Clustering_problem_statement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n",
    "In this session we are going to implement the clustering algorithm K-Means and Hierarchical Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Given a shopping dataset, which contains CustomerID, gender, Income, Age, Annual Income and Spending score. We will now perform Clustering algorithms based on Annual_Income and Spending_Score to segrigate customers into different clusters in following steps. \n",
    "\n",
    "\n",
    "### For K-Means Clustering\n",
    "\n",
    "1. Load the `shopping.csv` file uisng Pandas.\n",
    "2. Check the desriptive analysis of the data, and do changes if required.\n",
    "\n",
    "3. Perform k-means clustering with 5 clusters using sklearn library\n",
    "4. Print the cluster centers\n",
    "5. Attach clusters to the original data.\n",
    "6. Plot the Clusters using `matplotlib`.\n",
    "\n",
    "\n",
    "### For-Hierarchical Clustering.\n",
    "\n",
    "1. Segrigate both the columns `Annual_Income` and `Spending_score` from the dataset.\n",
    "2. Use `Aglomarative` `Hierarchical` clustering with 5 clusters using sklearn library on the segrigated data\n",
    "3. Plot the clusters using matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imporitng Required Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the dataset\n",
    "\n",
    "df = pd.read_csv('shopping_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Income  Spending_Score\n",
       "0           1    Male   19      15              39\n",
       "1           2    Male   21      15              81\n",
       "2           3  Female   20      16               6\n",
       "3           4  Female   23      16              77\n",
       "4           5  Female   31      17              40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the columns Annual income and spending score \n",
    "\n",
    "df.rename(columns={'Annual Income (k$)' : 'Income', 'Spending Score (1-100)' : 'Spending_Score'}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age      Income  Spending_Score\n",
       "count  200.000000  200.000000  200.000000      200.000000\n",
       "mean   100.500000   38.850000   60.560000       50.200000\n",
       "std     57.879185   13.969007   26.264721       25.823522\n",
       "min      1.000000   18.000000   15.000000        1.000000\n",
       "25%     50.750000   28.750000   41.500000       34.750000\n",
       "50%    100.500000   36.000000   61.500000       50.000000\n",
       "75%    150.250000   49.000000   78.000000       73.000000\n",
       "max    200.000000   70.000000  137.000000       99.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking descriptive analysis\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing k-means clustring with 5 clusters\n",
    "# import KMeans\n",
    "\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=5)\n",
    "kmeans = kmeans.fit(df[['Spending_Score','Income']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[49.51851852, 55.2962963 ],\n",
       "       [79.36363636, 25.72727273],\n",
       "       [20.91304348, 26.30434783],\n",
       "       [82.12820513, 86.53846154],\n",
       "       [17.11428571, 88.2       ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Income  Spending_Score  Clusters\n",
       "0           1    Male   19      15              39         2\n",
       "1           2    Male   21      15              81         1\n",
       "2           3  Female   20      16               6         2\n",
       "3           4  Female   23      16              77         1\n",
       "4           5  Female   31      17              40         2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Clusters'] = kmeans.labels_\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    81\n",
       "3    39\n",
       "4    35\n",
       "2    23\n",
       "1    22\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to check how many values has been assigned\n",
    "\n",
    "df['Clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc5083f610>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ajQ2NmLlypVYs2ZN/3cagGzWUJ8H4Lnu70cgHLBVjd3bqAhJKdG0ZVuvMK1SQ/VTL/0O1TWVWRghERFlg1riEcttFyyLfL94RQPAeuq8o85IKyGlx39TOVPtcDgwdepUvPnmmxg/fnzKzpuVLh9CiGsBBAH8Ud0U4zAZ576/EkKsEkKsamnhLGUhEkLAaivFiJG1cY8Zs++eMBhZFURERFQozKVmAOgxQ63dPlgtLS1ob28HAHi9XrzzzjvYb7/9kjpntIzPUAshzgZwMoBjpZRqaG4EMFJzWB2ArbHuL6V8CMBDADB58uSYoZvyX2VVBX735O349ewr8dWab3rsm3rcYbhx2UI4KuxZGh0RERGl2rI3lgAALp56KYDwDPV9H9yV9Hmbm5tx9tlnIxQKQVEUnH766Tj55JOTPq9WRgO1EOJEAFcBOEpK6dHsehXAn4QQdwIYDmAMgJWZHBvlnlihmmGaiKh4Gc2mcDlHN1e7O1LqcdUjC2BzWCPHUf4yl5rh8/iSnplWHXjggfjss89Scq540haohRDPAJgKoFoI0QjgeoS7epgBvN3druRfUsqLpJRfCCGeB/AlwqUgc6WUXMmDeoTqocOqGaaJiIpYqa0kbm20zWGFvZq/HwqBOlOdT9IWqKWUP4+x+dE+jr8ZwM3pGg/lr3CoXgqdTjBMExERUc7hSomUFyqrHP0fRERERUVbAsIyD8omBmoiIiLKS32VgBBlUlba5hERERERFQoGaiIiIiKiJDBQExEREVHBC4VC+N73vpfyHtQAAzURERERFYF77rkHY8eOTcu5GaiJiIiIKGes/OenOOp7P8HKf36asnM2Njbi9ddfxwUXXJCyc2oxUBMRERFRTlj5z08x77yFaNvVjnnnLUxZqL7kkktw++23Q6dLT/RloCYiIiKirFPDdJfXBwDo8vpSEqpfe+011NTUYNKkSakYZkwM1ERERESUdVfMvSESplVdXh+umHtDUuf96KOP8Oqrr6K+vh6zZs3Ce++9hzPPPDOpc0ZjoCYiIiKirFv6wPWwlJh7bLOUmLH0geuTOu+tt96KxsZGbNy4Ec8++yyOOeYYPP3000mdMxoDNRERERFl3SGHTcT9jy2JhGpLiRn3P7YEhxw2Mcsj6x8DdYELBkPo6HBlexhERERE/VJDdUWlIy1heurUqXjttddSek6AgbqgBYMhbNnUhMeW/wnO9o5sD4eIiIiywOPywtnqhLPVCY/Lm+3h9OuQwybiw89eyYuZaZUh2wOg9FDD9Jmn/hquDjc63R7MW3A+7I7ybA+NiIiIMijg82PRzAYAwOIVDYCtJKvjKUScoS5A0WEaAJ576s+4f9mjnKkmIiIiSjHOUBeYWGFa9dxTfwYAzlQTEREVOI/Li4DPDwBwte/OA9rvAcBoNqE0DTPWUkoIIVJ+3kyQUg74PgzUBSYYDOKCWZf0CtOq5576M/YbNwYnTz8eZrMpw6MjIiKiTNCWeWjddsGyHrfTUQJisViwc+dOVFVV5V2ollJi586dsFgsA7ofA3WBEQK466GbcP7PLoGv+y9TraOPPwLHnvgDhmkiIiJKi7q6OjQ2NqKlpSXbQxkUi8WCurq6Ad1HDGZaO1dMnjxZrlq1KtvDyDk+nw9rv1zXK1QfffwRuOH2K+GosGdxdERERJRu0SUf6sz0VY8sgM1hjRyXrpKPQiSE+ERKOTnWPl6UWIDMZjP23X9vPPrc3ZGZaIZpIiKi4lFqK4G92g57tb1HgLY5rJHt9mo7w3SKMFAXKG2oPvGUYximiYiIiNKENdQFTA3VixZfhnK7LdvDISIiIipIDNQFzmw2w2w2Z3sYRERElCVGsynczaP7e0o9BmoiIiKiAlZqK+HqiGnGGmoiIiIioiQwUBMRERERJYGBmoiIiIgoCQzURERERERJYKAmIiIiIkoCAzURERERURIYqImIiIiIksBATURERESUBAZqIiIiIqIkMFATERERESWBgZqIiIiIKAkM1ERERERESWCgJiIiIiJKAgM1EREREVESGKiJiIiIiJLAQE1ERERElAQGaiIiIiKiJDBQExERERElgYGaiIiIiCgJDNREREREREkwZHsARMlo29WOTrcHtSOGQq/XZ3s4AIAOpwvbm1swfOQwlJWVZns4RFTgPC4vAj5/5LbRbEKprSSLIypu2veD70Xx4Aw15a22Xe34zQXX4uc/vhCNm7ciFAple0jocLrw+O+fxcwTz8NHH6xEZ6cn20MiogIX8PmxaGZD5EsbrinztO8H34viwUBNeUkN06s/WQNnewfOPHVO1kO1GqYfeeBpSClxxdwGhmoiIqIiwEBNeUcbplXZDtXaMK1iqCaidPG4vHC2OuFsdcLV7u6xz9XujuzzuLxZGmFxifd+aN8Lvh+FTUgpsz2GQZs8ebJctWpVtodBGRQrTGvZHeV4+s/LUTdqeMZqqmOFaS0hBJY+0IDDjzoEZVbWVBNR8pytTiya2dDvcYtXNMBebU//gIoc34/iIIT4REo5OdY+zlBTXhFCoNxujbvfYjHDZDJBp8vcj7YQAlVDKuPu1+t1KLdbodOJjI2JiIiIMoeBmvKKo8KOxXdegyOPObTXvqHDhuDJlx7AsOE1ECJz4dVWbsUpM07AVQ3ze+0zGPRY/sTtOGjSeJSUlsDt6syJiyeJKP9oywoURcn2cIhIgyUflJfa25xYdNkt+Nt7/wKQvTCt5epw49UX38JtDfcCiArTJRbs2tmGq3+zGNfcdElGS1KIqDBoywrm3z0X1cOroNPp4Gp347YLlkWOu+qRBbA5wp/ksW1bZmhb5WnfD+17AfD9yHcs+aCCo52pzoUwDfScqY4VpuecfRU+/vuqnOhIQkT57d5LHoBOp4O92t4jsAGAzWGFvdoOe7Wd4S1