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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>MLDM Lab week 1: Introduction to Iris dataset and using a simple Decision Tree classifier from the sklearn library</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Introduction </h3>\n",
+    "<p>In this lab session we are going to learn about loading and exploring a dataset in Python. We will also use the sklearn library to create a simple decision tree classifier.</p>\n",
+    "<p>The dataset we are going to use is <b>Iris flowers dataset</b>, which is well-known dataset for demonstrating ML and pattern recognition algorithms. The data set contains 3 classes of flowers with 50 instances for each</p>\n",
+    "<p>More details about the dataset can be found from <a href=\"https://en.wikipedia.org/wiki/Iris_flower_data_set\">here</a></p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Lab goals</font> </h3>\n",
+    "<p> 1.  Introduction to the Iris dataset </p>\n",
+    "<p> 2.  Learn how to load and explore a dataset and to understand its structure using statistical summaries and data visualization </p>\n",
+    "<p> 3.  Use sklearn to learn and evaluate a simple Decision Tree classifier. </p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2> <font color=\"blue\"> About the Iris dataset</font> </h2>\n",
+    "<p> The dataset contains 150 observations of Iris flowers. There are four columns of measurements of the flowers in centimeters: 'sepal-length', 'sepal-width', 'petal-length', 'petal-width'. The fifth column is the class of the observed flower: Setosa, Versicolor and Virginica. </p> <i>Please refer the image below for more information.</i>\n",
+    "<img src=\"https://miro.medium.com/max/1100/0*SHhnoaaIm36pc1bd\" width=500></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Import libraries </font> </h3>\n",
+    "<p>First, let’s import all the modules we are going to use to load the dataset. We are using pandas to load the data. We will also use pandas next to explore the data both with descriptive statistics and data visualization.</p>\n",
+    "<p><b>You may need to install librariles (e.g. pandas) if you get error messages. These could be installed using pip as you did for Jupyter. Please inform the lab demonstrators if you need help with this. </b></p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Load  dataset </font> </h3>\n",
+    "<p>We are using pandas to load the data and explore the data. Note that we are specifying the names of each column when loading the data. This will help later when we explore the data.</p>\n",
+    "<p><b> You can also download <a href=\"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\">iris.csv</a> file into your working directory and load it using the same method, changing URL to the local file name. </b></p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\"\n",
+    "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
+    "dataset = read_csv(url, names=names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2> <font color=\"blue\"> Summarize the dataset</font> </h2>\n",
+    "<p>In this step we are going to take a look at the dataset in different ways:</p>\n",
+    "<p>1. Dimensions of the dataset.</p>\n",
+    "<p>2. Peek at the data itself.</p>\n",
+    "<p>3. Statistical summary of all attributes.</p>\n",
+    "<p>4. Breakdown of the data by the class variable.</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 1. Dimensions of the dataset </font> </h3>\n",
+    "<p>We can get a quick idea of how many instances (rows) and how many attributes (columns) the data contains with the shape property.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 2. Peek at the data </font> </h3>\n",
+    "<p>We can get a quick idea about the contents of the data</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "First 5 rows of the dataset\n",
+      "\n",
+      "   sepal-length  sepal-width  petal-length  petal-width        class\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\n",
+      "\n",
+      "Last 5 rows of the dataset\n",
+      "\n",
+      "     sepal-length  sepal-width  petal-length  petal-width           class\n",
+      "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
+      "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
+      "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
+      "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
+      "149           5.9          3.0           5.1          1.8  Iris-virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First 5 Data\n",
+    "print(\"\\nFirst 5 rows of the dataset\\n\")\n",
+    "print(dataset.head(5))\n",
+    "# Last 5 Data\n",
+    "print(\"\\nLast 5 rows of the dataset\\n\")\n",
+    "print(dataset.tail(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 3. . Statistical summary </font> </h3>\n",
+    "<p>Now we can take a look at a summary of each attribute.\n",
+    "This includes the count, mean, the min and max values as well as some percentiles.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       sepal-length  sepal-width  petal-length  petal-width\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\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 4. Class distribution </font> </h3>\n",
+    "<p>Let’s now take a look at the number of instances (rows) that belong to each class. We can view this as an absolute count.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Class Distribution\n",
+      "\n",
+      "class\n",
+      "Iris-setosa        50\n",
+      "Iris-versicolor    50\n",
+      "Iris-virginica     50\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# class distribution\n",
+    "print(\"\\nClass Distribution\\n\")\n",
+    "print(dataset.groupby('class').size())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>  <font color=\"blue\"> Data visualization </font></h2>\n",
+    "<p>We now have a basic idea about the data. We need to extend that with some visualizations.</p>\n",
+    "\n",
+    "<p>We are going to look at two types of plots:</p>\n",
+    "\n",
+    "<p>1. Univariate plots to better understand each attribute.</p>\n",
+    "<p>2. Multivariate plots to better understand the relationships between attributes.</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 1. Univariate plots </font> </h3>\n",
+    "<p>We start with some univariate plots, that is, plots of each individual variable.\n",
+    "\n",
+    "Given that the input variables are numeric, we can create box and whisker plots of each.</p>\n",
+    "<p><b>These plots give us a much clearer idea of the distribution of the input attributes</b></p>\n",
+    "\n",
+    "<p><i>We have to import the package <b>pylot</b>  from the <b>matplotlib</b>.</i></p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot\n",
+    "dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>We can also create a histogram of each input variable to get an idea of the distribution.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset.hist()\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p>Note that from the above histograms; two of the input variables have a Gaussian distribution. This is useful to note as we can use algorithms that can exploit this assumption.</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> 2. Multivariate plots </font> </h3>\n",
+    "<p>We start with some multivariate plots, that is, interactions between the variables.\n",
+    "<p><b>First, let’s look at scatterplots of all pairs of attributes. This can be helpful to spot structured relationships between input variables.</b></p>\n",
+    "<p><i>We have to import the package <b>scatter_matrix</b>  from the <b>pandas</b>.</i></p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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bgjyxuaVSj+GdyLGxFDMZx8zVB9zU+V14NAW3prC7r57nT01z75qFrp7OOj8PrW8kmTe4d00DnXU+XjoT48ElErdeOxdjOlPi6W1tfGBHO6enMmxpD2NaNicm0tT5ncz0h9Y3UdAt7l3TQHe9n7PTWbZ3RvC7NTyaSnPIc1m102oY/k+Xf/6Lea9JYGUplzXellwudFJKiRCCeLbE1w+OYlk28azOuuYgR0eTrG0O8ZO3JkkXDDJFkzNTOf7105sq+/3FKwMk8yUO+ZN89oE+fueJDTfug90m1PndbO6IEM+WCHtVfnrSkQC+oytaWYRf1xLiQizHi2dmOD2VYWdXlLfGU3TU+fnmoTGklNzdV8ejG1v4271DpIsmR0ZT72gdpr7GAMdGU3hcClvawwzFcyQLBls7orw5OEvBsNg3OMuOruiC/IW7ei5OkPYOJMjrJnsHEmzpiFTG/ZGRWf77c2cBmMkU+ZWH+ulp8COE4PnT0xwamkVRBJ+8t4d71zRU9lvTFFwgz/D4CmL+qxHV07faNmq8cxhLFvj24TF8LpXdPfX88Pgk+ZIJCO5eU4/EkUn2udVKwoth2fzNG0OkCwZPb2vjhdPTXJjJsaYpwGcfqA2vajOZKvLNQ2O4VMG2jgg/PTFN0KuxoTlIyLfQJFi2REqQEl4/H+flMzHuX1vPmakc52ay/ODYBI9taqnE9F+6EPlOY+5pRlUU/B6NT93Xi2HbeDSVL785zJmpzKLaE5dyejLDYCxHT0OAwViO7x+bIOzV6G+6GJNv2JKfnpjirfE0d3RHGU3k2Tc4i8+lEtta4rtHxskbFh/Y0X5NGj+rNvxCCD/wO0C3lPJzQoh1wAYp5fdW23aNm0sip3NsLEVfQ4DuhoWD68fHJzkfy/KROzsX1Pw8MZ7i/HQWTVEIejRsWyKAWLbIGxcSjM3myesm//rpjfw/PztPS8TLzu46vnXICYU7PZmmoFsEvRoF3SKV1/nyvhFaI16e2dnB0dEk2aLJXb11V6VhUuMiZ6czFA2LsdkSh4Zn6arzY5d9yrFMiXiuxI+OTzKVKtLXGKA+6Obxhha+cXCUoEfjyGiKtc0hDo8myetOXeU/+PkdzOYNNreFeP18HJ9bXVEi0duNExMpBuM5NEVhOJ5nc3sYj+KMQ8N2ak80Wx6KhsWhkSRBj8b2cmnJOSxbki4aGLbNsbEk56azeDWF+9c28sv39zKTKfHhOzv405cHkBKOj6doi3jpqPPhcylMpIrM5p31gbNTTu7r+ZkcW9rD+N0qB4ZmaQ55F4kfzqcarp4vAAeAPeW/x3Aifd5xhv9KPvp3AqZlUzRtgh6NHx6fYDpd4uhIkn/0cD9uzZntnJvK8BevDgAQz+r8m/dtruxfMmymMyU0RVDn09BUQcmElrCPeK7EeNIRp4r43Pxeeb+SadEe9ZLMG2xpj/DYphZePR/j/v5G/uLVAd644CwXaarCyfE0tpQYtnxHuxSuJxtaQ+wbSDCSyNMY8jCYyPHEphaGE3kG43m+f2yCCzNZJlMlIn4XLWEv2zsjPLiuiRMTafb0NxDyuvjhsUl00yJbMllXrmX82rkYewec7yvk1d5xi/G6YTORKuJSBLZc+HRzbjpHMm9wPpZj74U4h0ZSgJP9PD/D9sxUmmTB4NxUhnv66pnJFCtPTPNj8u/oruP4WIo7uupoi3iZSpeoD7i5o6uOoXiObNFifUuIbx0eQzdtBmM5mkIeTk86oc8NlyllWQ3D3y+l/AUhxC8BSCnz4p0szvEORjdtvvTmMImczgPrGvGWZ9QuTVkg+OQpqy2atiRwSZJVW9RbSUevD3lpDnkoel3U+d1MZ4oIIbBsucD/6dHUBZmOv/qufn71Xf0A/PGL5wFH8MulONoopmVfVuekxuUp6Ba2dPRU8rrpJGjpFqoiGI7lGIzniGVKIBwJAE0RqELw64+uZTie56cnp3AZFnf1RkkXzAW1cj2ui+PBo73zFnlt6VTNMmFJDSIhHIlin1ur/H3pk6kiBKK8f1edn7t66hFicbH5h9c3LZjc/OrDzjWRzOvkdafmQUE3catOXWuPplSiejRF4LrMIns1DL8uhPDhLOgihOhnXnTPcgghPoWzMKwCH5dSjlWhLzVWQapgkMg5iR+DsRzv39HO+ZksHVHfgkiNrjo/v/feTVyYyfHuTc2MJPIcHkmyviXEnd11hL2uygymZFikCiaWtPG61IoU7WSqwBdeHaA+4OFXHuxDUZYepP/g/j66G/y0R7wEPBrrW4Lopk3EXyvosVJyJZOXz8YIeFTu729kJOE8dW1sDZHMGzQFPewbSOBzq5ydyrChLYRbVVjfEuL9O9rQFJX2Oi9hr4vBeA4pIVM0ef+Odg4MzvLe7RdnqXd2Rwl7HV2faujL3wrMje91LUHcqsDjUhxjLuHffec4I4kC/+zx9dy7poEzUxk66nzs6qmjLuDG71ZpvaTW88bWMLZMs6ElyL1rGqgPugl7XSuWEp9MFyvJXyOzBT66u4uRRJ7+piAuVdAW8dEQcFdKOC5FNQz/53GUNruEEH8L3A/88uV2EEJ0AA9LKR+rwvFrVInGoJu+Jj+nxjPc3VuH16WyZRmRtM3tkYqA2rMnRkgVDC7M5Ohv6ifid+FRVc7NZIjnDEzL5ux0Fo+qIKXElpK/e3OYNwdmUQRsbA3x8IbmJY+jaUrl8dewbDa1hZnN61cUuapxkTcHE+wfTKCpgraIl22dEcZTBTyaQl9jgD976QJ53eTgsLN4eGBwlk3tYYJejal0ie2dUQLlGezu3nrSBZP6gJtz0xmifjf7h2YrobZCiIrb5+3GTKaESxWLVC2fPTFVGd9rmv24VAVFCN4YiPHsiSkA/uDZM/yTR9ZSNCweXt+EqiqYlo1k8TqUqgj8bhVFUVAUsahQy5VY0xhkbbOTsbuzK+rU6Z0nkbGp7crtVSOq51khxEHgXpynnN8q6/dcjvcAqhDiOeAE8NtSyuted+x28NGvhlTB4NuHxsmXF1f/yaPrVrRfQ9BNqmAQ9bs4NZnm2RPTaIrg4Q2NtEa86KbNuuYQZ6Yy2BKKho2UMJ0poiqCoGdlwzBTNJlKFzEsyViyUJNfXiHJ8iK9qgg+eEcHEZ+Lj+7qAuA//+Akg4k8maKJpipYtiM4ls6bHM0nOTWZ4fvlbOpP3ddDS9jLx+5x3HJfetOJ/28IvP2LpZ+ZyvD9oxMoQvDR3Z0LMs3nj+++hgBb2iOoimBrewSXqmBYNh1RL187MML5mRxT6RJT6SJf3T+Kpgj+tw9sYe28m2HRdFxthnVtEVBuTeH9O9qvvOFlWE2x9TsveWmi/LNbCNEtpTx4md1bALeU8jEhxO/jyDd845L2a+qcN5hYpkRed+6/Y7OFK2x9kfdua2Mi5aSgzy3smbbE69L4Lx/exmSqxF09dfy/L5zD51IJ+5zHWpeqEHCr+C9ZJzg2muLoWJLtHdEF0razeR3DcjIgY9krehNrlIkG3Gxtj6CpgpFEgQNDSda3BNnVW89EqojP5azZ/KunN/DNg+NMpIq4NYWWkIfBRJ6ReB5NUcgUDfzuiybjw3d2MpUu0hJePlHo7cLceLKlJJ7VFxj++ePb61JpCnlxqwp1ATd//Mm7GIjleXJrKx/9n68znSmS10166p39TVsyPFtYYPhzusVMpkT0Mq6Y681qZvz/52Xek8Cjl3k/BbxY/v1nwK5FDdTUOW84a1tCfGBnO2ensvzi7q4V72fakmzJJOJ3sbMjxFf3DdMU8rC2KUgsWyLkdcI6n9raxtHRJJvawnytPBsybclYqsimeS6lF89MY1iSF3PTCwx/X0OAXb11ZIrmkhrl18JIIo9u2VWLPhlJ5DFtSd8tsvic101CHo2mkBuvS2XvQByvS2UqXWRHV5TP3N/Ltw6Ps7Mzyu7eRg4NpUjkDMI+jae2tfGVfcOMJQqkCzrikpI5bk1ZUFLwepDM60ykiqxpClzX8N07u+s4O5XF71LZeEkYZLZkcmw0xY7uCB1R/4Ib3YbWMBvKrpo1TQGKpkV3vY+P3dND0bSJ+t08tHZhhu50uoQQMJ0pYZo2L5yZpjnsXRT2uVLSRYOx2QJ9jYHK2tqVWE2x9UeudV/gNWCuatdOYGAVbdWoIh+/Z3lhp+X4zuFxxpIFQl6NY2MpXjozgxCCjjq/o+9uS+7qqeOh9U0Vg3hXTx1Hx5J4XeqieOOuej8XZnKLjIqiCB5cV70QzuF4nq8fHAXgsU3N13zhzTEQy1XyER7f3HJLSBN/4+AYR0aSjCULSAl+t0rQq3F/fyOaIriju26Bhv7Wzgg53aIh6GZXbz3/47lzTGeKJPI6sUxpQc7G9UY3bb68b4SCbrGmKbBAmrjanJ3KksjpJHCSEOeHX/7vPzjJcDxP8KDGH3/iLrRlopUeXt9ES9hLd72ftqiP33vv5iW329oR4dREmvUtIb7w+gA/PTGNIgSff/9mNq7APz8fKSVf3TdCpmheVR2AaizuVhBC/ImU8nNX2k5KeVgIURBCvADEgD+sZj9qXF9My+Z7RyeYzhR5bFMLp6cynJpM0xLykio4UUFSSsZm8+wfnCVXMlGEk7E7V2x9W2eEe/oaqPO7FolQvX97O5miSchbneH5/KlpTk9l2N1bz109F43cfBGrgr6yJSYpJT9+a4rBeI4HLpnJzW+jaFjkdZNvHRqnYFjc2RVl/9AsdQE3H9jRXsmJqBZFw+Kbh8bI6xbv3dZWiSQpGhaGZaObFomcgWnbaIpSkQI4Npri1fMx+hoDPLG5hT39jWxpjxBwqzx/eoapstvH61Ipmjc2K9eWsiIGWNAtTk06RWA66/zs6W/gu0fHkRKe2dl+2TKDK2EyXeTwSBJFwMMbGhcY/rHZPOPlic0LZ6f4rz86g8+l8Ucf20lX/cXtntzayp7+xiuO23/z9CYmUkXaIl7+6Pmzlc+ayJf46r4R0kUng/1KGcDOflQifFY6hqHKhp8lXDbLIaX851U+do0bxEzWyfjM6xZRn5ugR6Mx4CHsc/Hb717Hb3/lMHV+F09va+PVczFMWzKRKvLK2RnGkwUSWZ1MwVlw9LtVHljXyNrmi7N+RRFVC9c0LJvDI0kADg3PLjD861uCZEuNlMry0ishWzI5OZF22iu3O8fG1hB53SzLGkedghnpIgDPnpzCpToFTSZTxUWZ0KtlKJ5nMuX4l795aJRf3N1NXcDNg+saKRom/U1B3rgQ562JNF5N4eUzMT73YD+vX4hxbirLdLrIA2sbCXg0wl6Ng8NJfnpyivvXNnB0LMX7d7Sv+BxVC69L5f072hmM59jZGeXHb02S1y3OTGXwuxXiWWeScWYqe0WxwCvhUgUNQTeaItAUhcFYjplsiW0dEba0R7AldER9fPOA83SrCMH3j07wq+9aW2kjmTc4M5WhrylweYE0TaGr/P1/5v4+PC6VlrKo2g+OTqJbNu1R34oMv6oIntnZwbmZLFvaV/60UG3DP13l9mrcZHTTrsxOi7qJoiioQjAymydXstjQGuSevnpsKdnUFmY8Vay4Y0qmhRACiRMqmsjpjMwWkMCRsRRnpjKoQjCZKi4w/Nfav6VwqQobWp2Ios2XPEYLIRaIZy3VtksVCxJ1Am6N3kY/Q/E8m9sW9llRREWHXTdtWiMeQl6Nkmmzu7eOQ8NJon4XzeHqRyN11PmI+FwcHpmlrzHI1w+O8sv39fLSmRiGBSXdqBS2LxgWqirwuhzjOTKbp8FwM5eqcXoqw0tnpknldaJ+N7/33s2rNqzXSk+9j6agh6BXY1NbmKl0ifaoly3tEc5MZZFQlfWUja1hjo+lcGsKUZ/LERe0nYCHRzY043WpbGwN8/f7HdcTAgxr4Qz7O0fGSeR0Do0k+UcPrVlRkfmo382vlW8e56YzjMzm0U2bvG6uuO9d9f6rXmupquGXUj5ZzfZq3Fzm0u97Gvx01fn578+dwaUq/Maja0EKpJRIBEZZj8ewHI39OcIeN09va8OyJHf0RBmdLVAfcBP0agigPeJFCEFgheGcl/L9oxOcmcqwoyvCoxuXVyR8elsbT25pvariIEdGkjx/epqmkIeP7uqqZEEqiuCDd3QuW1pPSsm3Do/x5kCCkmlzR3cdn7y3B49LZU9/43UrUBL0aHz2gT5My2ZktsBLZ2bYN5CgYFi4VIUz0xlcisKm1hB1fhebOiJ8ad8wA7EcvQ1+uhsCFUPlfIeCDa1hHtnQxM6blDNhmjb/+lvHGIrnec+WVj77QB/bOiKVc/i5hxwB4GoIBVjSiR+xpXPTPjaWJlcyCXtdBNqc8WraNj63imAuFn/huJ37aq/1K/a7NXZ21QFyUeGiarOacM7vUs7WXQop5Qeute0atwany+UOh+J5RmfzGJbEsCyOjqboafCT102iPhdnJjMIIbgwk+PJLa34yuqba5uD1AXcJPI6W9rD5HWLc9NZ+hoDaIpgOlOiJeS5qgLdX3x9kHhe51P39nB22unf6cnsAsMvpWTf4Cx53eTeNQ14XWrFWIwl83zlzRH6GoN88M7lFwvPTGWcXIN0ycluvSSrcjkDXjJtDg0nOTWRweNSiGdLjCZy/OLubr51eIyOOn8lhv568JFdXXzpzWEMy2YglqOvMcBQPEdT0INlS+7uq+e+/kYifkdrpyPqZTqjs6vnoujd+pYQcptj6C59SrqRxPM6h4eTZEsmL56Z4bMP9FXOe7ag87//4BQAv/f0RoK+1fn4B2ZylfE9Olugp94Z3xG/xpmpi+O7r9GpMqapCi3hhcb57r4Gnj81zd299dd0M2qP+nhmZzvZkrls4mS1WM2M/w+q1osatyS7e+t540Kctc1BusrFHryaylPbWtk3OMt02inEkswb7BtMsKE1hMelLgjB7G7wV/zZEZ9S8bE/f3oav1sjU7IYjOXoXcHj+itnZ/ju0YnK33f31XNiPL3oxnF+JssLp6exbImmKDwwr+DFn740wMmJNHsHEmxqCy0bRXFXTx3pokl7xHtVCUpCOIlmqiJIFXRKhk2mYHBkJFWeJSVY1xy8qpvd1VAfcPPBOzr44fEJVEWwpjHA09ta+fK+EUJeF5+5v4+I341tSwZac3zv6AQNQTdvXEiwoytaCQe8nLLjjcKlKORKJlndLEt7X+R/vjzAS2edGgLRF9387pMbV3Wsze1hBuI53KpgV28dOd1iKl3knr4G0kWDNwec8d0S9jIUz+P3aAvGFcDLZ2coGBavnIuxvTN6TU93a26QqN1qwjlfvPJW1aWWeXtj2doRWRCS+Ee/dDFn733bL852ehouX5hlKUJl944iBH7PymKP64NuFCGwpaQx6GFPfyN7+hdXMdLLC7qWLRfNnOoCzqKxW1Muq2VyaXGLlaIpToGO7no/BcPkh8cmmcrorG0KUDRtNEVQf50zXdujPv7BAwvrID20fqEkhqIIntrWhmFLzk9n8bkV1FusTq5LEwS8GggIX/Jdtc0LK22PrD7EtD7g5pPz6t/O1x8CFoT6/j8fv2vJNkJejUzRJODRbvmaw9XQ418H/GdgM1D5BqSUtQpctyGZosELp2fwuVQe2di8rDHZ1VtPY9CD36NeNgJiPhtbwrxnSwszmRLv297OifE0JyfS7OiKLIwKEoLNbWFM2yZ8SWGRX394LTs6o/SUY63nc3gkyfnpLLt766856kZVBL+wu4tTE2leOhNDUcCtKPzcHe3UBzy0RX0LQgWvN2enMhwdTbG5PbykhstTW1sZiudoDnsvq+Z4o5ASfnR8EsuW3NNXT1PQ0bbvrFv4XX383h4agh6klDy1rW2Z1q53XyWvnIsRz+o8uK6RZ3Z2MDqbX1E0zs2mWnr8n8eJxX8E+Axw80dQjetKKm8wm9cr8eBzHBx2CkuAE21wOZfBStw74CRaBb0as3mdsWQBw5LsG4hzcDjJeLLAWDLPbz528Thrm4JsaA2RKRrc2VPHSCLHa+fiPLG5lbqgm3ctIQhXNCxeOD2NlE7I5qf39K7wTCwm5HUxOlvgjQsxYpkSXpfKgaEkn97TS2PZkI0nC7RHfSvOtJwjWzKZyZToqvOtqLbtT09OV463sTW0yPfsUpVrjqi6HhQNqxIuqwqnWlihZDEYz6KbNnsH4qxpCtAR9dMevThhyBQNYlmd7np/VZ5clhvf85lIFdk/OAs4BdDft739ljqXl6Maht8npXxOCCGklEPAvxNCHAD+7XI7CCF6gb3ASUCXUj5RhX7UuEFkSyZf3DuEXo5/n68Z3hL2IISjB365QhAr5c2BBK+ei6Epgj39DZyYSGNaki3tYV4+O0Mi56T0/+Y8nddYVmc8WcSWkvNTaf7VN94iUzL41uExvvS5+5Y8jltVaAi4iWX1qmjPvHEhzumpDDndpmDYvHB6mql0iR1dEbwulUzRpDnsuapMacOy+dLeYbIlk/UtoUXuiKVojXgYjOVpCXurEv1yvdFUgao47ryGgMZkpohh2gzFCvy3n57hwNAsPrfKR3d18levDQHw2fv7GIznyOsWm9rCPLm1dVV9uNz4nk/E58LnVinoFq1vM72iahj+khBCAc4KIf4JTgWulThHn5VSfqIKx69xlRQNm798dYCexgCPLCOHDHByIl1Z3J0vlZDXzUpGZbpgLNhnY2uY5pAXt6ZcVnVzOlPkx29NEfJoPL2tbUEs/oGhBEdHU2zvjDAYz3FkNIlXczRUkjmdgmmRLxlkigbZklOecSxZ4LmTUzQEPPQ3BbDL4XkzWYNcOSZ6rlzdUiiK4Bd2d5PM6zReo+pnXjf53tEJTk2kOTicpGg458iWTmaooihsbg+T03XcqkKqsHx/lsK0ZOWzzO376rkYZ8qF0AfjOXIliye3tlY+wwd2dBDPlq77ukK1cKkKv3x/L7YtyZUsSoaFaUOqoHMhlmUyVcTrUjgzlWE64yTHDcSzlXOdLhqcncrw6rnYFcf3fIbiOf7w2bN4XAr/8P6+yvi+3HcU8Gh8+r5esiVzxVr6twrVMPy/BfiB3wT+A44426dXsN8jQoiXgW9IKWuSDVx58Xrwv7y3KvvndJPZvMHscJJdPXWLKv/M8caFOMm8wf7BWXb11DEYz+PWFPqbgmztiHAhluW+NQ2L9luJkTk8nOTEeAqPprC1I8zJ8TSD8Tz/8ME1vHYujmlLXj8fp7POT8jjFPaYzhSxpbOAOjJbwOvS8LtsvG6Ng0OzxLM68azOto4w96ypp2hY7Olv5Fcf7ueFM9N89K7Lh1G6NWVVWjRnprIMxXK8NZ6moJuVakwSx6AVdIuH1jfSFPRyYiLF5rarC9nzuVWe3NrKYCzPnT1RdNPmxTMzpAoGI4l85Xs8OpqshLeqirjsZyqUM2Hbo75bxnh9+9AYJdNmS1sQuxwwXjRtNrdFmEmXaAp52NQa5s1wAglsaQ9TH/Awkiiwu7eOHx6fXNH4ns8Pjk1wfsYJ2zw+nuaRjc1MpYvc27d4fM/H53ZCl6/ESCJPqmCwqS18SyyiV0OPfx9Aedb/m1LKzAp2mwDW41Tq+rYQ4jkp5dH5G9Rkma8fcyXx2qPeSoGNpVjbHGT/4Cxd9X5OTqR58YxTZuGxTc2cnEhj2ZJDI0ke37x88tRypIoGA7EcLkXh0HCC//7ceaSUjMzmeWRDM6cmM/SXI2uG4nkCHpX71jTys1NOyNyetU0YNpyaSLOrt541TQHOz2SJ+Fw0h710z1tA/fi9PXz83pW7VK6Vzjoffo9Knd9FpuhiNm+gY1U0Le/uq69kCl/r4vHG1nClcIdlS8aTBWYyJfqbArRGvBQNm77GlUcj/fD4BEPxvDPTfWBN1TWErpZ4Tud/lsttvndbCx5NoWTatIS97OlvwLIlzWEP2zoj3F9+Cl3fEqYl7K1EcPU3BZnJlK44vucT9rlI5g0UIaj3u6paKH4mU+LrB0eREhI5nYdugVrR1Yjq2YWzwBsq/50CPiulPLDcPlLKEuXyjEKI7wFbgaOXbFOTZb6EaoWzBj0a//hd/Xg05bJ+3wfXNbG7tx6PprB/aLbyum7aFVeKeY3FJGZzjnvGoymOdk/RwC5fGE9ubeVdG5rxupz+9TQ4VY9SeQOXJigYEHCrfP59m0jmTerLawlO6bmbF5bYGPTwuYf6sW3J//GjU4zPFlAEuBSnvzu7HMP0wulpjoyk2NEVWXKheaVIKdnYGqK73k971Mcv7u7CkvKq5IvNcn0Dy5KV7/RmYlk2maLjzsoWLRr8LhJ5nd6y5LCUEp+m0hr2VTJ3L41Guq+/gTu6o7hVZcVhlRtaQrxvezsK0FZX3agcy5bMnVrTvrFCd8tRDVfPXwC/JqV8GUAI8QDOjWD7cjsIIULzngzuB/6oCv2ocRWsNJpkbrs7u+tQBLhVJ0GrLuBmOl265pnRbE4n6NHQFEHI52ZHZ4RUweSJzc28NZ7mxTMzPLy+ia0dkUofDo3MMptzfK4vnY1xT7le6dV+puuJS1UYSeU5N5OjKezFXdDRDZs6v4sXTs/wKw/1c3wshS0lx0ZTqzL8mqrwoTs7GYzl2NQWRlOVq76g37O1lbfGU3TV+W+J8zenyWPaNtu6ovzw+BQgGEnkOT6WQgjBUNltUncZl+LVfpY7uusQ5fG9rrm6SVStES9Pb2sjmdfZ2R2tatvXSjUMvzVn9AGklK8IIa6kMPSgEOI/4Mz6X5ZS7q1CP2pcR1RloaBZf1NwVcVLHtvUwqnJDBGfm3dvaqk8Bt+7ponPf+cY6aLF/sE4X/jMPZV97uqpoyXsIZk3eWTD4sflvG7iVpUVhTlWE8uWFAyLoEdDN22+dWiMiM/FUMKRbh6dzTOZLhEsy/Xu7KrjyGiS7Z1X5+NfipWqOC5HxOdaMgnuZuHRVJ7e1oZh2ezqimBLG8OycauC7Z1RJlOT9DQGLpt8dy1cOr6rza2QCT2fahj+F4UQfwx8CWcd6xeAF+ZKMy5VglFK+QPgB1U4do23Kbt66/nCZ+4GIFcyyesWlpSVheeZTAnLXqyPs6U9QtGwF134x8dS/PTkFEGPxsfv6VnRglu1+PK+YabTJe7pq+fuvnpcmkK+ZCJtODaWoino4ZENzZVKTQ+sa1yU7l/DQVVEJYcikSlhS0eETTclmaKBxAm3tKVE4eYvkr5dqYbh31H++flLXr+DK5dgrHELM54scGg4yZqmwJJZn9ViJlMiW9ZiGYrnuKevgcF4jt5LMlyn08VKTeDBeJ518+qYDsRySOno5MSypeteEnAOS0qm00691oF4jj1rG7mjK8qPjk/g0QQ53aI14qWvMcAzO1dXIPt2I1k08LlVNFXBpSkcG0tydirDVLpIZlvbZV09NS5PNaJ6VlOCscYtzE9PThHP6pybzlal5qlp2UxnSjQGPbg1helMEa9LpbPOx4bWEMm8k2nbGPTw+oX4olDRnoYA61qCZIomd1ziK93dW0+6aNAQ8NBxA1PmVSG4q6eOoUSe+9Y0UDKdDOCGgJtcyWR9a4jdvXU8ubX9muWnb1fWNDm1Ho6MpvjFu7so6JYjM62tfNG2xtJUI6qnBfhPQLuU8ikhxGbgPinln6+6dzVuKvUBN/GsTsiroSmr95t/58g4Q3Eni3RrR5jnTk7jUgUfu6eHp+fprbxwagbTklyI5SqFTcBZOH3f9qVnza0R7zXVC64G88PzSrrFC2dmmE47Rebv6WvgmTs6F2m317gy2YLO2akcli1543yCn9/VSdGwCXhUvK6aKsxqqMZo/EucKJ7fK/99BvgKUDP8b3Oe3NLK9o4iTSFPVUIkZzKOSySWLXFhJsuJcafiUSxTXJD0NZMtLdj+7cBIIs+r52JE/S6CHo2cx5FmLugW2aJZM/wrREpngmDZNptbw2RKThTXeLLAIxuaWd8Soj7gXvXT5+1ONUZjo5Tyq0KIfwUgpTSFECuv+lvjlkVTlarWhn18cwvHxlJsbA1zZjKDS1WcGOxLcgnes6WFt8bTV1VD9Gbz+vk4E6kiE6kiz+zs4MhIkvaok3S0mmzg242CYXG+LPLXGvbx4Ts7OTic5NP39aAo4oat3bzTqYbhzwkhGihX4xJC3AukqtBujXcY8zXuJZLzsRAeTV0kira2OfS2UTmco7Pex1iyQJ3fxUd3dfGJG5Ap/E7EmQyISnHz33liw83u0jsSIVeZrVcO2/wjnOzb40AT8JFLJRhWQ2Njo+zt7a1Wc1dFybRJ5nWEENT53bjUt/+i0uDgIDfrfL7TuNy5NG1JIqcjpSTic9f80itgpWNTN22SZdG9qN9106UmblUOHDggpZSLTk41Zvz9wFNAF/Bh4J4qtVuht7eX/fv3V7PJFfPa+Rh7LyQAx1UxvyLV25Vdu3bdtPP5TuNy5/LUZJofHpsEnOSzW0Gj5VZnpWPzwNAsL51xSi8+uK5xQRBAjYsIIRblUUF1DPR/xYnqacOpwKUDb1ap7ZvOzq4oszkDlypuuey7Grc2a5uCbOuIUDAs7uy5PjV2b1e2tIeZyRSRknfEZOxGUw3j3AbsBD4FHJNS/p0Q4lAV2r0l8Lu