Prediction using Unsupervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "![logo](images/logo.png)\n",
    "<p style='text-align: center;'> THE SPARKS FOUNDATION</p>\n",
    "<h1 align=\"center\">K-Means Clsutering on Iris Dataset</h1> \n",
    "<div style=\"text-align: right\">created<br>\n",
    "-- by Mayur R. Machhi<br>\n",
    "-- for The Sparks Foundation</div>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the libraries and the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Pandas (version : 1.1.5) \n",
    "import numpy as np # Numpy (version : 1.19.2)\n",
    "import matplotlib.pyplot as plt # Matplotlib (version :  3.3.2)\n",
    "from sklearn.cluster import KMeans # Scikit Learn (version : 0.23.2)\n",
    "import seaborn as sns # Seaborn (version : 0.11.1)\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data from .csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we read the data from the dataset using `read_csv` from the pandas library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data\\iris.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the data that we imported to pandas dataframe object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing and Describing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we view the Head and Tail of the data using `head()` and `tail()` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "      <th>0</th>\n",
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       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the sample size of data - how many samples are there in the dataset using `len()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the dimensions/shape of the dataset using `shape`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing Column names of the dataset using `columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column number 1 is Id\n",
      "Column number 2 is SepalLengthCm\n",
      "Column number 3 is SepalWidthCm\n",
      "Column number 4 is PetalLengthCm\n",
      "Column number 5 is PetalWidthCm\n",
      "Column number 6 is Species\n"
     ]
    }
   ],
   "source": [
    "for i,col in enumerate(data.columns):\n",
    "    print(f'Column number {1+i} is {col}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, our dataset has 5 columns named:\n",
    "* Id\n",
    "* SepalLengthCm\n",
    "* SepalWidthCm\n",
    "* PetalLengthCm\n",
    "* PetalWidthCm\n",
    "* Species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View datatypes of each column in the dataset using `dtype`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "SepalLengthCm    float64\n",
       "SepalWidthCm     float64\n",
       "PetalLengthCm    float64\n",
       "PetalWidthCm     float64\n",
       "Species           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering Further information about the dataset using `info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describing the data as basic statistics using `describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the data for inconsistencies and further cleaning the data if needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking data for missing values using `isnull()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0    False          False         False          False         False    False\n",
       "1    False          False         False          False         False    False\n",
       "2    False          False         False          False         False    False\n",
       "3    False          False         False          False         False    False\n",
       "4    False          False         False          False         False    False\n",
       "..     ...            ...           ...            ...           ...      ...\n",
       "145  False          False         False          False         False    False\n",
       "146  False          False         False          False         False    False\n",
       "147  False          False         False          False         False    False\n",
       "148  False          False         False          False         False    False\n",
       "149  False          False         False          False         False    False\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking summary of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Id' column has no relevence therefore deleting it would be better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting 'customer_id' colummn using `drop()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('Id', axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K - Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-means clustering is a clustering algorithm that aims to partition n observations into k clusters. Initialisation – K initial “means” (centroids) are generated at random Assignment – K clusters are created by associating each observation with the nearest centroid Update – The centroid of the clusters becomes the new mean, Assignment and Update are repeated iteratively until convergence The end result is that the sum of squared errors is minimised between points and their respective centroids. We will use KMeans Clustering. At first we will find the optimal clusters based on inertia and using elbow method. The distance between the centroids and the data points should be less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to check the data for any missing values as it can ruin our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude that we don't have any missing values therefore we can go forward and start the clustering procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now view and select the data that we need for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the value count of the target column i.e. 'Species' using `value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into Training and Target data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Iris-setosa\n",
       "1    Iris-setosa\n",
       "2    Iris-setosa\n",
       "3    Iris-setosa\n",
       "4    Iris-setosa\n",
       "Name: Species, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_data = data.iloc[:,4]\n",
    "target_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0            5.1           3.5            1.4           0.2\n",
       "1            4.9           3.0            1.4           0.2\n",
       "2            4.7           3.2            1.3           0.2\n",
       "3            4.6           3.1            1.5           0.2\n",
       "4            5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_data = data.iloc[:,[0,1,2,3]]\n",
    "clustering_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to visualize the data which we are going to use for the clustering.\n",
    "This will give us a fair idea about the data we're working on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.scatterplot(x=data['SepalLengthCm'],y=data['SepalWidthCm'], s=70, color='#f73434', edgecolor='#f73434', linewidth=0.3)\n",
    "ax.set_ylabel('Sepal Width (in cm)')\n",
    "ax.set_xlabel('Sepal Length (in cm)')\n",
    "plt.title('Sepal Length vs Width', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a fair Idea and patterns about some of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining No. of Clusters Required"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Elbow Method**\n",
    "\n",
    "The Elbow method runs k-means clustering on the dataset for a range of values for k (say from 1-10) and then for each value of k computes an average score for all clusters. By default, the distortion score is computed, the sum of square distances from each point to its assigned center.\n",
    "\n",
    "When these overall metrics for each model are plotted, it is possible to visually determine the best value for k. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is the best value of k. The “arm” can be either up or down, but if there is a strong inflection point, it is a good indication that the underlying model fits best at that point.\n",
    "\n",
    "We use the Elbow Method which uses Within Cluster Sum Of Squares (WCSS) against the the number of clusters (K Value) to figure out the optimal number of clusters value. WCSS measures sum of distances of observations from their cluster centroids which is given by the below formula.\n",
    "\n",
    "![formula](images\\formula.png)\n",
    "\n",
    "where Yi is centroid for observation Xi. The main goal is to maximize number of clusters and in limiting case each data point becomes its own cluster centroid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this simple line of code we get all the inertia value or the within the cluster sum of square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([680.8244    , 152.36870648,  78.94084143,  57.31787321,\n",
       "        46.53558205,  38.93096305,  34.29998554,  30.21678683,\n",
       "        28.23999745,  25.95204113])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    km = KMeans(i)\n",
    "    km.fit(clustering_data)\n",
    "    wcss.append(km.inertia_)\n",
    "np.array(wcss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inertia can be recognized as a measure of how internally coherent clusters are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we visualize the Elbow Method so that we can determine the number of optimal clusters