Prediction using Unsupervised Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sparks Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science and Business Analytics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2 : Prediction using UnSupervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Author: Subramanya K S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the given ‘Iris’ dataset, predict the optimum number of  clusters and represent it visually.\n",
    "Dataset: https://bit.ly/3kXTdox "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Required Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Iris DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "data = pd.read_csv('iris.csv')\n",
    "#show first five column\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the id column since it is not required.\n",
    "data.drop(['Id'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#get the information about the data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>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>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>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>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>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": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     150.000000    150.000000     150.000000    150.000000\n",
       "mean        5.843333      3.054000       3.758667      1.198667\n",
       "std         0.828066      0.433594       1.764420      0.763161\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.350000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the Description about the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# know about rows and coulmn in dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 150 rows and 5 coulmns in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    float64\n",
       "SepalWidthCm     float64\n",
       "PetalLengthCm    float64\n",
       "PetalWidthCm     float64\n",
       "Species           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#know about datatypes present in dataset\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
       "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
       "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
       "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for correlation\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x247398085c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap to analyse correlation\n",
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    False\n",
       "SepalWidthCm     False\n",
       "PetalLengthCm    False\n",
       "PetalWidthCm     False\n",
       "Species          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null value\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "data['Species']=le.fit_transform(data['Species'])\n",
    "data['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2473a00c288>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting the Scattered plot of PetalLength vs PetalWidth\n",
    "plt.scatter(data['PetalLengthCm'],data['PetalWidthCm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.iloc[:,[0,1,2,3]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### within-cluster-sum-of-squares(wcss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[680.8244,\n",
       " 152.36870647733906,\n",
       " 78.94084142614602,\n",
       " 57.31787321428571,\n",
       " 46.53558205128205,\n",
       " 38.930963049671746,\n",
       " 34.318753220611924,\n",
       " 29.954086421037225,\n",
       " 27.992635739389613,\n",
       " 25.94725937001595]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the optimum number of clusters for k-means classification.\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(data)\n",
    "    # inertia method returns wcss for that model\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ELBOW METHOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the results onto a linear graph which allow us to observe 'the elbow method'\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.set(style='whitegrid')\n",
