6.5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a54ac8a9-be63-47c6-966f-f5d8cacf61e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sklearn\n",
    "from sklearn.cluster import KMeans # Here is where you import the k-means algorithm from scikit-learn.\n",
    "import pylab as pl # PyLab is a convenience module that bulk imports matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "75d65192-b9b9-428b-9455-5636f4597b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This option ensures the graphs you create are displayed in your notebook without the need to \"call\" them specifically.\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8e8c6c4b-fdfd-4fa7-907e-d28dee80bb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('merged_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0c98ae9b-a142-477f-b9c9-664d9bbccdff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782, 30)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09427eb0-4f05-4861-a7cc-4bd6356c7e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>...</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "0             1.0            7.587         0.03411                   1.39651   \n",
       "1             2.0            7.561         0.04884                   1.30232   \n",
       "2             3.0            7.527         0.03328                   1.32548   \n",
       "3             4.0            7.522         0.03880                   1.45900   \n",
       "4             5.0            7.427         0.03553                   1.32629   \n",
       "\n",
       "    Family  Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0  1.34951                   0.94143  0.66557                        0.41978   \n",
       "1  1.40223                   0.94784  0.62877                        0.14145   \n",
       "2  1.36058                   0.87464  0.64938                        0.48357   \n",
       "3  1.33095                   0.88521  0.66973                        0.36503   \n",
       "4  1.32261                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  ...  Trust..Government.Corruption.  \\\n",
       "0     0.29678            2.51738  ...                        0.12312   \n",
       "1     0.43630            2.70201  ...                        0.12312   \n",
       "2     0.34139            2.49204  ...                        0.12312   \n",
       "3     0.34699            2.46531  ...                        0.12312   \n",
       "4     0.45811            2.45176  ...                        0.12312   \n",
       "\n",
       "   Dystopia.Residual  Overall rank  Country or region     Score  \\\n",
       "0           1.850238          78.5        Afghanistan  5.391506   \n",
       "1           1.850238          78.5        Afghanistan  5.391506   \n",
       "2           1.850238          78.5        Afghanistan  5.391506   \n",
       "3           1.850238          78.5        Afghanistan  5.391506   \n",
       "4           1.850238          78.5        Afghanistan  5.391506   \n",
       "\n",
       "   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0        0.898298        1.211026                 0.661295   \n",
       "1        0.898298        1.211026                 0.661295   \n",
       "2        0.898298        1.211026                 0.661295   \n",
       "3        0.898298        1.211026                 0.661295   \n",
       "4        0.898298        1.211026                 0.661295   \n",
       "\n",
       "   Freedom to make life choices  Perceptions of corruption  \n",
       "0                      0.423538                   0.111299  \n",
       "1                      0.423538                   0.111299  \n",
       "2                      0.423538                   0.111299  \n",
       "3                      0.423538                   0.111299  \n",
       "4                      0.423538                   0.111299  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c5dcb9b8-5322-4abd-b013-f76dc17bb69c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values After Imputation:\n",
      " Happiness Rank                   0\n",
      "Happiness Score                  0\n",
      "Standard Error                   0\n",
      "Economy (GDP per Capita)         0\n",
      "Family                           0\n",
      "Health (Life Expectancy)         0\n",
      "Freedom                          0\n",
      "Trust (Government Corruption)    0\n",
      "Generosity                       0\n",
      "Dystopia Residual                0\n",
      "Lower Confidence Interval        0\n",
      "Upper Confidence Interval        0\n",
      "Happiness.Rank                   0\n",
      "Happiness.Score                  0\n",
      "Whisker.high                     0\n",
      "Whisker.low                      0\n",
      "Economy..GDP.per.Capita.         0\n",
      "Health..Life.Expectancy.         0\n",
      "Trust..Government.Corruption.    0\n",
      "Dystopia.Residual                0\n",
      "Overall rank                     0\n",
      "Country or region                0\n",
      "Score                            0\n",
      "GDP per capita                   0\n",
      "Social support                   0\n",
      "Healthy life expectancy          0\n",
      "Freedom to make life choices     0\n",
      "Perceptions of corruption        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute missing values for numeric columns with mean\n",
    "numeric_columns = df.select_dtypes(include=['float64']).columns\n",
    "df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].mean())\n",
    "\n",
    "# Impute missing values for categorical columns with mode\n",
    "categorical_columns = df.select_dtypes(include=['object']).columns\n",
    "df[categorical_columns] = df[categorical_columns].fillna(df[categorical_columns].mode().iloc[0])\n",
    "\n",
    "# Check if there are any missing values left\n",
    "missing_values_after_imputation = df.isnull().sum()\n",
    "print(\"Missing Values After Imputation:\\n\", missing_values_after_imputation)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cab26404-e043-40f0-9b73-89c1a8187e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming 'df' is your DataFrame\n",
    "columns_to_drop = ['Country or region']  # List of columns to drop\n",
    "\n",
    "# Drop the specified columns from the DataFrame\n",
    "df = df.drop(columns_to_drop, axis=1)\n",
    "\n",
    "# Now 'df' will not contain the dropped columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "166db1ab-7613-4066-a4e6-6c80de1e20ed",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>...</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "0             1.0            7.587         0.03411                   1.39651   \n",
       "1             2.0            7.561         0.04884                   1.30232   \n",
       "2             3.0            7.527         0.03328                   1.32548   \n",
       "3             4.0            7.522         0.03880                   1.45900   \n",
       "4             5.0            7.427         0.03553                   1.32629   \n",
       "\n",
       "    Family  Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0  1.34951                   0.94143  0.66557                        0.41978   \n",
       "1  1.40223                   0.94784  0.62877                        0.14145   \n",
       "2  1.36058                   0.87464  0.64938                        0.48357   \n",
       "3  1.33095                   0.88521  0.66973                        0.36503   \n",
       "4  1.32261                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  ...  Health..Life.Expectancy.  \\\n",
       "0     0.29678            2.51738  ...                  0.551341   \n",
       "1     0.43630            2.70201  ...                  0.551341   \n",
       "2     0.34139            2.49204  ...                  0.551341   \n",
       "3     0.34699            2.46531  ...                  0.551341   \n",
       "4     0.45811            2.45176  ...                  0.551341   \n",
       "\n",
       "   Trust..Government.Corruption.  Dystopia.Residual  Overall rank     Score  \\\n",
       "0                        0.12312           1.850238          78.5  5.391506   \n",
       "1                        0.12312           1.850238          78.5  5.391506   \n",
       "2                        0.12312           1.850238          78.5  5.391506   \n",
       "3                        0.12312           1.850238          78.5  5.391506   \n",
       "4                        0.12312           1.850238          78.5  5.391506   \n",
       "\n",
       "   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0        0.898298        1.211026                 0.661295   \n",
       "1        0.898298        1.211026                 0.661295   \n",
       "2        0.898298        1.211026                 0.661295   \n",
       "3        0.898298        1.211026                 0.661295   \n",
       "4        0.898298        1.211026                 0.661295   \n",
       "\n",
       "   Freedom to make life choices  Perceptions of corruption  \n",
       "0                      0.423538                   0.111299  \n",
       "1                      0.423538                   0.111299  \n",
       "2                      0.423538                   0.111299  \n",
       "3                      0.423538                   0.111299  \n",
       "4                      0.423538                   0.111299  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5900ee8b-d62d-44c5-98c5-b99502aabaa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Happiness Rank', 'Happiness Score', 'Standard Error',\n",
       "       'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n",
       "       'Freedom', 'Trust (Government Corruption)', 'Generosity',\n",
       "       'Dystopia Residual', 'Lower Confidence Interval',\n",
       "       'Upper Confidence Interval', 'Happiness.Rank', 'Happiness.Score',\n",
       "       'Whisker.high', 'Whisker.low', 'Economy..GDP.per.Capita.',\n",
       "       'Health..Life.Expectancy.', 'Trust..Government.Corruption.',\n",
       "       'Dystopia.Residual', 'Overall rank', 'Score', 'GDP per capita',\n",
       "       'Social support', 'Healthy life expectancy',\n",
       "       'Freedom to make life choices', 'Perceptions of corruption'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f7a5d303-84f0-4150-9ded-ed4705d9fdd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Happiness Rank                   0\n",
       "Happiness Score                  0\n",
       "Standard Error                   0\n",
       "Economy (GDP per Capita)         0\n",
       "Family                           0\n",
       "Health (Life Expectancy)         0\n",
       "Freedom                          0\n",
       "Trust (Government Corruption)    0\n",
       "Generosity                       0\n",
       "Dystopia Residual                0\n",
       "Lower Confidence Interval        0\n",
       "Upper Confidence Interval        0\n",
       "Happiness.Rank                   0\n",
       "Happiness.Score                  0\n",
       "Whisker.high                     0\n",
       "Whisker.low                      0\n",
       "Economy..GDP.per.Capita.         0\n",
       "Health..Life.Expectancy.         0\n",
       "Trust..Government.Corruption.    0\n",
       "Dystopia.Residual                0\n",
       "Overall rank                     0\n",
       "Score                            0\n",
       "GDP per capita                   0\n",
       "Social support                   0\n",
       "Healthy life expectancy          0\n",
       "Freedom to make life choices     0\n",
       "Perceptions of corruption        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d48d257a-6810-48b6-af4c-c0ae78cd4f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 782 entries, 0 to 781\n",
      "Data columns (total 27 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Happiness Rank                 782 non-null    float64\n",
      " 1   Happiness Score                782 non-null    float64\n",
      " 2   Standard Error                 782 non-null    float64\n",
      " 3   Economy (GDP per Capita)       782 non-null    float64\n",
      " 4   Family                         782 non-null    float64\n",
      " 5   Health (Life Expectancy)       782 non-null    float64\n",
      " 6   Freedom                        782 non-null    float64\n",
      " 7   Trust (Government Corruption)  782 non-null    float64\n",
      " 8   Generosity                     782 non-null    float64\n",