for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vV1hYmK677jrdddddXs+ROh8ul5sSWQHwZ1x65n/2oyTpoSFnf3A5zg3nKPwd5yj8HecoAkEgnaeGlchmzZrp3Xff9Vrmdrv16KOPKjMzUz169FBKSorGjBmjvn376p577tGmTZs0depUud1u3XrrrZKkPXv2aPjw4WrdurVmzJihnTt36qWXXlJGRoYef/xxIz4aAAAAAJRbhpXI8PBwtWrVymvZG2+8obS0NL3zzjuKjIzUfffdp6ZNm2ratGmSpO7du8vpdGru3LkaOnSobDab5s2bp4iICM2ZM0c2m009evRQSEiInn32WY0ePVoxMTEGfDoAAAAAKJ/8ZmKdv/76Sy+//LJuuukmtWzZUtnZ2UpOTlZcXJzXdvHx8Tp+/LhSUlIkSevXr1fPnj1ls9k82yQkJCgnJ0fr1q0r088AAAAAAOWd35TImTNnymw2695775Uk7du3Tw6HQw0bNvTarn79+pKktLQ0ZWVl6cCBA4W2iYyMVHh4uNLS0sokOwAAAABUFH4xO+vhw4f18ccfa+TIkapcubIk6cSJE5JyL3stKCwsd4agjIyMYrfJ3y4j48JmOIqKKrxflD/R0RFGRyg3bu7XRBLfaWnj+4S/4xyFv+McRSAIpPPUL0rke++9J5fL5TVbq9udOzuRyWQq8j1ms/ms27jdbpnNFzbQmp6eEVCzJOHcRUdH6NChE0bHKDdqV8t9tAffaenhHIW/4xyFv+McRSDwx/PUbDYVO6jmF5ezJiYmqlu3boqMjPQsi4jIbeJnjibmv46IiPCMQBY14piZmenZB4CysffgCe096F9/AQIAAKB0GV4iDx48qB9//FF9+/b1Wl6vXj1ZLBbt3bvXa3n+64YNGyosLEwxMTHas2eP1zbp6enKyMgodK8kAN96O2mH3k7aYXQMAAAA+JDhJXLr1q2SpLZt23otDw4OVrt27bR8+XLPZatS7qhlRESEmjdvLknq0qWLVq1aJbvd7rWNxWJRhw4dyuATAAAAAEDFYXiJ/OWXX1SpUiXVrl270LqxY8cqJSVF48eP15o1azRjxgzNnz9fo0ePVqVKufdejRo1SocOHdLtt9+uVatWaeHChZo0aZIGDRqkWrVqlfXHAQAAAIByzfAS+ddff3lmZD1Tp06dNGvWLO3cuVPjxo3TZ599pgkTJui2227zbNO4cWMtWLBAmZmZuvvuu7Vw4UKNGDFCjz76aFl9BAAAAACoMEzugteKwguzs5Z//jgTViCbsjhFkvTQkDYGJyk/OEfh7zhH4e84RxEI/PE89fvZWfH3Qqc+r+galRVdo7JCpz5vdBygSAN7NNbAHo2NjgEAAAAf8ovnROLsQqc+r7AXJnte5/+cOeERoyIBRbq4ThWjIwAAAMDHGIkMAAUL5NmWAUb79bdj+vW3Y0bHAAAAgA9RIgGUmg/X7NSHa3YaHQMAAAA+RIkMACcfmFiiZQAAAADga5TIAJA54RGv0uho2577IQEAAAAYghIZIDInPKKjny6TJJkOp0s8mQUAAACAASiRAcTRroNc1aopKG2XLL/uMDoOAAAAgAqIEhlIgoJk7x0vSbItW2JwGKCwm3pfopt6X2J0DAAAAPgQJTLAZCf0kyQFL19qcBKgsHoxEaoXE2F0DAAAAPgQJTLAOK7oJbfVqqCNG2RKTzc6DuDlh92H9cPuw0bHAAAAgA9RIgOMO6KyHF26yeRyyZaUaHQcwMvn63fr8/W7jY4BAAAAH6JEBqDs+L6SpOBELmkFAAAAULYokQHIHpdbIq2rvpSysw1OAwAAAKAioUQGIFfdenI2bS7zyQxZ1681Og4AAACACoQSGaCyE/IuaWWWVgAAAABliBIZoPIvabUlLpXcboPTALluSYjVLQmxRscAAACAD1EiA5SzVRvl1IiRZf9vsvyw3eg4gCSpZlSYakaFGR0DAAAAPkSJDFRms+xxCZKk4MQlBocBcm3Z8Ze27PjL6BgAAADwIUpkALPH95Mk2bgvEn4i8bu9Svxur9ExAAAA4EOUyABm79ZD7pAQWTenyPzHAaPjAAAAAKgAKJGBLDRU9h49JUm25csMDgMAAACgIqBEBjjPLK1c0goAAACgDFAiA1z+5Dq2r1ZLmZnGhgEAAABQ7lEiA5wr5iI52rSV6dSp3CIJGOi2/k11W/+mRscAAACAD1EiywHPJa086gMGi6wcosjKIUbHAAAAgA9RIsuB7LxHfQQvXya5XAanQUX2XepBfZd60OgYAAAA8CFKZDmQ07SZcurUlfnQnwravMnoOKjAVqXs16qU/UbHAAAAgA9RIssDk0n2eGZpBQAAAOB7lMhyIjvvvsjgZZRIAAAAAL5DiSwnHJ27yhUeoaDUH2Teu8foOAAAAADKKUpkeREcLEfPKyVxSSsAAAAA36FEliPZefdFBidSImGMOwY01x0DmhsdAwAAAD4UZHQAlB577zi5zWZZv14n04njckdUNjoSKpiIUJvREQAAAOBjjESWI+7IKDk6XC6TwyHrqi+NjoMKaN33B7Tu+wNGxwAAAIAPUSLLGbtnltYlBidBRbR+2wGt30aJBAAAKM8okeWMPaGfJMmWlCg5nQanAQAAAFDeUCLLmZyLL5GzUWOZjx6VdeMGo+MAAAAAKGcokeWQPT5vNJJLWgEAAACUMkpkOWTPe9QHz4sEAAAAUNoML5EbN27UTTfdpJYtW6pr16565plndPLkSc/6devWaeDAgWrZsqV69eqlBQsWFNrHtm3bNHToULVu3Vpdu3bV9OnT5XA4yvJj+BVHh8vlqlpVQTt/leXXHUbHQQVy76CWundQS6NjAAAAwIcMLZFbtmzRiBEjFB0drVdffVXjxo3Tp59+qscee0ySlJKSojFjxqhRo0aaNWuW+vfvr6lTp2r+/PmefezZs0fDhw9XcHCwZsyYoZEjR2rhwoWaNGmSUR/LeEFBsveOlyTZEhmNRNkJtloUbLUYHQMAAAA+FGTkwV944QW1atVKL7/8skwmkzp37iyXy6WFCxcqKytLM2fOVNOmTTVt2jRJUvfu3eV0OjV37lwNHTpUNptN8+bNU0REhObMmSObzaYePXooJCREzz77rEaPHq2YmBgjP6Jh7PF9FfLBu7IlLlHWuLuNjoMKYmXKb5KkXm3qGJwEAAAAvmLYSOThw4eVnJysm266SSaTybN8yJAhSkpKktlsVnJysuLi4rzeFx8fr+PHjyslJUWStH79evXs2VM2m82zTUJCgnJycrRu3bqy+TB+yN7zSrmtVlm/+1amw+lGx0EFsTH1T21M/dPoGAAAAPAhw0rkL7/8IrfbrSpVqujee+9Vq1at1LZtWz3xxBM6deqU9u3bJ4fDoYYNG3q9r379+pKktLQ0ZWVl6cCBA4W2iYyMVHh4uNLS0srs8/gbd+UqcnTqKpPLJVvScqPjAAAAACgnDLuc9fDhw5KkiRMnqk+fPnr11Vf1888/a8aMGcrOztbgwYMlSeHh4V7vCwsLkyRlZGToxIkTRW6Tv11GRsYFZYyKKrzfgHL9AOmrVaq8Jkkad7vRafxWdHSE0RHKDast935IvtPSxfcJf8c5Cn/HOYpAEEjnqWElMn/21DZt2uiJJ56QJHXq1Elut1tTpkzRoEGDJMnrUteCzGaz3G53sdu43W6ZzRc20JqeniGXy31B+zCSuXNPRUlyLV2m9P3pUoFLfpErOjpChw6dMDpGueGw50gS32kp4hyFv+Mchb/jHEUg8Mfz1Gw2FTuoZtjlrPkjit27d/da3rVrV7ndbm3btk2SCo0m5r+OiIjwjEAWNeKYmZmpiIjAafO+4KpXX84mzWTOOCHr1xX3/lAAAAAApcewkcgGDRpIkux2u9fy/BHKOnXqyGKxaO/evV7r8183bNhQYWFhiomJ0Z49e7y2SU9PV0ZGRqF7JSui7Pi+Ckr9QcGJS+S4opfRcVDOPTSkjdERAAAA4GOGjUQ2btxYtWvX1pIlS7yWr1q1SkFBQWrdurXatWun5cuXey5blaTExERFRESoefPmkqQuXbpo1apVXmU0MTFRFotFHTp0KJsP48fs8X0lSbblyyR34F6aCwAAAMA/GFYiTSaTHnjgASUnJ+uBBx7Q119/rXnz5unVV1/V0KFDFRkZqbFjxyolJUXjx4/XmjVrNGPGDM2fP1+jR49WpUqVJEmjRo3SoUOHdPvtt2vVqlVauHChJk2apEGDBqlWrVpGfTy/4WzdVq7oGrLs2yvLjz8YHQfl3LINe7Vsw96/3xAAAAABy+R2Gzs8lZSUpNmzZ+vXX39VVFSUBg8erNGjR3smxVmxYoVmzpyptLQ0xcTEaMiQIRo5cqTXPpKTkzV16lSlpqaqWrVquvbaa3XXXXfJarVeULZAn1gnX/j4O1Vp8X90cuJjyrxvgtFx/Io/3sQcyKYszn1+K5e1lh7OUfg7zlH4O85RBAJ/PE/PNrGO4SXSn5WXEmlb+oWqDLtJjjZtdXTZKqPj+BV//A82kFEiSx/nKPwd5yj8HecoAoE/nqd+OTsryo69+xVyh4TImrJJ5oN/GB0HAAAAQACjRFYEYWGyd+shSbKtSDQ4DAAAAIBARomsIOzx/SRJtsQlf7MlcP6sVrOsVv5aAQAAKM8Me04kypY9LkGSZPtqtZSZKYWGGhsI5dJ9g1oZHQEAAAA+xpBBBeG6qKYcrVrLlJUl29o1RscBAAAAEKAokRWI55LW5UsNToLy6tP1afp0fZrRMQAAAOBDlMgKJDuuryTJlrhUcrkMToPyKHX3EaXuPmJ0DAAAAPgQJbICyWneQjm168jy50EFbd1sdBwAAAAAAYgSWZGYTKcn2GGWVgAAAADngRJZwWTn3RcZnLjM4CQAAAAAAhElsoJxdOkmV1i4gn7YJvO+vUbHQTkTXsmq8EpWo2MAAADAhyiRFU1wsBw9r5Qk2ZYzGonSNe66Fhp3XQujYwAAAMCHKJEVUHbefZHB3BcJAAAA4BxRIisge+94uc1mWdevlenEcaPjoBz5YPVOfbB6p9ExAAAA4EOUyArIXb26nO06yORwyLp6pdFxUI7s3H9MO/cfMzoGAAAAfIgSWUF5ZmldxiWtAAAAAEqOEllB2eP7SpJsXy6XnE6D0wAAAAAIFJTICirnkv+Ts2EjmQ8fljX5O6PjAAAAAAgQlMiKymSSPe+SVlviUoPDoLyoVjlY1SoHGx0DAAAAPkSJrMA8l7TyqA+Uktv7N9Pt/ZsZHQMAAAA+RImswBwdLperalUF/bpDlp07jI4DAAAAIABQIisyq1X2Xn0kSbbEZQaHQXnwVtIveivpF6NjAAAAwIcokRWcPSHvvsjl3BeJC7fvYIb2HcwwOgYAAAB8iBJZwdl79ZY7KEjWDd/IdOSw0XEAAAAA+DlKZAXnrlxFjk5dZcrJke3LFUbHAQAAAODnKJGQPT5BEo/6AAAAAPD3KJFQdlzeoz5WJkl2u8FpEMhiIkMVExlqdAwAAAD4UJDRAWA8V4OGcl7aREE/pcr6zXo5evQ0OhIC1PC+lxodAQAAAD7GSCQkSfb4vFlaE5cYnAQAAACAP6NEQpKUHZd7X2Tw8mWS221wGgSqRUt/0qKlPxkdAwAAAD5EiYQkydmmnVzVo2XZu0eW1B+NjoMAdfBwpg4ezjQ6BgAAAHyIEolcFouy+8RLkoKXM0srAAAAgKJRIuHBfZEAAAAA/g4lEh72Hj3lDg5WUMommQ4eNDoOAAAAAD9EicRpYWGyd+shk9ut4KREo9MgANWNCVfdmHCjYwAAAMCHKJHwcvqSVu6LxLn7Z+//0z97/5/RMQAAAOBDlEh4sec96sO2ZqWUlWVwGgAAAAD+hhIJL66ateRo2VqmrCzZ1q0xOg4CzLzPftC8z34wOgYAAAB8iBKJQjyjkcu4pBXn5sjxbB05nm10DAAAAPgQJRKF2BPy7otcsUxyuQxOAwAAAMCfGFoinU6nLrvsMsXGxnr9at26tWebdevWaeDAgWrZsqV69eqlBQsWFNrPtm3bNHToULVu3Vpdu3bV9OnT5XA4yvKjlCvO5pcpp1ZtWf44oKDvtxgdBwAAAIAfCTLy4GlpacrOztaUKVPUoEEDz3KzObfbpqSkaMyYMerbt6/uuecebdq0SVOnTpXb7datt94qSdqzZ4+GDx+u1q1ba8aMGdq5c6deeuklZWRk6PHHHzfiYwU+k0n2uARVWjRftsSlcrZqY3QiAAAAAH7C0BL5008/yWw2Kz4+XpUqVSq0fubMmWratKmmTZsmSerevbucTqfmzp2roUOHymazad68eYqIiNCcOXNks9nUo0cPhYSE6Nlnn9Xo0aMVExNT1h+rXLDH9/WUyMyHHjU6DgJE49pVjI4AAAAAHzP0ctbU1FTVq1evyAKZnZ2t5ORkxcXFeS2Pj4/X8ePHlZKSIklav369evbsKZvN5tkmISFBOTk5WrdunW8/QDlm79Jd7tAwWbd/L/Nv+4yOgwBx/RWNdf0VjY2OAQAAAB8ytET+/PPPstlsuvXWW9W6dWu1b99ejz/+uDIyMrRv3z45HA41bNjQ6z3169eXlHspbFZWlg4cOFBom8jISIWHhystLa3MPku5ExIi+xW9JEm25csMDgMAAADAXxh+OWtGRoZuuOEGjRkzRtu3b9esWbOUlpam++67T5IUHh7u9Z6wsDBJUkZGhk6cOFHkNvnbZWRkXFC+qKjC+61QbrhOWvKZIlYtV8RD9xmdxmeioyOMjlBuPL/oO0nSI8M7GJykfOEchb/jHIW/4xxFIAik89TQEvnSSy+pSpUqio2NlSS1b99eUVFRevDBB7V+/XpJkslkKvK9ZrNZbre72G3cbrdngp7zlZ6eIZfLfUH7CGSmDt0VZTJJq1YpPe13ucMD58QuqejoCB06dMLoGOXG4aNZksR3Woo4R+HvOEfh7zhHEQj88Tw1m03FDqoZejlrhw4dPAUy3xVXXOH1+szRxPzXERERnhHIokYcMzMzFRFR/kpPWXJHR8vZroNMdrusq1YaHQcAAACAHzCsRKanp+v999/Xvn3ek7acOnVKkhQVFSWLxaK9e/d6rc9/3bBhQ4WFhSkmJkZ79uwptO+MjIxC90ri3GXH95UkBS9fanASAAAAAP7AsBJpMpn0+OOP68033/RavmTJElksFnXu3Fnt2rXT8uXLPZetSlJiYqIiIiLUvHlzSVKXLl20atUq2e12r20sFos6dOC+rAtlj+8nSbIlJUo5OQanAQAAAGA0w+6JjIyM1JAhQ/Tf//5X4eHhateunTZt2qS5c+dqyJAhql+/vsaOHasRI0Zo/PjxGjBggDZv3qz58+fr/vvv9zwWZNSoUfriiy90++23a9iwYdq9e7emT5+uQYMGqVatWkZ9vHIj5/9ildOgoSy70xSUvFHOjpcbHQl+rEmDakZHAAAAgI+Z3AWH+cqYw+HQokWL9OGHH2r//v2KiYnRoEGDNGrUKM+kOCtWrNDMmTOVlpammJgYDRkyRCNHjvTaT3JysqZOnarU1FRVq1ZN1157re666y5ZrdYLylfRJ9bJF/aviQr99xxl3nmvTj7+tNFxSpU/3sQMFMQ5Cn/HOQp/xzmKQOCP5+nZJtYxtET6O0pkLuu6r1T1un/I+X+xOrJuo9FxSpU//gcLFMQ5Cn/HOQp/xzmKQOCP56nfzs6KwODo2EmuylUU9MvPMu/aaXQc+LHp723R9Pe2GB0DAAAAPkSJxN+zWmXv3UcSs7Ti7BwOlxwOl9ExAAAA4EOUSJSIPS73UR+2REokAAAAUJFRIlEi9l695Q4KkvXbr2U6esToOAAAAAAMQolEibirVpPj8s4y5eTI9uUKo+MAAAAAMAglEiVmj8+/pHWJwUngr1peXF0tL65udAwAAAD4ECUSJZadf1/kyi8lu93gNPBHCR3rKaFjPaNjAAAAwIcokSgxV8NGcsZeKvPxY7J++7XRcQAAAAAYgBKJc+KZpZVHfaAIUxanaMriFKNjAAAAwIcokTgn2fH9JEnBy5ZKbrfBaQAAAACUNUokzomzbTu5oqJk2btblp9/MjoOAAAAgDJGicS5sVhk75MgiVlaAQAAgIqIEolz5rmkNZH7IgEAAICKJsjoAAg89h495bbZFLRpo0yHDskdHW10JPiJ9k1qGB0BAAAAPsZIJM5deLjs3XrI5HbLlpRodBr4kV5t6qhXmzpGxwAAAIAPUSJxXvIf9RG8jPsicVq2I0fZjhyjYwAAAMCHKJE4L/b4vOdFrlkpnTplcBr4ixnvbdWM97YaHQMAAAA+RInEeXHVqi1Hi5YyZWbKtm6N0XEAAAAAlBFKJM6bZzQycZnBSQAAAACUFUokztvpErlEcrsNTgMAAACgLFAicd6cl7VSzkU1ZfnjgIK+32J0HAAAAABlgBKJ82cyeWZptSUuNTgM/EGXFjXVpUVNo2MAAADAhyiRuCD2BEokTut6WU11vYwSCQAAUJ5RInFB7F17yB0aKuu2rTL/vt/oODDYiUy7TmTajY4BAAAAH6JE4sKEhMjeo5ckRiMhzfnfds3533ajYwAAAMCHKJG4YJ5ZWpdTIgEAAIDyjhKJC5bdO15uk0m2tWukjAyj4wAAAADwIUokLpi7Rg0527aXyW6Xbc0qo+MAAAAA8CFKJEpFdt4lrcGJSwxOAgAAAMCXKJEoFfb4fpIkW1KilJNjcBoYpWeb2urZprbRMQAAAOBDlEiUipzYS5VTr4HMf/2loE3JRseBQTo0iVGHJjFGxwAAAIAPUSJROkwmZSfkXdLKLK0V1uHjp3T4+CmjYwAAAMCHKJEoNfa4vEd9cF9khfXaZz/qtc9+NDoGAAAAfIgSiVLj6NRFrspVFPTzTzKn7TI6DgAAAAAfoESi9Fitsve6UhKXtAIAAADlFSUSpcozS2siJRIAAAAojyiRKFX2K/vIbbHI+s16mY4eMToOAAAAgFJGiUSpcletJsflnWXKyZFtZZLRcVDG4jvUU3yHekbHAAAAgA9RIlHqPLO0cl9khdPqkupqdUl1o2MAAADAhyiRKHX2+ARJki1pheRwGJwGZelA+kkdSD9pdAwAAAD4kN+UyDvvvFN9+vTxWrZu3ToNHDhQLVu2VK9evbRgwYJC79u2bZuGDh2q1q1bq2vXrpo+fbocFBdD5TS6WM5L/k/m48dk3fCN0XFQhv6z7Gf9Z9nPRscAAACAD/lFifzkk0+0YsUKr2UpKSkaM2aMGjVqpFmzZql///6aOnWq5s+f79lmz549Gj58uIKDgzVjxgyNHDlSCxcu1KRJk8r6I+AMp2dpXWJwEgAAAAClKcjoAAcPHtRzzz2niy66yGv5zJkz1bRpU02bNk2S1L17dzmdTs2dO1dDhw6VzWbTvHnzFBERoTlz5shms6lHjx4KCQnRs88+q9GjRysmJsaIjwRJ2fH9FPrKDAUnLtXJpydJJpPRkQAAAACUAsNHIh977DF16dJFnTp18izLzs5WcnKy4uLivLaNj4/X8ePHlZKSIklav369evbsKZvN5tkmISFBOTk5WrduXdl8ABTJ2a69XFFRsuxOk+UXLm8EAAAAygtDS+T777+vH374Qf/617+8lu/bt08Oh0MNGzb0Wl6/fn1JUlpamrKysnTgwIFC20RGRio8PFxpaWm+DY+zs1hk7x0vSbIlMksrAAAAUF4Ydjnr/v37NWnSJE2aNEmRkZFe606cOCFJCg8P91oeFhYmScrIyCh2m/ztMjIyLjhjVFThfeMc3HCd9O5bCl+ZqPBnHjc6TbGioyOMjlBu3NyviSS+09LG9wl/xzkKf8c5ikAQSOfpeZdIu92uX3/9VSEhIWrUqNE5vdftduuRRx5Rjx49FB8fX+R6STIVcx+d2Ww+6zZut1tm84UPsqanZ8jlcl/wfioqU5tOirLZpG++UXpqmtzV/e/5gdHRETp06ITRMcqN2tUqSRLfaSniHIW/4xyFv+McRSDwx/PUbDYVO6h21qZ17Ngxvfjiixo6dKjX8i+++ELdu3fXwIEDddVVV6lfv37asmVLiQMtXrxYP//8sx555BE5nU45nU5PKXQ6nYqIyG3hZ44m5r+OiIjwjEAWNeKYmZnp2QeM4w6PkKNLN5ncbtmSEo2OgzKw9+AJ7T3oX38BAgAAoHQVOxKZkZGhwYMHa/fu3brooovkdDoVFBSkbdu26cEHH5TL5dKNN96oSy65RB9//LFGjBihjz/+2HPf4tkkJibqyJEj6tq1a6F1zZo105NPPimLxaK9e/d6rct/3bBhQ4WFhSkmJkZ79uzx2iY9PV0ZGRmF7pWEMbLj+8m26ksFJy5V9o1DjI4DH3s7aYck6aEhbQxOAgAAAF8pdiTytdde02+//aYZM2Zo9erVCgrK7Ztz5syR2+3W0KFD9eSTT2rIkCF68803VaNGDf373/8u0UGfeuopffDBB16/evbsqYsuukgffPCBEhIS1K5dOy1fvtwzQinlls+IiAg1b95cktSlSxetWrVKdrvdaxuLxaIOHTqc1xeC0mWPS5Ak2VZ9KZ06ZXAaAAAAABeq2JHIL7/8Utdcc40SEhI8y7KysjyPzhgy5PSoUnBwsK6++mq9//77JTpoUfdQVq1aVTabTS1atJAkjR07ViNGjND48eM1YMAAbd68WfPnz9f999+vSpVy77saNWqUvvjiC91+++0aNmyYdu/erenTp2vQoEGqVatWibLAt1x16srR/DJZt38v2/qvZL8y7u/fBAAAAMBvFTsSuX//fs+IX77k5GQ5HA7VqVNHDRo08FpXs2ZNpaenl1qwTp06adasWdq5c6fGjRunzz77TBMmTNBtt93m2aZx48ZasGCBMjMzdffdd2vhwoUaMWKEHn300VLLgQvnGY3kUR8AAABAwCt2JNJsNsvlcnkt+/rrryVJnTt3LrT94cOHi3zcRklNnjy50LI+ffqoT58+Z31fu3bt9N577533ceF79oR+Cps+Vbbly6Qp06ViZt0FAAAA4P+KHYm8+OKLtXnzZs9rt9ut5cuXy2QyqVevXoW2//LLL9W4cWPfpERAc17WSjkxF8ny+34Fbf/e6DjwoYE9GmtgD/4eAAAAKM+KLZHXXnutli1bpjfeeEO//vqrJk2apP3796tu3brq1q2b17Zz587Vli1b1LdvX58HRgAym2WPyz03bMuWGBwGvnRxnSq6uE4Vo2MAAADAh4q9nPXGG2/Upk2bNGnSJJlMJrndblWpUkUvvPCCzObc7vnBBx9o3rx52rdvn9q2baubbrqpzIIjsNjjE1TpvwtlW75MmQ8+bHQc+Mivvx2TJIokAABAOVZsiTSZTHrhhRc0ZMgQbd68WWFhYerTp48iIyM92/zxxx9yu90aO3asRo8e7SmXwJns3a6Qu1IlWbdulvnA73LVZPbc8ujDNTsl8ZxIAACA8qzYEpmvdevWat26dZHr7rzzTt15552lHgrlUKVKsvfoqeBlS2Rbvkynho00OhEAAACA83BBQ4fHjh1TdnZ2aWVBOWeP7ydJsiVyXyQAAAAQqM5aIh0Oh9555x09/LD3PWzJycm66qqrdPnll6t169YaNWqU9u7d69OgCHzZfRLkNplkW7tGOnnS6DgAAAAAzkOxJdJut2vYsGF68skn9fnnn8vpdEqSdu/erVtvvVW7du1St27dNHz4cKWlpenGG2/UX3/9VWbBEXjcNWrI2aatTNnZsq1ZZXQcAAAAAOeh2BL5xhtvaPPmzXrwwQe1ceNGBQXl3j45a9YsZWdn66qrrtK8efM0YcIEffjhh7JYLJo7d26ZBUdg8lzSunypwUngCzf1vkQ39b7E6BgAAADwoWJL5NKlSxUfH69bb71VISEhknJHJ1euXCmTyaRbb73Vs23VqlV13XXXafXq1T4PjMCWnfe8yODlyySXy+A0KG31YiJULybC6BgAAADwoWJL5J49e9SuXTuvZVu2bFFWVpaio6PVpEkTr3X16tXTn3/+6ZuUKDdymjRVTr36Mv91SEEpyUbHQSn7Yfdh/bD7sNExAAAA4EPFlkiXyyWLxeK17JtvvpEkde7cudD2J06cUKVKlUo5Hsodk0nZcQmSpOBELmktbz5fv1ufr99tdAwAAAD4ULElsl69ekpNTfValpSUJJPJpCuuuKLQ9uvWrVO9evVKPSDKHx71AQAAAASuYkvkVVddpU8++URJSUnKysrSokWLtGPHDkVFRalXr15e23766adav369rrzySp8HRuBzdOoiV0RlBf2UKvPuNKPjAAAAADgHQcWtGD58uNauXas777xTJpNJbrdbVqtVzz33nGw2myRpxYoVevPNN/Xdd9+pYcOGGj58eFnlRiCz2WTv1Vshn3yk4OVLlXX7HUYnAgAAAFBCxZZIm82mRYsWacmSJdqyZYvCwsJ09dVX6+KLL/Zss337dqWkpOjqq6/WxIkTPbO4An/HHpegkE8+ki1xGSUSAAAACCAmt9vtPt83Z2VlKTg4WGZzsVfFBrT09Ay5XOf99eAsTEcOK6