf+fED2lHKCvscYa1T0MAAAAAKrZ2LFj0+qrr17dwwBqECF6SrkCvfQfyaZNm1b3cAAAAABYwqZMmZKLLEs5EUCJED2l8hYuEaAL0QEAAACWXVr9AnPS4AkAAAAAAAoI0QEAAAAAoIAQHQAAAAAACuiJDgAAAABUMmvWrDRz5szqHgYsNg0aNEj16tWbp32F6AAAAABAVlZWlsaPH58mT55c3UOBxa558+apVatWPzqhsBAdAAAAAMhKAfpqq62WVlhhhR8NF6G23iyaNm1amjhxYn7dunXrH9xfiA4AAAAA5BYupQB95ZVXru7hwGK1/PLL58cI0uM7/0OtXUwsCgAAAACU90CPCnRYFqzw/77rP9b/X4gOAAAAAJTTwoVlxbx+14XoAAAAAABQQIgOAAAAACxz2rVrl6688srFdvzBgwen5s2bp6XRUUcdlXr06JGWFUJ0AAAAAGCpMXbs2PTzn/88tWnTJi233HJprbXWSieddFKaNGnSEh3HIYcckt57773y1wMGDEibbbbZQh/3iSeeyG1IYhJYlgwhOgAAAACwVPjwww9Tp06d0vvvv59uv/32NGrUqHT99denRx99NG233Xbpiy++WGJjWX755dNqq622xD6PxUeIDgAAAAAsFfr27Zurzx9++OG08847pzXXXDN17949PfLII+mTTz5J/fv3r7T/119/nXr37p0aN26c2rZtmwYNGlRpe1R7H3vssWnVVVdNTZs2Tbvuumt69dVXy7fH8y5duqQVV1wxb99yyy3Tyy+/PFc7l3g+cODAvH9UkccS66Jifp999qn0mTNnzszh+w033LBA12DGjBnp9NNPz+cT57XNNtvk6vUwZcqUHO4/+OCDld5zzz335HOYNm1aeTV/z5498/hbtGiRfvrTn6bRo0enZZUQHQAAAACo9aLK/KGHHkrHH398DooratWqVTrssMPSnXfemcrKysrX/+EPf0ibbrppeuWVV9JZZ52V274MGzasfPvBBx+cJk6cmEPnESNGpC222CLttttu5RXtcczVV189vfTSS3l7HKNBgwZVtnY57bTT0kYbbZTGjRuXl1gXAf3QoUPz65L7778/h9mxfUGccMIJafjw4emOO+5Ir732Wj6HPffcM1fnR9Afof2QIUMqvee2227LPc5XWGGFHOJ369Yth+pPP/10evbZZ1OTJk3yMb777ru0LKpf3QMAAAAAAFhYERJHQN6xY8cqt8f6L7/8Mn322WflbVZ22GGHHHyH9ddfPwfGV1xxRdp9993TM888k1588cUcojds2DDvc+mll6Z777033X333alPnz5pzJgx6YwzzkgdOnTI29dbb70qPztC/Qii69evnwP9ku233z5tsMEG6ZZbbklnnnlmXnfTTTfl4Dv2n18xnnh/PEZP+BBV6RHUx/qLLrooB/+HH354DuojNI/q9H//+9+5Gj3EjYbZs2env/zlL7liPsR7oyo9Ktr32GOPtKxRiQ4AAAAALDUqVpr/mOiTPufrt99+Oz+P1itTp05NK6+8cg60S8tHH32UPvjgg7zPqaeemqvJu3btmn73u9+Vr58f8f4IqcOECRNy1Xu0eVkQr7/+epo1a1a+IVBxzE8++WT52Pbaa69cLX/ffffl13//+99zhXqcQ+m8o5d8VKKX3t+iRYs0ffr0BTq/pYFKdAAAAACg1mvfvn2unI4QfP/9959re6xfaaWVcn/zeREBeuvWrcv7iVdU6nU+YMCAdOihh+ZK7gi/zzvvvNxGparPL3LEEUfkavhowfLcc8+ltddeO3Xu3Hme3z/nmOvVq5dby8RjRaXK9ugZf9BBB+WWLr169cqP0TomquRLx4je7tHiZU6rzuO1W9oI0QEAAACAWi8qxqMNy7XXXptOOeWUSn3Rx48fn0PhCKxLLUrC888/X+kY8brUDib6n8f7Ilxu165d4edG1Xcs8ZkxSWlUlVcVokd4HVXiVY07+pHH+yJIP/rooxf4Gmy++eb5M6IFzQ8F8dHSJa7Vm2++mR577LF0wQUXlG+L846WLtHyJirU0c4FAAAAAFhKXHPNNWnGjBl5YsynnnoqjR07NvcDj8C4bdu26cILL6y0f/RAv+SSS9J7772XBg0alO666648uWiI9ibR3iUC7ocffjiNHj06V4r3798/vfzyy+nbb7/Nk3hGpfrHH3+cjxUTjBb1ZI8gPlrBjBw5Mn3++ed5nBVbutx88825Wv7II4+c59YtcazSEm1YIsyPgDxuFvzjH//Inxd93S+++OJcLV+y0047lU+2GpXv22yzTfm2WLfKKqukn/70p3li0Y8++iif469//ev03//+d75/JksDIToAAAAAsFSIiT0j4F5nnXVSz54907rrrpsnAO3SpUuu8o7e3hWddtppef+o4I5q7MsvvzwH8CEq1h944IEcOEd1eATU0f4kAvOWLVvmdimTJk3KgXVsi8/r3r17GjhwYJVjO/DAA9Oee+6ZxxJtUW6//fbybRHYR+uY+OzShKA/JsYV4y4t0YIlREV7jCnOLSYtjZsAEe6vueaa5e+Nc4uq+QjeIzSvKCYbjRsQsf8BBxyQbwocc8wxuSf6slqZXqdsfjrtL6ViBtpmzZqlr776apn9IgAAAAAsy+RDKYekUXUclcmNGjWq7uEsU6IPeVTKRwAewTU16zuvJzoAAAAAQDWYPXt2bu1y2WWX5clK99tvv+oeElUQogMAAAAAVIMxY8bkKujVV189DR48OE9iSs3jpwIAAAAAUA1islHdtms+E4sCAAAAAEABlegAQKFRT99U3UOoEdp3Prq6hwAAAEA1UYkOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAwH95555207bbbpkaNGqXNNtusynWjR49OderUSSNHjpynYx511FGpR48eaWk0YMCA8us0r3bZZZd08sknp5qgfnUPAAAAAACo2U578K9L7LMu637EfL8nAujJkyene++9Ny0J5513XmrcuHF69913U5MmTapc17x58zRu3Li0yiqrzNMx//d//zeVlZUt8vA6rsm8Bvm9evXK13Ho0KHl64YOHZq6d++ezy+OV/HYN954YxozZsyPHvf0009PJ554YlrU4ibFPffcs9hvPqhEBwAAAACYDx988EHacccd01prrZVWXnnlKtfVq1cvtWrVKtWvP291zM2aNcvBe3Xq0qVLevbZZ9P3339fvu7xxx9Pa6yxRnriiScq7RvrY/95ETcVStepNhKiAwAAAABLjWgD8utf/zqdeeaZqUWLFjnIrlhBHdXe8XrNNddMDRs2TG3atMn7V6xunrOiPcLtwYMHl28fMWJEOv/88/PzOFZV66pq5/Lmm2+mffbZJzVt2jStuOKKqXPnzjl8r6qdy+zZs9PFF1+c1l577bT88sunTTfdNN19993l2yPUjuM/+uijqVOnTmmFFVZI22+/fa6EDzHegQMHpldffTXvF0vpHIpEKD516tT08ssvV/qcs846K73wwgtp+vTpeV08xutSiB7V68cee2xaddVV87ntuuuu+XOL2rlESB/XPK5rhOu/+c1v0pFHHjlXRXlcg6KfY7t27fLj/vvvn8+t9HpxEKIDAAAAAEuVm2++ObdWiaD3kksuyeH2sGHD8ra///3v6Yorrkh//OMf0/vvv58D84033niejx0tWjbaaKN02mmn5efRqqSqdXP65JNP0k477ZSD+8ceeyyH7j//+c8rVX1XFAH6X//613T99dfn8P2UU05JP/vZz9KTTz5Zab/+/funyy67LAffUfUexwyHHHJIHk+MK8YUS6z7Ieuvv36+qRBV5uHrr79O//nPf9LBBx+cQ+rhw4fn9c8991yaMWNGeYge2ydOnJgefPDBfF5bbLFF2m233dIXX3xR5ef8/ve/T7fddlu66aabcuX7lClTqmzF80M/x5deeik/xjHi3EqvFwc90QEAAACApcomm2ySe3iH9dZbL11zzTW5Ynv33XfPPbyjqrlr166pQYMGuSJ96623nudjl1q0RIuSeB7i+ZzrPv/880rvGzRoUG7Zcscdd+TPLYXWVYmA+qKLLkqPPPJI2m677fK6ddZZJz3zzDM5/N95553L973wwgvLX0fF+N57750rxaN6vTSu0pjmRQTjUX3er1+/9PTTT+cxRoV53ACI9aXtUSEfrWtiTC+++GIO0eMGQbj00ktzKB6V83369JnrM66++up8/KgiD/HzeeCBB+br5xhjClHNPj/ntyBUogMAAAAAS5UIXytq3bp1DnlLVdPffvttDqV/8Ytf5Ikpi6rBF6Vo6xLtW0oB+g8ZNWpUmjZtWg6LIwgvLVGZXmr/UtW5xnmG0rkuaDucqA6fOXNmDst32WWXvD6C+lJf9FKYHqJtS7SAibYsFcf60UcfzTXW8NVXX6UJEyZUunER/eO33HLL+fo5Lkkq0QEAAACApcqcQXX0zI7+2iEmyYy+4VHlHa1Bjj/++PSHP/wht0mJ98W+0Te9ogiUF1ZUhs+rCKXDv//979S2bdtK20rV3lWda4w9lM51QUQ4/s033+T2KNHW5YwzzigP0aNVTLRoifYqv/zlL8vHGuH2nBOPhoWdKPWHfo5LkhAdAAAAAFimRKC977775qVv376pQ4cO6fXXX8+9vKNNSPTYLom+6VEVvrCiqjp6fEcg/2PV6BtuuGEOy6P1TMXWLfNrueWWS7NmzZqv96y77rr5RsN9992Xq+d3/n+fH2F+LNF//bvvviuvRI9rNn78+Nw2Zl4m94yWNi1btswhfbSICTHG6L1ecfLReRHXcX7Pb0EI0QEAAACAZcbgwYNz8LrNNtukFVZYId166605VI/+3mHXXXfNvbejF3ns95vf/GaeWrD8mBNOOCH3Au/Vq1fuBx5h8vPPP5/bmmywwQaV9l1xxRXz5KQxmWhUXu+44465DUq0WWnatGk68sgj5+kzI9SOtioRhq+++ur5uHNWslclAvJrr702tW/fPgfeJRGoxzmUJiAN0Vs+rlWPHj3y5J+x7dNPP81V9NHzvFOnTnMd/8QTT8wTp8bx4wZGHPPLL78sr6SfV3F+0SN9hx12yOe10korpcVBT3QAAAAAYJkRLUb+/Oc/5+A1qsOjrcu//vWv3NM7RKV1VGJH//JDDz00h9kRti+sOP5jjz2W259EGB09wGMcRQH9b3/723TOOefksLljx45pzz33zMF0TOg5rw488MD8vgjFo8L+9ttvn6f3xf5ff/11eT/0khh3rC9VoYcIvmNS0KgqP/roo3OIHjcKPv7440oBfEVxY6J3797piCOOyAF89FDv1q1batSoUZof8bOKljzx89p8883T4lKnbM4GP8ugKVOm5Ds/cTcn7uQAAP9n1NM3VfcQaoT2nY+u7iEAALCYyYdSmj59eq5ajpB2fsNMWBhRbR83Cnr27JlvHtS077x2LgAAAAAALDEff/xxevjhh3Nl+4wZM3L7nAizo/K/JtLOBQAAAABgGXDbbbfl1ilVLRtttNESG0fdunVzb/qtttoqt9WJSV2jrU5Uo9dEKtEBAAAAAJYB++23X55QtSqLYvLUeRU9zGOS1NpCiA4AAAAAsAxYccUV88L80c4FAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACWsCeeeCLVqVMnTZ48udrG0K5du3TllVfO8/6jR4/OYx45cuRiHVdNU7+6BwAAAAAA1GwTrztziX3WasddMt/vOeqoo9LNN9+cLr744nTWWWeVr7/33nvT/vvvn8rKylJtNXXq1LTSSiulW265JfXq1at8fTy/884700cffZTD8JJ4fvjhh6ff/va3P3rsl156KTVu3HiRjnfw4MHp5JNPrtabA4uaSnQAAAAAoNZr1KhR+v3vf5++/PLLRXbM7777LlW3Jk2apE6dOuXK9Yri9RprrFFpfQTqH3/8cdp1113n6dirrrpqWmGFFRb5mJc2QnQAAAAAoNbr2rVratWqVa5GL/L3v/89bbTRRqlhw4a5Yvuyyy6rtD3WRQX3EUcckZo2bZr69OmTK6ubN2+e7r///rTBBhvk0Pmggw5K06ZNy9Xv8Z6oFP/1r3+dZs2aVX6sqByP8HvFFVfM4zr00EPTxIkTF+jcunTpUiksf/vtt9P06dPTcccdV2l9PI9z22677fLrZ555JnXu3Dktv/zyOXCPMX7zzTeF7VzeeeedtOOOO+YbEhtuuGF65JFHcvuWqOiv6MMPP8xjimux6aabpuHDh5d//tFHH52++uqr/L5YBgwYkGo7IToAAAAAUOvVq1cvXXTRRenqq69O//3vf+faPmLEiNSzZ8/cBuX111/P4e4555yTQ/KKLr300hwMv/LKK3l7iMD8qquuSnfccUcaOnRoDoujTcwDDzyQlwjM//jHP6a77767/DgzZ87Mgfyrr76aQ+joJx5tZxZEBNbvvvtuGjduXH79+OOP57A7Ks4rhuixPgL0CME/+OCDtOeee6YDDzwwvfbaa7n1S4TqJ5xwQpWfETcAevTokYPxF154If3pT39K/fv3r3LfWH/66afn3ujrr79+6t27d/r+++/T9ttvn0P5uAERY40l9qvt9EQHAAAAAJYKEWxvttlm6bzzzks33HBDpW2XX3552m233cqD8Qh/33rrrfSHP/yhUrgdwfRpp51W/vrpp5/Ogfh1112X1l133bwuKtEjOJ8wYUJutxJV2xF0R4h9yCGH5H1+/vOflx9jnXXWySH8VlttlXucx3vmxw477JCWW265HJhHYB2PO++8c9pyyy3T559/ntu4rL322unJJ59MxxxzTH5PVOQfdthhuT95WG+99fIY4n1xLhG0VzRs2LAcvMexo3I+XHjhhWn33XefazwRjO+99975+cCBA3N1/6hRo1KHDh1Ss2bNcgV66RhLA5XoAAAAAMBSI/qiR5uVaHlSUbyOMLqieP3+++9XasMSLVjmFNXZpQA9tGzZMrdCqRiGx7qK7Vqi8n3fffdNa665Zm7pEuF1GDNmzHyfU3x+BPClqvMIy3fZZZdUv379XP0d66PFShw7wvwQFfBRZR9jLC3dunVLs2fPzqH7nKLSPVq+VAy/t9566yrHs8kmm5Q/b926dX5c0FY1tYFKdAAAAABgqbHTTjvlsLhfv34L1D6lcePGc61r0KBBpddRaV3VugioQ/QdjzHEctttt+UJPCPgjtcLOllphOPRkuXNN99M3377bdpiiy3y+gjnowI+PjvC9m222Savj4r3X/7yl7kP+pwi2F8YDSqce5x3KJ370kiIDgAAAAAsVX73u9/lti4xEWhJx44d07PPPltpv3gdbV2in/qiFBN0Tpo0KY8jqrvDyy+/vFDHjBD9ggsuSEOGDMn90EtjjpsG0b+8rKysvO1LiJA92tW0b99+no4f12rs2LG5RU1U1YeXXnppvscZn1+xsn9poJ0LAAAAALBU2XjjjXM/8OgBXhJ9zh999NE82ed7772XW75cc801i2Xiy6j0jjA5JjmNNiv33Xdf/tyFEW1bGjZsmI9Zag1TarkSrVT++c9/lrdyCb/5zW/Sc889lycSjQlAo21N7FM0sWj0Po+WNUceeWSeiDRuMJx99tmVqs3nRbS5iSr4uNbRrz0mZa3thOgAAAAAwFLn/PPPr9RiJCqz//a3v6U77rgj/eQnP0nnnntu3mdBWr78mGjfEv3I77rrrjzpaFSkX3rppQt1zJgIdNttt01ff/117odeEsF6aX3FED36lkfv9Lhh0Llz57T55pvnc27Tpk2Vx4/K9nvvvTcH4NF//dhjj039+/cv/+z5Cft/9atf5QlW4zpccsklqbarUxZ1/tUkZoGNZfTo0fl1zOIaP8ju3bvn19OnT893iOKLPWPGjNwz6Nprry3/c4IQvYSOO+643PcnmuPHnZKYeTaa6s+rKVOm5Fljv/rqq9S0adPFcKYAUDuNevqm6h5CjdC+89HVPQQAABYz+dD/ZXEx4eTaa689X6EpS6+oRt9xxx3TqFGjKk2surSY1+98tfZEX3311fNdmPXWWy/37Ik/ofjpT3+aXnnllRyon3LKKenf//53vmMT/xGLPzU44IADynsXRW+dvffeO88YG3+aMG7cuHTEEUfkxvYXXXRRdZ4aAAAAAECtcs899+RC5chrIzg/6aSTcp/1pTFArzXtXPbdd9+011575R9KNPC/8MIL8w/p+eefz3f9brjhhnT55ZenXXfdNW255ZbppptuymF5bA8PP/xwbo5/66235okCooI9egsNGjRogWe5BQAAAABYkiLXjFy0qmVJFgtHS5i+ffumDh065DY30dbln//8Z1rWVWslekVRVR4V5998803abrvt0ogRI9LMmTNT165dy/eJH1405R8+fHju8xOPMUlAxfYu0fIl2ru8+eabuc9PVaI1TCwV/1wHAAAAAKA6/OUvf0nffvttldtatGixxMYRXT5ioYaF6K+//noOzaP/TNxZiT8ZiGb7MWNszGDbvHnzSvtHYD5+/Pj8PB4rBuil7aVtRaJn+sCBAxfL+QAAAAAAzI+2bdtW9xCoqe1cwgYbbJAD8xdeeCFXkMfEoNGiZXHq169fbhdTWsaOHbtYPw8AAAAAgNqp2ivRo9q8ffv2+Xn0PX/ppZfS//7v/6ZDDjkk9zWfPHlypWr0CRMm5IlEQzy++OKLlY4X20vbijRs2DAvAAAAAABQoyvR5zR79uzcrzwC9QYNGqRHH320fNu7776bxowZk9u/hHiMdjATJ04s32fYsGGpadOmuSUMAAAAAADU2kr0aKsSM8/GZKEx8+uQIUPSE088kR566KHUrFmzdMwxx6RTTz01N8+PYPzEE0/MwXlMKhr22GOPHJYffvjh6ZJLLsl90M8+++w8g6xKcwAAAAAAanWIHhXkMdvruHHjcmi+ySab5AB99913z9uvuOKKVLdu3XTggQfm6vRu3bqla6+9tvz99erVS/fff3/upR7heuPGjXNP9fPPP78azwoAAAAAgKVFnbKysrK0jJsyZUoO8WOS0ah4BwD+z6inb6ruIdQI7TsfXd1DAABgMZMPpTR9+vT00UcfpbXXXjs1atSouoezTBswYEC6995708iRI+f5PbvsskvabLPN0pVXXpmWJrssxvOa1+98tU8sCgAAAADUbNfe+swS+6zjf7bjAr1v7Nix6bzzzktDhw5Nn3/+eWrdunXq0aNHOvfcc9PKK6+capPTTz89t7Ze1OrUqZPuueeefF3mxeDBg9PJJ5+cJk+evMjHsjgsrsC9xk0sCgAAAAAwPz788MPUqVOn9P7776fbb789jRo1Kl1//fXp0UcfzW2gv/jii8X22TNnzlzkx2zSpEmtC/6XZirRAQAAgEo+fXGv6h5CjdFm6weqewjAPOjbt29abrnl0sMPP5yWX375vG7NNddMm2++eVp33XVT//7900orrZRD9RdeeKHSezfddNM8J2NUrIe//OUv6bLLLsttPtq1a5d+/etfp+OPPz5vGz16dG79cccdd+S5G+NY1113XTrzzDPz40EHHZT3i2roCRMm5LkgwzPPPJN222239OWXX6YVVlghV3ZHtfk///nPPBdk3ACI+SFjLFW1c/n+++/Tqaeemv7617/meSKPPfbYNH78+Nx+KPYrmT17dh5LnENcj1/96lf5WCHOJey///75ca211srnszAm/8B5vPfee2mDDTZIb7/9durQoUP5e2L7Nddckz744IP8+o033khnnHFGevrpp/Ocl3vssUfeZ5VVVkk1hUp0AAAAAKDWiirzhx56KAfdpQC9pFWrVumwww5Ld955Z3588cUXy8Pb8Oabb6bXXnstHXroofn1bbfdlsP0Cy+8MIe/F110UTrnnHPSzTffXOm4Z511VjrppJPyPnvuuWfaaaed0hNPPJG3RVAe67/99tv0zjvv5HVPPvlk2mqrrXKAHg4++OA0ceLE9OCDD6YRI0akLbbYIofsRRXzv//97/PYbrrppvTss8/mHv4Vw/OSGGcE0RHuX3LJJen8889Pw4YNy9teeuml/BjHiHC/9HphHPwD57H++uvnUD3GXVG8Ll3vCOF33XXXfLPj5Zdfzq144uZDz549U00iRAcAAAAAaq1o4VJWVpY6duxY5fZYH8H2qquumiukhwwZUinQ3WabbVL79u3z6+ipHlXoBxxwQK44j8dTTjkl/fGPf6x0zOgTXtoneq9HL+5SiP7UU0/lULjiunjceeedy6vSI8y/6667csi83nrrpUsvvTQ1b9483X333VWew9VXX5369euXq8ijqjsquWP/OW2yySb5HOKYRxxxRD5+VN+HOP8Q74ubC6XXC+qZeTiPuHER7XVKojo9wvZYH+I84lrFzYo4r3h+4403pscffzzvW1MI0QEAAACAWi+C9B8T4W0pRI/9I+AtBbrffPNNrlI/5phjck/y0nLBBRdUql4PERpXFAH5W2+9lT777LNcdR4BeilEj57pzz33XH4dXn311TR16tTc87zi50T7mDk/J0TLlqjO3nrrrcvXRUuXLbfcssoQvaII+KNSfHF4dR7Oo1evXrllzPPPP19+0yKq1UvtXeIYEZhXfH9pW1XXorroiQ4AAAAA1FpRRV6nTp3cQqXU77uiWB/90KPyunfv3uk3v/lN+s9//pPbrYwdOzYdcsgheb8IhMOf//znXJ1eUYTWFUXLlIo23njj1KJFixygxxLtYKLaO9qwRNuUCNK333778s+JcLtUpV5RVdXl86NBgwaVXsd1iT7pi8PUeTiPuAbRriVuXGy77bb58bjjjqt0jH333TdfpznFsWsKIToAAAAAUGtFJfTuu++eJ/qM1isV+6LH5JtR/RytTSJQXn311XPVeKyLED3et9pqq+V9W7Zsmdq0aZM+/PDD8ur0eRXH7ty5c55gM/qs77jjjrn/eUy2Ga1gonK9FLxHJXaMq379+uWTff6QZs2a5bFFGB+918OsWbPyjYCYwHR+Q/Z476KwxTyeR1zLmOw0bmDEtY3q9IrH+Pvf/57fH8epqbRzAQAAAABqteitHYF1t27dck/yqDCPSSojJG/btm2uDK8Y6t5xxx25l/ecYfnAgQPTxRdfnK666qrck/v111/PE3FefvnlPzqGaNcS7WEi2I62JHXr1s2hdwT2pX7ooWvXrmm77bZLPXr0SA8//HBudxLtXvr3758n16zKiSeemMcVIf27776bJzWNPu8R3s+PCKujR3qE3/H+eTFr1qw0cuTISktU98/reUTv+K+//jpXoHfp0iXfqCjp27dvnoQ0Ava4SRAtXGKS2KOPPnqRhf2LghAdAAAAAKjVYlLLCG7XWWed1LNnz7TuuuumPn365NB2+PDhudVKyUEHHZQmTZqUpk2blgPgio499tj0l7/8JQfn0aIlwu/BgwfnCUR/TOwbwW+p93mI53Oui+D7gQceyAF7hMXrr79+rs7++OOPc8V5VaIFTQTNUVEfwXWE9HHDoFGjRvN1nWLS1GHDhqU11lgjT+I5L6ZOnZr3rbhEC5Z5PY8VV1wx7x/9z+e8aRGB+rPPPpuv0R577JGveUzaGu1g4iZETVGnbF467i/lpkyZkv8sIpr0N23atLqHAwA1xqinb6ruIdQI7TsfXd1DAIAl6tMX96ruIdQYbbZ+oLqHwBIiH0pp+vTpeVLICIznN5xlyYo+5x07dsw3DH77299W93CW+u98zW00AwAAAABAru6OlilR7R5ta6J9TYS/hx56aHUPbZlQc2riAQAAAACYS7Q2ibYyW221Vdphhx1yr/ZHHnkkV6MvjI022ii3hqlqiV7uNcnTTz9dONZYFieV6AAAAAAANVj0MI/e4Yta9DSfOXNmlduK+rNXl06dOuVJTauDEB0AAAAAYBm01lprpdpi+eWXT+3bt6+Wz9bOBQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAapABAwakzTbbbL7es8suu6STTz45LW12qQHnVb9aPx0AAAAAqPFGXffkEvus9sftvEDvGzt2bDrvvPPS0KFD0+eff55at26devTokc4999y08sorp9rk9NNPTyeeeOIiP26dOnXSPffck6/LvBg8eHAOsCdPnpxqS+AeNx+uvPLKKl8vKJXoAAAAAECt9uGHH6ZOnTql999/P91+++1p1KhR6frrr0+PPvpo2m677dIXX3yx2D575syZi/yYTZo0qXXB/9JMiA4AAAAA1Gp9+/ZNyy23XHr44YfTzjvvnNZcc83UvXv39Mgjj6RPPvkk9e/fP/3P//xP2mabbeZ676abbprOP//88td/+ctfUseOHVOjRo1Shw4d0rXXXlu+bfTo0bma+84778yfE/vceuutadVVV0133313+X5R/RyV8CXPPPNMatiwYZo2bVp+HZXdxx57bH5f06ZN06677ppeffXVwnYu33//ffr1r3+dmjdvnsP13/zmN+nII4+cq6J89uzZ6cwzz0wtWrRIrVq1yscpadeuXX7cf//98zmUXi+MyT9wHu+9917+nHfeeafSe6644oq07rrrlr9+44038s8qbhy0bNkyHX744fkvCWoSIToAAAAAUGtFlflDDz2Ujj/++LT88stX2hZB8mGHHZZD73h88cUX0wcffFC+/c0330yvvfZaOvTQQ/Pr2267Lbd/ufDCC9Pbb7+dLrroonTOOeekm2++udJxzzrrrHTSSSflffbcc8+00047pSeeeCJv+/LLL/P6b7/9tjxAfvLJJ9NWW22VVlhhhfz64IMPThMnTkwPPvhgGjFiRNpiiy3SbrvtVlgx//vf/z6P7aabbkrPPvtsmjJlSrr33nvn2i/G2bhx4/TCCy+kSy65JN8cGDZsWN720ksv5cc4xrhx48pfL4yDf+A81l9//fzXATHuiuJ16XpHCB/B++abb55efvnl3IpnwoQJqWfPnqkmEaIDAAAAALVWtHApKyvL1eNVifURbEe1dFSdDxkypFKgG9Xp7du3z6+jp/pll12WDjjggLT22mvnx1NOOSX98Y9/rHTM6BNe2icqzqP3dilEf+qpp3IoXHFdPEbleqkqPcL8u+66K4fM6623Xrr00ktzlXnFavaKrr766tSvX79cRR7V8ddcc03ef06bbLJJPoc45hFHHJGPHy1tQpx/iPfFzYXS6wX1zDycR9y4iPY6JVGdHmF7rA9xHnGt4mZFnFc8v/HGG9Pjjz+e960phOgAAAAAQK0XQfqPifC2FKLH/hHwlgLdb775JlepH3PMMbm1SGm54IILKlWvhwiNK4qA/K233kqfffZZrjqPAL0UokfP9Oeeey6/DtHuZOrUqbktS8XP+eijj+b6nPDVV1/l6uytt966fF29evXSlltuWWWIXlEE/FEpvji8Og/n0atXr9wC5/nnny+/aRHV6hGYl44RgXnF95e2VXUtqkv96h4AAAAAAMCCiiry6L0dLVSiUntOsX6llVbKlde9e/fO/cT/85//5HYrY8eOTYccckjeLwLh8Oc//3mu3ukRWlcULVMq2njjjXMf8gjQY4l2MFHtHW1Yom1KBOnbb799+edEuF2qUq+oqury+dGgQYNKr+O6RJ/0xWHqPJxHXINo1xI3Lrbddtv8eNxxx1U6xr777puv05wq9pSvbkJ0AAAAAKDWikro3XffPU8AGq1XKvZFHz9+fK5+jtYmESivvvrquWo81kWIHu9bbbXV8r4xqWWbNm3Shx9+WF6dPq/i2J07d07//Oc/c5/1HXfcMfc/nzFjRm4FE5XrpeA9KrFjXPXr15+nyT2bNWuWxxZhfPReD7Nmzco3AipOPjqvIXu8d1HYYh7PI65lTHYaNzDi2kZ1esVj/P3vf8/vj+PUVNq5AAAAAAC1WvTWjsC6W7duuSd5VJjHJJURkrdt2zZXhlcMde+4447cy3vOsHzgwIHp4osvTldddVXuyf3666/niTgvv/zyHx1DtGuJ9jARbEdbkrp16+bQOwL7Uj/00LVr17TddtulHj16pIcffji3O4l2L/3798+Ta1blxBNPzOOKkP7dd9/Nk5pGn/cI7+dHhNXRIz3C73j/vJg1a1YaOXJkpSWq++f1PKJ3/Ndff50r0Lt06ZJvVJT07ds3T0IaAXvcJIgWLjFJ7NFHH73Iwv5FQYgOAAAAANRqMallBLfrrLNO6tmzZ1p33XVTnz59cmg7fPjw3Gql5KCDDkqTJk1K06ZNywFwRccee2z6y1/+koPzaNES4ffgwYPzBKI/JvaN4LfU+zzE8znXRfD9wAMP5IA9wuL1118/V2d//PHHueK8KtGCJoLmqKiP4DpC+rhh0KhRo/m6TjFp6rBhw9Iaa6yRJ/GcF1OnTs37VlyiBcu8nseKK66Y94/+53PetIhA/dlnn83XaI899sjXPCZtjXYwcROipqhTNi8d95dyU6ZMyX8WEU36mzZtWt3DAYAaY9TTN1X3EGqE9p2Pru4hAMAS9emLe1X3EGqMNls/UN1DYAmRD6U0ffr0PClkBMbzG86yZEWf844dO+YbBr/97W+rezhL/Xe+5jaaAQAAAAAgV3dHy5Sodo+2NdG+JsLfQw89tLqHtkyoOTXxAAAAAADMJVqbRFuZrbbaKu2www65V/sjjzySq9EXxkYbbZRbw1S1RC/3muTpp58uHGssi5NKdAAAAACAGix6mEfv8EUteprPnDmzym1F/dmrS6dOnfKkptVBiA4AAAAAsAxaa621Um2x/PLLp/bt21fLZ2vnAgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAADAMqFdu3bpyiuvXGzHHzx4cGrevPliOz7Vo341fS4AAAAAUEvc8kKfJfZZh2/zpwV639ixY9N5552Xhg4dmj7//PPUunXr1KNHj3TuueemlVdeOS0JhxxySNprr73KXw8YMCDde++9aeTIkYvk+HXq1PnB7XH+8ZnV5aijjkqTJ0/O57w0EaIDAAAAALXahx9+mLbbbru0/vrrp9tvvz2tvfba6c0330xnnHFGevDBB9Pzzz+fWrRosdjHsfzyy+dlcRk3blz58zvvvDPfIHj33XfL1zVp0mSxffayTDsXAAAAAKBW69u3b1puueXSww8/nHbeeee05pprpu7du6dHHnkkffLJJ6l///7l+3799depd+/eqXHjxqlt27Zp0KBBlY4VldTHHntsWnXVVVPTpk3Trrvuml599dXy7fG8S5cuacUVV8zbt9xyy/Tyyy/P1c4lng8cODDvHxXkscS6n//852mfffap9JkzZ85Mq622Wrrhhht+8DxbtWpVvjRr1iwfM57HWOIGQlThVxQV4XGecc6jR4/O+99xxx1p++23T40aNUo/+clP0pNPPlnpPW+88Ua+dhHIt2zZMh1++OG5sn9ZJkQHAAAAAGqtL774Ij300EPp+OOPn6sKPALmww47LFdtl5WV5XV/+MMf0qabbppeeeWVdNZZZ6WTTjopDRs2rPw9Bx98cJo4cWKuYB8xYkTaYost0m677ZY/J8TxVl999fTSSy/l7XGMBg0aVNna5bTTTksbbbRRriCPJdZFQB9hd8Wq8vvvvz9NmzYtb18QEZT36tUr3XTTTZXWx+uDDjooh+wlUZ0f44rzj+r9fffdN02aNKn8BkLcNNh8883zjYEY54QJE1LPnj3Tskw7FwAAAACg1nr//fdzQN6xY8cqt8f6L7/8Mn322Wf59Q477JCD7xDV288++2y64oor0u67756eeeaZ9OKLL+YQvWHDhnmfSy+9NFd033333alPnz5pzJgxOYju0KFD3r7eeutV+bkR6Ec1d/369XOYXxJV4BtssEG65ZZb0plnnlkedkd4vzDtWCKcj2NHOB/94OMcHnjggVyNX9EJJ5yQDjzwwPz8uuuuy0F5VMDHWK655pocoF900UXl+994441pjTXWSO+9916+XssilegAAAAAQK1XqjT/MVF9Pefrt99+Oz+P1itTp07NE5FGoF1aPvroo/TBBx/kfU499dQcWHft2jX97ne/K18/P+L9parxqPSOqvdo87Iwtt5661z1fvPNN+fXt956a1prrbXSTjvtNNf5lkTA36lTp0rn//jjj1c69w7/72bBgpzn0kKIDgAAAADUWu3bt8+9vktB8Jxi/UorrZR7nP+YCNCjinvkyJGVlpi8M6rPw4ABA/KkpXvvvXd67LHH0oYbbpjuueee+RrzEUcckSdDHT58eA67YyLUzp07p4UV4Xz0XQ8R0h999NH52syrOP9o7zLn+b///vtzhfHLEiE6AAAAAFBrRdV4tGK59tpr07fffltp2/jx49Ntt92We42XwuTnn3++0j7xutQKJvqfx3uiQjvC+YrLKqusUv6eaGtyyimn5IlMDzjggLl6kZfEZKezZs2qcsw9evTI74vQO8LuReFnP/tZ+vjjj9NVV12V3nrrrXTkkUfOtU/F8//+++9zX/eK5x83CNq1azfX+Tdu3Dgtq4ToAAAAAECtFr28Z8yYkbp165aeeuqpNHbs2NzrO8L1tm3bpgsvvLB83+iBfskll+Qe34MGDUp33XVXnlw0RIuWaHcSAXcE5KNHj07PPfdc6t+/f55oM0L66Cn+xBNP5LA6jhUTjBb1Y48wOlrBRDX3559/nsdYsWo8Wq9EpXxVYfeCiIr7CPWjan6PPfbIE6DOKc45Kuffeeed1Ldv39wvvtRKJl7HBKq9e/fO5xUtXB566KEc8ld1M2BZIUQHAAAAAGq1mNwzQu511lkn9ezZM6277rp5EtAuXbrkliktWrQo3/e0007L+8YEmhdccEG6/PLLc/geolo9JuOM1iURHEfFea9evXJg3rJly1SvXr00adKk3I4ltsVnde/ePQ0cOLDKccUEnnvuuWceR7STuf3228u3RWAfrWPis9u0abPIrsUxxxyTvvvuu8Ie69HHPZZNN900T6R63333lVfZxzjixkAE5hHCb7zxxunkk09OzZs3T3XrLrtRcp2yee24vxSbMmVKatasWfrqq69S06ZNq3s4AFBjjHq66j9JXNa077xo/rQSAGqLT1/cq7qHUGO02fqB6h4CS4h8KKXp06fnqunoz92oUaPqHs5SL/qPR5V8tHSJ6vFF5ZZbbsmtZj799NPcTqYkqurjZ/vKK6+kzTbbbJF93rLwna+/REcFAAAAALAMmz17dm7tctlll+UK7/3222+RHHfatGlp3Lhxucr8l7/8ZaUAnYWz7NbgAwAAAAAsYWPGjMmtYYYMGZJuvPHGPIlpxW1NmjQpXGJ7kejz3qFDh9SqVavUr1+/xTLuJgs4ttpOJToAAAAAwBISk40WddiOnuQxCWmRH+qdPmDAgLwsyOfOizYLMbbaTogOAAAAAFADRFV6+/btU01UvwaPbXHTzgUAAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAgGVCu3bt0pVXXrnYjj948ODUvHnzxXZ8qkf9avpcAAAAAKCW+PTFvZbYZ7XZ+oEFet/YsWPTeeedl4YOHZo+//zz1Lp169SjR4907rnnppVXXjktCYccckjaa6///1oNGDAg3XvvvWnkyJGL5Ph16tT5we1x/vGZ1eWoo45KkydPzudc1evaSogOAAAAANRqH374Ydpuu+3S+uuvn26//fa09tprpzfffDOdccYZ6cEHH0zPP/98atGixWIfx/LLL5+XxWXcuHHlz++88858g+Ddd98tX9ekSZPF9tnLMu1cAAAAAIBarW/fvmm55ZZLDz/8cNp5553Tmmuumbp3754eeeSR9Mknn6T+/fuX7/v111+n3r17p8aNG6e2bdumQYMGVTpWVE4fe+yxadVVV01NmzZNu+66a3r11VfLt8fzLl26pBVXXDFv33LLLdPLL788VzuXeD5w4MC8f1SQxxLrfv7zn6d99tmn0mfOnDkzrbbaaumGG274wfNs1apV+dKsWbN8zHgeY4kbCFGFX1FUgMd5xjmPHj0673/HHXek7bffPjVq1Cj95Cc/SU8++WSl97zxxhv52kUg37Jly3T44Yfnyv5lmRAdAAAAAKi1vvjii/TQQw+l448/fq4q8AiYDzvssFy1XVZWltf94Q9/SJtuuml65ZVX0llnnZVOOumkNGzYsPL3HHzwwWnixIm5gn3EiBFpiy22SLvttlv+nBDHW3311dNLL72Ut8cxGjRoUGVrl9NOOy1ttNFGuYI8llgXAX2E3RWryu+///40bdq0vH1BRFDeq1evdNNNN1VaH68POuigHLKXRHV+jCvOP6r399133zRp0qTyGwhx02DzzTfPNwZinBMmTEg9e/ZMyzLtXAAAAACAWuv999/PAXnHjh2r3B7rv/zyy/TZZ5/l1zvssEMOvkNUbz/77LPpiiuuSLvvvnt65pln0osvvphD9IYNG+Z9Lr300lzRfffdd6c+ffqkMWPG5CC6Q4cOeft6661X5edGoB/V3PXr189hfklUgW+wwQbplltuSWeeeWZ52B3h/cK0Y4lwPo4d4Xz0g49zeOCBB3I1fkUnnHBCOvDAA/Pz6667LgflUQEfY7nmmmtygH7RRReV73/jjTemNdZYI7333nv5ei2LVKIDAAAAALVeqdL8x0T19Zyv33777fw8Wq9MnTo1T0QagXZp+eijj9IHH3yQ9zn11FNzYN21a9f0u9/9rnz9/Ij3l6rGo9I7qt6jzcvC2HrrrXPV+80335xf33rrrWmttdZKO+2001znWxIBf6dOnSqd/+OPP17p3Dv8v5sFC3KeSwshOgAAAABQa7Vv3z73+i4FwXOK9SuttFLucf5jIkCPKu6RI0dWWmLyzqg+DwMGDMiTlu69997pscceSxtuuGG655575mvMRxxxRJ4Mdfjw4TnsjolQO3funBZWhPPRdz1ESH/00UfnazOv4vyjvcuc5//+++/PFcYvS4ToAAAAAECtFVXj0Yrl2muvTd9++22lbePHj0+33XZb7jVeCpOff/75SvvE61IrmOh/Hu+JCu0I5ysuq6yySvl7oq3JKaeckicyPeCAA+bqRV4Sk53OmjWryjH36NEjvy9C7wi7F4Wf/exn6eOPP05XXXVVeuutt9KRRx451z4Vz//777/Pfd0rnn/cIGjXrt1c59+4ceO0rBKiAwAAAAC1WvTynjFjRurWrVt66qmn0tixY3Ov7wjX27Ztmy688MLyfaMH+iWXXJJ7fA8aNCjdddddeXLREC1aot1JBNwRkI8ePTo999xzqX///nmizQjpo6f4E088kcPqOFZMMFrUjz3C6GgFE9Xcn3/+eR5jxarxaL0SlfJVhd0LIiruI9SPqvk99tgjT4A6pzjnqJx/5513Ut++fXO/+FIrmXgdE6j27t07n1e0cHnooYdyyF/VzYBlhRAdAAAAAKjVYnLPCLnXWWed1LNnz7TuuuvmSUC7dOmSW6a0aNGifN/TTjst7xsTaF5wwQXp8ssvz+F7iGr1mIwzWpdEcBwV57169cqBecuWLVO9evXSpEmTcjuW2Baf1b179zRw4MAqxxUTeO655555HNFO5vbbby/fFoF9tI6Jz27Tps0iuxbHHHNM+u677wp7rEcf91g23XTTPJHqfffdV15lH+OIGwMRmEcIv/HGG6eTTz45NW/ePNWtu+xGyXXK5rXj/lJsypQpqVmzZumrr75KTZs2re7hAECNMerpqv8kcVnTvvOi+dNKAKgtPn1xr+oeQo3RZusHqnsILCHyoZSmT5+eq6ajP3ejRo2qezhLveg/HlXy0dIlqscXlVtuuSW3mvn0009zO5mSqKqPn+0rr7ySNttss0X2ecvCd77+Eh0VAAAAAMAybPbs2bm1y2WXXZYrvPfbb79Fctxp06alcePG5SrzX/7yl5UCdBbOsluDDwAAAACwhI0ZMya3hhkyZEi68cYb8ySmFbc1adKkcIntRaLPe4cOHVKrVq1Sv379Fsu4myzg2Go7legAAAAAAEtITDZa1GE7epLHJKRFfqh3+oABA/KyIJ87L9osxNhqOyE6AAAAAEANEFXp7du3TzVR/Ro8tqW6ncvFF1+cttpqq7Tiiium1VZbLfXo0SO9++67lfbZZZdd8qy4FZdf/epXlfaJPxXYe++90worrJCPc8YZZ6Tvv/9+CZ8NAAAAAABLm2qtRH/yySdT3759c5Aeoff//M//pD322CO99dZbqXHjxuX7/eIXv0jnn39++esIy0tmzZqVA/To9fPcc8/l5vlHHHFEatCgQbrooouW+DkBAAAAALD0qNYQfejQoZVeDx48OFeSjxgxIu20006VQvMIyavy8MMP59D9kUceyQ35N9tss/Tb3/42/eY3v8k9gMxCCwAAAABArWznMqevvvoqP7Zo0aLS+ttuuy2tssoq6Sc/+UmeWXbatGnl24YPH5423njjHKCXdOvWLU2ZMiW9+eabVX7OjBkz8vaKCwAAAAAA1NiJRWfPnp1OPvnktMMOO+SwvOTQQw9Na621Vp7d9bXXXssV5tE3/R//+EfePn78+EoBeii9jm1FvdgHDhy4WM8HAAAAAIDar8ZUokdv9DfeeCPdcccdldb36dMnV5ZHtflhhx2W/vrXv6Z77rknffDBBwv8WVHNHlXvpWXs2LGL4AwAAAAAgJqsXbt26corr1xsx4921c2bN19sx2cZDtFPOOGEdP/996fHH388rb766j+47zbbbJMfR40alR+jV/qECRMq7VN6XdRHvWHDhqlp06aVFgAAAACg9opC2Z///Oe5o0XMkxjdLU466aQ0adKkJTaGQw45JL333nvlr2POxpjDcVGpU6fODy7xedXpqKOOSj169Jjn17VFtbZzKSsrSyeeeGKuLH/iiSfS2muv/aPvGTlyZH5s3bp1ftxuu+3ShRdemCZOnJgnJQ3Dhg3LwfiGG264mM8AAAAAAJZ+o56+aYl9VvvOR8/3ez788MOcE66//vrp9ttvzzljzJd4xhlnpAcffDA9//zzc83DuDgsv/zyeVlcxo0bV/78zjvvTOeee25ufV3SpEmTxfbZy7K61d3C5dZbb01DhgxJK664Yu5hHsu3336bt0fLlt/+9rdpxIgRafTo0em+++5LRxxxRNppp53SJptskvfZY489clh++OGHp1dffTU99NBD6eyzz87HjopzAAAAAGDpFllgVJ8//PDDaeedd05rrrlm6t69e3rkkUfSJ598kvr371++79dff5169+6dGjdunNq2bZsGDRpU6ViTJ09Oxx57bFp11VVzoe6uu+6ac8eSeN6lS5ecZ8b2LbfcMr388stztXOJ5zEvY+xfqhSPdVEtv88++1T6zJkzZ+YC4RtuuOEHzzM6b5SWZs2a5WPG8xhL3EAYOnRopf3vvffefJ5xzpGvxv7RTnv77bdPjRo1ynNTPvnkk5XeEy2349pFIB9zTx5++OHp888/T8uyag3Rr7vuutyTfJdddsmV5aUl7qKE+OLHFz2C8g4dOqTTTjstHXjggelf//pX+THq1auXW8HEY9xt+tnPfpaD9vPPP78azwwAAAAAWBK++OKLXFh7/PHHz1UFHgFzzLMYeWN0xQh/+MMf0qabbppeeeWVdNZZZ+WWL9HZouTggw/OXS+igj2Ke7fYYou022675c8JcbxoSf3SSy/l7XGMBg0aVNnaJfLMjTbaKFeQxxLrIqCPsLtiVXnkm9OmTcvbF0QE5b169Uo33VT5Lwbi9UEHHZRD9pKozo9xxflHnrrvvvuWt7yJGwhx02DzzTfPNwZinBMmTEg9e/ZMy7Jqb+fyQ9ZYY4257oRUJfobPfDAA4twZAAAAABAbfD+++/nnLFjx45Vbo/1X375Zfrss8/y6x122CEH3yGqt5999tl0xRVXpN133z0988wz6cUXX8wheqnLxaWXXporuu++++7Up0+fNGbMmBxER9FvWG+99ar83Aj0o5q7fv36leZujCrwDTbYIN1yyy3pzDPPLA+7I7xfmHYsEc7HsSOcj0LlOIfITKNIec75KaNQuVTkHEF5VMDHWK655pocoF900UXl+9944405p41e73G9lkU1YmJRAAAAAIDFWbBbEtXXc75+++238/NovTJ16tS08sor50C7tHz00Ue59XQ49dRTc2DdtWvX9Lvf/a58/fyI95eqxqPSO6reo83Lwth6661z1fvNN9+cX0cb7Sg+jtbYc55vSQT8nTp1qnT+jz/+eKVz7/D/bhYsyHkuLYToAAAAAECt1b59+9zruxQEzynWr7TSSrnH+Y+JAD2quEeOHFlpick7o/o8DBgwIE9auvfee6fHHnssz9d4zz33zNeYox11TIY6fPjwHHbHRKidO3dOCyvC+ei7HiKkP/roo/O1mVdx/tHeZc7zf//99+cK45clQnQAAAAAoNaKqvFoxXLttdemb7/9ttK28ePHp9tuuy33Gi+Fyc8//3ylfeJ1qRVM9D+P90SFdoTzFZdVVlml/D3R1uSUU07JE5kecMABc/UiL4k5H2fNmlXlmHv06JHfF6F3hN2LQswX+fHHH6errroqvfXWW+nII4+ca5+K5//999/nvu4Vzz9uELRr126u82/cuHFaVgnRAQAAAIBaLXp5z5gxI3Xr1i099dRTaezYsbnXd4Trbdu2TRdeeGH5vtED/ZJLLsk9vgcNGpTuuuuuPLloiBYt0e4kAu4IyEePHp2ee+651L9//zzRZoT00VP8iSeeyGF1HCsmGC3qxx5hdLSCiWruzz//PI+xYtV4tF6JSvmqwu4FERX3EepH1fwee+yRJ0CdU5xzVM6/8847qW/fvrlffKmVTLyOCVR79+6dzytauDz00EM55K/qZsCyQogOAAAAANRqMblnhNzrrLNO6tmzZ1p33XXzJKBdunTJLVNatGhRvu9pp52W940JNC+44IJ0+eWX5/A9RLV6TMYZrUsiOI6K8169euXAvGXLlqlevXpp0qRJuR1LbIvP6t69exo4cGCV44oJPPfcc888jmgnc/vtt5dvi8A+WsfEZ7dp02aRXYtjjjkmfffdd4U91qOPeyybbrppnkj1vvvuK6+yj3HEjYEIzCOE33jjjdPJJ5+cmjdvnurWXXaj5Dpl89pxfyk2ZcqU1KxZs/TVV1+lpk2bVvdwAKDGGPV01X+SuKxp33nR/GklANQWn764V3UPocZos/UD1T0ElhD5UErTp0/PVdPRn7tRo0bVPZylXvQfjyr5aOkS1eOLyi233JJbzXz66ae5nUxJVNXHz/aVV15Jm2222SL7vGXhO19/iY4KAAAAAGAZNnv27Nza5bLLLssV3vvtt98iOe60adPSuHHjcpX5L3/5y0oBOgtn2a3BBwAAAABYwsaMGZNbwwwZMiTdeOONeRLTituaNGlSuMT2ItHnvUOHDqlVq1apX79+i2XcTRZwbLWdSnQAAAAAgCUkJhst6rAdPcljEtIiP9Q7fcCAAXlZkM+dF20WYmy1nRAdAAAAAKAGiKr09u3bp5qofg0e2+KmnQsAAAAAABQQogMAAAAA5Ram5Qcsjd91IToAAAAAkBo0aJAfp02bVt1DgSWi9F0vffeL6IkOAAAAAKR69eql5s2bp4kTJ+bXK6ywQqpTp051DwsWSwV6BOjxXY/vfHz3f4gQHQAAAADIWrVqlR9LQToszSJAL33nf4gQHQAAAADIovK8devWabXVVkszZ86s7uHAYhMtXH6sAr1EiA4AAAAAVBLh4rwGjLC0M7EoAAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAAAUEKIDAAAAAEABIToAAAAAABQQogMAAAAAQAEhOgAAAAAAFBCiAwAAAABAASE6AAAAAADUxBD94osvTltttVVaccUV02qrrZZ69OiR3n333Ur7TJ8+PfXt2zetvPLKqUmTJunAAw9MEyZMqLTPmDFj0t57751WWGGFfJwzzjgjff/990v4bAAAAAAAWNpUa4j+5JNP5oD8+eefT8OGDUszZ85Me+yxR/rmm2/K9znllFPSv/71r3TXXXfl/T/99NN0wAEHlG+fNWtWDtC/++679Nxzz6Wbb745DR48OJ177rnVdFYAAAAAACwt6pSVlZWlGuKzzz7LleQRlu+0007pq6++SquuumoaMmRIOuigg/I+77zzTurYsWMaPnx42nbbbdODDz6Y9tlnnxyut2zZMu9z/fXXp9/85jf5eMstt9yPfu6UKVNSs2bN8uc1bdp0sZ8nANQWo56+qbqHUCO073x0dQ8BAJaoT1/cq7qHUGO02fqB6h4CS4h8CKgVPdHjP1KhRYsW+XHEiBG5Or1r167l+3To0CGtueaaOUQP8bjxxhuXB+ihW7du+T98b775ZpWfM2PGjLy94gIAAAAAADU2RJ89e3Y6+eST0w477JB+8pOf5HXjx4/PleTNmzevtG8E5rGttE/FAL20vbStqBd73FksLWusscZiOisAAAAAAGqzGhOiR2/0N954I91xxx2L/bP69euXq95Ly9ixYxf7ZwIAAAAAUPvUTzXACSeckO6///701FNPpdVXX718fatWrfKEoZMnT65UjT5hwoS8rbTPiy++WOl4sb20rSoNGzbMCwAAAAAA1NhK9JjTNAL0e+65Jz322GNp7bXXrrR9yy23TA0aNEiPPvpo+bp33303jRkzJm233Xb5dTy+/vrraeLEieX7DBs2LE8AseGGGy7BswEAAAAAYGlTv7pbuAwZMiT985//TCuuuGJ5D/PoU7788svnx2OOOSadeuqpebLRCMZPPPHEHJxvu+22ed899tgjh+WHH354uuSSS/Ixzj777Hxs1eYAAAAAANTaEP26667Lj7vsskul9TfddFM66qij8vMrrrgi1a1bNx144IFpxowZqVu3bunaa68t37devXq5Fcxxxx2Xw/XGjRunI488Mp1//vlL+GwAAAAAAFja1K/udi4/plGjRmnQoEF5KbLWWmulBx54YBGPDgAAAACAZV219kQHAAAAAICaTIgOAAAAAAA1sZ0LAAAA1CS3vNCnuodQI+xWp7pHAAA1h0p0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoUL9oAwAAAMuOUdc9Wd1DqBm2qO4BAAA1jUp0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACARRmir7POOmnSpElzrZ88eXLeBgAAAAAAy2yIPnr06DRr1qy51s+YMSN98skni2JcAAAAAABQ7erPz8733Xdf+fOHHnooNWvWrPx1hOqPPvpoateu3aIdIQAAAAAAVJP5CtF79OiRH+vUqZOOPPLIStsaNGiQA/TLLrts0Y4QAAAAAABqQ4g+e/bs/Lj22munl156Ka2yyiqLa1wAAAAAAFC7QvSSjz76aNGPBAAAAAAAloYQPUT/81gmTpxYXqFecuONNy6KsQEAAAAAQO0L0QcOHJjOP//81KlTp9S6devcIx0AAAAAAJY2CxSiX3/99Wnw4MHp8MMPX/QjAgAAAACAGqLugrzpu+++S9tvv/2iHw0AAAAAANT2EP3YY49NQ4YMWfSjAQAAAACA2t7OZfr06elPf/pTeuSRR9Imm2ySGjRoUGn75ZdfvqjGBwAAAAAAtStEf+2119Jmm22Wn7/xxhuVtplkFAAAAACAZTpEf/zxxxf9SAAAAAAAYGnoiQ4AAAAAAMuCBapE79Klyw+2bXnssccWZkwAAAAAAFB7Q/RSP/SSmTNnppEjR+b+6EceeeSiGhsAAAAAANS+EP2KK66ocv2AAQPS1KlTF3ZMAAAAAACw9PVE/9nPfpZuvPHGRXlIAAAAAABYOkL04cOHp0aNGi3KQwIAAAAAQO1q53LAAQdUel1WVpbGjRuXXn755XTOOecsqrEBAAAAAEDtC9GbNWtW6XXdunXTBhtskM4///y0xx57LKqxAQAAAABA7QvRb7rppkU/EgAAAAAAWBpC9JIRI0akt99+Oz/faKON0uabb76oxsVictqDf63uIdQIl3U/orqHAAAAAAAsrSH6xIkTU69evdITTzyRmjdvntdNnjw5denSJd1xxx1p1VVXXdTjBAAAAACAJa7ugrzpxBNPTF9//XV688030xdffJGXN954I02ZMiX9+te/XvSjBAAAAACA2lKJPnTo0PTII4+kjh07lq/bcMMN06BBg0wsCgAAAADAsl2JPnv27NSgQYO51se62AYAAAAAAMtsiL7rrrumk046KX366afl6z755JN0yimnpN12221Rjg8AAAAAAGpXiH7NNdfk/uft2rVL6667bl7WXnvtvO7qq69e9KMEAAAAAIDa0hN9jTXWSP/5z39yX/R33nknr4v+6F27dl3U4wMAAAAAgNpRif7YY4/lCUSj4rxOnTpp9913TyeeeGJettpqq7TRRhulp59+evGNFgAAAAAAamqIfuWVV6Zf/OIXqWnTpnNta9asWfrlL3+ZLr/88kU5PgAAAAAAqB0h+quvvpr23HPPwu177LFHGjFixKIYFwAAAAAA1K4QfcKECalBgwaF2+vXr58+++yzRTEuAAAAAACoXSF627Zt0xtvvFG4/bXXXkutW7deFOMCAAAAAIDaFaLvtdde6ZxzzknTp0+fa9u3336bzjvvvLTPPvssyvEBAAAAAEC1qT8/O5999tnpH//4R1p//fXTCSeckDbYYIO8/p133kmDBg1Ks2bNSv37919cYwUAAAAAgJobords2TI999xz6bjjjkv9+vVLZWVleX2dOnVSt27dcpAe+wAAAAAAwDIXooe11lorPfDAA+nLL79Mo0aNykH6euutl1ZaaaXFM0IAAAAAAKgtIXpJhOZbbbXVoh0NAAAAAADU1olFAQAAAABgWSJEBwAAAACARd3OBYClzy0v9KnuIdQIh2/zp+oeAgAAAFBDqEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAmhiiP/XUU2nfffdNbdq0SXXq1En33ntvpe1HHXVUXl9x2XPPPSvt88UXX6TDDjssNW3aNDVv3jwdc8wxaerUqUv4TAAAAAAAWBpVa4j+zTffpE033TQNGjSocJ8IzceNG1e+3H777ZW2R4D+5ptvpmHDhqX7778/B/N9+vRZAqMHAAAAAGBpV786P7x79+55+SENGzZMrVq1qnLb22+/nYYOHZpeeuml1KlTp7zu6quvTnvttVe69NJLc4U7AAAAAAAstT3Rn3jiibTaaqulDTbYIB133HFp0qRJ5duGDx+eW7iUAvTQtWvXVLdu3fTCCy8UHnPGjBlpypQplRYAAAAAAKhVIXq0cvnrX/+aHn300fT73/8+Pfnkk7lyfdasWXn7+PHjc8BeUf369VOLFi3ytiIXX3xxatasWfmyxhprLPZzAQAAAACg9qnWdi4/plevXuXPN95447TJJpukddddN1en77bbbgt83H79+qVTTz21/HVUogvSAQAAAACoVZXoc1pnnXXSKquskkaNGpVfR6/0iRMnVtrn+++/T1988UVhH/VSn/WmTZtWWgAAAAAAoFaH6P/9739zT/TWrVvn19ttt12aPHlyGjFiRPk+jz32WJo9e3baZpttqnGkAAAAAAAsDaq1ncvUqVPLq8rDRx99lEaOHJl7mscycODAdOCBB+aq8g8++CCdeeaZqX379qlbt255/44dO+a+6b/4xS/S9ddfn2bOnJlOOOGE3AamTZs21XhmAAAAAAAsDaq1Ev3ll19Om2++eV5C9CmP5+eee26qV69eeu2119J+++2X1l9//XTMMcekLbfcMj399NO5HUvJbbfdljp06JB7pO+1115pxx13TH/605+q8awAAAAAAFhaVGsl+i677JLKysoKtz/00EM/eoyoWB8yZMgiHhkAAAAAANSynugAAAAAALAkCdEBAAAAAKAmtnMBqte1tz5T3UOoMY7/2Y7VPQQAAAAAaiCV6AAAAAAAUEAlOgAALIMmXndmdQ+hRljtuEuqewgAANRwKtEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKCAEB0AAAAAAAoI0QEAAAAAoIAQHQAAAAAACgjRAQAAAACggBAdAAAAAAAKCNEBAAAAAKBA/aINsDSbeN2Z1T2EmmHF/ap7BAAAAABQo6lEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAAChgYlEAAGCZde2tz1T3EGqMPap7AAAANZRKdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAAChQv2gDwLJk1HVPVvcQaoYtqnsAAAAAADVLtVaiP/XUU2nfffdNbdq0SXXq1En33ntvpe1lZWXp3HPPTa1bt07LL7986tq1a3r//fcr7fPFF1+kww47LDVt2jQ1b948HXPMMWnq1KlL+EwAAAAAAFgaVWuI/s0336RNN900DRo0qMrtl1xySbrqqqvS9ddfn1544YXUuHHj1K1btzR9+vTyfSJAf/PNN9OwYcPS/fffn4P5Pn36LMGzAAAAAABgaVWt7Vy6d++el6pEFfqVV16Zzj777PTTn/40r/vrX/+aWrZsmSvWe/Xqld5+++00dOjQ9NJLL6VOnTrlfa6++uq01157pUsvvTRXuAMAAAAAwFI3sehHH32Uxo8fn1u4lDRr1ixts802afjw4fl1PEYLl1KAHmL/unXr5sp1AAAAAABYKicWjQA9ROV5RfG6tC0eV1tttUrb69evn1q0aFG+T1VmzJiRl5IpU6Ys4tEDAAAAALA0qLGV6IvTxRdfnKvaS8saa6xR3UMCAAAAAKAGqrEheqtWrfLjhAkTKq2P16Vt8Thx4sRK27///vv0xRdflO9TlX79+qWvvvqqfBk7duxiOQcAAAAAAGq3Ghuir7322jkIf/TRRyu1XYle59ttt11+HY+TJ09OI0aMKN/nscceS7Nnz86904s0bNgwNW3atNICAAAAAAA1qif61KlT06hRoypNJjpy5Mjc03zNNddMJ598crrgggvSeuutl0P1c845J7Vp0yb16NEj79+xY8e05557pl/84hfp+uuvTzNnzkwnnHBC6tWrV94PAAAAAABqbYj+8ssvpy5dupS/PvXUU/PjkUcemQYPHpzOPPPM9M0336Q+ffrkivMdd9wxDR06NDVq1Kj8PbfddlsOznfbbbdUt27ddOCBB6arrrqqWs4HAAAAAIClS7WG6LvssksqKysr3F6nTp10/vnn56VIVK0PGTJkMY0QAIClzWkP/rW6h1Aj/Ka6BwAAALVEje2JDgAAAAAA1U2IDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAAAWE6AAAAAAAUECIDgAAAAAABYToAAAAAABQQIgOAAAAAAAFhOgAAAAAAFBAiA4AAAAAALUxRB8wYECqU6dOpaVDhw7l26dPn5769u2bVl555dSkSZN04IEHpgkTJlTrmAEAAAAAWHrU6BA9bLTRRmncuHHlyzPPPFO+7ZRTTkn/+te/0l133ZWefPLJ9Omnn6YDDjigWscLAAAAAMDSo36q4erXr59atWo11/qvvvoq3XDDDWnIkCFp1113zetuuumm1LFjx/T888+nbbfdthpGCwAAAADA0qTGV6K///77qU2bNmmdddZJhx12WBozZkxeP2LEiDRz5szUtWvX8n2j1cuaa66Zhg8fXo0jBgAAAABgaVGjK9G32WabNHjw4LTBBhvkVi4DBw5MnTt3Tm+88UYaP358Wm655VLz5s0rvadly5Z52w+ZMWNGXkqmTJmy2M4BAAAAAIDaq0aH6N27dy9/vskmm+RQfa211kp/+9vf0vLLL7/Ax7344otzIA8AAAAAALW6nUtFUXW+/vrrp1GjRuU+6d99912aPHlypX0mTJhQZQ/1ivr165d7qpeWsWPHLuaRAwAAAABQG9WqEH3q1Knpgw8+SK1bt05bbrllatCgQXr00UfLt7/77ru5Z/p22233g8dp2LBhatq0aaUFAAAAAABqVTuX008/Pe277765hcunn36azjvvvFSvXr3Uu3fv1KxZs3TMMcekU089NbVo0SIH4SeeeGIO0LfddtvqHjoAAAAAAEuBGh2i//e//82B+aRJk9Kqq66adtxxx/T888/n5+GKK65IdevWTQceeGCeKLRbt27p2muvre5hAwAAAACwlKjRIfodd9zxg9sbNWqUBg0alBcAAAAAAFime6IDAAAAAMCSJEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACApT1EHzRoUGrXrl1q1KhR2mabbdKLL75Y3UMCAAAAAKCWq5+WAnfeeWc69dRT0/XXX58D9CuvvDJ169Ytvfvuu2m11Var7uEBUMt8+uJe1T2EGuTg6h4AAAAAVKulohL98ssvT7/4xS/S0UcfnTbccMMcpq+wwgrpxhtvrO6hAQAAAABQi9X6SvTvvvsujRgxIvXr1698Xd26dVPXrl3T8OHDq3VsAAAAQO026umbqnsINUL7zkdX9xAAqk2tD9E///zzNGvWrNSyZctK6+P1O++8U+V7ZsyYkZeSr776Kj9OmTIlLe1mTPu2uodQI3z97f//81+WfVv/m+oeQo3x9bezqnsINcK333xX3UOoEb5OM6t7CDXGtO/8/8ay8jvCssTvQ//H70P/x+9D/z+/D/0fvw/9H78P/f/8PrTs/D5UOseysrLqHgpQw9T6EH1BXHzxxWngwIFzrV9jjTWqZTwseYOqewA1xlXVPQCgxnukugdQQ/St7gHAIuf3oRK/DwE/xu9Dy9rvQ19//XVq1qxZdQ8DqEFqfYi+yiqrpHr16qUJEyZUWh+vW7VqVeV7ovVLTERaMnv27PTFF1+klVdeOdWpU2exjxmgqoqHuJE3duzY1LRp0+oeDgDAEuf3IaC6RQV6BOht2rSp7qEANUytD9GXW265tOWWW6ZHH3009ejRozwUj9cnnHBCle9p2LBhXipq3rz5EhkvwA+JfzD6RyMAsCzz+xBQnVSgA0tliB6iqvzII49MnTp1SltvvXW68sor0zfffJOOPtqkFwAAAAAALOMh+iGHHJI+++yzdO6556bx48enzTbbLA0dOnSuyUYBAAAAAGCZC9FDtG4pat8CUNNFi6nzzjtvrlZTAADLCr8PAQA1VZ2ymDUBAAAAAACYS925VwEAAAAAAEGIDgAAAAAABYToALVYu3bt0pVXXlndwwAAWCxGjx6d6tSpk0aOHFndQwEAlmFCdIB5dNRRR+V/xM25jBo1qrqHBgBQ435n+tWvfjXXtr59++ZtsQ8AQG0hRAeYD3vuuWcaN25cpWXttdeu7mEBANQoa6yxRrrjjjvSt99+W75u+vTpaciQIWnNNdes1rEBAMwvITrAfGjYsGFq1apVpaVevXrpn//8Z9piiy1So0aN0jrrrJMGDhyYvv/++/L3RcXVH//4x7TPPvukFVZYIXXs2DENHz48V7HvsssuqXHjxmn77bdPH3zwQfl74vlPf/rT1LJly9SkSZO01VZbpUceeeQHxzd58uR07LHHplVXXTU1bdo07brrrunVV19drNcEAGBO8XtRBOn/+Mc/ytfF8wjQN9988/J1Q4cOTTvuuGNq3rx5WnnllfPvShV/H6rKG2+