DSm0lkddc+35o3wu+H4WNC7tQ3lJDtd8XwJChVVkN0yo1VB974g9gd5T3CNNf/m8tgN0dSTJ98SQRERGlBwM15TVHRe5dLW0rt8JWHp6hiA7TKoZqIkpEdCmBlnpbZ9Djpheuj0wqGM2mzA6SejCaTVi8oiHyPRWHtNVQCyEeA3AygB1SyvHd2yoBPAegHsBGAKdLKdu6910N4HwAIQDzpZRv9fcYrKGmXNbp7sSD9zyBJx56Lu4xY8fvg8dfuA8lpZYMjoyI8gXbsRHljmzVUD8O4MSobQsBvCulHAPg3e7bEELsD2AWgHHd91kuhOCUHeW1MmsZzp9zRsyOJAAwvG4Y7n3kFobpNMvnC6+JiCg/pLXLhxCiHsBrmhnqtQCmSimbhRC1AD6QUu7bPTsNKeWt3ce9BaBBSvlxX+fnDDXlg+iOJEA4TD+x4n4MrR2SxZEVPr/fjy2btqKyqgIVlZy9o/wTr3sEwG4elN+0P9tAfvwM51KXj6FSymYA6P5vTff2EQC2aI5r7N5GlPeie2czTGeG3+/HurUb8YsfX4jLLvot2nY5sz0kogGL1z0CYDcPym8Bnx+LZjZEvrThOh/lStu8WO0ZYk6dCyF+JYRYJYRY1dLSkuZhEaWGGqpn/uLHDNMZoIbpc396MbzeLnzy788ZqomIKG0y3eVjuxCiVlPysaN7eyOAkZrj6gBsjXUCKeVDAB4CwiUf6RwsZZaUEh1OF3Q6XaRLRiFxVNhx6dUXFeRzyyXRYVqlhuo7H7yJ5R+Ul7TdI9TbVLjysSSiP4l0rQHy87lmuoZ6KYCdUsolQoiFACqllFcKIcYB+BOAQwAMR/iCxTFSyj5XvmANdeGQUqK5aTvOOm0OZv/yZ5g+60cMnjRgAX8A677diHNmzOsRprUmHnIg7n5ocU62XCQiUkV3eCmETi753rWmrxrqtM1QCyGeATAVQLUQohHA9QCWAHheCHE+gM0AfgoAUsovhBDPA/gSQBDA3P7CNBUObZhu2bETd9y8HAAYqmnA9AY9ysutKLOVxg3Uo+rrcmIRICIiKhxpC9RSyp/H2XVsnONvBnBzusZDuSk6TKsYqnOX3++H3xeA1VaW7aH0otPpUDtiKJ56aTnOmj4HrTt29dh/6ukn4fJrfw27ozxLIySiYqUtd4hX0lDIJRGFLq0lH+nGko/8Fi9Ma11+7RyG6hyi1iffcfNyLH2gAZVVjmwPKSZFUcI/W5pQzTBNRNmkLXeIV9KQ7yUR/cn3NpC51DaPKMLX5cO3a7/Dzta2uMe8/9d/cGGOHKG92O8/H3+Gi89biF0727M9rJi0M9XVNZUM00REOaCQ20AyUFPWWEosOPjQCVi2vCFmTevEgw/EXQ8tRrndloXRkVaszhn/W/1VXoTq519/hGGaiIjSiiUflHWeTg/++bf/4PJfXx+ZjVbDNNubZV+8NnSqAyaMxX2PLcnZ8g8iomyJV+KgLW8Adpc45HtJxEDkY1vArHT5IEpUaVkpDjvyYNzxuxtw+a+vx/cmH8AwnUOUkIKFv7kpbteM/63+Ci8/9zp+cc4MlJRaMjw6IqLcpa4GGE0blIFwTTRsJeFAGSdUqiURhaKv55qPWPJBOUEN1Q//6S6G6RxjKbHgoaeXYdjwmpj7Tz39JMz8xY8ZpomIqGix5INySjAYhMHAD05y0bat2zF7xjxs27ojso0X+xERxTfQko949413DGUWSz4obzBM565hw4fiyRfvj4Rqhmkior7FK2tIpHyj0EoiCh3TCxElTA3Vzz71Cs69cBbDNBEREVjyQUSD4Ha5YbVxsR0iokQlslIi5TaWfBBRSjFMExENDEs4Chu7fBARERERJYEz1JRxiqLA5/PDbDZBp+PfdERERKnAspLsYZqhjFIUBc1N2zF7+lw0bWmGoijZHhIREVFBUBeSWTSzoUfLPUo/BmrKGDVMnzV9DtZ+uQ5nMVQTERFRAWCgpozQhunWHbsAALta2xiqiYiIKO8xUFPaxQrTKoZqIiKiwfO4vHC2OuFsdcLV7o5sd7W7I9udrU54XN4sjrLw8aJESrtOtwf3LX2kV5hW7Wptw123/h433H4lbOVsx0ZERJQotW46mrrMuWrxiga27UsjzlBT2tnKrVh4w3wcctjEmPsnTTkIv73lMoZpIiIiyktcKZEypr3NiQVzGrDyn59Gtk2achDufPBGVFQ6sjgyIiKi/KRtledqd0dmpq96ZAFsjt0TVWyjl7y+VkrkDDVljKPCjmXLGyIz1QzTREREySm1lcBebYe92t4jQNsc1sh2e7WdYTrNWENNGaWG6j88+AzOuXAWwzQRERHlPZZ8UFZ0dnpQVlaa7WEQEREVDK6UmF59lXxwhpqygmGaiIgotUptJezkkSWsoSYiIiIiSgIDNRERERFREhioiYiIiIiSwEBNRERERJQEBmpKG0VR4OpwD+g+HR0uhEKhNI2IiIiIKPUYqCktFEXB1qbt+P09T8DZ3pHQfZztHXj4vqextXEbQzURUYI8Li+crc7Il8flzfaQKAbt+8T3qPCwbR6lnBqmzzptDna27EJ7mxNXXDcPdkd53Ps42ztw162/x0vPvob/e/FNPPXycgyvGwa9Xp/BkRMR5Z+Az49FMxsitxevaGDrtBykfZ/4HhUezlBTL4FAcND3jQ7TAPDqi29h6Y33x52p1oZpANi1sx1nnTaHM9X9CAQC2R4CERERgTPUFMXtcmPVvz7HhMnj4aiwD+i+scK06tUX3wKAXjPV0WFapYZqzlTHtmtnG77+4lscNHE8yqxcJIcoU7Qr0QHZW41OOw5Xe89rVbS3s7laHlfti/8+Rb9nufBzlM1xFAIuPU4Rbpcbr7/yDm6+9i4ccthELFveMKBQ7evy4bRp56Bx89a4x1x2za/xs7NORUmpBV1dPrz0zGtY0nBv3OOH1w3Dn999EhaLeUDPpZDt2tmGOWdfha/WfIOlDzTg8KMOYagmyhBnq7NXeYW9emCTD+kYRzzZGh/Qc4zZHEc25fr7lCs/z/mir6XHWfJBAHqGaQBY+c9PsWBOA9rbnAmfQ+h0uPPBG1FSGvuv24O//z2c+tMfoqTUAgCwWMw46SfH4fs/iPmzCYvFjDt+dyN0OjHAZ1O41DD95f/WQkqJK+Y24KMPV6LT7cn20IiIiIoWSz6oV5hWqaE60Zlqk8mIYcNrsKcBopIAACAASURBVPzx2zDnnKvg9ey+innSlINw07KFMJqMPe7jqLTjtnuvw1Xzb8THf9/9aYPFYsYfXrgPY/YbDZPJlOQz7F8oFMLOll0wW8x9XjyZTdowrVJDNWeqidInF8srjGZT+MK27jHcdsGyyL6rHlkAm8MaOS5TossH4pU4FFNZQbz3SfseqcdlSrp+nou9xIclH0Wu0+3BO3/5EL9dsCTuMVMOn4Q7H7wRtnJr3GMAwNXhxrxzF6J2xFBMn/UjXHz+NfB6vJg05SAsWDQHF59/Na687mIceez3URo1i92+yxkJ1dkI042bm3HWaXNw5DGH9tuRJBtcHW788heX9QjTWkII3PPIzTj08EmwlFgyPDqiwsaP7Qc3jniKtawgV0pg0vU+5crzSyeWfFBcJaUWTJpyECqrK2LuF0Lg1NNPgkig7EIIgZ/89ES88co7eOnZ13Hfo7fgB8ccGgnTOqHDgd/bHyUxAp86U330tCOyFqbb25z9diTJph9PPz7uvtoRQzF23D4M00RERFnAko8ip9PpMLxuGJ5+eTnOPG0OdrW2RfYJIXDL3Ytw1HHfh9Va1u+5rLYyHP+jowEADVctBQDMvew8zDtvIXRChydfegC1I4ZCiNjh3FFpx+I7r4bZYspKmFbF60iSDR1OF6SUsDvKccrMEwEAt91wX49jhtcNwxMr7sfQ2iHZGCJRwcvF8opcpH2dAMDV5sZtv+wucXh4AWwVfJ1yQSp/nnO9i0kmMVBTzFA90DCtig7Vb7zyDmqGVvcbplX9lZWkSrwwrcqFUN3hdOH39z6Blu07ce1Nl8Je0TtUM0wTpV+prSTuIhw2hzXrH21HB9lsBdbo10lRlMj3ZfbSrL9O2aZ9n7L5R0Uqf56jFxVSaUM6UBwL2TBQE4Ceofqs6XOxYNHcAYdplTZU/+6ux/HEi/cnFKYzSQkp8Hq88GkuoIm2s7Ud2brGQA3TTz3yQmRbdKh+6tEXGKaJqM+ARLmD71NhY6CmCDVU/9/7TwMCgwrTKjVUH/+jo1FmLc2pMA0ARpMRe46pxxMr7sPZMy/u0ZEEAA4/agpuvefaAS9ukwqxwvSb//cegJ6h+sczTkC53Zbx8REREVFP7PJBRc3vD+C7bzf2CNW5Fqa1TvzxMZFQTUSZx5Xl4ot+bSAEnK3hkjp7tR3ozht8zXJHsj/P0TXUfbUFLIT3vK8uH5yhpqJmipqpnnjwgVkL0wDg9XbhpWdfj7v/3bf+jgt/czYDNVGW8GP7+OLV00YrhnrafJHsz3O8++fCtQWZxkBNRU8N1c+99hAclfashWkAqKxy4MkXH8DsGXN7rX5oNBnx8J/uxIiRtVkaHREREcXCkg+iHBMIBLBx/ZYeoVoN0/sfsC8sFnOWR0hE1FuslRJjlQAUysf/1FMxrJTIkg+iPGI0GlG/18jITLXfH2CYJqKcl+vtBSm9ir0cioGaKAdpQ7XL5WaYJiIiymEM1EQ5Sg3VoaDCME1EeSdXFjIhygQGaqIcZjQaYTRmexRERANX7CUAVFx02R4AEREREVE+4wx1DlIUJbIkdkmJJcujISIiomJTDF07Uokz1DlGURRsbdyGU44+E198/jW83q5sD4mIiIiKjLpQz6KZDT1XwKSYGKhziBqmzzxtDrY3t+BXZ17OUE1ERESU4xioc4Q2TO9qbQMABANBhmoiIiKiHMdAnQNihWkVQzURERGlm8flhbPVGflytbsj+1zt7sh2j8ubxVHmrqxclCiEuBTABQAkgP8BOBdAKYDnANQD2AjgdCllW5xTFJROtwdvvPJOrzCtCgaCePR3f8LS+6/P8MiIiIioGKg107GoS8gDCPcW5wWKvSQ0Qy2E2EcI8a4QYk337QOFEIsG84BCiBEA5gOYLKUcD0APYBaAhQDelVKOAfBu9+2iYCu3Ytbs03DuRT+Puf+Qwybi1ruvhdVWluGREaWXr8uHYCCY7WEQERElJdEZ6ocBXAHg9wAgpfyvEOJPABYn8bglQogAwjPTWwFcDWBq9/4nAHwA4KpBnj/vlNttOH/OGQCAPzz4TGT7IYdNxLLlDXBU2LM1NKK08Pl8WPvlOpRZS7HH6JEwGNnFk4hyi7Z1HFDY7eO0K1sC4TIPdWb6qkcWwOawRo6j3hL9DVYqpVwphNBuG9S0kpSySQixDMBmAF4Af5VS/lUIMVRK2dx9TLMQoibW/YUQvwLwKwAYNWrUYIaQs6JDNcM0FSqfz4e1X6zDebMugdlswpMv3s9QTUQ5J7oMopDLHfpa2dLmsMJezSzSl0R/e7UKIfZCuOYZQoiZAJoH84BCiAoAPwEwGkA7gBeEEGcmen8p5UMAHgKAyZMny8GMIZepoXrU6Doce8IPGKap4GjDtN/nh9/nx+wZ8xiqiYgobyX6m2suwiF2PyFEE4ANABIOwVGOA7BBStkCAEKIlwAcBmC7EKK2e3a6FsCOQZ4/rTyeLuzY1oLycisqqyvS8hjldhtOPnUazBZzWs6fb6SU2N7cAqPJiKqo17xtVzs8nV4MG14DvV4/6McIBAJobtqBcrs14T9i2tuccHW4UTt8KENggqLDtMrV4WaoJsphqSx9GOgKfJkuu9A+nrbTRfTtTI4j+vHivYapXN1QWwLCMo/+JfRbS0r5HYDjhBBlAHRSSlcSj7kZwKFCiFKESz6OBbAKQCeAswEs6f7vK0k8Rlp4PF3YsG4TzvvZbzDl8IloWHJF2kI1w3SYlBLbmndg9vS5sDvK8fun74iE6rZd7bj0V7/Fd+s24ek/L8eIkbWDCtWBQAAb1m3G7BlzcfD3v4ebli3sN1S3tzlxzSU3Y/Una/DkSw+gniEwIU1btvUK0yo1VL/018cxtHZIFkZHRPGksvRBe65EzpPpsotc6XbR1/OO9xoO9LXtS18lINRbol0+HEKI+QBuAnCzEOJeIcS9g3lAKeW/AawA8CnCLfN0CM9+LwEwTQjxLYBp3bdzhjZMez1efPD2R2hYuDRuqztKnjZMb29uwTdfrceFZ16Ona1tcLZ34NJf/Raf/ue/aG9z4sxT56BpSzNCodCAHkMbpj2dXnz4zj/x2wVL0N7mjHsfNUz/44N/w+3qxOzpc7FxwxZ2q0hAVXUFjjzm+3H3/+jUaSgpsWRwRERERMlLdErtDQD/QjgAK8k+qJTyegDRTZV9CM9W55zoMK364O2P0ACkdaY6HzidLkhFSWm9d3SYVqmh+rb7rkPLjp2R7WqoHshMdXSYVqmhOtZMtTZMq9RQzZnq/tkd5bh+yQIAwDt/+bDHvlmzT8O8y89HucOGDqcLUkrYHeXZGCYRIfHSB73RgFD3hEK8MoN454o+r3r/bJZdaEsdtJ0ugPR3u4j3vOffPRehYAhtO8KTeJ1OT2Sfq80NRQlHM53md1+81zZXFFoHFSFl/9f1CSE+lVJOzMB4BmTy5Mly1apVaX0Mr7cL333bO0xrTZ12OBpuuwKVVcUXqp1OF+697SF8t24T7nzwRlRUOlJy3uat23uFaa19xu6FxXdcjYvPuxrbt+0+xlFhTyhUxwvTWkcddxhuWnY1HBXhUBcrTGtZbWUM1QlytnfghoXLIqE6Okw/+fBz+Nt7/8KDTy4t6j9WibLJ2eqMW/qgdePz1+G6028EEC4ziNUNItFzqfcf6PHpEj2OTD+e6orfX4alF97Z7/37Oi7dYx+oTL+2qSCE+ERKOTnWvkR/6z8lhPglgNcQnkkGAEgpd6VgfDlNp9PBbDbBYIgfzsrK8vcvqmSoYfqFP74KALjsoutSFqr1ej1Mpvh//VssZkgpEYwq8TAaDTAYDNDp+q5m0ul0MJqMMBji/y9QWlYKbadIAYFSa2nc4w0GA4xGI4