    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can clearly see why it is called 'The elbow method' from the above graph, the optimum clusters is where the elbow occurs. This is when the within cluster sum of squares (WCSS) doesn't decrease significantly with every iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimum value for K would be 3. As we can see that with an increase in the number of clusters the WCSS value decreases. We select the value for K on the basis of the rate of decrease in WCSS and we can see that after 3 the drop in wcss is minimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply K-means to the dataset\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 5)\n",
    "y_kmeans = kmeans.fit_predict(data)\n",
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2473a787e08>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q/L1KxkGKsdhtyo4dO2LevHlwc3ODWq2Gj48PfvvtN71jCgoK8N577yEkJASLFy9GdXW1pcJpVFCQ+F2MidG/ihsTI27nPxqIiEg2a82ZJKcfKUrP3URmsVgx1rNnTwwYMACAOLdUdnY2Ro0apdtfWVmJe++9FwkJCdi2bRvKysrw7rvvWioco3x8gNRU4No1oLZW/DM1lf9YICKiJrLWnEly+pGi9NxNZBaLjhkDgNOnT+PZZ5/Fc889hwkTJhg97vjx41iwYAEyMzMl26y/99oYV1dX9OjRo9nxku2cOXPGYPoMIiJHMWjIEKhk/EoVVCr85/Bhi/dj6Tio6aw+ZgwQB7/NmTMHCxYsQHBwsN6+3377DT/88INu0lBBEPTmu5LD2AB+pQfbNzbYnMzTWE7d3Nxw33332Sgix9eiB0XbCHNqGS02r97e4gLHElRt2ph3/jL7sXgcLZwlBvAbY7HblEVFRZg1axZWrlxpUIgB4uSfK1aswIULFyAIAj799FO9wf1EREQOxVpzJsnpR4o15m4i2Sx2ZeyDDz5AdXW1brFoQJwXKzc3F3PmzEH//v2xePFizJw5E1qtFoMGDcLTTz9tqXCMu64BLhQDJX8CtXVAKxegUwfgrs6AJ2eLJyIimeLjgQ0bTI/nUquBuDjL9yNFiThIMRYrxhYtWoRFixYZbJ88ebLuv8eNG4dx48ZZKgRpf14DjhcCDZf+qa0Dii4DxX8CfX2ADreZ1UXv3r0bnYF+48aNAPTzoZQLFy5gzZo1eOONNxRvm4iIjGjKnEmW7kfOPGN8Us1uOO0M/LiuMSzEGqqrE/df11ik+8mTJ1ukEAPE8XgXLlywSNtERGSCteZMuqUfQaXS7ycxkXM3ORCnXSgcF4qNF2L16uqAi8XAnX8xu7uDBw9ixYoVqKurQ8+ePdG1a1cAwIwZM7BgwQKcPn0aADBlyhQ88cQTBu9ftmwZvv/+e7i4uOChhx7C7NmzUVlZicWLF+P06dOora3F9OnTMX78eLz22mu4ePEikpOTkZSUhLVr1+KLL75Aq1atMHz4cCQkJOD69et48cUX8ccffwAQlzYaM2YMDh06hFWrVkGj0aCsrAzz58/HQw89ZPb5ExE5jfo5k1JTrdbPfxobbG6tOMhszntlrORPeccVyzxOhnPnzmHDhg1YtmyZbtuPP/6Ia9euITMzE++99x7y8vIM3nfp0iV8++23+OKLL7Bx40acOXMG1dXVWLNmDfr164etW7fi008/xdq1a3HhwgUsWrQIvr6+SEpKwt69e5Gbm4stW7Zg27ZtOH/+PD777DN89dVX6NKlC7Zu3YrXX39d1296ejpee+01bNu2Da+99hrefvttxc6fiIiIDDnvlbFaiatiTT1Ohu7du6NNmzZ623r27ImzZ8/imWeewciRIw3W7wSAzp07w93dHZMmTcLo0aMxd+5cuLu744cffoBGo8GWLVsAAFVVVTh9+rTelBEHDhxAcHAwPD09AQATJ05EZmYm5s6di7feegvFxcUICAjArFmzAAArVqzA119/jV27duHnn39GJdeCIiIisijnvTLWSuapyz1OBg8Pw6cz27Vrhx07diAyMhJnz57FhAkTUFZWhrCwMN3L1dUVmzdvxvPPP4/S0lJMmjQJZ8+eRV1dHVasWIGsrCxkZWVh06ZNePDBB/Xar2vkVmxNTQ3uvvtuZGdnIyQkBHl5eYiIiEBdXR2mTJmC/Px8+Pr6YsaMGYqdOxGRKYWFQGys/vCm2Fhxu93JzQV8fQGV6ubL11fcTtQMzluMdeog77jOMo9rppycHCQkJCAgIACLFi2Cl5cXioqKdAVWVlYWjh8/jsjISAwZMgQvv/wyfHx8cPbsWQwdOlT3VGZJSQlCQ0NRVFSEVq1a6WayHzp0KHbs2AGNRoOamhps2bIFQ4cORXp6OlavXo2goCAkJSXhypUrKCsrw7lz5/D8889j5MiRyMnJQW1trUXPn4goOxvw8wPWrxfnMhUE8c/168Xt2dm2jrCBxYuBMWOAY8f0tx87Jm5fvNg2cZFDc97blHd1FseDmRrE7+ICdO0M1FmuIBk5ciT27NmD4OBguLu7IzQ0FL1799Y7pm/fvhgwYADGjx8PT09PDBo0CCNHjsT999+PV199FePHj0dtbS0SEhLQrVs3tGnTBuXl5UhISMCKFStw4sQJTJw4ETU1NRgxYgQiIyOh0Wjw4osvIiQkBK1atUJCQgJuv/12REREIDg4GK6urhg6dCg0Gg2qqqrg5eVlsRwQkfMqLBRnaKiqMtxXPytDRIT4AKDNZ2LIzQWSkkwfk5QEjBgBBAZaJyZqESy+NqUl1C8rYGw5pHvvvVdeQ43NM1bPxUU3zxiXQ1JeYzlt0mdHBlrsEjM2xJxaRsO8xsaKV8Ck5kmNibGDhwJ9fQ2viBk77uhRy8fTAL+ryrPEckjG1qZ03tuUgDihq39f4M6ON8eGtXIRf/bva/aEr0REZFp6uvRE8lotkJZmnXhMklOIAYCJNQiJGuO8tynreXoAPf8qvoiIyKoqKpQ9jsgROfeVMSIisilvb2WPI3JELMaIiMhmIiPFMWGmqNVAVJR14jGpXz95x/n6WjYOanFYjBERkc3Ex8srxuLirBOPSe+8I+84rlxCTcRijIiIbMbHB8jIALy8DIsytVrcnpFhB9NaAOJ0FcnJpo9JTua0FtRkLMYsPO3zrXOG1du4caNuwtbmysnJafLakWFhYYq3SURkjqAgcR6xmBj9/xXHxIjbg4JsHWEDiYlATo7hrUhfX3F7YqJt4iKH5txPU2Zni7MJ1s8sCNyc9nnDBvGfYxb6v8DkyZPNbmPMmDEYM2ZMk96TlZWleJtEROby8RHnEbP5XGJyBAZafR4xatmctxhryrTPd9xhdncHDx7EihUrUFdXh549e6Jr164AgBkzZmDBggU4ffo0AGDKlCl44okn9N6bk5ODzZs3Y+3atQCAtLQ0nD9/Hn379sWhQ4fw5ptvIjAwEH5+fjhx4gT+9a9/YceOHUhPT0ebNm1wzz33oFu3bnjuuefQu3dvnDp1CqtXr0ZxcTHOnz+PS5cu4fHHH8fMmTOxdetWXZs//PAD3nzzTQiCgDvvvBMpKSkAgAULFqC4uBglJSUYNmwYXn/9dahUKrNzRERE5Iyc9zZlSoq8mQZXrVKsy3PnzmHDhg1YtmyZbtuPP/6Ia9euITMzE++99x7y8vIM3jdy5EgUFBTg2rVrAIAdO3YgNDS00eN2796Ny5cv49NPP8XWrVvxr3/9C+fPn280nlOnTuGDDz7A5s2bsW7dOpSVlen23bhxA3PnzsWyZcuwfft29OrVC9u2bcM333yDe++9F59//jl2796Nw4cP45jciRCJiIjIgPMWYzaY9rl79+5o06aN3raePXvi7NmzeOaZZ7Br1y689NJLBu9Tq9V4+OGHsWfPHvz2228oLS2Fn5+fwXH33XcfAGD//v0YPXo0vL294e7ujuDg4Ebj+dvf/gY3Nzd06NABt99+O8rLy3X7Tp06hc6dO+uWJ4qPj0dUVBTGjx+P4cOH4+OPP8Zrr72G0tJSVDV2dZGIqCnkjN+VOkaJNqx1Lo7SRktix/lw3mLMBtM+e3h4GGxr164dduzYgcjISJw9exYTJkxAWVkZwsLCdC9AHHi/c+dO7Ny5EyEhIY22X7/elYuLC+pMLYB+y/EAoFKp0HCZUrVarXfrsby8HL///jvS0tKwfPlytG/fHpGRkfDx8YEDLm9KRPYkOxvw8xPH65aXA4Jwc/yun5+4X+qYxYvNbyM72zrn4ihttCR2ng/nLcbsZNrnnJwcJCQkICAgAIsWLYKXlxeKioqQlZWlewHAgAEDUFJSgqysrEZvUTY0bNgw7N27FxUVFbhx4wb27NnT5DFd3bt3x59//okzZ84AANavX4+NGzfi+++/x5NPPonQ0FBUV1fj5MmTsgo/IqJGNRy/e+vdCq1W3B4eDkycaPqYpCTz24iIMO8qiZxzkerDXtpoSRwgH85bjNnJtM8jR46Eh4cHgoOD8fjjjyM0NNTodBhBQUFo3bo17rrrLpNt9urVC0899RSefPJJTJ06Fa1bt250lXhT3N3dsWLFCrz00ksICQnBmTNnEBMTg2nTpiE1NRUhISF44403MHDgQFy8eLFJbRMR6cgZv1tdDWg05vUjpw1zxwkrMRbZXtpoSRwgHyrBAe8xVVdXo6CgAL6+vgZFxokTJ3TjnEwqLBQvTZoa7+TlBeTno/KOO9C6dWszo7aes2fPYu/evYiOjgYAzJw5E48//jgC7WgiwsrKSoOcyv7sqFFHjhzB4MGDbR1Gi8KcWoZeXtu2FW8X2Yu2bYH/PSzVrPfKORdTfTSzjWbl1JxzdSRK5NRMpuoWwJmvjDnUtM9N06VLFxw9ehTjx49HSEgI7r77bowePdrWYRERGVJwXK4izIlHibHI9tJGS+IA+XDeecaAm9M+r1olPjVZUSGOEYuKEhdCc8BCDADc3Nx0c4IREdk1b2/7ujJmzjhhuediqg97aaMlcYB8OO+Vsf8puaMNlk/uhsiPQhCS9igiPwrB8sndcPmOtrYOjYio5ZMzflelEl/mkNOGueOElRiLbC9ttCQOkA+nLcYOXzqM8M/D8dd//BVJ3yTh06Of4svTX+LTo5/i1W9eRbd/dEP45+E4fOmwrUMlImq54uOlf1G6uwONTA3UJHLaUKvFuyLNJedcpPqwlzZaEgfIh1MWY2vy1iBgQwAyT2ZCU6OBpkb/CZvrNdehqdEg82QmAjYEYP1P620UKRFRCydn/O7WrcCWLaaPSU42vw1zxwkrMRbZXtpoSRwgH05XjK3JW4O5e+aiSlsFAaYfJBUgoEpbhQXfLMCavDVWipCIyMnUj9+NidGfHT0mRtweFCR9TGKi+W0EBVnnXByljZbEzvPhVFNbHL50GAEbAlClbfryPV5qL+yN3gv/O/2bFTPp49QWyuM0DMpjTi2DeVUec6o8Tm1hIUv3LcV17fVmvfe69jqWfre0ye+rqKhAcnIyxo8fj7CwMERFRTV7Ye358+fj0qVLTXpP/XJKtwoMDORkrURERHbAaYqxksoSZJ/Jlrw1aYwAATvP7MTlysuy31NXV4fp06fjtttuQ2ZmJrKysjBr1ixMnz4dV69ebXIMBw8ebPI6kPXLKREREZF9cppi7OOfPja7DRVUTWrn4MGDKCoqwpw5c+DqKk7pNnToUCxduhR1dXVYt24dJkyYgNDQUCxfvhyCIODixYt47LHHkJCQgPHjx2PatGkoLS3FunXrUFJSgpiYGFy9ehWBgYF44YUXMG7cOPz555/YsmWLbpLXefPmobKyEgB0SyuVlpZi+vTpCAkJwQsvvIDq6moAwMmTJ/HEE08gPDwckydPxrlz58zOExFRi1ZYCMTG6o89io1t2tqGSrThTFp4vpymGMsvzjd4arKprtdcx9GSo7KPP378OPr06QMXF/00jxo1CseOHUNBQQEyMjKQmZmJ4uJifPHFFwDEAunpp5/Gl19+ibZt22L79u2IiYlBp06dsG7dOrRr1w6AuK7l7t278ccff2Dt2rVIS0vD9u3b4enpidTUVL0+33nnHfTt2xfbt2/H1KlT8ccffwAANmzYgKeffhpbt27FE088gZ9++smcFBERtWzZ2eJSeuvXixOJCoL45/r14vbsbOu04UycIF9OU4xd0yiz/tZVjfzbiy4uLkYX6N6/fz/y8/MRHh6OCRMmoKCgAGfOnAEAdOjQAX379gUA9OzZE9eMrB123333AQAOHz6M0aNH64q0J598EgcOHNA79tChQ3j00UcBAEOGDNEtNj5q1CgsWbIECxYsQJs2bRASEiL7/IiInEphIRARIa5pfOvC01qtuD0iwvTVGiXacCZOki+nKcZu87hNkXbaebSTfayvry+OHz9uMM7rrbfewv79+zFt2jRkZWUhKysLmzdvxowZMwBAr4BTqVRGx4nVH1dXV6e3XRAE1NTU6G27tZ1WrVoBAB555BFs27YNfn5++Pjjj5GUlCT7/IiInEpKimFBcCutVlxiz5JtOBMnyZfTFGN+nf3g4WreDM6erp7o3wDhCZYAACAASURBVKm/7OP9/f3RoUMHpKamora2FgDw3XffYevWrbpCrLKyEjU1NZg1axZ2795tsr1WrVrp2mno/vvvR25uLkpLSwEAmzZtwt/+9je9Y4YNG6YbzJ+fn4///ve/AIAXXngBR48exaRJk/D888/j+PHjss+PiMippKfLKwzS0izbhjNxknw5zULh0QOikfSNeVd9BAiIHhAt+3iVSoV3330XS5cuxfjx4+Hq6op27dph3bp16Nu3L4qKivDEE0+gtrYWDz74ICZMmGBy6oqAgADExMRg/Xr9FQH69OmDZ599FlFRUdBqtejXrx+Sk5P1jpkzZw7mzZuH4OBg3HPPPbrblDNmzMDChQvxz3/+E2q1Gq+++qrs8yMicioVFeYfp0QbzsRJ8uVUk76Gfx6OzJOZzZreQgUVJvSZgC1PbmlWzKSPk74qj5M+Ko85tQyHzWvbtuLAcTnHGRnrq0gbjXDYnEqxUL7k4KSvFjJ/xHx4qj2b9V5PtSfmPzhf4YiIiMhhREbKW3A6KsqybTgTJ8mXUxVjQ7oMwcqxK+Gl9mrS+7xcvbBy7EouhURE5Mzi4+UVBnFxlm3DmThJvlpkMWbqzutM/5m6gkwFlcl2VFDBS+2FNwLewEz/mUqHSQ044N1yInI2Pj5ARgbg5WVYIKjV4vaMDPE4S7bhTJwkXy2uGGvVqhW0Ek9ezPSfib3RezGhzwR4uHrA01X/1qWnqyc8XD0woc8E7I3ei78P+LslQyYAWq1Wt0oBEZHdCgoC8vOBmBj92eBjYsTtQUHWacOZOEG+Wtxvv9tvvx3FxcXo0qWLwcz3Dfnf6Y8tT27B5crL+Pinj3G05Ciuaq6inUc79O/UH9EDotGxdUcA0C0tRJZRV1eH4uJi3HabMnPBERFZlI8PkJoqvmzZhjNp4flqccXYX/7yF1y8eBGnTp2S/Z7x7cdjfPvxetv++O8f+APikkE3btyAm5ubonE6u1tz2rp1a/zlL3+xYURERES20eKKMRcXF3Tr1k3RNo8cOaJbeoiUwZwSERGJWtyYMSIiIqMKC4HYWP2xR7GxN9c2lNrfjDZKOntj+dxhiNzwGEL+FYLIrZFY/v1yXK683Pw4WxpnO99btLgrY0RERI3KzhYXldZqby6xU14OrF8PbNgAvPwysGyZ8f0ZGeI2mW0c7qjF0keB7B6VAA5Ac+5mKFtPbEXSN0kI6hGE+SPmY0iXIfLjzMhoEYPWdZztfBvBK2NERNTyFRaKv/CrqgzXOtRqxe1JSab3h4cDEyfKamPNfVoERAOZvQGNWnw1dL3mOjQ1GmSezETAhgCsyVsjP86IiJZzxcjZztcIFmNERNTypaRILzgtpboa0GgkD1vjD8wdC1S5AYLEb1kBAqq0VZi7Z65YkMmJU6sFVq1qQuB2zNnO1wgWY0RE1PKlp5tfjAmC+DLh8J03C7GmqC/I8r7aIK84SUtrWgf2Ss7n0pLO1wgWY0RE1PJVVFilm6UPAtebORr7uvY6lg6qknewlc7H4uSeR0s5XyNYjBERUcvn7W3xLkpaA9k9pG9NGiNAwM5ewGU5yydb4XysQu55tJTzNYLFGBERtXyRkdILTktRqcSXER8rMHWiyqUVPh4k8atZrQaioszvzB7I+Vxa0vkawWKMiIhavvh484sxd3fAw8Po7vzOhk9NNtV1l1ocvcN4wQdAPI+4OPM6shdyPpeWdL5GsBgjIqKWz8dHnK/Ky8vwl79aLW5PTja9f+tWYMsWo8dca63Mr9SrIwabjiMjQzyflkDO59KSztcIFmNEROQcgoKA/HwgJkZ/pveYGHF7YqLp/UFBJtu47aEQRcJs1623dBwtidTn0tLOtxGcgZ+IiJyHjw+Qmiq+mrPfxDF+3y/Hlou7oamRnovMGE9XT/Tv1F9eHC2Js53vLXhljIiISAHRA6LNbkOAoEg75FhYjBERESmgU+tOCOoRBBUkBuAboYIKj/Z4FB1bd1Q4MrJ3LMaIiMgshYVAbKz+cJ/YWP3lBBseM2TIIMNjmtqIsWNsbP6