      " 9   Dystopia Residual              782 non-null    float64\n",
      " 10  Lower Confidence Interval      782 non-null    float64\n",
      " 11  Upper Confidence Interval      782 non-null    float64\n",
      " 12  Happiness.Rank                 782 non-null    float64\n",
      " 13  Happiness.Score                782 non-null    float64\n",
      " 14  Whisker.high                   782 non-null    float64\n",
      " 15  Whisker.low                    782 non-null    float64\n",
      " 16  Economy..GDP.per.Capita.       782 non-null    float64\n",
      " 17  Health..Life.Expectancy.       782 non-null    float64\n",
      " 18  Trust..Government.Corruption.  782 non-null    float64\n",
      " 19  Dystopia.Residual              782 non-null    float64\n",
      " 20  Overall rank                   782 non-null    float64\n",
      " 21  Score                          782 non-null    float64\n",
      " 22  GDP per capita                 782 non-null    float64\n",
      " 23  Social support                 782 non-null    float64\n",
      " 24  Healthy life expectancy        782 non-null    float64\n",
      " 25  Freedom to make life choices   782 non-null    float64\n",
      " 26  Perceptions of corruption      782 non-null    float64\n",
      "dtypes: float64(27)\n",
      "memory usage: 165.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ebb04a01-b2a4-47e1-a996-c16b00e3fc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>...</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551341</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "0             1.0            7.587         0.03411                   1.39651   \n",
       "1             2.0            7.561         0.04884                   1.30232   \n",
       "2             3.0            7.527         0.03328                   1.32548   \n",
       "3             4.0            7.522         0.03880                   1.45900   \n",
       "4             5.0            7.427         0.03553                   1.32629   \n",
       "\n",
       "    Family  Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0  1.34951                   0.94143  0.66557                        0.41978   \n",
       "1  1.40223                   0.94784  0.62877                        0.14145   \n",
       "2  1.36058                   0.87464  0.64938                        0.48357   \n",
       "3  1.33095                   0.88521  0.66973                        0.36503   \n",
       "4  1.32261                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  ...  Health..Life.Expectancy.  \\\n",
       "0     0.29678            2.51738  ...                  0.551341   \n",
       "1     0.43630            2.70201  ...                  0.551341   \n",
       "2     0.34139            2.49204  ...                  0.551341   \n",
       "3     0.34699            2.46531  ...                  0.551341   \n",
       "4     0.45811            2.45176  ...                  0.551341   \n",
       "\n",
       "   Trust..Government.Corruption.  Dystopia.Residual  Overall rank     Score  \\\n",
       "0                        0.12312           1.850238          78.5  5.391506   \n",
       "1                        0.12312           1.850238          78.5  5.391506   \n",
       "2                        0.12312           1.850238          78.5  5.391506   \n",
       "3                        0.12312           1.850238          78.5  5.391506   \n",
       "4                        0.12312           1.850238          78.5  5.391506   \n",
       "\n",
       "   GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0        0.898298        1.211026                 0.661295   \n",
       "1        0.898298        1.211026                 0.661295   \n",
       "2        0.898298        1.211026                 0.661295   \n",
       "3        0.898298        1.211026                 0.661295   \n",
       "4        0.898298        1.211026                 0.661295   \n",
       "\n",
       "   Freedom to make life choices  Perceptions of corruption  \n",
       "0                      0.423538                   0.111299  \n",
       "1                      0.423538                   0.111299  \n",
       "2                      0.423538                   0.111299  \n",
       "3                      0.423538                   0.111299  \n",
       "4                      0.423538                   0.111299  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0e05aec6-c8e7-4053-a914-380a1e346cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Clean_data_Numeric.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "92a72431-29e4-4a58-8abe-b999fa6d4e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns which are object type (categorical)\n",
    "categorical_cols = df.select_dtypes(include=['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b324fff9-150d-4d37-a900-57c61b5e23cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use pd.get_dummies to convert categorical columns to one-hot encoded variables\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0407a61b-9825-47b3-81d5-7ca3493b4a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cl = range(1, 10) # Defines the range of potential clusters in the data.\n",
    "kmeans = [KMeans(n_clusters=i) for i in num_cl] # Defines k-means clusters in the range assigned above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e568b9c4-21b1-4fe2-b611-61dbc395c1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       " -454181.4295334231,\n",
       " -291186.84515546495,\n",
       " -177807.79464920092,\n",
       " -153546.89981791627,\n",
       " -129693.5858382197]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = [kmeans[i].fit(df).score(df) for i in range(len(kmeans))] # Creates a score that represents \n",
    "# a rate of variation for the given cluster option.\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "97644c96-622d-44a0-8faa-8f00bb9ed89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the elbow curve using PyLab.\n",
    "\n",
    "pl.plot(num_cl,score)\n",
    "pl.xlabel('Number of Clusters')\n",
    "pl.ylabel('Price')\n",
    "pl.title('Elbow Curve')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40dc544-83cf-48a1-9b59-c04cab469c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "The optimal cluster count is three, as indicated by the large jump from two to three on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6baf26b6-7dbd-4c7f-ae39-70a83267f02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the k-means object.\n",
    "\n",
    "kmeans = KMeans(n_clusters = 3) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2b94e5c4-d1d3-4533-9cb4-09e6e530f973",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the k-means object to the data.\n",
    "\n",
    "kmeans.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e41334ac-65c7-46ed-ab8a-9fb478597b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "df['clusters'] = kmeans.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47020608-4c5e-4a6a-a038-fb9e22f93c7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a5604e3f-71d2-4e0e-b808-886a5b9add82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
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       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>...</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
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       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12312</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "0             1.0            7.587         0.03411                   1.39651   \n",
       "1             2.0            7.561         0.04884                   1.30232   \n",
       "2             3.0            7.527         0.03328                   1.32548   \n",
       "3             4.0            7.522         0.03880                   1.45900   \n",
       "4             5.0            7.427         0.03553                   1.32629   \n",
       "\n",
       "    Family  Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0  1.34951                   0.94143  0.66557                        0.41978   \n",
       "1  1.40223                   0.94784  0.62877                        0.14145   \n",
       "2  1.36058                   0.87464  0.64938                        0.48357   \n",
       "3  1.33095                   0.88521  0.66973                        0.36503   \n",
       "4  1.32261                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  ...  Trust..Government.Corruption.  \\\n",
       "0     0.29678            2.51738  ...                        0.12312   \n",
       "1     0.43630            2.70201  ...                        0.12312   \n",
       "2     0.34139            2.49204  ...                        0.12312   \n",
       "3     0.34699            2.46531  ...                        0.12312   \n",
       "4     0.45811            2.45176  ...                        0.12312   \n",
       "\n",
       "   Dystopia.Residual  Overall rank     Score  GDP per capita  Social support  \\\n",
       "0           1.850238          78.5  5.391506        0.898298        1.211026   \n",
       "1           1.850238          78.5  5.391506        0.898298        1.211026   \n",
       "2           1.850238          78.5  5.391506        0.898298        1.211026   \n",
       "3           1.850238          78.5  5.391506        0.898298        1.211026   \n",
       "4           1.850238          78.5  5.391506        0.898298        1.211026   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  \\\n",
       "0                 0.661295                      0.423538   \n",
       "1                 0.661295                      0.423538   \n",
       "2                 0.661295                      0.423538   \n",
       "3                 0.661295                      0.423538   \n",
       "4                 0.661295                      0.423538   \n",
       "\n",
       "   Perceptions of corruption  clusters  \n",
       "0                   0.111299         2  \n",
       "1                   0.111299         2  \n",
       "2                   0.111299         2  \n",
       "3                   0.111299         2  \n",
       "4                   0.111299         2  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dc76b2da-84a0-4bc1-b829-40f2c3769b60",
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "clusters\n",
       "1    540\n",
       "2    122\n",
       "0    120\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "77b068ff-e8e9-4572-bc97-5e1aabfe772b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting \n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# custom color palette\n",
    "palette = sns.color_palette(['#9b59b6', '#3498db', '#2ecc71'])  # Dark purple, blue, green\n",
    "\n",
    "# scatterplot \n",
    "ax = sns.scatterplot(x=df['Happiness Score'], y=df['Economy (GDP per Capita)'], hue=kmeans.labels_, s=100, palette=palette)\n",
    "\n",
    "# Removing the grid from the background.\n",
    "ax.grid(False)\n",
    "\n",
    "# x-axis and y-axis.\n",
    "plt.xlabel('Happiness Score')\n",
    "plt.ylabel('Economy (GDP per Capita)')\n",
    "\n",
    "# title \n",
    "plt.title('Happiness Score by Economy (GDP per capita')\n",
    "\n",
    "# assigning names to clusters based on the label\n",
    "df.loc[df['clusters'] == 2, 'cluster'] = 'dark purple'\n",
    "df.loc[df['clusters'] == 1, 'cluster'] = 'blue'\n",
    "df.loc[df['clusters'] == 0, 'cluster'] = 'green'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{int(x/1000)}k'))\n",
    "\n",
    "# Your code...\n",
    "\n",
    "\n",
    "#saving chart to PNG\n",