ROxD2GwrQz1dVDKnuF6YfuewoAcNHsKxiqiYgo5yVUQw3AD2ApgI8BfNL9ld6p4RxhNpswsr4OT6y4H7Zya6/9Pzr1OFxx3byim52ODtMA8Mm/P8dlF12Htl3tSZ+/Zmg1/vDCvRi5x4he+w6cOA43LL0K8y+4Bjtbdv9NN6SmCk+9vBy1I4Yiqmd6L3q9HqPqR+Cpl5ej3G7rtf+HPzkO19z4mx4lB/aKcvx28WU4/uSjex3vqLDjqZeXo27U4C6MLEZqqI4XpgHg6y++xUWzr0jptQpSSijBAJSAP/wVDCCRT+qIiNLB4/LC2eqEs9UZKd2g/JNoycd6AFOklK3pH1LiMlHyofL5AtiysRFnz5wHV0e4Lolh+tWY+ydNOShlM9U7trfi3J/Ox5ZNTQDCYfq+R26BTq/DwvmL8dGH4fKLgYRprVAohM0bm3DWaXPQ4Qw3r4kVprWcbR24adGd+Otr7wNgmE6FWGFaa79xY1IyU62Eggi6XfBua0LI1wUA0JktKBk6AkarDbo+PrEgKjbR9bzaWuIrH74cVnsZgHDd7qIZ4cuiomuMY7Vzi7cCn/Y+fT12vMdIh0zU+WpLH+bfPRfVw6ug0+l6PO/5d89F1bDKyCegnU4Pbvtl92v48AKU2cOfnsZ7L9I19oHKlfd1sFJR8vEFAE+/RxUws9kYmak+e+Y8HHnMoUUZpgEg6A/g/bf/EXf/mtVfoXXHLtgd5f2WXvRHnak+96fzUVHlwH2P3IKKqnBQv/Wea3H1b27GN1+tG1SYBnrOVJ912hwcPnVKn2Ea2D1TDQArP/qUYToFFEXBG6+8G3f/urXfYdPGRpQ7bH2W6fT5GKEgfLta4W1u7Lnd14XOzetRMmwELFU1EHwfiQD03TZNKjJSN60Vr61cvHPFW4EvV1bty3TruHsveSBmLXH0du3vVlvF7tfD2bq7Q1Uurm6YKy0J0yHR30whAKuFEO+jZw31/LSMKkepofqFvzwKS4k5rWHa6+lCSWlutg+rqHLgqZeW46zpc9C6o2cZvdlswmPP34NRo+uSDtOqmqHVeHzFfTDo9ZEwDYRnhm+951r4uvyoGVY94DCtUkP1ijcfQ0mpJaHOEmqo9nq7MGRoFcN0khwV9sgfTo2bt/bYZzDosfyJ27HfuDGDDtMAAEWBt7kp7m7vtiaYK6oYqImIaMASLfk4O9Z2KeUTKR/RAGSy5COTnO0uvPz86/jJzBNT1jUj1RRFQXPT9h6hWg3T+4zdG2auqkSDsH1bS49QrYbpgyaNT7o/tbd1O7xbt/R5TEltHUqGDEvqcYgKkfajeikl9EYDlGD4ovB4JRyJtNDLxZUSMyGR0gedQY9QIBiZLMr0SompPFescxZlyYeU8gkhhAnAPt2b1kopA6kaIO3mbHfh3tvD9cnv/OVvuO/RW3IyVOt0OtSOGBqZqXY53QzTlLShw4ZEZqq3bd2esjANAIrf1/8xvv6PISpGiZY+JFJmMNAyikJcsW8gpQ8DKYlJ5WuVylUXVblSypMOCQVqIcRUAE8A2AhAABgphDhbSvm39A2t+GjDNAD899MvcPH512Q8VEspEyqf0IbqtjYnxuy7J8M0JU0N1Vs2NmHcQfulbOVEvaX/XwaJHENERBQt0ZKPTwD8Qkq5tvv2PgCekVJOSvP4+lRIJR/RYVrrwInjMhaqPR4vtmxswrDhNQmvUqcoCoLBEEwmY5pHR8XE5/On9A80JRBA+9f/BeL9mycEHPsdCJ2RP8dUvAbzMX86SgMKXS6WPkSX1gy0lCfZx8uHn51UdPkwqmEaAKSU3wgh+FsnRdyuTjz5yHNx29D999MvsGBOA+595BaU9bGwSLI8Hi8+/ffnuPj8a3DyadOw4LdzEwrVOp0OJlNqLkAkUqX80w6dDmV19ejcsiHm7rIRewApupCWKF8N5mP+QizJSLdcLH3IdAeOQvu5SfS3xyohxKNCiKndXw8jvLgLpUCZtRQ//cWPMWx4Tcz9FosZly68MK3LWWvDdCgUwisr3sSymx6As70jbY9JlEk6vR7GcgfK994PBuvuxXwMZTbY9toPRrsDOnb4ICKiQUg0of0awFwA8xGuof4bgOXpGlSxEUJgaG0NnnzxfsyeMQ/btu6I7LNYzHjsuXswZuxefc7YOds6ICHhqBj4X7XRYVr1yoo3ASDhmWqiXKfT66ErtcI6ai8AaumH4IIuVNSiyw9U2u+jO3vkw8fz+cJoNoVnfTW3gcyXRESPo6+SD+ot0d8iBgD3SCnvBAAhhB6AOW2jKkJqqFYXjtm2dQcsFjMeTTBMX3/V7XC7OrFsecOAQrXH48WnK//bK0yrGKqpEDFAE+0W76N+7cf8AHDF7y/D0gvvBJCfC2/kqnilD9HvS7pf81wsQ8kniZZ8vAtA+yqXAHgn9cMpbqFgCMFQCPc+cgvq9xyJux5aDNm9PR41TL/31t+x8p+fYsGcBrS3OeMeH81gMKB6SCVMfQT2kfUjIsudEhEREVFPiQZqi5Qy8tlP9/fpuzquCAUDQWza2IhZP/olbrrmDjz+wn148//ew+zT5uBfH30CT6e31320YVo10FBtMhmx55h6PLHiPpSU9v7LdN6C8zHr7NNQXm6LcW+ivklFgRIMIOT3Qwn4oQTZvp4oF3hcXjhbnXC2OpFIty8i6luin3t2CiEmSik/BQAhxCQAvRMeDYoapmdPnwtXhxv/W/0Vjjt0JoKBIADgsgt/izt/fxMOPXwSSsvCoTdWmFapoTrR8g9tqD575sXwesJvLcM0JUMJBuBt2Q7/zhZIJfwpi6HUitIRI6EzWXgBIFEWacsJbvnzTZHa2R51sw8vQJk9PHcmFYmd23ZF7q+tr2Y9derEq2ePvp2J11xbU8266f4lGqgvAfCCEGJr9+1aAD9Lz5CKj9frw7/+8QlcHbv/Z1HDNBDu8/zGK+9g8pQJkW1dXT6s/mRN3HN+teYbuDo6E66njg7V58/5BcM0DZoSDMC9cT2Cnp6/EIIeNzq+/Rq2vfaBKLUmtIAQEaWXEgzFrI8ts5fiutNvjHmfdLRRo8y3rutLobW1S7dElx7/jxBiPwD7Itzl42suPZ46tvIynDLjBAQCAdx58+967T/uh0dh0eLLUG63RrYNGVqFp19ejjNPm4NdrW1R57PiqZeXo3bE0D4f19Ppjcx4A7tD9esf/hEmi6lXmO50e2A2m9Lavo8KQ7Czs1eY3k2is3EjyvfcFxLdgVqn44w1ERHlrYRWSgQAIcRhAOqhCeFSyifTM6zEFNJKiQDg6nDjxWdf6xGqj/vhUbju1gVwVPTusKEoCrY2busRqtUwPaq+DgZD/IDStqsdf/zDizjzvJkJzWK73Z346IOV2Hvf0dijvo6hmuJSgkG4N61HsNPV53Hle4+Fe/N3kIoCY7kdJTW10BmMEFxchSgt4q3Op22JpjPoEQoEIYSA3mhAqPvT0sGs5ud2dvZYQdFqL0vfkysQubiCYqKKYcXMpFdKFEI8BWAvAKsBqC0nJICsBupCYyu3YsaskwEAd978uz7DNBBeoXB43bDITHXAH0g4TF/yy0X4bNX/8O3X3+GG26/sM1S73Z34+7sfY+FvFsNqK8OTLz3AUE19kAldfKgE/ICUkMEA/Lta4W/bCdue+8BQUsZQTZQGibbHW7yiod8WaYm0UQv4/JGSkRufvw4AA3V/8rl13WBW2SwkiSaiyQD2l7wUOO3UUL1HfR2+d/CBccO0Shuq/f7AgMI0ALz/138AQNxQrQ3TUkq4OtyYPX0uQ3UeUhQFukwEVSGgN5mh+Lr6PExnNEEJ7b5WAFLCvXEd7PuOZ6AmIqK8kmgaWgNgGIDmNI6FutnKrfjBMYfCkODiE2qoVhQ5oDCtiheqo8O0iqE6/3g6vVj3zQaMqq/r94+0ZOn0BlhqahFwxW/dqC8pQ8jfBShKj+0yFELQ0wlTuSOtYyQqRtquDfFKPtTj+rpvvGO0JR4A4NZ0pXBHdaiIVf6R6ZUBc10ir3m2JbLKJlAc72WiSagawJdCiJUAfOpGKeUpaRkVJRymVTqdDn1N6rk63LhuwW29wrTq/b/+A1VDKnHpwgthK7ciFArhmy/X9wrT2vPNnj4Xb/z9Ga6gmOM8nV6s/PgzXPLLa3H0CUegYckVaX/P9GYLTBXV8Le19ton9HqUDR+Jzq2bY9436PUwUBOlQbxygkRKCRLp+KAt8Yh2+6/ujHwfr/wj0ysD5rp86LIxkDKiXH8uyUo0tTWkcxCUfiWlJbjs2l/j0//8t0d7PlXN0GpcMOcMWG3hf+T0ej32HLMHjj3xSLzzlw9jnnP+lb+ETs+P5nOZNkwrioJ3//I3AEh7qNYZDCitrYPJVg7vjmaEurwQOj1MjkqYq4bAu60JIa8nzn2NaRsXERFROiTc5SMXFVqXj3QLBkPYsqkJZ5766x6humZoNZ586QHUjhjaqy9we5sTN159R69Qfe3iS3HSqcfBZrOCclN0mNY69odHZmSmGsDuCxQlIKVEx7dfQmprp6M4xh4InTH3Ptqk3KAEA5DBIIKeTgidDoYyG4ROB8G2iwOSjo4MsUo+1JnpKx+6DFZNhwq15COfu1rkk3R14Eikc0yqHzObBt3lQwjhQribR69dAKSUkp/15xGDQY+Re4zA03/+XSRU9xWmAcBRYcd1t14OAJFQzTCd+7weL1b9e3XMMA0gMlN9w21Xotye3sV7tDPOSjAIY7kd/radMY+11NQCOgYjii3k98O9aV3PTzeEgLmqprvtIq/nSFQ6ygnCITl2Jw+rw4qKmope23NpIZNClq4OHMmUERWaPv/1kVJymbwCow3Vl190He7/w5K4YVqlhmohgEMOm8gwnQeMJhNGjKxFmbU0ZokPAIw/aCyQ4ZUKw6UgI6E3mdHVuh0yFO7CKQxGlNTUwuSo5AIvFJMSDMC1/utwu0UtKeFr3Q4IEQ7V/Pkhoizgn/NFSA3Vf3r1QZgt5oSWf3ZU2HH9kisgdIJhOg8YDHrsMXoknnzpAcyePrdXqP7Nwgvx0zNOQXl55t9LncEAy5BhMFcN2R2o9XoInZ7t8igmKSUCro7eYVrD17odJdVDAQZqIgDswJFprKEmKmDBYAibNmzpEaqzGaaJBkMJBuHe/B2C7o4+j7OOHgOTrbg+Zs5liayUyBrq9HG2OuOW02glspBPIrhSIhEVrOiZ6vPmnMEwTflJ9r4WoJcY1wtQ9vRVU63K55UBqad8aPOXTgzURAVODdX/98EfYTAaGKYBKKHQ7vClE9DpB/9PoXaZdZatDJxUlHDpjwAgAWEwQAgBKSVkMBjZbrSWI9gZ+3oAlb6kNCNjJiKKxpIPIioaSigEGfDDs30rgq4OSEgYreUoGTYCOqNpQBe0KcEgAu4OdO1oRsjng86gh7lyCMxVQ9hLO0FKMADfrlb4drZACQahN5thGTIMRms5Ap2u3a+t0QTb6L3h/OYLIM7vLIO1HNZRe7LTRx7jSompVWwt7TKBJR9EVPSkoiDY6YJ747oe2wMd7Qh0tMO6x14w2MqhS6BtnxIMorNxIwId7bu3BRR4t2+Fb1cryvfej720+6EEAuhY/zUUf2TxXYS6vOjcsgFGazksQ2sR6vKGj/V3oatlG6yj9oR783e9QrXeUgLryNEM03mu2EsGUo0t7TKLn00SUVGQoRDcm76Lu9+9+buEa3CDna4eYVpLCfjR2bQZSh+L1xQ7GQrBs62xR5jWCrg7EPJ6YNRcYOjb1YqAywn7PuNQMqwORpsdRnsFrKPHwLbnPtAZ+akAEWUP/5wnoqIQcHf0fWGblPB3tMNSOaTP8yjBALw7tvX9WB1OQJEAO7jFJBUF/vZdfR7j29WKkppaBFzOHtt87bvg2O9AWKqGQALsO02UAKPZFF7Qpft7Sj0GaiIqCkFvZ7/HhDweoLKfg2S4BKG/g6QSAsBZ01ikVOLWQqtCvq7YZTOKAqmEoDOZkdlliYjyF8tp0o8lH0RUFHRGc//HmBKbuREJdAVht4/4hOj/tREGAxQlNOj7ExFlEmeoiagomBwV8DZv6eeYqn7PIwx6mKtq+jyXobQMSGPok1JChoII+boQ8nog9AYYreUQOh1EnBIIJRiEVBQE3OFyFIPVBp3BkJ2OJELAYLUh6HbFPcTsqELA2dZru8FqA2Ks7qoEg5ChULi0p7t7i9Dr2XGFiDKCgZqIioLQ6WCpGYauOPXPluqhCc0qC6GDuaISvp07Yl9UJwRKR+yRto4TUlEQ8vvg3vBtz6W4hUBJTS3MVTW9HlsJBuHZurlX3bK+pBTW+r2hz3BHEp3BgLLho+D89quYde06owkmRyU61n3dc4fQoWz4qBjPL4DOLRsQcPVcSdFQZgu30uMFi0SUZvzcjIiKgk5vgKV6WDjsagKWMBpROmIULDW1CYdgKSWse+wFk6Oyx2ypocwK2+h9IENBpKvHvwwF4Vr3dc8wHR5UuG1f+05ITbcSJRSEp3lLzIsAQ14PXOvX9licJlN0JjPKx4yFocy2e6MQMDkqYdtrv+7x7n4NDVYb7GPGQmfqWbqjBINwb/quV5gGwt1YXBvXZeX5EVFx4Qw1ERUNncEAc2UVTHZHJHSqZRJ91eUqwSAgu1f00xvQtb0Z/o42WKpqUL732PAFdjodQp5OeJq3QAaDKB8zFiLBcgO1hEMqElBCEHpDzPINqSjoam3pvuAxtq7tzTA7KiOz7VJR4G/bGf+5+X0IdnbCZHckNNZUETodDJYSWPfYC5ASUirh5yzCz7ukphaWIUMhFSX83ggR8w8eGQwg2Bm/dCTk7YTi97P0g4jSioGaiIqKEDoIQ2IfzqnlFZ7GjQh6wl1CrKPHwN/RBhkMwrt9K7zbt8a9byKUUAghbyc8TZsR8nWpg4TJXonS2roes+kyFIK/o+92czIUDC/Z3R0g+6pTVvnad4ZrqrPQgi7epwJCr4dIoO+gP0addTRf285wXTsRUZowUBMRxaH4/eiIqvMVEP22fAOgrVaIf4iUCHk64drwTfQO+Nt3IuTthG2vfXvOribw2Npyk4RKvtzkTgAAIABJREFUT/