8kbp3755/P4rfkw4//PD0+eefL9bzAQCWbUJ0gIX09NNPpyOOOCKddNJJ6a233sph+eDBg9OFF15Yab/f/va3eb/o6dmhQ4d06KGHpl/+8pepX79+6eWXX05lZWXphBNOKN9/6tSpaa+99kqPPvpoeuWVV3IV/L777pvGjBlTOJaDDz44TZw4MT344INpxIgR+R+wu+22W/riiy8W6zUAAJjTz3/+83TTTTeVv77xxhvT0UcfXWmfb775Jp166qn5d6H4nadu3bpp//33T7Nnzy4sGIgigQji4z0Rwk+YMCH17NlzsZ8PALDsqlMWqQ0APyp6d95666252rwkqqC+/PLLHFRHGF4S+5155pnp008/La9EP/vss3OQHp5//vm03XbbpRtuuCH/AzPEnzzHPywr/tnznH7yk5/k/qKlsD0mFj355JPz8swzz6S99947h+hRMV/Svn37PJY+ffoshqsCADD370wRdv/5z3/O1ejvvvtuXh9FBGPHjs1/NReV51F0MKeoKI+/qHv99dfz7z0xsWi0zouCgs022yxdcMEFuYDhoYceKn/Pf//73/LPWX/99ZfouQIAy4b61T0AgNqkS5cu6brrrit/HW1YNtlkk/Tss89WqjyfNWtW7vs5bdq03L4lxH4l8afHYeONN660Lt4zZcqU3IolKtEHDBiQ/v3vf+fe69EeJgL2okr0aNsS74k/ha4o3vNjfxYNALCoRRgeN/gjLI/arXi+yiqrVNrn/fffT+eee2564YUXcoBeqkCP33ciRK/q953HH388t3KZU/y+I0QHABYHITrAfIjQPCq7K4rgOnqgH3DAAXPtX7FqvUGDBuXPozK9aF3pH4+nn356GjZsWLr00kvzZy6//PLpoIMOSt99912VY4txtG7dOj3xxBNzbYtqLwCAJS3+4q70F3SDBg2aa3u0qltrrbVy1XqbNm3y70ERnv/Q7zvxnt///vdzbYvfgwAAFgchOsBCir7j8efDc4brCyuq2+PPoaMvaOkfjfEnzT80jvHjx6f69evnNi8AANUt5nSJQDyKBbp161Zp26RJk/LvUBGgd+7cOa+L9nQ/JH7f+fvf/55/14nfeQAAlgQTiwIspPgT5L/+9a+5Gv3NN99Mb7/9du5vHj3QF8Z6662X/vGPf+SJSONPl2Mi0qJJtkLXrl1zn/UePXqkhx9+OAfuzz33XOrfv3+eeAsAYEmrV69e/t0oJl+P5xWttNJKuQ3dn/70pzRq1Kj02GOP5UlGf0jfvn3zhOm9e/dOL730Um7hEv3RY16ZaKcHALA4CNEBFlJUVd1///05uN5qq63Stttum6644or8p8kL4/LLL8//uNx+++3zny3H50T1VZGo8HrggQfSTjvtlP8hGT1Be/XqlT7++OPyHuwAAEtazPUSy5zq1q2bCw9GjBiRW7iccsop6Q9/+MMPHitavsRf60Vgvscee+T5ZWKC9WhdF8cDAFgc6pTFDC8AAAAAAMBc3KoHAAAAAIACQnQAAAAAACggRAcAAAAAgAJCdAAAAAAAKCBEBwAAAACAAkJ0AAAAAAAoIEQHAAAAAIACQnQAAAAAACggRAcAYJ7ssssu6eSTT67uYQAAACxRQnQAgFpk/Pjx6aSTTkrt27dPjRo1Si1btkw77LBDuu6669K0adOqe3gAAABLnfrVPQAAAObNhx9+mAPz5s2bp4suuihtvPHGqWHDhun1119Pf/rTn1Lbtm3Tfvvtl2qqWbNmpTp16qS6ddVxAAAAtYd/wQAA1BLHH398ql+/fnr55ZdTz549U8eOHdM666yTfvrTn6Z///vfad999837TZ48OR177LFp1VVXTU2bNk277rprevXVV8uPM2DAgLTZZpulW265JbVr1y41a9Ys9erVK3399dfl+3zzzTfpiCOOSE2aNEmtW7dOl1122VzjmTFjRjr99NNzeN+4ceO0zTbbpCeeeKJ8++DBg3Pgf99996UNN9wwB/5jxoxZ7NcJAABgURKiAwDUApMmTUoPP/xw6tu3bw6sqxJV3uHggw9OEydOTA8++GAaMWJE2mKLLdJuu+2Wvvjii/J9P/jgg3Tvvfem+++/Py9P/n/t3T9I1WsYB/DnFgUOLhoWIi2GQkMSNNRQLQYNDe2iKEJD1BQN0VpU5ChNiQgNCSE0KC4JLYUiBqHYP6ihIGiIQEg0OpfnhdPtwD0Q92Z14PNZjuf9/TjvOb9Jvjx834cP4/r169+uX7x4sazdv3+/7Jvh+NLSUs1+586di8ePH8fdu3fj6dOnZd+TJ0/Gy5cvv92TFTM3btyI27dvx8rKSrS1tW3B0wEAANg66lwAABrAq1evolKpRHd3d836rl27Yn19vfydAXtOoy8sLJQQPSe/08jISAnM7927F2fOnClrX79+LZPizc3N5X1/f388ePAgrl69GmtrazE2NhZ37twp4XuamJiIjo6Ob/vmRPn4+Hh5bW9vL2s5lT47O1vWs24mbW5uxq1bt6Knp+eXPCcAAICfTYgOANDAMjDPQLyvr6/Uq2RtS4bgra2tNfd9/vy5TJ9XZY1LNUBPWdmSwXvK+zY2Nko9S1VLS0tNgJ897Nlx3tXVVbNPfofv9965c2ccOHDgJ/9qAACAX0eIDgDQAPbt21fqWp4/f16znp3oqampqbxmgJ6B+Pfd5FXZT161Y8eOmmv52RnG/6jcZ/v27aUuJl+/lz3qVfm9qjUzAAAAjUiIDgDQAHK6+8SJEzE6Ohrnz5+v24ue/efv378vB5DmtPl/0dnZWUL2+fn52Lt3b1n7+PFjvHjxIo4fP17eHzx4sEyi5/T60aNH/8cvAwAA+LM5WBQAoEFkt/iXL1/i0KFDMTk5Gaurq2UyPbvLnz17VibCe3t748iRI3H69OlyIOibN2/i0aNHcfny5VhcXPyhfXKSfHh4uBwuOjc3F8vLyzE4OBjbtv3zr2PWuGSFzMDAQExNTcXr169Ltcy1a9dienp6C58CAADAr2USHQCgQeSE+JMnT8qhnZcuXYq3b9+Ww0P3799fDvU8e/ZsqU6ZmZkpofnQ0FB8+PAh9uzZE8eOHYvdu3f/8F43b94slS15UGl2p1+4cCE+ffpUc08eIHrlypVy7d27d+WQ08OHD8epU6e24NcDAAD8Hn9VKpXKb9obAAAAAAD+aOpcAAAAAACgDiE6AAAAAADUIUQHAAAAAIA6hOgAAAAAAFCHEB0AAAAAAOoQogMAAAAAQB1CdAAAAAAAqEOIDgAAAAAAdQjRAQAAAACgDiE6AAAAAADUIUQHAAAAAIA6hOgAAAAAABD/7m+MwgxxegVuIAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(15, 7))\n",
+    "\n",
+    "obesity_order = [\n",
+    "    'Insufficient_Weight', 'Normal_Weight',\n",
+    "    'Overweight_Level_I', 'Overweight_Level_II',\n",
+    "    'Obesity_Type_I', 'Obesity_Type_II', 'Obesity_Type_III'\n",
+    "]\n",
+    "\n",
+    "sns.countplot(\n",
+    "    x='Gender',\n",
+    "    hue='NObeyesdad',\n",
+    "    data=df,\n",
+    "    hue_order=obesity_order,\n",
+    "    palette='Set2'\n",
+    ")\n",
+    "\n",
+    "plt.title('Obesity Levels by Gender')\n",
+    "plt.xlabel('Gender')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend(title='Obesity Level', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "398a6dac",
+   "metadata": {},
+   "source": [
+    "### Obesity Levels by Gender\n",
+    "\n",
+    "This grouped bar chart shows how obesity categories (`NObeyesdad`) are distributed across genders. Each gender is broken down by the number of individuals in each obesity class.\n",
+    "\n",
+    "Notably:\n",
+    "- Females have a higher count in `Insufficient_Weight` and `Obesity_Type_III`.\n",
+    "- Males tend to be more represented in mid-range categories like `Obesity_Type_I` and `Overweight_Level_II`.\n",
+    "- `Normal_Weight` and `Overweight_Level_I` are relatively balanced.\n",
+    "\n",
+    "This plot helps us understand how obesity levels vary between genders, which could be useful for interpreting clustering results or designing interventions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "cf545e9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3BupOcxnwOKXQrRXthOsAAAxWhOgAuisNjxlWIOOKPfLkpnbTxkInHZ1ZETVtLHxMJIchWq22fGdEXt0WlLau9Hx0oVvOGl0kNQE+IoG+aLXlSV1naLyxIyhLGsPm4Ov69lZTrX5yTUCG8/4BMIC05YoG5ds647IjGJeGcEIaQ3ETaNvRMyrLvE4zj0mxV8Ns565g25O6LHA7xO1wyJIl78kxxx4t9z/zukybNWe33xX0NwQTqHe1f4mml0T6azFfhxNJczA+krDMsjMjgnd2bZNuS7HHKSVeJxOUAwAwiPAXDoAeqgvccunUMnltR1DeqgvJB80R2dQeM1XpE0s4XR9Dq+/5C1s7zR/jqsTjlFNHBuSwch89T4F+KPQ45ewxRTJ3WIEJ05c3R2Rta9Qs08q8clJNwHymAMC+ao8mZGswFZpv64yZz+q+AnO3I/W7a3WBS6r9bin3uaTMlwrO9yWg3tMt9XcCV1cbF0OrzPdydluqbYwloa7LzljSbL8esE8ftFfa/qXU60xtt9dJGxgAAHIYf9kA2I3+An/ayEKZVOKVv29ql9ZoUv6wrk2OqPDJmaMKpYD2FhjEmsPa97xTVrdGzffax/T44QXmTAwqwoB9p+HP+eOKzftI+6WvaInKqq5FD0qdMLxAqgjTAexBeywhte0x2dQRM5fay7w3DZxHFrrNmWLDCtwyzO82gXmutUjR3yU0xC/19qxmDyUsaY8mpb0rSNe+7Nq7PRxKSF0oYYL8dKBe7nOa8XJQHwCA3MFfNABsjSnyyOXTy+XV7Z2yqCEsy5ojsr4tKmePLpLp5fRKx+CiVWCm9URT2JyOrX+Wzqnym2pZragFcGAq/W65YEKJnBCKy2vbg+ZAlU7Sq4tWph8/PECbJABGOJ40E9rrUtsRk+ZIosf1+hmt1eWjCj0yMuA2lxosD9ZQWbfb9EZ3O2V417p40pI2nfg0mpBm7eOuk6Ga75OyoT110KDKrxX2LtNTHQAAZBd/yQDYa+9b7Q89o9wnT9V2SFM4IX/Z2C5Td0bMafza2xHIZZFEUt6uD5mJQ9NnUE8s8cjpIwtpNQEcBPq+umhiiWm/8OaOVJierkyfVOKRE2oCJhADkF+TgOrPhPVtMdnQHjVtWnp3Z6kpcMvYYo+MLfLImCL3kJ+Px+10SIXfZZYJXZXqOyNJM1mqhutapb6lM24WDeA1TNdQ3c8ZoQAAZEVW04NXX31VfvCDH8jixYtl+/bt8sQTT8gnP/nJHqe93XjjjXL//fdLS0uLnHjiiXLffffJlClTum/T3NwsV199tTz55JPidDpl/vz58pOf/ESKioqyNCpgaNLA47JpZSYQ0V7pGops6tgpp44ImGreXDuVFtAKr3cbw7JgR9D8YapGBNxy2siAjCumvz9wsGnVuYbpDaG4LKgLyYqdEVnXFpN1ba0yrsgjxw0vkPHFnkFbWQpgz3SSzfWtMVnTGpEN7TETCmeq1PC42GMmtR9T6MnrcDizUn1UodtMXqrV+Y3hhAnWta/6po64WYo9jlQfeL/LBPEAACAPQvTOzk6ZPXu2/Pu//7tcdNFFu11/5513yv/+7//KI488IhMmTJBvf/vbMm/ePPnwww/F7/eb23zuc58zAfxzzz0nsVhMLrvsMvnSl74kjz32WBZGBAxt+ov6KSMLZVqZT56u7ZAdobg8u6VT3m8Km6p0KguRK9Vu2npIezOnJ++q8LnMpKFTS70EdsAhpmHPJ8YXm9ZJb9UFzftT+x7rMqzAJR8ZVmDOdnLx3gQGvbZowkwuvMYUW8RM+7Q0n8thDpxNKPbKhBKP6RsO+/mJUhOmuk1RgJ4J2hBOmHmK2mOWtMdSrXA0SNcDlpwZCgDAEA/RzznnHLP0RavQf/zjH8sNN9wgF1xwgVn36KOPyvDhw+Uvf/mLfPrTn5YVK1bIM888IwsXLpRjjjnG3OanP/2pnHvuufLDH/5QRo4ceUjHA+SL4QG3XDKtVN5rDMur24NmMqRfrW6VWZU+OW1EIX0bkbXwfHlzxPQ9T09IVuxxykkjAjKzwsfZEkCWacuCc8cVy4kjAvJOfUiWNoWlPpSQv2/qkFe2BeWYar/MrvKLf4i3cACGEv2bTaulNTTXZXswvlu1+ZRSr0wu8ZpJQfks3r8iFv3dW5dowpKGcFzqgglzlp3+Dq5LkTt1Gw3VNYAHAAADL2ebwW7YsEF27NghZ511Vve60tJSOfbYY2XBggUmRNfLsrKy7gBd6e21rcvbb78tF154YZ/3HYlEzJLW1tZmLuPxuFmU3ocuyWTSLGnp9YlEwvzSuLf1LpfLVD2m7zdzvdLb92e92+0295u5Xu9Xb997G+3W242pe3stS6zMx3XqjPBOsZIJ6dG00Jma1KfHbbvWGxn3vaf1Dpcr9diZ6x0iDqeuT2oitvt6vW3G8ysOhziczj2s773tfYzJsszzonL5dcrFfe/ISp9MLnbJazvCsmxnVJY2RWR1S1ROrimQmeWe7j+UBtOYhuLrNNTHpOH5ytaYLKgPm1OeVYHLIR+p9slR1QXic6duH8+4n1wf06F+nfR+vF5v6nMgfZvB9rP8QD6fLMuM3+wn8XjOvk5DZd8r8bjko6OL5Lgqr7zfHJV3myLSHkvKS9uC8saOkMys8MqRlV4p66pSHQxjGoqvE2NiTHbbro++I2zJ6paICc7TB67TRgXcMqnELZNLPOZMsMxtj+fqmOz+Hsr251Ov9V6Xy0y0OsLnkLaYJXXhpDRFktIRt6SjLVWdPtzvlBEFLvG5Hf3/zLWsHs9jru57Q/H9xJgYE2NiTIwp+2PK/P+DMkTXAF1p5Xkm/T59nV4OGzZstxeuoqKi+zZ9uf322+Xmm2/ebf17770nhYWF5uvq6mqZNGmSCfMbGhq6bzN69GizrF69WlpbW7vXT5w40WzLsmXLJBQKda+fPn26Cfr1vjNfqFmzZpk/2BctWtRjG/SAQDQalaVLl/Z4QefOnWseb+XKld3rCwoKTDucxsZGWb9+fY+DDTNmzJBt