    "plt.savefig('Happiness Score by Economy .png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a17b600d-2e58-45b8-869a-8d0fe3dba0e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iZZqS3RlSR9UCANDwcCQdAABY7A6Hti1eK9M0VVbiU0y7LopIb6PwJs0V3rS5ItLbKKZdZ5WV+GRK2rboZ9kd9rouGwCABoOQDgAALDaHXcntmunHd2cqNCZSrsIy5e0pUN6+UuXtLVX+ngKVF5UpNDpSP77znVI6NJcthJAOAEBN4XR3AABgMWyGopPjlDGws75+9B2ldkxXqwGdFB4XJUkqzS/WsvdmKnvTbg2+5TxFJcXKMIw6rhoAgIajzo+kT5w4URkZGQoLC1OfPn00f/78aq23cOFCORwO9ezZM7gFAgDQyNidDiW3a6ZzHr9OUSlx+vHd7zTzmSma+cwULXtvpmKaJmjMY9cpqU2a7CHs7wcAoCYZZh3eN2XKlCm65pprNHHiRA0ePFivvfaa/vOf/2jt2rVq0aLFcdcrKChQ79691bZtW+3bt08rVqyo9jYLCwsVGxurgoICxcTE1MBPAQBAw+WpcMvmsMtd4pIMKSQiVD6PVw4miwMAoNoCyaF1GtL79++v3r1765VXXrGWderUSWPHjtWTTz553PUuv/xytWvXTna7XdOnTyekAwAAAADqrUByaJ2d7l5RUaFly5Zp9OjRfstHjx6tRYsWHXe9SZMmacuWLXrkkUeCXSIAAAAAALWqzi4ky8nJkdfrVWpqqt/y1NRU7d2795jrbNq0SX/84x81f/58ORzVK93lcsnlcllfFxYWnnrRAAAAAAAEUZ1PHHf0jLCmaR5zlliv16srr7xSjz32mNq3b1/t7//kk08qNjbW+peenv6LawYAAAAAIBjq7Jr0iooKRURE6KOPPtK4ceOs5XfddZdWrFihuXPn+o3Pz89XfHy87PbD92L1+XwyTVN2u13ffvutRo0aVWU7xzqSnp6ezjXpAABUg8fllmSqJLdIkhSZGCPJlCPUWad1AQDwaxLINel1drq70+lUnz59NGPGDL+QPmPGDF144YVVxsfExGj16tV+yyZOnKhZs2bp448/VkZGxjG3ExoaqtDQ0JotHgCABs7jcqskp0DrvvlRu37aJJ/HK0myh9jVvHd7dTyrryITY+QIZZZ3AABqUp3e3PSee+7RNddco759+2rgwIF6/fXXlZmZqVtuuUWS9OCDD2r37t16++23ZbPZ1LVrV7/1U1JSFBYWVmU5AAA4dR6XW7tWbNYPb82Q6fP5PeZ1e7VjyTpl/rBB/X9zltK6tyaoAwBQg+o0pI8fP165ubl6/PHHlZWVpa5du+rLL79Uy5YtJUlZWVnKzMysyxIBAGhUfF6v8nZm64e3vpXpO/4VcabPpyWTvtGo+y5VQqtUGbY6n+YGAIAGoU7vk14XuE86AADH53V7NP/lT7V//c5qjW/SpZUG33ye7M463e8PAEC99qu4TzoAAKh/XEVl2r+hegFdkvau3a6K0vIgVgQAQONCSAcAAJbcbVlSIOfYmdKBHfuCVg8AAI0NIR0AAFh8Xt/JBx29jifwdQAAwLER0gEAgCU6Nf4U1omr+UIAAGikCOkAAMAS1zw5oKAe0zRBMU0Tg1gRAACNCyEdAABY3GUutRvZs9rj243sJU95RfAKAgCgkSGkAwAAy4Ed+9Wyf0e16NfhpGNbDuik9NPaK2/n/lqoDACAxoGQDgAALJ4ylxa9/j/1ufJ0dbtwkEKjI6qMCYuJULexg9X78pFa9OoX8pS766BSAAAaJkddFwAAAOqPmLRE7d+wS/vWZartqJ7qMLqPdq/YosKsA5Kk2LQkpfXIkM/jVdaa7cretEt9rhxVx1UDANBwGKZpBnI31F+9wsJCxcbGqqCgQDExMXVdDgAA9YrP69W+9TsVFhOpBS9Pl90Zopb9Oyo8LkqSVJZXrO3fr5PP69XQCWNVmlekJh1byOaw13HlAADUX4HkUE53BwAARzAUm5aoeS9OU1l+iUpyCpW7da8ObKv8l7s1S6UHilSWV6y5L0xTfPNkyWbUddEAADQYnO4OAAAsPo9Xqz9dJNM01eW8AWo9pKt8Xp+K9uZJkqKbxsuw2bR1/mptnr1Caz5frF6XjZAtlP3+AADUBEI6AACwmKapvB17dfoDl6v0QKF+ePc77ft5hw5dHWcYhpp0baUOZ/RWy34dteCVzyQOpAMAUGO4Jh0AAFj2/rxdUSnx2rN6q1Z8NFc63qcEQ+o9fqRSO7dQaV6xUjuk12qdAAD8mnBNOgAAOCXh8dEq3HvgxAFdkkzppymzVZJdqLCYqrdpAwAAp4aQDgAALJFJMVr/zQ8nDuiHmNL6b39QZAJnpgEAUFMI6QAAwFJeUKqczXuqPX7/hl2qKCkPYkUAADQuhHQAAGDJ27k/8HV2ZQehEgAAGidCOgAAsJg+X62sAwAAjo2QDgAALDFNEwNfp0lCECoBAKBxIqQDAABLTJMExTZLqvb4+BYpikqODWJFAAA0LoR0AABwmGmqw+g+1R7e4cw+MqszEzwAAKgWQjoAALDYHHal92mvtsO7n3Rsu1E91axnG9kd9lqoDACAxoGQDgAALJ4Kt7YvWquuFwxSnytHKSolrsqYqJQ49bnqdHU+p792LF0vT4W79gsFAKCBctR1AQAAoP6w2e1a8/libZz5k7qcN0BnPXS1cjbvUWFWrmQYimmaqKQ2TbVr+WbNfGaKPC63Wg3oXNdlAwDQYBDSAQCAJWfLHrmKSuUqKtX3//1KYTERSu/bQeFxkZKkPau26Pv/filXUZm1zoHte5XUJq2uSgYAoEEhpAMAAEtZfrHf1+WFpdo0a/mJ1ykoCWZJAAA0KlyTDgAALCFhzoDXcYSGBKESAAAaJ0I6AACwJLVtJlsAs7XbQ+yc6g4AQA0ipAMAAIvNYVPzXm2rPb55n/YyDCOIFQEA0LgQ0gEAgMXhDFG3sYMVEh560rHOiFB1u2AQp7sDAFCDCOkAAMBPWEyERtxzsUKjwo87JjQ6XCPuvVSh0ccfAwAAAsfs7gAAwI89xKGYJgk694nfatuiNdo8Z5WK9udJkmKaJKjt8B5qNaizDJtN9gCuXwcAACdHSAcAAFXYQyo/IrQe0k1th/eQVHnduSlTps8knAMAECSEdAAAcFyHwvohhgwulgMAIIh4mwUAAAAAoJ4gpAMAAAAAUE8Q0gEAAAAAqCcI6QAAAAAA1BOEdAAAcFyeCrdMn08VpS5VlLpk+nzyVLjruiwAABosZncHAABVeN0eucsrtHHmcm1buEauojJJUlhMhDIGd1X7Ub3kCAupMvs7AAD4ZXhnBQAAfrxuj3K3ZmnBxM/kcfkfNS8vLNW6r5Zq0+wVGnr7hUpomSq7k48TAADUFE53BwAAFtNnqjinQPP/9WmVgH4kT3mF5r30iUrzimSaZi1WCABAw0ZIBwAAFq/HqzWfLpLX7Tn52AqPVn9WvbEAAKB6COkAAMDirXBrz6qt1R6/Z8UW+dzeIFYEAEDjQkgHAACWnM17ZPqqf/q6z+tT7rasIFYEAEDjQkgHAACWU7m9mqeC090BAKgphHQAAGCJTIwJfJ2E6CBUAgBA40RIBwAAloSMJgqPj6r2+MjEGMWlpwSxIgAAGhdCOgAAsPg8PrUd3qPa49uO6CHT5wtiRQAANC6EdAAAYHE4HepwRm+ldEw/6dgmnVuq3ciesoc4aqEyAAAaB0I6AADwY3PYNXTCWLUZ1l02h73K4/YQu9oO764ht11wzMcBAMCpM0zTrP59VhqAwsJCxcbGqqCgQDExgU+OAwBAY+GpcMv0mdq28GcV7M6RDCmuWZIyBneRDEMOZ0hdlwgAwK9CIDmU89MAAMAxHQrhbYZ3k89Ted25zWGXnaPnAAAEDSEdAACckN3hkJ1PDAAA1AquSQcAAAAAoJ4gpAMAgOPyeb3yebyHv/Z45fNyyzUAAIKFk9cAAEAVXo9XhiHtXLZJm2evVGFWrmQYiklLULuRPdW8VztJpmx2rk8HAKAmEdIBAIAfr8er0gNFmvv8VJXmFfk9lrslS7lbshSRuFAj7r5I4fHRTCQHAEAN4nR3AADgp6KkXLP+PqVKQD9SaW6hZv39Q7nLXLVYGQAADR8hHQAAWDwVbq2evlCuorKTji0vLNWaTxfJU+GuhcoAAGgcCOkAAOAw09TOHzdWe3jmDxuCWAwAAI0PIR0AAFj2b9wlr9tT7fEel1s5m/cEsSIAABoXQjoAALC4yypOYR2uSwcAoKYQ0gEAgCU0KrxW1gEAAMdGSAcAAJbk9s3ljAyr9vjQ6HAltUkLYkUAADQuhHQAAGBxFZUqY1Dnao/PGNRF5UWlQawIAIDGhZAOAAAsNrtNnc7up5imCScdG5uWqI5nnSabw14LlQEA0DgQ0gEAgMUR5tTulVs04veXKLl98+OOS+mQrhH3XKLdKzbL4QypxQoBAGjYHHVdAAAAqD9sdpsiEmK09sslGnLr+Sran6+t81erICtXhgzFpCWqzdBuikyO1erpC9WibwfZHOzzBwCgphDSAQCAxTSl2GaJKs0t1Bd/+q9a9O2gVoO6KDwuUpJUllesLfNWKXPZJvUeP1zRTeIls46LBgCgATFM02xUb62FhYWKjY1VQUGBYmJi6rocAADqHa/Hq/KCEpUXlWrjd8u1e/km+bw+SZLNYVfz3m3V/vTeCo0KV1hspOxckw4AwAkFkkM5kg4AAPzYHXaFx0aqNK9Y3cYOUu/LR6h4f75kGIpKjpW7zKXyghICOgAAQUBIBwAAVdgcdiW0TJFhsyl/d45cxWWSIdlC7IpNS1R4XBQBHQCAICCkAwCAY7KHVH5MSGiRIjM9WZJkGEblgzYmiwMAIBgI6QAA4KSscA4AAIKK3eAAAAAAANQThHQAAAAAAOoJQjoAAAAAAPUEIR0AAAAAgHqCkA4AAAAAQD1BSAcAAAAAoJ4gpAMAAAAAUE8Q0gEAwHF5PV75vD7ra5/XJ5/HW4cVAQDQsDnqugAAAFD/eD1eyTS1Y8k6bZq9UkV7D0iSYtIS1XZ4D7Xs31EyDNkd9jquFACAhsUwTdOs6yJqU2FhoWJjY1VQUKCYmJi6LgcAgHrH6/aoOLtAc1+YqvLC0mOOCYuJ0IjfX6LI5FiCOgAAJxFIDuV0dwAA4MdVXKbZz3103IAuSeWFpZr93EeqKC6rxcoAAGj4COkAAMDiqXBr9fSFqigpP+lYV3GZ1ny2SJ4Kdy1UBgBA40BIBwAAFtPr085lm6o9PvOHjVKjunAOAIDgYuI4AABgyd6025q9PSwmQq2HdFPLAZ0UER8lSSrNK9L2xeu0beEalReWyuv2KGfzbjXp0qoOqwYAoOEgpAMAAIu7vEKS1PX8geowuo/2b9iplVPnqTCrcnb32LREtR7aTZ3P6ad1X/+gtf9bInc5p7sDAFBTCOkAAMASHhupXuNHqGm3DH335GQV7Mn1e7x4f752r9iiuObJGnzr+XJGhCosNqKOqgUAoOHhmnQAAGBJatdM6X3aac7zU6sE9CPl78rWnOc/Vot+HZWY0bQWKwQAoGEjpAMAAIu3wqOf/7dEpbmFJx1bklOodV8tldftqYXKAABoHAjpAADAYrPbtGPJ+mqP3754nWwOexArAgCgcSGkAwAAS/bGXfIcnDyuOtxlLuVs3hPEigAAaFyYOA4AAFhcxWV+X0elxKll/46KiIuWVHkLth1L1qk4u8AaU1FSXqs1AgDQkBHSAQCAJTQqXJIU3yJF3cYOVnK7Ztq9cqsKs3JlGIZimiborIev0f6Nu7R6+kLl78yWMzKsjqsGAKDhIKQDAABLcvvmSuvRRv1/c5Y2zV6hJZO+kauo1G9MWEyE2o3qpZH3Xqolb3yt5LZpdVQtAAANj2GaplnXRdSmwsJCxcbGqqCgQDExMXVdDgAA9YrH7ZZ80o/vfafMpRtOOLblgE7qc8UoyZAczpBaqhAAgF+fQHIoE8cBAACLIUPbl6w7aUCXpB3fr1Pmjxtk2Pg4AQBATeFdFQAAWAy7TZtmLq/2+I3f/STDMIJYEQAAjQshHQAAWAp2ZatoX161xxdmHVBhVm4QKwIAoHEhpAMAAEvRvvyA1ynODnwdAABwbIR0AABgsdkD/2hgs9uDUAkAAI0TIR0AAFjiW6ZKgVxibkhx6SlBqwcAgMaGkA4AACzOyDCldEiv9vgmnVspJMwZxIoAAGhcCOkAAMDiKi5T5zH9ZNhOfjjdsNnU6ezTVFFaXguVAQDQOBDSAQCAJSI+WmGxkTrtmjNPeP9zm92mftedKWdkmMLjomqxQgAAGjZCOgAAsJQXlChrzTbFpSfr9AfGq0W/jrI5Dk8MZ3PY1bJ/J53+wOWKaZqofeszVV5QUocVAwDQsDjqugAAAFB/2MNC1GZody185XPFNE1Q53P6qdf4ESrJzpcMQ1FJsSorLNGWuatUtD9fg28+Tz6fr67LBgCgwSCkAwAAi91h17ZFP2vQLefpp8mz9c3j7yq+RYrC4yIlSWX5xcrLzFbL/h016Hfnatvin5UxqEsdVw0AQMNR56e7T5w4URkZGQoLC1OfPn00f/78445dsGCBBg8erMTERIWHh6tjx456/vnna7FaAAAatvKCEiVmNNXulZvV56pRuuDpG9W0WyvZHHbZHA6ldW+tC565Sb0vH6mdyzYpqXWaygtL67psAAAajDo9kj5lyhTdfffdmjhxogYPHqzXXntNY8aM0dq1a9WiRYsq4yMjIzVhwgR1795dkZGRWrBggW6++WZFRkbqd7/7XR38BAAANCwHtu9TYuumatolQ3Nf+ETOCKdaDeys1I6V78ulecVa8uY38pRVaMhtF6iizKW8ndmKSoqt48oBAGgYDNM0zbraeP/+/dW7d2+98sor1rJOnTpp7NixevLJJ6v1PS666CJFRkbqnXfeqdb4wsJCxcbGqqCgQDExMadUNwAADdWB7XsVmRSrWX//UEX78k44NqZpgkb94TIVZxcooWVqLVUIAMCvTyA5tM5Od6+oqNCyZcs0evRov+WjR4/WokWLqvU9li9frkWLFmn48OHHHeNyuVRYWOj3DwAAHFt0kwSt/XLpSQO6JBVmHdC6r35QdGp8LVQGAEDjUGchPScnR16vV6mp/nveU1NTtXfv3hOu27x5c4WGhqpv3766/fbbdeONNx537JNPPqnY2FjrX3p6eo3UDwBAQ2Rz2LX9+7XVHr9t0c+yH3GLNgAA8MvU+cRxhmH4fW2aZpVlR5s/f75+/PFHvfrqq3rhhRc0efLk44598MEHVVBQYP3buXNnjdQNAEBDlL1pl9ylrmqPrygpV87WrCBWBABA43JKE8dt3LhRc+bM0f79+6vcG/Xhhx+u1vdISkqS3W6vctR8//79VY6uHy0jI0OS1K1bN+3bt0+PPvqorrjiimOODQ0NVWhoaLVqAgCgsXMVldXKOgAA4NgCDun//ve/deuttyopKUlNmjTxO+ptGEa1Q7rT6VSfPn00Y8YMjRs3zlo+Y8YMXXjhhdWuxzRNuVzV3+MPAACOzxkR+I7tkHBnECoBAKBxCjik//Wvf9UTTzyhBx544Bdv/J577tE111yjvn37auDAgXr99deVmZmpW265RVLlqeq7d+/W22+/LUl6+eWX1aJFC3Xs2FFS5X3Tn332Wd1xxx2/uBYAACAlt2suu9Mhb4XHWmZz2BUaFS5JchWXyefxWo85QkOU3K5ZrdcJAEBDFXBIz8vL06WXXlojGx8/frxyc3P1+OOPKysrS127dtWXX36pli1bSpKysrKUmZlpjff5fHrwwQe1bds2ORwOtWnTRk899ZRuvvnmGqkHAIDGzuNyq8VpHbRt4c9KyGiitsO7K71Pe9lDKj8yeN0eZf64UVvmrtSB7fvUsn8nuctc1uMAAOCXCfg+6TfccINOO+0062j3rw33SQcA4PgOZO5TRFy0crbsVkrHFtqxeK22zF+tor2Vt2SLaZqg1sO6qVX/Ttq7NlMpHZqr5ECxElok13HlAADUX4Hk0IB3e7dt21YPPfSQvv/+e3Xr1k0hISF+j995552BfksAAFBPRCbFyjAMRSXH6auH36wyKVzBnlwt/2CO1n25VMPvukiGzVBUUnQdVQsAQMMT8JH0QzOrH/ObGYa2bt36i4sKJo6kAwBwfD6vVyW5hfruqQ/kLnXJHmJXk64ZioiPkiSVHihS1prt8nm8ckaG6YwHr1BEXJRs3CsdAIDjCuqR9G3btp1yYQAAoH7zur36+YvvZXfY1emiIcoY1EWu4jIV7j0gSYptmqi+V5+hbQt/1obvlmnt/5ao12UjCOkAANSQXzTLy6GD8Efehg0AAPx6mT6fCnbn6vQ/Xq6CXTla9Pr/lL1xl9+YlA7N1eHMPjrjj1dowcRP66hSAAAaJtuprPT222+rW7duCg8PV3h4uLp376533nmnpmsDAAC1LHfrXg29/UJlLt2gBRM/qxLQJWn/hl2a/69PtXPZRg257UId2J5VB5UCANAwBXwk/R//+IceeughTZgwQYMHD5Zpmlq4cKFuueUW5eTk6Pe//30w6gQAALUgIiFKeZn7tXr6wpOOXTVtgWKaJCgyObYWKgMAoHE4pYnjHnvsMV177bV+y9966y09+uij9f6adSaOAwDg+Lxuj+b8Y6pyt1Xv6HhS22Yaduc4OZzcJx0AgOMJJIcGfLp7VlaWBg0aVGX5oEGDlJXF6W4AAPyaleYVVzugS1LO5t0qLywJYkUAADQuAYf0tm3b6sMPP6yyfMqUKWrXrl2NFAUAAOpG/s79Aa9TsDsnCJUAANA4BXxu2mOPPabx48dr3rx5Gjx4sAzD0IIFCzRz5sxjhncAAPArEtBFcL9gHQAAcEwBH0m/+OKLtWTJEiUlJWn69OmaNm2akpKStHTpUo0bNy4YNQIAgFoS0ywx8HWaJgShEgAAGqdTmuWlT58+evfdd2u6FgAAUMeiU+IUl56s/J3Z1Rqf0CpVkYlMxAoAQE2p1pH0wsJCv/8/0T8AAPDrZfpMdRzdt9rjO47uy9nuAADUoGodSY+Pj1dWVpZSUlIUFxcnwzCqjDFNU4ZhyOv11niRAACgdhjyKa17K3UY3Ucbvl12wrGdzj5NTTq3kGH6JNlrp0AAABq4aoX0WbNmKSGh8nqz2bNnB7UgAABQdzwlJXLl56rr+QMV1zxZ67/+QQV7cv3GxDZLUqezT1Oznm1UumubQhOT5QyJq5uCAQBoYKoV0ocPH279f0ZGhtLT06scTTdNUzt37qzZ6gAAQK0yDSkspZl2LF0vSTr9j5crf2e2CrMOSIYU2zRRsc2TlLl0vXYu26hm3VvJdFfUcdUAADQcAU8cl5GRYZ36fqQDBw4oIyOD090BAPgVs4dFKGdzln585ztJ0qqp89W8dzuFx0VJkg5s26udyzbJXeaSDGn4XeOUmNGkLksGAKBBCTikH7r2/GjFxcUKCwurkaIAAEDdMOwOrf1yqfV1RalLWxesOfZgU1r75Q8adsfY2ikOAIBGoNoh/Z577pEkGYahhx56SBEREdZjXq9XS5YsUc+ePWu8QAAAUHvKDhQpZ8ueao/P3rhL5YWl3IYNAIAaUu2Qvnz5ckmVR9JXr14tp9NpPeZ0OtWjRw/dd999NV8hAACoNXnVvD/6kfJ3ZRPSAQCoIdUO6Ydmdf/Nb36jF198UTExvBkDANDgmIHf9dw8hXUAAMCxBXxN+qRJk6z/37lzpwzDUPPmzWu0KAAAUDdimiYEvk6TwNcBAADHZgt0BY/Ho4ceekixsbFq1aqVWrZsqdjYWP35z3+W2+0ORo0AAKCWRDdJUGyzJOvryKQYdT6nv/pec4b6XnOGOp/Tz+/U9vgWKYpKjq2LUgEAaJACPpI+YcIEffLJJ3rmmWc0cOBASdLixYv16KOPKicnR6+++mqNFwkAAGqJaarD6D7aOOMn9bh4iJLbN5crP1/yuCRJRqem6nxuf+3fsFMrpy5Qh9F967ZeAAAaGMMM8EKy2NhYffDBBxozZozf8q+++kqXX365CgoKarTAmlZYWKjY2FgVFBRwXT0AAEfxVLgl05Rht8mVu1+u3GyZHv8z5QxHiEITUxSamCyfxyfDZsjhDKmjigEAqP8CyaEBH0kPCwtTq1atqixv1aqV34zvAADg18iUYbOpbM9OVeTnHnuEx63yfbvlqyhXeGpzMW0cAAA1J+Br0m+//Xb95S9/kcvlspa5XC498cQTmjBhQo0WBwAAaldFiUsV+bnHDeh+Y/NyVVGQp4qS8lqoDACAxiHgI+nLly/XzJkz1bx5c/Xo0UOStHLlSlVUVOj000/XRRddZI2dNm1azVUKAACCLjw2UgWbdlR7fEVetmLadgpiRQAANC4Bh/S4uDhdfPHFfsvS09NrrCAAAFB3PGWl8rmqf2TcW14mT3mZQiIig1gVAACNxy+6TzoAAGhYfO6KgNcx3RWSCOkAANSEgK9JBwAADZch4xRWOoV1AADAMQV8JD0jI0PGCd6Mt27d+osKAgAAdcd+CqetO8IjglAJAACNU8Ah/e677/b72u12a/ny5fr666/1hz/8oabqAgAAdcDmcMgRFS1PcVG1xodExciwB/xxAgAAHEfA76p33XXXMZe//PLL+vHHH39xQQAAoO6YphSa2KTaId2Z1ESmqVM5SR4AABxDjV2TPmbMGE2dOrWmvh0AAKgDxTkFcrt8Cm/a4iQjDYWntVRFmUeleYW1UhsAAI1BjYX0jz/+WAkJCTX17QAAQB0o2JWt2c9PlcfnUGSr9nLGJfhPDGcYcsYlKLJVO7ndNs15fqoKduXWXcEAADQwAZ/u3qtXL7+J40zT1N69e5Wdna2JEyfWaHEAAKD2leUVa8bfJqv1kK5qP6qXYlKby1NWJklyhIfLVVymtV8v17aFa+RxuTnXHQCAGhRwSB87dqzf1zabTcnJyRoxYoQ6duxYU3UBAIA6ENssSZLkcbm1ceZybZy1XImtmyo8LkpSZYDP3ZYlmUesk5ZYF6UCANAgBRTSPR6PWrVqpbPOOktNmjQJVk0AAKCORCXFKqFVqg5s31e5wJRyt2Qdd3xim6YKj4+upeoAAGj4Arom3eFw6NZbb5XL5QpWPQAAoC4ZUqezT6v28E5nn+Z3GRwAAPhlAp44rn///lq+fHkwagEAAHXM5/UpqW0zdTlvwEnHdr1goBJbNZHP662FygAAaBwCvib9tttu07333qtdu3apT58+ioyM9Hu8e/fuNVYcAACoXabXp3n//ERDbrtAMWmJ2jBjmQ5s2+s3JrF1U3UY3UcJLVM195+faOS9l9ZRtQAANDyGaZrmyYcdZrNVPfhuGIZM05RhGPLW873phYWFio2NVUFBgWJiYuq6HAAA6pU9q7ZqwcTPFB4XpY6j+6jlwM4qzS1UYdYBSVJM0wRFJMRo++K1Wv/tjyovKNHQO8aqaZdWdVs4AAD1WCA5NOAj6du2bTvlwgAAQP3mLq+QJJXlF2v5h3O1+tNFSuvRWhFxlZPD7Vm1VbtXbpG3wmOt43G566RWAAAaooBCutvt1siRI/XFF1+oc+fOwaoJAADUkUO3WjvE43Irc+mGE68TG3nCxwEAQPUFNHFcSEiIXC4Xs7gCANBAJbVJU2h0RLXHh8VGKqEVt2UFAKCmBDy7+x133KGnn35aHo/n5IMBAMCvis/rVeshXas9vs2wbszuDgBADQr4mvQlS5Zo5syZ+vbbb9WtW7cqs7tPmzatxooDAAC1y+EMUedz+mnv2h3K27HvhGMTMpqo41mnye6w11J1AAA0fAGH9Li4OF188cXBqAUAANQDht2mkfdeoiVvfK3dK7dIR98HxpCa92qrfteffcy7vgAAgFMX8C3Yfu24BRsAANXjdXtUUVKuTbNXqGBPrgzDUExaotqN7ClnRKjsIQHv6wcAoFEK6i3YDsnOztaGDRtkGIbat2+v5OTkU/1WAACgHrKHOBQeF6XO5/aX6avcp2/YDDmcIXVcGQAADVfA56iVlJTot7/9rZo2baphw4Zp6NChSktL0w033KDS0tJg1AgAAOqQwxmikDCnQsKcBHQAAIIs4JB+zz33aO7cufr888+Vn5+v/Px8ffrpp5o7d67uvffeYNQIAAAAAECjEPA16UlJSfr44481YsQIv+WzZ8/WZZddpuzs7Jqsr8ZxTToAAAAAoDYFkkMDPpJeWlqq1NTUKstTUlI43R0AAAAAgF8g4JA+cOBAPfLIIyovL7eWlZWV6bHHHtPAgQNrtDgAAAAAABqTgGd3f/HFF3X22WerefPm6tGjhwzD0IoVKxQWFqZvvvkmGDUCAAAAANAonNJ90svKyvTuu+9q/fr1Mk1TnTt31lVXXaXw8PBg1FijuCYdAAAAAFCbgn6f9PDwcN10002nVBwAAAAAADi2gK9Jf/LJJ/XGG29UWf7GG2/o6aefrpGiAABA/eEur/D7BwAAgifgkP7aa6+pY8eOVZZ36dJFr776ao0UBQAA6p7X7VFJToHWfLZYi177Qote/59+/nyxSnIL5XV76ro8AAAapIBPd9+7d6+aNm1aZXlycrKysrJqpCgAAFB3vF6vTI9Pi//zpbJWb/N7bN/aHdo4c7nSerTWgN+Okc1hk81ur6NKAQBoeAI+kp6enq6FCxdWWb5w4UKlpaXVSFEAAKAOeU3NevbDKgH9SHtWbtXs5z6S6Qt4/lkAAHACAR9Jv/HGG3X33XfL7XZr1KhRkqSZM2fq/vvv17333lvjBQIAgNrjLq/Quq9+UP7O7JOOzcvcr3VfLVWH0X0VEuasheoAAGj4Ag7p999/vw4cOKDbbrtNFRWVk8eEhYXpgQce0IMPPljjBQIAgNpjD3Fo26I11R6/dcEadT53QBArAgCgcQk4pBuGoaeffloPPfSQ1q1bp/DwcLVr106hoaHBqA8AANSi7I275Coqq/b48sJS5W7NUnK7ZkGsCgCAxuOU7pMuSVFRUTrttNNqshYAAFDHygpLAl+nIPB1AADAsQU8cRwAAGi4TuXa8pCwkCBUAgBA40RIBwAAlpT2zWVzVP+WavYQh5Lbcqo7AAA1hZAOAAAshs2m5r3bVXt8et/2Mo0gFgQAQCNTrZDeu3dv5eXlSZIef/xxlZaWBrUoAABQNxyhIep24SA5I04+IawzMkxdLxikkFBuvwYAQE2pVkhft26dSkoqJ4V57LHHVFxcHNSiAABA3QmNDteIey5RaHTEcceExURo5L2XyhkZVouVAQDQ8FVrdveePXvqN7/5jYYMGSLTNPXss88qKirqmGMffvjhGi0QAADULoczRFEp8Trvb7/V9sVrtXnuKhVmHZAkxaYlqs2I7mrVv5OkymvSAQBAzTFM0zRPNmjDhg165JFHtGXLFv3000/q3LmzHI6qb8qGYeinn34KSqE1pbCwULGxsSooKFBMTExdlwMAQL3mcXtks9lks1eefOfz+mR6fbI7CecAAFRXIDm0WiH9SDabTXv37lVKSsovKrKuENIBAAAAALUpkBwa8G5wn893yoUBAAAAAIDjO6Vz1bZs2aIXXnhB69atk2EY6tSpk+666y61adOmpusDAAB1zOf1yefxSpJsDrt16jsAAKh5AYf0b775