I+fBUezbrvZ5qT8x/cL7CEVmBNT4XB/jszcFijIiImi07G/DzA9avB8rLxdWCysvFn/38xP2Gx6j0jjm8uFmNGB5jB4Z0GYKVY1fCSy1n5tabvNReWDl2Jfzv9LdQZBZijc/FQT57c1h0AH9qaiqy/5ekUaNG4aWXXtLbf+LECSxcuBCVlZXw9/dHcnIyXF35TAERkSMoLAQiIoCqRlbw0WrF18SJ4u/OxtbX1mqBu7SF6JsUAcBEI+Hh4mSr168bPyYiQnzyzg6mQJjpPxMAMHfPXFzXXocA4+tZqqCCp9oTK8eu1L3PYcj5Apj7uVijDztgsStjP/zwA/bt24dt27YhMzMTx44dw1dffaV3TEJCAhITE7F7924IgoBNmzZZKhwiIlJYSor0Gs8aDVBdbXz/i0iBGhKNVFc3Xs01pNUCq1aZPsaKZvrPxN7ovZjQZwI8XD3g6ap/69LT1RMerh6Y0GcC9kbvdbxCDJD3BTD3c7FGH3bAYpehOnbsiHnz5sHNTVy63sfHB7/99ptu/6VLl6DRaDBgwAAAQHh4ON555x1MmTLFUiEREZGC0tOlf08Kxi8KAQAikQ43qWJMqhFADCQtza6mRvC/0x9bntyCy5WX8fFPH+NoyVFc1VxFO4926N+pP6IHRDv2YH05XwBzPxdr9GEHLFaM9ezZU/ff586dQ3Z2NjZu3KjbVlJSgo4db34JO3bsiOLi4ib1UVBQYH6gMh05csRqfTkL5lR5zKnymFPjKioGAc18crBeG1QoEwwAobwc/7HTzyvQIxCB3QL1tv335H/xX/xXsT6s/V0dVFEh69M353OxRh+mWCunFh+gdfr0aTz77LN46aWXcPfdd+u219XVQdVgwVVBEPR+lsPX1xfu7u5KhWrUkSNHMHjwYIv340yYU+Uxp8pjTk3z9hbHUZujHN64DWY28j+qNm2c9vOyyXdV5hfArM/FGn0YoWROq6urTV5AsujTlEeOHEF0dDTi4+MxYcIEvX133HEHLl++rPv5jz/+QKdOnSwZDhERKSgyUnqNZ5VKfBmTjkjcgJmNAGIgUVGmjyFlyfkCmPu5WKMPO2CxYqyoqAizZs3CypUrERwcbLC/S5cucHd3110CzMrKwsiRIy0VDhERKSw+Xvr3pIcHYOoGxluIh1aqGHN3FxsyRa0G4uJMH0PKkvMFMPdzsUYfdsBixdgHH3yA6upqvPnmmwgLC0NYWBg2btyI6dOn4+jRowCAlStXYunSpXjkkUdQVVWFp556ylLhEBGRwnx8gIwMwMvL8PelWi1u37IF2LrV+DG/e/ngeLJEI1u3ig2ZOiYjw6GnNnBIcr4A5n4u1ujDHggOSKPRCHl5eYJGo7FKf3l5eVbpx5kwp8pjTpXHnMpz5owgzJolCG3bCoKLi/jnrFni9saOUanqDI9paiPGjnFSNv2uWuNzscFnr2ROpeoWlSDIeWbYvtQPhOMAfsfFnCqPOVUec2oZzKvymFPlWWIAv7G6hcshEREREdkQizEiIiIiG2IxRkREFldYCMTGAm3bAkOGDELbtuLPhYXi/txcwNf35iwWKpX4c25uMztxcYFBJ86G+XAYLMaIiMiisrMBPz9g/Xpx/k5BUKG8XPzZz0+cImrMGODYMf33HTsmbl+8uFmdQK+T7GyLnJvdYj4cCosxIiKymMJCICICqKoyXGJQqxW3p6ebbiMpSeIKmZxOIiKc54oQ8+FwWIwREZHFpKRIr/Msx/PPm9mJVgusWmV+II6A+XA4LMaIiMhi0tOVKcZMLOsnrxOtFkhLMz8QR8B8OBwWY0REZDEVFXbUiVWCsQPMh8NhMUZERBbj7W1HnVglGDvAfDgcFmNERGQxkZHS6zzL4etrZidqtfjYpjNgPhwOizEiIrKY+HhlirG33zazE7UaiIszPxBHwHw4HBZjRERkMT4+QEYG4OVlWB+o1eL2yEjTbSQnA4GBZnaSkSEe5wyYD4fDYoyIiCwqKAjIzwdiYsRJ4FUqAW3bij/n54sP9eXkGN6K9PUVtycmNqOT+hnn6zsJCrLIudkt5sOhuNo6ACIiavl8fIDUVPF15Mh/MHjwYL39gYHA0aMKdkLMhwPhlTEiIiIiG2IxRkRERGRDLMaIiMik9HSgfXtApbr5at/+5pqSubni+K6G+319JdaTvEVhIRAbqz+8KTbWBssnyjkZJYJVuI1BQ4ZYJml288G0cIID0mg0Ql5enqDRaKzSX15enlX6cSbMqfKYU+Uxp4IQGSkIgPGXn5/p/cnJhm3emtedOwXBy0sQ1Gr996rV4vadO610ssnJ0iejRLD20oY99GHHlPz7L1W38MoYERE1Kj395tUvY/LzTe9PSjJ9haywEIiIAKqqDJdT1GrF7RERVrgQk5srBmtKUhLw2GPmBavECVsjaXbzwTgHFmNERNSoOXOUaef5543vS0mRt6b1qlXKxGKU3JO9ccP0fqlglThhayTNbj4Y58BijIiIGnX1qjLtFBQY35eeLu93flqaMrEYdeyYMu1IBavECVsjaXbzwTgHFmNERGQzFRXKHmcXTAWrxAlbI2kt8oOxXyzGiIjIZry9lT3OLpgKVokTtkbSWuQHY79YjBERUaPatVOmnVuXOWooMlLemtZRUcrEYlS/fsq0IxWsEidsjaTZzQfjHFiMERFRo955R5l23n7b+L74eHm/8+PilInFKLkn6+Zmer9UsEqcsDWSZjcfjHNgMUZERI2KjBRfpvj5md6fnCyuO2mMjw+QkQF4eRn+7lerxe0ZGeJxFhUYKAZrSnIykJlpXrBKnLA1kmY3H4xzYDFGRERGpaWJr/bt9be3by9u//lnICfH8Fakr6+4PTFRuo+gIHG+spgY/YneY2LE7UFByp2PSYmJ0iejRLAWaENQqZRPmt18MC2fShAEwdZBNFV1dTUKCgrg6+sLd3d3i/d35MgRDB482OL9OBPmVHnMqfKYU8tgXpXHnCpPyZxK1S28MkZERERkQyzGiIiIiGyIxRgRERGRDbEYIyJyUIWFQGys/tjq2Fg7Xbu5QbCDhgyx82CJrIvFGBGRA8rOFqeVWL8eKC8HBEH8c/16cXt2tq0jbOCWYFV2HSyR9bEYIyJyMIWFQEQEUFVluJazVituj4iwk4tODhUskW2wGCMicjApKYZ1za20WmDVKuvEY5JDBUtkGyzGiIgcTHq6vPomLc068ZjkUMES2QaLMSIiB1NRoexxFuVQwRLZBosxIiIH4+2t7HEW5VDBEtkGizEiIgcTGWm4dvOt1GogKso68ZjkUMES2QaLMSIiBxMfL6++iYuzTjwmOVSwRLbBYoyIyMH4+AAZGYCXl2Gdo1aL2zMyxONszqGCJbINFmNERA4oKAjIzwdiYvRn4I+JEbcHBdk6wgZuCVZQqew4WCLrc7V1AERE1Dw+PkBqqviyew2C/c+RIxg8eLCtIyKyG7wyRkRERGRDLMaIiIiIbIjFGBGRgyosBGJj9ceMxcY2bZlHJdpQgr3EYTVOd8JkiuSYMY1Gg5ycHJSWluptnzp1qsWCIiIi07KzxfW1tdqbqw2VlwPr1wMbNogPKEqNi1eiDSXYSxxW43QnTFIki7EZM2agrKwMXbt21W1TqVQsxoiIbKSwUPxdXlVluK/+93tEhPigorEZI5RoQwn2EofVON0JkxySxVhxcTGys7OtEQsREcmQkiJv7e1Vq4w/aalEG0qwlzisxulOmOSQHDPWq1cvXL582RqxEBGRDOnp8n6fp6VZtg0l2EscVuN0J0xySF4Ze+SRRxAUFIRevXrB1fXm4Z988olFAyMiosZVVJh/nBJtKMFe4rAapzthkkOyGPvnP/+JZ599Ft26dbNGPEREJMHbWxzvLec4S7ahBHuJw2qc7oRJDslizNPTE9OnT7dGLEREJENkpPjgnam7XWo1EBVl2TaUYC9xWI3TnTDJITlm7IEHHsCnn36KkpISlJaW6l5ERGQb8fGGa27fSq0G4uIs24YS7CUOq3G6EyY5JIuxjz76CEuWLMHIkSMxdOhQDB06FMOGDbNGbERE1AgfH3EqKi8vw9/rarW4PSPD9MwISrShBHuJw2qc7oRJDsliLD8/HydOnMDJkydx7NgxHD16FCdOnLBGbEREZERQkDgVVUyM/iTuMTHidjlzhirRhhLsJQ6rcboTJimSxdjBgwcRFhYGAPj1118REBCAH3/80eKBERGRaT4+4lRU164BtbXin6mpTbuookQbSrCXOKzG6U6YTJEsxpYtW4alS5cCAHr27Il169bpfiYiIiIi80gWY1qtFv369dP93K9fP9y4ccOiQRERERE5C8lizNPTE99++63u5/3798PLy8uiQRERObrCQiA2Vn9IUGysuF0p8+cDrVoBKtXNV6tW4na5cSgR