rqz53jZCJjlwqklBAxLmYkIkoF1lATEcWgBIPwNG/uFTaDXZ6edb8xCL0eQt//P68yGERn06a4+0O+LgRczt2hWKfr97EhdD0CuLHM2u84jDZHzAsypZRQgoHIVyKz7kowqLlP+leLNNr6L1UxllcwTBNRWnGGmogoFiljXujm29mCsro94i49DgDmqhoIXf/lCjIUirv8tqqrtQVGmx3CYISuu7a4r5poc2UVoAnHQq+HodSKoMcd83ih08Nkd/QKnEowAF/bLvhat0MJ+CEMRliqhsBcNSRmPbISCkHxdcG7rQkBtwsQgNFWjpKhI6AzmdNWTqI3m6EzmeO+jsJohKG0NC2PTUSk4gw1EVEscUolFL8PQY8HpcNHxtxvLHfAUlWTUAs+qfQ/gytDwZ7lIzo9ykaOBmKsE2i0lqOkZniP8Cp0epSNGg292dLreKHTwzp6TK/qFCUYgGvDOnibt0S6ichgAN7tW9Gx/msogZ5dM5RQCIGONnSs+yrSBxpSItDhRMe3XyLo7ki4pnygdAYjbHvuE3NVRWEwonzPfRNaiIeIKBn8V4aISENRA6wQ4ZneGEHQu60RlppalI/ZH/6ONoS8XugMBpgcVeGeyM1bYvZLjiYM/fd/1lssgG53eA51eRD0elA+Ziz8zjaEurwQej3MjipIqaCzcRPKRtZHHlsqIbg3b0Bp3R5QAgEEOtohpYSx1AqD1Qbv9q09/jiQigJfW7h+O+br4/PBu6MZJbUjoFNn4ZXw48bj3rwBjrEHdC853t1ZRfMHhwyFwmUtOrH7nAOg727BF+x0R9oDGu0VMFpt4Y4pLPcgojRjoCYiQnhWNtjphm9XK6QSgrl6KMwV1fDt3BHz+K4dzdDpDRA6PfSWEshQCJ1bNuzuD10bewY7Wn8rBlqqaiIz1EowiK4d2xDsdMG3cwdM9orwYysKOps2RcoetLPBoa4uhDxuuNavhb6kFEZrOQAg4O6Ap3u1xy6TGaXDRkDodJBKCF0t2/scs6+tFSVDa4Hu8Otz7ur7YkmpwO9yQvF6EfR2wlBmg7lqCAABxd+FrpbtkKEgdGYLSqqHQRgN0A1gVllKGZ4UR3gFxvDrJcL/lTLmyopERKnEQE1ERU8JBOD6bu3utnUIB9HyffZHsNOFUJe3132M1nLoS0vhWr825jnDvaL77n0sQwGU1tbBtXEdZKD34iPmqiHhcKyGVSl31wpLGXOxFvW8QHj2u8dz8noQ8np6Ha/4unoEYtnfQiiaMUkpocR4fXrdxeuN/NES9Hhgstnh3b61u0SkW6cb/l2tsNTUwlI9NKGFdqSUUHw+dKz/OlweoyF0etj22hd6i6XPPuNERMnivzBEVNSUYBCdjRt7BE8AMJbbEXC2oayuHiW1ddBbSiD0BuhLylBaVw9LTS3cG9fHPW8iFyUKnR6eps2w1Y8Jr9RoMkPoDTBYbbDusRcMZTZ0te7YPcMqkNBiMUK3O4jGqi3udbzB2GMWt9+xCxE5XgiR8GPIUHhBGnNlFfwd7T3DtEbXjmaEunoH/1hkKAjXhrW9wjQQ/qPG9d03kcclIkoXzlATUVGTioKAy9lru7myGu5N6yGDwfCFhjW10On14SqCYBCu72LPTAPhJcgTKTMQej1kKISOdV/D5KhAaW0dhE6HkD9cpxzyemDdY6/ITK0QOpgrq+Fpil3fDAB6SwkgBLw7mgEhuldN1Pe5uqJlyNBITbPQ6WGqrIavNX7Zh6m8oseMr6miKu4CN93PFEZbObzbmwAA5orq3r23o3i3b4XeUtrvLHWoy9vrIkktGQoi2OmGyV7R53mIiJLBQE1ERU3xxS5XEEIXXnEQQKCjfXebPCFQvvd+0JstvWa1w/t1KB2xR0LlCkJvQNnIenSsX4v/Z+/OgiS7zjux/8/dcq+sfd+rFywE16YEElywEyCJRfJIQTlmgmPLoReHLdsTYVF+90hPE9aD7QjG2B46ZkLWDDUhgpQIkWgAAigQEBskSAJsoNeq6qqufc315r33HD/czKzKysyqrM6qyqzK/y8Cge6TN/PezOyu/vLkt+Q21sra4RnhSEnfaSUljFAYeihSuWhQCIQHRqDcXDEvWdo2QgPDSFfpd221d5a0wROahlBPP5ytjZ188JJr1hEaGCoZiy40HcHefmSXFyueI9Q34E803J1WcsCusT+Z8uChNJVaG5YfswWzrbw1IBHRUWHKBxG1tmrpDdWCL6WQnL2NyOgkAp3dJT2fzWgb4ucfgG4Fajq1EAJaMIS2c/eXBM5C0xHo6UN0/FxZYJ5emENkeLSs17URiSE2cQG5rfV8kZ4fjNrrK4CUiI5N+bvXhXMYJkIDwwhX6EYiDANt5+6D1dFV8jqYbe1oO/9AWYqHZhgIdvcjMjxecptmBRAeHoNmBZBdXtj9xA9+bWpoOwigJLCvStMZTBPRseIONRG1FCUllPT8KX5K+cFvhfZ4np2BHo7AS5fvBEs7i8StjxAeGEGobzDfSMLPK65lZ3o3TdOhhcKIjk0BSvkjsjUBoekQmlacUKg8F8KwYMbakLh5DVZnN2KTF/LtqAXcTArpu7OQrguro6skxSO9cMfPyx4/V1zzr1ereL1CCAjTQnhgGOH+IT9AFwKAgG5WzuHWDANWRxfMtjgg8zvLAkjNz5YNwZE5G3owVLHYs8CKdwI1FBJa8Q5kFuf3PSbQ2X3g4xAR1aMhAbUQoh3AvwXwMfjf6f3XAD4C8FcAxgFMA/h9pdRGI66PiM4m6bqw15b9dIh8wBno7keoZ6CY31tgr64g1DuA5PSNio+lpCrmK1cLMg9jb2CrpISbzSB1Z3onvUNoiJ+/H9mVJdirSxXznMODo7DXVsrW3WQCuc11uMmEXwwoBAId3Qj1D5ZNPlRKQuZySM1Nw03tTFjUQ2FEhsehB4IVd5CFECVFk9LzoIdCZQF1di3/2s7eqvJiaMWc9YMI3YDZ1l51cqURiR36Qw4R0WE1KuXjLwC8rJS6D8AnAFwF8C0Al5VS5wFczv+eiOhISNdFenEemaW7Jbu39uoi9GAQwb7BkvQNN52EdHKIjp2D2BOQaVYAsfFzyFbpUX0k1+s42L5xtTRXWkmk7s4iNnG+JH0D8NNEQvmixmrt9PyBNYVfK9jrK0jO3PJ360vO7WL7xtWSYBrw2+5t37gKmSvPra5E03UEu/r813bXbnNhcmJkdLJsiqFmBdA2dV/NQbBmGIgMj/npKXuY8Q5ExyYrjkonIjpKJ/6xXQjRBuBLAP4lACilcgByQogXADyaP+w7AF4H8CcnfX1EdDYp6SG3Xr5zCwDJmZsI9vSj/eLH4GYzgOdBD4XhZTPIJbcRGzsH6bmQjlPMj86sLNRSM3dPpOf6Q1cqTGl0kwmk5mcQHhyFMAxIOwuh636e8soiMmtzVR/XiEaRXS/9EOCmEpA5uxjASs9DZvlu9aJBpZBeuIPI6ERNw1f8/Oo+BDt74GbSgJLFFoRC02BGY/DsLKTrQrcC0Ezz0NMNNcNEeHAE4f6h/DkAIxwGNO1QA2KIiO5VI37STAJYAfD/CCE+AeBdAH8MoE8ptQAASqkFIURvA66NiM6oqru2edmVRfjbtxJOMoHI2BSkk0NubRm5teV8j2gdynWKbdp25yQfKangbJe38ivwMmkkbl9D+/0fhxHsgJLSv9at6llyejgC5boVB8hkV5f84kFN96cabuz/WjmJLT9PuoZ6QCUllOsgvTjvD5ARwm//190LM9oGzTCPZAdZ0w1AB6waemITER21RqR8GAA+DeD/VEp9CkAKh0jvEEL8kRDiihDiyspK5d0mIqK95EHT/wAo6UJ5nj9N0HWhWYH8iGy/kM7LpIvBdKh/uObUh8NTOHD7Oz9u2/+lQnZjDbEK6SmA35s6MjSG9ELl3WvleRC7T6fKd8YrX+NBl6jgZdPYuvYBnK0NeNkMvEwabjqJ1OwtZJbmy9JNiIhOo0YE1HMA5pRS7+R//134AfaSEGIAAPL/r5icqJT6tlLqklLqUk9Pz4lcMBGdfmY4euAxeiAErzDaGwqpO9PQA35bu2B3H6z2TgR7B9B2/gEo6fk51EeY9iFdNx/4iwMnImqmWcyHFpoGTdeRXpxHbPw8IsPjsDq6YHX4nUDCg6NIzt7aGVu+93mHI4AmCk8b2gFt/4Su19T6TnkukjO3SvpP72avrVTsdX0cCq+t3GfADRHRvTrxgFoptQjgjhDiYn7pCQC/AfASgG/m174J4HsnfW1EdHYZ0dj+I7U1DUYkCjeZAADYm+uwOjqRvjuLxO1rkJ4LzTAhcza2b3yI7PICQj39FXeED0u6DnJbG0jO3kTi9nXYiU0Eu/fPegt09xd7MIv8REQ3ncT2jauwN9bybff81nipuWk/3aLaY3V2FycfCsNAsKd//3Pv6YFd9Xk5zoEBc3ZlEfIYR4NL14G9uY7kjP/aZu7OwbOzHEdOREeqUdUa/x2A/yCEsADcAvBfwQ/u/6MQ4g8BzAL4vQZdGxGdQULTEZ04h8Sta+U7pkIgOjKRz6P25dbXEJu8ADeVgpdJlU0xNOPtRzJ9TzoOErevlfRkzszPom3qIpxIDG4qUXYfI9qGQEdXyfhvaDqio5NIztyEm0oU7+dlM4iMjCM5faNiEBkZmSgJjoUQsOIdcLa34CTKW9EZ4QiC3b01DV6ptiNeekyu6g52vaTjYPvWRyUfJrxMGvb6ir+LH++obTAMEdEBGhJQK6XeA3Cpwk1PnPS1EFFrEJoGIxRB/OJDyK4s+oV1AMxYGwKd3bDXVksKF5X0kJy5iejoJNxsBvb6KpTrQLMCCPb2wwhF6u5vLD0Xqbuz5QNOlEJi+gaio1P+DuvqMqSTg2ZaCPb0VeytrOk6jGgb4hc/hszKItyE32vaCMegWUHELzyI7PqK/xylghGNIdTTD820yoJKzfBHorvppL+DnMtBmCaC3X35QsIaW9rV0J/bT105+imG0nX33ZlPzU3DiEShM6AmoiPAfkJE1DKEpkG3LIQGhhDqHQAAKAEkb1+Dlymf2iedHLZvfoi28w/Airfn+zgffhpiVVLCqdKZQ7kuErc+QqhvENGxSQAHT2LUdB3QdYQHRoA+v7BQ6HpxNznUM4Bgp59KIjRt391ZzTBgtbXDCEeK/asP241DswIQhgm1T0FosKe/pgEugF/kqDwXXiYDJ7kNoev5XWajfDCOksUPTdVkVhYRHhip+fxERNUwoCailqNpOpBPc1BKITw4isTNa6hUYWjG4tDMo2nttpes0MJuL3tjDYHOnpp2ewsKgfVeQtNqStUoeaw6nrfQdERHxpG4fb3i7Wa8A5pVW5s7pRSkncX27Wslrf8yi/Mw29oRGR4rudb9csYLvHQq39GEATUR1adRkxKJiJqCEAJ6MIy28/fDiMZ21g0Tof5hREYmjm/SXg3B7WED4GailIR0XcQmzsOI7HRZEaaJUP+QX3hZY/q0cl1s3/yoYh9tZ3sT6YU5SG+nBV8tRZOn+bUloubCHWoianmarkMLhREdncoXyPk5DrvTJY7lvIZxYEqE1dl9JJ1EGkJKpO7chh4KI9jVi/DgKAAF5UnY6yvILN1FeHAEgc6efYs7lVLIbW1AedV7Vuc21hDuHypuNmuWnxu+XzePQGdP2ehzIqJ7wZ8kRER5R5YbXSOh6QgPDCN153bl6zFNBNo76+4k0ihuJg3A76yRmpuueExucwNWvHPfDw3K8/adArn7fIVJiULTEeofQnp+tuKxmmXBbIuf2teWiJoLv+8iImoQoWkwY3FEhsfLdkqNcBSxqftO9w7q8XTDq4nQNFjxToSHRsuKL41IDLGp+44vlYeIWs4p/klNRHT6aYYBq70TZlsc0rYhpQc9EITQ9BPfMT9qRjh84DFmW/uBvaCFrsNsa6/Yk7vkfKHS82mGgUBHN6x4BzzbhjpDry0RNRf+RCEiarBC940zt2MqNFjxjqrpGkLTa0ppKUyCzC7dhaoyOtyKdwCi/EvXM/vaElFTYcoHEREdC80wEB4aLemeUiB0A7GpizVPKhS6nj++fB/IiMYQHhrjrjMRNQx/+hAR5RUGhwAAhFYy8EO6LgDlD1epIa9Zeh4gpT+PRTcO3IXdfW4hSoeuFM+N0sEuyvOgVGGAy8Hn2Pd6851Gys7tufnOJ6Xnlp6X7+Gc74aSP7eSsthZQ+g6NMPcmfi4vgpID0YsDjMSPfCa955bDwYRv/gg3FTSH9qi6Qh0dkMzTAbTRNRQ/AlERAQ/oMxtbRaDPj0cQainH0I34GZSyK4uQToO9PzocT0QqhjESc+D8lxklxf9nF9Ng9XehUBHZ9UAUroOnO0t2OsrkJ4HIxRGsHcAQjcgs2lkVpYgnRx00/InCwZDUK6D7PIC3GwGmm4g0NUDM9Z26NQG6Tpwkgl/vLnnQg+GEOrth2ZY8OwssiuL8HI2tPzocSMUgfJcZFYW/cEouh/UWrF2KAD26tLOWPd4BwKd3RBCwE2n/BaEmgbPzsIIhiCEVnEAjXRdeLksssuL8OwsNMNEsLs3P3LdhBXv8HOv2aGDiJoEA2oianmek0PixoeQTm5nzc4it7GG0MAIoCTcpF8QJ+0snMQWrI4uf2z1nl1bJ7GF1OytksfPZNLIriygbep+6IFAyW3ScbB960NI2y6u5ewscpvrCPYNQtMNuMnt4rnNeAecVALZ5YWdxwDgppPQAkG0TV6seaqidB0kbl2Dl90Zuy7tLJytDQS6+2CEwsXgWNpZJJMJP72ibwi5zY38DjWQTqeQtRYRHZtCbmsDMrfzXKxYHNszN8oGsmQX5xEZGYfZ1lH2TUB6cR659ZWSa0qmEjDCEUTHz0EzTAbTRNRUmENNRC1Nui5Ss7dLgundMgt3YEZiZX2ScxtrcPKBboGSXlkwXbzNdZGcuVFMrSiee36mJJjeLbt0F3owWBzPrQUC0C2rJJgueS521n+8fQagFK/H85BemCsJpnezV5cgNA16MFSy7iYTyCW2EOjoLD13zkZ6YQ7Bnv7iWqh3AKn52YrTDQEgdWe6bFiLm06WBNOlt6WQWV6ErFKYSETUKAyoiailKekd2I7N3lhFoKO7bD27vFAMkJWUfrrIPrxsJp8PXTw5nO3Nfe+TXVtBoKMHABDo6EF2rXKwWeBsbwLy4AbQSkrkNtf3PcZeW0Ggs6d8fX0FVkdX2bqb3PZb5QkNQjegmRa8TGrfc2RXl6Gkv9MtXQeZKh8Wdp8b+eOJiJoFA2oiamnSzh54jJtJQ7MCZeu7d3eVkvBSyQMfa3eAWW1XvPT4NLR8mogeCMDLTx/cj9xnlHmBKhb8VVfteSvXrTqS3bP9fGvNsuBW2f0uOT6TKgbU/u8PeH5SlhxPRNQMGFATUUsT2sFt24SmF/OFS5QElaJigV3ZY+3qEFLTuXW9uCOrpKypzVy1YLdEDccIvcrz9m+tcm7d7/RR87XqOw+lanxNmD9NRE2GATURtTQtEDgw8LPaO5GrkJphtXcWh4louo5gV3l6RAmhwQhHdn5rGBAHFBBa8Z1zO4ktWPHOfY/XTKvmoFsLBPc/d5XnbUTb4KbLd+OFbkDTdSjXgbSz0APBAwP3QFdPsQ2h0PWKqSS76aEIwICaiJoMA2oiamlC0xEeGK56uxYIwgiFi10+dt8v1DtY0qFCD4b9gK+KUO9AyTQ/oesID4xUP7dpwYy1wdn2O23ktjZgtsWhmVbV+4QHRysOP9lL6AYig9XPLfLt6XKbe6YcCoFQ7wDsCrncob5BZHcVFNoba/5zrkIPhko/YGgaQj19+34giAyNcOohETUdBtRE1NKEpsGMdyAyPF4WiJqxOGITF5Bemi9Z10NhtJ27r6wPtWYYiE2c88dg7z6HriM0MJzfjd0JFoXQYMbiiIxOQuwJEo1oG2JTF5FZugt/qAsApZBdWURs6mLZ9EFhmoiOTcGIRmtKiRBCwAhHER0/XxagG5Eo2qbuQ2Z1qSTlQwsEEZu8CDeTLskfF4aB8Mg4tGCwpNDRXlvx2+wNjpYFyWZbO2ITF8qCY2GYaDt3P/Rw6QcTzQogNnkBWqC06wgRUTMQ6oCilGZ26dIldeXKlUZfBhGdAX7erwfpOFDSg24FAE3zc4I9F8r1ID0HmmFB6Nq+u6TScwEp4eVyEEJAsywITa+a27z33JoZ8FMyDAPSdaA8D9J1/P7L+emD0nWhpIR0bAhNh2aa+YmFh9snKUxolK4L5bl+yoiml59bNyEMHUI3ihMaZW7XuTUdgIKSEp5tQ8BPp4HQIITIP78clFTQLcufRLnPdEPpOvnnl4OmG356TJ3TIImI6iGEeFcpdanSbRzsQkQEf6faD2LLA2VhmIBhQsf+OccFmm4AOvZNzaj13Frh3HvynQvBqG7Vdo6q5xYCwjAPde5CT269wvPzg/EKr2GV51dN4Vi9QpcRIqJmw5QPIiIiIqI6cIeaiOiMkZ5XbLUn9OqpJs2gkHICBUCIfdNAiIiaFX9yERGdEdLzoFwHmZVFvyuJELDiHQh29fj5x00WWEvXgZPY9qclug60QBCh3n7owTADayI6VfgTi4joDJCeBzeZQHLmRsl6dnkB9uoyYlMXoQdDTVPUJ10HidvXSyYjSieHRHIbVkcXwgMjDKqJ6NRoru0KIqIm1tQjr6VEcvZmxZuU9JCcvu6nVjSQUn4XEOl5yCwtVB0znttYg5tKVLyNiKgZ8eM/EdE+pOdCeR7s9VXIXA56MIRARyeg6SU9pRtJKQV7cw3Ypw2qdBx4drYhQ1Gk60A6Duy1FSglEeobhL2xuu99MssLMCJRDnEholOBATURURXSdZFdXkB2dalkPbM4h/DQKKz2rqYIqpWUcNOpA49z0ymYkdiBxx0l6TpITt8sGVUe7OotFk1Ws3twDBFRs2PKBxFRBUopONubZcF0QXp+tmmCPiFETYH9Se/2Ss9Fam6mJJgGANQyybEJPqgQEdWKO9RERBUoz0Vm+e6+x2QW56GPTR1b8VxximEu508MNC1/6qJuFKc3enYGwjAR6OqFvb5fGoWAGY0hl9gCpIIeChWnNyrpwbOzUK4LPRCE0A1oZv3Bt5ISzvZm2bqXzcAIR/bdVQ90dOenL9ZGOg4UFLz8Y+qhCCAE9CN4HkREB2FATURUiVKQudy+h/iFc9XzlushPQ9eJo3UnduQzs51aKaFyOgEnGQC2aWdgD86fg5mtA1Ocrvi4wV7+mCvryKz6z5mrA3hoTEkZ27Ay+zstuvBEKJjU9CsQF1dQbx05aLD7Noywn1DSNy+jkqvnzAMBLt7a27zJx0H6cU55DbWSx7PincgPDh6JB8OiIj2w5QPIqImJHM2EreulQTTQL613K1rMKNt0HaNBE/dmUaofxCBrh5A7PxoF7qBUP8QjEgUmeWFksdyEttITt9AeHC0ZN3LZrB94yqUW2dXkCqxuJdOIbe9iej4ubKx5kYkhrZz9/vj3msgHQep+RnkNtawNzjPbW0geecWpOPcy9UTEdWMO9RERJUIAT0QhGdnqx5ixuKoGjXWQbouMgtzqLr7rRSyywsIdvUgffeOv+S5SNy6jkBvP9rvewjSdfzJg7qOzNICMovzFR/Ky2agHAd6KFzSxk55HrJry34BoVJ+3rMmoOm1/7NhhMLwX5/y52GvLcPLZhAZnfLTTjwXwjQhhHaoFBolvYppJQVuMgHpOtylJqJjxR1qIqIKCju7+wn1Dx1P/rRSVVM3CpzEFoxItPRu0kN2cR7Kc2GEwjCCITipJOz1lX0fK7e1ATPaVr6+sQY3ncTmh7/C5tVfIXVnGl7OhtqnPV8JTYfV2VX1Zs/OQDMN6IEAjHAEumkd+vW0N9cPPuaAFn1ERPViQE1EVIEQAkYk5qdD7M0j1jREx89BswLHdPb68rJ3B7xKejUcLyvmKysld6WP+F1Ptq9fLUtDqUbTdYT7h2G1d5bfZlpom7oP4hA73hXVMmxHSshmHspDRKceUz6IiKrQDANWRxeseAecxBak4w92MSLRYoeMo6KkhPJ2gl/NsvYtitSsQOXcYCFK2uMZ4Wj5MXsYoUjFFoBGKAJpl64rz0VmYR6R4bGaWttphoHw4ChC/UPIbW0AUsKIxordRADsPA8hDr1DbURiwMri/sdE26Ad4XtFRLQXA2oion1oug7oOgKd3cfy+IXWePb6Kuz1VSjpwersQbCnH+n52ar3C3ZXbpNnxTtLAn3NMKAHQ9V7ZgsBs629rGCxcI5UPkd7t9z2BsJypKaAWikFKAl7Yw0yZwMA5KaLQGcXhFJwtrdgry5Bei50K4hgbz+McLTmwFoPhSBME6pK4aHQDZiRgz9UEBHVgx/ZiYgaSDoOtq//BpnFecicDeW6sJcXoVtBWPGOivexOrqgB4JlxXh6KIzw4HBJoKsZpp+eYlrlDyQEomNT/vCaPXnRob5BeDkbslJRplKoNS1F5mxsXfsA2aW7yG2sIbexBnttGdvXryK3vlbSA9tNJ5GcvoHU/AxkjR1GnFQSsYnzFVNHhKYjNnEeuVSy9rxvIqJ7wB1qIqIGka6L1Nx0hdQNhcTMDUSHxxHs6UN2ZQnScaBZFoI9/X5LOSkR6OqFl0lD6DoC3b0wQuGK0xB1K4C28/fDSWz7BXpKwYy2wWrvhJISZixeHBSjB0MIdHbBTSWr75BrGmrpbiJdB8mZmyWpLLtllubRNnUfsvlBNQXO1gaceAcCFXKv9xJKwV5fQ/zCA8htbiC3vQlAwYzFEWjvQnZjFULX6+qnTUR0EAbUREQNopSEW62bh5RIzt6C1dmN0NAoBESxDV5BeGAYSsqy9Uo0w4TV3plv9QdAKWx++GtASeihMKx4h58qouvIJRPIVmmzB/hTDFFDTrJy3QPHs9sbqwh0dJWNeM8uL8CMxg4cl25G25Cam4FQORelAAAgAElEQVS9uoxg/yBCfYMAADeTxua19wGl0H7fxw+8ViKiejCgJiJqEFVDtww3mYBQgGZWSmnQDlUYKYSAyOcme9kMoPzOF14mjUyhB7UQaJu6D05oo6QvdYFmBRDqHTgwgAewbw/v3cdUSm2p5b4AAF1DdHQCyZmbyC7OY++9IsPjNQX/RET1YEBNRNQgQjv4R7DQjeOYHQNoVQJipZCcuYno2BScxBbs9VVIJwdhGAh09SLY1XPgrnFBLS3xhG5UTAkRulFTmram6TCjbWg7/wAyi/NwElsA/M4e4f4haIFATcE/EVE9GFATETWIMHS//V2++0Ulwa6e+ns1A5CeB0gPbjoFpSSMcLRq9w/p5LB980NERqfQdu7+YkB/2FaBejAIoetVc6gBwGrvRLZC27tAZ3dNXUQAQOg6jFAYkdGJXcWVh2/BR0R0r/jThoioQYRuIDw0iuTt6xVv1wNBmG3xugvqpOsiszifb7PnB5xGJIbQwHDVc2umCTMSqXk3uhKh6QgPjCA1N13x9kJ7vL2pJcIwEezqPXSf78OMRSciOkpMLCMiahSlIISG6NgUtEBwZ10IWO2diIxO1js0EdJzkV6cz48f33kwN5WAm9hGdOI89GCo5D5mWzvapu6rK5gG/BxvM96OyOhkads+oSHQ2YPo2CQyy6W702YsjrZz9xdzvYmITgP+xCIiypOuAygFpVS+4O+IpyEqBeW6+Z7ICkIIJGduwgiFER4YhmaYUFJC6DqcxBYSt68j2N2LYHffvV+HlMitr1S8Kbu6BCOTRmRsCkJogPT89BJN3NNur1L+tEclJQABoQkI3YAV74AZjUF5ElAyfw4Nmq4jOjoBJdXOue9hWuJB9r6v0DRo1XLIiYjuAQNqImp50vPbu6Xv3immHwhNh9XVjVBPf907tYVzOInt4gAXoRuIjk1CeS6c5Dac5DYg/NZ4kLJ4v9zmup9PfI8BtZNK7nu7m0ogsziPyPA4NKvC8JcaSddFbnMdmeUFKNfvq62ZFkJ9gzDb2v3XsMK/OEfx2la9Js/Lv6+zO2klmr87Huo9mveViAhgygcRtTilJNx0CombH5Xk8irpwV5ZQnLmpr/DWQfpebDXV5GavbVTgChEfie35GJKgmn/OmRdaR9l56h8UF2NRKTnIrN8F+m7s8VgGvCLG1Nz07DXlvctTDwOSil46RQSNz8szdGWEvbqEpLTN2uexkhEdBAG1ETU0pTnVS2aAwA3lYRboR/zoUiJzJ5BKcp1/LziAwoOjUi0rj7KZjhy4DFGtK3iOZRSkI5T/K/a+G7lebBXl6s+fmbpLpQ84YDac/d/X9NJuOn9d++JiGrFgJqIWpp0HKiy0d+l7NWlunYzneT2rnZuu9YTWxWHmuxW6xCVaoRhQA+F9zlAQyDeWdZJRLouchtrSNy+hs2Pfo3tWx/CXluuuFufW1878DpyW5uHvvZ6SNeFPGBwTrbO95WIqIABNRG1tFrSOfyg697zLqoFdtmVJQR7+mFU2UWOjEzUneerGSai4+egWYHyG4WGtskLZf2epesiOXMDqblpv0+1lJC2jfTdO0jculbymimlIN2DJz4eFNweNVXD+6pct+IHHSKiw2JRIhG1NL1SoFnhGCHuff9hb1u6AuW5SE5fR2TE73Rhb6xCeR6MUASBrh4IXa95uMm+5zcttJ27D24qCXtjFZAKRiyOQHunf45d6R5KKdiba3CrFDN62Qwyy4sI9w/5nVCEgB7cZwe8cA377ZIfg5I2fdWOsQJH2sWFiFoXA2oiamlC16tODCwI9g7UFdga4UjViYHScZC4dQ3t938CRmQcfjs97cgDPc0w8+3r2qCg/JaAFfK3levCXlna97Fy66sI9fYXr9GKdyC9cKf6bq+mwYzG6n4OhyIE9FC4bGjMbsHu3hO8ICI6y/jRnIhammaYiI5OQlTpSxzs7qtpt3M/QtMRHZuqWoAYGR4v9mXWdONYd01F4RzViiHFwekZSnqlwbOmITIyUfX46OhU1df3uEjXQWRwtOoHoUBnN5Qna+uCQkR0AO5QE1HL0wIBtF18ENnlReQ216Gkn3YR7BuAEYrUPWhEaBr0cATxCw8is3QXue1NQAFmNIZQ3yC0QLCuwsMjp2ll7fvK7QTkmq7DisWhn38AmaW7cBLbgACsWByhviEI0zzx1AohNGSW5hGbvAh7fQW5zY38+xpGID/WPLu2jGhk8kSvi4jOJgbURNTyhNCgmxZCA0MI9Q4UY0XNMCE9D9Jx4KaTkE4OWiAIIxgu7ijXStN0IKAjMjSG8MBI/hz7TwVUUkJJD24mDWnb0AMB6KHwkU9w3HOhCHR0w16r3gbPbGsHtNIdbqHrMEJhREbGAenvXgtNg9B1SM/vuOEkE1BSwghHoJnmsQ5W0UwTXjaDxK2PEOjoRnR0EtA0SDsLe20FbjqJ6Ni5I5/KSEStqWE/SYQQOoArAOaVUl8XQnQC+CsA4wCmAfy+UmqjUddHRK1H03RgV2qC5zpwt7eQujtbsmMrdAPR0UmocAi6frigsNZCQ+V5cJLbSM1Nl+ReC11HZGQCRiR2LLvamqYj1NuP3Na63wWj/ACEB0eqjibXdAPYdVnSdZFZnIe9Z/y5Hgz53UdMq3r6SR2EbiA8PIbk7evIri4hu1qaF66HI36PbyKiI9DIHOo/BnB11++/BeCyUuo8gMv53xMRNYyXTvvDQfZOL/RcJKavQznH18PYs7NIztwsK2RUnofk9I2diYvHQBgm2s7d7+9E72JEY4iff6DmnWXlecgsL5QF04DfLSRx80Mo73heQyEEzHAUsckLpV1WhIZAVw9i49ydJqKj05CfJkKIYQBfA/C/Avif8ssvAHg0/+vvAHgdwJ+c9LUREQF+YV5mca76AUohs3QXkcFRaObRpi5I1/W7ZuwjvTCH6OjksQSFQgjoVsAvllQKSko/xUTsn6Kyl5Jy39QR6ThwEtuw2ssHyxwFoeswo22ITV7wpzxK5T+PfLqOdF1Ayfzz0wFNVN15vxdeLud3G9n156PSGhGdfo36eP6/AfifAezuo9SnlFoAAKXUghCC/YyIqGGUwr6t9ADA2d4EBkeO5eTV+kAXuFWmLx6leoN1N5M+8BrtjTWYsTjEMe4W791RV1LCs7NIzc3ATSWK62YsjvDQ6JGkobhZG3OXf4qO+6cQHemHbpoV14jobDjxgFoI8XUAy0qpd4UQj97D/