ppY8lkUvpPaXJXrmJIjujoCB06dMKQY5dHB9JPSpJqRoUZnKT84ByFv+Mchb/jHEUg8Mfz1Gw2KSoqvOh1F7LjzMxMXUAHRQXmrhYpR4fLZXI6ZVuZZHQclJKaUWEUSAAAgHLub0vkm2++qf79+8vpdBZa9/zzz6tbt25atGiRL7KhnPPM0rqMWVrLiy07/tKWHX8ZHQMAAAA+VGyJdLvdmjBhgp599ln9+eef+v333wttU6dOHZnNZk2ZMkX33XefT4Oi/LHH5z4v0vblCsnhMDgNSkPid3uV+N1eo2MAAADAh4otke+//74+/fRT/fOf/9RXX31V5DMgx48fry+//FLXXHONli5dqo8//tiXWVHO5DS+RM6LL5H52FFZv/vW6DgAAAAASuCsJbJ9+/Z6/PHHFRwcXOwOgoOD9fzzz+vSSy/VO++845OQKL88l7QmLjU4CQAAAICSKLZE/vrrr7ryyitLthOzWfHx8fr5559LLRgqBnt8X0mSLXGJxCRNAAAAgN8rtkRaLBbZbLYS76hatWrl9lEf8B1Huw5yRUYqKG2XLL/uMDoOAAAAgL9RbOurX7++tm/fXuIdbdu2TbVq1SqVUKhAgoJkvzJOErO0lge39W+q2/o3NToGAAAAfKjYEnnVVVfps88+044dfz86tGPHDn322Wfq3r17qYZDxZCdkHtfZPBy7osMdJGVQxRZOcToGAAAAPChYkvk4MGDVatWLQ0dOlSffvqpcnJyCm3jcrn0+eefa8SIEQoLC9OwYcN8Ghblk+OKXnJbrQrauEGmv3jGYCD7LvWgvks9aHQMAAAA+FBQcSvCwsL06quv6o477tBDDz2kp556Ss2aNVN0dLRcLpfS09P1ww8/KDMzUzVr1tTs2bNVo0aNssyOcsIdUVmOLt1kW71StqREZd84xOhIOE+rUvZLkjo0iTE4CQAAAHyl2BIpSY0aNdKnn36qxYsX64svvlBKSoqcTqckyWq1qlWrVoqLi9PgwYPPaRIe4EzZ8X1lW71SwcuXUSIBAAAAP1Zsibz//vt1+eWXq2PHjhoxYoRGjBghSTp8+LAsFouqVKlSZiFR/tnj+koPPyjrqi+l7GzpLM8mBQAAAGCcYkvk0qVLtWRJ7myZNWrUUMeOHT2/6tSpU2YBUTG46taTs2lzBf24Xdb1a+Xo1dvoSAAAAACKUGyJTE5O1tatW7V582Zt3rxZa9as0aeffiqTyaSLLrpIHTt2VIcOHdSxY0fVrl27LDOjnMpO6KugH7crOHEJJRIAAADwUya32+0u6cY7d+5USkqKNm/erC1btigtLU2SVLNmTc8o5bXXXuurrGUuPT1DLleJvx5coKCUZFVL6KWc2nV0OOUHyWTy+TGjoyN06NAJnx+nojiRaZckRYRyj3Rp4RyFv+Mchb/jHEUg8Mfz1Gw2KSoqvMh151Qiz3TkyBF98cUX+uijj/Tjjz/KZDIpNTX1vIP6G0pkGXO5FHlZrCx/HtThL9cpp8VlPj+kP/4HCxTEOQp/xzkKf8c5ikDgj+fp2UrkWWdnPZPD4dDmzZv13XffadOmTdq6dauysrJksVjUqlUrdezYsVQCo4Iym2WPS1ClN99Q8PKlyiyDEonSte77A5KkrpfVNDgJAAAAfOWsJdLtdmv79u365ptvtGHDBqWkpOjUqVMym81q0qSJbrrpJl1++eVq27atQkNDyyozyjF7fD9VevMN2RKXKPP+h4yOg3O0fhslEgAAoLwrtkTeeeed+u6773TiRO6wamxsrAYPHqyOHTuqffv2Cg8vemgTuBD2bj3krlRJ1i2bZf7jgFwXUUYAAAAAf1JsiUxKSpLVatW1116rsWPHql69emWZCxVVaKjs3a9QcOJS2ZYv06lbRhidCAAAAEAB5uJWXHPNNapevbr+97//qW/fvrruuus0depUffXVVzp58mRZZkQFY4/vJ0myLV9qcBIAAAAAZyp2JHLKlCmSpD179njuifzkk0+0YMECBQUFqXnz5urYsaMuv/xytWnTRsHBwWUWGuWbvU+8JMn21WopM1PiflsAAADAb5zzIz5+/vlnffvtt9qwYYOSk5N1/Phx2Ww2tWzZUpdffrnGjRtX4n253W698cYbevvtt3XgwAE1aNBAt912m/r37+/ZZt26dXrppZf066+/KioqSjfffLNGjhzptZ9t27Zp6tSp2r59u8LCwnTdddfprrvuktVqPZePVgiP+DBO1YSesqZs0rH/vCN7Qj+fHccfp1MOZNmOHElSsNVicJLyg3MU/o5zFP6OcxSBwB/P07M94qPYy1mLExsbq2HDhmnOnDlat26dpkyZoqZNm2rjxo165ZVXzmlf//73vzV16lRde+21+ve//60uXbrogQce0JIlSyRJKSkpGjNmjBo1aqRZs2apf//+mjp1qubPn+/Zx549ezR8+HAFBwdrxowZGjlypBYuXKhJkyad60eDH7HH9ZUk2RKXGJwE5yLYaqFAAgAAlHPn9JzIvXv3auvWrdq6dau+//57/fTTT3I4HAoJCVGXLl3Uvn37Eu/L4XBowYIFuummmzR27FhJUqdOnbR9+3a9+eab6tevn2bOnKmmTZtq2rRpkqTu3bvL6XRq7ty5Gjp0qGw2m+bNm6eIiAjNmTNHNptNPXr0UEhIiJ599lmNHj1aMTEx5/IR4Sey4/spbPKzCl6+TBkul2Q+53/vgAFWpvwmSerVpo7BSQAAAOArxZbIY8eO6fvvv/cUxu+//17Hjh2T2+1WRESE2rZtq7vvvlsdOnRQs2bNZLGc2+iDxWLRf//7X1WtWtVrudVqVWZmprKzs5WcnKx7773Xa318fLxef/11paSk6PLLL9f69evVs2dP2Ww2zzYJCQl66qmntG7dOg0cOPCccsE/5DRtppw6dWX5bZ+CNm+Ss23J/4ECxtmY+qckSiQAAEB5VmyJ7Nixo0wmk9xut6pVq6Z27dqpffv2at++vZo0aSKTyXRBBzabzYqNjZWUe29kenq6PvroI3399dd6+umntW/fPjkcDjVs2NDrffXr15ckpaWlqWXLljpw4EChbSIjIxUeHq60tLQLyggDmUyyx/dVpfnzZEtcSokEAAAA/ESxJTIhIUEdOnRQ+/btdckll/g0xPLly3X33XdLkq644gpdffXVSk1NlSSFh3vfzBkWFiZJysjI0IkTJ4rcJn+7jIyMC8pV3I2kKCODBkrz5ynsy0SFvTTNZ4eJjo7w2b4rGqst94oEvtPSxfcJf8c5Cn/HOYpAEEjnabElcsaMGWUWomnTpnrzzTf1888/6+WXX9btt9/uuYy1uBFPs9ms/Illi9rG7XbLfIH30TE7q8GatlFUeITM27YpfdN2uerVL/VD+ONMWIHMYc+dnZXvtPRwjsLfcY7C33GOIhD443laqrOz+kLdunXVvn173XzzzXr00Ue1YcMGT0E8czQx/3VERIRnBLKoEcfMzExFRAROm0cRgoPl6HmlJMm2fKnBYQAAAABIBpbIo0eP6uOPP9bBgwe9ljdt2lSS9Ntvv8lisWjv3r1e6/NfN2zYUGFhYYqJidGePXu8tklPT1dGRkaheyUReLLjcx/1EbyMEhkIHhrSRg8NaWN0DAAAAPiQYSXS5XJp4sSJevfdd72Wr1+/XpLUokULtWvXTsuXL/eMSkpSYmKiIiIi1Lx5c0lSly5dtGrVKtntdq9tLBaLOnToUAafBL5k7x0nt9ks6zfrZDp+zOg4AAAAQIV3Ts+JLE2RkZH65z//qXnz5ikkJEQtWrTQpk2b9O9//1s33HCDGjVqpLFjx2rEiBEaP368BgwYoM2bN2v+/Pm6//77ValSJUnSqFGj9MUXX+j222/XsGHDtHv3bk2fPl2DBg1SrVq1jPp4KCXuyCg5Olwu27dfy7bqS2Vfc53RkXAWyzbkXimQ0LGewUkAAADgKyZ3wWG+MuZwOLRo0SJ98MEH+v3333XRRRfphhtu0KhRozyT4qxYsUIzZ85UWlqaYmJiNGTIEI0cOdJrP8nJyZo6dapSU1NVrVo1XXvttbrrrrtktVovKB8T6/iHSrNnKvypx3Tq+sE6Mee1Ut23P97EHMimLE6RJC5pLUWco/B3nKPwd5yjCAT+eJ6ebWIdQ0ukv6NE+gfLrzsU2bmtXFWrKv3HXVJQ6Q2g++N/sIGMEln6OEfh7zhH4e84RxEI/PE89fvZWYGzybn4EjkbXyzz0aOybtxgdBwAAACgQqNEIiDY43JnabUtW2JwEgAAAKBio0QiINgT+knieZH+zmo1y2rlrxUAAIDyzLDZWYFz4WjfUa6qVRW081dZft2hnIsvMToSinDfoFZGRwAAAICPMWSAwBAUJHvveElc0goAAAAYiRKJgGGPz7svkkta/dan69P06fo0o2MAAADAhyiRCBj2nlfKbbXK+t23Mh1ONzoOipC6+4hSdx8xOgYAAAB8iBKJgOGuXEWOTl1lcrlkS1pudBwAAACgQqJEIqBkJ+Re0hqcyCWtAAAAgBEokQgo+c+LtK76UsrONjgNAAAAUPFQIhFQXPXqy9mkmcwZJ2T9ep3RcXCG8EpWhVeyGh0DAAAAPsRzIhFwsuP7Kij1BwUvXypHzyuNjoMCxl3XwugIAAAA8DFGIhFwPI/6SFwqud0GpwEAAAAqFkokAo6zdVu5omvI8ts+WX78weg4KOCD1Tv1weqdRscAAACAD1EiEXjMZmXHJUiSghOXGBwGBe3cf0w79x8zOgYAAAB8iBKJgGSP7ydJsi3nUR8AAABAWaJEIiDZu18hd0iIrCmbZD74h9FxAAAAgAqDEonAFBoqe/crJEm2FYnGZgEAAAAqEEokApY9Ln+WVu6L9BfVKgerWuVgo2MAAADAh3hOJAKWPW9yHduaVVJmphQaanAi3N6/mdERAAAA4GOMRCJguS6qKUer1jKdOiXb2jVGxwEAAAAqBEokAppnllYuafULbyX9oreSfjE6BgAAAHyIEomAlp1/X+TyZZLLZXAa7DuYoX0HM4yOAQAAAB+iRCKg5TRvoZzadWT586CCtqQYHQcAAAAo9yiRCGwm0+kJdpYvNTgMAAAAUP5RIhHwsvPuiwxeRokEAAAAfI0SiYDn6NJNrrBwBf24XeZ9e42OU6HFRIYqJpJHrQAAAJRnlEgEvuBgOXpeKYlLWo02vO+lGt73UqNjAAAAwIcokSgXsvPuiwxOpEQCAAAAvkSJRLlg7x0vt9ks6/q1Mp04bnScCmvR0p+0aOlPRscAAACAD1EiUS64q1eXs31HmRwOWVevNDpOhXXwcKYOHs40OgYAAAB8iBKJciM7rq8kKXjZEoOTAAAAAOUXJRLlhj0h91Efti+XS06nwWkAAACA8okSiXIj5+JL5GzYSObDh2VN/s7oOAAAAEC5RIlE+WEyyR6fNxrJJa2GqBsTrrox4UbHAAAAgA9RIlGu2ONz74vkeZHG+Gfv/9M/e/+f0TEAAADgQ5RIlCuODpfLVbWqgn7dIcvOHUbHAQAAAModSiTKF6tV9l59JEm2xGUGh6l45n32g+Z99oPRMQAAAOBDlEiUO55ZWhO5L7KsHTmerSPHs42OAQAAAB8ytES6XC69/fbb6t+/v1q3bq3evXtr0qRJysjI8Gyzbt06DRw4UC1btlSvXr20YMGCQvvZtm2bhg4dqtatW6tr166aPn26HA5HWX4U+BF7r95yBwXJ+t23Mh1ONzoOAAAAUK4YWiJff/11PfPMM7riiis0e/ZsjRgxQh9//LHuueceSVJKSorGjBmjRo0aadasWerfv7+mTp2q+fPne/axZ88eDR8+XMHBwZoxY4ZGjhyphQsXatKkSUZ9LBjMXbmKHJ26ypSTI9uXK4yOAwAAAJQrQUYd2O126/XXX9fgwYN1//33S5I6d+6satWqafz48UpNTdXMmTPVtGlTTZs2TZLUvXt3OZ1OzZ07V0OHDpXNZtO8efMUERGhOXPmyGazqUePHgoJCdGzzz6r0aNHKyYmxqiPCAPZ4xNkW7tatuXLlH3DjUbHAQAAAMoNw0YiT548qauvvlr/+Mc/vJY3atRIkrRjxw4lJycrLi7Oa318fLyOHz+ulJQUSdL69evVs2dP2Ww2zzYJCQnKycnRunXrfPwp4K+y4/Ie9fHlCsluNzhNxdG4dhU1rl3F6BgAAADwIcNGIsPDw/XYY48VWp6UlCRJatq0qRwOhxo2bOi1vn79+pKktLQ0tWzZUgcOHCi0TWRkpMLDw5WWluaj9PB3rgYN5by0iYJ+SpX1m/Vy9OhpdKQK4forGhsdAQAAAD7mV7Ozbt26VfPmzVPv3r114sQJSblls6CwsDBJUkZGRrHb5G9XcIIeVDz2eGZpBQAAAEqbYSORZ9q0aZPGjBmjOnXq6Nlnn/WMIppMpiK3N5vNcrvdxW7jdrtlNl9YR46KKlxOEUBuvF56+UWFJiUqdN6rUjHnUnR0RBkHK7+eX/SdJOmR4R0MTlK+cI7C33GOwt9xjiIQBNJ56hclcsmSJZo4caIaNGig119/XdWqVdNff/0lSYVGE/NfR0REeEYgixpxzMzMVETEhf1BpKdnyOVyX9A+YKCGTRRVPVrm3bt1+KsNymnarNAm0dEROnTohAHhyqfDR7Mkie+0FHGOwt9xjsLfcY4iEPjjeWo2m4odVDP8ctaFCxfqvvvuU6tWrbR48WLVqFFDklSvXj1ZLBbt3bvXa/v81w0bNlRYWJhiYmK0Z88er23S09OVkZFR6F5JVDBms7LjEiRJwcuXGhwGAAAAKB8MLZHvv/++Jk+erL59++r111/3GjkMDg5Wu3bttHz5cs9lq5KUmJioiIgINW/eXJLUpUsXrVq1SvYCM3AmJibKYrGoQwcuqavo7PmztHJfJAAAAFAqDLucNT09Xc8995xq166tIUOG6Mcff/RaX69ePY0dO1YjRozQ+PHjNWDAAG3evFnz58/X/fffr0qVKkmSRo0apS+++EK33367hg0bpt27d2v69OkaNGiQatWqZcRHgx+x9+gpd3CwrJuSZTp4UG6eGwoAAABcEMNK5Nq1a5WVlaX9+/dryJAhhdZPnTpV11xzjWbNmqWZM2dq3LhxiomJ0YQJEzRy5EjPdo0bN9aCBQs0depU3X333apWrZpGjBihu+66qyw/DvxVWJjs3XooOGm5gpMSdWrILUYnKteaNKhmdAQAAAD4mMld8FpReGFinfIh5I0FinjwXmUn9NPx/7zjtc4fb2IGCuIchb/jHIW/4xxFIPDH89SvJ9YBfM2eN7mObc0qKSvL4DQAAABAYKNEotxz1awlR8vWMmVlybZ2tdFxyrXp723R9Pe2GB0DAAAAPkSJRIXgGY1MXGZwkvLN4XDJ4XAZHQMAAAA+RIlEhWBP6CdJsi1fKrkoOQAAAMD5okSiQnA2v0w5tWrLcvAPBX2/xeg4AAAAQMCiRKJiMJlOX9K6bInBYQAAAIDARYlEhWGP7ytJsi3nvkhfaXlxdbW8uLrRMQAAAOBDQUYHAMqKvUt3uUPDZN3+vcy/7ZOrTl2jI5U7CR3rGR0BAAAAPsZIJCqOkBDZr+glidFIAAAA4HxRIlGhZOfN0hqcyH2RvjBlcYqmLE4xOgYAAAB8iBKJCsXeO15uk0nW9WtlyjhhdBwAAAAg4FAiUaG4q1eXs10Hmex2WVetNDoOAAAAEHAokahwsuO5pBUAAAA4X5RIVDieR30kJUo5OQanAQAAAAILJRIVTs7/xSqnQUOZDx+WvvnG6DjlSvsmNdS+SQ2jYwAAAMCHKJGoeEwmuaKicn/u1k2hU583Nk850qtNHfVqU8foGAAAAPAhSiQqnNCpz8u6KdnzOuyFyRTJUpLtyFG2g0uEAQAAyjNKJCqcsBcmF73M5TIgTfky472tmvHeVqNjAAAAwIcokUCeat06KGTxf6TsbKOjAAAAAH6LEokK5+QDEwstc0VUVtCOXxQx/k5Ftm2uSi+/KNPRIwakAwAAAPwbJRIVTuaER7yK5MkHJir9pzQdn/OanM1ayPLnQYU/95QiWzdT2L8elvm3fQamBQAAAPwLJRIVUuaER3Toz+OS263MCY9IVquyrx+sIyvX6ei7/5O9e0+ZT2Yo9N+zFdmhpSLuuE2WH7YbHRsAAAAwHCUSKMhkkqPnlTr2wSc68uVanbruesntVsgH7yqyZ2dVGTxA1rVrJLfb6KR+qUuLmurSoqbRMQAAAOBDlEigGM4WLXVi7gId3rBFmbeNkTs0VLZVX6rqwP6q2qeHgj/+UHI6jY7pV7peVlNdL6NEAgAAlGeUSOBvuOrV18nnpio95QednPiYXNWjZf1+iyrfPkKRl7dWyOtzpZMnjY7pF05k2nUi0250DAAAAPgQJRIoIXdklDLvm6D0Tdt1YtoMORs1lmXvHkU8MkFRbZoqdPKzMh06ZHRMQ83533bN+R/3jgIAAJRnlEjgXFWqpFPDRurI+mQdW7hYjrbtZT5yRGHTpyqqbTOFPzhe5l07jU4JAAAA+AQlEjhfFovsV/XX0SVJOvJporLj+8p06pQqvTFfkZ3aqPLIoQratNHolAAAAECpokQCF8pkkvPyTjr+33d1eN1GZf1zqGS1KvjzT1St75Wqck1f2ZYvlVwuo5MCAAAAF4wSCZSinP+LVcaM2TqcvE2Zd42Xq3IV2b5Zryo3D1a1Hpcr+O03pexso2MCAAAA540SCfiA66KaOvmvp3R48w/KeOp55dSqraCff1Lle+5QZPvLVGnWDJmOHzM6Zqnr2aa2erapbXQMAAAA+BAlEvAhd0RlZY29U4e/26rjr/xbziZNZfnjgMKfeVyRrZoq7MnHZP59v9ExS02HJjHq0CTG6BgAAADwIUokUBZsNmUPuklHVn+jo+98KHvX7jJnnFDonJmKbH+ZIu4aI0vqj0anvGCHj5/S4eOnjI4BAAAAH6JEAmXJZJKjVx8d++hzHVm+WqeuuU7KyVHIu28pssflqvzP62Vdv1Zyu41Oel5e++xHvfZZ4JdhAAAAFI8SCRjE2aqNTry2SIe/3ayskbfJXamSgpOWq+qAq1Q1oadsn/5PyskxOiYAAADghRIJGMzVoKEyJr+o9JQfdXLCI3JFRcm6OUVVRg1TZKc2ClnwmpSZaXRMAAAAQBIlEvAb7qgoZT4wUembftCJKdOVU7+BLLvTFDHxfkW1babQaZNkSk83OiYAAAAqOEok4G9CQ3VqxCgd/nazjs3/jxyt28icnq6waZMU1aapwifeL/PuNKNTAgAAoIKiRAL+ymKRvf+1OrpslY5+vETZveNkyspSpQWvKfLy1oq4bbiCtqQYndJLfId6iu9Qz+gYAAAA8CFKJODvTCY5OnfV8bc+0OGvNujUjUMki0Uhn3ykanFXqMp1/5Dty+V+MaNrq0uqq9Ul1Y2OAQAAAB/ymxKZmpqqZs2a6Y8//vBavm7dOg0cOFAtW7ZUr169tGDBgkLv3bZtm4YOHarWrVura9eumj59uhwOR1lFB8pMzqVNdGLmqzqcvE2Z4+6RKzxCtnVfqcpN16vaFZ0V/O5bkt1uWL4D6Sd1IP2kYccHAACA7/lFidy1a5dGjx4tp9PptTwlJUVjxoxRo0aNNGvWLPXv319Tp07V/PnzPdvs2bNHw4cPV3BwsGbMmKGRI0dq4cKFmjRpUll/DKDMuGrW0sknntHhLT8q4/FnlHNRTQWl/qDKd41RZIeWqjRnlkwnjpd5rv8s+1n/WfZzmR8XAAAAZcfQEul0OrV48WJdf/31ys7OLrR+5syZatq0qaZNm6bu3btr/PjxuvXWWzV37lzZ80Zb5s2bp4iICM2ZM0c9evTQyJEj9fDDD+udd97RwYMHy/ojAWXKXbmKsu68R4eTt+n4zFfljL1Ult/3K/zJRxXZupnCnnlC5j8OGB0TAAAA5YihJXLTpk164YUXNHLkSD3wwANe67Kzs5WcnKy4uDiv5fHx8Tp+/LhSUnInFFm/fr169uwpm83m2SYhIUE5OTlat26d7z8E4A9sNmXfOERH1nyrY4vfk71zV5mPH1PorJcU2ba5wu8dJ8svjBACAADgwhlaIhs3bqykpCTdeeedslgsXuv27dsnh8Ohhg0bei2vX7++JCktLU1ZWVk6cOBAoW0iIyMVHh6utDQeg4AKxmyWvU+Cjn28REeWrVR2/2slp1OV3vqvIru2V+Whg2X99mu/mIQHAAAAgSnIyINXr178LI4nTpyQJIWHh3stDwsLkyRlZGQUu03+dhkZGReULyqq8H5R/kRHRxgdwTfie+b++vVXafp0aeFCBScuVXDiUunyy6UHH5SuuUY64x9wLoTVlruvcvudGoTvE/6OcxT+jnMUgSCQzlNDS+TZuPNGSkwmU5HrzWbzWbdxu90ymy9soDU9PUMuFyM25Vl0dIQOHTphdAzfqhIjPTVFpjsfUKX5/1alha/J/O230sCBcjZqrKyxd+nUoJukSpUu+FDx7etKUvn/TstQhThHEdA4R+HvOEcRCPzxPDWbTcUOqvnF7KxFiYjIbeJnjibmv46IiPCMQBY14piZmenZBwDJHR2tzImPKT3lR514fqpy6tVX0K6dinjwXkW1babQ6VNlOnL4go7RrEGkmjWILKXEAAAA8Ed+WyLr1asni8WivXv3ei3Pf92wYUOFhYUpJiZGe/bs8domPT1dGRkZhe6VBCApLEynRo3R4W836/i8hXJc1krmv/5S2ORnFdW6qcIenSDz3j1/v58i7D14QnsP+te/ogEAAKB0+W2JDA4OVrt27bR8+XLPZauSlJiYqIiICDVv3lyS1KVLF61atcrzyI/8bSwWizp06FDmuYGAERSk7GsH6uiKNTr64Wey97xSpsxMhb42V5EdWylizEgFbdt6Trt8O2mH3k7a4aPAAAAA8Ad+WyIlaezYsUpJSdH48eO1Zs0azZgxQ/Pnz9fo0aNVKe/+rVGjRunQoUO6/fbbtWrVKi1cuFCTJk3SoEGDVKtWLYM/ARAATCY5uvXQsXf/p8Mr1+vU9YMlk0khH32gald2U5Xrr5F11ZfM6AoAAABJfl4iO3XqpFmzZmnnzp0aN26cPvvsM02YMEG33XabZ5vGjRtrwYIFyszM1N13362FCxdqxIgRevTRRw1MDgSmnOYtdGLOazr83VZljh4nV1i4bF+tUtXBA1StV1cFf/Cu5HAYHRMAAAAGMrndDC8Uh9lZyz9/nAnLn5iOHlHIfxaq0rxXZfnzoCQpp05dZY2+Q1lDhklnPF5nyuIUSdJDQ9qUedbyinMU/o5zFP6OcxSBwB/P04CcnRWA8dxVqynr7vt0eNN2nXjpFTkvvkSW3/Yp/F8PK6p1U4U+/7RMBw8aHRMAAABliJHIs2Aksvzzx3/18Wsul2zLlyn0lRmyfvetJMlts+nU4H9KVpt2L10jSao3ZIAyJzxiZNJyg3MU/o5zFP6OcxSBwB/P07ONRAaVcRYAgcxslj2hn+wJ/RT03QaFzn5ZtmVfqNJ/F0mSmuRv98JkmY4f08mnnpcsFqPSAgAAwAcYiTwLRiLLP3/8V59AY/l1hyI7t5UkpdaMlSQ1OfCzJMlttSqnbj25GjRUTv0GymnQSDn5P9dvIIWFGRU7YHCOwt9xjsLfcY4iEPjjecpIJACfybn4Es/P/+k6VJI06f3HJEkmh0NBu3ZKu3YW+V5XdI3cUukpmQ2VUz/3tbtGDclk8v0HAAAAwDmhRAK4YCcfmKiwFyYXWpZ5572y7N0jy+40WfakybI7TeY9u3Nf790j86E/ZT70p6wbNxTapzs0NG/E8nTBdDXIK5p160s2W1l9PAAAABRAiQRwwTyT6OzJ/e3kAxM9y3IubaKcS5sUflNOjsx/HMgrmLtlLlA0LbvTZD5yREGpPyoo9cdCb3WbTHLVruM1gunKH8ls0FDuqtV89VEBAAAqPEokgFKROeEROfKeE5lZkudEWixy1a4jV+06cnTpVmi16djR3HK5Z7csafkFc7cse9Jk/m2fLHm/tO6rQu91VanqKZiuMy6XddWqzWQ/AAAAF4ASCcAvuatUlfOyVtJlrQqvdDhk3rdXlvxLY/NGMz2jmMeOyrx1s6xbNxfeb/5kP56Ry0YF7sdswGQ/AAAAf4MSCaDU3NT7kr/fqDRYrXI1aixXo8ZynLnO7Zbpr79k2b3rdLHM+928Z7csfxwo2WQ/BYpl/qyyTPYDAABAiQRQiurFRBgdQTKZ5I6OljM6Ws72HQuvz8zMnexnz25Zdu86PdHPOU32kzfhT4MCl8vWqScFB5fBBwQAADAWJRJAqflh92FJUrMGkQYnOYvQ0OIn+3G5ZD7we4GRyzSvy2XNhw///WQ/BSb4cRUYzXRX8+PvBAAA4BxQIgGUms/X75bk5yXybMzm05P9dO5aaLXp+LHTM8mmpXldLmv+be/pyX7Wry30XleVqgUeVeJ9uayrdp1Ck/2ETn3e89iU0AKz3QIAABiNEgkAJeSuXEXOFi2lFi0Lr3Q4cmeNPXOin7zSaT52VObvt8j6/ZbC+z1jsh/Lrl2yrVnpWZ9fJimSAADAH1AiAaA0WK1yNWwkV8NGxU/2syet8EQ/u9P+drIfKbdIBn/0vtzVqsldpapcVavm/l6tmtxVqnleu6tWlatqtdzfq1SVQkOZDAgAAJQqSiQA+FrByX7adSi8PjNTln178wpmmsIfm1jkboLOUjKL47Za5a56umS6qlb1en26dFaTK++1u1ruzwoJOefjAQCA8o8SCQBGCw1VTuylyom9VJJkOnrUcwlrvsw77tapIbfIdPSIzMeOynT0aO7PR4/KdOxo7u8FXpuOHpX56BGZTp2SKW/G2XPlDgnJLZZFjn6efp1fOgsWUdlspfHNAAAAP0SJBFBqbkmINTpCuZB/72N+kTx5IRPrnDp1unQeyS+geb8fOXK6gB47KvMZr02nTsly6g/p4B/nfFh3aFgJRj/PLKLV5K5SRQrif5oAAPBnJrfb7TY6hL9KT8+Qy8XXU55FR0fo0KETRscAimXYOep2S5mZngJauHQeKX708+hRmXJyzvvQrvCIAqObfzf6WfX0aGnlKpLZXOx+C854e0HFHF74exT+jnMUgcAfz1Oz2aSoqPAi1/HPvQBKzZYdf0mSWl1S3eAkuGAmkxQWJldYmFSrts6pErrdMp3MOGP083QR9YyMHjtSePTz6FGZM05IGSdk2bf3nCK7TSa5K1cpsnQG/Zwq63fferYNe2GyLL/tU9ao0XKHhckdFp77e2hYocetAAAAb5RIAKUm8bvc/9NPiazgTCa5wyPkDo+Q6tQ9twLqcsl04njRo5/5I50FXhe8R9R8/JhMx45Kx47Ksnf33x4q5J3FCnlncaHl7tBQuUPDvMtlWFjuZworYnlYuNzh4Wcsz/s9PJxiCgAodyiRAAD/YTbnXq5apapc5/renJzTo5lnXF4b8dB9Rb7F0aJl7qhpRoZMJ0/KfDJDpsxMmTIzpb8OXfDHyeeuVClvpLNAuSxRGS1YSAuU2NAw7h0FABiG/wUCAJQPFovckVFyR0YVKqDmQ38WmvG2yPsiXS4pK0umkye9yqUpM+/3M5efLOHyrCyZsrIk/VVqH9cdEpJXKguOkJ4xClpkGQ0vPGKa9/O5FNOC95iGco8pAFQolEgAQLlX4hlvzWYpv1SpRukcPG+SoiLL5ckzS2fBInrG9hknvJefOiXTqVNSenrp5FTBYnr6HtGiymjQ5k2yfb3O876wFybLsutXnbppqNzBIVKwTW5bsBQSLLct+PSy4JDcx7+cZQIkAID/o0QCACqEzAmPGDNaljdJUakX0/wR0zPLZbEFtQQl9QKKachHHyjkow9KFt9qLVQs3cHBp38OyfvdFix3SLCUV0Tdwbbcn4taFhwsBZ9lmc3m+dlz3KCg3D8fP8EMwgACBSUSQKm5rX9ToyMAFYPJJIWG5k4CFB1dOvssWEyLHAE9vSz82SeK3IW9W4/cEmq3y5R9SsrOlsnzc+7vJrtdJodDJodDyiid6OfLbTJJISG5o6Z55TW3bBYsonk/5xXR08U0uPAyW3De/opZ5rW+wIhtcLBCX5zidcl1/s8USQD+iBIJoNREVg4xOgKA83UOxdR0Kqtk95gWxe0uUDLzi2W25+cil9ntuaOk9mzpVHbu7wWXZWfLlJ3t2a8pO29ZgX2Ysu1S3n49P+fk5BbnrKwL+eZ8JuyFyQpe8rncNmtuobXZ8kZo88qn1ZpbVq223KJb8HdbcG5JteaNwBa1rS1YsllPl2jP/gu812bzq9Hac8F9u4DvUCIBlJrvUg9Kkjo0iTE4CQBfKvE9pkUxmU6P5Ely+yRhCTmdnrJpstulU6cK/H7GsrxyWnBZwdFW5RXV3NJboKgWtSyvAMtuzz3OqVPFRgz6cXsZfiFFc1utZy+cnoJaoKjmj8YWV3CL29cZy/9um+Lury1YICVGdksLl1wjn8ntdhv697c/S0/PkMvF11OeRUdH6NChE0bHKDemLE6RJD00pI3BScoPzlH4O87RUuB2K3Tyswp7aZrX4qwRtylr6PDThTavdMruOD0aW9Ryhz23sHr9nrePvJ/lcJz+/Sz7MjkcBn0pJeMOCjpdMq1599ZarQpK21Xk9vZeveW2WHKf3WrO/d0dVODnguuC8l7nrfNaX/B1/vogi9zmItYXt+2Z+847ptdry1n2XWD7M9fJbC71EeQzi7lEkSxN/vh3qdlsUlRUeJHrGIkEAAAwksmkzIf/JVks/jfKk3/5scOee2lwictrXgHNzj79Xof99IjsGdvml9nT+83b1u7w/r3AvkzZ2TI5nZLTmfts1xKwrUzy8RfmP9xnFs68MirzGWXYbM4t4wUKdO56s2QJ8hTggjMy5wt7YbIsv/5yerugoLyfzQV+tkhBQafLelBQ7rr8ou75OSh3ndl8+meLJXcf+WW54P7yjnP6Z0uB9xTcX97n9vycu9wXRft8BOpl15RIAAAAP2DYDMJnU/Dy43CDLz8+k9stORynLwsuUForzZ2jSm/M99r81MBByh54g5TjknJypJwcmVw5uZc15+RILlfufbKeX86813nbu3JyXzudeT+f3o9cObmFNn/7/G3z9qOcAvt25R3b6fRsq5y87V05kvOM1zkFXucUs23B7ZxOSTq9vSRfVqWQjz/y4d59y51fNvMLapFltUApteS9DrIU+Pl0AS9YnN1525ytXFuTv5P1u289eQLpsmtKJAAAAAKPyZR7X6TNJrfCvQpuxrSX5IqO9r+R3bLiKlBwC5bMIkpnoYJacJ3z9OuQt99UyPvveB3mVP9rZb+qv3eBzSvapvySm1+680aNc38+c3l+KXYWKOMFi7nzdOnOcXotlzM/v/OM9+TtL8d5+rPkFMznKrOifS7CXpgcEOcqJRIAAADlTv7IbnR0hDL97F4znzObc39ZrZIKjyCfz4iyo2t35dRvUH6KudvtKZSnS6/zdHH2Wn56xNqUk1eGc1zepTRvxDl3nfP0qHVOgRHsM/YX/szjRn8L540SCaDU3DGgudERAACAj/jlJdfny2Q6fX+kvIt1WV22bcrKLHKyokBAiQRQaiJCbUZHAAAACAgX9LgkgxX9cB0AOA/rvj+gdd8fMDoGAABAQMic8IgO/XlccrsDpkBKlEgApWj9tgNav40SCQAAUJ5RIgEAAAAAJUaJBAAAAACUGCUSAAAAAFBi5aZEfv7557rqqqt02WWXqW/fvvr444+NjgQAAAAA5U65eMTH0qVL9cADD+iWW25Rt27dlJSUpIceekghISFKSEgwOh5QYdw7qKXREQAAAOBj5aJETp8+XX379tUjj+ROi9utWzcdO3ZML7/8MiUSKEPBVovREQAAAOBjAX856759+7R3717FxcV5LY+Pj9euXbu0b98+g5IBFc/KlN+0MuU3o2MAAADAhwK+RO7atUuS1LBhQ6/l9evXlySlpaWVeSagotqY+qc2pv5pdAwAAAD4UMBfznrixAlJUnh4uNfysLAwSVJGRsZ57zsqKvzvN0LAi46OMDpCuWG15V7Oyndauvg+4e84R+HvOEcRCALpPA34Eul2uyVJJpOpyOVm8/kPtqanZ8jlcp9/OPi96OgIHTp0wugY5YbDniNJfKeliHMU/o5zFP6OcxSBwB/PU7PZVOygWsBfzhoRkdvYzxxxPHnypNd6AAAAAMCFC/iRyPx7Iffu3avY2FjP8j179nitPx9ms+nvN0LA48+59FSrHCyJ77S08X3C33GOwt9xjiIQ+Nt5erY8Jnf+dZ8B7Morr1SrVq304osvepbde++9Sk1NVWJiooHJAAAAAKB8CfiRSEkaN26cHn74YVWpUkVXXHGFVq5cqaVLl+qll14yOhoAAAAAlCvlYiRSkt555x0tWLBABw4cUN26dXX77bfr2muvNToWAAAAAJQr5aZEAgAAAAB8L+BnZwUAAAAAlB1KJAAAAACgxCiRAAAAAIASo0QCAAAAAEqMEgkAAAAAKDFKJAAAAACgxCiRAAAAAIASo0QCAAAAAEqMEgkAAAAAKDFKJAAAAACgxCiRqHBcLpfefvtt9e/fX61bt1bv3r01adIkZWRkGB0NKNKdd96pPn36GB0D8LJx40bddNNNatmypbp27apnnnlGJ0+eNDoW4OXtt99W37591apVK/Xv31+ffvqp0ZEApaamqlmzZvrjjz+8lq9bt04DBw5Uy5Yt1atXLy1YsMCghH+PEokK5/XXX9czzzyjK664QrNnz9aIESP08ccf65577jE6GlDIJ598ohUrVhgdA/CyZcsWjRgxQtHR0Xr11Vc1btw4ffrpp3rssceMjgZ4vPvuu3ryySd1xRVXaM6cOercubMefPBBLV261OhoqMB27dql0aNHy+l0ei1PSUnRmDFj1KhRI82aNUv9+/fX1KlTNX/+fIOSnp3J7Xa7jQ4BlBW3262OHTvqqquu0hNPPOFZvmTJEo0fP14ff/yxmjRpYmBC4LSDBw+qf//+qlSpkmw2G2USfuPmm2+WJP33v/+VyWSSJC1evFgLFy7UZ599pkqVKhkZD5Ak3XjjjbLZbPrPf/7jWTZkyBCZzWb997//NTAZKiKn06l3331XL774oqxWq44ePao1a9booosukiQNHz5cmZmZeu+99zzvmTZtmt577z2tX79eNpvNqOhFYiQSFcrJkyd19dVX6x//+IfX8kaNGkmS9u7da0QsoEiPPfaYunTpok6dOhkdBfA4fPiwkpOTddNNN3kKpJT7f86TkpIokPAb2dnZCgsL81pWtWpVHT161JhAqNA2bdqkF154QSNHjtQDDzzgtS47O1vJycmKi4vzWh4fH6/jx48rJSWlLKOWCCUSFUp4eLgee+wxtW3b1mt5UlKSJOniiy82IhZQyPvvv68ffvhB//rXv4yOAnj55Zdf5Ha7VaVKFd17771q1aqV2rZtqyeeeEKnTp0yOh7gccstt2jt2rVaunSpMjIytGzZMq1evVrXXHON0dFQATVu3FhJSUm68847ZbFYvNbt27dPDodDDRs29Fpev359SVJaWlqZ5SypIKMDAEbbunWr5s2bp969e6tx48ZGxwG0f/9+TZo0SZMmTVJkZKTRcQAvhw8fliRNnDhRffr00auvvqqff/5ZM2bMUHZ2tiZPnmxwQiDXVVddpW+//Vb33nuvZ9mAAQM0atQo40KhwqpevXqx606cOCEpd7CjoPyRdH+c/JESiQpt06ZNGjNmjOrUqaNnn33W6DiA3G63HnnkEfXo0UPx8fFGxwEKcTgckqQ2bdp47i3v1KmT3G63pkyZonHjxqlu3bpGRgQkSWPHjtXmzZv18MMPq2nTptq6davmzJnjuSoJ8Bf5U9QUvEWgILPZ/y4e9b9EQBlZsmSJRowYoZo1a2rRokWqVq2a0ZEALV68WD///LMeeeQROZ1OOZ1Oz/+4FPwZMEr+v4x3797da3nXrl3ldrv1888/GxEL8JKSkqJ169bpscce0/Dhw9WhQwfddtttmjhxov773/9ynsKvRERESCo84pj/On+9P2EkEhXSwoULNWXKFHXo0EGzZ8/2y/84UTElJibqyJEj6tq1a6F1zZo106RJk3TdddcZkAzI1aBBA0mS3W73Wp4/Qlncv6QDZen333+XlDtiXlC7du0kSTt37lRsbGyZ5wKKUq9ePVkslkITPOa/PvNeSX9AiUSF8/7772vy5Mnq16+fpkyZ4ndTJqNie+qppwo9sH327NlKTU3VK6+8ojp16hiUDMjVuHFj1a5dW0uWLNE///lPz/JVq1YpKChIrVu3NjAdkCv//3Rv3LjR8w8fUu4zTiWpdu3aBqQCihYcHKx27dpp+fLlGjZsmOcf4xITExUREaHmzZsbnLAwSiQqlPT0dD333HOqXbu2hgwZoh9//NFrfb169ZjIBIbKf9xMQVWrVpXNZlOLFi0MSAR4M5lMeuCBB3TffffpgQce0HXXXaft27fr1Vdf1dChQ/k7FH6hWbNm6t27t55//nmdPHlSTZo00fbt2zV79mx1795dLVu2NDoi4GXs2LEaMWKExo8frwEDBmjz5s2aP3++7r//fr98dBIlEhXK2rVrlZWVpf3792vIkCGF1k+dOpWpvwHgb/Tr1082m02zZ8/W6NGjFRUVpXHjxmn06NFGRwM8XnrpJb3yyitatGiR0tPTVbt2bY0cOVK333670dGAQjp16qRZs2Zp5syZGjdunGJiYjRhwgSNHDnS6GhFMrmZpQEAAAAAUELMzgoAAAAAKDFKJAAAAACgxCiRAAAAAIASo0QCAAAAAEqMEgkAAAAAKDFKJAAAAACgxCiRAABDTJw4UbGxsVq8eHGR63/77TfFxsZq1qxZZZysMLfbrWnTpqljx45q1apVsZnzZWRkaMGCBbruuuvUtm1btWrVStdff73effdduVwur2179eqloUOH+ix7RkaGDh8+7LP9n49x48Zp7ty5kqSPPvpIsbGx2rBhQ7Hb79u3Tx07dtTBgwfLKiIA4CwokQAAQ7300kv666+/jI5xVqtXr9brr7+uVq1a6dFHH1WnTp2K3XbXrl0aOHCgpk+frtjYWN133326++67FRwcrMcff1wTJkxQWT2iefv27erbt6927NhRJscridWrV2vz5s265ZZbSvyeunXrKiEhQc8//7wPkwEASirI6AAAgIrtxIkTmjRpkl588UWjoxTr559/liTdd999io2NLXa77Oxs3XHHHTp69Kg++OADXXrppZ51I0eO1FNPPaW33npLl1122TmVqPP1yy+/6M8///T5cUrK5XLp+eef17BhwxQaGnpO773tttsUFxenjRs3qn379j5KCAAoCUYiAQCG6tWrlz7//HN98803RkcplsPhkCSFhYWddbu33npLaWlpevjhh70KZL6HHnpIVapU0TvvvOOTnP5u5cqV2rNnj/r373/O761Tp45at26tN954wwfJAADnghIJADDUY489pkqVKunJJ5+U3W7/2+2Tk5M1fPhwtW7dWq1bt9Ytt9yijRs3nvfx/25/vXr10iuvvCJJuvLKK9WrV69i9/XFF18oNDRUV111VZHrQ0JC9N577+njjz8udh/F3SN55vJjx45p4sSJuuKKK9S8eXP17t1bL774orKzsyVJs2bN0sMPPyxJuuWWW7xy//HHH5owYYIuv/xytWjRQtdee60+/fRTr+NNnDhRCQkJWrx4sdq3b6/27dvrq6++ktvt1iuvvKL4+Hi1aNFCnTt31oMPPqgDBw4U+5nyvfXWW2ratKlq1ap11u1ee+01xcbG6rnnnvNa3rt3b61cubJExwIA+A4lEgBgqNq1a+uOO+7Q7t27NW/evLNu++WXX2ro0KE6cOCAxo4dq7Fjx+rAgQMaPny4vvzyy3M+dkn298gjj6hPnz6SpIcffliPPPJIkftyu91KTU1V8+bNZbVaiz1mgwYNZLPZzjnrme69916tWrVKN9xwg5544gl16NBB8+bN07PPPitJ6tOnjwYPHixJGjNmjCf3wYMHdcMNN+jrr7/W0KFD9dBDD6latWp68MEH9frrr3sd48CBA5o9e7buvPNODRo0SC1bttTcuXM1e/ZsdevWTY8//rhuuOEGJSUlaeTIkcrJySk2b1ZWlr777jv16NHjrJ/r/fff1wsvvKDBgwfr0Ucf9VrXs2dP5eTkaN26def8fQEASg/3RAIADDdixAh9+umnmjdvnvr376/69esX2sbpdOrpp59WTEyMPvzwQ4WHh0uSbrzxRv3jH//QU089pe7du5+1wJ3P/nr37q3U1FStWLFCvXv3Vp06dYrc35EjR+R0OhUdHX2e30LJpaen6+uvv9aECRN06623SpJuuOEGud1u7du3T5J06aWXqlWrVnr33XfVuXNndezYUVLuREZ2u12fffaZatSoIUm6+eabdf/99+vll1/WgAEDFBUVJUk6deqUnnjiCV133XWeY3/22Wfq3r27HnvsMc+ymjVr6u2339b+/ftVr169IjNv3bpVDofjrPeUrlixQk888YSuueYaPfXUU4XW169fX5UqVVJycrJuuOGGc/nKAACliJFIAIDhrFar53LWp59+ushtfvzxR/3xxx8aMmSIp/BJUuXKlXXzzTfr4MGD2r59e4mPWdr7M5tz/yf1bKNxpSUiIkKhoaF66623lJiYqMzMTEnSpEmTtGjRomLf53K5lJSUpHbt2ikoKEiHDx/2/IqLi5Pdbtf69eu93tO1a1ev1xdddJE2bNigN954wzOr7o033qhPPvmk2AIpyVNuiyvh3377re677z516NBBkyZNkslkKrSNyWRS7dq19dtvvxV7HACA71EiAQB+oV27dhowYIDWrVunL774otD6/OLQsGHDQusaNWokSfr9999LfLzS3l+VKlVktVrL5JmMNptNTz/9tNLT03X33XerY8eOuvXWW/Xuu+967oksypEjR3TixAklJSWpU6dOXr/uvvtuSSp0v2H+qGS+CRMmqFq1anr++efVtWtXDRw4ULNnz9ahQ4fOmvno0aOS5FXYC5ozZ46cTqd++eUXZWRkFLuf8PBwHTly5KzHAgD4FpezAgD8xoMPPqiVK1dq0qRJhe7PO9uzFfPXlfRSVl/sz2QyqXXr1tq+fbucTqeCgor+n9iXXnpJ+/bt08MPP3xOl76eOcLZv39/devWTUlJSVqzZo2+/vprrVu3Tm+99Zbef//9Iu+7zN9HfHy8brzxxiKPU7duXa/XFovF6/Wll16qxMRErV27VqtWrdLatWs1c+ZMLVq0SO+8844aN25c5H7zR2pdLleR6zt16qRhw4ZpzJgxeuGFF/TMM88UuZ3L5SqUCQBQthiJBAD4jcjISD3wwAM6dOiQZsyY4bWudu3akqRdu3YVel9aWpqk3EstS6q09yflTmaTmZmpJUuWFLn+1KlT+uCDD/T111+ratWqRW5jNpsLzVLrdDq9Rt9Onjyp5ORkmUwmXX/99Zo1a5a++eYb3XLLLfrpp5+KnXgmMjJSlSpVktPpVOfOnb1+NWjQQJmZmapUqVKxny8nJ0c//PCDDhw4oCuvvFLPPvus1qxZo5deeknHjx/X+++/X+x780c080ckzzR27Fj17NlT/fr10/vvv69NmzYVud3Ro0cLjY4CAMoWJRIA4Feuv/56tWnTRqtWrfJa3qxZM0VHR+vtt9/2utwxIyNDb731lqKjo9W8efMSH6e09ydJgwcPVu3atTVlyhT98ssvXutycnL05JNP6q+//tJtt91W7Chn9erVlZaWplOnTnmWrVy50usy1R07dmjIkCH64IMPPMtsNpuaNm0q6fTo4Zmjf0FBQerevbvWrFmjn376yeu4kydP1rhx4856qWhOTo5uueUWPf/8817LW7Zs6XW8ouSX9j/++KPYbaTcR4vkP/Il//mcBY9/6NAh1axZ86z7AAD4FpezAgD8islk0pNPPqnrrrtOTqfTs9xqtepf//qX7r33Xg0cOFDXX3+9JOmDDz7Qn3/+qZkzZ3pKzL59+5SSkqI2bdoUujzzfPZXUsHBwXrllVc0cuRIXX/99erfv79atGiho0ePatmyZUpNTVVCQoJGjBhR7D7+8Y9/6JlnntGoUaN09dVXa8+ePXrvvfc8JUzKLW3t2rXTSy+9pAMHDig2NlYHDhzQm2++qUaNGqlTp06SckceJentt9/WX3/9pf79++uBBx7Qhg0bNGTIEA0ZMkS1atXS6tWrtWrVKg0ePFiXXHJJsdlsNpuGDh2qV199VePGjVO3bt106tQpvfvuu6pUqZIGDhxY7Htbtmyp0NBQbd26tdjnaEpSTEyM7rzzTk2dOlULFy7U7bff7ln3yy+/KCsry/P5AADGoEQCAPxObGysbrnlFi1YsMBreXx8vBYsWKA5c+Zo9uzZCgoKUsuWLfXcc8+pXbt2nu02btyohx9+WJMmTSq2RJ7L/s5F06ZN9cknn2jRokX66quvtGTJErndbsXGxur555/XddddV+TMo/n++c9/6ujRo/rggw/0zDPP6NJLL9Urr7yiBQsWeGZhNZlMmj17tl555RWtWrVK7777rqpUqaK4uDjdc889nvshO3XqpL59+2rVqlX69ttvFRcXp3r16um9997TzJkz9d577ykzM1N169bVww8/rKFDh/7t57v77rtVtWpVffjhh5oyZYosFovatGmjadOmFXs/pJRbQDt27Kjk5OS/PcawYcP0v//9T7Nnz1bfvn09f4abNm2S2WwuNGMsAKBsmdxnm1kAAACglCQlJWncuHFKTExUgwYNzvn9N954o6pXr65XXnml9MMBAEqMeyIBAECZuPLKK9WgQQN9/PHH5/zePXv2aPPmzRo5cmTpBwMAnBNKJAAAKBMmk0n3339/ocmMSmLevHnq2bOn2rRp46N0AICSokQCAIAyExcXpzZt2mjRokUlfs/evXu1YsUKPfHEE74LBgAoMe6JBAAAAACUGCORAAAAAIASo0QCAAAAAEqMEgkAAAAAKDFKJAAAAACgxCiRAAAAAIAS+3938dfR/9PbAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "ax = plt.plot(range(1,11),wcss, linewidth=2, color=\"red\", marker =\"8\")\n",