5/ncQuz1jUWZqi3qVC4oU7XFXt9YnM+92UjDfoYMGWSROIgcliDhp59+EoYPHy6MGjVKCAgIEEaPHi0UFBRIvU2nvLxcCA4OFi5cuGCwb/Xq1UJAQIAQGhoqhIaGCunp6bLa1Gg0Ql5enqDRaGTHYY68vDyr9ONMmFPlMafKa25Od+4UBC8vQVCrBQG4+VKrxe07d5ofm5+fftu3vvz8pONITjY/zkPJO4UKeAnV0G+kGmqhAl7CoeSdVonD2fHvv/KUzKlU3SI56et9992Hb775Br/88gtatWqF7t27w83NTVah9/PPP2PRokU4d+5co/sLCgrw1ltvYeDAgU0qIImI7FVhIRARAVRVGe7TasVXRIT40Fxzx2rPny++35T8fCA0FKipMR5HUlLj75Ub5/ncQvRNikBrGJ6sG7RwgxZ9kyJwv3s+qqoNG1EqDiJHJ3mbEgBcXV3Rt29f9O7dW3YhBgCbNm1CUlISOnXq1Oj+goICvPfeewgJCcHixYtRXV0tu20iInuUkiJvHehVq5rfx/Ll8o5rrBBrCqk4z81JgRqmT1YNLWZWm3GyMuIgcnSyirHmev311+Hv79/ovsrKStx7771ISEjAtm3bUFZWhnfffdeS4RARWVx6urxiLC2t+X3U1TX/vU0hFefAY+lwkyjG3KBFFMw4WRlxEDk6lSAIgqU7CQwMxCeffIKuXbsaPeb48eNYsGABMjMzJdurrq5GQUGBkiESESliyJBBEASV5HEqlYDDh/9jdP+V6ivYfmE7zpSfQYW2At5qb/Ro0wOhd4Xi4eFjAEj3oQRTcQ70HwIXSP8KqYULXFFrsTiIHIWvry/c3d0NtkuOGQOAGzdu4Pr162hYt91+++1mBfTbb7/hhx9+QEREBABAEAS4usoKR8fYSSntyJEjGDx4sMX7cSbMqfKYU+U1J6fe3kB5ufRxbdqoGm378KXDWLpvKbLPZAMANDUa3T5PV0+8f+Z94Mkg4Lv5wG9DmhRbcxiLEwDK4I22kD7ZCnhbNA7i339LUDKnUheRJG9Tbty4Ef7+/ro1KZVam9LDwwMrVqzAhQsXIAgCPv30Uzz88MNmt0tEZEuRkfLWgY6KMty+Jm8NAjYEIPNkJjQ1Gr1CDACu11wXt/XOBKIDAP81ygXehDjr/dgvEjdg+mRvQI00mGhEgTiIHJ1kMfbBBx9g48aNOHHihG6NSnPWppw+fTqOHj2K9u3bY/HixZg5cyYeeeQRCIKAp59+utntEhHZg/h4ecVYXJz+tjV5azB3z1xUaasgSN36cxEAtypg7FyTBVkTbzbIirOhu9+Jh1aiGNNCjTXuJhpRIA4iRydZjP3lL3/Rm4G/OXJzc3Xjxd5//330798fADBu3Dh8+eWX2L17N5YuXdqkJzWJiOyRjw+QkQF4eRkWZWq1uD0jQ3+ahsOXDusKsSapL8juzDPY5ecHfPGF6TiSk5sW563+GuiD48kZqISXwRWyG1CjEl44npyBldt8LBoHkaOTLMZGjBiBf/3rXyguLkZpaanuRUREjQsKEufFionRn1E+JkbcHhSkf/zSfUtxXXu9eZ25XgdG3Fwv2MUFmDcP+Pln6TgSE5sWZ2OGJAbhj5x87PeNQRnaohYuKENb7PeNwR85+RiSGGQQh0olKB4HkSOTfJrSz8/PYC1KlUpl1q1Kc9UPhOMAfsfFnCqPOVWeNXJaUlmCv/7jrwbjw5rCw9UD/33hv+jYuqOCkVkOv6vKY06VZ4kB/M1+mjJfappnIiJqto9/+tjsNlRQ4eOfPkbC8ATzAyIiqzNajGVlZSEsLAwfffRRo/s52J6IyHz5xflmXRUDxKcsj5YcVSgiIrI2o2PGzp8/DwD45ZdfGn0RETmiwkIgNlZ/bFJsrLjdFq5prinSzlXN1Ua3S51vbi7g6wuoVDdfvr7idrltOB0mhBRm9MrYnDlzAABLly41dggRkUPJzhYXna5fgBoQJ2hdvx7YsEF8as/ag8Vv87hNkXbaebQz2CZ1vuHh4vJNtzp2DBgzRnzKccgQ+8uZTdnjl4gcnkXXpiQisheFheLv0Koqw7UjtVpxe0SE9S9u+HX2g4erh1lteLp6on+n/nrb5JxvY4VYQ0lJYsFmbzmzGXv9EpHDYzFGRE4hJUXeAt6rVlknnnrRA6LNbkOAYNCOnPOVQyMxnM0WObMZe/0SkcNjMUZETiE9Xd7v0bQ068RTr1PrTgjqEQRVMxf+VkGFR3s8ajCthZzzVYItcmYz9volIocnObWFRqPBnj17cOXKFb2Fwvk0JRE5kooKZY9T0vwR87G7cHfTZ+AH4Kn2xPwH5xtst+Z52CJnNmHPXyJyaJLFWHx8PIqKitCrVy+oVM37lxsRka15e4vjrOUcZ21DugzByrErm74k0g0vrAxeCf87/Q12yT1fJdgiZzZhz18icmiSxdgvv/yC3bt3w8WFdzSJyHFFRooPvJm6y6RWA1FR1oupoZn+MwFALMiqr4uLgRtTpwJqPPF/BSsx8/WZjR4i53yVYMucWZ29f4nIYUlWWB06dEBNTY01YiEispj4eMOFqG+lVgNxcdaJpzEz/Wdib/RejOg4AdB6AFpP/QO0nuL2kxOAj/ci/fnGCzFA3vnK4SHxoKetc2ZVjvAlIodk9MpY/cz7HTt2RFRUFMaMGQN1gy8hx4wRkSPx8RGngLp1iihA/P2pVov7fXxsFyMA+N/pj+9mb8HLyZexfM/HQOejgMdVQNMOKO4P/BQNVHVEcjIQGGi8HTnna2yesXrG5hlr2IY95MxqHOVLRA7H6JWx+pn2vb29cc899+Ds2bOcgZ+IHFpQEJCfD8TE6E+eHhMjbrenuTqXJXVEzpIE+J7+BNi4Hdj2CfBDAnzv6YicHCAxUboNqfNNSwNycsQZ9xvy9YWuD0fKmVUwIWQBRq+M1c+8/+9//xsPPfSQ3r7MzEzLRkVEZCE+PkBqqviyd4GBwFEzl5yUOl85fThSzqyCCSGFGS3GcnNzUVNTg+XLl0MQBN20FjU1NVi9ejUee+wxqwVJRERE1FIZLcZOnDiBAwcO4M8//8Qnn3xy8w2uroiOjrZGbEREREQtntExY7NmzUJaWhrmzp2LtLQ03eujjz5CFB/bJSKFFRYCsbH6w3BiY62/zF96OtC+PaBSAf7+g6BSiT83HOiemyuOq1Kpbr58fcXtco+R04ZUTuTkzF7ySkTGqYSG0+o3IDUuzJa3Kaurq1FQUABfX1+4u7tbvL8jR45g8ODBFu/HmTCnynPknGZnSz+gZo1x0VFRpp8ujIwEevYUF9A2JjlZ/NPUMaNHA19/bboNqacYX34ZWLbMdM4A+8jrrRz5u2qvmFPlKZlTqbrF6G3KXbt2AQAuX76MX3/9FUOHDoWrqysOHjyIe++9l2PGiEgRhYViwVDVyMTz9UVERIT4oJolZwxITzddiNUfI8VUEVbPVCFW34a7O1BdbbivPifG+qnfHx4uXm27ft34MdbIKxFJM3qbcu3atVi7di06dOiArKwsrFmzBqtXr0ZmZiZcXSUn7icikiUlRd7ay6tWWTaOOXMs235TNVaINfX9Go3pY6yRVyKSJjkDf1FREbp166b7+c4778Tvv/9u0aCIyHmkp8srxtLSLBvH1auWbd/aBEF8mWKNvBKRNMlLXB07dsQ777yDCRMmAAA+//xz3HXXXRYPjIicQ0WFssdR0zCvRLYneWXszTffxKlTpxAWFobHHnsMly5dwhtvvGGN2IjICXh7K3scNQ3zSmR7klfGOnXqhH/+85/WiIWInFBkJLB+velblWq1+KSjJbVr17JuVapU4p+mblVaI69EJM1oMfb888/j7bffRkhISKP7t2/fbrGgiMh5xMcDGzZIF2NxcZaN45137KswMfY0ZVPeb+xpynrWyCsRSTNajE2fPh0A8Morr1gtGCJyPj4+4nxXUvNhWXr6hchIYPdu55tnjNNaENme0TFjH374IbKystCjRw/cf//9Bi8iIqUEBYnzXcXE6M8UHxMjbrfWxKRpaeKrffv6LeI9vvbtb+5LTARycsTZ8hvy9RW3JyZKH5ObK92GVE4SE6VzZi95JSLTjM7Av379ehw4cAA//vgjevbsiVGjRiEgIAD33nuvtWM0wBn4HR9zqjzmVHnMqWUwr8pjTpVnFzPw//3vf8ff//53aLVa/Pzzzzhw4ADeeOMNFBUVYdiwYViyZIkiARIRERE5M8mnKdVqNXr06IGSkhKUlpbiypUrOHXqlDViIyIiImrxjI4ZO3fuHD788ENMnToV48aNw+7du9GvXz988skn2LRpkzVjJCJSTGEhEBurP4YqNlbcfuv+IUMGGexXqh9rtWEPfRCRBMGI3r17CzNnzhT2798vaLVaY4fZhEajEfLy8gSNRmOV/vLy8qzSjzNhTpXHnErbuVMQvLwEQa2uXyxIfKnV4vbkZNP7d+5Uph857SjRhj300Rh+vynpgAAAIABJREFUV5XHnCpPyZxK1S1Gr4xNnz4dFy5cwKuvvoqUlBQcPHgQtbW11qwTiYgUU1goTvNQVWU4p5lWK25PSjK9PyJC+oqRnH6k2lGiDSnW6IOI5DFajMXHx2P79u344IMP0KVLF6xbtw4jR45EXFwcvvjiC2vGSERktpQU6QXJpWi1wKpV5vcj1Y4SbUixRh9EJI/k2pRdunRBZGQk3nrrLcybNw+//vorXn75ZWvERkSkmPR0ZYqxtDTz+5FqR4k2pFijDyKSx+jTlGVlZTh8+DAOHTqEQ4cO4bfffsOwYcPw1FNPYeTIkdaMkYjIbBUV1mlHbj+mjlOiDaXeq1TeiMg4o8XYsGHD0Lt3bzz44INYuHAhBg4ciFatWlkzNiIixXh7A+XlyrSjRD+m2lGiDTnvtXQfRCSP0duU33zzDbZu3Yq4uDj4+/uzECMihxYZKa7HaA61WnoxcTn9SLWjRBtSrNEHEcljtBjr2LGjNeMgIrKo+HhlirG4OPP7kWpHiTakWKMPIpJHcgA/EVFL4OMDZGQAXl6GRYhaLW5PTja9PyNDbMfcfqTaUaINKdbog4jkYTFGRE4jKAjIzwdiYvRnnI+JEbcnJurvV6kEvf1BQcr0I6cdJdqwhz6ISJpKEAShsR0fffSRyTc+/fTTFglIDqnVz5Wm5MrtJGJOlcecKo85tQzmVXnMqfKUzKlU3WL0acpffvlFkQCIiIiIyDijxdjSpUutGQcRERGRU5IcM/bjjz9i5syZmDZtGp566ilERkYiICDACqERkZTCQiA2VhznM2TIILRtK/7srOsJNsxH/finW/Mh5xgiImuSLMYWLVqEgQMHoqKiAiEhIfD29sbYsWOtERsRmZCdDfj5AevXi5N3CoIK5eXiz35+4n5nYpgPGORDzjFERNZm9DZlPZVKhZiYGFy9ehX33HMPQkJCMHHiRGvERkRGFBYCERFAVZXhPq1WfEVEiE/EOcPUBHLyMXGiWHxpNMaPcaacEZH9kLwy1rp1awBAt27dcPr0aXh4eMDFhTNiENlSSoq8RZ5XrbJOPLYmJx8aDVBdbfoYZ8oZEdkPyaqqf//+eOGFFzB06FB8+OGHePPNN+HqKnlBjYgsKD1dXjGWlmadeGxNTj4EQXyZ4kw5IyL7IVmMLVy4ENHR0ejevTsWLFiAuro6vPXWW9aIjYiMqKhQ9jhHp+R5OkvOiMh+yCrGBgwYAAAICAjAggULsIrX8Ylsyttb2eMcnZLn6Sw5IyL7YfR+Y1JSEoqLi3HkyBFcuXJFt72mpgYXLlywSnBE1LjISPEJQFO35tRqICrKejHZkpx8qFTin6ZuVTpTzojIfhgtxiIiInD69GmcOnUK48aN021v1aqV7koZEdlGfDywYYN0MRYXZ72YbElOPjw8jD9NWc+ZckZE9sNoMda/f3/0798fDzzwAO644w6UlZWhbdu21oyNiIzw8QEyMsSpGOqnZainVouvjAznmaJBbj4A5oyI7I/kmLHr16/j0UcfRXBwMIqLixEUFIRCTlVNZHNBQeKcWDEx4izyKpWAtm3Fn/Pzxf3O5NZ81M+u3zAfco4hIrI2yWJsyZIlWLhwITp06IDOnTsjMjISiYmJ1oiNiCT4+ACpqcC1a8Dhw//BtWviz856dadhPmpr0Wg+5BxDRGRNksVYaWkphg8frvt56tSpqOCz30RERESKkDWVfnV1NVT/exTp8uXLqKurs2hQRERERM5CshibMmUKnnnmGfz5559ISUnBk08+icmTJ1sjNiKyE7m5gK+vOD1E/cvXV9xuzTYKC4HYWP3xXrGx4nalNOxjyJBBjfZhjTiIyHlIFmMRERF4/vnnERISgpqaGixZsgRTpkyxRmxEZAcWLwbGjAGOHdPffuyYuH3xYuu0kZ0N+PmJ84mVl4vTVJSXiz/7+Yn7zWXYh8qgD2vEQUTOxWQx9ssvv2DPnj3o1q0bEhIS8PLLL+uNH5NSUVGB8ePH4+LFiwb7Tpw4gfDwcIwbNw4LFy5ETU1N06MnIovKzQWSkkwfk5Rk+uqWEm0UFopTUlRVGc4lptWK2yMizLsyJaeP8HBg4kTLxkFEzsdoMbZlyxZERkbi/fffR2hoKPbt29ekhn/++WdMnjwZ586da3R/QkICEhMTsXv3bgiCgE2bNjWpfSKyvDlz5B33/POWbSMlRd7C6Oas1Canj+pq05PGKhEHETkfo8VYWloatm/fjs2bN2Pt2rVYt25dkxretGkTkpKS0KlTJ4N9ly5dgkaj0c3kHx4ejl27djUxdCKytFtvKxpTUGDZNtLT5RVjaWny+mpuH4JgejklJeIgIudjdAZ+AOjcuTMAYODAgbh69WqTGn799deN7ispKUHHjh11P3fs2BHFxcVNah8ACkz931thR44csVpfzoI5VZ7yOR0EQCXjOAFHjvzHYm1UVMhro7zcVBymye1DDnPicBb8+6885lR51sqp0WKsfiqLeq1atVKs07q6Or32BUEw6E8OX19fuLu7KxaXMUeOHMHgwYMt3o8zYU6VZ9ucqhTo23gb3t7iIHkpbdo0Pw65fchhThzOgH//lcecKk/JnFZXV5u8gCRrnjHAsDgzxx133IHLly/rfv7jjz8avZ1JRLbVr5+843x9LdtGZKS4dqQpajUQFSWvr+b2UT8lhyXjICLnY7QYO3XqFAYNGqR71f88cOBADBo0yKxOu3TpAnd3d93lv6ysLIwcOdKsNolIee+8I++4t9+2bBvx8fKKsbg4eX01tw93d8DDw7JxEJHzMXqb8quvvlK8s+nTp2POnDno378/Vq5ciUWLFqGiogL9+vXDU089pXh/RGSewEAgOdn01BTJyeJxlmzDxwfIyBCnjdBq9Qfaq9XiKyPDvPUl5fYBWDYOInJCggPSaDRCXl6eoNForNJfXl6eVfpxJsyp8iyZ05wcQfD1rX+WUHz5+orbrdnGmTOCMGuWILRtKwguLuKfs2aJ25XSsA+Vqq7RPqwRR0vGv//KY06Vp2ROpeoWk09TEhEB4lWro0dt34aPD5CaKr4spWEfR478p9EBvNaIg4ich+wB/ERERESkPBZjRERERDbEYoyIiIjIhliMEREREdkQizEiIiIiG2IxRkRERGRDLMaIiIiIbIjFGBEREZENsRgjIiIisiEWY0REREQ2xGKMiIiIyIZYjBERERHZEIsxIiIiIhtiMUZERERkQyzGiIiIiGyIxRgREdH/b+/Og6Ou7z+Ov3ZDQhKgIYk5QC4hUQiRo8FWxJFDJUiJQYozlI5chY4OFGX4wYAitsjRopXhqMdQj2mlnD8JYrlEW6YSioJCiPjjsoEgcRMSCFfO3f398S1rQrKbTbKbL5s8HzOZye73s9/P+/thxrz8fj/7+QAmIowBAACYiDAGAABgIsIYAACAiQhjAAAAJiKMAQAAmIgwBgAAYCLCGAAAgIkIYwAAACYijAEAAJiIMAYAAGAiwhgAAICJCGMAAAAmIowBAACYiDAGAABgIsIYAACAiQhjAAAAJiKMAQAAmIgwBgAAYCLCGAAAgIkIYwAAACYijAEAAJiIMAYAAGAiwhgAAICJCGMAAAAmIowBAACYiDAGAABgIsIYAACAiQhjAAAAJiKMAQAAmKiV2QWgCZSUSrk2Kb9QsjukIKsUGy11jpPCQs2uDgCAFo0w1twVFkvHz0gOxw/v2R1SXoFkK5SSekjREebVBwBAC8djyuaspLRmEKvK4TCOl5Q2bV0AAMCFMNac5drcB7GbHA7pvK1p6gEAADUQxpqz/ELv2tm8bAcAAHyOMNac2eu4K1bfdgAAwOcIY81ZkJf/vN62AwAAPsdf4eYsNtq7dnFetgMAAD5HGGvOOsdJ1jr+ia1WqVNc09QDAABqIIw1Z2Ghxjpi7gKZ1WocZ+FXAABMw6KvzV10hDQgyVi+wlZlBf64aOOOGEEMAABTEcZagrBQKbGr8QMAAG4rPKYEAAAwEWEMAADARDymhKGk1Ng+Kb/KvLLYaOMbmcwrAwDAb/x6Z2z79u0aOXKkhg8frnXr1tU4vmbNGg0dOlTp6elKT0+vtQ2aQGGxdOi4lFfww2r8dofx+tBx4zgAAPALv90Zs9lsWrFihT744AOFhIRo3Lhx+ulPf6qEhARXm+zsbL322mvq37+/v8pAXUpKpeNn3G8o7nAYxwckcYcMAAA/8NudsczMTN1///1q3769wsPDlZqaql27dlVrk52drbfeektpaWlatGiRysrK/FUO3Mm1uQ9iNzkcxtIYAADA5/wWxvLz8xUTE+N6HRsbK5vthz/o169fV69evTRnzhxt3bpVV65c0euvv+6vcuBOfqF37WxetgMAAPVicTqdTn+c+I033lBZWZmee+45SdKmTZuUnZ2tRYsW1dr++PHjev7555WRkVHnucvKypSdne3TeluqH19zyuJFO6ekL9t60xIAANQmOTlZrVu3rvG+3+aMxcfH69ChQ67XBQUFio2Ndb2+cOGCMjMzNXbsWEmS0+lUq1b1K8fdRfna4cOHlZKS4vd+TPHZlz9M2vfAEmRVSsqPfdZtsx5TkzCmvseY+gfj6nuMqe/5ckzruonkt8eUDzzwgA4cOKCioiKVlJRoz549euihh1zHQ0ND9corryg3N1dOp1Pr1q3To48+6q9y4E5stHft4rxsBwAA6sVvYSwuLk6zZs3ShAkTNHr0aI0aNUp9+vTRtGnTdOzYMUVFRWnRokV65plnNGLECDmdTk2ePNlf5cCdznHuNxK/yWo19rEEAAA+59dFX9PS0pSWllbtvbVr17p+T01NVWpqqj9LQF3CQqWkHu6Xt7BajeMsawEAgF+wAj+k6AhjHbHzNuNbkzdX4I+LNu6IEcQAAPAbwhgMYaFSYlfjBwAANBk2CgcAADARYQwAAMBEPKY0W0mpsSVRfpW5WrHRxrccvZ2r9X2BdPq8ZLf/8F5QkJTQSYqP8a4PX9Thi3MAANDCEMbMVFhc81uMdoeUV2BMpE/qYUyu9+Sbb6X8oprv2+3SibPS94XS1Rue+5AaX4cvrgUAgBaIx5RmKSl1v5yEZLx//IzRzp3vC2oPYlUVX6ujj9PS16cbV4cvrgUAgBaKMGaWXJv78HKTw2EsN+HO6fONr8PhlOranrSuOnxxLQAAtFCEMbPkF3rXzuahXdU5Yv7mqQ5fXAsAAC0UYcwsXmzOXa92/uapjkC7FgAAbiOEMbMEeTn03rbzN091BNq1AABwG+Gvo1lio71rF+ehXVCQb2ppbB2+uBYAAFoowphZOscZm3B7YrUae0O6k9Cp8XVYLZLF0rg6fHEtAAC0UIQxs4SFGmtvuQsxVqtx3NNiqfExUmyU534i2tbRR4LUO6FxdfjiWgAAaKFY9NVM0RHSgCRjyQdblVXr46KNu0jehJde3aXIdnWvwF9XH42twxfXAgBAC0QYM1tYqJTY1fhpqPgY46cxffiiDl+cAwCAFobHlAAAACYijAEAAJiIx5Rmu1Qsnc6VblTZtzE8VEroLEVGGPtPepoPJhlzwnJtxkr4N+dqxUYb33L0dq6WL84BAADqjTBmppwL0tkLNd+/USplnTJC2Y1aNte226UTZ6VLV43AdOsm3XaHlFdgTKRP6mFMrveksLjx5wAAAA3CY0qzXCquPYhVVVsQqyq/SMo+5X6TbofDCFklHs5TUloziNX3HAAAoMEIY2Y5nds0/TgcxnIT7uTa3Acxb88BAAAajMeUZqnrrlcd8suL9N73Hynr2ikVV15TRKu26tM2UZPj0xQTElm9sa3Q/XIT+YXedejpHAAAoMEIYwHmiytfa9m597SzMFOSVOosdx374OI/9FLOW3os6gHN7zJJ9/2ot3HA7uHOl6djDWkHAADqhceUAeSN77ZoyJGnlXFxn0qd5dWCmCSVOMpU6ihXxsV9GnLkab3x3RbjQJCHf2ZPxxrSDgAA1At/Yc0SXr/lIt74bov+58xK3XCUyimnx7ZOOXXDUar/ObPSCGRx0e4bx3o4VpWncwAAgAYjjJklobPXTb+48rUriNXHzUB2KOi8+0ad49xv8H2T1WrsLwkAAHyOMGaWyAipa0fPbf5792zZufdU4ihrUDcljjIt++I19w3CQo11xNwFMqvVOM7CrwAA+AVhzEzdOkp9Ems+sgwPNd6/L1n5XcK1syizzkeT7jjl1I7TO1RwvcB9o+gIaUCS1DHmh7lhQVbj9YAkFnwFAMCP+Dal2SIjpPvch533Lnxk3J1qxJcZLbLovSPvac6gOe4bhYUaS1ewfAUAAE2KO2O3uSxblkorG7cmWUlliY7lH/NRRQAAwJcIY7e54tJin5znUukln5wHAAD4Fo8p3SkpNbYKyi/Uj+1O6bMvjWUgOsd5P5n9UrGx7VHV1fbDQ41vUkb+99HkuQvSf2rZo/KujlKXjoootTT+WiRFFtulfYeqv9kpVurRxfi9yvXK7jDmjNX3en1xDgAAWhjCWG0Ki6ttnm2RjHCRV2BsC5TUo+5J7TkXat8I/EaplHXK+Cbl5StS8bXaP/8fI6T1Ce6m/7WE1FjgtT7CrK11b5uEmgfO50tFV6TunWtuFl7f671lzBp0DgAAWiAeU96qpLRmqKjK4TCOl3iYx3WpuPYgVtXZC+6DWBWT4kfV2aYuTjk1KT6t9oM3SqXsU427Xl+MGQAALRRh7Fa5Nveh4iaHQzpvc3/8dK7PyokNidJj0Q/IooY9rrTIopFRg2puHl4fdV2vL8YMAIAWijB2q/xC79rZPLS74ds7QPO7TFKYtXWDPhtmba35XSY1vghP1+uLMQMAoIUijN3K7uWCXt6284H7ftRbr/Z4VuHW+k2CD7eG6tUez2rAj5IaX4Sn670NxwwAgEBBGLtVkJdD4m07H3nmzrGuQFbXI0uLLK4g9sydY31TgKfrvU3HDACAQMBfx1vFRnvXLs5Du1u3N/KRZ+4cq3393tITdwxRqDWkxqPLMGtrhVpD9MQdQ7Sv31u+C2KS5+v1xZgBANBCsbTFrTrHGXObPE1It1qlTnHujyd0Npav8IMBP0rS/yYvV0H5Jb33/XYdu35alyquKTK4re5tk6BJ8WmNm6xfm7qu1xdjBgBAC0UYu1VYqLEmlrulGqxW47inRUwjI4x1xDwtb1HXOmN1iAmJ1Jxuk6TgIKmsokHnkGTcxattnbGbvLleX4wZAAAtFGGsNtER0oAkYykGW6GcdocsQVbjMVsnL1eT79ZRimhTxwr8HetcgV9Z/yddqiWwRbaV+vQ0fj/xrfR9Uc028VHSPd2lM+eMBV5vVXUF/irX61o9vz7Xe8uYNegcAAC0QIQxd8JCpcSuUmJXfXn4sFJSflz/c0RGSPfVsep8l/+GLnduBi5P7ulu/LjTo8sPocudKtfbYL44BwAALQwT+AEAAExEGAMAADARjykbo6TU2Aoov8ocqdho49uFvpoj5U0fdc07AwAAty3CWEMVFtf89qDdIeUVGBPYk3oYk9r93UdunvtvZP7nglR0RernxbwzAABgCh5TNkRJqftlHCTj/eNnjHb+7CP7VN1LYxRfM+6cAQCA2xJhrCFybZ4XOJWM4+dt/u3DW7U9wgQAALcFwlhD5Bd6187mZbvG9AEAAAIaYawh7F7esfK2na8/CwAAAgZhrCGCvBw2b9v5+rMAACBg8Be/IWKjvWsX52W7xvQBAAACGmGsITrHGZtfe2K1Gnsy+rMPb93FWmMAANyuCGMNERZqrPHlLixZrcbxxiz86k0fyYlSRFvP54loy8KvAADcxlj0taGiI6QBScbyFbYqq+PHRRt3xHyxAr83fURHsAI/AAABjDDWGGGhUmJX48fMProQugAACFQ8pgQAADARYQwAAMBEhDEAAAATEcYAAABM5Ncwtn37do0cOVLDhw/XunXrahz/5ptvNGbMGKWmpuqFF15QZWWlP8sBAAC47fgtjNlsNq1YsUJ/+9vflJGRoY0bN+r06dPV2syZM0cLFy7U7t275XQ6tWnTJn+VAwAAcFvyWxjLzMzU/fffr/bt2ys8PFypqanatWuX6/h3332n0tJS9evXT5I0ZsyYascBAABaAr+tM5afn6+YmBjX69jYWGVlZbk9HhMTI5vNVq8+srOzG1+olw4fPtxkfbUUjKnvMaa+x5j6B+Pqe4yp7zXVmPotjDkcDlksFtdrp9NZ7XVdx72RnJys1q1bN77YOhw+fFgpKSl+76clYUx9jzH1PcbUPxhX32NMfc+XY1pWVubxBpLfHlPGx8eroKDA9bqgoECxsbFuj1+8eLHacQAAgJbAb2HsgQce0IEDB1RUVKSSkhLt2bNHDz30kOv4nXfeqdatW7tuAW7btq3acQAAgJbAb2EsLi5Os2bN0oQJEzR69GiNGjVKffr00bRp03Ts2DFJ0quvvqply5ZpxIgRunHjhiZMmOCvcgAAAG5Lft0oPC0tTWlpadXeW7t2rev3nj17asuWLfU+r9PplCSVl5c3rsB6KCsra7K+WgrG1PcYU99jTP2DcfU9xtT3fDWmN/PKzfxyK4vT3ZHb2NWrV3Xy5EmzywAAAPDa3XffrXbt2tV4PyDDmMPh0PXr1xUcHFzvb2ACAAA0JafTqYqKCrVp00ZWa80ZYgEZxgAAAJoLNgoHAAAwEWEMAADARIQxAAAAExHGAAAATEQYAwAAMBFhDAAAwESEMQAAABP5dTuk5uDatWsaN26c3nzzTXXq1MnscpqFNWvWaOfOnZKkwYMHa+7cuSZXFPhWrlyp3bt3y2KxaOzYsZo8ebLZJTUbf/jDH3Tp0iX9/ve/N7uUgPfUU0+pqKhIrVoZf3oWLVqkvn37mlxVYPv000+1Zs0alZSUaNCgQVqwYIHZJQW0zZs36/3333e9Pn/+vNLT07Vw4UK/9ksY8+Do0aNasGCBcnJyzC6l2cjMzNRnn32mrVu3ymKxaOrUqfr444/16KOPml1awPr888/173//Wx9++KEqKys1cuRIDR48WN27dze7tIB34MABbd26VUOGDDG7lIDndDqVk5Ojf/zjH64whsbJzc3VSy+9pM2bNys6OloTJ07Uvn37NHjwYLNLC1hPPvmknnzySUnSqVOnNH36dM2YMcPv/fKY0oNNmzbppZdeUmxsrNmlNBsxMTGaN2+eQkJCFBwcrB49eujChQtmlxXQfvKTn+gvf/mLWrVqpcLCQtntdoWHh5tdVsC7fPmyVqxYoaefftrsUpqFb7/9VpI0ZcoUPf7449XuPqBhPv74Y40cOVLx8fEKDg7WihUruNPoQ7/97W81a9YsRUVF+b0v/vfEgyVLlphdQrOTmJjo+j0nJ0c7d+7U+vXrTayoeQgODtaqVav0zjvvaMSIEYqLizO7pIC3cOFCzZo1S3l5eWaX0ixcuXJFAwcO1IsvvqiKigpNmDBBd911lwYNGmR2aQHr7NmzCg4O1tNPP628vDwNGTJEzz33nNllNQuZmZkqLS3VY4891iT9cWcMpjh16pSmTJmiuXPnqlu3bmaX0yzMnDlTBw4cUF5enjZt2mR2OQFt8+bN6tChgwYOHGh2Kc1G//79tXz5crVr105RUVEaO3as9u3bZ3ZZAc1ut+vAgQNaunSpNm7cqKysLG3dutXsspqFDRs2NOncW8IYmtzhw4c1adIkzZ49W0888YTZ5QS8M2fO6JtvvpEkhYWFafjw4Tpx4oTJVQW2HTt2aP/+/UpPT9eqVav06aefaunSpWaXFdAOHTqkAwcOuF47nU7mjjXSHXfcoYEDByoqKkqhoaF65JFHlJWVZXZZAa+8vFxffPGFhg0b1mR9EsbQpPLy8jR9+nS9+uqr+tnPfmZ2Oc3C+fPntWDBApWXl6u8vFyffPKJUlJSzC4roL377rv66KOPtG3bNs2cOVPDhg3T888/b3ZZAe3q1atavny5ysrKdO3aNW3dupUv7jTS0KFD9dlnn+nKlSuy2+3617/+pd69e5tdVsA7ceKEunXr1qRzb/nfEjSpt99+W2VlZdWWCRg3bpx+8YtfmFhVYBs8eLCysrI0evRoBQUFafjw4QRd3HaGDh2qo0ePavTo0XI4HBo/frz69+9vdlkBrW/fvpo6darGjx+viooKDRo0SD//+c/NLivg5ebmKj4+vkn7tDidTmeT9ggAAAAXHlMCAACYiDAGAABgIsIYAACAiQhjAAAAJiKMAQAAmIgwBsAnzp8/r169eik9Pd318/jjj2vLli11fjYrK0sLFy6ss928efP09ttv13h/9erVWrRoUYPqrq81a9Zo7969Huu51cqVK5WRkeHv0gAEKNYZA+AzoaGh2rZtm+u1zWbTqFGjlJycrJ49e7r93OnTp2Wz2ZqixEY7ePCgEhIS6vWZZ5991k/VAGgOCGMA/CYuLk5du3ZVTk6Oevbsqc2bN2v9+vVyOBxq3769XnzxRYWHh2vVqlW6evWq5s+fryVLlmjp0qU6evSorl+/LqfTqcWLFzd4V4EzZ85oyZIlunz5sux2u5566imNHTtWBw8e1IoVK9S5c2edOnVKlZWV+t3vfqeUlBQVFRVp/vz5OnfunNq3b6+YmBglJiYqKipK2dnZWr58uYKCgiRJX331lcaNG6eLFy8qMTFRf/zjH2us3D1v3jwlJibqV7/6le699179+te/1v79+5Wfn+9atPNWR48e1eLFi1VSUqLg4GDNnTtXAwcO1L333qvJkycrMzNTN27c0IwZM7Rr1y6dPHlSsbGxevPNN5t05XAAjcdjSgB+89VXX+ncuXPq27evPv/8c2VkZGjdunXKyMjQ1KlTNWPGDHXo0EEzZ87UgAEDtGzZMh09elT5+fnauHGjduzYoSeeeEJr165tUP+VlZWaOXOmZs+erQ8++EDvv/++3nnnHR05ckSS8Xh0ypQpysjI0JgxY7RixQoWswDhAAADnklEQVRJ0uLFi5WQkKCdO3dq5cqV+vLLLyVJv/zlL5WcnKy5c+e6tvKx2Wx69913tXv3btlsNu3Zs8djTeXl5YqMjNSGDRu0atUqLVu2TGVlZdXaVFRUaPr06Zo+fbo++ugjvfzyy1q6dKkcDofKy8t1xx13aMuWLRo9erQWLFigF154QTt27NC1a9f0ySefNGisAJiHO2MAfKa0tFTp6emSJLvdrsjISL3yyivq0KGD/vrXv+rs2bMaN26cq/2VK1d0+fLlaufo37+/IiIitGHDBuXm5urgwYNq06ZNg+rJycnRuXPnqu0rWVpaquPHj6tHjx7q2LGjevXqJUlKSkrS1q1bJUn79u1z/R4bG6sRI0a47eORRx5RWFiYJCkxMVFFRUV11vXwww9Lknr37q3y8nLduHFDrVu3dh0/efKkrFarhgwZIklKTk7W9u3bXcdTU1MlSV26dNHdd9+tuLg4SVKnTp1UXFxcZ/8Abi+EMQA+c+ucsaocDofS09M1Z84c1+v8/HxFRERUa/fPf/5TS5Ys0eTJk/Xwww+re/fu+vDDDxtUj91uV7t27arVdPHiRbVr105HjhxRaGio632LxaKbu8O1atVKVXeKs1rdP0Ro1eqH/4xWPYcnN4OXxWKRpBqfCQoKch276eTJk+revbskKTg42PV+1d8BBCYeUwJoEg8++KD+/ve/Kz8/X5K0fv16TZw4UZIRPiorKyVJ+/fv19ChQzV+/HglJydr7969stvtDerzrrvuqhYQ8/LyNGrUKGVnZ3v83ODBg13fAr106ZL27t3rCkdVa/WX7t27y2KxaP/+/ZKkr7/+WhMnTpTD4fBrvwDMwZ0xAE3iwQcf1LRp0zRlyhRZLBa1bdtWa9askcViUb9+/fSnP/1JM2bM0KxZszR79mylpaWpsrJSgwYN0p49e+oMIps2bXI9WpSke+65Rxs2bNDrr7+uJUuW6M9//rMqKyv17LPPKiUlRQcPHnR7rvnz52vBggVKS0tT+/bt1bFjR9ddtGHDhum1115TRUWFbwamFiEhIVq9erWWLl2q5cuXKzg4WKtXr1ZISIjf+gRgHovTm3vqANCCrFu3TklJSerfv7/Ky8s1fvx4/eY3v9HgwYPNLg1AM8SdMQC4RUJCgl5++WU5HA5VVFRoxIgRBDEAfsOdMQAAABMxgR8AAMBEhDEAAAATEcYAAABMRBgDAAAwEWEMAADARIQxAAAAE/0/9NBP1+FFiroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the clusters and plotting the centroids of the clusters.\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.title('Clusters with Centroids',fontweight ='bold', fontsize=20)\n",
    "plt.scatter(data[y_kmeans == 0, 2], data[y_kmeans == 0, 3], s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(data[y_kmeans == 1, 2], data[y_kmeans == 1, 3], s = 100, c = 'pink', label = 'Iris-setosa')\n",
    "plt.scatter(data[y_kmeans == 2, 2], data[y_kmeans == 2, 3],s = 100, c = 'red', label = 'Iris-virginica')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 2], kmeans.cluster_centers_[:,3], s = 300, c = 'green', label = 'Centroids')\n",
    "plt.title('Iris Flower Clusters')\n",
    "plt.ylabel('Petal Width in cm')\n",
    "plt.xlabel('Petal Length in cm')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________END______________________________-"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}