f8IwB8BwOjo6BFfHdHplEtn4eUqf3WuWwascPCEr+jeNNfzqCFYVQq7u12c6LlPA1VDS7pajjli0slh6/pvylJ5nMQWtm9cRfz8AxB1tEr07BzmLr+F1PwSUvOLGH7yEUSH+0vWRp7+IiJDfdBNpp0QnQWN+Jv8CIDnhRBfBRAE0CaE+PcAloQQA/nd6QEAFb8nVEp9G8C3AeDSpUtn5F8dovp4ORff/9a/rXjbc3/+3wAnO6TunjXT8xBCQDNNSKf6CGsjHIU6juBXCGiBIKSdrXqIHgwdTyx/hGqZjmhG4yfaUs9Pp5mr2hZQuS4yy4sIDQz5Rar3KDoygNS8Xwg598o/woxG4CRTAAA9GECwMw7NaKJWidRyvJwD3TIPXKPanHhRolLqT5VSw0qpcQDfAPCqUuqfA3gJwDfzh30TwPdO+tqIiIp0A8Hu/n0PCfYNQK9z6EslQjf89n37nbt38Fjbzh0FoekwY237HCAQ6Ow62R7VSvmpOvvwx7Pf+865HrDQcXESfQ9/sri2O5iefPEpmLHIseSNE9XCzWSx9E+/gpvJ7rtGtWumSYl/DuApIcR1AE/lf09EZ0wunUVmM1nxP3XMOcGHoWkarPZOWO1dFW8P9g3CqGEH9l4IIWDG4gh291U+d0//yY/yvgeaYSAyPFHaZaNAaIhNXoA4wiLA2tTwZ+wIpifqAQud95+DHgqUrHfcNwkjHGQwTQ3jZrKY/dGb2PjNdcy+/AbcTLbiGh1OQ5O3lFKvw+/mAaXUGoAnGnk9RHT89kvr+Pq//sMTvpr9aaaJ8MAwgr19sFeXIV0XmhVAsKvX7xRxnINJDAPB3gEEunqQXV32h8pYAQS7eiAM40i7URwnzTQRm7wAL5uFvb7iD3aJxhCId/o9uU94giIgIDTdH59eRb2j5oFCUeJb8DKl7Q1X37uKYHfnmS5KlJ5X1iO90ho1hpISbtIvuM6srGPm714HhEB21R/94aQzUN7J1zacdqfjJzIRUYNopun/NzBSbB93UoGBZhiAYSA8MAylFIQQDQhA66cZJrSoCT0cBlR+gmKDdmiFrsPq6oa9slT1mGBPf10757uLEgE/zSM2NoTNj24B8HOqz2pRovQkclsJ6JYFMxquukaNY0bCGH/hCUx/7zKcVBrZtZ0UKCMSwsQLT8KIVPhWifZ1tv4mE50RzdXtggD4QXSDdtiEpjV7/WFN6inyOypC0xDq6Yeb2K7YFtGIxI6kL3ahKLGQM60HAwh0tGHp7ffObFGi9CRym9u4/dIr0AMWJp5/EnooWLbGoLrxrGgEo89+CTe/+3LJ+uhXvgQzEmZK0j1gQE3UhA7b7UK3DH+9At06PX/NNVM/E8+Dmptm+Gkoua1N2KtLkK4DzbQQ7O2HGY3X3X+7UJSoGQaiw/3FAsRKa2eJ8jws/vTnkI4L6bi4/dIr6P3sx7HwkyvFtbX3P0LPpx6EHjj6Yl6qnZvJ4u6bPytbX3jzZxh95kswQty0OSz+C0V0Bljh4Klpjbcf5UmE2qONvgxqAZphItDZDSve7tcpivIBMPXQAxbi5/wpmoXAudLaWaJbJoaf+DxmXn4D2ZV1OMk05l97u3h7/NwYuj/5AIPpBisUIGaW1gD4KUkQgJexkVlZx+zLbzCovgenLxmPiIjoCAgh/Pxu0zyWAlPdKp+4WGntLDFCQYw9+2VYbaUfjCNDfRh45DMwgoEq96STsrso0YiEMPm7T2Pyd56GGfF3ZViUeG+4Q01EJ+qspKcQUTnpSbipDNx0adu13GYC0nG5O90ECkWJc5ffwvATny/mTI+/8ATmXnkLw09+nkWJ94D/ehHRiTor6SlEVGp3UaJ080XVQvjDdFJp3H7pFRYlNgkrGsHYVx+FZhjFb0ysaARjXytdo9ox5YOIiIjqtrsoEfBzpu/75u8i1NMJAHCSaaz9+iN4dq6Rl0l5eoVc/kprVBvRTJPJDuvSpUvqypUrjb4MoiPHtnlEdBq5mSxmXn4DgXgM/Z//NIxgwC+Ce/kNWPEo+j/PPGo6vYQQ7yqlLlW6jSkfRE2IaRFE1Cy8XA66ZR24BuwUJQIoBs5GKIjRPWtEZw1TPoiIiKgiN5PF4lu/gJvJ7ru2mxEMlAXOldaIzhLuUNOpxJQIIqLj5WaymPm715Fd20R2fbO487x3jf2KiRhQ0yl12EmCRER0OEpKeLYDAMiubmD6B68BULA3tgEAnu1AydNThyU9D5quH7jWKvh6HC2mfBAREVEZMxLGxPNPwIxGAAD2xlYxmDZjUUw8/zjMU9Kv2EllkFleg+e4+661CjdrY/v2HLysvWftTska1Y471A3QqukKrfq8iYhOKzMaxuhXvoCbf/33Jeujz3yxOFmv2TmpDKZ/8Cpy20mMPftlhPu74dlOyVqorxu62RohkZu1sfyzX2Pj6g203zeJ/t/+BJQCln/2K2xcvemv/dYn/JHkVLPW+NPTZFo1XaFVnzcR0WnlZrKYf/2dsvX5194+FfnTnuNg+/Yd5LYSAICZH/4D+j/3Kax/cL24tvTOexh//olGXuaJkjkHm9duAwA2P7wF5UkIIYprW9dn0PPJ+xlQHxJTPoiIiKjM7qJEADAiYRhhP8Uju7qBmR/+Q9VOH81CN03Ez4+j59MP+gtKYfGtnxeD6UBHHGPPfhm6aTbwKk+WEQlh4vknIPK50lvXp4vBtNB1jD/3OIwwd7gOiwE1ERERldldlGjGoph88UlMvvhUMaf6tBQlGgELXR+/D+0XJ0vW9VAgHzyejjzwo6LpOgKdcYx99ctlt40+8yUEu9qhGSxMPCymfNCppFuGnyZS5TYiIqpPoShx/vV3MPTYbxdzpnfWHj41RYnScZFeXClZ87I5ZFfXEerraZn86QLpuNi6PlO2vnVjGqGudoCdPg6ttf4E0ZnBSYJERMfPjIYx8vQXoFvmvmvNrFiUmE/zEJoGJSWgFGZ++AbGnv1SSwXVflHir7Dx4U1/QQj//0ph86PbgBAsSrwHTPkgIiKiqioFzqclmN5blBjoiOPiv3ixJKd66Z1fQqD5U1eOil+UOA3Az5meeMFP5dnJqZ6Bl8s18ApPJ6HU6f1DdOnSJXXlypVGX8ahtWr7uFZ93kRE1DiuncP6rz/C9u05jH/tURjhEDw7h7U9a61Ceh7s9S3M/N3rGH32ywh2tgMCZWvMoy4nhHhXKXWp4m0MqImIiKgaL5eDblkHrjUz184BSsHYlcbg2TmoPWutQnoeoBQAUQycK61Rqf0CaqZ8EBERUUVOOoO7b/yspD1epbVmZwSsssBZr7DWKjRdh2YYJYFzpTWqXWtk4DcZpj4QEVGzc9IZzPzta7A3tmFvbmP8a49BKVW21uzDXYhOAgPqBuDEQCIianoKUJ4E4OfX3n7pMqAUcttJ/2ZP4jSnjTYD6XnQ9rSoq7RGzY8pH0RERFTGjIQw/twTsNqiAIDcVqIYTFvxGMafexxmCxXzHTU3a2Pr+jTcrL3vGp0O3KEmohJMSSKiAjMSwshTX8DNv365ZH306S8wmK6Dm7Vx941/QmJ6Hh2rG+j97EOAQunapYdaNsf7NGJATUQlmJJERAVOOoO5V98qW79z+S3mT9fBy2SRvLMIANj4zQ0o14OXtZGYvQsA2Loxg55PPdDIS6RDYsoHERG1HCeVKetSUWmtle0uSgQAMxaBGY0A8HOqp//2tVP/enl2DrmtBDzH3XftqJmxCMafe7w4TGXz2u1iMK1ZJiZfeJIfVk4Z7lDTiWEqwemnpEJmM1m2zvePThMnlcHtly5DtwyMffVRGKFgxbWWt6sosZAzDQVMf/8yctvJU1+UWBjusvKL32D0mS8h3N8DSFm2dhwjyTXDQLCrHSNPfwGzP/yHktsmnnscVjwGoXHP8zRhQN0AumX4X51Xue2sYirB6ZdNpPHKn/1l2TrfPzotvJyD+dffhpNIwgEw83evY+ixhzH79z8pri2+/R4GHvnMqRmvfVwKRYl33/wZBr/02WLOdKW100YphezaJlZ+/gEAYPblNzDy1CPIrqxj5Re/AQDc+dGbuPjPXzy2a5COi42rN8rW16/eZP70KXR2o7cmZoWDDD6IiBpAt0wMPfrbuP3Sq3ASSWTXNnHzuzsFd8GudvQ//MmWD6YLzEgIw48/XDIVsdLaaSOEQLCrHT2fftAPqpXCnR/9ZPcBGHn6i8Ax7RLvLkoEAKFrUFIBSmHjN36QzaD6dOH3CURE1FLMSBgTzz8BI1K6sxHoaGO6RwWVAufTHEwX6AELXQ9dRHeF4r/RrxxfugdQWpSoWSamfvcZTLzwZDGneuvGDJTnHcu56Xhwh5qISlRLSVJSIbm61YArIiI6RhXzwI83N7xQlHjnR29i/GuPwYpHoaTcWfv64/xgd8pwh5qISljhIELt0bL/hCbw+r/5bqMvj6huhQJEN5UuWbc3tjHzd6+f+s4VVJtCUeLqe1fLbpv9+zeRXlw5tk4fmmEg2BnH+W88VyxALFlri7Io8ZThu0VERC1jd1Ei4OdMT/2zZ2DG/GmA2bVNLL79Hryc08jLpGO2tyjRz5n+Ano+/WDhANz50ZvHmnahGQY0Qy8JnCut0enAd4yIiFpGoSjRjEUR7GrH2FcfRbCzHRPPP1FcY1Hi2be7KBFCYPSZLyEy1I+uhy6i5zMfKxYlFnKaiQ4iTnMPyUuXLqkrV640+jKoRuxDfbrx/aOzxEllIDRRkqdaaY0A6brQDOPAtdPIs3PwbBt6KFQsQPTsHLysDT0cOraiRDqdhBDvKqUuVbqNf1LoxLBd4OnG94/OEjNS3j+50lqrc7M2tq5PI35+vNjCrdLaaaUHLOgB68A1ooMwoG4i3AEkIqJmsbtXcnppFQNf8Dfm9q6d9qCa6CgwoG4inCRIRETNQuYcpOaXAQDbt+4UR40nZ+8CAJJzi5COCzCgJmJRIhEREZUzIiFMvPAENNMv0EzMzBeDac0yMfHCk0yTIcrjDjURnSimNhGdDsr14CTTGH7ic5h9+Y2S20aefAROMg0zEjoTUxOJ6sWAmohOFFObiE4HN5uFbplY/WX54JP139xA10MX4GVzDKiJwJQPIiIiqkC3LKz+8kMkZvw0D6Hrxb7Miek5rH1wDRrbytER8tsY5g5ca0YMqImIiKiMdFyk7vpFiZplYvxrj2Lsq18u5lSn5pYg3eObJEitxUlncOtvfoyND28WA+hKa82KHy2biG4Z/lfeVW4jOizmKxPRvSoUJc6+/AaGH/8cVt67CqFpGH3mi5h/7W2MPvtlFiXSkfAcB9M/eA25rQSW3vkloID2+yYx/f1Xi2t6MID41GjTDhRqzqtqURycQUeN+cpEdK+U6yG3ncTk734F86+/U+zwAQBT/8UzSC2usCiRjoYC+n7r47jz438ElMLSP/0Sy1d+DSUlACDQEUdsZKBpg2mAATURERFV4GayuPOjn0DoOpTnYejxzwFSYv71d/DRnQUoz8P5b3ydATXVTbdMRAb7MPLUI7jzo58AQEkwPf61R2GEm/vbEAbURHSimNpEdDoY4RCGHnsY86+/g+EnH0F0pB9QwNBjorimhzjUhY6GbpkI93VDs0zInFNcjwz1QTTxznRB818hEZ0pTG2iViddv65h99fXldYaTbdMxMaGcOEPnoMWMKHnixErrRHVy0lnMP39V0uCaQBYf/8azEgIHfdNQQ8077ch7PJBRER0QqTrIru+hezqZjGIrrTWLHTLhBkNlwTOldaI6rG7KBHw0zz6PvcpQAgAwNI7v8T29FzT/f3YrXk+ChNRS2DnEWpVhcB5+vuvAlAY/9rjCHa3V1xrpp1qomO3qygx0N6G8a89Cs00YcUixbVmL0oUSqlGX8M9u3Tpkrpy5UqjL4OoaTVj8JrZTO7beSTUHj3hKyI6Ga6dw+ovPsDarz4CAAhdQ9fHLmLt/WtQnt/PufNjF9D7mY819Vfbx0EpBZHfjdxv7V6Pp+bn5RxkVtYQ7IgXCxArrTWSEOJdpdSlSrc1b6hPRHVjvjJR8zACFno++SCUJ7H+wXUoT5aM9e544Bx6Pv1gywXTbtaGk0zDikeLaST+WgpWPFaWWiJdF7ntJIxwCEYwUHWNThfdMhHu74GWn8ZZba1ZnXhALYQYAfD/AugHIAF8Wyn1F0KITgB/BWAcwDSA31dKbZz09VH9Drsr2oy7qEREx0EPWuj97MeRXd9CemG5uB7u60bfb30CutVaeclu1sbiWz/H1s1ZjD79RUQGeyE9b9faFxAe7C0G1dJ1kV3bxPQPXkV4oBfDj38OmmGUrTGoPp0qBc6nIZgGGrND7QL4V0qpnwshYgDeFUL8GMC/BHBZKfXnQohvAfgWgD9pwPVRnQ47TITDR4ioVUjXhb25jczyWsl6ZnUd9vpWS+VPSymRWVrF1o0ZAMDsj97E0GMPIzl7t7g299rbuPBfPl9yvzuvvAXlSaTmFjH36k/R+eB5zL3yj8W17Vt30H5hAppxOgIxOhtOvMuHUmpBKfXz/K8TAK4CGALwAoDv5A/7DoAXT/raiIiIjsvuosRCzrSR/wZOeRLTf/tqwzp9eHYOnp3D7rqqSmtHSdM0hPt70PvZh/wFpTD/6k+LwbRmGhh/7nGIXTuUQtMw8fXHoOd3oFNzi7jz929Cef4QkM4HzyM+NcJg+oxxszbcrH3gWiM1tG2eEGIcwKcAvAOgTym1APhBN4Dexl0ZERHR0ZKexPat2WIw3fHAOZz/xnPofPA8AD+o3ro1WwwOT4pn57Dx0S3c+E8/hJNMQylVce046AELnQ+cR/enHii9QQhMPP8EAu1xaPpOqCI0DWYsgskXnoLYEzS3X5hA76WHoAeY7nGWuFkbc5ffwtxrbxcD6Eprjdaw75WEEFEAfw3gf1BKbddamSuE+CMAfwQAo6Ojx3eBR4C5weWUVMhsJsvWqHVwUiK1qt1FiUop9F56CJqho/cz/g6tUurEixKl5yE5t4ilt98DANz+3iuYfPEpbN2a3Vl76TLO/f6zx9Z3WimF3HZy7yKcZAZmWxTQS/f+lJRws9myDx5O6vgCf2oMz85h4