27bJli1butfbjUnb4KhAPCgd6z7oXu8fPkY8pZUSrF0jyWh41+OOmijuwhLp3LA89ctXl8C46eJ0e3rchyqaNFOS8ZgEN+3adv3FrWjyLEkE2yW0dde2O71+KRw/XeJtOyVct3nXcxAolsDoSRJtrpdo867X1lNSIf6asRKp3yKxtubu9d6KGvFV1Uho20bzGHsaU0msTcaPH2++zuXXKZf3vSoROdLhl/W+4dKa8MhzW4Py5uaITIw3SYUVlGGDcExD8XUaamNau369NDiLZKOrQkLOVI9zn8OSUbEmGRVpFccmS7YEB9eYsvU66fwi1113nfl5mIxGBuXP8gP5fNJx6/ibmprM85Grr9NQ2/dWfvC+uBMJ0VKIOmex1BfWyM6oJYsbI7K4ISwVyaCMSrbK2UfOML+rDIYxDcXXiTExJh3TmvUbZKezQBqdRdLsKpKo7DpjxGElpTwZkgmFDjlh8mjZUbtBGjY1iD4L6wfJ65R+fF8y0uOzItufT3v6zNUoQM+7rvEGpKN6gmzviErEcsq2UFK2BRNSboVkXHW5uFv3/pmrn4M6/5jK5ddpqLyfGBNjYkyMiTHNyKkxZf7/PXFY/Y3bDzI9SpA5seibb75pPsj1SR0xYkT37f7t3/7N3Pb3v/+9fO973zP90letWtXjvvRJ1JD8iiuu6Hcl+pgxY8wfzyUlJXl3VOf99983O839T78mU4+YlXeV6KuXLZUvn3eqaQukb6pcfZ0Gw76nj750Z8y0eIl0TR41tjA1kePIIu+gHNNQfJ0G+5gsh1OWNoXknfpwd/Wb3+WQY4cVmLMj3A5r0I0p26/TkiVLzGfuPX99QabOmjMof5YfyOfT6qVL5MoLzpQ33nhD5syZk7Ov01Dc93q+HE5Z3x6TRfUh2dix67oyr1OOrPLL4WXuHq1eBsOYhuLrxJjyZ0w6EeiGjoSs1olB22IS79XffFKJRyYXu2V8kUe8LsegGJPd62H791COVaLv6TM3mUzIznBStoUSpko9rdTjkJEBl5R5dNscfY5J/x76z/NPk3feeccEHrn6Og3m9xNjYkyMiTExptwdk+bCGrxrSJ/OhQdVJXpNTY25rKur6xGi6/f6B276NvX19T3+nz5JWlGX/v998fl8ZulNX3RdMqVflN7ST35/1/e+3/1ZrztBX+vttrG/680vU6kvzC9muz2us+8x9XVbYx/Wm8fuc71TTHnFbtvSd59U+/Wuva93OLrfoLn8Og2Wfe/oarccVu6TBXUhWdwQktrOuDy6pk0OL/fJKSMDUup1D7ox7c96xjTwY4okkrKkMWx6KXd2/SWvbVvmDiuQo6v94ttDH+VcHVOuvE56P1pRYD4H0p8Jg+1n+YF8PjkcZvz6PKRfm1x8nQ50/WAY0+RSn1mawwl5tzEkHzRHzMEybfXy2naR6eU+mV3pl9GFvE77u54xMaY9jakj4ejub765I9YjBy7xOGVKmdf0OB9T5LGdDHgwvk57/3soS59P+/CZ63S6pDKgi8e0yNrWGTf96ltjlrS2xqXQ7TCvW4XPsfuYHI7uUCGXX6f9Xc+YGJPdesbEmPa07Ywpf8bk6OecLTkbok+YMMEE4S+88EJ3aK5HBrTXebrC/Pjjj5eWlhZZvHixHH300Wbdiy++aAJR7Z0OIDsK3E45Y1ShHFXll9e2B2X5zohZVrZEzLrjhgekkMlH0U+dsaQ5ILO4Mdx9hoNOGPqRYQUmTNPqNwBDbxLSs0YXySkjCuXDndreJSQN4YQsa46YRfss62TWR1T4pYjPE2C/aeWWTvCr1eYanOvXmXSiyqkmOPfJ8IJUZRdyn/6eNK3MK+Pi2tolbiYf1QKElS3RjDA9VR0PAAAk90P0jo4OWbt2bff32vdGTynXnuZjx46Vr33ta3LbbbfJlClTTKj+7W9/W0aOHNnd8kV76HzsYx+TL37xi/Lzn//c9Mu86qqrzKSjejsA2VXmc8nHxxebSuGXtnbKpo6YLGwIy3uNYXNq/rHDA4QfsFUXjMuihpAJ0Lqyc6n0ueTY4QXmzAaXkz/8gKFOD5LNqfLL7EqfbO2My/tNYXNAtjmSkJe3BeWVbUGZXOo1gfrEYi8/F4B+0Am5tcp8dVfFeVvGxKD6Dhpd5JappT5Tca6/y2Hw8rudMrFEzxywzM/Q7cF4n2E6AADI8RBdm7mffvrp3d9fe+215vLSSy+Vhx9+WL7xjW9IZ2enfOlLXzIV5yeddJI888wz4vf7u//Pb37zGxOcn3nmmabcf/78+fK///u/WRkPgL7VBNzy6cklsqE9Jq9vD5qKmHSYPttUphdIsYc/0pD6w35ta1QWNYSltiM16bEaEXCb/WRqqZeqKSAP6ft+dJHHLGeNLpSVO6OytDlsQqF06wmdG2F6mc+0FBtT5OZnBZAhqv3N26OyuiUq69qipt95mtshMqHEaz5jJ5V6JeAmVB1qPE6HjC/2yKhCt2nzkhmmB9wOcXsLs72JAADkvKyG6KeddlqP5u+96R8/t9xyi1nsaNX6Y489dpC2EMBA0fezVsJMKPaYMP2NHUETfixuCJs+19qWQ9tzUPGUn0LxpOl9rC0bWrsq4jT+ml7mlWOGFcioQk+2NxFAjtD5D/QArC6NobgsbY7Ih80R6YgnZUlT2CzaymBGeSpQpwUF8lVbNCHr22KypjUiG9tj3Wd1pecU0bM4tFXL+GKvCVkx9OnrPK7YIyMzwvSgzjNTNlq+9Mu/SYt4s72JAADkrJztiQ5g6Ifpm7QyfUdQtnTG5d3GVGW6/jF3THWBmTSO0GNo04Oom7U9Q2OqPUP6j3t/V/sG7Z9f4uWgCgB7VQVuOWOUW04bGTBnr2iYvqo1aibV00mIdSnzam9gn/l8GRngswVD+2wuLVBY3xY1Z3XpPAKZ9L2gLVqmlPnM71lO3gt5KzNM131ma0dUJhx1vLwjIs3r2+TUEQGpLiAqAAAgE5+MALJCQ4zxJV7zC7wGH2/VhUyF+qqWqFm0Bczcar9M197X/JE3pATjSTMxoJ6BoH2N04YVuOSoqgI5vMJHRRyAfaJhoFbT6nJ20jLtKnQ+BQ0SW6JJebs+ZJYit9OE6dq2Ykyxh88XDInPVA3N17VGze9RmW1alLbv0OIF3eer/JyVgb7bvAQ3rZJ/vvqGHHvh583PTd2fjqjwyckjAhQ0AADQhRAd6LJixQrJZ1VVVWZC30NN/5gbV6xhulcaQqmJJDVg3RGMy5ObOszEcToJ6cwKnxTzS/yglUhasr49KsubI6Z3cfpvfJ1XVtstaOV5TUH2KkRra2ulsbFR8lW+//zD0OJ2OkzluS7aB1oDxtVdoZC2fNEzn3TxuVLhkQaME0s8zM2BQXMWV10oYQ4U6T6t88xk0rO5dJ+eVOIxfc7pb47+cCXj8sRt/y3XXHiWNJaNNwUt2mZvxc6IfGR4gRw3LGAmegaAoSrf/x7MZiY0mBCiI+811e/QJFcuvvhiyWeBQMAEadn8oamnjZ4ztlhOHVEo7zWF5d2GkDkl/9XtQXlte9CEHLMq/aaHJ9WDg+MPfT1FePnO1B9hmdVxGphrcD6j3Gv6G2f7F6YZM2ZIMBiUfNfR0ZHtTQAGlIY+ekaTLvGkZdqIrW5NHczTPsDps5/SZ8OkAnWvjAq4xcUZMcgR7bGE2Xe1r/nGtpg5GJRJ991JJjj3mvYctGnB/iqUuJw8oUS2d8bkxW2dsrkjLm/uCJnWe6eMLDRFLexfAIYa/h7MnUwo1xGiI+91tLZq2idX3fojmT33WMlHm9auktuuutwcec2FH5gBj1NOrAnIscMKTK9sbfuhfdPXtcXMEnA7ZGaFX2ZV+qTSz4+xXKyQW90SMeF5epJQVeh2mKrzwyv8pl1PrtD9Xn9huuFnD8i4ydMkH7310rPywB23SDgczvamAAe1Qn1Sqdcs8yzLnPGk1bw68aJOrlcfSkh9KGTai+lZMjqh8bgij2k7pj+zCI5wqIQTSaltj8mmjlRw3tSrt7nun9q6SENzLTCg3QYG2ohCj3x2cqk5i+elrZ2mLdbTtR1mAvgzRhWa/Q8Ahgr+Hsy9TChX5U6KAWTZqAmTZNqsOdneDPQKPI6o8JulKRyXpU0RWdYcls641d3fdniBS2ZolWGZT8p8/BGZzYnMNDjXP7Yyg3Ov02H6Dx9R7pOxxZ6cDqH0F6Z8/Rmwac2qbG8CcEjpz6KRhTqpnkdOHiESjCVNyykN1De0RyUUt1JVv+0xke2pn2VjitwypshjwnUN1Zm7AQNF97/NnTHZ3JFa9GB0b7rPafuh8UUeGV3kMb8jAQeTttjTtlh6sEZbYL2xI2gONv5ubZuZoPbMUYX87g1gSMnnvwfRP4ToAAYFrTg/fZRbThkZMD1ANVDXCkL9Q7MuFDS90/UPzBllXvMLP7/UH1zaZ3hTh/Zjjcma1og5sJHmdohph6AHNqaUeQmaAOQ8PQMqfdBWz6hpDKfaZ2zqCjW1HVX6bCilTaiGB9xm0kYN1fWy2ONk0kbsle5fOyNJ2doZMwegNTzvXWmuyn3OrslyPTK2yCMF9DZHlugBm48MKzATjb6+PSjvNYZNSyydb+I47Zc+PMDvegCAvECIDmBQ0V7oU8t8ZgnGk7K6JWpavmjYoafm6/LStmB3f1DT25b+oAPyR79WH23oqtTc0hmT5K7c3EzQN7nEa6rO9TnnjykAg5UG4TpHhy7HDCswZ9voz7/ajphs6dDgM2YOHGoLGF0WNaTaIGmrMZ3vQQ/oasCulyUE63kvHE+a/UQD823BmGzrjPeYIySt2u8yZzroMrpID8pQDIDcopPUnj2mSI6q8stzWzrNQcY3doTMBKRnjSo01en8vAMADGWE6AAG9S/zOjmlLp2xpJksbuXOqAk60r1tF9SFTMCrlVxmwrhijxTTO7RfoXlTJCFbOuKmCnNje7RHtbkq9TrNczq11Guq5JiED8BQpAdhNRDXRasx9eejtq1KVxLrpX7m6ESl69tjZkkrcDlkWIFbqgpcUu3XYN4lVX5X1idUxsGhv4vowfy6UGrRrzNbnGWesaX7k7YTGl2YahNEpTkGi6oCt3x6comZlPnFrZ3SFk3Knze0y4Rij5w1upD5igAAQxafcACGhEKPU46sKjBLZm9bPdVUK770F31dVJnXafqJmmqvQrdU+Fx5XzmTSGqlpZ5WngrNtdJcewL3nshMw3INzicUe82p5vn+vAHIP/pzT1uG6XJ4RWpdLGlJQ1domg5PG8IJCZnWV6m2MJm0Ql3D9ApdfKml3O+icn2QiCSSpuWPWUKp17oxlJCO+O6Befr3Dm37M7JQg3O3DPO7OfCMQU1/Tk0v95nfCd+qC5p5ija0x+SBlS3ykeoCOaEmIF4X+zgAYGghRAcwpHvb6mn4ehq1hukaquvXLdGktDTrJKWR1O3dDhld6JERXafga9VgkSbGQ1Q8HfZ0BT3psCezPUu6Um6EVsgVesykoPocMZEZAOxOW1ilJyrN/FmrIav+vG3outTv22NJaetaMqvW0z93y7sCeg1eS70uKfWlLsu8roMaStXW1kpjY6Pkq6qqKhk7dmz39wk94yCSlJ2RhDRHEuZSF+1frq+dnUqfy/wuoROfm9Y+BW7xU2WOIUp/Jp0yslBmVvrl+S0dZt6It+pDsnxnRE4fVWjmKuLAIABgqCBEBzDkT8NPTfrmkZNHpHqT6un3Wmmti/Ym1VPwV7dGzZJW6E6dgq9//Orp96ZK0OcaVH8I6+Sf+od/Uzhu/ujX9izNeqmBeR+397v0uUqdVq6L9valUg4A9o8edEy3gcmkn0OmcjkcNxNM6s9l/VndEkmIngBkAvc+JppMt4cp9jrNJKbaM1sP+Ka/L3Q7zUFhbXW2rz+7NUCfMWOGBINByRcuj1dKho2QsppRUj5ijFSPnSD/fuVXJeEpkLZYwgTo9lG5SJHbadr06BkF2qon3bKH6lvkI/0d+V8nlcra1qgJ07Vg5W8b2+W9IrecPbrIzDEBAMBgx6cZgLyiIfikUq9Z0pWCWomtPW3rQglzGr4GGtr/W09L1aXH/3c5zB8KqWVXiFHocZg/qLUKXic/PRTtV7RNQIdWM0a7Khujqa9NhWM0adbZKeg9AV6B2/Q4p1oI+WzFihWSr3pX4eLgfg6NKdJlV9W60jOntH+2fga1RBPm69ZoKlzXr7U1mf7cD5k5PzRk7/n5lEnnAkkH6tprO/11oNfXejuv0yF1jU0mQL/hZw/IuMnTZDDSk6ksh1Msp0uSDlfq0ukSy6GXbkm6ui67Fr2+t/V6glokttuZAeUZLXf0UoNzepgDu5tc6pXxxeWmvcuCHUHZ3BGXB1e2yNHVfjlpRED8zAcBABjECNEBSL5XCmp/dF3S0r1t67tCdf1aK2o0sNYQQ1vC6GInHVBoMGECiq6QQi99TodoTq0V8hpXm8WRulRahajBvlkyvjbBSTxpLsNxS6K9e6/Y0Ip67blb6XN3XbpMZb1WLRKYAylN9TvMG/Hiiy+WfBUIBMxBBIL07NHPhXRg25dwousAadfnkVZL62V710HTYFwXy4TJkYRlFq107xfHGLntne3mM7HN7RYtptasSw8Km6/1sut7/eTQQvfU55dDnBmfY+bzrWt978+39KeWZWV83bXC6uN6/ZjTAwvmUnZ9ndCl6zNSW64kkqnLXtN49G/YXQccEqEOefMfT8inP3m+TB83OtU+xxwo57MS2Ff6c+TEmoAcUeEzE4/qnESLGsKyoqvFy+HlPt5XAIBBiRAdAPrR2zbdHkWrA9N9UXXRAKMzZpnJxDpjSfOHv4YYwXjfp+IPNA3JS7ypULzEq0tqYjr9umKQtZ8BsqWjtdUkd1fd+iOZPfdYyTeb1q6S2666XF577TXT0iNf5Xo1vlZw+gucMqzA/jaWZZmDvelAPXW56+tQr3X6uZY+KOtyu81nWPdBWvMxth/JdJZ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+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 6))\n",
+    "sns.histplot(df[\"FAF\"], kde=True, bins=20, color='skyblue', edgecolor='black')\n",
+    "plt.title(\"Physical Activity Frequency per Week (FAF)\")\n",
+    "plt.xlabel(\"Hours per week (continuous)\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f67ba048",
+   "metadata": {},
+   "source": [
+    "### Physical Activity Frequency per Week (FAF)\n",
+    "\n",
+    "This histogram shows the distribution of the `FAF` variable, which measures the number of hours of physical activity per week. The data is continuous, with many values concentrated around 0, 1, and 2.\n",
+    "\n",
+    "- A significant portion of individuals report no activity (`FAF` = 0).\n",
+    "- Smaller peaks at 1 and 2 hours suggest some engagement in moderate activity.\n",
+    "- Very few people exceed 2.5 hours per week.\n",
+    "\n",