RhdccIF69uypwYMHyzRNLVq0SF26dNHnn3+uM888Mxh1AgCAWuapcMswDO1Ysl4Fu3MkQ4prnqwWp3WQaZpyOEPqukQAABqcgK9J79Wrl8466yw99dRTfsv/+Mc/6ttvv2XiOAAAGgCfx6vVny7Ulnmr5XG5/R5zhDnVdnh3dT1/oGwOex1VCADAr0dQJ44LCwvT6tWr1a5dO7/lGzduVPfu3VVeXh54xbWIkA4AwIn5PF4tfO0LZa3edsJxzXq20cAbzyGoAwBwEoHk0IAvKktOTtaKFSuqLF+xYsWvdsZ3AABQyev2aPOclScN6JK0e8UWbZm/Wl63pxYqAwCgcQj4mvSbbrpJv/vd77R161YNGjRIhmFowYIFevrpp3XvvfcGo0YAAFBLbHabNs1eUe3xm2avUJvh3YNXEAAAjUzAIf2hhx5SdHS0nnvuOT344IOSpLS0ND366KO68847a7xAAABQew7s2KeS3MJqjy/en6+CXTmKb8HZdAAA1ISAQ7phGPr973+v3//+9yoqKpIkRUdH13hhAACg9pXkVD+gH1KcU0BIBwCghpzSfdIPIZwDANCwnMokcPYQJo4DAKCmBDxxHAAAaLgSM5rIMIxqjzdshhJaNgliRQAANC6EdAAAYLGHhqhJ11bVHp/WvbVsDj5OAABQU3hXBQAAFneZS53H9KvWae82h12dzj5Nbpe7FioDAKBxqJGQnp+fXxPfBgAA1LHwuCjZQhwacOOYE15rbg9xaOBN50g2Q+GxkbVYIQAADVvAIf3pp5/WlClTrK8vu+wyJSYmqlmzZlq5cmWNFgcAAGqX6fUpZ/MuhUVHaPSfr1a7kT0VEh5qPR4SEap2o3pp9ENXyRkRqgPb9sn0+uqwYgAAGhbDNE0zkBVat26td999V4MGDdKMGTN02WWXacqUKfrwww+VmZmpb7/9Nli11ojCwkLFxsaqoKBAMTExdV0OAAD1js/j1cLXv1BK+3S17NdRzsgwuYpLJRkKjQpXRUmZdixZr+wtuzXoxnNPaUZ4AAAak0ByaMAhPTw8XBs3blR6erruuusulZeX67XXXtPGjRvVv39/5eXl/aLig42QDgDAyfm8Xvk8HlXkZstdWijDqAzips+rkKgYOROSZXM4ZLMT0AEAOJlAcmjAp7vHx8dr586dkqSvv/5aZ5xxhiTJNE15vd5TKBcAANQnps8nb1mpCjf9rPKcvfKWlspTUiRPSZG8ZaUqz96rwk0/y1teKtPHqe4AANSkgEP6RRddpCuvvFJnnnmmcnNzNWbMGEnSihUr1LZt2xovEAAA1B7TNOWrcKlo2ybpRAHc51PR1k3yuSsU4El5AADgBByBrvD888+rVatW2rlzp5555hlFRUVJkrKysnTbbbfVeIEAAKAWmaZK9+6WzGocITd9Ktu7W5HpGZJhBL82AAAagYCvSf+145p0AACOz+dxK39t1bu1GPbK/fqm13PUA4biOvWQzRHwfn8AABqNQHLoKb2jbty4UXPmzNH+/fvlO+pUuIcffvhUviUAAKgHPCXF1v/bw8LljE9SaHyiFcK9bo8q8nNVkZcjb3mZZJrylJbIGRNbVyUDANCgBBzS//3vf+vWW29VUlKSmjRpIuOI09sMwyCkAwDwK2b6fJJhKLxJupxxCcpcul6b585UYVauJEMxaYlqO6K7WvTtoIr8XJVl7ZLpY+JYAABqSsCnu7ds2VK33XabHnjggWDVFFSc7g4AwPG5S4rlLXfJ5TI198VPVF5Qcsxx4XFRGn73ODlDTDnCw+WIiKzlSgEA+PUI6i3Y8vLydOmll55ycQAAoP5yRETKZ4Ro9nMfHzegS1JZfrFmP/exTHuo7OERtVghAAANW8Ah/dJLL9W3334bjFoAAEAd81Z4tPqzxaooKT/pWFdRmdZ8/r28FZ6TjgUAANUT8DXpbdu21UMPPaTvv/9e3bp1U0hIiN/jd955Z40VBwAAapdpmtr548Zqj89cukG9xo8IXkEAADQyAV+TnpGRcfxvZhjaunXrLy4qmLgmHQCA49uzaqsWTPwsoHWG3TlOTTq3DFJFAAD8+gX1Fmzbtm075cIAAED95i6vCHydssDXAQAAxxbwNelHMk1TAR6IBwAA9VhYTOCTwJ3KOgAA4NhOKaS//fbb6tatm8LDwxUeHq7u3bvrnXfeqenaAABALUtu11zOyLBqjw+NjlBi66ZBrAgAgMYl4JD+j3/8Q7feeqvOOeccffjhh5oyZYrOPvts3XLLLXr++eeDUSMAAKglPq9XGYO7VHt86yFd5fN6g1gRAACNyylNHPfYY4/p2muv9Vv+1ltv6dFHH63316wzcRwAACfmqfBo5tMfqGB3zgnHxaUn6/T7x8seEvAUNwAANCqB5NCAj6RnZWVp0KBBVZYPGjRIWVlZgX47AABQz9gcNo36w2VKPcGM7U26ttKo+y6VYf9F09sAAICjnNJ90j/88EP96U9/8ls+ZcoUtWvXrsYKAwAAdcNms8kIDdGQ2y5QWV6RNs5croI9uTIMKaZpotqf0VvhcVGy2W0yDKOuywUAoEEJOKQ/9thjGj9+vObNm6fBgwfLMAwtWLBAM2fO1IcffhiMGgEAQC0zDEN2h11RyXHqPm6I32OO0JA6qgoAgIYv4HPULr74Yi1ZskRJSUmaPn26pk2bpqSkJC1dulTjxo0LuICJEycqIyNDYWFh6tOnj+bPn3/csdOmTdOZZ56p5ORkxcTEaODAgfrmm28C3iYAAKg+R2iI3z8AABA8AU8cV5OmTJmia665RhMnTtTgwYP12muv6T//+Y/Wrl2rFi1aVBl/9913Ky0tTSNHjlRcXJwmTZqkZ599VkuWLFGvXr2qtU0mjgMAAAAA1KZAcmi1QnphYaH1jQoLC084NpDg279/f/Xu3VuvvPKKtaxTp04aO3asnnzyyWp9jy5dumj8+PF6+OGHqzWekA4AAAAAqE2B5NBqXZMeHx+vrKwspaSkKC4u7piTxJimKcMw5K3mvVIrKiq0bNky/fGPf/RbPnr0aC1atKha38Pn86moqEgJCQnVGg8AAAAAQH1WrZA+a9YsKwjPnj27Rjack5Mjr9er1NRUv+Wpqanau3dvtb7Hc889p5KSEl122WXHHeNyueRyuayvT3YmAAAAAAAAdaVaIX348OHW/2dkZCg9Pb3K0XTTNLVz586ACzjW96nO7VwmT56sRx99VJ9++qlSUlKOO+7JJ5/UY489FnBdAAAAAADUtoBnd8/IyFB2dnaV5QcOHFBGRka1v09SUpLsdnuVo+b79++vcnT9aFOmTNENN9ygDz/8UGecccYJxz744IMqKCiw/p3KjgQAAAAAAGpDwCH9eEe6i4uLFRYWVu3v43Q61adPH82YMcNv+YwZMzRo0KDjrjd58mRdf/31ev/993XuueeedDuhoaGKiYnx+wcAAAAAQH1UrdPdJemee+6RVHl6+kMPPaSIiAjrMa/XqyVLlqhnz54Bbfyee+7RNddco759+2rgwIF6/fXXlZmZqVtuuUVS5VHw3bt36+2335ZUGdCvvfZavfjiixowYIB1FD48PFyxsbEBbRsAAAAAgPqm2iF9+fLlkiqPpK9evVpOp9N6zOl0qkePHrrvvvsC2vj48eOVm5urxx9/XFlZWeratau+/PJLtWzZUpKUlZWlzMxMa/xrr70mj8ej22+/Xbfffru1/LrrrtObb74Z0LYBAAAAAKhvqnWf9CP95je/0YsvvvirPW2c+6QDAAAAAGpTjd8n/UiTJk2y/n/nzp0yDEPNmzcPvEoAAAAAAOAn4InjPB6PHnroIcXGxqpVq1Zq2bKlYmNj9ec//1lutzsYNQIAAAAA0CgEfCR9woQJ+uSTT/TMM89o4MCBkqTFixfr0UcfVU5Ojl599dUaLxIAAAAAgMYg4GvSY2Nj9cEHH2jMmDF+y7/66itdfvnlKigoqNECaxrXpAMAAAAAalMgOTTg093DwsLUqlWrKstbtWrlN+M7AAAAAAAITMAh/fbbb9df/vIXuVwua5nL5dITTzyhCRMm1GhxAAAAAAA0JgFfk758+XLNnDlTzZs3V48ePSRJK1euVEVFhU4//XRddNFF1thp06bVXKUAAAAAADRwAYf0uLg4XXzxxX7L0tPTa6wgAABQf5g+U16PV/YQuyTJ6678f8Mw6rgyAAAapl90n3QAANAw+bw+yZDyd2bLXV4hR2iIJMnrcssR7lR882SZpmSzB3zlHAAAOIGAQ7pUea/0OXPmaMuWLbryyisVHR2tPXv2KCYmRlFRUTVdIwAAqEVej1ee8gp5XG6Fx0Zq75rtKsg6IMOQYtISlTGoi0rziuUIDVFIuFM2u72uSwYAoMEIOKTv2LFDZ599tjIzM+VyuXTmmWcqOjpazzzzjMrLy7lPOgAAv3I+j1c2h11rPpqnzB82KC49WRHxlTvhdy/frJ8//14t+3dU78tHyuvxEtIBAKhBAYf0u+66S3379tXKlSuVmJhoLR83bpxuvPHGGi0OAADULk+FRzabTfNf+UwxqfEa89i1Co+NlLusTIYkR3i4yvKLtXHmci187QsNueV8eSo8cjhP6eQ8AABwlIDfURcsWKCFCxdWuSd6y5YttXv37horDAAA1AGfqc3zV6n3ZcMUGhUqd162CvZukUyz8nHDkDMuQV3PO01lhS5tmb9arYd0rduaAQBoQAIO6T6fT16vt8ryXbt2KTo6ukaKAgAAdcMWYlOznm1kN9wq2bZRkuk/wDRVkZerivwDCk9rqabdWsnm4HR3AABqSsBTsp555pl64YUXrK8Nw1BxcbEeeeQRnXPOOTVZGwAAqGVlecUKcRoq27NDVQL6kUxTZbt3yBlqU1l+ca3VBwBAQ2eYpnmCd+Cq9uzZo5EjR8put2vTpk3q27evNm3apKSkJM2bN08pKSnBqrVGFBYWKjY2VgUFBYqJianrcgAAqFe8brdKdm6Tp7iwWuNDomMV0ayV7M6QIFcGAMCvVyA5NODT3dPS0rRixQp98MEHWrZsmXw+n2644QZdddVVCg8PP+WiAQBA3TN9vmoHdElyFxVIpi+IFQEA0LgEfCT9144j6QAAHJ8r/4BKMrcGtE5Uy7ZyxsYFpyAAABqAQHJowNekP/nkk3rjjTeqLH/jjTf09NNPB/rtAABAfXIqu+4b1/5+AACCKuCQ/tprr6ljx45Vlnfp0kWvvvpqjRQFAADqhj00NOB1bKewDgAAOLaAQ/revXvVtGnTKsuTk5OVlZVVI0UBAIC6YQ8Pl81Z/dBtDw2TPTQsiBUBANC4BBzS09PTtXDhwirLFy5cqLS0tBopCgAA1A1fhVthSanVHh+alCqf2x3EigAAaFwCnt39xhtv1N133y23261Ro0ZJkmbOnKn7779f9957b40XCAAAao/hcCg0PlGe0mJV5B844VhnXKJC4xJqqTIAABqHgEP6/fffrwMHDui2225TRUWFJCksLEwPPPCAHnzwwRovEAAA1C5TUmTzVrKHhqk8Z79Mr8fvccPhUFhSqsKSUmWapoy6KRMAgAYpoFuweb1eLViwQN26dZPT6dS6desUHh6udu3aKfRXMmkMt2ADAOD4fO4KuUuKVZ69VxFNmskRGa2Kwnx5y8tkGIbsoWEKiYmTu6RIZXt3Kyy5iRyRUbKHOOu6dAAA6q1AcmjA90kPCwvTunXrlJGR8YuKrCuEdAAAjs/0+VS4eZ285WWSJJszVKHxibIdDOE+d4VcebnyVbgkSfbwCMW06SjDFvA0NwAANBqB5NCAT3fv1q2btm7d+qsN6QAA4Pi8rnIroEuSr8Klsn17jj++rFTeCpccYeG1UR4AAA1ewLu9n3jiCd1333364osvlJWVpcLCQr9/AADg18vrKg94Hd8prAMAAI4t4CPpZ599tiTpggsukGEcnirGNE0ZhiGv11tz1QEAAAAA0IgEHNJnz54djDoAAEA94AiPCHgdO6e6AwBQYwIO6cOHDw9GHQAAoB6whTjliIiUp7SkWuMdkVHWpHIAAOCXO6WpWOfPn6+rr75agwYN0u7duyVJ77zzjhYsWFCjxQEAgNoXltwkgLFNg1gJAACNT8AhferUqTrrrLMUHh6un376SS5X5S1YioqK9Le//a3GCwQAALXH566QPTxC4U2anXRsRNPmsoeFyedx10JlAAA0DgGH9L/+9a969dVX9e9//1shISHW8kGDBumnn36q0eIAAEDt8pSVqmjbJjnjEhTVqp0ckdFVxjiiohWV0U4hsfEq2rpRntLSOqgUAICGKeBr0jds2KBhw4ZVWR4TE6P8/PyaqAkAANQhn6tchZvXKSwpVVEtW8v0eivvnW4YsoeGybDb5TqQo5LMbTK9Hh1xsxcAAPALBRzSmzZtqs2bN6tVq1Z+yxcsWKDWrVvXVF0AAKAO2EPDJEmmx6OyvbtVtm+PQqJjrcnhXAdy5C4qkEzTWsd2cB0AAPDLBRzSb775Zt1111164403ZBiG9uzZo8WLF+u+++7Tww8/HIwaAQBALbGHhckeFl555FySTFPuwvzjjw+PkN0ZWjvFAQDQCAQc0u+//34VFBRo5MiRKi8v17BhwxQaGqr77rtPEyZMCEaNAACglnhdLoUlpapk1/ZqjQ9LSpG3wiUH90oHAKBGGKZ5xPlqASgtLdXatWvl8/nUuXNnRUVF1XRtQVFYWKjY2FgVFBQoJiamrssBAKBeqSgqkCM8UmX7dsuVm33CsaGJKQpPTZOntFRO3lMBADiuQHJotWd3Ly0t1e23365mzZopJSVFN954o1q1aqV+/fr9agI6AAA4MUdYhIq3b1Z4ajNFpLWQ7RinstucoYpo1kLhqWkq2r5ZjoiIOqgUAICGqdqnuz/yyCN68803ddVVVyksLEyTJ0/Wrbfeqo8++iiY9QEAgNpks8lw2FW4Zb3CU9MU276LPCXFB2d3l+yh4XJERqmiME+Fm9fJHhYupncHAKDmVPt09zZt2uiJJ57Q5ZdfLklaunSpBg8erPLyctnt9qAWWZM43R0AgONzlZTJbjNVuHmdZJoyHCEKjUuQERIiSfK53arIz5Xp8UiGTTHtOsnrlUIjuSYdAIDjCSSHVvtI+s6dOzV06FDr6379+snhcGjPnj1KT08/9WoBAEC9ERIWKp/bo6iWbVW8Y4tMj1vlOfuqDrTZFN2yrUzZFBIWUvuFAgDQQFX7mnSv1yun0+m3zOFwyOPx1HhRAACgbvi8Xu34YYMMR6jiOnZVWHKqDPvhffqG3aGw5CaK69BNpi1EO5dtks/rrcOKAQBoWKp9JN00TV1//fUKDT08gUx5ebluueUWRUZGWsumTZtWsxUCAIBaY3q9ajWgk5ZM+lqJbdLUelBnhTdpLtNbuVPesDvkLnNp05xVytm2V/2vP0umj5AOAEBNqXZIv+6666osu/rqq2u0GAAAUMd8bpmmXf2uO0sbZy/XlgU/q1X/TgqLqZzBvbywVDuWrJPP61P/60dLMiWPW1LVWeABAEDgTvk+6b9WTBwHAMDxecpKVFGQr5C4FNlD7Nq7doc2zVquwqwDkiHFNk1Uu1E9ldqppXweryoO7JczPl6OMG7DBgDA8QRl4jgAANDw2cMiZCsrV0VJmea+OE3F+/P9Hi/LK9betTsUnRqv4XdfJFtoqOyhzOwOAEBNqfbEcQAAoOHzVrhlhIRr1t+nVAnoRyral6eZz0yR4YyUt8JdewUCANDAEdIBAIDF6/Zq9aeLVJZfctKxZXnF+vnzxfK6mTgOAICaQkgHAAAWu9OhzB82VHv89iXr5QjlPukAANQUrkkHAACW7I275XFVnr7uCA1Ri34d1XpIF0XERUmSSvOKtXXBGu1Yul7eCo885RXK3rxHTTq1qMuyAQBoMAjpAADAUlFaLklqO7y7ul80VF5XuTyFuarIzpckhYaFq9uF/dXzkmFaOXW+tsxfLffBdQAAwC9HSAcAABZnZJg6n9tfHUf3UemurfKUFPk97iktlutAthxRMepxyVA5I8PkjAyro2oBAGh4COkAAMCS0iFdKe2aqXjbBnldxz9C7ikuVMmOTep8bj/JxhQ3AADUFN5VAQDAYT6fXLl7TxjQD/GWl6k8Z59Mr68WCgMAoHEgpAMAAIvNYZfrQE61x1ccyJE9xB7EigAAaFwI6QAAwOIuKZbprf59z02vR56Sk99THQAAVA8hHQAAWEyPO+B1fKewDgAAODZCOgAAsBj2wE9dP5V1AADAsRHSAQCAJSQyWjKM6q9g2CrXAQAANYKQDgAA/DjjEqo9NjQ+QTLNIFYDAEDjQkgHAAAW0/QpPLWZDLvjpGMNh0PhqWkyfdyCDQCAmkJIBwAAFsNml7e8VNGt28sWEnLccbYQp6Jbd5CnrJRr0gEAqEGEdAAAYPG6ylW8fYs8pSWK7dBNkc1byRERKdlsMmx2OSIiFZneSrEduspTXKTi7ZvlrXDVddkAADQYJz+XDQAANBpeV7kkU6W7d8iVs0+hiSmKzmhvHS33eT2qyMtVwaa18rnKK5e5yqWw8DqsGgCAhoOQDgAAjsnrKlfpnkyV7smUYasM6abPe4yRAcwGDwAAToiQDgAALI7wiGMuP3Y4r2QP5yg6AAA1hWvSAQCAxRbilCMiqtrjHZHRsjmOP8EcAAAIDCEdAABYfB6PwlKaVnt8eEpT+byeIFYEAEDjQkgHAAAWw2aTIzxC4U2bn3RsRNN02cPCZBh8nAAAoKbwrgoAAPwUbtkgZ2y8ojLayREVU+XxkKgYRWe0V0hMrAq3bmTeOAAAahATxwEAAIunpEi+inIVblqnsKRURbXIkOn1Hrw1m2QPC5dh2OQ6kK3yzP0yvR55SorljImr28IBAGggCOkAAMBi+nyV//V6VLZvt8r271FIdIxsDqckyZWbLXdxoWSaVdYBAAC/HCEdAABYbCFO/wWmKXdhQWDrAACAU8Y16QAAwOKIjJIRwC3VKm/ZFhnEigAAaFwI6QAAwOLzeBSWmFzt8aGJyfJ5uAUbAAA1hZAOAAAshmEoLDlVjsiok451REYrLClFho2PEwAA1BTeVQEAgMVTUqzSPTsV3aqdnPGJOub91QxDzvhERbdqq5LdmfKUFNd6nQAANFRMHAcAACymzyvXgRw5IqIU2ayFIpo2lys32+8WbKEJyTJshiryDqgiL1ch0bF1XDUAAA0HIR0AAFgMR4jCmzRTSHSMCrdulM1ulzM+SY6oaEmSz12hksytMn1eRbVsq3CPW7YAJpoDAAAnRkgHAACWkMgoOcIjVLh5nXwVLnkluYsKjzm2cOsGxbTpyDXpAADUIN5VAQCAxef1qmzfHvkqXCcf6ypX+f4s+bzeWqgMAIDGgZAOAAAsNrtdFXm51R7vysuRzW4PYkUAADQuhHQAAGBxFxfK9FX/yLjp9crN7O4AANQYQjoAALCYp3Dquun1BKESAAAaJ0I6AACwGKdw6vqprAMAAI6NkA4AACwhkdGSEcDHA5utch0AAFAjCOkAAMBimlJofEK1x4fGJ8o0zSBWBABA40JIBwAAlqJ9eQpPbSbDEXLSsUZIiMJT0lS0Pz/4hQEA0EgQ0gEAgMWUKVdxuSJbtpXNGXrccTZnqKJatJOrxCXTx5F0AABqCiEdAABYopLitOStb7RjyUbFtOus8Gat5IiKluEIkeEIkSMqRuHNMhTTrrO2Ll6npW/PUGRiTF2XDQBAg+Go6wIAAED9UVFSrv3rd6lwzwHZnQ616NdRoXHxsh2cwd3n9crn8Wnbop+1/ptlchWXylNeoZAwZx1XDgBAw8CRdAAAYDmwfa9imibojD9eodjmySrenyfDMOQuK5e7rFyGYah4f57i01N0xh8vV3RKvPIy99d12QAANBgcSQcAABbDZmjYXeNk+kzFpMSqIj9H+etWyfR6Kh+3O+RMSJIzKVEVpW4Nu2ucCnbn1nHVAAA0HIT0eqikpFSRkRHyuD1yhDhUUeGW6fPJ4XDIMAyZMmUYhjwer3xer8LCw6yxLpdLPq9P4RHh8nq8MmyGvF7vwdvjGAoJccjr9crhcKiiokIej1cREeFHrF8h0+eTM9QpHZwHyJQpr9crn89UWFio31iv9+D6Ho/sdrs8Ho98PlM2m012u02mz5TNbpPb7ZHH4/HfVnmFfKZPoaFOmWblz+Tz+eTz+mRKCg11+m/L41VE5OH1K1xumWbVvng9Xnm8XoWftC8+mabv2H1x+2/L5XLJNCWnM8S/Lx6vfGbVvvi8XoWfal9cFfL5fnlf7A6HbDbD+h4ej1feavbF5/PJMI7qi+vg8yXyqOeLaSokxCFDhtUXn88nr8ensPDDfSktKZMzNEQOh8OvL6bPlHHKzxdTPq/32H3x+j+3D72Oqt8XU+ERYSd9vhzrd3C8vni9lb/HI/viKnfJ5/Md4/nik81mt/pid9iP+5qt+nw52BfTVOjxXrO/oC9h4aHyeX1H9UUKCQmx+lJaUipnqLPy933Etrxer5zOkKp98fn8X0fH6YshyWbYZNhsMs3K54vX7a38GxkaIp/XK5vdLm+FR6ZM2R0OHXrRmqYOTnBmyh7iODzW7ZHP61NImPPwMo9HpinZbDYZhlH5PQxDPo9Xps/035a78nlsdzoqn8+GIdP0WZOp2Rx267ldOdYnR+jhbXncHsms/B0fZli/Q4fzyG255fNJIaEh1u/A5/VJB39vxhGvo2P2xe2RaZ64LwmtmlTW7C5X0dbNks/n9x5lej1yZe+VK3e/IppnyHCEKr5lirxuT5W+HrcvhiHbkbV6PDK9/n05VKvNYT/4bDl+Xzxut+TTwZ+1On3xHnNb1Xm+eNweyfod+g7/Xk3JZrcd/DlNq4bKn+vUni/+2zq8fcOUDIftpH3xut0yA+pLdZ8vlc+JKr9vr0+Og68jw2arfL0EqS+mz6eQGuyL6fVZf/9+UV9Mye6sRl8k2WyGDMN27L54vLI5TuH5csS2Dv0tk6/6fTn670tN9EWmWbn8yOdLhUc+8/DvMDh98R3zd+D398Vul2Ec0Rffwb/xTv+fy+czA+jLMf6++Cp/fqsvkuQz5fOZcjiP93508DV7nNeRx+WWzWGX3WE/3BePV6bXd1RfTOs1W6UvR/198R78XFbdvhx+P/TvS5W/8cfaVqB9sV7zh/siVe5Y9nu+uL2yO3/9EffX/xM0IOXlLmXvy9Xb/5miL6d/p7LSMj30t/s0+rwR+t+0b/XB29O1eeM2DRs1UM/862GtW7NJ7036WHNnLlZ0dKReeuMppTZN1ofvfqpPPvifsvfn6nd3XqvfTbhGM79ZoMlvTdXKZT+rY5e2ennS0zpwIF/vvfGxvvlitrwer5588c/qN6i3pn/0lT5691Pt2LZL54w9Q489fb+WLV2l9yZ9rMXzflBSSqImvvWMwsPD9MHbn+izj79Wfl6h7v2/W3XZNWP17Rez9f6b07RuzUb1Pq2bXvj3E8rcvlvvTfpYM7+apxBniP7x6uPq0LmtPn7/c02d/IX27Nqr8deM1R8eul0L5/2g9yd9rB8Wr1CLVs008a1n5PV49d6bU/W/T2b49+WTGZry9nRt2rBVQ0cN0N//9UiVvvzzv0+qSVqKX19umnC1br7zOs2esUDvT5qmFcvWqEPntpr4ZmVf3p80VV9/Pktej1d/e+H/1H9wH3368df68J3p2rFtl8ZccLoe+/sD+mnpKr0/aaoWzVuqxOQETXzrGUVGRmjyW5/os4+/Un5eoX7/4C26/Lpxfn3p1bebXvzPE9q5Y4/VF7vDrn+8+rg6dmmnqZO/0Mfvf649u/bqsqsv1P0PTzjYl6n6YfFypbdM08S3/y6fx6v335qmL6Z9q7LSMv35iXt09vmn64tPvrX6MmRkfz378qNa//NmvTfpY835bpGioiL0z/8+qabNUvXRe5/qkw++1P59Obrx9qt1y13Xac6MhZr85jQt/3G1OnRuq5cnPaX8/EK998bHVl+eeP7/NGBIZV8+evdTbd+6Uw88eofGXzNWy5as0rv//UiL5i21gv2Y80/XNTddprRmqXr4/qd11W8uUedu7fX1F7M0+c1PtHb1BvXs01Uv/ucJ7crM0vuTpuq7r+Ye0Zf2mjr5c02d/Ll279yrS6+6QA88cocWzf9B770xVT9+v0LN0pvolbf/Lp/P1PtvTrX68n9/vUdjLjhd/5s+Qx+8/Yk2b9imwcP76e8vP6oNazfpvUnTNOe7hZV9+c+TSktvoo/e/VTTPvif9u/L0W9vvVK3/f43mn1EX9p3aqOJbz6t/PxCvf/GVH31+Ux5PF498Y8/adCw06zny/atO3XWeSP1l+ce1IofV+u9N6Zq4dwlSkxK0MS3nlZUdJQ+ePsTffrRV8o7UKA7779JV/32En37v9lWX3r06aJ//udv2r0zS++9UdkXm92m5155XJ27ddC0D77Qx+9/pt079+riK87Xg4/dqcULftR7b1Q+X47uy/8+maGS4lL96S9369yxZ/r1ZdCw0/TsxMe0Yd1mvffGVM35bqEiIyP0z//8Tc1bNK18HU35Uvv2Zutfk57SoGGnafaMRZo8aaqW/7hGPp9P8QmxuuCSs3X1DZcqIiJcv7/5z3rkqT8oMTlB0yZ/oQ/f+0zbt2Rq9Lkj9dfnHtSKZYf7kpAYr5fffFoxsdF+fbn7gZt19Q2XaOdPm7Rlzkod2LFPiRlNNeS2C1SSW6hNs1Zo10+bZNgMDfzduUpomaqtC9do6/zVKskpVOshXdVr/AjtW5epTbNXaP+GnYpMjNHQO8ZJkjbPXqEdS9bJXV6h3pePVMv+nbRj6XptnrNSBXtylNqppQbddI4Kdudq4+zl2rNyqxyhIRpy6/mKTI7T1vmrtXXBGpXlF6vD6D7qev5A7Vm1VZtmr1Tulj2KSUvU0Alj5S5zadOs5cr8caN8Hq/6XTdaTbu20vbFa7V57ioV7ctT897t1O/60crdmqVNs1Yoa812hcWEa+jtY+WMDNPmOSu1bfFauYpL1e2CQWp/Rm/tXLZJm+esqNqX2Qf7YhgaeNM5SmjVxK8vGYO7qPflI7VvfaY2zVqhoux8jXnsOnnLylSya2tlmHM45IxNkM1Zec25r6JCFfkHZHo9Kt25TVEZ7eWMCNeKKXO0bfFaOUJDNPjW8xV1dF/O7KOuFwzUntXbtGnWisq+NE3Q0DvGyl1WoU2zVyjzhw3yebw67brRSjvUl3mrVLQ3T817t1W/68+y+rL35+1yRoVr6IQLFRoZfrgvRaXqeuEgdTijt3b+tEmbZ6/UgR17ldCqiYbedqFK84q0cdZyqy8DbjxHiRlNtG3hz9oyf7VKcgqUMaizel8xSvvX79TG2Su0f32mIhJiNOyOsTJshjbNXqEdS9bLXeZSr8tGqNXAztrxw3ptmbNK+buzldqxhQb97lwV7MnVplkrtHvlFjmcDg2+9QJFp8Zpy7zV2rpwjcryitX+jN7qduEg7Vm9TZtnr1DO5sN98ZRXaNOsyr54PV6dds0ZSuveWtuO6EuzXm3V/zdnKXfbXm2atVx71xzsy+0XKjS6si/bF69VeWGpup4/UB1G99GunzZr05wVOrDdvy+bZq/QzmUbD/ZljBIzmlajL9Eadsc4GTbbwb6sk7vUpZ6XDVfG4C7KXLpBm+euVP6ubKV0SNfgm89TQdYBbZq1XLtXHOrL+YpOjffvy+m91G3sYGWt3qZNc1YqZ9NuRTdN0LAJY+Vx+fel7zVnqFmPNtq2eK22zFulwqwDatazjfr/9mwd2LZXG2et0N412+SMDNPQCWMP9mWVti/+WeWFpepy/gB1HN3Xvy8tUzXk9gtVllds9UWSBt54jpLapGnrwjXaMq+yL60GdlafK0dp/4Zd2jRrufatz1REfLSG3jFWNrtdm+es0PbvD/bl0mHKGNLVvy/t0zX4Fv++2J0ODb7lfMU0SdCW+au1bcEaleYVqd2oXuo+brCy1mzXptkrKvvSJF5D7xgrr8tT+Tpaul5et6eyLz3bavsRfUnr0UYDfnu2Dmzfq02zVyhrdWVfhtx+ocJiIvz7ct4AdTyrr3Yt36zNs1cqd3vW4b7kF1t/d03T1IAbxii5bTNtXfSzts5bpeLsArUc0El9rzq9si+zV2jfuh2H++Kwa/Psw33pcekwtR7SVTt/2KhNc1Yc7EtzDbr5fBXtPaCNs1Zoz8rNsoU4NPjm8xTTNNH6++LXl5+3a9Osavbl+3XaMnflwb601oDfjtGBHfu0adbyKn3ZMneVti3y78vuFVu0afYK5W7LUnyLVA29/UKVF5RYf1+O25f+ndT36tO1f+MubZpV2Zfw+GgNm3ChbCGOw30pq9DpD4xXbLMkZf6wXptnV/ZFphQeH6XWg7uq7YgecpumFr76uQbdfJ7sdrs2zVl5+Pkysqe6XzTEry9RqXEadsc4ed2H+rJB3gq3+lx9hpr3aqsdS9Zp85yDfeneWgNu8O9LSESYhk64UOGxkdo8d5W2LVyj8sJSdT6nvzqNOc2/L+kpGjphrMoLS7Rp1nLtXLZJps/UgBvOVnK75tq2+Gdtmbdaxfvz1bJ/R/W9+gxlb9yljbNXaN/aTIXHRWrohLGyOw/3paKk8rKrpLZpajuyp9K6ZWjVp/O18bufKvsypKvajugph9NxcAfAr5NhVh5ibTQKCwsVGxurgoICxcTUn9loXa4KvffGx3rx6detPXNPvfiQMtq20O3XP6Ds/ZWnEg4Z0V/PvfKY/nzvU5rx5RxJUnRMlN786CVt3rhND933lCpcFZKkW+++XhddcZ5uu+5+bVq/VZLUrkNrTfron/rvy+9p0muTJUkOh13//O+TioyK0J03/EkF+YWSpHMuPEN/fuIe3XfbI1o07wdJUkJSvN6b/ooWzftBf3voBXm9lXux/vjYXRoyop9uvfZ+7dyxW5LUvXcXvf7us/r74y9r6gdfSKo82vnvyc+rpKhE9972iEpLyiRJ468dq9vv+a3uuOFBrVz2sySpWXoTvTNtoj79+Gv985l/W3352wv/p7btM3T79Q9o/74cSdLg4f30j1cf9+tLVHSk3vr4X9q6eYf+756/WX25+c7rdMlV5+v26x/QxnVbJElt22fozY9f0n8nvqdJr1b2xW6368X/PKGY2GjdecOflJ9XIEk6+/xReuSpP+jeWx/268u7n0zU9wuW6Yk/P2/15YFH79TQkf2P3Ze/TNTUyZ9bfXntvedUVlque299+HBfrhmrCffdoDtueFArflwjSUpr3kTvfjJRn0/7Vi889ZoOvYT/9sL/qX3HNrr9+ge0b2+2JGnQsNP0/Gt/0cN/eFrffDFbkhQZFaE3P3pJ27fu9OvL7+68VpddfaFuv/4BbVi7WZLUpl0rvTX1X3rjlff1xivv+/UlNi5Gd/z2Qasv9z88QZdefaF+/7s/a8GcJcd9rt9w+1W65a7rlZ9XoBvG36XM7ZV96dazk15/7x967omJ+vj9yr44Q516/d3nVF7u0r23PqyS4lJJ0qVXXaA7779Jd97wJy3/cbXVl3c+maj/ffKtnn/ycF+e+Mef1LFLO9123f1WXwYO7asXXv9rlb5M+vCf2rljt/509xNyHezLTROu1uXXXaTbrrvf6kvrdi319tSXNenVyfrvxPesvrzw+l8VnxCrO254UHkHKvty5jkj9PjfH9AfJjyqBbMr+5KQGKe3p72sH5es1F//9Jw8B/cG3/fQ7Ro1eohuvfYP2rFtl19fnn/yVX347qdWX15751lVVLh1zy0PWX255Mrzdfcfb9adN/xJP/2wSpLUJC1F705/RV99NlP/eOIVqy9/fe5Bde7WXrded7/2ZVX2ZcCQvnrx33/Vow/8XV99NlOSFBEZrjc/ekm7Mvfowbv+avXllbf/rq49OuqmK+/R+p83HfN3bbfb9cjTf9BZ543Ujq07dfPV9x7Rl+F6/O9/1P13PKb5s76XJMUnxOrtaRP10w+r9JcHn7X68oeHbtf5F5yhxS9/rqJ9eZVjW6ZqxO8v1qpPFmjL3Mqf1eawa9id42R6fVr02hdyl1fW2npIV3UfN0QLXvlcOZsrn28R8dE6/YHxyvxxo1ZOnWedIXPadaOV0DJV81+artK8IklSSod0DbntAv347nfK/GGDpMojcCPvvVQluYVa8sZX8rora+14Vl+1G9VL8/81Xfk7K/sa3SReo/4wXhu/+0nrvloqSTIMQ4NuPk9hcZFa8PKnchVVvuab9WqrfteN1vf//UpZq7dV/r4jw3T6/eOVs2WPfnx3pnVEpPtFQ9S8dzvNf2l6lb6s/mSBNh/dF59Pi1493JeMwV3U46KhWvjq58reVNmXdqf3Uo9xg1Wya7u85WUKT02TMzZenpJieV1lkgzZw8LkiIhSRUGeyvbtkT08QpHNWmrp2zO0Z+XWyr4cKNSS/x7Rl9F91f6MXpr/r0+t69ejU+M16g+XadOs5Vr75eG+DLz5XIXHRfn3pWcb9bv+rGP3ZWuWfnznO7++pPdpr3n//ORwX1qkaMQ9l2j19IXaPGfl4b7cMVamqcrnS5mrsi+DuqjHJcMq+7Kx8nUYHhel0x8Yr10/bdbKj+dZr6O+15yhpNZpmvfSJyo9UPl8SW7fXENvv1A/vjdTmUvXW8+XEfdcorK8Yn3/36/kdVdeOtDhzD7qcGbvY/dl9gqt/d+Sw3353bmKiI/S/Jc/k6uo1K8vS974WntWbbX6MuoPl+nA9r368Z3vKo+cS+o2drBanNZB8176REV7j+jL7y/Wms8Wa9PsFZV9sds09I6xMgxDC189si+d1eOS4VX7cv947VqxWSs/OqIvV5+hpDZpmvev6SrNLfTry7L3Z2nHknWSKo84j7znEpUVlOj7/xzZl97qcGZfzX/5U+Xt2CdJikqJ0+n3j9fmOSv18xffH+7LTecoIjFG8//1qdWXtB6t1f83Z2vJpK+1Z+XBvkSEatT944/dl34dNP+l6SrMOiBJiktP1sh7LtGazxdr06wj+jJhrAy7TQtf+dzqS6uBndXz0uFa9Nrn2r/hUF8idfr9l2v3yi1a8eFcqy99rjpdye2aad5LR/SlXTMNnTBWyybP0o7v/ftSXliqxf/5Ut6Kyr60P72XOp512kn7IqNyZ0JkUqwWvPypygsP9qV7a/X/7dlaMukb7Vm55XBf/jBeeZn79MPbM6y+dL1wkFr273jMvvz8xffaOHO51ZchEy6UzW7Xwlc/l7u0si8tB3RSr/EjtOjVL7R/w05JUlhspE5/YLz2rNqqFVOO6MuVpyu5fTPNf2m6Sg72JaltMw2dcKGWT5mj7YvXWn0Zcc8lch2rL2efpgUvf6oD2w/2JTlWo+4fry3zVunnz/37EpUcp/n/mm71pWm3DA24YYyWvvmNdq+o7EtIRKhG/eEy5e/MruzLwfejrhcMVKsBnTXvX9NVuKfys3lc82SNuPcSrT2iL4bNpqETLpQ9xK4FrxzRl/4d1evykVr02hfav/6Ivtw/Xlmrt2n5lDlWX0b/+SqFhIdqzvMfqySnsi9Hs4c4NOjm85TcrplytuzRote+kMflliS1G9VTncb004KXP9OB7XslSZFJsTr9/vHaumC11ny22OrLgBvOUXRKNfty32Uq2J2jpW99a/Wly/kDlDGoi+a9dLgvsc2SNPK+S7X2f0u08bufDvfl9gtldzq08JXPVHGwLy36dVSfK0Zq0ev/0751mZV9iYnQ6Q9crqw1B/tynFt9xrdI0dAJF2rDjJ+0YcYy/760by5HPTqqHkgOrT9VN2KlJWX6fNo3euGp16xl540brR59uujKC27Wgdx8SVJ4RLie+udDevSBv1tBVJLu/uPN2r83W3+6+wkrHHbr2UnX3jReV4+9VVs2bbfGPvXPh/T+pKlWQJeky6+7SE3SUnTNuNusD/sJSfF69Jn79fvf/dkKopL0f3+5W6t+Wqu//Ok5a9mQEf015oJRuuL8m7VnV+UfAYfDrmdfflQvPvNvK6BL0k13XCObYeiu3/3ZCoctWjXTvf93m2684vda9dPP1tjH/v5Hfff1PL349OvWsnPGnqHep3XXFRfcrAM5eVZfnn7pYT32x2f9+/LAzcren6s/3vkXqy9de3TU9TdfrmvG3abNG7f59+XNaVZAl6TLrx2rZulNdc2421RcVFLZl8Q4Pfb3B3TvLQ/79eXBx+7SmpXr9Zc/PWf9cR08vJ/OufB0XXnBzdq9s7Ivdrtdf//XI3rp7/+xArok3TjhaoU4HLr5pv+zQlB6y2a698+36aYrf2/tuJCkx565X7O+WaDnn3zVry99+vXw70t4mJ5+6WE9/qfnrCAqVT5fcnPy/PrSuVsH/faWK3TNuNu1acNWa+yTL/5Zk9+aZgV0qXLHQfMWabpm3G0qKiyWJLVqk67LrxunO2/80wkDuiStXbVBFa4KXX/JHdq9M+twX15+VC8/918roEvSjbddpRBniG6++l6rL83Sm+oPD0/Q7666x9pxIUmPPv0Hzf52gf7xt8N9GXPB6eo3qJcuP/9m5WZXfsgICwvV0y89rL/86R9+fbnz/puUn1egB+543AqHnbq21w23XaVrLrrd2tFV2ZeHNOWd6VZAl6RLr75ALVo109VH9CUuPlZ//ceDuu+2R6yALkkPPHKnNqzdrMce+Lv1fBkwpK8uuPgsXXH+zVZfbDabnvnXI5r4/CQroEvSDbdeqdCwUN167R9UXu6y+vLAI3fod1fda+24qOzL/Zr73SI999eJ1rKzzx+l/oN764oLblbO/iP78pCe+PPzVkCv7MvvVJBfqPsnPGb15ezzR+m0gb105QU3Wzu6jsXr9Wrtqg3q27+nbrj8bqsvsXEx+utzD+oPtx8O6FLlTq1N67fo0fufsfrSf3BvXTz+XM1++kOV5FQGfMMwNPDGc/TzF99bAV2qDIKO0BDNfvYj68N+RGKMel42QvP+OU05m/dYY/tefYay1mzTyo/nWcua92mn1E4t9N2Tk1VeUPmat4fYNfCmc/TTB7OtgC5VfoCtKHVp8b+/tMJhXHqyOp/TX7P+/mHl0Y6D+v/mbG2dv9oK6JLUemg3xTRN0HdPf2B9eHNGhqn/UUFUknpdNlwFWbn64Z0Z1s6ElA7N1XpwV3331GQVZx/ZlzFa+78lVkCv7Eufqn1JiFbvy0dq7oufWDsuJKnTmNNk+nwyPW7FtO2kioI8FWz8Wb4Kl9/v1hYaprDkVMW07aSibZVHjFr266SElk3kLnPp+39/aX3Yj22WpM7n9tesZz+0dlxU9uUsbVv4sxXQpcodKrFpiZr51AfWh7eQiNDKIDrpa7++9Lz0YF/e/tbqS3L7g315+gMV78+3+jLgxnO09sslVkCXKoNgSHioZj37ofVhPyI+Wr0uH6n5L31i7biQpL5Xn659azO14qO51rJmvdqqaZdW+u6pySrLr3y+2Bx2DbzxHC2fMscK6JLU5fyB8rjcWvzv//n1pcv5AyqfL0f0pd/1Z2nbop+tgC5V7lCJbZakmU9NrtKXpW9+YwV0Sep5yTAV7cvTD2/NsF5Hye2aqc2w7pXPl4N9kSENuHGM1n39gxXQK/vSR86IML++hMdHqfcVozTvpelWQJekPleO0r71mVrx4RF96dlGTbtl6LsnJ6ssv/iIvozRio/mWgFdkrqeP1CeCo8Wv364LzFpiepy/kDNfvYjvwkJ+19/lrYvXns4iB7sS1x6sr576gNVlJRX9iU8VP1/c3ZlX1Ye7kuPS4dX6UtS22ZqM7y7Zj71gbVD51CQW/f1j1ZAl6T2Z/RWaHS4Zv39QysEhcdV9mX+v47uy+nav2Gnlk+ZYy1L69FGad1bV+nLgBvPqezL94f70uW8AfK6vVr0+v+sEBTTNEFdLxxUpS/9rhut7d+v8+/LoC6Kb5Hi35cwp/r/9mwtfetbK6BLUo+Lh6k4O19L3/z2cF/apKntiB5V+jLghjFa/+2PVhCVpPan91ZYdMRRfYlUnytP14KXP7UCemVfRil74y4t/+DIvrRWs56tNePJySrLO9gXu00DbzpHq6bOtwL6ob74PMfqy2DNfu4ja8eFVPk6yly6/nBAl5QxsLPiW6Tqu6cmW31xhDk14Ldn64e3v7WCaGVfhqokp1BLJ31j9SWxTVO1G9lT3z39gbWj61BfNny77Ki+9FJYbKRmPTPF6ktYbKT6XHWGFkz81AroktTnilHK2bxbP31w+DNJl/MHKCIhRt/+5V1rh/GxeN0eFezOUWhUmBZM/MzqS3STeHUbO6RqX64brcwfNxwO6JJaDeishFapmvnUB3IVl1l96f/bs/XDOzP8+3LREJUeKNSSSV9boTmxdVO1H9XLvy+q/PuyccZPVkCv7EtPhcUd1ZeYCPW9+gwtfOUzK6BLUu8rRylnyx79NPlwX44lL3O/5r00XaP+cJn2rt2hgt058ro9WvjKZxp576WKb5kqm/3XN1f6r6/iBijEGaKX/v4fv2WXXzdOk16dbAV0STp37BnalbnH7wN0dEyUzrtotF54+nUrcEnS5deO0/SPvvIL6H3691ByaqL+c0SwMAxDl187Vq+9+JYV0CXpovHn6qelq7Rw7uEPT6lNkjXijMF64enDOxMO1Tr5zWlWQJekEWcOltfr1ZS3p/v9nJdccb5eeva/VkCXpEuvvlCzvpnvF9Dbts9Q916d9fJzb/ht64prx2nSa5OtICpVHvHfvTNLX376nbUsKjpS5188Wi8+/VqVvnz68dd+Ab1Pv+5KbZqs/778rl9fxl87Tq+9+JYV0CVp3PhzteLHNX5BNCU1SaNGD9GLTx8+enuoLx+89YkV0CVp+BmDZJqmJr/1yTH74jqiL5ddfYFmf7vAL6C3btdSPfp01UvP+j9frrjuIr35+gd+fRlz4enK2r1P//tkhn9fLjp2Xz6b+o1fQO/Vt5uaNkvVf18+6vly3Ti9/s+3rcAlSX/48+1atXytXxA9niuuu0jvTZpqBVFJGnb6QEnS+29Os5Y5Qhy65Mrz9fJzR/flQs2ZsdAvoGe0balefbvpX8/+129bl183Tm++9oEV0A/1ZV9Wtr745FtrWWRUhC64+Gy98NTrVhA9tP7n0771C+g9+3ZVs+ZN9O+X3vH/ua4dp9de8u/LuMvO0erla/2CaFJKgk4/e6jf0f7KvozTlHem+/dl1ADZ7Xa9P2mqf1+uukAvP/dfK6BLlWcXzJ252C+gt2rTQn36dde/nq369+Wt16dYAV2Szjp/lHL2H9BnU7+xlkVEhuvCS6r25Xd3XqvPpn59woB+5LYm/uMNv76MvWyMVq9cr3mzDn9ISExO0BlnD6vSl6t/c4m2zV9jBXRJatK1lWwOmzbNOvyByLDZ1GZYN635fLEVRCWpzdBuylqzzS+gR6fGK7l9M63+dJFfre1G9NDGGcusgC5J6X07qLygxO+DoiM0RBkDO2v19IVWQJektsO7a8eS9X4BPSGjiaJS4vwCuiS1HdFDa79cagV0qfKI3IEd+/wCV2h0hJr3bqdV0xZYQbRy/Z7acvDUSasvXVrKFuLw+6Bo2GxqPay7fj5mX7b7BfSolDg5I8LkKStVVKt2Ktu3R6W7d1QJ6JLkc5WrdNcOle3PUnRGW3nKShWdGqdWgzpr1fSFVuCq7EsPZf6w3i+IJrRqoujUeK390v9vRtsRPbTuqx+sICpVfrDO37nfL3CFRocrvU87rf5k4VF96aEt81cfDqKSUru0lMPp0MbvjuyLoTbDulc+XyoO96X1sG7au3a7X0CPSo5VSod0rf50oV+t7Ub00MaZP1kBXZLS+7RTRUm5ti06/Hfb7nQoY1AXra7Sl+7KXLrBry/xLVMV0zTBL6Af7stSv760GtBZ+buy/T5Ah0aFK71ve62atsDvddR2RA9tPaovTTq1lCPU6fcB+lBffv7ie7++tBnSTXvX7vALopFJsUrt1EKrp/v3pe2Ingf7cvg137x3W1WUurR1weG/23anQxmDu2r1p1X7svOHjX5BNL5lqmLSEvXzMfvygxW4KvvSSQW7c/z64owMU4u+7bXqk2P3xQqiklI7tZQjzKmN3y073BfDUJvh3bXm8++tYCFJrYd21b51R/clRqmdWmjV9KrPl00zl/v3pVdbuctc2rrwiL6EHNGXI/7uth3eQzt/PKovLVIU2yxJa48I6JLUbmRPrfvavy8tB3RSwZ5c7V6+2a8v6ad1OGZfti1Y49+Xji3kjAjVhhlHPF8O9uXnL47qy5Bu2r8+0y+gRybGqEnnlsd4vvTQxpkrrIAuVe4A85RXaMuCw+9n9hB7ZV+mL/LrS5th3bVr2Ua/IBqXnqy45sl+Ab1yWz21/pujni/9O6ow64B2/eTflxandazal+E9tHXhz35BNKVDupyRYdYR3BP3pav2b9jpF9AjEmPUpGsrrfrEvy8Zg7pqw7c/njCgH+5LF62ctsD/+TKsu3b9dFRfmicrvkVKlb60G9lT67/50QroUuUR/6J9edq17PCZciERoWrRr2Pl3xffUc+XRUf3pblCo8K1/tsf/fsyrIfW/q9qX7I37vIL6BEJ0WraNUOrPllwwp//kPyd2cr8YYPaDOtuLfN5fVpxxJlPvzaE9DpW4arQV599p8KCwy/CTl3bq3Xbln4hQqoMJ1Peme637PyLztL6nzdZp+FKlUepRp87wu/I26H1P5nypV9AHjCkjyIiwjXzm/nWMsMwdOlVF1TZ1sVXnKeFc5cqa/fhF3xa8yYaMLiPpk7+wm/sZVdfqA/f/VS+Iz7AnnH2MBUWFGnposN/4J2hTo29dIxfmJcqj0p+/dks69R7SerYpZ3adsjQF9OO6ss1Vfty3kWjtXH9Vq1bc/iPS0xstEafN1Ifvus/9rKrx2r6h1/6BcF+g3orOjpS3319+CibYRi65Mrzq2zrosvP1aL5P/iF8abNUjVwSN9j9uWj9z7z68vpZw1VUVGJliw8/Ac+xBmisZedow+P2talV12obz7370uHzm3VrkOGPj8iXB3a1pR3/J8D5447U5s3bPPrS3RMlM6+YFSV38Fl11yo6R995RcE+w3qpZiYKM34aq7f2N79e+jd/36kk2mSlqJBw07T1COOlh+q9eP3P/fry6jRQ1VSUqbF8w//gQ9xhmjc+HOq/A4uu+oCffu/Odap95LUrmNrdezc1i90HtrW0eufO/ZMbd28Q2tXHz5SGh0TpTEXnK4P36n6Ovr0qL6cNrCXYuNjNeNL/75cenXV19G48edq6eLl2pV5ODSmNk3WkBH9/c4ikKTLrhmrj97/zG+Hysgzh6i8rNzvTA5HiOOYfbn0yvP17ZdzrVPMpcpLXjp1aadPP/66Wn3ZtiVTP686fEQwtWmyWrVO1+QjdqgcT98BPRUXH6tvjzjDxdrWUc+3i8afox8WL7cuC5EqdwwOGt5P2+av8Rvbdnh3bZ2/2u9DQlqP1vK6vdr38w5rmc1uU8bgLtoyd6Xf+q2HdtOu5ZutU6mlyqMx8S1T/cK4VPkBcPO8VX7LWpzWQUX7863TB6XKo1QtTuuoLUeNbTusu3YsXuv3gSSpbTOFxURY17ge3lY3vzMDpMojYtmbdvuFq/C4SDXtlqEt81cftf6hvhx+HaV1z5DP49XeI/pi2GzKGNxVm4/qS5uh3SRJ9tBQuQvz5co9+W3VXDn75C4qlN3pVERijEqyC3Rg2+G+OMKcatm/o9+RfUlqM7y7tn+/7qi+pP1/e/cdH0WZ/wH8s303vfeQQkISQg9duhRBBWwgKuo19XeiIuqhp17xzuPOO+88+6lnV8SCiFggKr0TCAQIIaRCKull++78/thkktnZQPCQDfB5v168Xjp5ZufZz/Ps7nxnZnegD/TFyX0F0raThsjWTxqfidMnKiRFhD7QFzGDk1DslkvKpCEo3nZYkkv04GQITgHVh0sluSRfkSkbg+SJg1GRWyReAgq4zlKFJEWjZIeH+eKWa7+RaWivb0F9cdcBOLVOg36j02VtUyYNQZlbLqH9o2EI9sfJfWefL4njB6LuRKU0lwAfxAxJls3N/pOHoHhbnqRAjh6cBEEQJFcsKJQKJE0YJB/DiYNRcbBYmktkMEKTXd9jd8/Fffv9Rg6AsaEF9UXSXBLGZMjn5qTBKNudD7u563M6NDkaPsH+kitcOrflvn7S+EzUFVVJigidvw9ihyajaIt8vpRsOyzJJWpQIgCgKq/rQJFCqUDyBPkYJE8YjMpDxZKDfX4RQQhLiZEcvOnKJU9yoCl+5ACYmtokBxZVWjUSxmbIx2DSYJTvOSZ+hQUAQpOi4RMSIM9l8hDZe2HiuIFoKKkSL2d35WJA7LD+8vkyaQiKtx+RFIJRgxKhUCokBxZduQySvW8mTxyMyrwSyUEtv4gghKfEomSH9D1enC/dconLGgBzU5vkwKJKq0aix1xcB0y75xKSGAXf8MAe5ovb62hsBhpKq8XLtgHXAbC44Ske3+NLth+W5BKZmQClWiU5sKhQKJA8cbD882jCIFTllUgO3kSkxUEf4INit9eRJ3EjUmFuMUoOFKk0aiSMG+h5vuw9Jn5Vw5VLJPzCAyVX/nTmInt/GTsQjWU1aO6Wi9ZXj7jhKR7HoMRtvkQOTIBKo0JFrjwX99ds8oRBqDpcIjl4czZFmw8hYUw61DqNuKy+uEr8asnFhkW6l1ksVmzfJD3Dkp6ZgkMHjkrObCuVSqRnpkoKFgBIy0zB7m05kmVJKf3Q2NCMkhNlkuUZmanYtU26fvrAVOzdlev6RdAOgUEBiI6NxC73bQ1MwS63bQ3I6I/iE2Xid+bP1DZtYAp2bc+RHNGKiY2ETq+TnP3rWt+tr5mpyMvNl5zZVigUSMvoL9uWp6ySUvqhuakFxYXSXNIyU2RtMwalYt/ug5Jc/AP8EBsfLWubnpkqy2pAen+UFp8UvzN/pn6lDUzBnu37JblEx0RAr9chZ4/0TS8jMxW7tstzPXzwmDyXgSnYtW2vpG36QPn6Sf37oaW5VXLVRWdf3edW2sAU7Nt9EDZr1w5kTFwUfH19ZM/LkwHp/VFWckr8bnhXvzyM98AU7HabL1HREfD19UHObumbedpAD2OYmYrDB49JzuB2tvU0X9y3n5gcj7aWNsnVBZ392ulhbu7fI83Fz98Xcf1iPPbLfVlqejLKSk+J3w3vvi33uZWemYLdbvMlMiocfv5+2LvzgFvbVOzeJp9vRw4VyHLpaW665zL2iiyYjGbJ1QU9SR+Ygv17D0kODPr6+SA+IVb2uGkDU2W5pqYno7mmQXYmISguHDX5JyXLguPCUVtQLsnFEOQHrY8etd12XrrWL5cui49AY1mt5Eylq22Yx7buy/wigmC32iRn0V1tw1Ht1ja4XzjqTlRKdl7UOg38I4JRk18ma+u+rYCYMLTXNYvfgRa31U/er6D4cNQWnHTLxRdaP73kLFdnWwBQaXUw19Wgt8x1NVBpdQCAmmPS7ftHBMFhs0vOFgMdY+DWNiguHHVFleL32F19UcM