SdXkJpfAuCnAI088XnMXX6ruLb41i8w8MinG17M25B/vYQQJvxg+j8opf5zfnlJCDGglFoQQgwAWK50X6XUtwF8G/Db5p3IBd8j5gaXyybSeOXP/rJk7ck//YMGXQ01AjuPUCvTgxZ6P/MQFFSxcK7S2knRdB2RoT6E+7qRXlqFm87g2l9+H9gVmPZ/7lPHdv5CUeL2zdmy22Z/9CZGn/4iwoM9FYsSsSd4Ts0vYe7Vn7Io8Zi4dg5CALpl7bt2lDTTQO9nP47U/BI8O4fUnQV8+J3/XHzv9WAAvZc+Bq0JinlPPOVD+FvR/xeAq0qpf7PrppcAfDP/628C+N5JXxsREdFx04NWWcBXae2kGMEARr7yRYT6uv2FXYHq8JOPIDrSfyy703uLEjXTwOTvPI3eSzs51XOv/RTYs3VWKEoEgM4HzuHc73+1JKd6+9YdSNc78uttZZ6dw8q772Prxiy8XK7q2lETmgYrFsHEi0/t7EDvCqYnX3wKZizSFP3HG7FD/QiAfwHg10KI9/Jr/wuAPwfwH4UQfwhgFsDvNeDamtJpSx2p9pW+kgrJ1a2yddd28PV//YcQWvlfCKYAEBEdP6FpiAz2IrO0urOm6wj3dgULMSoAAA6oSURBVB1b15HdRYmr713FeD5n2mqLAgL+WpWixFvfewXxyRH0fvYhaKaJyRee9NemRhGfGmVR4hFy7RxW3v0A6+9fK661nx/H8rvv76wJgfjUyLHsVAtNgxG0YEbD8OydwN2MRaAHrKYIpgFOSjxWRzUR7qxMljsrz4OI6CwpFCAWcqZ3M8IhTLzwJMxouO7ARXoelCdLem1Lz4PMuYBS0AJWsQDRs3NQnixZK/BzqG1oulYsQNxZ0xueS3vWeLkcEjN3Mf/a28W1QEcc9oa/QSY0DWNffwyh7o5j+fBVKEAs5EzvFhkZwPBjD5/Ytzv7TUpsaJcPIiIiapy9RYkA0P/IZ4rpH246g9svXa67lZ/0POS2kkjM3i2mBxTWkvOLEKZeEjjrAQtGOFgWTAP5Th/hUEk3j501BtNHTbcsxMYGMfTYw8W18mC681iC6b1FiXowgKHHHy6+z6k7C1h86xclO9eNwu/Tiahl7Jc+BTRnChXRcdpblOjnTA8gPjWKO3//JtJLq3UXJRYC59vfewXScTD02MOIjQ/BSaRL18YGj624jeqjWxZi48MIdncgu7ozxDo2NoRgV/uxpdjsLkqEEJh88SkY0TBCPV24/Tc/BoRomqJEBtTHiO3BSvH1oEbbr/MO0Lrdd6i1FYoS7bVNBHs7oZsGYBp71u49YFGeh8zyGqTjAADmX3sbHQ+cw9b1meJaYmYesdHB0vtJCaHxi/Rm4Nk5LL/7QUkwDQDbt+8gMtSH+LnRY8ufLhQlCiGKBYiV1hqNUcwxYnuwUnw9iIiakxEMQBvogbYrgK20di90y0Lb5AiUlFj4iV/3tPGbG8Xb2yZH0P/wp4Bdhelu1kZucxuBrvZj639NtXHtHFZ//gHW3/8IgB/ktk2NYuv6NAD47+kxFyVasQggRDFwrrTWaPzodwrsVzh6motKiYioeVQKnOsNpgt0y0T8/BjiFyZK1q14DAOf/zRu/KcfInlnEZ7jwM3auPvGz3D7pcvFNWocIYBgd4f/63zO9MAXPlPMqRaahkBH27F+myA0rSxwrrTWSNyhPgU0Q686/IStgYiIqNlJz4OTSCNxe65kPbeVwPbtOfR8+gHMvfKPGPjiJSTvLCIx7R+38ObPcP4PnmvEJVNeoShx+PHP+fnL3Z3QDL3iWitjQH0KKE+WTRcsqJaTTERE1Az2FiUCfg9hJ5ECACz847sYeOQz6PvtT2DhzZ1WuJplYvz5JyAqdPo4LDdrA0rBCAX3XaPKdMtCdHQQQhPFwLnSWitjygcREREdm71FiW2TIzj3z57FwBd22vkm7y4jXJjUCPgdHV54ClZbFJpeX7DmZm0s/+zXuPU3P4aTSlddo/3pllnWGq/SWqviq0BEZ0619nhC1/D1P/vDqnl37DZDdPR2FyWm7i5h4AuXoJkG4ufGAACpu0vo/uQDmP7+qzt3UgrZjU0Y0RBQR0DtOS42P7yFjat+EeTtly5j4oUnsfLuB8W1mb99HZO/8zQ0k3//6d7xTw8RnTn7tcfjVE6ik6dbJuLnxhCfGi0O5dAtE7GJYYT7u3H7e5eLO9gFc6+8le+L3X/PnT5000D7xQls3ZxFdm0DTiKFa//+e8Xbha5j8NHf9ivviOrAlA8iIiI6drpllkwylFIiu7KOm999GdJxoFkmpn7v2ZJBMnff+CegzmZWRiiIsa9+GcGu9pJ1oWkYf+5xBDuPbzAJtQ7uUJ8CHIhCRERnjaZpCPd1o/9zn8Lyu+9j4oUnYbVFYeZb6y2/+z4mnn/ySIoShabBam9Ddm1z5/ymATMaZjBNR4LR2CnAgShERHQW6QEL7RcmED8/Ds00/AJEXS9fq0OhAHH75mzJumfncPt7r2Di+SdgRviPLNWHKR9ERETUMHrA8qcy7gqcK63di71FiULXMfzk5xHs8geVOIkUZv72dUinvIiZ6DAYUBMREdGZVChKDHa1Q+g6xp97HLGxoWJONYsS6aiI0zy6+tKlS+rKlSsHH0hELaVa2zzArzuwwhzkQNRK3EwWTiqDQHtbMWe60hrRfoQQ7yqlLlW6jTnURHTmsO6AiHYzQkHoAQtC0/ZdI7pX/FNEREREZ16lwJnBNB0V/kkiIiIiIqoDA2oiIiIiojowoCYiIiIiqgMDaiIiIiKiOjCgJiIiIiKqAwNqIiIiIqI6MKAmIiIiIqoDA2oiIiIiojowoCYiIiIiqgMDaiIiIiKiOjCgJiIiIiKqAwNqIiIiIqI6MKAmIiIiIqqDUEo1+hrumRBiBcDMMZ+mG8DqMZ+Dmgff79bC97u18P1uLXy/W89xv+djSqmeSjec6oD6JAghriilLjX6Ouhk8P1uLXy/Wwvf79bC97v1NPI9Z8oHEREREVEdGFATEREREdWBAfXBvt3oC6ATxfe7tfD9bi18v1sL3+/W07D3nDnURERERER14A41EREREVEdGFBXIYR4RgjxkRDihhDiW42+HjpaQogRIcRrQoirQogPhBB/nF/vFEL8WAhxPf//jkZfKx0dIYQuhPiFEOIH+d/z/T7DhBDtQojvCiE+zP9d/xzf87NLCPE/5n+evy+E+EshRJDv99khhPi/hRDLQoj3d61VfX+FEH+aj+E+EkJ85bivjwF1BUIIHcD/DuBZAA8A+AMhxAONvSo6Yi6Af6WUuh/AwwD+2/x7/C0Al5VS5wFczv+ezo4/BnB11+/5fp9tfwHgZaXUfQA+Af+953t+BgkhhgD89wAuKaU+BkAH8A3w/T5L/h2AZ/asVXx/8/+efwPAg/n7/B/52O7YMKCu7LcA3FBK3VJK5QD8fwBeaPA10RFSSi0opX6e/3UC/j+0Q/Df5+/kD/sOgBcbc4V01IT4/9u7/xi5qjKM498Hl/6gLcGAkmIhlFhKTIxtddFKVaAbTLDSIGohkhSNGhMV+aMxaBNLNf5hNIKpUk0QNJHUohSsMWpRUovRlKYF2yhgCGyhUikVsYpka+vjH/dsuGlmFpaZ2elen08ymTvn3jn3PfvuTt49c+aO5gDvAW6pNSffDSXpZOCdwHcBbB+2/RzJeZMNANMlDQAnAU+RfDeG7W3As8c0t8vvcuCHtkdsPw48SlXb9UwK6tZeBzxZe7yvtEUDSTobWAhsB063vR+qoht4bf8iiy67Cfgs8N9aW/LdXOcAzwC3lWU+t0iaQXLeSLb/AnwNeALYD/zD9haS76Zrl98Jr+NSULemFm25HEoDSZoJ3AlcZ/tQv+OJ3pC0DDhge2e/Y4kJMwAsAtbbXgg8T97ub6yydnY5MBc4A5gh6er+RhV9NOF1XArq1vYBZ9Yez6F66ygaRNKJVMX07bY3leanJc0u+2cDB/oVX3TVBcBlkoaplnBdLOkHJN9Ntg/YZ3t7efxjqgI7OW+mIeBx28/Y/g+wCXg7yXfTtcvvhNdxKahb2wHMkzRX0hSqhe2b+xxTdJEkUa2tfMj212u7NgMry/ZK4CcTHVt0n+3P2Z5j+2yqv+d7bV9N8t1Ytv8KPClpfmlaCvyJ5LypngDeJumk8vq+lOqzMcl3s7XL72bgSklTJc0F5gH39zKQfLFLG5IupVpz+SrgVttf7nNI0UWSlgD3AXt4cU3t56nWUd8BnEX1Av0B28d+CCImMUkXAqtsL5N0Ksl3Y0laQPUh1CnAY8CHqSaSkvMGkrQWWEF1FacHgI8CM0m+G0HSBuBC4DTgaWANcDdt8itpNfARqt+H62z/vKfxpaCOiIiIiHjlsuQjIiIiIqIDKagjIiIiIjqQgjoiIiIiogMpqCMiIiIiOpCCOiIiIiKiAymoIyIiIiI6kII6IqJHJK2W9EdJuyU9KOmtPTzXDZJWle0vShrqcv8TNpaIiMlmoN8BREQ0kaTFwDJgke0RSadRfcFIz9n+Qjf768VYJA3YPtKVACMi+iwz1BERvTEbOGh7BMD2QdtPSRqW9BVJ95fb6wEkvUbSnZJ2lNsFpf0GSbdK2irpMUnXjp6gzBo/IulXwPxa+/ckvb9sD0taK2mXpD2Szqud757S/h1Je0uh/LLHUvoZlPQ7SX8o45klaZqk28r5HpB0UTn2Gkk/kvRTYIukGWVsO8pxy7uagYiICZKCOiKiN7YAZ0r6s6SbJb2rtu+Q7fOBbwI3lbZvADfaHgSuoPrK7FHnAe8GzgfWSDpR0puBK4GFwPuAwTFiOWh7EbAeWFXa1gD3lva7qL66d1xjkTQF2Ah8xvabgCHgBeCTALbfCFwFfF/StNLXYmCl7YuB1SWGQeAi4KuSZowRR0TEcSlLPiIiesD2v0rR+w6qYnGjpOvL7g21+xvL9hDwBkmjXZwsaVbZ/lmZHR6RdAA4vfR7l+1/A0jaPEY4m8r9TqriG2AJcHmJ9ReS/v4KxrIT2G97RznuUIllCbCutD0saS9wbunuHtvPlu1LgMtG134D06gK+4fGGEtExHEnBXVERI/YPgpsBbZK2gOsHN1VP6zcnwAstv1CvY9SYI/Umo7y4mt3vZ+xjD6//ly1ObalNmPZ1SaGsfp+/pjjrrD9yHhiiYg43mTJR0RED0iaL2lerWkBsLdsr6jd/75sbwE+VXv+gpc4xTbgcknTy0z2e8cZ4m+BD5ZzXQK8ut2BY4zlYeAMSYPluFmSBkpsHypt51LNOrcqmn8JfFrlvwZJC8c5hoiI40JmqCMiemMmsE7SKcAR4FHg41RXy5gqaTvVpMZV5fhrgW9J2k312rwN+ES7zm3vkrQReJCquL1vnPGtBTZIWgH8BtgP/HM8Y7F9uDx/naTpVOunh4CbgW+XmewjwDXl6iDH9vslqjXku0tRPUz184mImFRkv9x3DCMiolOShoG32D7Y5zimAkdtHymXxVtv+6VmxSMiooXMUEdE/H86C7hD0gnAYeBjfY4nImLSygx1REQAIOlU4Nctdi21/beJjiciYrJIQR0RERER0YFc5SMiIiIiogMpqCMiIiIiOpCCOiIiIiKiAymoIyIiIiI6kII6IiIiIqID/wN1nMEQgWADrQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Clusters on Chart using seaborn\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.scatterplot(x=\"Spending_Score\", y=\"Income\",hue = 'Clusters',  data=df, style='Clusters',s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15,  39],\n",
       "       [ 15,  81],\n",
       "       [ 16,   6],\n",
       "       [ 16,  77],\n",
       "       [ 17,  40],\n",
       "       [ 17,  76],\n",
       "       [ 18,   6],\n",
       "       [ 18,  94],\n",
       "       [ 19,   3],\n",
       "       [ 19,  72],\n",
       "       [ 19,  14],\n",
       "       [ 19,  99],\n",
       "       [ 20,  15],\n",
       "       [ 20,  77],\n",
       "       [ 20,  13],\n",
       "       [ 20,  79],\n",
       "       [ 21,  35],\n",
       "       [ 21,  66],\n",
       "       [ 23,  29],\n",
       "       [ 23,  98],\n",
       "       [ 24,  35],\n",
       "       [ 24,  73],\n",
       "       [ 25,   5],\n",
       "       [ 25,  73],\n",
       "       [ 28,  14],\n",
       "       [ 28,  82],\n",
       "       [ 28,  32],\n",
       "       [ 28,  61],\n",
       "       [ 29,  31],\n",
       "       [ 29,  87],\n",
       "       [ 30,   4],\n",
       "       [ 30,  73],\n",
       "       [ 33,   4],\n",
       "       [ 33,  92],\n",
       "       [ 33,  14],\n",
       "       [ 33,  81],\n",
       "       [ 34,  17],\n",
       "       [ 34,  73],\n",