+    "This skewed distribution toward lower physical activity may play a role in clustering patterns related to lifestyle or health outcomes.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86872a61",
+   "metadata": {},
+   "source": [
+    "### Dropping target variable as this is not needed for clustering (unsupervised) task "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "c8b5d25d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Keep the original with labels for later evaluation\n",
+    "df_with_labels = df.copy()\n",
+    "\n",
+    "# Drop the target variable for clustering\n",
+    "df = df.drop(columns=['NObeyesdad'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a744247",
+   "metadata": {},
+   "source": [
+    "### Encoding Categorical Features\n",
+    "\n",
+    "To prepare the dataset for clustering, we encode categorical variables:\n",
+    "\n",
+    "- Binary features (`yes`/`no`) are mapped to 0 and 1.\n",
+    "- Ordinal features like `CAEC` and `CALC` are encoded based on logical order.\n",
+    "- Nominal features like `MTRANS` are one-hot encoded.\n",
+    "- The target column `NObeyesdad` is removed, as clustering is an unsupervised task.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2687405c",
+   "metadata": {},
+   "source": [
+    "### Binary encoding of variables with yes/no values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "29952dd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binary_cols = ['FAVC', 'family_history_with_overweight', 'SMOKE', 'SCC']\n",
+    "for col in binary_cols:\n",
+    "    df[col] = df[col].map({'yes': 1, 'no': 0})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37712c03",
+   "metadata": {},
+   "source": [
+    "### Ordinal encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "30c1974f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordinal_map = {'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3}\n",
+    "df['CAEC'] = df['CAEC'].map(ordinal_map)\n",
+    "df['CALC'] = df['CALC'].map(ordinal_map)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7822bbf6",
+   "metadata": {},
+   "source": [
+    "### Ordinal Encoding: CAEC & CALC\n",
+    "\n",
+    "To prepare the data for modeling, we convert the following ordinal categorical features into numeric values using a custom mapping:\n",
+    "\n",
+    "- **CAEC** *(Consumption of food between meals)*  \n",
+    "- **CALC** *(Alcohol consumption)*\n",
+    "\n",
+    "We use the following encoding:\n",
+    "\n",
+    "| Category   | Encoded Value |\n",
+    "|------------|----------------|\n",
+    "| no         | 0              |\n",
+    "| Sometimes  | 1              |\n",
+    "| Frequently | 2              |\n",
+    "| Always     | 3              |\n",
+    "\n",
+    "This transformation preserves the natural ordering of the responses, allowing models to interpret increasing frequency as increasing magnitude.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f82ddc54",
+   "metadata": {},
+   "source": [
+    "### Binary encoding of gender variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "ba013c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gender'] = df['Gender'].map({'Female': 0, 'Male': 1})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "b06f87d0",
+   "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>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>family_history_with_overweight</th>\n",
+       "      <th>FAVC</th>\n",
+       "      <th>FCVC</th>\n",
+       "      <th>NCP</th>\n",
+       "      <th>CAEC</th>\n",
+       "      <th>SMOKE</th>\n",
+       "      <th>CH2O</th>\n",
+       "      <th>SCC</th>\n",
+       "      <th>FAF</th>\n",
+       "      <th>TUE</th>\n",
+       "      <th>CALC</th>\n",
+       "      <th>MTRANS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.52</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Walking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>89.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Public_Transportation</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender   Age  Height  Weight  family_history_with_overweight  FAVC  FCVC  \\\n",
+       "0       0  21.0    1.62    64.0                               1     0   2.0   \n",
+       "1       0  21.0    1.52    56.0                               1     0   3.0   \n",
+       "2       1  23.0    1.80    77.0                               1     0   2.0   \n",
+       "3       1  27.0    1.80    87.0                               0     0   3.0   \n",
+       "4       1  22.0    1.78    89.8                               0     0   2.0   \n",
+       "\n",
+       "   NCP  CAEC  SMOKE  CH2O  SCC  FAF  TUE  CALC                 MTRANS  \n",
+       "0  3.0     1      0   2.0    0  0.0  1.0     0  Public_Transportation  \n",
+       "1  3.0     1      1   3.0    1  3.0  0.0     1  Public_Transportation  \n",
+       "2  3.0     1      0   2.0    0  2.0  1.0     2  Public_Transportation  \n",
+       "3  3.0     1      0   2.0    0  2.0  0.0     2                Walking  \n",
+       "4  1.0     1      0   2.0    0  0.0  0.0     1  Public_Transportation  "
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "id": "ec014fc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Select only numeric columns (this includes all encoded columns now)\n",
+    "numeric_df = df.select_dtypes(include='number')\n",
+    "\n",
+    "# 2. Compute correlation matrix\n",
+    "corr = numeric_df.corr()\n",
+    "\n",
+    "# 3. Optional: mask upper triangle for cleaner view\n",
+    "import numpy as np\n",
+    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
+    "\n",
+    "# 4. Plot the heatmap\n",
+    "plt.figure(figsize=(14, 10))\n",
+    "sns.heatmap(corr, mask=mask, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
+    "plt.title(\"Correlation Heatmap (All Numeric Encoded Features)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e337b79",
+   "metadata": {},
+   "source": [
+    "### Correlation Heatmap (with Binary and Ordinal Encoded Features)\n",
+    "\n",
+    "This heatmap includes all numeric features and encoded variables (binary and ordinal). These transformations allow us to uncover meaningful relationships, such as:\n",
+    "\n",
+    "- A positive correlation between `Weight` and `family_history_with_overweight`\n",
+    "- An inverse relationship between healthy eating (`FCVC`) and calorie surplus (`CAEC`)\n",
+    "- Gender-related patterns in height and weight\n",
+    "\n",
+    "One-hot encoded features (e.g., `MTRANS`) were excluded to avoid artificial correlations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "id": "c86b2884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run get_dummies (as you're already doing)\n",
+    "df = pd.get_dummies(df, columns=['MTRANS'], drop_first=True)\n",
+    "\n",
+    "# Fix: Convert all boolean columns to integers (0/1)\n",
+    "df = df.astype({col: int for col in df.select_dtypes(include='bool').columns})\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "8ac1963c",
+   "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>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>family_history_with_overweight</th>\n",
+       "      <th>FAVC</th>\n",
+       "      <th>FCVC</th>\n",
+       "      <th>NCP</th>\n",
+       "      <th>CAEC</th>\n",
+       "      <th>SMOKE</th>\n",
+       "      <th>CH2O</th>\n",
+       "      <th>SCC</th>\n",
+       "      <th>FAF</th>\n",
+       "      <th>TUE</th>\n",
+       "      <th>CALC</th>\n",
+       "      <th>MTRANS_Bike</th>\n",
+       "      <th>MTRANS_Motorbike</th>\n",
+       "      <th>MTRANS_Public_Transportation</th>\n",
+       "      <th>MTRANS_Walking</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>1.52</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>89.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender   Age  Height  Weight  family_history_with_overweight  FAVC  FCVC  \\\n",