    "plt.axvline(x=3, ls='--')\n",
    "plt.ylabel('WCSS')\n",
    "plt.xlabel('No. of Clusters (k)')\n",
    "plt.title('The Elbow Method', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear, that the optimal number of clusters for our data are 3, as the slope of the curve is not steep enough after it. When we observe this curve, we see that last elbow comes at k = 3, it would be difficult to visualize the elbow if we choose the higher range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will build the model for creating clusters from the dataset.\n",
    "We will use `n_clusters = 3` i.e. 3 clusters as we have determined by the elbow method, which would be optimal for our dataset.\n",
    "\n",
    "Our data set is for  unsupervised learning therefore we will use `fit_predict()`\n",
    "Suppose we were working with  supervised learning data set we would use `fit_tranform()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kms = KMeans(n_clusters=3, init='k-means++')\n",
    "kms.fit(clustering_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the clusters created, we will enter them into a different column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Cluster_Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0            5.1           3.5            1.4           0.2   \n",
       "1            4.9           3.0            1.4           0.2   \n",
       "2            4.7           3.2            1.3           0.2   \n",
       "3            4.6           3.1            1.5           0.2   \n",
       "4            5.0           3.6            1.4           0.2   \n",
       "\n",
       "   Cluster_Prediction  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = clustering_data.copy()\n",
    "clusters['Cluster_Prediction'] = kms.fit_predict(clustering_data)\n",
    "clusters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get the centroids of the clusters by the `cluster_centers_` attribute of KMeans algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
       "       [5.006     , 3.418     , 1.464     , 0.244     ],\n",
       "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kms.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all the data we need, we just need to plot the data. We will plot the data using scatterplot which will allow us to observe different clusters in different colours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7)) \n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 0]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 0]['SepalWidthCm'],\n",
    "            s=70,edgecolor='teal', linewidth=0.3, c='teal', label='Iris-versicolor')\n",
    "\n",
    "\n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 1]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 1]['SepalWidthCm'],\n",
    "            s=70,edgecolor='lime', linewidth=0.3, c='lime', label='Iris-setosa')\n",
    "\n",
    "\n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 2]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 2]['SepalWidthCm'],\n",
    "            s=70,edgecolor='magenta', linewidth=0.3, c='magenta', label='Iris-virginica')\n",
    "\n",
    "plt.scatter(x=kms.cluster_centers_[:, 0], y=kms.cluster_centers_[:, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(4,8)\n",
    "plt.ylim(1.8,4.5)\n",
    "ax.set_ylabel('Sepal Width (in cm)')\n",
    "ax.set_xlabel('Sepal Length (in cm)')\n",
    "plt.title('Clusters', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing Data using the above graph becomes much more easier as it gives us a visual aid for better understanding of the \n",
    "data.\n",
    "Kmeans has divided the dataset into 3 clusters based on Annual income and the spending scores of the individual customers.\n",
    "The following clusters are created by the model, \n",
    "1. Iris-versicolor\n",
    "1. Iris-setosa\n",
    "1. Iris-virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Iris-versicolor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![versicolor](images/versicolor.jpg)\n",
    "Iris versicolor is a flowering herbaceous perennial plant, growing 10–80 cm (4–31 in) high. It tends to form large clumps from thick, creeping rhizomes. The unwinged, erect stems generally have basal leaves that are more than 1 cm (1⁄2 in) wide. Leaves are folded on the midribs so that they form an overlapping flat fan. The well developed blue flower has 6 petals and sepals spread out nearly flat and have two forms. The longer sepals are hairless and have a greenish-yellow blotch at their base. The inferior ovary is bluntly angled. Flowers are usually light to deep blue (purple and violet are not uncommon) and bloom during May to July. Fruit is a 3-celled, bluntly angled capsule. The large seeds can be observed floating in fall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7)) \n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 0]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 0]['SepalWidthCm'],\n",
    "            s=70,edgecolor='teal', linewidth=0.3, c='teal', label='Iris-versicolor')\n",
    "\n",
    "\n",
    "plt.scatter(x=kms.cluster_centers_[0, 0], y=kms.cluster_centers_[0, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(4,8)\n",
    "plt.ylim(1.8,4.5)\n",
    "ax.set_ylabel('Sepal Width (in cm)')\n",
    "ax.set_xlabel('Sepal Length (in cm)')\n",
    "plt.title('Individual Clusters', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Iris-setosa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![setosa](images/setosa.jpg)\n",
    "Iris setosa is similar in form to a miniature Japanese iris, or a dwarf version of Iris sibirica but a shorter lived version.\n",
    "The shallowly rooted, large, branching rhizomes spread over time to create large clumps. The rhizomes are grey-brown, thick, and are covered with old (maroon-brown) fibrous leaf remains (of last seasons leaves).\n",
    "It has branched stems, which are very variable in height, ranging from 10 cm (5 inches) up to 1 m (3 ft) tall. The larger plants can grow beyond the height of the leaves. The roundish stems are between 1.5–9 cm in diameter with 1 to 3 branches.\n",
    "Iris setosa has mid-green leaves, which are grass-like, and lanceolate (sword-shaped). They have a purplish tinged base and the leaves can measure 30–60 cm (12–24 in) long by 0.8–2.5 cm wide.\n",
    "The plant has 3–4 flowers per stem (between 6 and 13 for the whole plant, in groups of 3,) and it blooms between June and July.\n",
    "The large flowers are between 5–8 cm (3–6 in) across, usually 7–8 cm, and come in a range of shades of blue, which can depend on the location. and range from violet, purple-blue, violet-blue, blue, to lavender. Very occasionally, there are pink or white forms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7)) \n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 1]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 1]['SepalWidthCm'],\n",
    "            s=70,edgecolor='lime', linewidth=0.3, c='lime', label='Iris-versicolor')\n",
    "\n",
    "\n",
    "plt.scatter(x=kms.cluster_centers_[1, 0], y=kms.cluster_centers_[1, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(4,8)\n",
    "plt.ylim(1.8,4.5)\n",
    "ax.set_ylabel('Sepal Width (in cm)')\n",
    "ax.set_xlabel('Sepal Length (in cm)')\n",
    "plt.title('Individual Clusters', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Iris-virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![virginica](images/virginica.jpg)\n",
    "Iris virginica is a perennial plant. The plant has 2 to 4 erect or arching, bright green, lance-shaped leaves that are flattened into one plane at the base. Leaves are 1–3 cm (1⁄2–1 1⁄4 in) wide and are sometimes longer than the flower stalk. The fleshy roots (1–2 cm or 1⁄2–3⁄4 in in diameter) are rhizomes that spread underground. Pale brown, variably shaped seeds are born in three-part fruit capsules (3–6 cm or 1 1⁄4–2 1⁄4 in long, 1–2 cm or 1⁄2–3⁄4 in wide).\n",
    "The slightly fragrant flowers (4 cm or 1 1⁄2 in long, 7 cm or 2 3⁄4 in across) consist of 3 horizontal sepals, or \"falls\", and 3 erect petals. The petals and sepals can vary in color from dark-violet to pinkish-white. The sepals have a splash of yellow to yellow-orange at the crest. Each plant has 2 to 6 flowers that bloom from April to May upon a single, erect, 30–90 cm (12–35 in) tall stalk. The stalk is sometimes branched and has a slight zigzag