/Mlj+vDyMYWBMKNrrW8TvtJ6pbXB8OGqPueUS6Audn4/kLBfgykW+/Qg0nayVnJHrsW2/cNm4+kUEwelwynPxNF/iI1BfVCm5EkKlUSEgKsTD84qQ5RoQEwpjQ6s8F4/PKxy1x09JctEH+MIQ6ItaT3PLwxg2nTotyyXYw2vG07j4hQdBcDglZ4t7ahsU3zlfunJRqlUIiA6RvT94eq6BMaEwNrZJrtIR2x6T53q64JTkwKA+wAeGQF/U9Ga+xIej6VSd5ExlV9syD8s85CIIkrOirrYRsvkSFB+O+uJKyZUQSrUKgdGhHl8HsvkSHQpTU5vkKp0en1dcOGrdctH5+8AQ5Ieaox7aytYPQ1NFneSAqdivXvTVLywQACQHTMW+us/N+HDUF1VJDoAplEoExoZ5bCt/3w2FqbldcsC0s638NRuO0wUnJQcGdf4G+AT5yeeLh7kdlZmI1ppG8TcWziTIw2veNywACoVCcsC0s61svsSFo7642i0XBYJiw+SfR/Hyz96A6FCYW4wecvH8mq89fkqSi9bPAJ9gf89t3ZadTWN5LZx2B/zCgyTLKw4W9fh99r6MRbqXKRQKmEzSDzO9QQ+z2zKdzvWrumaTdAdSr9fBZHRbXy9fv+tx3dY36CRnBDuXAfCwXA+TSfpGajDoZe262p79eekNelg8rG/wuL5O1n+tVgOlUgmT0a1fHjLQ6/WyrFzL5Y/rWiZtazDoAUDW355yde+/6zF0MJvlGZg8LOt+Zr/743rql/u2NFoNVCoVTJ765T5fPDwm0DG3ejGGgUGuH75wz8ATT+t3LvfYLw9zs6f55ilX97ZqjRpqtbqHueVhXM7hdeQpawAeH9fjGHqam562pdfJ53sPryPXczh7hmq1ChqNuldzyy/Az+O2POnpNQ94en/x8Do06GDvtqPZSaXVwN7tgIhrmVrWVqVVu3bg3T6b1Vq1bH21Vi3ZqQVc788qjRqOXrRVaTWyZWIf3NtqPPcfgPw59LD97jtU4nKNfFtqrQYOi3xbTptDlotK6zr74LCY4TBL59iZOExG8ZJ4936pPGTd03NQe8hQ3dknT2N7DmPgcb7Y5Mtct96TBqPWamTj4mkO9PgcNPJlKq1aNi5dz6s3c9uVi6fn5SlDT6+jnvL2NF86b1cl75en7Xt4XhrPz8HzuHjuq8cxsMmXAfCYocPTePfUV4/z2MN867itlHsf5H3VeN6Wx/HyPAd6ni/yuSXrv6aH9xePc0De/5761dMyp8Mhy8Vjhh5eG5398tTW8xzy0FdNT+/x8mWAp/cX+Xh5yrqr7dn75botn1NycLazD+591ei1HueLJz1/HvV2vnh4HfY4X+SvI0/976lfPY2B4BRkufQ0tmfjsNplt19zWOWv04sBi/Q+IDQsRPL/Lc2tCA0LliwzmcywWqyy5a3NbQgJly5raW5FcGgQlEqlbHmoh7buj9l56X2Ih+W96euZHtfT+r5+PuJBiLOt794ni8UKi9kif9yWHtbvdS5tCA13f0zXmceQsCD54/Yi167l0sdt7eG59pyLpzGQLrNarDCbLT2Mgfy5BgUHynNpaevVeFdXuo7se3q+7jyNofi4vZybvn4+0Ot1Htqeva92mx0mo8ljBiEe1g8KDoRKpZIt702urc2u+dKbfp1pvshfhx7mZnMr/Px9xQNJksf1NLfdc7E7YGw3enxc9/7XVtfBL8BPlosnHud7S2cuvRtvQ6Cv7HGtRjP0AT5uyyzQ+8uXafRayaVvZ2qrc3tMQRBgM1mg89DWffs2oxlaXz0Ubq+jntrKlplcB+U8PS/3fnl6zJ7bmqELkGZoM1qg1mlkudg6DhQ5LPIDRmfTuY7H/vsaepWL1WiWZd15WaY8Q/kY2oxm2fMXtyVr63kOqLUaqPXS911ru6f51sO22uXLPc1Xm9ECrZ98vtg8zhcPy0xWCE6hl/PFDL2/Qd5XD8/B0zKb0QKVRg2NWy49Zei+DACsJk9tzdD7+8qWaX30sh95crV1W79d/tq0mT3nYmv3PAfc1xe31YsxsLabXbn4SD+PepzbHuaLrZevb5vRAq2vPBfP80Xef7vZCqfT2avXnGsMDeh2U3Vxuae2nvqvVKmgdcvFY4YexlVc3ou2rvmig1Ktclveu/d4m8UKp0OeiytDt/fNdgt0fp5y8TRfPH9GKVVKaH31suXubc0tRo/zxRPP729maHz0slxsPWQomy9WG5wOh+cMPS3zmEsPn0ce1lcoFa7HOEtfz0ahVELrp5dczg8A+iBfKPjDcXSufHwNmL9gtmTZrm05SM9MRXxCrGT51o27MWf+dMmyLT/sxFXXTJXsMB/PPwGLxYrxk0fJ2s6ZJ11/26bdGDN+BEJCg8RlxnYTcnYflLXd+sNO2fb3781DcEgQBg/LcGu7C1fPnyFbf8qMK2Dw6XohVp6qFu8jLe3rLtn2d23LQVpGfyQkxUkfd+Muj7nMvGYq1OruuRTBZDJjwpQxsvVny3LZhdHjhksKJJPRhL07D3jIRd7X3H2HERgUgCEjMs/6vLb8sBNTpktzqa6sRXFhGWZdO+2s6+/enoPUtCQkJse79Us+3lt/2CXLpfBYMUxGEyZOHeth/Ssly7Zt3I1R44ZLirnGhmbU1TbIxsCTgzmH4R/gh6FZ0ly2/rALc9zny8bdmDL9Cvj4duVSU3UaJwpKcNXcK93W34nZbn3dvT0H/VMTkZSSIFm+pYe5OfPqKVB3u5/miYIStLW2Y+I0t1w2ysdg+6Y9GDlmKMIiunKxWKzYvT3H43i7LzuYcwR+/r4YPnKwfFse5vaU6ePh69f14VVbU4djRwpx1VzpfPE0N/fs2I+klASPubiP95YfdmKWWy4bN2yD0+EUf+zvTLZv3o2s0UMQHhEqLrNarNi1bZ/HuTl73nQoFF2f9If2H4VKq0ZYSoykbdXhEvQbnS5bFj04SVJImJvb0Vhei36j0s66fm3BSfhHBiMgOsStbSkSPGwrbkSKZAeopaoB1nYzYoYkSdpWHy6Vb/9oGcJSYmEI6toJdNodqD5ahn6j5NvqNypNsgPUUFIFlUaN8NRYWdsE923llSLGPZcWo+dcun0P+ceKG5EqyaW1qgHmViNihibLtuWea/XRMoT1j4YhyE9c5nQ4UX2kDP1GS/taebgE8W651JdUQ6VWIXyA9DPC83wpdc0XQ1chYWk1oqGsRp7h4VLZ9muPn4JfeCACY0Ld2pYgYbR8vsUOd5sv1Q0wNxsRO6y/vK9uc6D6SJn4/etOgtPpeb7klaDfyAHS+VJaA4VKKc8lr9Tjc40elCgpPC1tJjSUVsszzJPnevr4KfiGBSIwNsxDW/m23F9HrTWNMDe3IcZDLrL5kt+RS0j3XARUHy2V5VJ5uATxI9Mk7y8NZTVQKBWISJN+drrGW/46jMpMlBSe1nYz6kuqPbzm5H09fbwCviEBCIoLl7f1sH7ssBSoNN1yqW2EsbEVscNTZG3dX8fVR8sRkhQNn9Cu2zsJguD5vahj/e65dH7VQJaLh+dVdbgEUQMTJYWntd2M+uIqjxm6b7+usAKGYD/xqzZdbeWvuc7XUfdc2k43wdjQithhKbK27n2tyS9DSEIkfLvlAgGoPiLPpTKvFPEjB7jlUgNAQGR6P+m2PIxB1eFSRA1MkORiM1pQV1TpMUP3ZUVb82AI8EVwvwicjWu+9JfcD7ztdDPa65oRN8J9vshzrckvR3C/CPiGSXPpaQzcc2k6WQvBKSAyI0HWVv5e6Mqle0FuM7ly8bQt92VnEzusP8zN7Wip7vqNBaVahX5ur/2LBYt0L1MqlcgckoaUAV07dvWnG/D9+q1YsHiepO2q99bghpuvgUbbdfZj26bdsNsdmDJjvLjMbnfgsw+/xM23XydZ/9MPv8SkaeMQGdX1ZlhWcgr79+bh+puvkW1rwW3zJJN6/bqNCI8IRdaYoeIyk9GEtZ+tl23r4/e/wNXzp8M/oGtnKy83H6fKK3G1W9Gx6r01WLh4vmTZF598g0HDMpCa1rVj11DXiO++3YIFt7nn8gWuX3g1tN3OOm/ftAd2mx1TZ04QlzkcrlwW3i7d1qcffonJ08YhMrorl/LSCuzdlYsbbr5a9rwW3DZPctZ5/VcbERoWjFHjhnflYjJj7WfrZc/r4/e/wBy3XA4fPIby0lO45roZsrbuuX7xyTfIGDwAqendcqlv6iGXNbj+ZrdcNu+B1WLF1JkTJbl88uGXuNlDLhOmjEFUTNeHxKnySuzdeQA3LJLOl6++yMZtv7hJdjbenclkxtpPv5U9r1Xvr8HsuVciILBrZ+vIoWMoKSrHtdfPkj2vhW6vjbWfrUfGoAFIG9j1gdTY0IwNX2+W5fLx+19g/oLZkqsUdmzZC7PJjCtnSXP5dOWXsjHszCU6NlJcdqq8Ert37McNN18r29aCxdL5kv31ZgQEBWDMFVniMrPZgi8++UY2Nz9+7wvMnnul+JUCAMg/fBxFhWW45vqZsm255/rl6vVIy+gvyaWpsRnZX22SPa9V763B/AVzJLns2paD9nYjpl81SVxmt9uRuy8Pi3+5AGdTcbIau7bn4MZbpLmsem8Nbrp1riSX777dgoAAP1kun3/8NRInSg9eFG0+hPisAZIdoKaTp9FSWY+EsdIDhkVbDkl+7RUAynblIyg2TLJjaG0342TOcaRMHippe2LzISSOz5TsANUcK4fNZEVctx1mQRBQtCVPtn7xtjxEZvST7AAZ61tQc7QMyRPcnteWQ+g/aTAUyq733YoDJ6DRayU7QA6bA6U7j6K/27aKthxC/MgBkh2gplOn0VRRh8RxA92e10GkTJGuX7r7GCAAasO5nb0AAJXBBy2V9bC2myU7hoIgoNhTLtsPIyItHr4d3ykFAGNDK6qPlok/YCf2dctBJE+U5lKZWwS1ToOobrk47Q6U7DiClMnS8S7acgjxWanyXE6eluVStPkQ+rutX7Y7HwFRrh8W7GQzWly/JOw+X7YcQuK4TMnllrUFp2BtNyM+K7WroQAUbT0k62vxtsOISIuDX3i3XBpbUX24FMkTB8meV/+JgyVn4ytyi6DSahA1MFGSS+mOo7LxLtpyCHEjUqHrdpa9uaIOjeW1SJLNF3lfy/Ycg39EEEISu+VicuXiPt5Fmw8hcdxAydUbp4+fgrnViPisAdJcthySrV+y/QjCB8TCLyJIXGZqbEPV4RL5fNl8CMkTB0lyqTxYDJVGhahMt/my/YjnXIanSM74NVfWo7GsGonjM2Vt3edA+d4C+IUHIiQpSpJL2Z5jsrl1YsshJLjnUlgBc4sR8SO7FSgCOt5f3ObL9iMIT42Ff2S3EwpNbajKk+dStOVQx+uoWy6HiqFQKcUfxjtjLlvzEDusv+Ssa0tVAxpKq5E0Xj5f+k8eIjlYVLbnGHzDAxGaFN2Vi9mK8j2e5stBJI7JkORSd6IS5qY2D7kcQsoUaS4lO44gLCXGLZd2VOaVyD4PTmw+hOQJgyRXKVTlFUOhUCB6cNe+udPhRPG2w7JcinvIpb64CklXeJgvk6S5lO8tgE9IAEL7d+XSWWimTJVuy5O6okoYG1tlBwo8fR6V7DiCsP4x8I/qysXc3I7KQ8WyXIo2H0LyhMFuubgO5EYPkeZSsu2wh/fdPMQMSYa+29VwrTWNqCuqQpLb6+jE5oMecjkOn2B/hPWXHqQ/k5QpQ+U/xJiVKvnsuJiwSO8DHHYHnnnp95Izhu++/jEW3DoXY64YIS7bvT0HlRU1eOLpZWLx7HQ68fZ/PsKjf3xAUmR+8uFaDB2RiRu6Fd+Vp6qR/c1m/OW5xyWF25uvfIhf/PpWZA7pOpL33beuWxYs++094jKLxYr3/vsJfv/XRxAc0rUD8eFbn2LarImYMWeyuOzYkULs33MIf/rHo5Kz/P99+UM8sPwuyVm8das3ICI6HL/49a3ispbmVnz6wVr8+Z+PSc4YvvPaKtx4y7WSHfk9O/aj4lQ1nvizNJe3Xl2J5X+4X1JkfrrySwwZPhA3LOoqGqoqarD+q41Y8dwTklze/s9K/Pz/bsWgoV25fL9+K5wOJx7slou1I5ffrXhYksvKtz/DtJkTMGPOFHFZwdETyNl9EE//87duuXyA+3/zKySnduXy5er1CI8MxS/vvU1c1trS1pHLbyW5vPv6Ktyw6BqMnTCyWy4HcKqsEr/7y0NiLoIg4M1XPsTyP9wnKTI/W7kOmUPTcdOtc8Vl1ZW1WL9Onstb/1mJn9+zSHL1xEv/eAO+vgY88uS9OJtPP/wSM2ZPxsyru66eKDxWjL07D8hyefOVD3DfI79E/9REcdm6z7MRFh6CX923WJLLJx+sxZ+ffQx+3S6ffO+Nj3H9wjkYP6nrqpK9Ow+gvLQCv1vxsFsuK/Gb39+HmLiuHavPVq7DwMEDJIV+TdVpfLP2e6x47glJQfvWqytx5903Y8jwrp2VjRu2wWKx4uEnfi0us1lteO+Nj/G7FQ9JrtRY+c7nmDRtnOSqksKCYuzenoM///MxydUP/335A9z3sDSXr9Z8h+CQINx1/+2SXD5+/wtZLu++8TGuWzAbV0weLS7L2X0QpcUn8fu/PSKbL7/53RLExnfl8sxTL2LwsAzZQQVPPvvwS/zi17dKrirZlL0DZrMFj/xuiSSXd15fJcvlw7c/Q/SgRMmOWUtVA2ryyzD6zlmSHc789fswaO54ydnNsj3HoPXTY+CcritobGYrTmw+iNF3zJScST3+3X4kjhuIqMyuXOtOVKC1ugEjF0/v2oEQgGPr92HYTZMkxXfJ9sMIjAuT7MSZmtpRvqcAY352laTQP7YhBwOmj5DsmFUeKobdYsOwm7reS50OJwq+24+sW6ZJdgJPbDqIqMwEyY5ZS1UDqo/Kczm2fi8yrx0nyaV8bwE0Bh0yr+m6UsRutsJmtkCp0ULt03Ug8WzUvv5QqjVQqFWuXG6U5lK8/TACY0OROnWYuMzc3I6y3ccw5mezJLkUZOcg9crhklyq8kpgM1kxfMEUSS7Hv8vBCPdcNh9C5MAEyVm81upGVB0pxeifuc2Xb/ci89qxkrO+HnOx2HBi00GMvn2G5Axz4fcHkDAmHdHdCpz6oio0V5zGqMUzJDuc+d/uxdAbJkkOSpRsP4KA6FCkTuuWS4sRZbvz5fMlex9Spw2X7LBW5ZXAarJg+MKu+SI4nSjIzpHPl80HEZnRDwljuuVS04iqw6UYc+dVkh3xY+v3YeA10lxO7iuAWq/FoGu7rqDpzGXU7TMluRz/4QD6jU6TFDj1JVVoOnkaIxfPkBz8P/btPgy9YaLkoETJ9iMIiApB6rSuA9/mFiNKd+ZjzJ2zJAdACjbkuHJJ6bqqpPpwKaztZgxfOEWWy4hF0yRFQ9HmQ4hIi5cc3GurbUJlXgnG/GyWPJerx0jOhp/cVwCVRo1Bc6W5FG46iFG3z5CceS/84QD6jUpDzJBuB9lLqtFUXotRt0tzyf92L4ZcP0FyUKJ0xxH4RwZjwJVduVhajSjdeRSjfybPJWXKUGkuR8pgaTVhxM1dnzGCU0DBhhyMWDRVcmXPiS2HEDHAQy6HijHmZx7my5wxkoOeJ3OOQ6VWY/DcrhNIDqsdJ37Ileey8QDiRw6QXG3TUFqDhrIaeS7r97rmS7dcSnYehV94EAZM79pftrSaULLjCMb8bJak0C/IduXS/aqS6qOlMLcYMdw9l+9yMOJmaS5FW/IQnhonObjXdroZFQc7cun2OX1s/T4MnD3aLZdCKNVKDJ53hSSXwo0HMGrxDMmB50Ofb0PCqHTEDJVeVeJJyY4jGHbTZMlBidKdR+EbFoi0Gd1yaTOhZPsRjPnZVW657EfKZLdc8stgbm7HiEVdV+iJ82XhVMkVT0VbDyE8JVZysKa9rhkVuUXyXDbsQ8bsUZKrBE7tPwGFUokh87udWLPZUbgxFyMXT5d9RcCT1GnDEBAVIrmDgm9YIIbfPFX8bZOLDYv0PkCn1yE2Phrvf/6KWKQdOXQMf/rtP/Hv15/G3BtmQaVSQRAEPPDL32LU2GFY8e8nxB3ZVe+twcYN2/DuZy9hxCjXkay62gYs/dXjePjJe/HLe28Tv8P71KP/gEarwX/e+wfi+rk+7Hdvz8ELf38Dr33wLKbPngSFQgG7zY5771yOOfNn4Imnl4lnOF9/4T3k5x3HO5++iIxBrqPf5aUVePT+P+HPzz6GRXdeL57p/819TyGuXwyee/3P4gGEDV9txMq3V+Otj58XLztvbzNiyc8exZ1334ylj94tFp///MsrqDvdgDdX/VssRvIPH8dTv30W/379z5h302wxl6W/ehxZY4ZIcvn4/S/w/bdb8O5nLyFrdFcuD/zycTz8xK/xqyXSXFRqFV57/9luuezH839/Hf95/1nMmDMFSqUSdpsdv75zOWbPvVKSyxsvvo/DB/PxzmcvYeDgNDGX39z3R/z52UcluSy/7ynExEXh3288LR5AyP56Mz5481O8uaorF2O7CffeuRx33LUQDz52j5jLv1a8itM1dXjz4+e75VKIPz72Dzz32p8w76bZYkH3wF2PY/iowfjr80+Kl6h/+uGXyP56M9759EUxl/rTrlyW/fb/8Kv7Fou5/Om3z0KhVOC1958Vv36xZ8cBPPe31/Dqe/8QczGbrbjvF7/FDYuuxR/+9hvJmd/u+iXG4ul//hY2mx0r/v04bv3ZDWIujz3gmifP//cvYi7ffbMF773xCd78+N+YMNWVi8lowq/vXI7Fv7gJDz3+f2Iuz614FdVVtXhz1b/FK1OOHSnEHx/9B/756lOYv2COmMvSu57A0KxM/O2F34m5fLbyS2xYtxHvfPoCRo4dBsB19cYDv3ocSx+9G3fff4f4ne8//fZZCIKA/3zQlcu+Xbl47q//wavv/QMzr57qmi92B+69czlmXD0Fv//rI2Iub77yIXJzjuCdT18UD46dKq/Eb5b8EU/9fTlu+/mN4oGR3y59GpFR4Xj+vyvEXH5YvxXvvL4Kb378b/FyfJPRNV9u+/mNeOjx/xOL8n//7TVUV9birY+fF69MKTh6Ar//zTN49pU/4rqFXbk8ePeTGDxsIJ558ffipfurP/oK36z9Hm9/+qKYS/GJMvztD8/jN08uwT0P3CH7LnyngYPTsPwP98NstuCNlf/CrGtcuTgcDiz52XJMv2oi/vC3rlze/s9H2L/nEN797EXx4FjFyWos+7/fYcSt05A6bZj4Yb/nrfUwBPpiwr1zxcuAKw8WoSB7H6Ysu9FVIChcO0BbX/wCqdOGYeiNk8TLvvPWbEd7fQumPnSjWLw2V9Rh77vZGH/X1Ui6IlMs6La/ug4hCZEY98s5YuFTsuMIyvYUYOrDC8QdG0ubCdtf/hKD543HwKvHiDvN+z/6AU6HE5OXXi/uXNadqMDBz7Zi0pL54uWDglPA1pe+QOyw/pIdk4INOThdWIFpjyxASKLrYEl7fQt2vv41sm69EgOuHC7JRR/gg4lL5omXAVceLMax9Z5yWYOUKUNduXQcrFBr1TC3GKEN7TqIdza60AiYW43wDfZH6c6jKNt9DNMeXoCINFcu1nYztr28FplzxyHzmrHizuH+jzbCaXdg8oM3dMulEgc/3dKRS5qYy7aXvkDMkGTJjmxB9n6cLjglycVY34Kdr32FrFumYcD0EV25vL0BOj+DJJeqvBIc+3Yvpiy70VU4KVw7hltfXIP+k4dg2E2TxVwOr92B1tNNmPrQTWLx2lxZj73vZmPcr65G8oSuM7c7XvsKQfHhGP+rq8WCsGxXPkp3HcW0RxaIlxGLuVzrnssm2K12THnwBnGnu76oCgc/3YKJ980XL08WBFcu0YOTMOr2GeKVAse/24+aY+WY9shC8WyusaEVO/6zDiMWTUPajG65vLMBWj89Ji6ZL14eXZVXgvxv9rjl4sCWFz5H8qTBGLagWy5f7kRrTSOmdculpbIee9/dgHG/nIPkiV1n4na+9hWC4sIw7q5uuezOR+nOo5j6cLdcjBZse/kLZF47FoOuHSfmcuDjTbBbbZjy4I1duRRX4cDHmzBxyTz0G909l7WIHpyI0XfM7Mrl+wOoyS/HtEcWiGdzjY0dudw8FWkzssRLqfe+swFaHx0mLpkvXh5ddbgU+V/vwZQHb3AVlB25bH1xDZInDMbwbrkcWbsTrVUNmPrwArGob6lqwJ6312PsL2ajf7dcdrz2FQJjwjDu7qvFgrB8zzGUbD+CqQ/dhMiMfmIuW19ai4FXu+eyGXaTFVOWdculpAq5Yi7pUCg7cnn5C0RlJkhyKfzhAGqOlmHaIwsRmuzKxdTYhh3/+dKVy8xuubybDbVBi4n3XSfmUn2kFEe/3o0pS29wFZQK19n4rS+uQdIVgzB84RTxIM6RdbvQXFmHqY94yOXns9F/0pCu+fL61wiICcX4u6/plksBirfmYdrDXbnYjBZse+kLDJwzBoPmjRdzyf1kC6wmi+R11FBSjf0fbcSEX89FwhhXLhCAbS+vRdTABIy+c6Z4ZUnhD7moOlzqyqXjoKGpqQ3bX/0SwxdOQfrMkWIu+97LhlqvlcyX6qNlOPLVbkx58EbX11rEXL5A0viBGH5zVy5H1+1Gc0Udpj58k1jUVx8pw7Hs/Rj/qznoP3mI7PcIOkWkxWPg1WPhcDhw5fKFXbmYXLlkXDUagyW5bIa13eyaLx1n1BtKu+eSIcklIj0eY342S8zlxKaDqDpcgmmPLBAPGpqa2rH9P+swbMEUpM/qlsv730GtVWPS/deJB25rjpbhyLpdmLz0Brdc1iBhbAZG3Dy1K5evdqPpVB2mPbxA9pWITmqdBpnXjEXmteOw7eW14p0lItLiMeOxRRdtgQ4ACuFivcP7j9TS0oLAwEA0NzcjIMBzIeEtFrMFarUaeQfz8fmqr1B/uhHDRw3GjbdcC6dTwEfvrMaxoyfg5+eDO+9ZhKT+/bBxw3Z8981mtLW145rrZmLazAmorKjBR+9+jsqT1UhKicdtv1gAP39frP7oK+zfcxCCACz+5QIMGT4Q+/ccxNpPv0VjYzPGTxqNeTdeBZPJjA/f+gxFhaUICQ3Gz+6+GTHxUVi/bmPHGTAzblh0LSZMHo3iE2X4+P0vUFN1GhmDU7Hojhug0ajxyQdrkZd7FBq1BnfcvdB1i6wt+/DVF9lobWnDlbMm4aq509DU0IwP3voMJ0tPITo2CrfftRBhYcH4cvUG7Ny6D3abHTffPh+jxg/H0bwCfLZynSyXVe9+jmNHT8DX14A7716EpJR+2JS9Hdlfu3K5ev4MTJs1EdWVtVj5zmpUnqxGYv94LP7FAvgF+OLzVV8hZ3dHLr+4CUNGZOLA3kP44pNv5Lm8/RmKjpciJDQId969CLHxUdiwbhM2Zm+H2WzG9TdfgwlTxqC0qByr3lvjymVQKm6+43potRoxF7VajTvuuhnpma5bbH31xXdoaW7FtJkTMXvulWhqbMaHb3+G8pJTiIqNxB2/WoiwiFCsW70BO7bshc1mw823X4dR44bj2JHj+GzlOtTVNmDYyEG46da5EAQBH73jysXHR48771mE5JQEaS7zZmDaVZ5yuQl+AX5Ys+pr7NudC8Ep4LZf3IShWYOQuy8Paz7+Go2NzRg3cRTm3XgVLGYLPnx7NU4cL0Fichx+8evb4OPrg/XrfsDm73bAaDQhOCQI8268CiNGD0FtTR3+9fQrGDtxJK669ko4nU58/P4XXbn8aiEyBg3Arm37sG5NtiSX5qYWfPj2ZygrPomomEjcftdChEeEYt3nG7BjsyuXhYvnY/T4ETh2pBCfrfxSlsuq99Yg/3AhfAwduaQmYPN3O7Dhq01oa2vHnHnTceVVk1BTVYuVb3+OipNVSEyOw22/XICAAH98vuorMZdbf3EThnXm8sk3aGxowrgJozDvJmkuQcGB+Nk9ixDXLxrZX7nmi8lkxnULr8bEqWNRWlyOj9/7AtWVtUjLTMEtd94AnU6LTz5Yi0MHjkCtVuP2Xy3AwEFp2LU9B+s+34CW5lZMnTEBc+ZNl+QSGR2BO+6+GeERofjq82xs37ynI5d5GD0+CwVHC/Hph65chmZl4qZb50GhAD5693Mxlzvuvhn9ByRiy/c7sX7dRrS1tWP23Csx/apJqKmuw8q3V6PiZBVGjRuG6xddA5VKhc8/cuVitdgQGR2Om26bh/6piSgvPYXn/vof3HTrXIweNwJtbe1Y+fZnOHG8FIFBAfjZPTcjrl8Msr/ejI0btrlyWTAHE6eORVnJKax6b40rl4H9cfsvFsDHoEfR1jzUF1dBoVQibUYWgvtFoPZYOcr2HIO13YzoIclIGJ0Oq9GCExsPoLWmCfogP6TPyIIhyBflewpQdaQUTocD/ScOQWRGPJpO1qFo6yGYW4wITY52XSqqUODEpoNoOlkLlVaDtBlZCIwJRVVeCcr3HYfdbEH8qDTEDU+BqakdJzblou10M/wjXGd1tD56lOw4It7SakDHmdD64iqU7DgCS6sRERn9kDQuE06766xBc2UDtL46pM/Igl94EE4dKERFbjHsFiuSrshE9KAktNU24cSWQzA2tCIwNgwDpg2DSqtB0ZZD3XIZgeB+kagtOImy3fmSXGxGCwplufihfM8xJIzNQMXBIkSkxUEwtcBY6brdjkpvgFLjOsDhtFnFX3/3ie0HhdYPdUVViB6ShD1vrYfNZEH8yAGIG5EqycUvIghpV46A1k+P0h1HUXv8JJxOAQOmDkNYSizqS6pQsr0jl/R4