+       "0       0  21.0    1.62    64.0                               1     0   2.0   \n",
+       "1       0  21.0    1.52    56.0                               1     0   3.0   \n",
+       "2       1  23.0    1.80    77.0                               1     0   2.0   \n",
+       "3       1  27.0    1.80    87.0                               0     0   3.0   \n",
+       "4       1  22.0    1.78    89.8                               0     0   2.0   \n",
+       "\n",
+       "   NCP  CAEC  SMOKE  CH2O  SCC  FAF  TUE  CALC  MTRANS_Bike  MTRANS_Motorbike  \\\n",
+       "0  3.0     1      0   2.0    0  0.0  1.0     0            0                 0   \n",
+       "1  3.0     1      1   3.0    1  3.0  0.0     1            0                 0   \n",
+       "2  3.0     1      0   2.0    0  2.0  1.0     2            0                 0   \n",
+       "3  3.0     1      0   2.0    0  2.0  0.0     2            0                 0   \n",
+       "4  1.0     1      0   2.0    0  0.0  0.0     1            0                 0   \n",
+       "\n",
+       "   MTRANS_Public_Transportation  MTRANS_Walking  \n",
+       "0                             1               0  \n",
+       "1                             1               0  \n",
+       "2                             1               0  \n",
+       "3                             0               1  \n",
+       "4                             1               0  "
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c40fe936",
+   "metadata": {},
+   "source": [
+    "## Checking outliers using boxplot method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "4390c03f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "numeric_cols = ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE']\n",
+    "\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i, col in enumerate(numeric_cols):\n",
+    "    plt.subplot(2, 4, i + 1)\n",
+    "    sns.boxplot(y=df[col], color='lightblue')\n",
+    "    plt.title(col)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36fd07d",
+   "metadata": {},
+   "source": [
+    "### Outlier Handling Decision\n",
+    "\n",
+    "Boxplots revealed the presence of mild to moderate outliers in several numeric features, such as `Weight`, `Age`, and `FAF`. These outliers likely represent realistic variations in individual behavior or physical traits, rather than data errors.\n",
+    "\n",
+    "Since the goal is to explore meaningful clusters across the full range of real-world observations, we decided **not to remove outliers**. Instead, we applied `RobustScaler`, which reduces the influence of outliers during scaling without eliminating valuable data points.\n",
+    "\n",
+    "This approach preserves potentially informative edge cases while keeping the clustering process stable and fair.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "1ec30e51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import RobustScaler\n",
+    "\n",
+    "# Making a copy if needed\n",
+    "df_robust = df.copy()\n",
+    "\n",
+    "# Initialize RobustScaler\n",
+    "scaler = RobustScaler()\n",
+    "\n",
+    "# Apply RobustScaler\n",
+    "df_robust_scaled = pd.DataFrame(scaler.fit_transform(df_robust), columns=df_robust.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "4912df59",
+   "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>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>family_history_with_overweight</th>\n",
+       "      <th>FAVC</th>\n",
+       "      <th>FCVC</th>\n",
+       "      <th>NCP</th>\n",
+       "      <th>CAEC</th>\n",
+       "      <th>SMOKE</th>\n",
+       "      <th>CH2O</th>\n",
+       "      <th>SCC</th>\n",
+       "      <th>FAF</th>\n",
+       "      <th>TUE</th>\n",
+       "      <th>CALC</th>\n",
+       "      <th>MTRANS_Bike</th>\n",
+       "      <th>MTRANS_Motorbike</th>\n",
+       "      <th>MTRANS_Public_Transportation</th>\n",
+       "      <th>MTRANS_Walking</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.305183</td>\n",
+       "      <td>-0.587756</td>\n",
+       "      <td>-0.458016</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.39728</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.644237</td>\n",
+       "      <td>0.369134</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.305183</td>\n",
+       "      <td>-1.306126</td>\n",
+       "      <td>-0.648137</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.60272</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.139397</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.288474</td>\n",
+       "      <td>-0.630866</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.023948</td>\n",
+       "      <td>0.705310</td>\n",
+       "      <td>-0.149071</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.39728</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.644237</td>\n",
+       "      <td>0.369134</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.682209</td>\n",
+       "      <td>0.705310</td>\n",
+       "      <td>0.088580</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.60272</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.644237</td>\n",
+       "      <td>-0.630866</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.140618</td>\n",
+       "      <td>0.561636</td>\n",
+       "      <td>0.155122</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.39728</td>\n",
+       "      <td>-6.725583</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.644237</td>\n",
+       "      <td>-0.630866</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender       Age    Height    Weight  family_history_with_overweight  FAVC  \\\n",
+       "0    -1.0 -0.305183 -0.587756 -0.458016                             0.0  -1.0   \n",
+       "1    -1.0 -0.305183 -1.306126 -0.648137                             0.0  -1.0   \n",
+       "2     0.0  0.023948  0.705310 -0.149071                             0.0  -1.0   \n",
+       "3     0.0  0.682209  0.705310  0.088580                            -1.0  -1.0   \n",
+       "4     0.0 -0.140618  0.561636  0.155122                            -1.0  -1.0   \n",
+       "\n",
+       "      FCVC       NCP  CAEC  SMOKE      CH2O  SCC       FAF       TUE  CALC  \\\n",
+       "0 -0.39728  0.000000   0.0    0.0  0.000000  0.0 -0.644237  0.369134  -1.0   \n",
+       "1  0.60272  0.000000   0.0    1.0  1.139397  1.0  1.288474 -0.630866   0.0   \n",
+       "2 -0.39728  0.000000   0.0    0.0  0.000000  0.0  0.644237  0.369134   1.0   \n",
+       "3  0.60272  0.000000   0.0    0.0  0.000000  0.0  0.644237 -0.630866   1.0   \n",
+       "4 -0.39728 -6.725583   0.0    0.0  0.000000  0.0 -0.644237 -0.630866   0.0   \n",
+       "\n",
+       "   MTRANS_Bike  MTRANS_Motorbike  MTRANS_Public_Transportation  MTRANS_Walking  \n",
+       "0          0.0               0.0                           0.0             0.0  \n",
+       "1          0.0               0.0                           0.0             0.0  \n",
+       "2          0.0               0.0                           0.0             0.0  \n",
+       "3          0.0               0.0                          -1.0             1.0  \n",
+       "4          0.0               0.0                           0.0             0.0  "
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_robust_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50dc2a13",
+   "metadata": {},
+   "source": [
+    "### Exporting the dataset to csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "f3e9d843",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved scaled dataset to ../../data/processed/clustering_preprocessing_robust_scaled.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_path = \"../../data/processed/clustering_preprocessing_robust_scaled.csv\"\n",
+    "df_robust_scaled.to_csv(output_path, index=False)\n",
+    "print(f\"Saved scaled dataset to {output_path}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391