appearance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OnVKVKlVUs2bN9C8MAAAAAJAmmX4P5oQJExQcHGy/ZyYlJ0+etD/uHAAAAADgXjI1YK5atUrHjh3Tv/71r3u2vXLlisLDw3X8+HG1bdtWVapUUZs2bbRu3br0LxQAAAAAcE+ZNkX2woULmjx5siZPnqyAgIB7tj958qSkhPddjRkzRhaLRevWrdMrr7yi+Ph4PfHEE+ldMgAAAAAgBZkSME3T1KuvvqpmzZrZ3zl2L1WrVtWcOXMUFBQkf39/SVLjxo0VHh6uGTNmPHDAvH49SjabWzxQF0kIDPRXeHhkZpeBZNA/7o8+cm/0j/ujj9wffeTe6B/35uFhKF8+P5fsK1MC5pIlS3Tq1Cl9+eWXiouLk5QQOiUpLi5Onp6eMgzDYZuAgAA99thjTvtq1qyZdu3apYiIiFSNhCbHZjMJmG6O/nFv9I/7o4/cG/3j/ugj90cfuTf6J3vIlIC5efNmXb9+XY0bN3ZaV6VKFU2ePFndunVzWH7w4EH99ttveuqppxyWW61WeXl5KVeuXOlaMwAAAAAgZZkSMN944w1FRUU5LPvwww914sQJzZ49W8WKFXPa5tChQ5oyZYqqVaumihUrSpJsNps2b96s2rVry9vbO0NqBwAAAAAkLVMCZpkyZZyW5c2bVz4+PqpWrZokKSIiQmfPnlW5cuXk7++vbt26KTQ0VMOGDdOIESPk5+enpUuX6pdfftGSJUsy+hQAAAAAAH+T6e/BTM63336rHj166NixY5KkPHnyKDQ0VNWrV9fkyZM1YsQI3blzRwsXLlSNGjUyuVoAAAAAgGEmPl0nmwsPj+TGYzdWoEAuXb16O7PLQDLoH/dHH7k3+sf90Ufujz5yb/SPe/PwMBQY6O+afblkLwAAAACAbI+ACQAAAABwCQImAAAAAMAlCJgAAAAAAJcgYAIAAAAAXIKACQAAAABwCQImAAAAAMAlCJgAAAAAAJcgYAIAAAB4YPPnz1WzZvVTbDNs2HMaPnxIBlWUPp58spOmTHnL7feZWbwyuwAAAAAA2cPLL4+TYRiZXcYDmTTpHfn5+Wd2GW6LgAkAAAAgQ5QuXSazS3hg5ctXzOwS3BoBEwAAAMjKTMmywkuWMC95XDNky2/K2i5O1h5xUiYNFm7c+KXefXeyXnrpZc2fP1e+vhZNn/6xJk9+U56eXpox4yNJ0r59uzVv3hydPv27PD29VKtWbT3//IsqWbJUkvuNiYlRp06t9cQT3fX888Psy63WGHXs2Fo9e/bRs88Ols1m02efLdRXX63X1atX9MgjRdS7d1917Pi4fZthw55T4cKP6M6dO9q/f6/q1Wugt9+eqi1bNumzzxbp3Lmzypkzp+rVa6AhQ15S/vwFJCVMZ61bt57GjfuXJCkqKlIhIR/p22+/0Z07USpduqwGDXpBQUEJ04Xj4uK0Zs1KffHFOl28eEGBgfnVsWNnPf30AHl6eiZ5nrdu3dT8+XO1a9f3Cg+/plKlyqh//3+qWbPm9jaNG9fVwIHPa+fOb3Xx4nkNGDBQPXs+fd995ioETAAAACCrMiX/ERb5LveWYf4vTfps8pL37lhFfmDNtJAZGxurpUsX69VXX5fNZlXRosUc1l+4cF7jxr2sDh066/nnh+nWrZsKCflIY8YM14oV65KcSuvr66tmzR7Ttm1bHALmDz98r+joO2rdup0k6d13Jyss7Cv17/+sKleuqr17d2vq1ImKiYnRk0/2tG+3ZcsmtWzZWpMmvSPDMPTzz4f09tv/1oABA1WzZm1duXJZH300Q2+8MUGzZs11qic+Pl6jRr2o8+fPauDAF1SsWHF98cVajR07QnPnLlD58hU1Zcpb2rbta/Xt+4yqVauun38+rAUL5unixQsaP/51p33GxMRoyJCBun37lgYOfEH58xfQli2b9NprY/Xaa/9Ru3Yd7W0XLJinwYOHqUSJkipevETaOykdEDABAACALMqywsspXEqSYRryXeat2IbxCSOZmcA0TQ0YMFANGzZWgQK5dPXqbYf1J04ck9VqVb9+/7SPDhYsWEjffbdD0dF3lDOnX5L7bdOmvTZt2qBjx46qSpWqkqRt2zarcuWqKl68hM6e/VNffrlOQ4YMV69eCSN69eo1kM0Wr08+maOOHR+Xr6+vJMnLy0tjx74miyXhc2joQlksvurTp798fHwkSblz59HJk8dlmqZT6N29e5eOHTuid96ZroYNG0uSatWqo+ee66+fftovLy9vbdq0QUOGDFfv3n0lSUFBDeTr66uPP56lHj16q0yZcg773LDhC505c1rz5i1SpUpVJEkNGwbr1q2b+vjjWWrdup195LNatRr2/boLniILAAAAZFGWMC+ncJnIMA1ZNmbueNLfw9NfValSTT4+Fg0c2E8zZrynPXt+VLly5TV48FDlzOkn0zQVFxfn8E+S6tQJUoECBbVt22ZJ0p07Ufrxx11q0yZh9PKnn/bJNE0FBzdx2LZx42aKjIzU8eNH7TUULVrMHi4lqVat2oqJiVa/fj01Z85sHT58UPXqNdAzzwxKckT1558PycfHRw0aBNuXeXl56dNPl6hnz6d1+PBBSVKrVm0ctkscaT148CenfR4+fFDFihW3h8u/bhMREa4//zxjX1a2bPLfb2ZhBBMAAADIojyupTz/1QjP3Ce2BgQEJLvukUeKaPbsufrss0X66qt1WrVqmfz9c6lbt6c0aNALCgv7SpMmveGwzapVX+iRR4qoVas22rJls4YNG6kdO7bLZotXixYJIe7mzZuSpN69n0jyuNeuXbP/73z5Ah3WVa1aXe+8M0MrVizRihVL9NlnCxUQEKh+/Z5xmFqb6Natm8qbN1+yT8a9devm/x/H8XtI/BwVFZnkNgEBgU7Lk9rm7/t1BwRMAAAAIIuy5TdTXG8Gprw+s1WuXFWTJr2j2NhY/fzzIa1fv0aLF3+q8uUrKDi4iT75ZLFD+8SptG3adNDSpaE6cuRnffPNFtWr11B58+aVJPn7J7xCZPbsEPtU2L965JEiKdZUv35D1a/fUDExMTpwYJ9WrVqm6dPfVdWqNVSxYiWHtn5+/rpx44bTPk6cOCZvbx/lypVbknT9eoQKFChoXx8enhBy8+TJ67Rtrly59euvp5yWp7SNO2GKLAAAAJBFWdvFyTSSDpGmYcraPnPuv0yNzz9frief7KS7d+/K29tbdeoEaezY1yRJV65cVp48eVWxYmWHf97e3pISpoaWK1demzdv0L59e9SmTXv7fmvUqC1JunXrlsO2ly9f0iefzFF0dEyyNX300UwNGtRPpmnK19dXwcFNNHToCHtNf1e9ek3dvWvVvn277cvi4+P1xhv/0qpVy1SzZkItW7Zsdthu69bN9u3/rmbN2jp//pxOnDjmtE1gYKCKFSuebP3ugBFMAAAAIIuy9oiT94+xTg/6MQ1TMb1iZe3uvgGzdu0gffTRTL366mg98UR3eXp6ad261fLxsahRoyb33L5Nm/aaM2eWfH191bhxU/vycuX+oZYt22jy5Dd18eJ5lS9fUadP/665cz9ShQoVVbhw4WT3GRRUT8uWhWrixP+oTZt2io2N09Kli5U3b17VqlXHqX1wcBNVqlRFb731bw0a9IIKFy6sL79cr2vXrvz/A3zKqk2bdpo37yNZrTGqWrWajh49osWLP1WbNu2TfC9o+/adtHr1Co0bN0oDB76gAgUKasuWTdq9e5fGjZsgDw/3HiMkYAIAAABZlSFFTrcqNjhelg1eMsINmYEJI5fW7pn3HszUKFOmrKZO/UCffhqi//znNcXHx6tixcr64IPZqRqla9WqrebMmaVmzZrLYrE4rJsw4Q0tWjRfq1ev0tWrlxUQEKiOHTtr4MDnU9xnUFAD/ec/E7VkyWK9+upYGYah6tVraObMOcqVK5dTe09PT73//mx9/PFMzZ37oazWGFWoUEnTp39kf8DR+PH/VtGixbVhwxdavPhTFSpUWP/853Pq3btfkjXkyJFDs2eHaM6c2ZozZ5aio2NUtmxZTZw4zeE9mO7KME3TvSdmZ5Dw8EjZbHwV7iqpR1vDfdA/7o8+cm/0j/ujj9wffeTe6B/35uFhKDDQ3zX7csleAAAAAADZHgETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAQJZmmmZml4D/55XZBQAAAAB4MKZp6sqVy7p166Zy586jggULyTCMTKnl2LGjWrVqmX7++ZBu3LihAgUK6NFHm+nJJ/uoQIGCLj1WZGSkZsx4Vx06dFbNmrUfeH+NG9fVwIHPa8CAgcm2GTbsOXl6emnGjI8e+HgPIwImAAAAkEVFR0frhx/mStqk0qUPKX/+O4qIyKkjR2pKaqvg4MHKkSNHhtWzcuUyffjhdNWtW18vvPCiAgPz688/z2j58lBt2rRZs2eHqHjxEi473u+//6awsK/Url1Hl+xvzpwFKlSokEv2lV0RMAEAAIAs6MqVC9q7d4B6994ji+Wva+6oQYNdslp3aenSDapXb6EKFiya7vX8/PMhzZ79gZ56qpdefHGkfXnt2nXVpUt7de7cWe++O8WtR/6qVq2W2SVkeQRMAAAAIIuJjo7W3r0D9Mwze5JtY7FIzzyzRwsWDFCLFl+k+0jmsmWhyp07j557bojTukKFCmno0BG6du2q4uLi5OXlpS++WKuVK5fqwoXzyp+/gDp37qann+5vn9o7ceJ/FB4erubNW+izzxbp8uVLKlmytF544UXVr99QP/20Xy+99Lwk6aWXnlfNmrU1e3aIhg17ToULP6I7d+5o//69qlevgd5+e6pu3bqp+fPnateu7xUefk2lSpVR//7/VLNmze11/n2K7KVLlzRz5ns6cGCvLBZf9e7d1+nc9u3brXnz5uj06d/l6emlWrVq6/nnX1TJkqXS4Vt2fwRMAAAAIIvZtStEvXsnHy7/qnfvPVq7NkQtWgxPt3pM09SePbvVpEkzWRyHU+3+Oo01NHSBQkI+UvfuvVS/fiOdOHFM8+fP0Y0b1x1GP48fP6IrVy5p4MDn5efnr08+maMJE8Zq7dowVahQUWPGvKp33pmkUaNeUa1adezbbdmySS1bttakSe/IMAzFxMRoyJCBun37lgYOfEH58xfQli2b9NprY/Xaa/9JcoptdHS0hg0bJC8vL40dO0EeHobmz5+r8+fPqUaNhPs9L1w4r3HjXlaHDp31/PPDdOvWTYWEfKQxY4ZrxYp1mXYfbGYiYAIAAABZiGmaMs0wJZPjnFgskmmGyTRfSrfAc+PGDd29a1Xhwo/cs21kZKQWLZqvbt2e0osvjpIk1avXQDly5NSHH07XU0/1UuHChe1tP/10iYoUSZjimyNHDg0b9pwOHtyvJk0eValSZSRJpUqVVunSZezHSAiFr8li8ZUkrV69UmfOnNa8eYtUqVIVSVLDhsG6deumPv54llq3bidPT0+HOjdu/FJXrlzW4sUrVKpUaUlS5cpV1bNnV3ubEyeOyWq1ql+/fyp//gKSpIIFC+m773YoOvqOcub0S/uXmcXxmhIAAAAgC7ly5bJKlz6Upm1Klz6sK1cup09Bkj2c2Wy2e7Y9evRnxcTEqHHjpoqLi7P/Cw5uovj4eP300z5728DA/PZwKcn+FNro6JgUj1G0aDF7uJSkw4cPqlix4vZwmah163aKiAjXn3+ecdrHzz8nbJMYLiWpUKHCqlLlf/dpVqlSTT4+Fg0c2E8zZrynPXt+VLly5TV48NBsGS4lRjABAACALOXWrZvKn/9OmrbJly9KERG3VKhQ4XSpKXfu3MqZ00+XLv032TZRUZGSEuqXpJEjhyXZ7tq1q/b/7evr67DOwyNhfMw0Uw6y+fIFOny+deumAgICk2gX4FCb4za3lDdvPqflgYGBunEj4RweeaSIZs+eq88+W6SvvlqnVauWyd8/l7p1e0qDBr3AFFkAAAAA7i137jyKiMgpKfUh8/p1P+XKlTv9ilLCNNeDB/fLarUmeR/mypXLtHDhJ3rppYRpsW+8MUlFixZzapc41dSVcuXKrV9/PeW0PDz8miQpT568Tuvy5MmrkydPOC2/efOmw+fKlatq0qR3FBsbq59/PqT169do8eJPVb58BT36aAvXnEAWwhRZAAAAIAspWLCQTp+umaZtTp+uoYIF0/f9jj179tHNmzc1f/4cp3UXL17U6tUrVblyFbVo0Ube3t4KD7+mihUr2//FxcVp7twPde3atVQf09MzdXGmZs3aOn/+nE6cOOawfOvWzQoMDFSxYsWdtqlbN0gXLpxzCKY3btzQsWNH7Z8//3y5nnyyk+7evStvb2/VqROksWNfk6R0nZLszhjBBAAAALIQwzBkGO1kte5K1YN+rFbJMNql+3TNqlWra8CAgVqwYJ7OnDmjtm07KE+ePPr111NasWKJPDw89K9/vaW8efOqZ8+nNXfuh4qMjFSNGrV06dJ/FRLykfz9/R0e1nMv/v65JEk//viDcuXKrX/8o3yS7dq376TVq1do3LhRGjjwBRUoUFBbtmzS7t27NG7cBPvU279q06aDVq1arldeGfX/91Tm1KJFn8pmi7e3qV07SB99NFOvvjpaTzzRXZ6eXlq3brV8fCxq1KhJGr/BhwMBEwAAAMhiGjV6TkuXfpXiezATLV1aXy1aPJcBVUnPPjtYFSpU0po1qzRjxru6ffu2ChYspLZt2+qJJ/oof/78kqTnnhuiwMBArV37uUJDFyh37jyqX7+hBg8emuxrTpJSsmQpdejQWatXr9TevT9q0aLlSbbLkSOHZs8O0Zw5szVnzixFR8eobNmymjhxmsN7MP/K29tbM2bM0cyZ7+mDDxJed9K5c1cVKVLUPk22TJmymjr1A336aYj+85/XFB8fr4oVK+uDD2YnOSqaHRimaZqZXYQ7CA+PlM3GV+GuChTIpatXb2d2GUgG/eP+6CP3Rv+4P/rI/WXHPrpy5YL27h2g3r33JDmSabUmhMt69RaqYMGizg0yUHbsn6zEw8NQYKC/S/bFCCYAAACQBRUsWFQtWnyhtWtDZJphKl36sPLli9L16346fbqGDKOdWrR4Tjly5MjsUpGNEDABAACALCpHjhxq0WK4TPMlXblyWRERt5QrV261aFEoW74iA5mPgAkAAABkcYZhqFChwun2nksgtXhNCQAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFzCbQLmsGHD1KpVqxTbREVF6Y033lBwcLBq1aqlQYMG6cyZMxlTIAAAAAAgRW4RMNevX68tW7bcs93IkSO1adMmjR49WlOnTtXly5fVr18/3b59OwOqBAAAAACkJNMD5uXLlzVx4kQVLlw4xXb79+/Xjh07NHXqVHXt2lWtW7fWwoULdfv2bS1btiyDqgUAAAAAJCfTA+aECRMUHByshg0bptjuhx9+kJ+fn4KDg+3LAgICFBQUpJ07d6Z3mQAAAACAe8jUgLlq1SodO3ZM//rXv+7Z9o8//lDJkiXl6enpsLxEiRI6ffp0epUIAAAAAEglr8w68IULFzR58mRNnjxZAQEB92wfGRkpf39/p+V+fn6KjIx84HoCA533DfdSoECuzC4BKaB/3B995N7oH/dHH7k/+si90T/ZQ6YETNM09eqrr6pZs2Zq06ZNqrdJjofHgw/EhodHymZL/hjIXAUK5NLVqzzMyV3RP+6PPnJv9I/7o4/cH33k3ugf9+bhYbhswC1TAuaSJUt06tQpffnll4qLi5P0vwAZFxcnT09PGYbhsI2/v7/Onz/vtK+oqKgkRzYBAAAAABkrUwLm5s2bdf36dTVu3NhpXZUqVTR58mR169bNYXnp0qX1448/yjRNh/D5559/qnTp0uleMwAAAAAgZZkSMN944w1FRUU5LPvwww914sQJzZ49W8WKFXPapnHjxpozZ4527dplf5JsRESE9u/fr8GDB2dI3QAAAACA5GVKwCxTpozTsrx588rHx0fVqlWTlBAez549q3Llysnf319BQUGqV6+eRo0apdGjRytv3ryaNWuWcuXKpV69emX0KQAAAAAA/ibT34OZnG+//VY9evTQsWPH7Mtmz56t5s2ba9q0aRo3bpwKFy6shQsXKk+ePJlYKQAAAABAkgwzpcezZiM8Rda98eQx90b/uD/6yL3RP+6PPnJ/9JF7o3/cmyufIuu2I5gAAAAAgKyFgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACX8MrsAgAAAAAg05iSZYWXLGFe8rhmyJbflLVdnKw94iQjs4v7iyxSJwETAAAAQPZkSv4jLPJd7i3D/F9K89nkJe/dsYr8wOoe4S2r1CmmyAIAAADIpiwrvJxCmyQZpiHfZd6yrHSP8bisUqdEwAQAAACQTVnCvJxCWyLDNGTZ6B7BLavUKREwAQAAAGRTHtdSnldqhLvHvNOsUqdEwAQAAACQTdnymymuNwNTXp9RskqdEgETAAAAQDZlbRcn00g6nJmGKWv7uAyuKGlZpU6JgAkAAAAgm7L2iFNMz1in8GYapmJ6xcra3T2CW1apU+I1JQAAAACyK0OKnG5VbHC8LBu8ZIQbMgMTRgSt3d3o/ZJZpU4RMAEAAABkZ4Zk7R7nVqOAScoidTJFFgAAAADgEgRMAAAAAIBLEDABAAAAAC5BwAQAAAAAuAQBEwAAAADgEgRMAAAAAIBLEDABAAAAAC5BwAQAAAAAuAQBEwAAAADgEl6pbXj37l0dOHBABw4c0Pnz53X9+nV5eHgof/78euSRR9SwYUPVrFlThmGkZ70AAAAAADd1z4B5/vx5LVmyRKtXr9bt27dlmqZy5MghPz8/maapmzdvKi4uTrNmzVLu3LnVrVs39e/fX4ULF86I+gEAAAAAbiLZgBkTE6NZs2Zp0aJFKliwoDp37qzmzZurQoUKCgwMdGh77do1HTp0SAcOHFBYWJhCQ0PVu3dvjRgxQjlz5kz3kwAAAAAAZD7DNE0zqRWPPfaY/vGPf2jw4MGqU6dOqndomqZ2796tkJAQnTlzRtu3b3dZsekpPDxSNluSXwXcQIECuXT16u3MLgPJoH/cH33k3ugf90cfuT/6yL3RP+7Nw8NQYKC/S/aV7Ajmu+++m6ZgmcgwDDVs2FANGzbU/v37H6g4AAAAAEDWkexTZO8nXP5d3bp1H3gfAAAAAICsIdVPkZUkm82m8+fP68qVK0pmZq2CgoJcUhgAAAAAIGtJdcA8efKkhg8frrNnzya53jRNGYahEydOuKw4AAAAAEDWkeqA+cYbb+jq1asaPHiwihUrJk9Pz/SsCwAAAACQxaRpBHPIkCEaNGhQetYDAAAAPFxMybLCS5YwL3lcM2TLb8raLk7WHnGSkdnFAa6V6oBZqFAhRi0BAACAtDAl/xEW+S73lmH+L036bPKS9+5YRX5gJWTioZLsU2T/bvDgwVq4cKF+//339KwHAAAAeGhYVng5hUtJMkxDvsu8ZVmZpmduAm4v1f9Fd+nSRRs2bFDnzp1VqlQpBQQEyDD+dqEYhhYtWuTyIgEAAICsyBLm5RQuExmmIctGr4SpssBDItUB891339X3338vLy8vRUZGKjo6Oj3rAgAAALI8j2spz381wpkfi4dLqgPm2rVr1aRJE33wwQfy9/dPz5oAAACAh4Itf9Lvjk9kBqa8HshqUn0PptVqVevWrQmXAAAAQCpZ28XJNJIOkaZhytqe6bF4uKQ6YDZu3Fi7d+9Oz1oAAACAh4q1R5xiesY6hUzTMBXTK1bW7gRMPFxSPUV2yJAhGjx4sEaMGKGWLVsqMDBQXl7OmwcFBbm0QAAAACDLMqTI6VbFBsfLssFLRrghMzBh5NLanfdg4uGT6oD5+OOPS5I2bdqkzZs3O603TVOGYejEiRMuKw4AAADI8gzJ2j2O0UpkC6kOmJMmTXJ6LQkAAAAAAIlSHTC7desmSbp9+7b8/f3tYfP3339XgQIFlDt37vSpEAAAAACQJaT6IT+maWrKlCkKDg7W6dOn7cs//vhjNWrUSLNnz06XAgEAAAAAWUOqA+Ynn3yihQsXqk2bNsqTJ499+T//+U89/vjj+vDDD7V8+fI0Hdw0Tfs+q1evrs6dO+vLL79McZv169erQoUKTv/efPPNNB0bAAAAAOBaqZ4i+/nnn6tbt26aNGmSw/LKlSvr7bffVmxsrJYsWaKePXum+uBz587VzJkz9eKLL6pmzZrauXOnRo8eLU9PT7Vv3z7JbU6ePKmSJUtq2rRpDsvz58+f6uMCAAAAAFwv1QHz0qVLqlGjRrLra9eurU2bNqX6wLGxsfr000/Vq1cvvfDCC5Kkhg0b6ujRo/rss8+SDZinTp1SlSpVVLNmzVQfCwAAAACQ/lI9RbZw4cL66aefkl1/5MgRBQYGpvrAnp6eCg0N1XPPPeew3NvbW1arNdntTp48qQoVKqT6OAAAAACAjJHqgNmxY0d98cUXCgkJUVRUlH15dHS0Fi1apDVr1qhTp06pP7CHhypUqKBChQrJNE1du3ZNISEh2rVrl3r06JHkNleuXFF4eLiOHz+utm3bqkqVKmrTpo3WrVuX6uMCAAAAANKHYZqmmZqGsbGxGjJkiL777jt5enoqICBAHh4eunbtmuLj4xUcHKyPP/5YPj4+aS5i8+bNeumllyRJjz76qGbMmCFfX1+ndjt37tSgQYNUpUoVDR06VBaLRevWrdOXX36pSZMm6YknnkjzsQEAAAAArpHqgJlox44d2rFjhy5cuKD4+HgVKVJETZs2VYsWLezvxkyrc+fO6dKlSzp16pRmzJihSpUqafHixU7tIiIidPjwYQUFBcnf39++/JlnntHvv/+unTt33tfxJSk8PFI2W5q+CmSgAgVy6erV25ldBpJB/7g/+si90T/ujz5yf/SRe6N/3JuHh6HAQP97N0yFVD/kJ1GzZs3UrFkzlxw8UfHixVW8eHF7cHzllVd08OBB1apVy6FdQECAHnvssSRr2rVrlyIiIhQQEODS2gAAAAAAqZPqezBd7caNG1q3bp0uX77ssLxy5cqS5LRckg4ePKhVq1Y5LbdarfLy8lKuXLnSp1gAAAAAwD1lWsC02WwaN26cVqxY4bD8hx9+kCSVL1/eaZtDhw5pwoQJOnnypMN+Nm/erNq1a8vb2zt9iwYAAAAAJCvNU2RdJSAgQL1791ZISIh8fX1VrVo1HThwQHPnztVTTz2lMmXKKCIiQmfPnlW5cuXk7++vbt26KTQ0VMOGDdOIESPk5+enpUuX6pdfftGSJUsy61QAAAAAAMrEgClJ48eP1yOPPKLPP/9cs2bNUuHChfXiiy9q4MCBkqRvv/1W48eP1+LFi1W/fn3lyZNHoaGheu+99zR58mRFRkaqatWqWrhwoWrUqJGZpwIAAAAA2V6anyL7sOIpsu6NJ4+5N/rH/dFH7o3+cX/0kfujj9wb/ePeMvUpsmfPntXVq1dls9mSXB8UFPTARQEAAAAAsp5UB8wLFy5o5MiROnLkSJLrTdOUYRg6ceKEy4oDAAAAAGQdqQ6YkydP1rFjx9SjRw9VqlRJPj4+6VkXAABwB6ZkWeElS5iXPK4ZsuU3ZW0XJ2uPOMlwo30CANxCqgPmrl271L9/f40dOzY96wEAAO7ClPxHWOS73FuG+b/k57PJS967YxX5gTXtgTA99gkAcBupfg+ml5eXSpQokZ61AAAAN2JZ4eUUBCXJMA35LvOWZWXaH0afHvsEALiPVAfMJk2a6JtvvknPWgAAgBuxhHk5BcFEhmnIsvE+AmY67BMA4D5S/VN80KBBGjJkiIYPH662bdsqICBAHh7O+ZSnyAIA8HDwuJbyXFUjPO1zWdNjnwAA95HqgPn4449Lki5evKivv/7aaT1PkQUA4OFiy5/y+6HNwLS/Pzo99gkAcB+pDpiTJk2SYfBXRQAAsgtruzj5bEp6SqtpmLK2j3OLfQIA3EeqA2a3bt3Ssw4AAOBmrD3i5P1jrNNDeUzDVEyvWFm730fATId9AgDcR7IBc9++fSpbtqwCAgLsn1ODezABAHhIGFLkdKtig+Nl2eAlI9yQGZgwymjtfp/vrEyPfQIA3EayAbNv375655131KlTJ/vnlKbIcg8mAAAPIUOydo9z7chieuwTAOAWkg2YkydPVs2aNR0+AwAAAACQnGQDZteuXVP8DAAAAADAXzm/yPL/hYSEyGq13veOo6Ki9NFHH9339gAAAACArCXZgHn48GG1aNFC8+bN06VLl1K9w7Nnz2rmzJlq0aKFjh075pIiAQAAAADuL9kpsh9++KG+/fZbTZ48We+//74qVaqkRx99VBUqVFCxYsXk7+8vm82mGzdu6L///a9+/vln7d+/X8eOHVOpUqX01ltvqVWrVhl5LgAAICswJcsKL1nCvORxzZAtvylruzhZe/AU2ftik/xftshni6eMKEOmn6m7reIV+Z41haEEAEgfKb4H89FHH1WzZs20bds2LV26VCEhIYqLi3N6mqxpmsqRI4fq16+v2bNnq0WLFulaNAAAyKJMyX+Exek9mD6bvOS9O1aRH1gJmWlhk/I1zCnP0x4yEr+4KMl3iYe8f/TU9V13CJkAMlSKAVOSDMNQy5Yt1bJlS8XExOjw4cM6d+6cbty4IQ8PDwUGBqpo0aKqXr26fHx8MqJmAACQRVlWeDmFS0kyTEO+y7wV2zA+YSQTqeL/ssUxXP4/Q4Y8//CQ/2iLIt+//2dqAEBa3TNg/pWvr6/q16+v+vXrp1c9AADgIWYJ83IKl4kM05BloxcBMw18tng6hctEhgz5fO2ZwRUByO6YNAEAADKMx7WU578a4cyPTQsj6h7f1x2+TwAZi4AJAAAyjC2/meJ6MzDl9XBk+t3j+8rJ9wkgYxEwAQBAhrG2i5NpJB16TMOUtT3TY9Pibqt4mUrm+5Spu63jM7giANkdARMAAGQYa484xfSMdQqZpmEqplesrN0JmGkR+Z5V8WVsTiHTlKn4MjZFvssDfgBkrDQ95AcAAOCBGFLkdKtig+Nl2eAlI9yQGZgwcmntznsw08xDur7rjvzHWOSz2TPhnsucCSOXke/yHkwAGS/NATM2NlY3btxQbGxskuuLFCnywEUBAICHmCFZu8cxWukqHgkjmXovswsBgDQEzJs3b+q1117Tjh07FBeX/P8hnDhxwiWFAQAAAACyllQHzClTpmjr1q2qX7++qlWrJl9f3/SsCwAAAACQxaQ6YH7zzTfq2rWrJk+enJ71AAAAAACyqFTf+n337l3Vrl07PWsBAAAAAGRhqR7BrF+/vvbs2aOnnnoqPesBAABIO1OyrPCSJcxLHtcM2fKbsraLk7XHAzyZ9i/71E0pdx7fB99nVpHO36fL9plVZOdzR7aTbMC8ePGiw+f+/ftryJAhmjx5sjp06KCAgAB5eDgPgPIUWQAAkKFMyX+ERb7LvWWY//tt3WeTl7x3xyryA2vaf4lPYp8WeT/YPrOKDPo+H3ifWUV2PndkS8kGzObNm8swHP9rN01TixYt0uLFi5PdIU+RBQAAGcmywsvpl3dJMkxDvsu8FdswPmGkKJP3mVXwfbpWdj53ZE/JBsyhQ4c6BUwAAAB3YwnzcvrlPZFhGrJs9Ep7IEqHfWYVfJ+ulZ3PHdlTsgHzxRdfTNOO4uPjnabVAgAApDePayn/QdwIT/sfzNNjn1kF36drZedzR/aU6qfIVqpUSV999VWy69euXavHH3/cFTUBAACkmi2/meJ6MzDl9Rm1z6yC79O1svO5I3tKdgTz8uXL+vHHH+2fTdPUvn37FBfnPIRvs9n05ZdfMqUWAABkOGu7OPlsSnoaommYsrZP+/TD9NhnVsH36VrZ+dyRPSUbMAMCAjRnzhydOXNGkmQYhlasWKEVK1Yku7O+ffu6vEAAAICUWHvEyfvHWKcHqZiGqZhesbJ2v49AlA77zCr4Pl0rO587sqdkA6a3t7c+/fRTnT9/XqZpqn///ho8eLCCg4Od2np4eCggIEBlypRJ12IBAACcGFLkdKtig+Nl2eAlI9yQGZgwMmTtfp/vGfzbPi23vGXNHftg+8wqMuD7dMk+s4rsfO7IlgzTNFM18Xvt2rWqW7euihcvnt41ZYrw8EjZbMyBd1cFCuTS1au3M7sMJIP+cX/0kXujf9wffeT+6CP3Rv+4Nw8PQ4GB/i7ZV7IjmH/XtWtXlxwQAAAAAPBwSjZgVqxY8b4e2nPixIkHKggAAAAAkDUlGzAff/xxp4C5detWWa1WNW7cWGXKlJHNZtO5c+e0Y8cO+fv766mnnkr3ggEAAAAA7inZgDllyhSHz6Ghodq+fbvWr1+v0qVLO6w7f/68evfuzWtKAAAAACAb80htw08++UQDBgxwCpeSVKxYMT399NNatWqVS4sDAAAAAGQdqX7Iz+3bt+Xj45PsepvNprt377qkKAAAgDQxJcsKL1nCvORxzZAtvylruzhZe2SD10Bk8rmbpqkrVy7r1q2byp07jwoWLJT0rLZ4KU9XX3n95CkjzpDpZSqudrxuro2RPNO/TgAZI9UBs2bNmgoNDVWHDh1UqFAhh3W//fabFi5cqHr16rm8QAAAgBSZkv8Ii9OL7H02ecl7d6wiP7A+vCEzE889OjpaP/wwV9ImlS59SPnz31FERE4dOVJTUlsFBw9Wjhw5EhrHS4H/8JMRacj4/4KMu4a8dxsK/Iefwn+NImQCD4lUB8xRo0apb9++at++vZo1a6bixYvr7t27On36tL7//nvlypVLY8eOTc9aAQAAnFhWeDkFLEkyTEO+y7wV2zA+YTTvIZRZ537lygXt3TtAvXvvkcXy1zV31KDBLlmtu7R06QbVq7dQBQsWVZ6uvg7h0l6nDClSytPNVzfXx7i8TgAZL9X3YFatWlWrVq1ScHCwvv32W82dO1cLFizQ/v371alTJ61du1YlS5ZMz1oBAACcWMK8nAJWIsM0ZNmY6r+nZzmZce7R0dHau3eAnnnm7+HyL3VZpGee2aO9ewcoOjo6YVpsMkOphgx5HWD4EnhYpOmnTrly5TRz5kyZpqnr16/LMAzly5cvvWoDAAC4J49rKc8BNcIf1vmxmXPuu3aFqHfvPalq27v3Hq1dG6Ieca+l3DDu4e0jILtJ9QjmXxmGoYCAAMIlAADIdLb8ZorrzcCU12dlGX3upmnKNMOSHbn8O4tFMs0w2TxtKTf0enj7CMhukh3BrFSpkqZNm6ZOnTpJkipWrHjP91wahqHjx4+7tkIAAIAUWNvFyWdT0lNFTcOUtf3Def+llPHnfuXKZZUufShN25QufVjnq11QiZ+KJzlN1pSpuDrxLqoQQGZLNmB26dJFJUqUsH9+/PHH7xkwAQAAMpq1R5y8f4x1etiNaZiK6RUra/eHOGBm8LnfunVT+fPfSdM2+fJF6eKMqyrerpgUKYeQacqU6W/q5hoe8AM8LJINmFOmTEnxMwAAgFswpMjpVsUGx8uywUtGuCEzMGH0ztr9IX8PZgafe+7ceRQRkVNS6kPm9et+ypU3t8J/jVKeJ3zltd8z4Z5Lr4SRy5treA8m8DBJNmD27NlTDRo0UL169VS7dm35+vpmZF0AAACpZ0jW7nEP9WhlsjLw3AsWLKQjR2qqQYNdqd7m9OkaatGikGRIN9cxUgk87JINmOfOndOhQ4c0d+5ceXl5qVq1aqpfv77q16+v2rVry8fHJyPrBAAAQCYzDEOG0U5W665UPejHapUMox23WQHZiGGaZrKP7Tp37px++uknHTx4UIcOHdKvv/4qm80mb29v1ahRQ/Xq1VP9+vVVs2bNLB84w8MjZbPxBDN3VaBALl29ejuzy0Ay6B/3Rx+5N/rH/dFH/xMdHa1t2zrrmWfu/aqSBQvqq0WLL5QjR450r4s+cm/0j3vz8DAUGOjvkn2lGDD/LioqSocPH9ZPP/2kw4cP6/Dhw7p165YsFotq1Kih+vXra+jQoS4pLKMRMN0bP5TcG/3j/ugj90b/uD/6yNGVKxe0d+8A9e69J8mRTKtVWrq0vurVW6iCBYtmSE30kXujf9xbpgXMv7t69ao2b96sNWvW6Pjx4zIMQydOnEj19qZpatGiRVq2bJn++9//qlSpUho0aJD91ShJiYqK0rvvvquvv/5ad+7cUd26dfXaa6+pVKlS93sakgiY7o4fSu6N/nF/9JF7o3/cH33kLDo6Wrt2hcg0w1S69GHlyxel69f9dPp0DRlGOzVq9FyGjFwmoo/cG/3j3lwZMJO9BzMply5d0o8//qi9e/fqwIEDOnfunCQpf/786tChgxo0aJCmg8+dO1czZ87Uiy++qJo1a2rnzp0aPXq0PD091b59+yS3GTlypI4cOaKxY8fKz89Ps2fPVr9+/bRhwwblypUrTccHAABIVryUp6uvvH7ylOKkQC8/xdWO1821bvbUU5vk/7JFPls8ZUQZMv1M3W0Vr8j3rJLHfe7TlCwrvGQJ85LHNUO2/Kas7eJk7fG/J9PmyJFDLVoMl2m+pCtXLisi4pZy5cqtFi0KZe17LlNx7m6xz+yK/nF7KY5gXr9+XXv27NGPP/6o3bt36+zZszJNU/ny5bPff9mgQQOVKVMmzQeOjY1VcHCwOnXqpH/961/25X379lV8fLyWLl3qtM3+/fvVp08fzZs3T02bNpUkRUREqEWLFnrhhRf03HPPpbmORIxgujf+6uXe6B/3Rx+5N/rHDcVLgf/wkxFpJPnexvBfo9wjZNqkfA1zyvO0h1Od8WVsur7rTtpDpin5j7Ak+27NyA+sbvlLt0uuo/Q49yz6fboa/ePeMmQEs2vXrjp16pRM01Tu3LlVt25dPf3006pfv77Kly//wAf29PRUaGio8ubN67Dc29tbd+4k/W6lH374QX5+fgoODrYvCwgIUFBQkHbu3PlAARMAACBRnq6+TuFSUsLnSClPN1/dXJ/5r9zwf9niFC6lhDo9//CQ/2iLIt+3pmmflhVeTr9sS5JhGvJd5q3YhvEJIzsPofQ49+z8fboa/ZM1JBswT5w4IW9vb3Xs2FH9+vVTpUqVXHpgDw8PVahQQVLCvZjh4eFas2aNdu3apTfffDPJbf744w+VLFlSnp6OfzIsUaKEwsLCXFofAADIvrx+8nQKbYkMGfI64A7Dl0qYFptCnT5fp71OS5iX0y/b9n2ahiwbvR7aX7jT49yz8/fpavRP1pBswBwyZIj27Nmjr776SuvWrVOePHlUr149NWjQQPXr11fZsmVdVsTXX3+tl156SZL06KOPqnPnzkm2i4yMlL+/89Ctn5+fIiMjH6gGVw0JI/0UKMA9tu6M/nF/9JF7o3/czD1+n/SI83CPPotOebVntGfa67yZ8mrLLW8VKOCdtn1mkAfuk/Q49yz8fboa/ZM9JBswEwNfTEyM9u3bp927d2vPnj2aOHGibDab8ufPr/r169vvwyxevPh9F1G5cmV99tlnOnXqlGbMmKHnnntOixcvdmqX0gNvPTzu9y72BNyD6d64P8m90T/ujz5yb/SP+wn08pPH3eR/t7B52RR+NSoDK0paQI6c8ryd/ChlfI54RVxN+taj5OTO4yuLkv+F2po7VreuZv704L9zxXWUHueeVb9PV6N/3FuGPkXW19dXTZo0UZMmTSQljCLu3r1bu3fv1t69e7VhwwZJ0iOPPKIGDRpo0qRJaS6iePHiKl68uIKCguTv769XXnlFBw8eVK1atRza+fv76/z5807bR0VFJTmyCQAAcD/iasfLe7fzPZhSwgN04urEZ0JVzu62ipfvEud7MKWEOu+2Tnud1nZx8tmU9LRB0zBlbf/wThdMj3PPzt+nq9E/WUOah/38/f3VsmVLTZgwQXPmzNHkyZNVo0YNXbx4UWvXrk31fm7cuKF169bp8uXLDssrV64sSU7LJal06dI6d+6c00jmn3/+qdKlS6f1VAAAAJJ0c22MTH9Tphx/50h8iuzNNe4xohH5nlXxZWxJ1hlfxqbId9P2gB9JsvaIU0zPWJnG3/b5/0/VtHZ/eH/hTo9zz87fp6vRP1lDqt+DGRkZqSNHjujw4cM6fPiwfv75Z0VEREiSSpYsqe7duysoKCjVB7bZbBo3bpyGDBlin44rJTwpVlKST6pt3Lix5syZo127dtmfJBsREaH9+/dr8ODBqT42AABAijyl8F+jlOcJX3nt95RHnIdsXjbF1YlPCJfu8YwfyUO6vuuO/MdY5LPZU7pjSDkTRi4j373P92AaUuR0q2KD42XZ4CUj3JAZmDCSY+3+kL8XMD3OPTt/n65G/2QJyb4H88SJEw5h8vTp0/aRw3LlyqlevXqqW7eu6tWrp8DAwPs6+JtvvqmVK1fqpZdeUrVq1XTgwAHNnTtXXbp00dtvv62IiAidPXtW5cqVs0+B7du3r3755ReNHj1aefPm1axZs3Tjxg19+eWXypMnz31+DdyD6e64P8m90T/ujz5yb/SP+6OP3B995N7oH/fmynswkw2YFStWlGEYMgxDFStWVFBQkIKCglS3bl2nd1fer9jYWC1cuFCff/65Ll68qMKFC+upp57SwIED5eHhoTVr1mj8+PFavHix6tevL0m6efOmpkyZoq1bt8pms6lOnToaN26cypQp80C1EDDdGz+U3Bv94/7oI/dG/7g/+sj90Ufujf5xbxkSMN99910FBQWpTp062eIBOgRM98YPJfdG/7g/+si90T/ujz5yf/SRe6N/3FuGPEV29OjRLjkAAAAAACB7eLCXRwIAAAAA8P9S/RRZAAAAlzAlywovWcK85HHNkC2/KWu7OFl78MRGIFW4huDGCJgAACDjmJL/CIt8l3s7vNjcZ5OXvHfHKvIDK78gAynhGoKbY4osAADIMJYVXk6/GEuSYRryXeYty0r+9g2khGsI7o6ACQAAMowlzMvpF+NEhmnIspFfjoGUcA3B3SX7X2CLFi3SvDPDMLR169YHKggAADy8PK6lPHfPCGduH5ASriG4u2QDZpEiRTKyDgAAkA3Y8qf8zmkzkHdSAynhGoK7SzZghoaGZmQdAAAgG7C2i5PPpqSn+JmGKWv7uEyoCsg6uIbg7lx6D+bx48dduTsAAPCQsfaIU0zPWJmG4yiLaZiK6RUra3d+OQZSwjUEd5fqu4BjY2MVEhKir7/+Wnfu3JHNZrOvi4+PV1RUlCIjI3XixIl0KRQAADwEDClyulWxwfGybPCSEW7IDEwYdbF25x1+wD1xDcHNpTpgTp8+XfPnz1fhwoWVO3du/fLLL6pbt66uXr2qCxcuyNfXV6+99lp61goAAB4GhmTtHsdIC3C/uIbgxlI9RXbTpk2qV6+evvnmG82bN0+S9Prrr2vz5s2aO3eu4uLi5O3tnW6FAgAAAADcW6oD5uXLl9W6dWt5eHioUKFCCgwM1MGDByVJzZo1U9euXbVy5cp0KxQAAAAA4N5SHTB9fX0dRihLlCihX375xf65evXqOnfunGurAwAAAABkGakOmJUqVdLOnTvtn8uUKWMfwZQSRjgNg7uKAQAAACC7SnXA7NOnj7Zt26bevXsrMjJSHTp00PHjxzV+/HjNmzdPCxcuVLVq1dKzVgAAgKTZJP+RFgVUzanA0n4KqJpT/iMtku3emwKQZEqW5V7K3d9XeTvkUO7+vrIs95LMe2+a5WXnc08HqX6KbNu2bfXWW29pwYIFypEjhxo1aqRBgwbZH/hTpEgRjR8/Pt0KBQAASJJNytcwpzxPe8hIfEdDlOS7xEPeP3rq+q47Ln7zN/CQMSX/ERb5LveWYf5vRqLPJi95745V5AfWh/f1J9n53NOJYZrmA2Xzixcv6ubNmypbtqx8fHxcVVeGCw+PlM3GnyncVYECuXT16u3MLgPJoH/cH33k3uifB+M/0iLfJd7/C5d/YcpUzNOxinzf+kDHoI/cH310/yzLvZRruK9DwEpkGqZuz4yRtceDvRLFXfsnI849K/DwMBQY6O+SfaV6BPOvjh49qgsXLsjT01MlS5ZUpUqVXFIMAABAWvls8UwyXEqSIUM+X3tmcEVA1mIJ80oyYEmSYRqybPR6aENWdj739JKmgLlhwwZNmzZNV65cUeLAp2EYKlWqlF5//XU1bNgwXYoEAABIjhF1j/lrd5jfBqTE41rK14gR/vBeQ9n53NNLqgPm119/rZdfflklS5bUK6+8opIlSyo+Pl5nzpzRsmXL9Nxzz2nhwoWqU6dOetYLAADgwPQzpagUGuTkFhggJbb8KV8jZuDDew1l53NPL6kOmB9//LGqVKmipUuXymKxOKzr06ePevTooenTpys0NNTlRQIAACTnbqt4+S7xSPYezLut4zOhKiDrsLaLk8+mpKeKmoYpa/uHd4podj739JLqZ6r98ccf6tq1q1O4lKQcOXLoySef1NGjR11aHAAAwL1EvmdVfBmbzL+9U8CUqfgyNkW++2AP+AEedtYecYrpGSvT+Ns1ZJiK6RUra/eHN2Rl53NPL6kewSxWrJj++OOPZNdfuXJFBQoUcElRAAAAqeYhXd91R/5jLPLZ7Jlwz2XOhJHLyHetvKIEuBdDipxuVWxwvCwbvGSEGzIDE0bvrN3jHu7XdGTnc08nqQ6Yo0eP1vDhw1W6dGn16tVLXl7/23Tjxo1asmSJ3n333XQpEgAAIEUeCSOZei+zCwGyKEOydo/LniN22fnc00Gq34P5zDPP6Pfff9fVq1eVK1cuFS9eXN7e3jp//rzCw8Pl6empQoUKOe7cMLR169Z0KdzVeA+me3PXdychAf3j/ugj90b/uD/6yP3RR+6N/nFvmfIezLi4OJUsWVIlS5Z0WF66dGmVLl3aJcUAAAAAALKuVAdMng4LAAAAAEhJqgNmori4OB05ckT//e9/Va9ePfn6+io+Pl558uRJj/oAAAAAAFlEmp6rFhYWpkcffVS9e/fWyy+/rF9//VUHDhxQs2bN9Mknn6RXjQAAACkzJctyL+Xu76u8HXIod39fWZZ7STxeAQAyVKoD5vfff6+XX35ZpUqV0iuvvKLEZwMVK1ZM5cuX13vvvaf169enW6EAAABJMiX/ERblGu4rS5i3vPd5yRLmrVzDfeU/0kLIBIAMlOqA+eGHH6pq1apavHixunTpYl9etmxZLV26VLVq1dKiRYvSpUgAAIDkWFZ4yXe5twzT8YV1hmnId5m3LCvTfEcQAOA+pTpgnjhxQh06dJCHh/MmXl5e6tixo06fPu3S4gAAAO7FEublFC4TGaYhy0YCJgBklFQHTG9vb8XFJf/y0Rs3bsjb29slRQEAAKSWx7Wkw2UiIzzl9QAA10l1wKxXr54+//xzWa1Wp3VXrlzR0qVLVadOHZcWBwAAcC+2/CnfZGkGchMmAGSUVM8ZGTVqlHr06KHOnTuradOmMgxD27Zt07fffqu1a9fq7t27eumll9KzVgAAACfWdnHy2ZT0NFnTMGVtn/wMLACAa6V6BLNs2bJasmSJChYsqNDQUJmmqc8++0yLFi1SiRIltHDhQlWqVCk9awUAAHBi7RGnmJ6xMg3HkUrTMBXTK1bW7gRMAMgoabrrvUKFCgoNDdWNGzd09uxZ2Ww2FS1aVAUKFEiv+gAAAFJmSJHTrYoNjpdlg5eMcENmYMLIpbV7nMQtmACQYe7rsWp58+ZV3rx5dfPmTfn6+rq6JgAAgLQxJGv3OEYrASCTpThFNjY2VsuXL9f48eMdlu/fv18dOnRQgwYNVKtWLQ0cOFBnz55N10IBAAAAAO4t2YB59+5d9e/fX//5z3/01Vdf2V9RcubMGT377LP6448/1KRJEw0YMECnT59Wz549de3atQwrHAAAAADgXpINmIsWLdLBgwc1ZswY7du3T15eCbNpZ82aJavVqg4dOigkJERjx47V6tWr5enpqTlz5mRY4QAAAAAA95JswAwLC1ObNm307LPP2u+zvHv3rr755hsZhqFnn33W3jZv3rzq1q2bvv3223QvGAAAAADgnpJ9yM+ff/6pbt26OSw7dOiQoqOjVbBgQadXkpQoUUJXrlxJnyoBAMC9mZJlhZcsYV7yuGbIlt+UtV2crD14kioAJIufnS6VbMC02Wzy9PR0WPbjjz9Kkho1auTU/vbt28qRI4eLywMAAKliSv4jLPJd7i3D/N9vRD6bvOS9O1aRH1j5RQkA/o6fnS6X7BTZEiVK6MSJEw7Ltm7dKsMw9Oijjzq1//7771WiRAmXFwgAAO7NssLL6RckSTJMQ77LvGVZeV9vJgOAhxo/O10v2YDZoUMHrV+/Xlu3blV0dLQWLlyoX3/9VYGBgWrevLlD2y+++EI//PCDWrRoke4FAwAAZ5YwL6dfkBIZpiHLRn5JAoC/42en6yX7jQ0YMEDfffedhg0bJsMwZJqmvL29NXHiRPn4+EiStmzZos8++0x79+5V6dKlNWDAgIyqGwAA/IXHtZTncBnhzPECgL/jZ6frJRswfXx8tHDhQm3cuFGHDh2Sn5+fOnfurHLlytnbHD16VD/99JM6d+6scePG2Z82CwAAMpYtv5niejMw5fUAkB3xs9P1Uhzz9fT0VKdOndSpU6ck1z///PMaPny4PDySnWkLAAAygLVdnHw2JT3VyzRMWdvHZUJVAODe+Nnpeg+UDHPkyEG4BADADVh7xCmmZ6xMw/Gv7aZhKqZXrKzd+SUJAP6On52ux12rAAA8DAwpcrpVscHxsmzwkhFuyAxM+Ou7tTvvcgOAJPGz0+UImAAAPCwMydo9jr+4A0Ba8LPTpZjfCgAAAABwCQImAAAAAMAlCJgAAAAAAJfI1IBps9m0bNkyderUSbVq1VLLli01efJkRUZGJrvN+vXrVaFCBad/b775ZgZWDgAAAAD4u0x9yM8nn3yi6dOn69lnn1XDhg11+vRpzZw5U7/99pvmz5+f5DYnT55UyZIlNW3aNIfl+fPnz4iSAQAA3IspWVZ4yRLmJY9rhmz5TVnbxcnagydgAsh4mRYwTdPUJ598oh49eujll1+WJDVq1Ej58uXTyJEjdeLECVWqVMlpu1OnTqlKlSqqWbNmBlcMAADgZkzJf4RFvsu9HV4U77PJS967YxX5gZWQCSBDZdoU2aioKHXu3FkdO3Z0WF6mTBlJ0tmzZ5Pc7uTJk6pQoUK61wcAAODuLCu8nMKlJBmmId9l3rKs5I10ADJWpgVMf39/TZgwQXXq1HFYvnXrVklSuXLlnLa5cuWKwsPDdfz4cbVt21ZVqlRRmzZttG7duowoGQAAwK1YwrycwmUiwzRk2UjABJCx3OqnzuHDhxUSEqKWLVuqbNmyTutPnjwpSTp//rzGjBkji8WidevW6ZVXXlF8fLyeeOKJ+z52YKD/fW+LjFGgQK7MLgEpoH/cH33k3ugf9+eWfXQz5dWWW94qUMA7Y2pxA27ZR7Cjf7IHwzRNM7OLkKQDBw7o+eefV4ECBbRkyRLly5fPqU1ERIQOHz6soKAg+fv/LxA+88wz+v3337Vz5877Pn54eKRsNrf4KpCEAgVy6erV25ldBpJB/7g/+si90T/uz137KHd/X1nCkg+Q1naxurUoJgMryjzu2kdIQP+4Nw8Pw2UDbm7xHsyNGzfqmWee0SOPPKKFCxcmGS4lKSAgQI899phDuJSkZs2a6fLly4qIiMiIcgEAANyCtV2cTCPpP5Cbhilr+7gMrghAdpfpAXPBggUaNWqUatasqSVLlqhgwYLJtj148KBWrVrltNxqtcrLy0u5cjHsDgAAsg9rjzjF9Ix1CpmmYSqmV6ys3QmYADJWpt6DuWrVKk2ZMkXt27fX1KlT5ePjk2L7Q4cOacqUKapWrZoqVqwoSbLZbNq8ebNq164tb+/sc48BAACADClyulWxwfGybPCSEW7IDEwYubR25z2YADJepgXM8PBwTZw4UUWLFlWfPn10/Phxh/UlSpSQlPC6knLlysnf31/dunVTaGiohg0bphEjRsjPz09Lly7VL7/8oiVLlmTGaQAAAGQuQ7J2j2O0EoBbyLSA+d133yk6OloXLlxQnz59nNZPmzZN8fHxGj9+vBYvXqz69esrT548Cg0N1XvvvafJkycrMjJSVatW1cKFC1WjRo1MOAsAAAAAQCK3eYpsZuMpsu6NJ4+5N/rH/dFH7o3+cX/0kfujj9wb/ePeHrqnyAIAAAAAsj4CJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJTI1YNpsNi1btkydOnVSrVq11LJlS02ePFmRkZHJbhMVFaU33nhDwcHBqlWrlgYNGqQzZ85kXNEAAAAAgCR5ZebBP/nkE02fPl3PPvusGjZsqNOnT2vmzJn67bffNH/+/CS3GTlypI4cOaKxY8fKz89Ps2fPVr9+/bRhwwblypUrg88AAAAAAJAo0wKmaZr65JNP1KNHD7388suSpEaNGilfvnwaOXKkTpw4oUqVKjlss3//fu3YsUPz5s1T06ZNJUl169ZVixYttGzZMj333HMZfh4AAAAAgASZNkU2KipKnTt3VseOHR2WlylTRpJ09uxZp21++OEH+fn5KTg42L4sICBAQUFB2rlzZ/oWDAAAAABIUaaNYPr7+2vChAlOy7du3SpJKleunNO6P/74QyVLlpSnp6fD8hIlSigsLCx9CgUAAAAApEqm3oP5d4cPH1ZISIhatmypsmXLOq2PjIyUv7+/03I/P78UHwyUGoGBzvuFeylQgHts3Rn94/7oI/dG/7g/+sj90Ufujf7JHtwmYB44cEDPP/+8ihUrprfffjvJNqZpJru9h8eDzfYND4+UzZb8/pG5ChTIpatXb2d2GUgG/eP+6CP3Rv+4P/rI/dFH7o3+cW8eHobLBtzc4j2YGzdu1DPPPKNHHnlECxcuVL58+ZJs5+/vr6ioKKflUVFRSY5sAgAAAAAyTqYHzAULFmjUqFGqWbOmlixZooIFCybbtnTp0jp37pzTSOaff/6p0qVLp3epAAAAAIAUZGrAXLVqlaZMmaJ27drpk08+ued7LBs3bqxbt25p165d9mURERHav3+/GjVqlN7lAgAAAABSkGn3YIaHh2vixIkqWrSo+vTpo+PHjzusL1GihKSE15WUK1dO/v7+CgoKUr169TRq1CiNHj1aefPm1axZs5QrVy716tUrM04DAAAAAPD/Mi1gfvfdd4qOjtaFCxfUp08fp/XTpk1TfHy8xo8fr8WLF6t+/fqSpNmzZ2vKlCmaNm2abDab6tSpo+nTpytPnjwZfQoAAAAAgL8wzJQezZqN8BRZ98aTx9wb/eP+6CP3Rv+4P/rI/dFH7o3+cW8P3VNkAQAAAABZHwETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAAOASBEwAAAAAgEsQMAEAAAAALkHABAAAAAC4BAETAAAAAOASBEwAAAAAgEt4ZXYB7sLDw8jsEnAP9JF7o3/cH33k3ugf90cfuT/6yL3RP+7LlX1jmKZpumxvAAAAAIBsiymyAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACXyFYBc9iwYWrVqlWKbaKiovTGG28oODhYtWrV0qBBg3TmzJmMKTCbS03/rF+/XhUqVHD69+abb2ZQldlPXFycqlev7vSd16pVK9ltuI4yzv30D9dRxtu3b5969eqlGjVqqHHjxnrrrbcUFRWVbHuuoYyV1v7hGso4e/bsSfK7Tvy3du3aJLfjGso499tHXEcZa9myZWrXrp1q1qypTp066Ysvvkix/YNcQ14uqDdLWL9+vbZs2aISJUqk2G7kyJE6cuSIxo4dKz8/P82ePVv9+vXThg0blCtXrgyqNvtJbf+cPHlSJUuW1LRp0xyW58+fPz3Ly9ZOnz4tq9WqqVOnqlSpUvblHh7J/32K6yjj3E//cB1lrEOHDumZZ55R8+bN9fHHH+vPP//U+++/r4iICH3wwQdJbsM1lHHup3+4hjJOlSpVtGLFCodlpmnqtdde0507d9SsWbMkt+Mayjj320dcRxlnxYoV+s9//qN//vOfatKkiXbs2KExY8bI29tb7dq1S3KbB7qGzGzg0qVLZlBQkNm0aVOzZcuWybbbt2+fWb58eXPHjh32ZeHh4WbNmjXNuXPnZkSp2VJq+8c0TfOZZ54xR4wYkUGVwTRN84svvjArVqxo3rlzJ1XtuY4yVlr7xzS5jjJanz59zD59+pg2m82+7LPPPjNbtGiRZL9xDWWstPaPaXINZbaFCxeaFStWNA8dOpTkeq6hzHevPjJNrqOM1KNHD7Nv374Oy3r37m0+/fTTSbZ/0GsoW0yRnTBhgoKDg9WwYcMU2/3www/y8/NTcHCwfVlAQICCgoK0c+fO9C4z20pt/0gJf+2qUKFCBlSFRCdOnFCJEiWUI0eOVLXnOspYae0fiesoI0VERGj//v3q1auXDMOwL+/Tp4+2bt2aZL9xDWWc++kfiWsoM127dk0zZsywT2lOCtdQ5kpNH0lcRxnJarXKz8/PYVnevHl148aNJNs/6DX00AfMVatW6dixY/rXv/51z7Z//PGHSpYsKU9PT4flJUqU0OnTp9OrxGwtLf1z5coVhYeH6/jx42rbtq2qVKmiNm3aaN26delfaDZ26tQp+fj46Nlnn1WtWrUUFBSk119/XZGRkUm25zrKWGntH66jjPXLL7/INE3lyZNHI0aMUM2aNVWnTh39+9//VkxMTJLbcA1lnPvpH66hzDVz5kx5eHhoxIgRybbhGspcqekjrqOM1a9fP3333XcKCwtTZGSkNm3apG+//VZdunRJsv2DXkMP9T2YFy5c0OTJkzV58mQFBATcs31kZKT8/f2dlvv5+SX7yxruX1r75+TJk5Kk8+fPa8yYMbJYLFq3bp1eeeUVxcfH64knnkjvkrOlkydPKjIyUk899ZSef/55HT16VLNmzdLp06e1ePFih7/6S1xHGS2t/cN1lLEiIiIkSePGjVOrVq308ccf69SpU5o+fbqsVqumTJnitA3XUMa5n/7hGso8ERERWrdunf75z38qd+7cybbjGso8qe0jrqOM1aFDB+3evdsh9Hft2lUDBw5Msv2DXkMPbcA0TVOvvvqqmjVrpjZt2qR6m+Sk9MAMpN399E/VqlU1Z84cBQUF2f+jb9y4scLDwzVjxgx+GKWTDz74QHny5LFPYwkKClJgYKDGjBmjXbt2OUyfkLiOMlpa+4frKGPFxsZKkmrXrq1///vfkqSGDRvKNE1NnTpVQ4cOVfHixR224RrKOPfTP1xDmWflypWy2Wzq169fiu24hjJPavuI6yhjvfDCCzp48KDGjx+vypUr6/Dhw/roo4/k7++vCRMmOLV/0Gvoob3KlixZolOnTunVV19VXFyc4uLi7F/WX//3X/n7+yf5WPKoqKgkUzzu3/30T0BAgB577DGnvmjWrJkuX75s/0s0XKtevXpO90g8+uijkv73F8i/4jrKWGntH66jjJV4z0vTpk0dljdu3FimaerUqVNO23ANZZz76R+uocyzefNmNWnS5J6znriGMk9q+4jrKOP89NNP+v777zVhwgQNGDBA9erV06BBgzRu3DiFhoamy/8PPbQBc/Pmzbp+/boaN26sKlWqqEqVKlq3bp3Onj2rKlWqJPlOntKlS+vcuXNO4ebPP/9U6dKlM6r0bOF++ufgwYNatWqV03Kr1SovLy8eO54OwsPDtWrVKp07d85heeK9Sfny5XPahuso49xP/3AdZazEV8fcvXvXYXniyNnfpzBLXEMZ6X76h2soc1y+fFnHjx9P9pUKf8U1lDnS0kdcRxnn4sWLkhJmavxV3bp1JUm///670zYPeg09tAHzjTfe0Oeff+7w77HHHlPhwoXt//vvGjdurFu3bmnXrl32ZYlPmGvUqFFGlv/Qu5/+OXTokCZMmOAwKmOz2bR582bVrl1b3t7eGXkK2YJhGHr99df12WefOSzfuHGjPD09VadOHadtuI4yzv30D9dRxipbtqyKFi2qjRs3Oizfvn27vLy8VKtWLadtuIYyzv30D9dQ5jh8+LAkJflz7e+4hjJHWvqI6yjjJAbCffv2OSw/dOiQJKlo0aJO2zzoNfTQ3oNZpkwZp2V58+aVj4+PqlWrJinhizp79qzKlSsnf39/BQUFqV69eho1apRGjx6tvHnzatasWcqVK5d69eqV0afwULuf/unWrZtCQ0M1bNgwjRgxQn5+flq6dKl++eUXLVmyJKNPIVsICAhQnz59FBoaKn9/f9WtW1cHDhzQnDlz1KdPH5UsWZLrKBPdT/9wHWUswzA0evRo+/XQrVs3HT16VB9//LH69u2rgIAArqFMdD/9wzWUOX755RflyJEjyV+GuYbcQ1r6iOso41SpUkUtW7bUpEmTFBUVpUqVKuno0aP68MMP1bRpU9WoUcP111BqX9D5MHjllVfMli1b2j+vXr3aLF++vLl79277shs3bpjjxo0z69ata9auXdscNGiQ+fvvv2dGudlOavrn/Pnz5siRI81GjRqZ1atXN3v37m3u27cvM8rNNu7evWuGhISYbdq0MatWrWq2aNHCnDt3rhkfH2+aJtdRZruf/uE6ynhbtmwxH3/8cbNq1apms2bNzI8++ohryI2ktX+4hjLev//9b7NJkyZJruMacg9p7SOuo4xjtVrN9957z2zWrJlZtWpVs02bNuasWbNMq9VqmqbrryHDNFN4TBAAAAAAAKn00N6DCQAAAADIWARMAAAAAIBLEDABAAAAAC5BwAQAAAAAuAQBEwAAAADgEgRMAAAAAIBLEDABAA/k7t27CgkJUefOnVWzZk3Vrl1b3bp1U0hIiKxWa2aXJ0nq27evmjdvnmKbWbNmqUKFCtqzZ08GVZV2NptN58+ft39es2aNS2qeNGmS/vWvf0mS9uzZowoVKmjNmjUPtM+McO7cOdWvX1+XL1/O7FIAAP+PgAkAuG9xcXF69tlnNWvWLNWoUUNjxozRiBEjVKxYMb3//vvq37+/7t69m9llPhQiIyPVvXt3rV271qX7PXnypFatWqWhQ4dKksqWLatp06YpKCjIpcdJD8WLF1fbtm01adKkzC4FAPD/vDK7AABA1hUWFqa9e/dq1qxZat26tX15v3799Mknn+idd97R559/rt69e2dilQ+HGzdu6MiRI2rWrJlL9ztp0iR16tRJhQsXliTlz59fXbp0cekx0tOgQYPUunVr7du3L0uEYgB42DGCCQC4bwcPHpQkBQcHO63r06ePvL29dejQoQyuCql18uRJ7dmzR506dcrsUu5bsWLFVKtWLS1atCizSwEAiIAJAHgAfn5+kqQVK1Y4rcuRI4d++uknTZs2zWH5b7/9pqFDh6pu3bqqUaOGevbsqe+++86hTd++fTVgwAB98803at++vapXr67HH39cmzdvdjrOpk2b9PTTT6tOnTqqWrWqmjdvrmnTpqXr1NxLly5p7NixatCggapVq6bHH39cX3zxhUObcePGqW3btvr555/19NNPq0aNGmrUqJHefvttxcTEOLT9448/9MILL6hu3bqqX7++3n77ba1cuVIVKlTQ+fPntWfPHrVo0UKSNHv2bPvyROHh4Ro9erTq1q2r2rVra+jQobp48eI9z2PJkiXKly+fateubV/293swEz//8MMPeuONN9SwYUPVqFFD/fv318mTJ+95DNM0tXjxYnXs2FHVq1dX8+bN9e677yo6Otph/7t27dJrr72moKAg1alTR+PHj9edO3e0Y8cOdenSRTVq1FCXLl30448/Oh2jZcuW+uabb/Tf//73nvUAANIXARMAcN86d+4sb29vTZ06VR07dtT06dO1Z88ee7jz8fFxaH/q1Cn16NFDv/32mwYPHqyRI0cqLi5Ozz33nDZu3OjQ9vfff9dLL72koKAgjR49Wh4eHnrppZf05Zdf2tusWrVKw4cPV65cuTR69GiNHTtWRYsW1fz58xUSEpIu53z58mU99dRT2rVrl/r27atXXnlF+fLl05gxY/TJJ584tI2IiNCzzz6rMmXK6LXXXlPt2rUVGhqqmTNn2ttcvHhRvXv31sGDB/XPf/5Tzz77rLZs2aL33nvP3qZs2bIaP368JKlVq1aaNm2aAgIC7OtfffVV3bhxQ6NHj1a3bt20fft2vfDCC/c8lx07dqhJkyby9PS8Z9sJEybo+PHjGjJkiAYNGqTDhw9r0KBBiouLS3G7N954QxMnTlSxYsX0yiuvqHnz5vr00081ZswYh3bjxo3Tf//7X7388stq1qyZ1qxZo6FDh2rs2LFq1aqVRo0apWvXrmn48OG6deuWw7aPPfaY4uPj9f3339/zPAAA6cwEAOABbN++3WzYsKFZvnx5+7+aNWuao0aNMv/44w+Htk8//bTZsmVLMyoqyr4sNjbW7N27t9moUSPTarXa25UvX95csGCBvV10dLTZqlUrs3HjxmZ8fLxpmqbZtm1bs0ePHqbNZnPYX9OmTc2OHTs6HPexxx5L8Txmzpxpli9f3ty9e3eK7V555RWzXr165uXLlx2Wjxo1yqxatap57do1e7vy5cubixcvdmjXrl07s3HjxvbP48ePNytXrmz+9ttv9mWXLl0ya9asaZYvX948d+6caZqmee7cObN8+fLmzJkz7e1Wr15tli9f3nzhhRccjjFu3DizfPny5tmzZ5M9j7Nnz5rly5c3582b57B89+7dZvny5c3Vq1c7fH7iiSfMuLg4e7u5c+ea5cuXN7///vtkj/Hrr7+aFSpUMCdMmOCw/P333zfLly9v/vrrrw77T+zX+Ph4Mzg42Cxfvry5Y8cO+3YrV65M8pg2m82sUaOGOXbs2GRrAQBkDEYwAQAP5NFHH9X27dv1wQcfqEuXLipQoIDu3Lmjr776Sl26dNHevXslSdevX9fevXvVrFkzxcTEKCIiQhEREbp165ZatWqla9eu6ciRI/b95sqVy+HhQL6+vurVq5euXLmio0ePSpK++OILhYSEyDAMe7vw8HDlzp1bd+7ccfm52mw2bd26VXXr1pWXl5f9HCIiItS6dWvdvXtXP/zwg8M27dq1c/hcsWJFhYeHS0qYPrpt2zY1adJEZcuWtbcpVKiQOnfunOq6OnTo4PC5WrVqkqSrV68mu03iFNtixYql6hitW7d2GOmsVKnSPY/x7bffyjRN9e3b12H5s88+qy+++EIlSpSwL2vRooU8PBJ+LfHw8FDx4sXl6+urpk2b2tsk1vr3YxqGoaJFizpMGwYAZA6eIgsAeGAWi0Xt27dX+/btJUnHjh3Tp59+qq+++kr//ve/FRYWpnPnzkmSQkNDFRoamuR+/noPXYkSJZym2JYsWVKSdOHCBVWvXl3e3t7at2+fvvrqK/3xxx86e/asPbwVLVrU5ed5/fp13b59W1u3btXWrVvveQ6SHKaySgnThuPj4yUlPBn2xo0bKlWqlNN+ypQpk+q6/n4MX19fSVJsbGyy21y/fl2S5O/vf1/HSOwbm82W7DYXLlyQJKfzy507t3Lnzu2wLH/+/A6fvby8nI6ZGECTOqa/v7/9nAAAmYeACQC4L3fu3NHcuXNVpUoVh1eUSFKVKlX03nvv6datW9q5c6euX79uD1V9+vRRy5Ytk9xnuXLl7P/b29vbaX1isEgcSXvvvfcUEhKiypUrq2bNmurSpYtq1aqlt956K10e+JJ4Dm3atFHPnj2TbFO8eHGHz4mhKCmJ9y/+PUhLCaE9tVI6xr22SSkgPugxEr+v1EjqPtC/jkzfi81mS9W9pACA9EXABADcF4vFovnz56tWrVpOATNRuXLl9N1338nX19c+oujp6alGjRo5tPvtt990/vx55ciRw77s/PnzMk3TIWScOXNGUsJI5oULFxQSEqIuXbo4Pan22rVrrjhFJwEBAcqRI4fi4uKczuHixYs6fvy4wzncS2BgoHLmzGk/r7/6888/H7TcFCWOGN64cSPdjlGkSBFJ0rlz5xymAF++fFmTJ0/W008/7bJj3bhxI11GrQEAacM9mACA++Lp6an27dtr7969Wr9+vdP6GzduaPPmzWrUqJFy5MihggULqmrVqlq7dq0uX75sbxcbG6tXX31VL730ksMTSa9du6awsDD75+joaC1btkylSpVShQoVdPPmTUmOo55SwpNRz5w5c8+nm94PLy8vNW3aVDt27HB6RceUKVM0dOjQNE3T9PDwUPPmzbVz5077FGJJunnzpr766iuHtomjc6kdcbyXxPB36dIll+wvKc2aNZMkLVu2zGH5mjVrFBYWlurpufcSHx+vq1ev6pFHHnHJ/gAA948RTADAfRs3bpx+/vlnjR07Vl988YWaNGkif39/nT17VmvWrFFsbKxef/11e/sJEyaof//+euKJJ9SrVy/lzZtXGzZs0OHDh/Xyyy8rX7589rbe3t4aP368jh07poIFC2r16tW6fPmy5syZIykhWBYpUkRz5syR1WpV4cKF9fPPP2vt2rWyWCyKioq6r3NasGCBNmzY4LS8YcOGateunUaPHq09e/aoT58+6tOnj4oUKaJvv/1W27dvV48ePfSPf/wjTccbPny4duzYoR49eqhv377y8fHR8uXL7a/iSBzBzZs3rzw8PPTNN9+oSJEiyY4ap1aRIkVUokQJHT58+IH2k5JKlSrpqaeeUmhoqK5cuaKGDRvqt99+0/Lly/X444+rYsWK2rNnzwMf55dfflF0dLQaNmzogqoBAA+CgAkAuG8BAQFas2aNFi5cqG3btunDDz9UdHS0ChYsqNatW+v5559XwYIF7e1r1aqlZcuWadasWVqwYIHi4uJUunRpTZkyRV27dnXYd8GCBfXqq69q6tSpunr1qqpUqaIFCxYoKChIUsJ9iyEhIZoyZYoWL14s0zRVokQJvfrqq4qLi9PEiRN19OhRVa1aNU3ntH379iSXWywWtWvXTiVKlNDKlSs1c+ZMrVy5Unfu3FHx4sU1fvx4p6elpkaJEiX02WefaerUqZo7d64sFosef/xxeXp6av78+fb7M3PkyKGRI0dq/vz5evvttx2ewHq/mjZtqrCwMNlstvu6xzI13nzzTZUqVUqrVq2yh+OhQ4dq4MCBLjvGgQMH5OHhocaNG7tsnwCA+2OYpmlmdhEAAPxV3759deHCBX3zzTeZXUq6Cw8PV0BAgNMDbd566y0tW7ZMhw8fTvKBR65w4sQJPf7441qwYIHTPaVZSc+ePZU/f37Nnj07s0sBgGyPezABAMhEw4cPV4cOHRzurYyOjtb27dtVsWLFdAuXUsIU1kaNGmnt2rXpdoz09ueff+rgwYP65z//mdmlAABEwAQAIFN16dJFv//+u5577jktW7ZMCxcuVJ8+fXTp0iWNHDky3Y8/cuRIbd682eEhQ1lJSEiIHnvsMdWuXTuzSwEAiHswAQDIVE899ZQsFosWL16sd955Rx4eHqpataoWLlyoevXqpfvxq1evru7du+ujjz7S5MmT0/14rnT27Flt2bIlyacYAwAyB/dgAgAAAABcgimyAAAAAACXIGACAAAAAFyCgAkAAAAAcAkCJgAAAADAJQiYAAAAAACX+D+xk39g3SX7PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7)) \n",
    "plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 2]['SepalLengthCm'],\n",
    "            y=clusters[clusters['Cluster_Prediction'] == 2]['SepalWidthCm'],\n",
    "            s=70,edgecolor='magenta', linewidth=0.3, c='magenta', label='Iris-versicolor')\n",
    "\n",
    "\n",
    "plt.scatter(x=kms.cluster_centers_[2, 0], y=kms.cluster_centers_[2, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim(4,8)\n",
    "plt.ylim(1.8,4.5)\n",
    "ax.set_ylabel('Sepal Width (in cm)')\n",
    "ax.set_xlabel('Sepal Length (in cm)')\n",
    "plt.title('Individual Clusters', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}