       "       [ 37,  26],\n",
       "       [ 37,  75],\n",
       "       [ 38,  35],\n",
       "       [ 38,  92],\n",
       "       [ 39,  36],\n",
       "       [ 39,  61],\n",
       "       [ 39,  28],\n",
       "       [ 39,  65],\n",
       "       [ 40,  55],\n",
       "       [ 40,  47],\n",
       "       [ 40,  42],\n",
       "       [ 40,  42],\n",
       "       [ 42,  52],\n",
       "       [ 42,  60],\n",
       "       [ 43,  54],\n",
       "       [ 43,  60],\n",
       "       [ 43,  45],\n",
       "       [ 43,  41],\n",
       "       [ 44,  50],\n",
       "       [ 44,  46],\n",
       "       [ 46,  51],\n",
       "       [ 46,  46],\n",
       "       [ 46,  56],\n",
       "       [ 46,  55],\n",
       "       [ 47,  52],\n",
       "       [ 47,  59],\n",
       "       [ 48,  51],\n",
       "       [ 48,  59],\n",
       "       [ 48,  50],\n",
       "       [ 48,  48],\n",
       "       [ 48,  59],\n",
       "       [ 48,  47],\n",
       "       [ 49,  55],\n",
       "       [ 49,  42],\n",
       "       [ 50,  49],\n",
       "       [ 50,  56],\n",
       "       [ 54,  47],\n",
       "       [ 54,  54],\n",
       "       [ 54,  53],\n",
       "       [ 54,  48],\n",
       "       [ 54,  52],\n",
       "       [ 54,  42],\n",
       "       [ 54,  51],\n",
       "       [ 54,  55],\n",
       "       [ 54,  41],\n",
       "       [ 54,  44],\n",
       "       [ 54,  57],\n",
       "       [ 54,  46],\n",
       "       [ 57,  58],\n",
       "       [ 57,  55],\n",
       "       [ 58,  60],\n",
       "       [ 58,  46],\n",
       "       [ 59,  55],\n",
       "       [ 59,  41],\n",
       "       [ 60,  49],\n",
       "       [ 60,  40],\n",
       "       [ 60,  42],\n",
       "       [ 60,  52],\n",
       "       [ 60,  47],\n",
       "       [ 60,  50],\n",
       "       [ 61,  42],\n",
       "       [ 61,  49],\n",
       "       [ 62,  41],\n",
       "       [ 62,  48],\n",
       "       [ 62,  59],\n",
       "       [ 62,  55],\n",
       "       [ 62,  56],\n",
       "       [ 62,  42],\n",
       "       [ 63,  50],\n",
       "       [ 63,  46],\n",
       "       [ 63,  43],\n",
       "       [ 63,  48],\n",
       "       [ 63,  52],\n",
       "       [ 63,  54],\n",
       "       [ 64,  42],\n",
       "       [ 64,  46],\n",
       "       [ 65,  48],\n",
       "       [ 65,  50],\n",
       "       [ 65,  43],\n",
       "       [ 65,  59],\n",
       "       [ 67,  43],\n",
       "       [ 67,  57],\n",
       "       [ 67,  56],\n",
       "       [ 67,  40],\n",
       "       [ 69,  58],\n",
       "       [ 69,  91],\n",
       "       [ 70,  29],\n",
       "       [ 70,  77],\n",
       "       [ 71,  35],\n",
       "       [ 71,  95],\n",
       "       [ 71,  11],\n",
       "       [ 71,  75],\n",
       "       [ 71,   9],\n",
       "       [ 71,  75],\n",
       "       [ 72,  34],\n",
       "       [ 72,  71],\n",
       "       [ 73,   5],\n",
       "       [ 73,  88],\n",
       "       [ 73,   7],\n",
       "       [ 73,  73],\n",
       "       [ 74,  10],\n",
       "       [ 74,  72],\n",
       "       [ 75,   5],\n",
       "       [ 75,  93],\n",
       "       [ 76,  40],\n",
       "       [ 76,  87],\n",
       "       [ 77,  12],\n",
       "       [ 77,  97],\n",
       "       [ 77,  36],\n",
       "       [ 77,  74],\n",
       "       [ 78,  22],\n",
       "       [ 78,  90],\n",
       "       [ 78,  17],\n",
       "       [ 78,  88],\n",
       "       [ 78,  20],\n",
       "       [ 78,  76],\n",
       "       [ 78,  16],\n",
       "       [ 78,  89],\n",
       "       [ 78,   1],\n",
       "       [ 78,  78],\n",
       "       [ 78,   1],\n",
       "       [ 78,  73],\n",
       "       [ 79,  35],\n",
       "       [ 79,  83],\n",
       "       [ 81,   5],\n",
       "       [ 81,  93],\n",
       "       [ 85,  26],\n",
       "       [ 85,  75],\n",
       "       [ 86,  20],\n",
       "       [ 86,  95],\n",
       "       [ 87,  27],\n",
       "       [ 87,  63],\n",
       "       [ 87,  13],\n",
       "       [ 87,  75],\n",
       "       [ 87,  10],\n",
       "       [ 87,  92],\n",
       "       [ 88,  13],\n",
       "       [ 88,  86],\n",
       "       [ 88,  15],\n",
       "       [ 88,  69],\n",
       "       [ 93,  14],\n",
       "       [ 93,  90],\n",
       "       [ 97,  32],\n",
       "       [ 97,  86],\n",
       "       [ 98,  15],\n",
       "       [ 98,  88],\n",
       "       [ 99,  39],\n",
       "       [ 99,  97],\n",
       "       [101,  24],\n",
       "       [101,  68],\n",
       "       [103,  17],\n",
       "       [103,  85],\n",
       "       [103,  23],\n",
       "       [103,  69],\n",
       "       [113,   8],\n",
       "       [113,  91],\n",
       "       [120,  16],\n",
       "       [120,  79],\n",
       "       [126,  28],\n",
       "       [126,  74],\n",
       "       [137,  18],\n",
       "       [137,  83]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segrigating the data\n",
    "data = df.iloc[:, 3:5].values\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Agglomentric Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,\n",
       "       4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2,\n",
       "       1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')  \n",
    "cluster.fit_predict(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Clusters</th>\n",
       "      <th>Hierarchical_Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Income  Spending_Score  Clusters  \\\n",
       "0           1    Male   19      15              39         2   \n",
       "1           2    Male   21      15              81         1   \n",
       "2           3  Female   20      16               6         2   \n",
       "3           4  Female   23      16              77         1   \n",
       "4           5  Female   31      17              40         2   \n",
       "\n",
       "   Hierarchical_Clusters  \n",
       "0                      4  \n",
       "1                      3  \n",
       "2                      4  \n",
       "3                      3  \n",
       "4                      4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hierarchical_Clusters'] = cluster.labels_\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    85\n",
       "2    39\n",
       "0    32\n",
       "4    23\n",
       "3    21\n",
       "Name: Hierarchical_Clusters, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to check how many values has been assigned\n",
    "\n",
    "df['Hierarchical_Clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cc50108850>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "#plot the clusters to see how actually our data has been clustered\n",
    "\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(data[:,0], data[:,1], c=cluster.labels_, cmap='rainbow') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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