JI0fBKfdgcKNB6S5RATh1P4TqMgtgt1iReL4TMQMTkLb6Wac2HxQlkvx1jzUFVW6cpk+AsEJbrkMTkLCmAzYTBYU/pCL1ppG6AN9kT4zC4Ygf5TvPYaqw6Vw2B1ImTQYken90FRRh+KteTA1tyM0KQr9Jw2BQqHAic0H0VheC5VGjbSZWQiMCUPV4RKc3HccNpMFcSMHIH5EKszN7Sjc2JFLeCDSpmf1KpfwtHgkX9GRy6ZcNFfUQ+urQ9qMLPi75zIuEzFD3HMJRerU4VDresjl+CmU7Toqz2VjRy4BHnKx2dF/0hBEZUhzCUmMRMrkofJcZmQhMPZMuRxE2+mmbrkYULrzCGoLTsLpEJA6bRjC3XMZEI/kCW65+GiRNnOkK5cDHbmYrUgcNxAxQ5LRVteMok0H0e6ey7Y81BVVAQog7coRCEmM6sglH9Z2U4+5pM3I6rhve/dcBiMqIwHNlfUo2nJImotSgRObD6GxrAZKtQrpM0ciMDYM1UdKUb63wJVLViriRwyAucXYMV+a4BcWiLQZWdD5GVCy8yhqC8pduUwdivDUODSUVqNk+2GYW4wIHxDnysXhxImNB9FUUQeNjxbpM0bCPzIIp3KLUHHgRI+5BMSEYsC0nnM5XXgKpTvdcjFbceKHXLTUNEDn75ovPiH+OLm3AJV5JXDYbOg/cQiiBvYul7QZIxEU15HLvgLYjBbEjUhFfNYAmFuNOLExF621rlwGzBgBvZ8PSncdRc2xjlw6zpz3Lpcs+EcGS3JJGDsQsUOT0V7XjBNuuWh0GhRvP4zTHfe0T5suzyVqUBISx2bAbraiUMzFpyOXAEkuyR25tFTWo2hrHkxNbYgfkYK4EalwOgTX66isBk6HE37hQUidMhSGYD80njqN/K92I2P2aAT3i4CpuR0nOt5ffEL9kTY9C3r/s+cSlhqH/hMHQXA4UbjpIJpO1UFj0CJ9piuXioPFOLW/sCOXDMQO7Y/2+hZXLvUtCIgOceWi16J4+xGc7rinfWcudScqULLjqCuXzERXLhab51z2HUdlXjEcVhuSJwxBVGYCWqrqUbTFlYtKo0LEgHgkjh8Ia7sZBdk54vtp6tRhMAT7Q6FUnPVrmBfaudShLNL7IKfTCWN71+1vNFo1dDodjEYTnA7XLQQUSgV8fX1gs9lct0NSKABBgMHHAJVKhba29o7vZAhQqdWuW6WZzLDZ7OKlQ37+vnA4HOKtnwRBgE6vg1argbHdCGfHPQWVSiV8fA2wWqywWKziETaDj961rY77cwuCAI1WI94iytHZV4UCvn4+sNsdMJlM4pkRg0EPtUaN9jajeGsXlUoJg48BZrMFNqtN0ld5LhrodNpzyqW9rR3CueSiACAI0Oq00Gq1brko4OPrA6vFCqvV2rGts+RiMsPRcZuJzlwcDgeMxjPn0jkGFrMF1rPlolFDp9fB2G6C0/kjclGpYfA511y6tuVpvghOAT6+rm0ZjSbX8xJcc1ur1brlAvj6dW7LBHTkojfooNF4nptny0UQBGi1GlkunWNos9lgNlnEsw0+slwAlVr1I+aLh1ysVtd9w880XzRq8bZ0DocdgMJjLuh4zWo0GrS3G8X7gHbPxdbtFkU+vgYolUrJtjzlIr5mbXaYTGYPuXR7zXbkYjKaYLc7ZGPgugWbAgIE6HRaqNVqz6+jc8jFbrHB6XR2XE2sgMaghdPhhN1qg6JjWyqNGiq1CjazFZ2DqFAqoNZp4bDZ4bA7xKuRVTrXrRw7fxFWAKBUqcTbhIm3blEooNFr4XQ4YLe45oAAAWqtBkqVUtyW0DEGap2m41dyneK2NAYdnA5nx21oOvqqVkGlUUv62rkth90Oh61bXzu3Jemr0nWLqh+ZS+e27DY7lCol6gorXGfUBCcEp2tMlWoNHDbXL9CrNFo4bTYIcP2aLhRKNJTWILR/NOyWrm2ptWooVaqOX6535aLofF42B5yOrte8Wte9r67ndeZcHHDY7GJbtVsuAKD4X3NRKqDRuXJxdswXAa4zN2eaL+K2us8Xqx2KjkdQecjljPPF6ezI9cfk0rmts+TSsb7gdMJm+V9z6fa8eplLj/Olc1sXIpfOMZDMF9e2epWLOLe73l96zqXnMfixuQgAtP9jLhqdBgr3ud2rXDq29SNysZos4rLOXGwW16/0S1+zZ8/F8+uoY/0flYtrW2fMxWqD4Oi6vZfrPb6zr+c5F7MVgiDIxqD7rcpUWrVrvp3188hzLkq1CmqNGjaLFXD2MpcetvVT5OLps89hs3fVAQqF7M4lfQmL9DO4GIp0IiIib7FbrLC2NEEfHALBYYf5dDUsjQ2A0LHDpVRCFxQCfXg0FColzI2N0AUGi/fuJiIiIrlzqUPVZ/wrERERXVZUWg30ISGwtbWivby46wxHJ6cTloY6WBrr4dcvGfrgECi6/eAjERER/W+8fqH+yy+/jKSkJOj1emRlZWHr1q09tq2qqsItt9yCtLQ0KJVKLF269MJ1lIiI6DIgOBxwWixoL/NQoEsaCmgrL4bTZoHTYe+5HREREZ0Trxbpq1atwtKlS/H444/jwIEDmDhxImbPno3y8nKP7S0WC8LDw/H4449j6NCz3zuQiIiIzo1CqYSppgKSm832RBBgqqnscz/OQ0REdDHz6nfSx4wZgxEjRuCVV14Rl2VkZGD+/PlYsWLFGdedMmUKhg0bhueee+6ctsnvpBMREfXMabOiKf/QOa0TlDEUSg2/k05ERNSTc6lDvXbo22q1IicnBzNnzpQsnzlzJnbs2HHetmOxWNDS0iL5R0RERJ7Z29vPfR3jua9DREREnnmtSK+rq4PD4UBkZKRkeWRkJKqrq8/bdlasWIHAwEDxX3x8/Hl7bCIiokuNIDjP3ki20o9Yh4iIiDzy+pfIOu+p20kQBNmy/8Vjjz2G5uZm8d/JkyfP22MTERFdalRa3Tmvo/wR6xAREZFnXrsFW1hYGFQqleyseW1trezs+v9Cp9NBp+POAxERUW+ofHyh1OrgtFp61V6p00Nl8PmJe0VERHT58NqZdK1Wi6ysLGRnZ0uWZ2dnY/z48V7qFRER0eXNaXdAHxohWabU6qD29Yfa11921lwfFgGnw3Ehu0hERHRJ89qZdABYtmwZFi9ejJEjR2LcuHF47bXXUF5ejnvuuQeA61L1iooKvPvuu+I6ubm5AIC2tjacPn0aubm50Gq1GDhwoDeeAhER0SVFAKALDYfN2AaFQgl9aDhUBh84bVYAgFKjhd1khKW+FhAE6ILDYHc4oPJut4mIiC4ZXi3SFy5ciPr6ejz11FOoqqrCoEGD8PXXXyMhIQEAUFVVJbtn+vDhw8X/zsnJwYcffoiEhASUlpZeyK4TERFdkpQKwNraDL+4RDjtdpjrqmEtKYTgdJ0tVyhV0AaHwhAVC6VKDWtbM9Q+/l7uNRER0aXDq/dJ9wbeJ52IiKhngiDAYTbBabWgrbwY6Gk3QaGEX0IylBoNVHqf8/qjr0RERJeai+I+6URERNT3OKwWwOk8c4EOAIITbWVFgADxUngiIiL637FIJyIiIpFSrYapturMBXonQYCptgoKJb+RTkREdL6wSCciIqIuTidsrc29bm5raYLr5+aIiIjofGCRTkRERCK7sf2CrENERESesUgnIiIikSA4z30d57mvQ0RERJ6xSCciIiKRUqM753VU2nNfh4iIiDxjkU5EREQitY8vlBptr9srtTqoDD4/YY+IiIguLyzSiYiISOS0WaELi+h1e31oBG/BRkREdB6xSCciIiKRQqWCPjQcGv/As7bVBARBFxIGhUp9AXpGRER0eWCRTkRERCIFFDDX18EvIRn68EhAKd9VUChV0IdHwa9fEswNdVB4oZ9ERESXKh76JiIiIpHT6YA2IAhtpcUwRMXAEBEDS1MDHGYToABUOgN0QSFwmE1oLSuCb2wCBKcDCpXK210nIiK6JLBIJyIiIpG9rRVqX38YomLQWlIIpVYLXXAoNH7+AFzfWW8pLoDTaoV/8gAAgK29DbqgEG92m4iI6JLBIp2IiIhECpUKzccPIzA1E0Hpg2FuOA1zXS2cVgsAQKnTQx8SDl1IGJx2G5qPH4FfQn8v95qIiOjSwSKdiIiIRBo/f0AAmguPwDcuEbqQMOjDIiVtBKcD1pYmtJ8qAxSAxtffS70lIiK69LBIJyIiIpEgALrQcOiCQwGFAg6TEWofPyg6fkBOcDrhMBmhMvggICUd1sYGCIITCv4WLRER0XnBIp2IiIhETrsNhsgYCDYrlBotLG2tMFaedP1wHBRQ6fXQhYRDF+wLp80KfWQ0nHYblLwNGxER0XnBT1QiIiISKVRqCE4HBKcTTQV5EOz2bn8V4DCbYKwsh6m2Cv5JKVA4HSzQiYiIziNem0ZEREQipVIJOJ1oLTnuVqBLCXYbWosLIQiCeCk8ERER/e/4qUpEREQiQRBgqqmE4HCcva3D7morCBegZ0RERJcHFulERETUjQBrc2OvW1ubGqH4CXtDRER0uWGRTkRERCJ7W6vrJ957S3DC1t7603WIiIjoMsMinYiIiESC03lB1iEiIiLPWKQTERGRSKnRXJB1iIiIyDMW6URERCRS+/pDcQ63VFOoNVD7+P2EPSIiIrq8sEgnIiIikeB0QhcS1uv2upAwXu5ORER0HrFIJyIiIglDRDRUBp+ztlMZfGCIiIJCwd93JyIiOl9YpBMREZHI1tYCU00l/JMHQOMf2GM7TUAQ/JPTYKyugK2Vv+5ORER0vvT+S2dERER0yRMcDpjraqDSG+CX0B9Ouw2Wulo4zCZAAah0BujCIqBUa2BprIelrhZqA7+TTkREdL6wSCciIiKRUqOBPjwKmoAgtJUVQ6FSQhccKn5P3WmzwlRdAcHhhG98IpwWM5Rq7k4QERGdL/xUJSIiIpHG1x9qH1+0FhW4zp4DsDY1eGzbWnIcAclpAL+TTkREdN7wO+lEREQkEgQB5tpqsUA/E4fJCNPpagiCcAF6RkREdHlgkU5EREQihVIJS0Ndr9tbGk5DoeTuBBER0fnCT1UiIiIS2dtbITjsvW4v2O2wt7f9hD0iIiK6vLBIJyIiIpHTZjv3deznvg4RERF5xiKdiIiIRAqV6oKsQ0RERJ6xSCciIiKR2tf/3H6tXaGExof3SSciIjpfWKQTERFRF8EJbWBwr5trg4IhCM6fsENERESXFxbpREREJHJYLTBExfbqEnaFSg2fqFg4rNYL0DMiIqLLA4t0IiIiEilVGkChgH/SACjUmh7bKdQa+CcPgABXsU5ERETnB4t0IiIiEik1GhhPlcFhNiEobRB8YvtBpTe4vqeuUEBl8IFPbAKC0gfBYTLCWFEOlabnYp6IiIjODQ99ExERkUhw2GFrbYattRnm+lroQsMRkJIOhdJ1+bvgdMDS2ICWE8fgMJs61nFAoeRxfyIiovOBRToRERGJ7MZ28b8dJiOMp8pgPFUOhbqjSLc7AAjSdUzt0GqCLmAviYiILl0s0omIiEgkOD39UrsAwW7veSWn0PPfiIiI6Jzw2jQiIiISKbW6H7GO9ifoCRER0eWJRToRERGJ1D6+51SoK3U6qAw+P2GPiIiILi8s0omIiEgkOJ3Qh4b3ur0+NKKHS+SJiIjox2CRTkRERF0EAbrQCGgCgs7aVBMYDF1oONx/SI6IiIh+PBbpREREJBKcDgiCAL9+ydCHR4m3XutOoVJBHxENv/gkCE4BgsPhhZ4SERFdmvjr7kRERCRSqjWwtjTBUlcLQ3QcDJHRsDY1wGE2AwBUegO0QcGwm4xoLS6APjwKGv9AL/eaiIjo0sEinYiIiCTMp6vhMBnRWnQMKr0BuuAwqH18AQBOmxUtJ47BYTaJbVmkExERnT8s0omIiEjksFrgMBm7/t9sgrW5EUqNBgDgtNnEAh0A7MZ2OG0WqHSGC95XIiKiSxGLdCIiIhI5zB0FulIJfUg4dKHhUKhUcFgsUMB1yzXB4YC5/jQsDacBpxMOs5lFOhER0XnCIp2IiIi6UUCp0cAvaQDgdMBUXQFrSxMgdPyCu0IBbWAw9GGR0AWHorWk0Ku9JSIiutSwSCciIiKRWm+Af9IA2I1taD9VJm8gCLA2NcDa1AjfuAT4Jw+AQqG48B0lIiK6RPEWbERERCRSanVw2m2eC3QJAe2nSiHY7VBqtBekb0RERJcDFulEREQkYaqt6nVb8zm0JSIiorNjkU5EREQip90Ge1trr9vb2logOOw/YY+IiIguLyzSiYiISOQwGs/eyI3ddO7rEBERkWf84TgiIiISCRDE/1ZqNNCFhEMbHCp+79xps8LaWA9zw2kINlvHSoKnhyIiIqIfgUU6ERERiVRaHQDAEBULfVgkbK3NMFaUw2E2uf6uN0AXGo6gtMEwn66GqaYSyo51iIiI6H/HIp2IiIhEKoMBvvFJUPv4ornwKJwWs+TvTpsVttZmqHR6+CWlQqnVQaXTe6m3RERElx5+J52IiIhEgt0BjX8AWosLZAV6dw6LGa1FBdD4B0JwOi5gD4mIiC5tLNKJiIhIpFAqYaquhLPz++Zn4LRZYaqphELB3QkiIqLzhZ+qRERE1EWhgKWpvtfNrY31gELxE3aIiIjo8sIinYiIiES29lbA6ex1e8HpgL297SfsERER0eWFPxxHREREIsFul/y/UqeHzu0WbJbGesn31Z0O6TpERET047FIJyIiIpFCpQIAqAy+8ImOhdrHD9bmRtct2BQKqHR6BKYOhL29DcbqU3CYjOI6RERE9L9jkU5EREQijV8ANAHB8ItPhLmuBm1lxRDczpQbK09CHxaBgOQ0tJ0sgcbX30u9JSIiuvSwSCciIiKRIAjw65eI9pOlsDY3em7jsMNUUwmHxQy/fkkQBAH86TgiIqLzgz8cR0RERF0EwNJQ32OB3p21qQGWxnrXSkRERHResEgnIiIikUKpgLmuptftzXW1vE86ERHRecRPVSIiIhLZTUY4rZZet3dazK4flSMiIqLzgkU6ERERiZy23hfo4jpW60/QEyIiossTi3QiIiISKX7MroGSPxtHRER0vrBIJyIiIpHKx+ec11Ebzn0dIiIi8oxFOhEREYkUKjU0fgG9bq/xDwSU3J0gIiI6X/ipSkRERCLBYYc+IhrozZ3PFQroI6LhtDt+8n4RERFdLlikExERkUip0UKhVsM3PhFQnKFQVyjgG58EhVIJlVZ7wfpHRER0qWORTkRERCLB4YCttRkqvQEBqQOhDQ4Fut8HXaGELiQMAakDodLqYGtrheDgmXQiIqLzRe3tDhAREVHfog+NgNNuA5xOGCKi4RMTL95mTaXVuf4mAFCroQ8Nh+B0erfDRERElxAW6URERCRSKJWus+ktzTBWlgMAVAYfKNUaAIDTZoXDbAIA+MQmQBsQCIWKuxNERETnCz9ViYiISOSwWuG0WsQCHQAcJiM8XdBurCiDSjsACq0Wap3+wnWSiIjoEsbvpBMREZFIpdHAfLqq1+1Np6ug6jjLTkRERP87FulEREQkctptsLe39bq9va0VTof9J+wRERHR5YVFOhEREYnsJuM5r+MwmX6CnhAREV2eWKQTERHR/0jwdgeIiIguGSzSiYiISKTSnvsPwCm1up+gJ0RERJcnFulEREQkUun1UOkN59DeAOWPKOyJiIjIMxbpREREJDI1tUMbEt7r9tqQCJhb2n/CHhEREV1eeJ90IiIiEmn99IBTB2eICZaG02dsqwsNh9o3AEq16gL1joiI6NLHM+lEREQkEpwCTmw+CF1YNAzR8R6/b67U6mCIjoc2JArFW/PgdDi90FMiIqJLE8+kExERkUihUCBx3EBsfekLpF45HDGDB8La0gLBZgEUgEKtgzYgAJUHi1C4cSvG3TUHCm93moiI6BKiEAThsrpvSktLCwIDA9Hc3IyAgABvd4eIiKhPaa9vgVKlhMNmx5YX1sBusaHfqDQYgvwAAKbGVpTvLYDGoMPE++ZDpVHD6XDCN8Tfyz0nIiLqu86lDmWRTkRERCKnw4mmU6eh1mvhFxqAysMlKNp8CC1VDYACCIwORf/JQxCdmYj2+hZYTRYEx0dAqeI36IiIiHpyLnUoL3cnIiIikd1sRX1xFQJjw6BSq6DWaTH6zlkwBPoCAExNbWipaoCppR3Gxja01jTCPzwIWl/eho2IiOh84GFvIiIiEjWcrEXS+Ewc/34/Dq3eBqVKCa2PDu0NLTA2tELrZ4BCqcDBT7eiaMtBJIxJR1NFnbe7TUREdMlgkU5EREQia6sJu99ej7E/n43AuDA0ltXCbrHCNyQAhmA/2M0WNJbXIjg+AqPvmIXdb62Htd3s7W4TERFdMni5OxEREYn8IoJQkXsClXmpyJg1Era2FlhqT8F00gRAAZVej6Qx/aHxC0D53gJUHirCoGvHebvbRERElwz+cBwRERGJnHYHKvOKETkgFu2niuC0WDy2U+r08I1PRvWxU4gZ0h8qteoC95SIiOjicS51KC93JyIiIpHVZEF0ZgLaygt7LNABwGkxo73sBGIGJ8Fm6rkdERERnRsW6URERCTS+uhgrq2EYLOdta3TZoW5thJaH90F6BkREdHlgUU6ERERiRQKBSxNDb1ub21sgEKh+Al7REREdHlhkU5EREQiW1sL4HT2ur3gdMDW3voT9oiIiOjywiKdiIiIRILDcUHWISIiIs9YpBMREZFIqT73u7P+mHWIiIjIMxbpREREJFL7+kOh7P3t1BQqFdQ+fj9hj4iIiC4vLNKJiIhIJDid0AaH9rq9LjgMwjl8h52IiIjOjEU6ERERiWwWGwyRMVBqz35bNaVWB31kNGyWs9+ujYiIiHqHRToRERGJtAYdrM2NCEhOg8rg02M7lcEXAf3TYG1qgNbA+6QTERGdL/ylFyIiIhLZje0wVpTBGR6FgP7psBvbYKk/DYfFDABQ6Q3QhYRD7eMLU00lzKerodL7QOPL76UTERGdDyzSiYiISOS0WwEA5tPVsDTUQRcSCn1kDJQajevvViusjfVoKzsh3npNsPFydyIiovPF65e7v/zyy0hKSoJer0dWVha2bt16xvabN29GVlYW9Ho9kpOT8eqrr16gnhIREV36FN12DQSHHebTNWg5fgRNR3LRdCQXLYVHYa6rkd4bXanwQk+JiIguTV4t0letWoWlS5fi8ccfx4EDBzBx4kTMnj0b5eXlHtuXlJRgzpw5mDhxIg4cOIDf/va3uP/++/HZZ59d4J4TERFdmlS+vue8jtpw7usQERGRZwpBEARvbXzMmDEYMWIEXnnlFXFZRkYG5s+fjxUrVsjaL1++HGvXrkV+fr647J577sHBgwexc+fOXm2zpaUFgYGBaG5uRkBAwP/+JIiIiC4hgtOJttITsLW19Kq9xj8Qfgn9oVB6/eI8IiKiPutc6lCvfaJarVbk5ORg5syZkuUzZ87Ejh07PK6zc+dOWftZs2Zh3759sPH7cERERP8zY2MrtKGRAHpxCbtCAW1oJExNbT95v4iIiC4XXivS6+rq4HA4EBkZKVkeGRmJ6upqj+tUV1d7bG+321FXV+dxHYvFgpaWFsk/IiIi8qyuuBrmdht8YhMAxRkKdYUCPrGJMLVYUF9ac+E6SEREdInz+rVpCrcdAEEQZMvO1t7T8k4rVqxAYGCg+C8+Pv5/7DEREdGlS6VWYtO/VsNscsIvMQ26kDBA0W13QamELiQMfklpMLXZsfm51VCpVd7rMBER0SXGa7dgCwsLg0qlkp01r62tlZ0t7xQVFeWxvVqtRmhoqMd1HnvsMSxbtkz8/5aWFhbqREREPQhPjYO13Yzv/7YKCWMyMODK4QhMj4Pd7LpPulqvR9vpJhxdvRNle44BgoCwlBgv95qIiOjS4bUiXavVIisrC9nZ2bjuuuvE5dnZ2Zg3b57HdcaNG4cvv/xSsmzDhg0YOXIkNB33b3Wn0+mg0+nOX8eJiIguYSqtGjFDk1Fx4ASKtx1G8bbDCIwNgyHYDwBgamhFc2W92D5+5AAoVTyTTkREdL549XL3ZcuW4Y033sCbb76J/Px8PPjggygvL8c999wDwHUW/Pbbbxfb33PPPSgrK8OyZcuQn5+PN998E//973/x8MMPe+spEBERXVKUahUGzx0PlbbrOH5zRR2qD5ei+nCppEBX6zQYdO04qHWeD5QTERHRufPamXQAWLhwIerr6/HUU0+hqqoKgwYNwtdff42EhAQAQFVVleSe6UlJSfj666/x4IMP4qWXXkJMTAyef/553HDDDd56CkRERJcUpVIJnxB/TFoyH1tfXgu72eqxnUavxYT75sMQ5HeBe0hERHRp8+p90r2B90knIiI6O7vFBofNjsIfclG8LQ/mFiMAwBDki+QJg5EydShUajXPohMREfXCudShXj2TTkRERH2TWqeBWqdB+swsZF4zFnarDQoFoNJoYLfaoNFrvd1FIiKiSxKLdCIiIuqRuqMY716Us0AnIiL66Xj9PulERERERERE5MIinYiIiIiIiKiPYJFORERERERE1EewSCciIiIiIiLqI1ikExEREREREfURLNKJiIiIiIiI+ggW6URERERERER9BIt0IiIiIiIioj6CRToRERERERFRH8EinYiIiIiIiKiPYJFORERERERE1EewSCciIiIiIiLqI1ikExEREREREfURLNKJiIiIiIiI+ggW6URERERERER9BIt0IiIiIiIioj6CRToRERERERFRH8EinYiIiIiIiKiPYJFORERERERE1Eeovd2BC00QBABAS0uLl3tCREREREREl4PO+rOzHj2Ty65Ib21tBQDEx8d7uSdERERERER0OWltbUVgYOAZ2yiE3pTylxCn04nKykr4+/tDoVB4uzsAXEdV4uPjcfLkSQQEBHi7O3QWHK+LC8fr4sGxurhwvC4uHK+LB8fq4sLxurh4c7wEQUBraytiYmKgVJ75W+eX3Zl0pVKJuLg4b3fDo4CAAL64LyIcr4sLx+viwbG6uHC8Li4cr4sHx+riwvG6uHhrvM52Br0TfziOiIiIiIiIqI9gkU5ERERERETUR7BI7wN0Oh1+//vfQ6fTebsr1Ascr4sLx+viwbG6uHC8Li4cr4sHx+riwvG6uFws43XZ/XAcERERERERUV/FM+lEREREREREfQSLdCIiIiIiIqI+gkU6ERERERERUR/BIp2IiIiIiIioj2CR3ge8/PLLSEpKgl6vR1ZWFrZu3ertLl32VqxYgVGjRsHf3x8RERGYP38+CgoKJG0EQcAf/vAHxMTEwGAwYMqUKThy5IiXekzdrVixAgqFAkuXLhWXcbz6loqKCtx2220IDQ2Fj48Phg0bhpycHPHvHK++wW6344knnkBSUhIMBgOSk5Px1FNPwel0im04Vt6zZcsWXHvttYiJiYFCocCaNWskf+/N2FgsFtx3330ICwuDr68v5s6di1OnTl3AZ3H5ONN42Ww2LF++HIMHD4avry9iYmJw++23o7KyUvIYHK8L42yvre7uvvtuKBQKPPfcc5LlHKsLpzfjlZ+fj7lz5yIwMBD+/v4YO3YsysvLxb/3tfFike5lq1atwtKlS/H444/jwIEDmDhxImbPni2ZNHThbd68Gffeey927dqF7Oxs2O12zJw5E+3t7WKbZ555Bv/85z/x4osvYu/evYiKisKMGTPQ2trqxZ7T3r178dprr2HIkCGS5RyvvqOxsRFXXHEFNBoNvvnmGxw9ehTPPvssgoKCxDYcr77hb3/7G1599VW8+OKLyM/PxzPPPIO///3veOGFF8Q2HCvvaW9vx9ChQ/Hiiy96/Htvxmbp0qX4/PPP8dFHH2Hbtm1oa2vDNddcA4fDcaGexmXjTONlNBqxf/9+PPnkk9i/fz9Wr16N48ePY+7cuZJ2HK8L42yvrU5r1qzB7t27ERMTI/sbx+rCOdt4FRUVYcKECUhPT8emTZtw8OBBPPnkk9Dr9WKbPjdeAnnV6NGjhXvuuUeyLD09XXj00Ue91CPypLa2VgAgbN68WRAEQXA6nUJUVJTw17/+VWxjNpuFwMBA4dVXX/VWNy97ra2tQmpqqpCdnS1MnjxZeOCBBwRB4Hj1NcuXLxcmTJjQ4985Xn3H1VdfLfz85z+XLLv++uuF2267TRAEjlVfAkD4/PPPxf/vzdg0NTUJGo1G+Oijj8Q2FRUVglKpFL799tsL1vfLkft4ebJnzx4BgFBWViYIAsfLW3oaq1OnTgmxsbHC4cOHhYSEBOFf//qX+DeOlfd4Gq+FCxeKn1ue9MXx4pl0L7JarcjJycHMmTMly2fOnIkdO3Z4qVfkSXNzMwAgJCQEAFBSUoLq6mrJ2Ol0OkyePJlj50X33nsvrr76akyfPl2ynOPVt6xduxYjR47ETTfdhIiICAwfPhyvv/66+HeOV98xYcIEfP/99zh+/DgA4ODBg9i2bRvmzJkDgGPVl/VmbHJycmCz2SRtYmJiMGjQII5fH9Dc3AyFQiFeZcTx6jucTicWL16MRx55BJmZmbK/c6z6DqfTia+++goDBgzArFmzEBERgTFjxkguie+L48Ui3Yvq6urgcDgQGRkpWR4ZGYnq6mov9YrcCYKAZcuWYcKECRg0aBAAiOPDses7PvroI+zfvx8rVqyQ/Y3j1bcUFxfjlVdeQWpqKtavX4977rkH999/P959910AHK++ZPny5Vi0aBHS09Oh0WgwfPhwLF26FIsWLQLAserLejM21dXV0Gq1CA4O7rENeYfZbMajjz6KW265BQEBAQA4Xn3J3/72N6jVatx///0e/86x6jtqa2vR1taGv/71r7jqqquwYcMGXHfddbj++uuxefNmAH1zvNRe2SpJKBQKyf8LgiBbRt6zZMkSHDp0CNu2bZP9jWPXN5w8eRIPPPAANmzYIPl+kTuOV9/gdDoxcuRI/OUvfwEADB8+HEeOHMErr7yC22+/XWzH8fK+VatW4f3338eHH36IzMxM5ObmYunSpYiJicEdd9whtuNY9V0/Zmw4ft5ls9lw8803w+l04uWXXz5re47XhZWTk4N///vf2L9//znnzrG68Dp/6HTevHl48MEHAQDDhg3Djh078Oqrr2Ly5Mk9ruvN8eKZdC8KCwuDSqWSHaGpra2VHfkm77jvvvuwdu1abNy4EXFxceLyqKgoAODY9RE5OTmora1FVlYW1Go11Go1Nm/ejOeffx5qtVocE45X3xAdHY2BAwdKlmVkZIg/mMnXV9/xyCOP4NFHH8XNN9+MwYMHY/HixXjwwQfFK1Y4Vn1Xb8YmKioKVqsVjY2NPbahC8tms2HBggUoKSlBdna2eBYd4Hj1FVu3bkVtbS369esn7nOUlZXhoYceQmJiIgCOVV8SFhYGtVp91v2OvjZeLNK9SKvVIisrC9nZ2ZLl2dnZGD9+vJd6RYDryNmSJUuwevVq/PDDD0hKSpL8PSkpCVFRUZKxs1qt2Lx5M8fOC6688krk5eUhNzdX/Ddy5EjceuutyM3NRXJyMserD7niiitktzQ8fvw4EhISAPD11ZcYjUYoldJdBZVKJZ6Z4Fj1Xb0Zm6ysLGg0GkmbqqoqHD58mOPnBZ0FemFhIb777juEhoZK/s7x6hsWL16MQ4cOSfY5YmJi8Mgjj2D9+vUAOFZ9iVarxahRo86439Enx8srP1dHoo8++kjQaDTCf//7X+Ho0aPC0qVLBV9fX6G0tNTbXbus/d///Z8QGBgobNq0SaiqqhL/GY1Gsc1f//pXITAwUFi9erWQl5cnLFq0SIiOjhZaWlq82HPq1P3X3QWB49WX7NmzR1Cr1cLTTz8tFBYWCh988IHg4+MjvP/++2IbjlffcMcddwixsbHCunXrhJKSEmH16tVCWFiY8Jvf/EZsw7HyntbWVuHAgQPCgQMHBADCP//5T+HAgQPir4H3ZmzuueceIS4uTvjuu++E/fv3C9OmTROGDh0q2O12bz2tS9aZxstmswlz584V4uLihNzcXMm+h8ViER+D43VhnO215c79190FgWN1IZ1tvFavXi1oNBrhtddeEwoLC4UXXnhBUKlUwtatW8XH6GvjxSK9D3jppZeEhIQEQavVCiNGjBBv80XeA8Djv7feekts43Q6hd///vdCVFSUoNPphEmTJgl5eXne6zRJuBfpHK++5csvvxQGDRok6HQ6IT09XXjttdckf+d49Q0tLS3CAw88IPTr10/Q6/VCcnKy8Pjjj0uKBo6V92zcuNHjZ9Udd9whCELvxsZkMglLliwRQkJCBIPBIFxzzTVCeXm5F57Npe9M41VSUtLjvsfGjRvFx+B4XRhne22581Skc6wunN6M13//+18hJSVF0Ov1wtChQ4U1a9ZIHqOvjZdCEAThpz1XT0RERERERES9we+kExEREREREfURLNKJiIiIiIiI+ggW6URERERERER9BIt0IiIiIiIioj6CRToRERERERFRH8EinYiIiIiIiKiPYJFORERERERE1EewSCciIrpMKBQKrFmzxtvd8Lq3334bQUFB3u4GERGRRyzSiYiIfgJ33nkn5s+fL1u+adMmKBQKNDU1XfA+VVVVYfbs2Rd8u71RWloKhUIh/gsMDMTYsWPx5ZdfertrREREFxSLdCIiostEVFQUdDqdt7txRt999x2qqqqwe/dujB49GjfccAMOHz7s7W4RERFdMCzSiYiIvKi+vh6LFi1CXFwcfHx8MHjwYKxcuVLSZsqUKViyZAmWLFmCoKAghIaG4oknnoAgCGKbxMRE/OlPf8Itt9wCPz8/xMTE4IUXXpA8TvfL3TvPXK9evRpTp06Fj48Phg4dip07d0rW2bFjByZNmgSDwYD4+Hjcf//9aG9vF//+8ssvIzU1FXq9HpGRkbjxxhvFv3366acYPHgwDAYDQkNDMX36dMm6noSGhiIqKgrp6el4+umnYbPZsHHjRvHv3377LSZMmCDmcM0116CoqEj8e2+fl/sYjB49GnPnzoXZbD5j/4iIiH5qLNKJiIi8yGw2IysrC+vWrcPhw4dx1113YfHixdi9e7ek3TvvvAO1Wo3du3fj+eefx7/+9S+88cYbkjZ///vfMWTIEOzfvx+PPfYYHnzwQWRnZ59x+48//jgefvhh5ObmYsCAAVi0aBHsdjsAIC8vD7NmzcL111+PQ4cOYdWqVdi2bRuWLFkCANi3bx/uv/9+PPXUUygoKMC3336LSZMmAXBdWr9o0SL8/Oc/R35+PjZt2oTrr79ecmDhTGw2G15//XUAgEajEZe3t7dj2bJl2Lt3L77//nsolUpcd911cDqdvX5e3Z06dQoTJ05Eeno6Vq9eDb1e36v+ERER/WQEIiIiOu/uuOMOQaVSCb6+vpJ/er1eACA0Njb2uO6cOXOEhx56SPz/yZMnCxkZGYLT6RSXLV++XMjIyBD/PyEhQbjqqqskj7Nw4UJh9uzZ4v8DED7//HNBEAShpKREACC88cYb4t+PHDkiABDy8/MFQRCExYsXC3fddZfkMbdu3SoolUrBZDIJn332mRAQECC0tLTInkNOTo4AQCgtLT1DSl06+2MwGARfX19BqVQKAITExEShvr6+x/Vqa2sFAEJeXl6vn9dbb70lBAYGCgUFBUK/fv2E++67T5ItERGRN/FMOhER0U9k6tSpyM3NlfxzP/vtcDjw9NNPY8iQIQgNDYWfnx82bNiA8vJySbuxY8dCoVCI/z9u3DgUFhbC4XBIlnU3btw45Ofnn7GPQ4YMEf87OjoaAFBbWwsAyMnJwdtvvw0/Pz/x36xZs+B0OlFSUoIZM2YgISEBycnJWLx4MT744AMYjUYAwNChQ3HllVdi8ODBuOmmm/D666+jsbHxrJmtWrUKBw4cwNq1a5GSkoI33ngDISEh4t+Liopwyy23IDk5GQEBAUhKSgIAWV5nel4AYDKZMGHCBMyfPx/PP/+8JFsiIiJvYpFORET0E/H19UVKSorkX2xsrKTNs88+i3/961/4zW9+gx9++AG5ubmYNWsWrFbreenD2YrP7peSd7btvHTc6XTi7rvvlhxkOHjwIAoLC9G/f3/4+/tj//79WLlyJaKjo/G73/0OQ4cORVNTE1QqFbKzs/HNN99g4MCBeOGFF5CWloaSkpIz9ic+Ph6pqam4+uqr8cYbb2DhwoWS4vraa69FfX09Xn/9dezevVv8WoB7Xmd6XgCg0+kwffp0fPXVVzh16tQZ+0RERHQhsUgnIiLyoq1bt2LevHm47bbbMHToUCQnJ6OwsFDWbteuXbL/T01NhUqlOmOb9PT0H923ESNG4MiRI7IDDSkpKdBqtQAAtVqN6dOn45lnnsGhQ4dQWlqKH374AYCrOL7iiivwxz/+EQcOHIBWq8Xnn3/e6+1PnjwZgwYNwtNPPw3A9QNv+fn5eOKJJ3DllVciIyOjV2fnPVEqlXjvvfeQlZWFadOmobKy8kc9DhER0fnGIp2IiMiLUlJSkJ2djR07diA/Px933303qqurZe1OnjyJZcuWoaCgACtXrsQLL7yABx54QNJm+/bteOaZZ3D8+HG89NJL+OSTT2RtzsXy5cuxc+dO3HvvvcjNzUVhYSHWrl2L++67DwCwbt06PP/888jNzUVZWRneffddOJ1OpKWlYffu3fjLX/6Cffv2oby8HKtXr8bp06eRkZFxTn146KGH8J///AcVFRUIDg5GaGgoXnvtNZw4cQI//PADli1b9qOfn0qlwgcffIChQ4di2rRpHnMnIiK60FikExERedGTTz6JESNGYNasWZgyZQqioqIwf/58Wbvbb78dJpMJo0ePxr333ov77rsPd911l6TNQw89hJycHAwfPhx/+tOf8Oyzz2LWrFk/um9DhgzB5s2bUVhYiIkTJ2L48OF48sknxe94BwUFYfXq1Zg2bRoyMjLw6quvYuXKlcjMzERAQAC2bNmCOXPmYMCAAXjiiSfw7LPPYvbs2efUh2uuuQaJiYl4+umnoVQq8dFHHyEnJweDBg3Cgw8+iL///e8/+vkBrisBOvs8bdo0yaX1RERE3qAQhF7eC4WIiIi8YsqUKRg2bBiee+65HtskJiZi6dKlWLp06QXrFxEREZ1/PJNORERERERE1EewSCciIiIiIiLqI3i5OxEREREREVEfwTPpRERERERERH0Ei3QiIiIiIiKiPoJFOhEREREREVEfwSKdiIiIiIiIqI9gkU5ERERERETUR7BIJyIiIiIiIuojWKQTERERERER9REs0omIiIiIiIj6CBbpRERERERERH3E/wNRRV/sHH/TzwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the clusters for the \"availability_365\" and \"calculated_host_listings_count\" variables.\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = sns.scatterplot(x=df['Happiness Rank'], y=df['Perceptions of corruption'], hue=kmeans.labels_, s=100)\n",
    "\n",
    "ax.grid(False) \n",
    "plt.xlabel('Happiness Rank') \n",
    "plt.ylabel('Perceptions of corruption') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8c9dae6e-d9fb-45d4-9366-4b6333a663c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['clusters'] == 4, 'cluster'] = 'darkest purple'\n",
    "df.loc[df['clusters'] == 3, 'cluster'] = 'darker purple'\n",
    "df.loc[df['clusters'] == 2, 'cluster'] = 'purple'\n",
    "df.loc[df['clusters'] == 1, 'cluster'] = 'lilac'\n",
    "df.loc[df['clusters'] == 0, 'cluster'] = 'pink'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4e761576-ab87-486e-b4a0-60dd43769465",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Happiness Rank</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Happiness Score</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Perceptions of corruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lilac</th>\n",
       "      <td>90.140035</td>\n",
       "      <td>79.238095</td>\n",
       "      <td>5.111062</td>\n",
       "      <td>5.378949</td>\n",
       "      <td>0.115325</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pink</th>\n",
       "      <td>79.238095</td>\n",
       "      <td>79.238095</td>\n",
       "      <td>5.378949</td>\n",
       "      <td>5.378949</td>\n",
       "      <td>0.093183</td>\n",
       "      <td>0.082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purple</th>\n",
       "      <td>30.983607</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.564680</td>\n",
       "      <td>6.486500</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>0.111299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Happiness Rank            Happiness Score            \\\n",
       "                  mean     median            mean    median   \n",
       "cluster                                                       \n",
       "lilac        90.140035  79.238095        5.111062  5.378949   \n",
       "pink         79.238095  79.238095        5.378949  5.378949   \n",
       "purple       30.983607  31.000000        6.564680  6.486500   \n",
       "\n",
       "        Perceptions of corruption            \n",
       "                             mean    median  \n",
       "cluster                                      \n",
       "lilac                    0.115325  0.111299  \n",
       "pink                     0.093183  0.082000  \n",
       "purple                   0.111299  0.111299  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cluster').agg({'Happiness Rank':['mean', 'median'], \n",
    "                         'Happiness Score':['mean', 'median'], \n",
    "                         'Perceptions of corruption':['mean', 'median']\n",
    "                          })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c83d6cbb-75b0-4858-ad2a-f11edf548400",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>...</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>clusters</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>...</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "      <td>dark purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>...</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "      <td>dark purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>...</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "      <td>dark purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "      <td>dark purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>...</td>\n",
       "      <td>1.850238</td>\n",
       "      <td>78.5</td>\n",
       "      <td>5.391506</td>\n",
       "      <td>0.898298</td>\n",
       "      <td>1.211026</td>\n",
       "      <td>0.661295</td>\n",
       "      <td>0.423538</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>2</td>\n",
       "      <td>dark purple</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Happiness Score  Standard Error  Economy (GDP per Capita)  \\\n",
       "0             1.0            7.587         0.03411                   1.39651   \n",
       "1             2.0            7.561         0.04884                   1.30232   \n",
       "2             3.0            7.527         0.03328                   1.32548   \n",
       "3             4.0            7.522         0.03880                   1.45900   \n",
       "4             5.0            7.427         0.03553                   1.32629   \n",
       "\n",
       "    Family  Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0  1.34951                   0.94143  0.66557                        0.41978   \n",
       "1  1.40223                   0.94784  0.62877                        0.14145   \n",
       "2  1.36058                   0.87464  0.64938                        0.48357   \n",
       "3  1.33095                   0.88521  0.66973                        0.36503   \n",
       "4  1.32261                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  ...  Dystopia.Residual  Overall rank  \\\n",
       "0     0.29678            2.51738  ...           1.850238          78.5   \n",
       "1     0.43630            2.70201  ...           1.850238          78.5   \n",
       "2     0.34139            2.49204  ...           1.850238          78.5   \n",
       "3     0.34699            2.46531  ...           1.850238          78.5   \n",
       "4     0.45811            2.45176  ...           1.850238          78.5   \n",
       "\n",
       "      Score  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0  5.391506        0.898298        1.211026                 0.661295   \n",
       "1  5.391506        0.898298        1.211026                 0.661295   \n",
       "2  5.391506        0.898298        1.211026                 0.661295   \n",
       "3  5.391506        0.898298        1.211026                 0.661295   \n",
       "4  5.391506        0.898298        1.211026                 0.661295   \n",
       "\n",
       "   Freedom to make life choices  Perceptions of corruption  clusters  \\\n",
       "0                      0.423538                   0.111299         2   \n",
       "1                      0.423538                   0.111299         2   \n",
       "2                      0.423538                   0.111299         2   \n",
       "3                      0.423538                   0.111299         2   \n",
       "4                      0.423538                   0.111299         2   \n",
       "\n",
       "       cluster  \n",
       "0  dark purple  \n",
       "1  dark purple  \n",
       "2  dark purple  \n",
       "3  dark purple  \n",
       "4  dark purple  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb72668e-d238-4239-a661-ae491da3b69e",
   "metadata": {},
   "outputs": [],
   "source": [
    "With a mean happiness rank of 90, the cluster lilac is less happy than other clusters. With a mean happiness score of 5.11, less happiness is indicated.The study shows that different countries have different perceptions of corruption and different happiness scores and ranks. The lilac cluster's member nations are perceived as being more corrupt and are also the least happy. The happiest countries are those in the purple cluster, while those in the pink cluster have slightly higher happiness scores and lower perceptions of corruption.  The mean and the median agree with each other. The average perception of corruption in this cluster is 0.115, indicating a higher perception. The happiness rank and happiness score are consistent, slightly higher than the lilac cluster but still low. The mean and median happiness scores are around 5.38, while the perception of corruption is lower.Like the lilac cluster, the purple cluster has a mean perception of corruption of 0.111, a mean happiness rank of 31, and a higher happiness score of 6.56. These variables show average relative happiness.\n"
   ]
  }
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