1FRW8qHH1rFaG+lZHUxXefQ3KpTWujNel8uTW2nV5rVRlcRf4beBx4PeFEB7exqUX9w8mODySZFtHhHuW0JqvUWMlDMVz/PTkNC1hD09tbbslNNjfjjx3coqBWI771zZe1+zx241rNtBCiA8JIT6Eo9GzBWeB91Hg48C3qtK7m8DegQSZosnegcTN7kqNtzEHh2dJFwzOTmXfVvkItxLZksnR0RSZosn+wdr1WE1WMzN/f/m/HziLI8P8fuBBnIXeW5Z00eA7R8b52akpLHthVNP6sv7L+paaP7/GtbOuOYSUkli2xOvnYxUtohqXR0r48VuTfP/oBEJSiduvXY/V5ZpdPVLKzwAIIe6XUr46/z0hxP0raUMI8U+BD0spH7jWflwLBwZnK0ki3fX+BTHjj29u4eH1TbXwsBqrwllwlPzkrSkG43kODs3WonpWQNGwODGeBqAh6OYjd3ViWPaiYis1Vkc1zuZSRVSuWFilvBawswrHv2qaw47cr1tTqA8srjO6nNE/MpLk5bMzFI1aYnKNxdi2ZN9ggtfPxzEtm5awD7emIASLktRqLI2mClRFIAQ0hzxcmMnyyrkYszn9ZnftHcU1z/iFEPcBe4AmIcTvzHsrDKxESOMfAH8F/Ptr7cO1sqU9QmvYi8elElyhYuJIIs/PTk0DTmLOI6uonFTjncmpyQyvnHXqErtUwa7eej69pxfDkisqQF/Dydz95ft7sW2J16Xyxy9ewJaSqVSRX7y7Vnu7Wqxmxu8Ggjg3j9C8/2ngI5fbUQjhAt4lpfzZZbb5nBBivxBi/8zMzCq6uTQNQc8Co398LMXXD4yQKRpLbu9xKShlTRnfLVCirsatx1zxl7xuMjKbJ6+bhLwu6gNuTMvm+FiK0dn8Te7lrU/Y6yLqd6MIgaec7bxUYZ2fnpjkJ29N3ujuvSNYjY//RZzqW38ppRy6yt0/CfzdFdq/YcXWp9NF/vMPTmLakpOTGf7X925etE1zyMsv7O4iWzJWVXKwxjuXvsYAP3dHO1/aO8JgLM/3jk7w0V1OnMMr52IcGk4iBHzi3h4ag4tdjDUW4tYUfnF3FxOp4qJr7ofHJ/jLVwcBR77hfTtqRW6uhtW4er7LRWG2Re9LKT9wmd03ADuFEL8KbBFC/IaU8roXXNd1ix+fmGRnd5Su+ovVnQzbZi64RzftRftNp4uoiqA14sVJTr61SRcN0gWDzjonIsK2JWPJAg1B99tKHvhyCV63anJXR9RP2KdhWBLLlszmdNJFg4lkkWRex+dSKRoWIwmnLsHtEkSQK5kkcjqddT6EEOR1k3hWpz3qu2yOg0dz3LGqIjBNm4Mjs/Q2BCgZTi1egJL5zllzS+Z1CoZFW2RxMaGxZIGgR6tKveHVWIE/KP/8ENAKfLH89y8BU5fbUUr5u3O/CyFeuRFGH+A3v3KYY2NJ/G6Nr37uPuqCjt+1I+rn1x/p5+Rkmmd2dCzY58xUhu8fnUARgo/s6ryh1Z2uhUzR4G9eH0I3be5ZU8+e/kaePTnFifE0Ia/Gp+7rvdldfEfj1hQ+eGcnQ7EcTSEPf/PGEAeHZ1GAdNFkW2eEZ09MkcwbtEW8t4XfumhYfPGNIfK6xc6uKA+sa+Tv9g6TKZpsagvz5NbWJfczLZu/3TtU3i7EifE0ewcSBDwqv/Xousq6yY6O6A38NNePeLbE3+0dxrQl79rQxB3dF2U+9l6I89r5OG5N4RP39qza+K/W1YMQ4v+UUu6a99Z3hRArVgC7kaGck+kitoS8bjFb0KkLutFNG1tK7utv5I7uukXl8eJZJ5rAls7s7VY3/LmSVXlqSZQjIeZ+ZksmurX4iaZGdemI+uiI+jg9mSFTMEjndTRNwedWCXtdTKaLeDWV+G0SqVI0rEqt5HhORzftSl5D4jLnQLds0gWDnG4Rz+lMpIuAM8YnUsWKdlaqtPS63NuNdNHELLseLj0vc3/PnbubZvjnERBCrJFSXgAQQvQBgSvsc1N4cmsLX3pzmLVNIXoaAiRyOl/ZN0LRsBBIJIIH1jWye57S3x3dUbIlE5cq2Pg2EGlrjXi5f20jsWyJPf2O5MRjG5t5czBBT31gxVFMNVZP0TD57tEJiqbFoxua2NlVx/rWEM0hDycm0my+TSQIon43j2xsZmy2wD1r6gl4NN69qYXBeG7BtXYpbkXh2FiKkUQer9bKP7i/j78/MMqm1hBbOiL85OQUUkqaQu+M9ZLeBj/39NWTLpqL5GL2rG0EnHKo1Zh8VsMK/FPgBSHEBUAAPcA/qkK7VSfkdfFzOzsByOkmE6kCRcOioJtMZ0r0NAQYmFk4GL0ulcffZkJbd/ctvJiaw95la9XWuH68fj6OqkDArSIRfGpPb+W9NbdZgMDOrig7u6KVv7d2RK6oqhnP6+R1i4agh6FEno1tYf7N+5zAiwNDCbrKa1hT6RLd9bfkXPOqEEJUDPylRHwuntpWPVG6VRt+KeWPhBDrgI3ll05JKW+YOMmBoQRvXEiwviW0rIF+/tQ0JybSdER9NAYlPQ0Bwl4X/U1B+hqzFA2LbZ1RkgWDe9bcerreRcPim4fGyBQNnt7WVlm0rXFr8P2jEwwlcjy0rqlizM5NZ8noTq3dgEfl0/fdPhW5TMvm24fHmc6UeHxzC2ubr+0m1xR0I6XkQizH1kvKcG5sDTMQyyOlrIm3zePcdJZnT0zRHPLwzM7lJ3uriep5VEr5s7JQ23z6hRBIKb9xrW1fDYeGk+imEyP90PrGRUWYLVtyeCQJwHSmyOce6q+853Wp/NwdCxdzb0VGZwtMphz/5lvj6Zrhv4WwbMmZqQwAh0eSFcN/bCyJJhSe3tbGL93dXY4Iuz2YyZYYTjj5CsfHUtds+GeyTuW7/qYgU5cI3QU8Gh+5q3PVfX2ncXwsRdGwGE7kmb6MOOBqZvwPAz/DEWa7FAncEMO/tSPC3gsJ1rcEFxl9AFURzOZ09g0m2Nwe5ne+cpjeRj/tUT8Bt+PG0ebpgLw1nuLoaIqt7RG2dd4aBR6cJxU36aL5tlhnuJ1QFUFfY4DhRL5i9P/69UG+e2SciWSB+qAb07JoCHrZs7aBja3v/NlpY9BDe9TLdLq0qtl4U9BNqqAzOlvgyS2tvDkQ58tvjrC+JcSvvqt/2f2G4jlePx+npyHAff23l7T65vYwI4k8zWHPZdc+VhPV8/nyz89caxvV4N41Ddx7Gd183bQ5M50h7HPxyrkYvQ0Bjo+neGBtIyGvi7XNQdbNU/574fQMumkTy5RuGcPvc6t8shaGecsy/6kxldcd1088R9FwIsZePBtjbVMQ07ZvC8PvUhV+Yffqw1SHE3kyRSeC5fRUhq/sG2EsWWAsWeA9W1roW2ad5OWzMWYyJSZSRbZ2hAl5Vx/3/nZhfUtoRUqmq/bxCyHOA28ALwMvSynfWm2bV8NkqsjhkSRrm4OsbQ5S1E3+4rVBpITP3N+L363hUgXnprJEvC4SOZ2gR2U6XSRvWDSWY/m/dWiM05MZGsshnnNysFdzbNOyef1CHCnB51JIFkzuWVNP+DYaeLc7AbdGe9TH2GyeTMEgYTqhjEXdAuHUim0KXozq6W64uW676UyRQ8NJehsCKyotmikavHEhQX3AXfkcW9rDK7perpaWsI9M0SRbNIn6XHTV+zk6mqIl7CHi0/h/Xzi34Dqfo7vez0ymRGPIc9MSFgu6xesXYgTcGnf31S+Z5HqtSCnZO5Agr5vs6W9kLFng7FSW7Z0R2lcY8VONs7IZp8D6g8B/FUJsAI5KKT9YhbavyI/fmiSR0zkzleEfv6uf7x2d4MXTjrZPnd+RdTVNm+aQh5Jpc29/A/GsTkvYg8elksgbZIsWX3pzGIC+Bj//8ulNKzLWlx77xHia/YOz5Eom6aJBW8SHbtq3benGK5VWfCeiaQq//+Ht/O7Xj/D6uRniOYNM0cS0baxhSU/9DLpl41YVLsSy/Nq71t7U/v70xDRT6SKnJjJ01/uX1MSZz6vnYpyccNY0dMvCraoMxnP86sPLu16ulRPjKbIlEwlciOXprPNzf38Dfo/Gd49OVq7z+oCbX5qXCPfQ+ia2d0YqGb83gzcG4hwZSQHQFPJUNYrr/EyW18/HAVCE4NhoCtOWjCcLfPaBvhW1UY18cQswyj9tYLr8/4YQ9jn3roBHQxWClnmLaC1hD5oCPo+GbkuCHo1cycKjKXhdGm5VJeTRCPk0vGXhtcaQ1xGIWsGAufTY4XJShaYKUnmDY2NJXOry7fzw+ARf3T+CuYRMRI23L25Noafej6aqCCHQFFFZf0oXDBrKGafVSL1fLXNj2O9Wmc4U2TfozCSX3b48IXKpgnq/B9OyOT2Z5rmTTrL+XDDF6cnMqvvWEnYzdxn6XAphr0bE7ybsc9EenX+dexlLFhb0Pep3L1i7u9EE3SqTqQKzeZ2g99rm17FsiTcHEiTzC5O5Ql5XRTAy6nNV2g/7XGSKBl98fZDXzsUu23Y1Zvxp4BjwfwF/KqWMV6HNFfPebe2MzOZpi3hRFMGD65qI+FxIKdnRVYdtS7a2Rwh5XVi2Xc6e1HhmZzt1AXdFLOs/fnArg7Ec9/WtfDHo0mP3NQb4pbu7GZjJsn8wgWE5F8ETWxanpL94eroiMlU0rLellMLtOKNfKRtaQ+zuq6egW9zdV0eqYDKTKeHWFN6ztQXdlLdEFviTW1rZ3JYn7HXxpTcduYDR2TwfvGPpiJn7+htoj/qI+Fz43Cp/8ONTjCYK/MlLF6gPOG7S18qzUY+m0Nt47fH1jSEfz+xs58R4hg/f1c5jm5zQ0MaQh7DXRXN58XJdS4g/f3kA05aMzRZuiUg93ZL4ym7mknFtE7tvHBwlV7I4MZ7il++/OJNvCXv52D3dFA2Lrno/61tDTKSKdNX5+cNnz3BweBYhoO0ykWTVMPy/BDwA/BrwD4UQrwEvSSmfq0Lbi5jJlMiVzAUDSkqJZdm8fHaGtrCX7Z3RBfu4VGe2kC2Z1PnduFRBV72/MssH6KrzVxJCrkSmaDCVLtHT4F+kGtga8ZLTTTRVQVWgZFg8d3KKO7ujxHMGzWFn0M5/oigZFudnsrSGvUykCrSFfUxmijQE3ET97mU/d41bj4lUgal0kWNjabyaSkvYy5Nb23j+1AymLXGpCl5No6vu1sg2lYAtHUmSommTzOt4NEGqYBDxubBtyUA8R0PAjVtTGE8W6Kr3V55gGoNedGsWVQoM22YsWSCWLeFzqVTDra0pCi5VQeAUaJnvMtnR5WjZzH9CqaIrfUUkcjrJvE5fY4CSaTM6m6c96kNw8Ynu0j4l8zoHhma5o6uO+uDCOg1LXedLrQ+cnkoTz+h01fvxu7WKHZrbVHD5E1GNBK5vA98WQmwEngJ+G/hfgKpPZ2LZEl96cxjLlhVphe8dHWconuet8RTpgoFLVfhPH9pGT4Nz4oRw/hcNmy3tEe7ojtIe9S0w+leDYdl86c1hciWLtc1B3r+EHGx/U5Dffmw9F2Yy/Oz0DMdfuoAqYHdfA363ymfu7+PBdU0UdItErkQsq/Odw+Mkcjr1AXflp1tT+OU9vRQMa9HnrnHrMRzP89X9w3z/2CS2baMpCo9uauY7R8bJlSwE8HM7O24piYEfHZ/k3HQWj0vBsm0GYjni2RJi7zCfub+X1y/EOTzsuCxVRaFoWHTW+fj5stz0ts4I+wcThHwujo2mODOZ4ex0lq3tkVUrjybzJb59ZBzTspnJFvkHDy69juB3a3z4rk4mUgU2t924SLxUweDv9g5hWJLdvfWMlyOO6vwuPnlfLwGPRsCjLcq7+fx33mIyVaQ57OGPfunOyuszGUekzZaSB9c18uE7OxmI5VjXvHDR/flTU/ybb7+FlJKzM1n++RMbKu/NrXP2NQSWjXqC6kT1fB3YAZzHiez5FLB3te0uRa5kVoqjzxVMSRcMCoZFMm8ghFMdy5Eg9lAyLEZn80gJ7VEfihDc1bM6o2nZkoJul/uwvC/03v4GdvXU8b2jExiWTaZcrrFgWBiWjVtT2NQWYirtYiLlPBonsiVKpkWmoFcem0umveTnrnHrkS4aWLbEsGykLfG4BEXD4tREmuaQj+aw55Z6YtNNm6nUnPCZicB5OjZtSbZoMJvXyRTN8pgVSGni1lSm0yVSeR1LQixbZHN7GCEEiZyObkk8mkrU7yJbNOEa7PBgLItpSUrmRbn0on55d0l71LfiiJZq4ci9WJi2JFM0SOZLzOZ0hJAoAnbMk6iYTzKvY1j2It99rmRiy7nr3CTqd7OmSRC6ZI1gPFks2wPJdLqIadmkiyZ1fhchr2vBQvdyVMPV85+BQ1LK6y6K3dMQ4KH1jaQLZiV23+dWGU8WuLM7itetEnCrHBic5bmTU/z05DSmZfPguiYe3dhclbh8r0vl6W2tDMbzC7RHlkLTFDZ3RDg8nOTRjc1s7YjQXe8n4NHYOxDnf/naUSxb8syOdu7oruOlM9MMXcjTUefnw3dFaI14qQ+4qQ+4F33uGrcem9rCpIsGPrfKbN6gWDL45qFxDMumq863wE97symZFl98Y5ipTJGIz81TW1v5zpFxvJpCU8hDpmjy1X2jtIY9TCQLtIS9fOSuTl48M8NIIs9vfeUwqhA0Bj1E/C6e2NxCe9THoeFZsiWT3obANWXsZoomn/qLN7El/Oaja2kNeZjOlNjVF63+SVglIa9GrvzU/uC6Rs7N5Dg07IR3Xy58c0dnlNfOx9lxiUu6tzHAg+sayRSd6/ybh8YYSeQXeRae2NzM1w+NkiuZfHBnB18/OMp4ssjG1tCK9Xyq4eqpSDALIf5ESvm51ba5zHEQ5Rn73O8A2ZJV8W/9+iNrOTaW4sXTU0ymCuRKJh5NYTxV4PHNLVeMpZ3fLoBt2yiKsuj1dS0h+psCC96b23Y+VjmS6P61jQQ9Go9uaERVVaSUHBhKYFo2ti2ZypR4bFMz//H7J/C5VVIFnXeXdYeW+9zL9R8cn+CVtq1RfRQBe/ob2dPfiJSS//Dd44BESknJtC4bLXOjkNLpT7pgki4Y+FwqLSE3rREvmiLY2BZGNy3cmopl2wwl8vQ1BhBCEPVpdNT5GJ3NU9AtdNPC71bpbvCzq7ce27Z5eltbZdzNjcel+gBL+67zuvN0KyW8OZCgLeqjJezB69Iq+96McT3/uHO/z+addZCQx7nRz2RKRH0aybyBadqoqljQ17n9PC6Ve/vqllQa2NVbXzk/48kCUtqMzhYW7J/Imzy4tgkpbfK6I1ENTqGWlVLt7IZdV97k6jEsyf988QIgEQhM2+Z929vpbQzw8PomDgwlWNcSwq0p+FwKz52awbJsoj6NgmHTU+/nvz93ll099TywbrH6nWVLvn5wlIlksfJk8J0jY3z5zRGiPheb2kL43Bo/f1cXIa/K579zgrPTGTa3hQn7XJRMm6MjSeqDbv7jM1uJlBdkVUXwyIZmTk2mOTg8y8N/8CJNQQ+PbGgkp1sUDRvTtvGogv/x3Dnetb6Jo2MpHtvkFHKfShf5xsGxJT/3pSRyOl87MIJpSVRFoJs279nauqIsvhqr59hoip+dmqYt4uSHfO3AKCfG01i2o87ZEvYuemS/kZRMi68dGOUnb00ymzfY1VOHogheORvDtGy+tG+Eja0h/G6NTMng7FQWpKQh4GEqWyovSCb47ANr6G0M8Nr5OLppsb0jyu6eOn7jSwc5P51lXUuI92xpJVVwDOETW1oWZCvHsyW+dmAUgA/f1bmoBGXIq1E0LCwJj29q4vd/fJbJdJGWsIejo0mePzVDR52PD97RccNi9F87H+PNAUcIckdXlG8dGsPnUnlySwt7B+LEsiV6Gv0UDYu3JjKsaQowniry3aPjBNwq79/Rzg+OTZAqGDy1rY0fHZ/g7HSWNY1BfuOxdZXj5HWTv98/SrZk8r7tbewdSHBiPMVdPXWcm27mh8cmqQu42bO2nhdPT5M3LO7oruNdG5o5PZnmznmFW65EtUfidYnfL5kWRcMikXN8Yy1hL6enMvQ2BipZs3McGkkS9GiUTIuIz8f2zghnpzLUBzycmEgtafiTeZ2x8l315ESabZ0RXj8Xx7IdZcCo30VTyMtQIkdr2FsR5Xr9QpwnNrfyxvk4miqYTpc4OZHh3nn6IDu6ouzoivK3bwwjpWQokWMyHSKe02mPenGpChdiORpDXja3R/h3z2yt7HtuOrvs576UgViWXMkiXXQShjqiPk5OpGuG/wZxYiKFLSVD8TyGLRmIZTEtmzq/i129dfQ1BkkXbt6MfyZTYjpdYjhRQBGOuOG9axqwbElOtygYFnd21+FSFZCgCoEFzBYMDNN2tiuHFm5sDVUMdtDnwrQl0+kSmaLJcDzP/sEEQY+GEIJTE5kFhn8glqsUZRmI5RYZ/pJps628mHl4NE3JtKjzuxiMFzgxnsaWkpFEnnTBoC6wMCLmenFiPI2UcHoyg0t1JlW6afNqOUu/IeDh2FiGqXSJOr+LZN7gxFiqst2x0RSxckGnM5MZRhJ5XIpgdDa/4DjjyUKl4MrJ8RRD8Rx+t8q5mSynJjOYtmQmU+LVs3E8LhWPS+XIaIrffvf6K7qdL6WqGQ5Syier2d4cXpdKxOeiu8GHYUtOT2aWjVF9aG0TjUE3rWEve/rrCXo03rWhGa9LXVDKbD51fjdrm4P43GplQWZHd5RETifs0UgWDEZm87SGvTQG3dzZXYfXpbKzK8pb4yk6632kCk6G4empNF9+c5hvHBzlb14f5PtHx/mr1wa5ozuCW1NZ1xwklini0RQ66/w0BD08vKFpwbHn2NAaIuJz0dcUYFNbqJyAZvJXrw1Wbj5zrG0K0RB00xn1saMzgt+tLgprrXH92NEVxedW2dgWZldPHWsagyAg7NV4cnMrXpfKnT0rn5FdK/sHE/zVa4McKSvSztES9tJd76ct7KVQsohlSxwZTVYkS6SE42NJsiUDr1ulrynA2qYgG1pDbO+M0FIe+49uamZ3bz0dUR+WlOimBUh6Gvw0hTxsbA3xwLom+poC+N0qfY0BvrJvmK8dGKWgW6xrDlXWrdYtsQYQ9Gg0hTzU+V383I42WsM+iobNXT317Ox2zvG6luANTX67o3y97+iKMJkq8icvnecvXhmgv96PS3VcPnd1R9nSEaZo2PQ3BdjREyXk1WiLeLmzp46OOh9Bj8bWjghtES9F06YtvNCGddb5aY86T4bbuurY09+AW1N5aF0T2zoiBDwqXfV+ntjSSlvER9ir8ejG5mv6TGI5P9wVd5xXbH0prlBs/arYtWuX3L9/P+ems3z3yDgAW9rDSyZGVYuvHxhlOJFnOJGnIeAm4NF4bFPzAmP6Zy9fIFM0OTuVYU1TkKl0kYBHQwhnkao94uXMVJYNrSECHpXPPdTPcyenODrqpHK/d3vbVc3IcyWTP3npAgANQfc1J33t2rWL/ftXXB1zWW5mAtetUmx9uXP5zUOjDMacGd2NkmWWUvI/njuHLSUel7KkHMSfvXyB509NMRh34s276/0InNm3EII1TQF2dtXxsXuuHBnyP547i2VL3JrCrz+ytPTEa+di7B1IAPDoxuZlI13mmH8+syWTPy2P98ag+5YQKnzqv7/E+eksAI9saGJjOXy0vznIeLJAQbcQAn7z0XXLZv9/4s/ewLAkmiL421+597r2Vwhx4JLSuEB1iq3fMFojXoIejbxu0Xedw+Iagm5+enIKjyY4PJLErQr6mnyMzRZ4YJ2j7NnfFOTwSJLWiJdXzs0QdGvUB91IwKUIzs1kkdi8cHqa+/rr+e6RcVTF0dfwuZWKMTg4PMvYbIF71zRcNsbb51Jpj3oZTxYJeVx858g465qDi6Rv916IE8vq3L+2oZIAVuPGsqYxyFA8T9Tnoi5wY2anQgj6mgKcn84uSCzMlUxePjvDcDxPIqfTEPAwndHxuzU2toZ4+WyMTMlESsn5aZhKFTkzlWZPfyMddV6+fmCMNY0BPnGJ4S3oFq+dj/HgEu7TNwcSTGeK9DY4s2JFEXTUXQy3PDqaZDCe5+7e+mVvin6XSlvEy0SqeFMrlo0k8hwaSbKuOciOzginJzIg4D2bWzg8liaWLXHvmnoGZrK8cHqaXb31l5V8aY14OTycZEdXlHTR4JWzMSI+F/f21fGnLw8Sz5X47P19ZEsmJyczbOuIUO9388q5GPUB9yKp6dOTGU5PZdjZGV2x6N+qi63fSIIejc/c34tpy2tOwFopybzBmsYAR0dTmLZNyZT86NgUG1rDuFSFd29u4ZGNzdyzpp7/9uxpXKpCIq/TEnEe1bIlk+56P8+dnCLic/PimRgezTndn7qvh4jPhaYqzOb0ithUybQvW1xCUQQ/f1cXRdPiK/tGSOYNBmZy9DcFK8kyk6liJWVeImslF28SO7qirC8HHNxIobD3b2+jYFj45l0fbw4meHMgwVvjada3BHl4YzP/8YNb0VSFbxwcoyHgJl00UIQgnisykZKcm8kiJIyniiQLBm+Np9nVW8/GeZMMR4tKqTzBzjGdLvJqWSvGsiW/8tAaBKIyRnMlk5+dmkZKyBbNZZ8uFEXw0V3OeL9ZKpsAz56YIlVwrjXdkjSG3AhgeLZI1Oci5NVI5AyOjqZwqQonxlOYpo22TAJbX2OAurLm0Bvn4xVdo7HZPM+fdpZJ/27vEG5NxbQlE8kC3fX+inu3u8FfkfuwbMmPjk9iSyem/x8+uGZFn2nVPn4hxDohxNeEECeEEBfm/q+23eXQVGXVRl83bfZeiHN8LLXsNkGPykSqiM+toikKHk1lIJbnu0fHkWUP12xO5+BQkmBZNMnv0Yj4XM4glYLpTAm/x7kJRMs+yZBXI1w2+uDkIQQ8zudpCF55dq4oAr9bo6G8KBb1uxYIwQW9Gh6X03ZD4NbJEL0d8bnVG64OKYQzPubCCE9NpHnx1AzpgoFh2UymikylCmRKjjFtDLoJlY2X360S8blxawIpnYXdiE9jYCZLIqdj2pJXzsaIZ53KTs0hL6oiaLnEVx3wXBQ9bAp68Gjqgixet6YQ9DiG/Epjfm68X29My2bfYIIjI8lFYagel1KuKCbpa/A7SWVCsKUtRLZkMpEqEvJqtIQ9qIqgKeTFhmVtjEdTiWV1PJqCpgqePzXFoeFZ1rWEKtdyV0Ogco03BD2V392asiA6TBFQX36ivJqM8Gqc0S8Anwf+EHgE+AxVXjSuNm8OJNg36PgdQ16tIu8wn4JhEfG7aI14eWRDM6+cnebPXxlEAl94dZDHN7fyvWMTxMrCW7/75AbaIj7cmkIsq/PNQ6OUDIv2iI+gW2N9S5CntrfREPA4kRNlvC6VT9zbw2zeoP0q/MDv3dbGRKpAY9CzIFY46NH45L09zhrDLSACVuPm8n/+5DTpohMbv6MzysnJNG+NZ5CM8Y8eWsMTm1sXFD2XUrL3QoIjo0m8LpUDQwlsnIzxL785TEPQw/mZLJ/e08u/f2YrpybTbL6kHm7Ao/HJ+3pIF4wlx6BLVfj4PT3EcyXaI7fGGD00kuSVs85Tis+tLlh7M0ybqN+FxFnovTCTw+tSiAYdWZWoz0XBsPhf37eZE+NpNrSG2DsQZ//gLOAoms53wZiWJOpzYdmyUpt4JltiKl3iP394O6m8wdaOCLppM50p0hJ2ov+66p0F4vmFZYQQfHR3FzOZEq3hlduPahh+n5TyOSGEkFIOAf9OCHEA+LdVaPu6MH+G7LpEujVXMhmM57Cl84VpisDjUgj73CCcC8NXnlF7yvuqwjHgQggags5d3+fS8GgKed2mNeIl4HUtWyvX79auelajKmLZ9gq6E9bZYntvmh55jRvPXGLV/MxRj0tFFhw5E0URBDwamuJIRcdzOomczrrm4AIJ45JpM1IOb/aWx7EiBF63s01l9i4cI2kvEeIR9GiVWf1S+NwqnW5n/A7H8xi2vUjw8EbiVhUSOR1VEYtsgmVLJpNFvJrz5N9dnij6XRqW7ZwvrfxksqusozXXhhDg0hZegz63StjnwutS8bucp0IhBEGvWhaLLPepHPk3R9u8m+T879qjqVddh7sahr8khFCAs0KIfwKMATfvG1wBu3vrCftcBD3aohnJNw6OEsvqhL0aj29uIZYp8dzJaXIlky3tYUqGzSfv7QHgfTvaOD+dY3Q2z4/fmkIRgk/e10N9wM1Hd3cSz+q0RZzF2DVNN0ajJZU3+PK+ESxbMtFd5JEN1xbu9XbnShFHt0pUULUYTxb42oFRpIQH1zVWDNDvPb2Jv9s7TFZ3tHg+elcnLWEvTSEPf79/BMOSjFwSIbe2OcQzOwWWLfnsA7389WtDbO+MsKMrykiiUBnLX9s/wmzeWFWE2WAsxzcPjQHw7k0tqzoHq0M6/yRcOlc6Np5iZDZPVjf5nXevJ+TT8JVDzEFW9HXmc3dvPZGyjWm75KnmQ3d2MBjL09Pox6so/MVrA/Q0BHhgbdOKejo277t+aH3jNemPVcPw/xbgB34T+A/Ao8Cnq9DuqsmVTHIlk+byI5CUkql0iajftWwR6JxukS0ZuMuaJcm8QUG3yOkma5udxBVZnk353RrbOiNMpp2UaVtK9HJRFVUIFLHQP3cpqYKBadnLvn8tlCyrIuhWMq67fFKNG0yxLEjYHPIslPY2bebsT3Ge/ntTyMPaliBHR5NoqmB9a5i+xgDJvOOzl1IymS5SNKwFa2cNAQ/pgsFgLMc/fri/slA5P0qsWB7rRcMiWzLJ6ybNoasLWy2a1pK/32hKpqysiRUNm1i2yGSqxNaOCIZpE/Bojvy7lDQFPbg0R6l07mndsGRZRbREQ8BTFmG8aGPmnx+fS6Ux5MbnUnGpCh+6q7PyxJ8pGhQN+7L++pJhLfldXw3V0OrZB1Ce9f+mlHL1pXeqQK5k8tevD1E0LPb0N3DPmgZ+dmqao6Mpwj4Xn7qvZ9EjHTiPaLGsTl63+Lu9w+RLBrGcgUdTeGxzMx5N5a5LEnEeWNuIz6VSF3DWBKbTRf7F145SNCye3NrKZ5YQ55pOF/nKvhFMW/L0trYV1TtdCc0hL09ubSWWLS3qZ423N2ZZEjyZNxblsfQ1BnhkYzP5klmZ7QN858g4b1yIc246y6a2EOHywmDU7+a929r43tEJJlJFvvTmMJ+6rxdVEYwlC3z5zWF+eHwSv1vlvjX1/KunNy/qzzM72zk9maGjzsdfvz5IybB5aH3TVY27DS0hciVHsfaOq8w+rSZ3dkcrOQmNATf//O+PkNct3r25md9693p+dHySe/rqGYjn+dHxSTRF8At3d/GeLa3EcyV29dTzvaMTDMRylUIpc6QKBl98YwjdtHl4QxPnp7OMzhboqPOxviXE86emcWsK79vWxveOTaCb9qKcofmsaQou+V1fDdWQZd6Fs8AbKv+dAj4rpTyw2rZXQ7poUNBNbAnTGScKYSrt/EwXDIqGtaThL5kWbWEvw7N5mkNe4jmDxqAbv1ujOeRdsBA2h8+tLpCCmEgVKJZn20Px/KLtAWJZZ8YFTsHrahl+YNmnmXcSt2P1L92ySeYdWe65Me0IwNmVTPLKtqaNgiPhWzRsPJpCY9DjxPEHL1auagwliGVKJPMGpXLYZCxTQi9LDjvSAkuLf7VFfLRFfIwk8pUqU9Plp9+V4ggQ3vwJiqYq7OiKoCkK56Yz5Mq1fodiedY/GKos9r50xgm9Nm1JIqcvWNiezjifPZYtYduy8kSWyhsVT8B0ulT57qbTxUoGsm7aDCfyC7a7HFcr0bDo865qb4e/AH5NSvkygBDiAZwbwfYqtH3N1PndpAoGU5kSe9Y6CQ/v2tDEmwMJuhv8C1bG5zMcz7NvMMGmthBrmgLc1RMlV7LQVGXFxnlHVx3v3tTMyGyBT97Xs+Q261uCTKQiFA37qsSVaty++N0aj2xsZjCWY3efM9P71uExBmN57uyp4+H1jo/47FSGbx8e48xUlsagmzVNfrZ2RFjXHFy0gPrIhib2D87S2xiouBs2tYWZShcpWU49iA9eoZRhZ52PXb11zOaNRclFbxf2Dyb4bz89g6Yq/OunNlEfdDOeLC7Q3QK4q6eOTNHE51YWFUh5bFMLR0eTbGwNL3DDddXPOz9rGuhvCnB8PMWW9ggtIS8l0ybic7GnvwFLSjJFk7vXXN9iS9Uw/Nac0QeQUr4ihLjp+rPxnE7U75QunCnfYdujvivW4xxLFsozI4Nndl577c5feWjpakFzaKrCYzd1MavG25GdXdHKbE837YosxNmpTMXwn5/JkimapAoGrREvG1vDPLl1aZ32zjr/oogQt6bwxJbWFUuiCOHUun47s39wFsOSGJbFa+dibGgJs6ElXJmBzxHwaLx3+9Lnsr9p8Y0VFp+fiN/Funnhoh+Yp7X/rhsUjFENw/+iEOKPgS/haPf8AvCCEOJOACnlwSocYxE/PTHFQCzHff0NS7pfWsNe1rUEiWVKWLbkT146z67e+ivOrje2hnnxzDR7VlHwxLRsvn9swpGl3dy64jTqW43b0Z1SDZ4/Nc256Sz3rmmoSvGf5XBrCm5N4dDwLE/NM+w7uqKMJ4vYEnobAnTW+fjCqwMEPBof2NGO16Vi25IfHp9kIlXgkY3NNzWUcqWcmcrw4ukZuup9vGdLa1V1+e/pq+P7xyZwa4JHNzZzfCLNVLq4yA11aiLNf3/uLH63yu89vXlRzdy3C9Uw/DvKPz9/yet34NwIHq3CMRaQK5kcK2fEHRyeXdLwq4rgfdvbsWzJ/3juLOA8zl3J8Ef9Lp7a2raq+PepTIkLMzkADo3Mvm0Nf42rp2hYHC4rY+4fSlxXwz8n+7ulPcLsvDJ+bREfn33gYkDBcyenSOYNknmDoXieDa0hYrlSRQLg0HDybWH4Dw451b1OTmS4p6+hqrLMOd2uKF2miiZPL1PJ6scnJknkdBI5R6f/fUvU3H47sOoMWynlI5f5X3WjD+B3q/SUjemGK6hbqoqoLMxsaL3youfG1rltr+zPf/nsDL/79aN8bf8I4Fz0Pzg2wb6BBBGfI+NQzUXbGrc+Hk2pxLlf7+/erSn0l6WNN15mbLdGvJybzjCRKlQE0er8blrCXjJFg9HZPAeGZhftd3B4lq8dGGUonrs+H+Aq2dAaQgjoiPoIV1mWuTXi4cJM1pFfjywfSnnfmoaKbMIdPdEF7z13corf/fpRvn14rKp9ux5UI6qnBfhPQLuU8ikhxGbgPinln6+6d8sfkw/d2Ylp2QsyDpfjvdvbeI/VsqJtH9vUwsPrm1a07RdfHyJZMBiK53hyaxtnpjMVwaUH1jVyZ3ddLXP2NkMIwTM7O1Y8NlfLB3a0X/FY0+kS/U1BhLgYSeJSFT52Tzd/9fogiazOS2dm2NQWqizwFg2rIh6YK5l8es/NLxJ/R3cd2zoi1+W8TqdLFcXfqXSJpmXyEe7ua+DPP12HprCo1OoX3xgir1sMx/O8d2vbsiJttwLVcPX8JU4Uz++V/z4DfAW4boZ/jqsZANdj2+4GP8nRFC1hL363QnPIkWuQElpCt75cQs2Hf/24EUZ/pcdqjXhRylIElyYLdkR8JLI6DUH3ghqwblWhMegmVs4+v1W4Xue1pXyOVCFovoLYmXsZg97dEODURJr2qPeWNvpQHcPfKKX8qhDiXwFIKU0hxGVT8IQQ9+CIutnAPinlP61CP244v/uejZyZydDb4BRe76zz88v39yIlN7RCUI0al2NTW5jWsBe3phC4RD/nsU3NbO+KEPW5F0xUFEXwC7u7SRZ0mqqYWX6r0t8U5DN7+lBVcVmNocvxe09v4txMhjVLiD7ealTjtpQTQjRQrsYlhLgXWF7v2GEIeFRK+QDQLITYVoV+LEsip3NoeJZM0ahqu5qmsLktskBgLex11Yx+jZvChZksx8dS2EuoptWVq8hdihCC5pB3yVmsW1NoDnmrGj1zqyKlZCxZYDK1dLLaSnCX7YH3JtYOWCnV6OHvAN8B+oUQrwJNwEcut4OUcnLenwZw3UQ6pJT8/f4R8rrFiYk0H79n6YSqGrcXq3Vz3WoibyOJPN8+7JQlzZVM7llFOPLtyOGRJC+U1zSe2SluasWvG0E1Zvz9wFPAHuDHwFlWeEMRQmwHmqSUJ5Z473NCiP1CiP0zMzPX3DkpwSorGllL6cfWqPEOYL5CZG2cXz232/m75mLrlQaEOCql3F6WavgPOLV4/62U8p4r7FcPfAv46CVPAItobGyUvb29q+rn7US6YGDYkrBXW1KPaHBwkGqez4JhkS9ZeF2LfcjvdKp9Lm93aufzykjpCL/ZUjrV/C4TRHLgwAEppVxkBFY6M/8Q8PtAMyDK/6WUMsxFN817gT+VUn5fCPEfr9CeBnwR+OdXMvoAvb297N+/fyVdve0ZSxb40t4hDMtmc3tkSdmJXbt2VfV8/tnLF8gUHZWOf/Lo2gU3G8uWGJZ93WskXw4pJQXj+tRtrfa5vN253c+nadnL1hQvGhaaIjg3k+WHxxyzubMryiMbl5d5EEIsqZyw0ivh/wDeL6U8ucR7Y2XJhseB3xdCeLiyC+nngd3A/1FeOPpXUsrXV9iXGsuQyhtkCzo/PTlNtmQS9NyYReY1TQGOjKToafAvMPoF3eJLbw6TLhq8e1PLogzrWLaE16VecxTFSvnmoTGG4nl2dEV4dGNNH6nGrYlT3tJZj3zv9lZ6GgLEszqNQTenpzI8e2KKkNfFe7e14XerlEy7ksh6taz0iptaxugDfBR4EvgDKWVSCNEG/IvLNSal/BKOtk+NKjGSyPONg2OMJwu4NUGrx8tM9vLSrtXi0Y0t3NPXgN+9cJYSy5ZIFZxIqoFYboHhPz6W4tkTU7g1hY/d3V3V9Pv5GJZdkca+MJPj0Y3X5TC3XcWvGtVnKl0iW3KenAdiTjb1eLJIb6MzoZKy7Ma1bD77QB/WMk8GK+Gyhr/s4gHYL4T4Co5PvmJNpJTfkFLmgW/Me20CmLim3tS4ZmayJWwpaQ65KRgBbEnFzZPXTU5OpGmP+haVgasWS/n226M+NrSGiOf0RWJXU2Xddt20SeT162b4XarCxtYQ+4dm2fM2lQyucXvQ0+An6NVI5EpsaQ/xtQNpwLkhPLOz3SlzGXDTHvWVawNf+7GuNON//7zf88AT8/6WzDP4NW4MF2ayDCXy7OiMUj/PWG5pD5eLN0h+/dF1C2YCP35rksFYHpcq+IcPrrlhfVUVsUDsaipd5MR4mnUtQXb31ZPXLYJejb7rmPBi25LBeJ6gR+PcTLYW5ljjpjORKnBqIsOG1tCCmt8zmRLZoolbVRmI5XliSwsnJ9Js64jSFvFVan1Xg8safinlZwCEEPdLKV+d/54Q4v6q9aLGiigaFt87OoFlSyZTRX7p7ovl3TyaypNbl9ZPnwvckvLi7zeD7x4ZJ1M0OTGR5tfe1c/7b5CyoXRyC2/qZ69RY47vHB4nr1ucnsrwqw9frNsxf3hK6QjvXU58bzWs1Mf/R8CdK3itxnXAtGx+/NYUs3kdw7JRhCCZ1/mbN4ZY3xxcMIu1bcmzJ6eIZUs8urGZtoijXf7WeJqOOh8+942LrrFtyU9OTJHI6Ty6sRm/W3OqF7lUEjmdZ09MEfRqvGdL65Jhp1fDZKrIz05NUx9w88TmlkoFJEURfOTOTgbLcsQ1atxs/G6VvG7hd6tMZ4o8d3KaiM/FE5tbeP+ONtJFk+1LSM3PoZs2P3prkoJu8sTm1gVu0vFkgedPT9MU9PDuyxR6upKP/z6cxKwmIcTvzHsrDNy8+LxbHMOy+fbhcWLZEk9sbrliFqBtS/RlQh4Ny+b0RJpvHBylZDqa4XvWNvDSmRlimRKxTIk7uusqKfcTZXcKwJsDCZ7Z2UHAo3F33/Ut5bYUY8kCJyecvuwbTPDBOzoYSuTorPPz+vk4o7MFFOFIYa+dV8bulbMx9g8l2NVTT0+Dnx8cmyDqd/Fzd3QsEBKbz77BBFPpIlPpIlvaw3TVX4x2aA57aQ7fOkJjNW5v2qM+zs/k2NoR5uBQkslUkclUkU1t4QXXwfx6yvO5EMtydiqDlE7G8fxwzu8eGeelMzMEPBqb2paf6FxpmuUGgjg3iNC8/2muIMtwOzOdKTGSyFPQrUrBGCkl05kiJfOiOkW2ZDKTKfKlfcP8fy+cX6SJPjqb549fPM9XD4ySKhjkdROB8wg4Vzijo86HS72YwNEQcFe0yudkZq8HRcMidoWoocagh5BXQwinLz63ysbWMEGPhqLAK+diHBxOLooG+uq+YV4/H+er+4Y5PpYir1uMJ4uMJ5cv5N3bEEAICHk1Gm8DUbEatz6zOZ1caXEV2rfG02iq4K2xND0NfoSAoEej6RJV0O8fm+D/e+E8z56YAiCeLVEoPykcH0txcGh2kQJw0bAwbVn5uRxX8vG/iFNa8S+llEMr/cC3O01BD60RL/Fsic1tjo/uhdMzHB5JEvW7+OS9PSTyOn/03FmSBYOoz0V9wMOpiTQRn4vOOh9el8pQPI9hSTRFIAS4FIXOemcx6LFNLdzdV0/ArS0Q0fK6VD51Xw+6aV+3LNqCbvE3bwySK1ns6W9YdsHU51b59J7eJfvy+rk4U+kCcUXh7FSG9ujFGboQgqJhIYRgfUuQn52api3ivaw88LbOCGuaArg1ZdVuoxo1Vstb4yl+8tbS4cqZosHx0RRbOiJsagtX8l/mj1spJacmMqQKBicnUtQH3Lx0ZgafW+Xuvnr6GgPopr1IXuI9W1uZyZTobVhcS3k+K7UM/7cQ4tLbRwrYD/yxlHL5qdhtgGnZnJhIU+d301XvdyojNQXQBOimk8SUzDml8ZJ5g4Jh8fKZGX7y1hS2lNzZXUd/U5B4Vue7R8ZpDHn45L09bGkPMxTP49YU7u6tL2uRXzTyIe/SCVqXDqJqky4a5ErOk8tkuki2ZHJmKkNPvX+R3vvBoVlGEnme3t6GaUvOTWfpawggcRakVUVQMiVD8RypgsHmtjDNYQ+zeZ3msIeXzsYYTuQYSxYYSxZQhCBbNNncHl4027nd5CJq3HxGEnkSOZ3N7eEF19xk6mK4cjxXWmD4fS6VrnofXpez/VIZ5UIIErkSJ8bT3NVTx9hsgcl0EZ9LxTBtxpJFdNNCCDg3lWHf0CzvWt/EVLpEQ9BNyXJm/cux0ivlAo7q5lzS1S8AGWA98KfAJ1fYzjuSl8/FODycRAj4+D09ZIsGf/jsWSzbZvynBQJejYagmy3tEbZ1RAh5XWSKJoZlIwFVddQAD4/MgqQiHx31u/nYPd3YtuSFM9OkCyZ39954X/2ltIS97O6tZzpT5L7+Br5zeJypdBGfW+VzD66pLKyem8rwhz89g5QwmixQH3ATz+oc8Mzy83d1cnhklnq/m63tYf7v589TNCye3t5GfcDDHd0aXpdKLKsDAsuWnJ5Ic3oqC0CmZLCnv/EmnoUatzuJnM43Do5hS0k8V1qQFb6rt76cPa/R17hwjW8smefwSIptl1nAlVIyEMtRMCzOz2TZ3hUlmdcpuR3X6VyJWInkP/7gJAXd4o0LcR7f3AIIdNOmUAXDv0dKuXve398VQuyTUu4WQry1wjbesczpn5uW5OWzM2SKJpa0kVKSKZlIAZmiye7eBgZiefK6yZ7+Bn52apqCYeFSBQeGZjEsG8Oy2d27UHtDUcQtJzXwwLqLRne++un8x8L5PkbDkpXHUks6kUeaopAumjx3coqDw7OYtk1LxMPH7u7h5ESaTW1hPJqCpkBL2MfWjgjPnpxGN222tl+/IuY1aqwEW8pKqLBlL3wv4nMtqZMFcGQkTdGwODZ6+bIl2ZJJqmAQ9roIuLVKaGd3vZ8jniSposmW1nBFWdSyJQ+tb8KrqTSHPTQvUz4SVm74g0KIbinlMIAQohtn0RdAX2Eb71geWNdI2OdiMlXg3LRTmPrxTS24VAVxfIKBmRxddY5v3qUJpwB8a5jffWojqbzBgeFZTEsykSrQXR/gyGhygWG91Xn/9jZOTmRY0xQgUzR49sQUPrfKE5tb+dxD/Ywk8nzozg4MS3J6MkN/c4DvH3WSu4VwXDRuVUERTsm/2bzOTLZEW95gW2eEf/r4BsBJXgu4VTRFsPAWU2O11CQnrp7GoIf372gnntXZ3rnyiUhvo5+x2QLtUR+xbInnTk4R8bl4fHPrAvdlyOPCtCRBr8aD6x0bUx9wk8jrHBpOols2+4dm+V/es5G9F+K8e3MLYa+Ld2++8iRxpYb/nwGvCCHO4ziZ+4BfE0IEgL9aagchRC+wFzgJ6FLKJ+a9146jzunFkXD+6Qr7cVPRdYuCZRH2ujAsiVtT0E0bj6ayu7eeoXiO8zOO4X90YzOtER/fOzZOS9iLFJDM62xpD+NWFXIlk8MjSUqGzeObW7BtODKaZGy2sCAj9+1A1O/mvrIcwvOnp7kwnUVVBGubg7xrfSNF0674Mee2294R5psHR6kPuNnVV8epyQzZoskDa5t46cwMRcNiNqezbd4FFfG5aAl7MW1JSy08s8YtQH9TkP6mi3/rpl0JrTYsG1WIiutzjv/9g9t49dwM969tYv/gLEPxPGo58u3QcJJ00eB929t5bHMz56ezbOmI4NHUSkj286emODmRxrIl52ayPL29nfUtoWVrAS/Figy/lPIHQoh1wJzE1el5C7r/7TK7Piul/MQSr/9L4N8AR4DvAbe04U8XDUYTef7pVw6TK5nc299AR9SP360Qy+psagvzoTs76WkI8Il7eyiZFj86PslMWqfe7yYlTAqGyanJDGPJAk9sbmU0WWBstoAtJVPpEg+vb2IonuPMZOa6q1VejpJpkStZV7z5pPI6sZxeCSud4+xkhr99cxiXqrCrt47/8dxZZnMGn9rTw1NbL8o3fOPQOLGszmze4MBgkqjfjQTqAi5m8wbHx1KL1Dwbgh4+dV8vBcOi9RYqAF6jBsBPT0yxdyDO1o4I2zujfO/IOD63yi/e3Y2mOJFqUb+bIyNJhuIF/O4kk+k83zo0ht+jsqUjwnePjFMybXwulU/c28N0urRorAsBhi2xLIkAvnNknPPTWe7ojvKuDctLNM/naizMXUBveZ8dQgiklH99hX0eEUK8DHxDSvmH817fBvyWlFIKITJCiLCUMn0Vfbnu2LZkOJEnUzT42akZjo7OkioYCAGHhpN0RP388PgkHk3l3HSWbR1hGoNeGoMezk1n+eGxCbIli8c3NvPR3d389esDHB1LowiBFBBwa5yYSGNYNg+td6YMp6cy2FJydipzUz5zybT44hvDpAsG96ypX3bxNJHV+e2vHCJVMPjwnR18ak9f5b0Tk2m8LgWB4LmT00ylipi2ZO+F+ALDrwjnhqqpCkXD8WWalhP1E/Fq7OyKElgiyzjidxGhVtO4xq3HNw6NcmEmy9mpLD6XimlLMkWTc1MZ3hhIUNAtHtvUzNHRJGPJAqZlM5EqIIGiYXNyPMXIbB7dtJlMFfFo6oJExDnCPhe9DX4MS9IU8nB+2gl4ODuVra7hF0L8DU6JxcNcLLwigcsZ/gmcqJ8S8G0hxHNSyqPl91R5sfRXCojiJIXNP+bngM8BdHd3c6N58ewMh4eTTKWL1Afc9DeFmM6UMG3JA2sbyRQN2iM+xsvFmb99eAKvS2FPfwODsRwDsTymZXNyKkNXg5+P7OqmOTxNb2OQqM/FeLLAjs4oUkpi2RJHR5PkiiYXYjnWt9wcaYFM0SRdllG+XLLUmekMF2I5pJS8eCa2wPDv6qnj1bMxNE3w+JZm3hiIkyoYPB1ZqCMU9roIulXcLpWwz8VIIk5Ot9jdV8+2zigvn51hW2dNUK1arLbGcI2lGUsWmM3pbGwNcXoyQyxTIlM02d4ZYSJVJOjRCHg0CrpjNseTBV47H+fcdIY1jUGe3NbK0dEUPrdKW8TnhIRKEJfx2mxucwos5XWTRze1cGYyy6nJNLuuIuJvpTP+XcDmecb6ikgpS5QlnIUQ3wO2AnOGf/4aeBhILrH/nwB/ArBr164bvpI3ZwDrA246oj7qA27++XucRcY/e+UCJcO5WzcGPRR0i8MjCYRQiOd0CrqJWxO4NY16vxPXvqE1tEArZl1zkJfPzDCbNzg1nub8dI6ZbIk7u+sQwKvnYrSEPQtSuK83jUEPd/fVM54scP/a5Y1ub4OP7jofqYLJXb0L5ZZ7GgJ8rFzQfjqtky6YWDYcHkkxkylxajLN2uYgqiLwe5yycabtrBN4Xc7FcWwsxWxe5/hYivv6Gzg4PEvU52Zz+/URrKpR43IkcjpvjafoawwsSIpK5HT+yw9OkszrfHR3FxGfi1zJIuzVCHtcbGgNEfRo9DcF2dEVYTZncHdfA//sq0cwLJsz01m+sKubpqCXqN9Fd4OfV8/HKZk26y9z3XtdKj+/q6vyd3PIe9XBICs1/MeBVq5CZ18IEZJSzvks7scRdZvjaFkH6Chwy7l5AB5e34TXpdIa9rKjK1p5vWhYWJZzH/K6VNa3BHjpzAwTqQJCQEfUR8jrojPqQyLYWd53OlNk/+AszSEPiZxOqpzIJQTMZHXaoz62d0Zpj3oZTxZ5cyCBEPDLezxE/Tdusff+tVceQK0RP//iyY1MZ0rc27fwBrG7tx7dtPG5VSzLpmhaWLakoFv81WuDXJjJ0h71saMrQkPA7cx0wp5KKKsEjozMki1ZpAsGr52PcWTECXuL+l0LZGxr1LgRfP+osx51dDTFp+7t4Y2BBD6XCsLm9QtxbFvyrUPjrGkMMJvX6akPsG8owcHhJOA83c4Px67zu0jmDcI+F3UBNx++qxNwYvc/cW8PmaLJnstMvKrBSg1/I3BCCPEmCwuxfOAy+zwohPgP5e1fllLuFUL8kZTyN3BKOf414AM+f21dvzxSSp4/PU0so/OuDU1XFOl65WyMsWSe+9c20lnnp2BYJPNOpOqFQ6O4VIXHN7fgdan83B0djCTytIY9fP/YJAXDQlUE0paYlo2qCO5Z04CqKOQNky+/OcxUuogtHW389qiPgu4YxPqAm3dtaKIu4GZHZ5SAR+Mnb02SKhioQizKTr3ezN3EHljXRMcyRlZKSbJgVG5e833uXpfKY2VVwONjSQJujaJp0Rb1cmg4wXiqyGS6SHe9UyFszhfaUedDSoj6XLRHvRwfy9DeEsCtOn5+IZz/Pzo+SbZk8tjG5utWvKVGjfnMRctoiuDlczG+dWgMTRU8vK4RVQgQ4NYEr1+IM5kqki6YfOSuLk5MpPFoClLYPHvCUdd9dGMz//UjO/je0XGe2t624DhCiBtWL2Klhv/fXW3DUsofAD+45LXfKP8cBR692javholUsTJTfGMgwQcuo/2eyOnsG0wA8LdvDFciS9yqwt4LCeoDbgIejZ76ANs6I3TV++mq9/OXrw1iWDYg2dwWRghBfcCNJgQ53UIRNrM5nYjPxXiqQGedH6+mcHwshVt1ZB0My2J3b/2CG9MjG5tpj/poDnmWlWW4HsSypYpQ3Ovn42xoCfH6hRhrm4MLZixT6RKHy7OZNy7E6Wnws28wwYbWMA+vvxjbpqkKbVEvJcOmIeAhntXxajoBj8beC7MkcjqKIhhOOIVSpjIluhv8RP0eNrZC1O9hT38D9QE3Eb/zGD2n9nlgaHZF8co1aqyWngY/pyYyrG8Jcn46y8mJFKoQvHtjM2Gfi1RBZ0dXHc+emMKWTth2UTdpDnlwqwpDsTzHy2KN+wYSPLWtjftW8GR9PVlR4GdZrG0QcJV/3wcsWb39VqHO766ERXbWXd49EPRoRP2OgY3nSuRKFvGsTtGwaA57sCyJblq0RBx/vWk5Bn0ulHFzW4R/+77NfPaBXk5OZtg7mCCvO+na52eyvDmYoC3s5cN3dvLQ+ibWNTsLvHndwqWqnJxcGMXjUhW2dkRuuJRwyKsRKSt7dtb52D+UIFeyODKSWqD7EfW7CHkvntv9g7PkShYHy9nHmaJB0bDoqvPz8PpmdvfW8+7NLSgCYlkdKSVdDY6mkd+loCqCTNHE71J5ayxFf1OQ5rCX/qYgiiLY3B6mI+qjKejB61IRgopYXY0a15tjoylcmsKpyQzJvE48pxPP6wwn8oS9mhPkkSzQGvaiKs71sbYtRFPIQ1PYw9qmUEWBdqkonZvBSqN6fgUnwqYeJ7qnA/ifwGPXr2urw+dW+dSeHoq6TcR/+VmzW1P4+D095HWTA0OzHB1N0V3vfJm6aXNmKotbU3hkYzMlw+bFMzPMZErs6ony0V2dtEW8eN0a09kS2zoiWLbEtJ0Y254GPyGvi5DPWbyROBocDUEPUkqEEPQ3XT/55KvBozmxw3nddOLqpTOjX9MUwDMvOcRRAO2loFtE/C6Khs2+wQRrm4MMxnJ8Zd8IXpfKZx/o45fu7mImq7O5LcxQIo+qCMZTRR7d2IQiBKqqsL4lRK5kkS6arG0OsaYxQKpgVG5Cc0T8Lj5zfy+6ZRO+gU9CNW4PJlNFNFUskvXOliyOjCRZ0xRgJl1CN20EjmRDW9RHPKtzf38j2zvD/OzkNPf0N7CxNUx71IdbVfC6HJXakmkvGtM3i5W6en4duBsnExcp5VkhxMoCRm8iHk1dtnDHpbg1BbfmZnNbmNmcwcHhBOdncgzH89hSoiiC/+snp+luCCClpD3qZzCepznsI6dbXIjlCXmcxUcJfGB7O0GvxmvnY5ycSHNXjxNq1dMQ4B+/ay1zrnspWZTZdzNJ5nWmMyUCHieW3udSy5rhC/uYyOnEsiUCnhB39kQJelR6GwN8+/A4x8ZSCGBnV4R9Q7NkCgbv3d5OV52fgm7REfFyYiyNpghsWzIQy/PpPb3YksqaxnL+e69LXbJgTY0aq+HkRIq//f/be+84O8763v/9nDm9be+9yOqyZRVLtmxcwDZgigEbMIQQIEDqTXK5SSD3xyUkN4UEcpOQewmQAKGYmBawKbbBuGBbttVldWml3ZW27+l9zszz+2Nmj3ZX26Q926R5v1772nPmzDzznJk533nm+3y/n+9LPThsNj50W/uE+S23w0ZrhZeg28G54QSabuShIOHzD91YyNb912fOsKrGsB+6LicMTpbbdTtXw5+VUubGfvxCCDusXLEUVdPZ3xPB51JYP0ns6y9/fNTMKM3hd9mp8DvQdMjrOoPxLNF0ng0NJdSVuBlN5vj5sUEGohlqgi4Um4337mieIE1866oqbl1VNWEf4ydsxfKx+SSyeR7Z04uqSc6H0ySyeXpDKTxOhQ/tajNloSGaVvnCM2eIZ1TuWlNDLKPSH83gOxeiyu8kllZxOmxkVI2DPREk8MKZEf7wdddxsDfC2rogR/uiHBtM4LYrrKkNIIRAWUbHwuLaYm93hFOm8uvRPsO1ORTLckNTKbF0npODCdqrfCTMYkhSQjyb59RgnNFkjhuaSnnN6ioO9BjX93IazE3FXA3/M0KITwAeIcTrgN8GHl24bi0sL3WFCpO5fpedloqLrpZISiWUzJHXdWpL3Hiddm5bVclgLMP39p4nr0tqS9y8a3szX9/dzUg8SzavITEe/VRtxd4P0TRZUNTMaTq5vJFukdd00jmNPd0jOO02qgMujg/EkBL294YLfktVk1yIptF0SVbV0aSksdwY5a+q9rO2LshaszBNZ7WfCr8Lv9tu1cK1WHLaKn1UB10oQhBwOfjSs10ksnkuhFP0ho2aGEPxLB3VPs4OpxA2QXXQxWOm2GAsrXL3+toFK45ebOZq+P8U+CBwGPgIRrTOlxeqUwuNfdzQ0j6pYEllwMVALEOF18mq6gB2m+D2NdWkshpnR1NkVY2tLUbS0hs31nGkL8qbr69jMJ6l3Odc0RoyJV4H922qZzCW4fqmUtS8zpE+ozzcy+dCPLKnF8UmeO9NLayuCRDL5Lm+sZSdHRUc7Y/RXuXnF8cGC26auhIPH7q1jVAix40tExO9fC47d6+vnaobFhaLzvr6IG0VPtxOhTKvnXOjSaSE4wNxNjeVcvhClLpSDx++tY1SzylKPQ7eua2Fr7/YjS7liqv6NleRNh2j4MqXFrY7C4euy8Lj1/bWcgJuO36X/ZJY9YyqUel3IYBkNk97lQ+fQyHgsvNbr+kgmlYLo9Zyn7PgxulYxAzbYjD+eIyns9pPZ7UpvOa6qLv/7MlhIikjmzmb1/nIazoYTeRYUxfAodi4paMSm03w0LZmyr1OSr0Otk9K7trbHWZ/T5h19UGriIrFsuJInxEmnMlpRDMaNzSVEk2p3NxZwU1tFWwdStBU7kWTkvYqP26Hgtep8I6tjQXJhpXEjIZfCHGYGXz5UspNRe/RAvCzV/s5PhBna0s5u1YZBmqyb3+M166tKRT49rnsnBlK8rmfn6TM6+SBLU3LJhxrPqRyef7pqVO0VHh5y/UNc/JH7uiooGskiUMRrG8IUh1wF7JoXzg9wsvnQlxXE+ANG+u4b5qciZfPhsioGi+fDbGzveKSCWOL5cvVrtcfS6v88GAfdpvgjtXV/NbtnYSTuUJgw1j2/i9PDDGaMBM7h5OFUOOVxmwj/vsWpRcLRDyjEk6pHD5vGPJXL8xe4ORtNzZyx5pqzofSPH1yCB2JTRhx5t2hJJu8pYvT+QUkrepICedGUiRz+WmTxDRd0hdJUxVw0VHl56HtTbgcyiWVfY70Gf7+EwNxXreuZtrH3tW1fg72RllVHbCMvsWyons0hdepYAPOjabY3FI2ZehlZ5WfIxeiuOwKDbPkBy1nZjT8UsruycuEEPdJKR9buC4Vh3hG5eu7u8mqhhhSKpcvJB3NhGIz4ngr/S7WNwSJpnI8eqgfh2KjfZL2/ErF61TwOBVaK3wzav//7NUBTg7GTbdNOU8cGcQmBA9ua6Su5OJFf2NLmZG5WxOY0dd555oadnVWXVbBCAuLxcBltzEQzWATgoB7+rDLpnIvH31NB7YpCqysJK6k4senMYqnLGuSWY2sqqNLiVOxsa6lfE6GfzwOxUZlwM1v3NI2+8orCI9D4aOv6Zh1vdGkIcsUTasMx4zXupSEk+oEw7+lpYwtkyZvp8My+hbLkbzUC3LoqRmKlMOlASErkSsx/CviNldb4uaWzkpGEkZ1q/5ohpZyL08cGaC5wrtiwq6WkrvW1rCvO0xHlZ/2Kh85zUhUWV0b4ND5CEOxLNvaypdNNqKFxZXywJYmwkkVt0Ph3vV1V/31fSWG/yNF78UCMVajcoz/fKWHvkiGo/0xmsq8+JawxOFKoKHUM2Hiaiz8cjie5RfHhgBIqxpvmkEAz8JiJVDqdfLH9xqVZY0C6Mb1nVK1GQUeVyqzRfW8bZrljQBSyu8vRKcWCmMSM4PHoay4uNvlhNthKxSav1z3mYXFcsftUArXd/Aqvb5n+1ZvmuEzCUxr+IUQNwH/gFFt6xUp5R+O++xTwP1AGPiRlPJzc+3wfLh7XQ2raw3VPMvXfOUE3A4e2t5MOJWjtWJ5CMwtd672cMirCb/LzntuaiaUvHqv79mien5jHm13A3dKKTNCiG8KITZKKQ+P+/y/Syl/Po/2Lxu7YitIKVvMjzKf0yqEYnHVUup1Lmrlu8Vmzs8xQog3AuuBQhC3lPLT060vpRwY91blYpH2Mf5WCBEGPialPDDXflhYWCw/rCealcWc/B1CiC8A7wR+DyOq5wGgZY7bbgKqpJRHxy3+JynlFuC3mFiLd/x2HxZC7BFC7BkeHp7LriwsLCws5sBcHd03SynfB4SllH8O7ASum20jIUQ58HkMgbcCUsqQ+f/UdNtKKb8opdwqpdxaVVU13WoWFhYWFpfJXA1/2vyfEkLUY7hu6mZYf0yz/xsYrpyBSZ8Fzf+VXFlIqYWFhYXFFTJXo/uYEKIU+DuMWruS2WWZHwC2AZ8xdVk+DjxkFlz/OyHEBowbz59eQb8tLCzGMZuP3cJiPHM1/J+RUmaB7wkhHsOY4M3MtIGU8mHg4UmLXzQ/W7AksHAyx/f3X0BKyf2bGyZUwzp0PsKzJ4dprvDxpk11llDYCiOd0/juvvMkMnnu21Q3Z6XUJ44McGIgzva2cm5qr5h9A4tlgZSSHx/u59xIkls6K9ncfFEWZPzv/G03NlJuRZhdFnN19bw49kJKmZVSRscvW050jSSIpVXimTynhxITPjt0PkI4pXJiIEYsk5/wWSSV49ULUXRdv6TNUDJHNK0uaL8tpmc4njWqIUVS9IVThJJZjg/E57StqukcOh8lklY50BsBYCiWIZXLz7yhxZKTzGkc648VFHallAzGMmRUjTPDCYZiGYbjWU4PJcioGgPRDFKu3Ap4i8lsmbu1QANGycXNXNTpCQLLSpheSsmZ4SSlHgfRVA5dwipTdKlrOMGzJ4cJJbPsOReiqcyLx3HxnhfPqHzsOweJZ/Lcdl0Vv3NHZ+Gz00MJHjvUh00IHtg6UZXS4sqRUnJ2JInfZac66GYoniGeydNe6SOj6vSGUzSVeTk+EOPpE8M47TZet7aafT0R4hmVm1rLSGbzXIikaS73TlvI2m4TDMYznByIs6uzkhfPjLK7axSvU+HXdrbgdVpTTMsVlyI40BPm5GCcB7c088zJYfb3RAi47WxpKeNAbwQp4e71NXzzpR5iaZUbmkrZtaqSsyNJaoLuq1JnpxjMdtXfA7wfaATGZ9fGgE8sUJ8ui3hG5cRAnIFYhlODCXpCKbpHkzgUG//y1CnsCjx+ZIhENk9e0/E4FDJ5jW+91E1zhZ/GMg9dQwkuhFOkVZ1TgxNHkoPRDIMxQ651NJGzDH+R2Nsd5rlTI9iE4N4NNXz60aOEUjn+6LXXMRDPMhLPUhlwUWW66nJ5nZfPhjgxECOvS544PsjxwQTdoynW1gX5wK6JCqr/8cJZQskcH7iljd5QClXT6RpJcp1ZKSmV04hn8hMM/1AsQ08oxZq64Ixy1QPRDBciKUvob4H4y0ePkMnrvGd7Ay+cCSGBr7zQRV2ph75ImoDbjscu6IukADh6IUIsbTzBDcYyPH5kgFODCTxOhQ/c0mZl6U/BbJm7XwO+JoR4u5Tye4vUpxk50BshllbZ3laO26Hwpee6eKkrhF2BLc3lxNM5BqMZ8rrkSF8Uh00Qy+RRbIKcJoln8gwncnztxXPYhI3NzaWUepxE0nk0XSeRyfPL40NcVxugodSDYoNoSkWxCazrZ34MRDMc64/RWe3nfCjFk0cH8LnsqHmNV7rDICWfe/IEd6yp4Xw4jarpvG5tDQfPR6gJuBiKZ4iaP/AD5yKcHEgwHM/SG07xru1NvHIuTIXPyanBOP/6bBcAo8kcWVUnm9fJqBq3dBqFeKoCLmqCFwvKqJrOd/aeJ5fX6RpOcu/GWvZ2h6krcU8w8BlV43v7jPXOjaQW8eitbOaa4DUQy/DNl3sAONEfLZT/S6lGUaDnTg1TX+ohlVUZjhuVsI70JXhgWyO9oTQ3tZfz7Ekj7yer6uR1HeecPdrXDnN9zn1eCPFvQL2U8vVCiHXATinlvy1g3y7h3HCCz/z0GGlV57ZVlbRX+/nZqwMks3mQsLraGKkpiiCb11A1WfD5jV1AQhiVpRKZPMKsrFXiduB22LAJhd5wim+/0kNjmZf/7751OB1KwWWk2K6tC6g3lGJfjyHLvKFh6lKVl8Njh/qIZ/Ic7Y/x6vkIPaEUNiFYVe3HLkAKQYnHwbmRJEf7Y+Q1nSN9UZyKjXDKmLexCeNcKjajf4mshqZLnj05wrF+o25qmffi470QsLW1jIFYhnV1QQQghMA2w8S+RPLL40N0DSc5KKA26J6Qvj/5mrIoHnlNR9ONIzuazk747NFDFxhN5IikVa6r8eNQjHNYHXSxpaWcLWZK6evW1bK/J0xLhddy5U3DXI/KV8y/PzPfnwT+E1hUw3+4L0p3KIUu4Ymjg9wtBF6HgmITSAk+l8JIUlDudVLld6HpOiA5O5Ihr+u4FPC7FVyKoK0qQNDt4MFtzUgpiaRyhFIq/dE0kZSKqiUBuKGxFLtNIICTgwn2dId57doaqgKuGft6NfDU8SFCyRxnR5J0Vvun9aPPFa/TTjyTx+NQyEuJwDCeNzSVUlvipms4yf+8by1v/fzzxLN5Dp2P8N6dxq/ZJgR3r6vlqRPDZFWNt25u4P89cxabAE03qoqBUUHtrZsb0CWEUjl+5/ZO4lmVcyMpVtcGePqEYdDPDCWoL3WzrztCLq9zz/pa3rGl0XD11AZ48cwoYBTjGa/k6nYovH1LI+fDadbVBfmbeR2RuXOthGtWBdx01AVRdcm966r47M/PFD7LaxKJRNdhY32A7lHjiWtX58RIrXKfk7vW1ixqv1caczX8lVLKR4QQHweQUuaFEDOXqSkieV3y5ee6iGdUakvc5PI67ZWG2Nr7bm5FsYHDJjgxmGR1bYC3bq7HbTdG6iOJLH/x6BGGE1laK3287cZG1tYGyOvGjaKuxENG1Tg+ECeRzdNc7iWvS1abo3ybTbCpsZQzwwlOmv7//T3hgjb91UxVwEUomaPU48BZBBnr+zc30B1K0ljmpbHURddwgoDbya2rqqgbp/vvdzvIaTpBt4Od7RVUB1wE3Q6qg27++d2bGUnkuP26Svb3xjgzHGdjQym7Oiup8DmpCBjiWuN9/h7nxTrBVX4XXcNJPE6FoViWsyPGDf7whSi7VlUW3D93rqmmpcJHpd95Sd2GuhKPNdezQNhtgtvXVKNqOjvaKwHD8Pscgvs3N/DY4X5qAi42N1cyktSQSKqD1rm4XOZq+JNCiArMp1shxA4gumC9mkRGNSbiQPA7d3RQG/TQXuUnlMxxfCDGwd4oLRVeHrqpGZ/LPmFirirg4i2bGzgxEGdbazmv33BpwrHbofDeHS1E0jkqfE5GErlLRvTVARc+l0Iqp9FylUq1Tube9bXc0FRKuc9ZlPqiHqdS8JcLm8Idq2uwK4LkpNDK37y1nZfOjrKjvQIhBJ3VgcJnmxpLC6///oHrOTUUZ21tgOMDcZ48NkiJx8G7tzdP+3Ryc2cl7VV+Am47GVXj5XMh8pqkqXyi8bArRqUxi8Ulm9eREuw2G8m8xrp6PxdCGV63toqP3bOaezbU0lTmo9zvpK7UjQQrhv8KmKvh/yPgR0C7EOJ5oAp4x4L1ahJuu4LXqWBXbGxpqSiEaNWXevjJ4X4AukdTvHGTA5f90h/8A1uaSObyM0ZqeJwKHqen0O5kAm4Hv3FLG6qmXzN+Q5tNTHksisGa2gCnhuIEXHYaSidGBr/txgbuXl8z4/kC8LvthaSeM8MJpIRISmU4np0xuau2xBjV+1x2PrirDSmZtxvLoji47DaCHgd5Tael3Medq2tIqzqtFV5sNhvXN11M4rJkwa+cuVqwo8APgBQQB/4Lw8+/KNgVwUemKQ6+o72CV86FuK4mMKXRB8OAGdW35sdkf6/FldNU7uW3b++c8jMhLv983dhSRjiVo9znvKyb1XTXjMXSoNgEHxznptvaWs7ZkSTb26yM62Ii5pLpJoR4BCN2/5vmooeAUinlAwvYtwKVlZWytbV1MXZVFPK6ZDRhRCR4HArBKZJIsnmdSMoIR/O77fhmeIpQNZ1Q0ljX57Tjn2c5uHPnzrFSjudQPEvCzLKuK3HjcU5tqGMZlXTOmHaq8LuwF8E1NRdW0rFcCBLZvBFVh+FyyeV1Ypk86ZyG22HD77JPef1Px7V+PIvN3r17pZTyktHqXC3IBinlunHvfymEODrt2kWmtbWVPXv2LNbupmUoniGZ1Wit8M6o8xNK5vjCM6fJ5HR2raqcciL47EiSb+zuRtN17ttUz9bW8ilaMuiPpvn2y70AbGstZ9eqynl9j61bty7a8RxNZImkVdorfTMes+F4lnhGpW3Sep9/6hTPnRpBCPjkfeuoDLhIZjXaKifOs/zi2CCHzkcRAt67o4VK/+JEXS3msVwKdF3SNZKkYoqKa6FEjm/sPkciq+FxKrxrexO9oTS/ODbIwd4Ia+oC7Oyo5J7LCIS42o/nYiOE2DfV8rka/n1CiB1Syt1mYzcBhbMjhKgCfhNoHd+mlPIDV9rh5cZwPMvDL/WiS8muVZVsm8FQKzaBrkM2r03rGnIogpyZazCb+6iuxMNbbqgnnsmzvn7lZItGUyrfeqmHvC5nvGGNJrI8/HIPmi7Z2VHBjnFCah/a1UZ10E19iZvKgKtwDm67rpItLRfPwW3XVVHuc1Lhcy2a0b8WeObkMAd6IzjtNn795tYJ8y7/69FXGYxmcDsVPvWm9dSVeKgJuPE4FG5dVYnXZb/i69Wq6LWwzNXwbwFeEEL0mO+bgRNCiMMYkT4J4Dng51xaYvGqIJXLo5tusXhmesG2RDbPaCKLz2XH57KTyKqMJLKUeydGxiSzGgG3A103cwiSOYIuO12jSVorfJekmbdfRq3gqNle2yLVF5ZSMmqGfdrH3cTSqkbeTMZJZC89Zr2jKXwuO6mcRtZcN2G6CQ5diLCpoRSP086DW5sAODeSJJs3ErYmi+w5FNsE9UaL4hDPGucjreYZiKYo9boQGBPj0bSKEAJdl9SVeNB1SSiVY21dgLwuSee0Oc2hJLN5NCkJFmEezmJuzNXw3zvL5z+UUv7JfDuznGmp8HHbdVXEMio7pplouhBJ87295wHY0BBEsQl6Qym+/mI3q2sDvGHjxVDSMq+DUDJHKqfx/JkR9vVEONIXLQiV/fXbN11RP0cSGf74u4dIZjXeckMDD93UfEXtXA4/e3WA4wNxakvcvHv7xf3Vlri5c001o8nsJZNzjx3s4xsvdeO02/jTe9cQzajE03lKvHY++LVX6B5N0lLh41u/uaOwTanXQTStksjmKbPEtxYFv0uhezTBSFLl9FACp6Jgs8H1jaV8aFcbjx3qR+rwlRfOUul3cSGcpirgIp3TSGTzsz4dD8UyPLKnF02HN99Qv4jf7NpmToZfStk90+dCiMeEEG+QUv6kON1anmxpmXlEORDNFNLNS71Obmwu45+fOoWUkgvhNLouC6P+4USWmqCbtKoxFMvQUmGnezRFuc9JT+jKNWB6Q2mSWQ0pJScG5yZdPF8uRIwCbYOxDHlNnzDqv76pdML3HuP4QBxdl2RVnYO9USp8Lip8LkYTKhfCqQntjjGSyFHpN1w5I4ncZfVxqj5YzE40rVLhdxNOqURSKootj9tu5LO0V/m5c001x/rjZE2BQ6/TTvdoEo9DQQhB36RzOB5dlwzGsqia8Zvpn2Fdi+Iyr/AQIUQcw9UjgE8IIbIYZRkFIKWUK8chXQTW1wfpj6aREjbUl6DYBG67jb3dEVbV+Pmnp07RVObl/s0NXFcToDeUIqPqbG8tI5xS+fWdrRw8H+HW66588nZNrR+/y85gPMOOtulHWsXk9tXV7OsOc11tYILRz+Y1HtlznnAyx+s31BY0jwBu7jDEtEo8Du5ZX8P+3giRlCG+9+7tzfz82BCvXVs9YT9tlT42NJQQz6hsbZ27W+fHh/o5NWQk8I2JtFnMjZs7KsmqOpUBJ4oQ5nyUZG1dCcf6YuzviZDNa+TyOopNEPQ4eNP19URSKoOxDDunKHyj65L/OnCBnlCKHe3lrK4NoGo61zeVLvr3u1aZl+GXUlqpjeNwOxTu23TxcVXTJWlVZ119kBMDcVbXBugJpYhn85R4HNw7RRbxe2mZVx9CqTwbGkrYQAk57dKiMgtBZ7WfzupL5xOGYoa8Mhgj/PGGP5nTuWNNtdnn3ARtlY/e3slHp4jxV2yC1627PA0WVdMLUhvH+mOW4b9MaoJu3rV9anfhP/3iFG6HgqZLSgJGxE97pX9CdvVUJHP5gs7OqaEkv7Zjfte8xeVTlBRUIcQvpJR3zbbsWkOxCbxOhVfOhdjQUILHodBS4SVoygX8+FA/mbyG12EnllGp9DsZjmfZ2FjClpZyoimVn7zaj0Ox8caNddPGsIMxqfyTQ/3kdZ2aoBHyeP0sP8CFpirgIpzKMRTLcMskIa10Ls8je3rxuRQe3NrAjw72EU2r3L2uZoJc8mAswxNHDSmGN2yonfBEMRccio0bmks5MRC3Jn+LQF8kzeNHBugeTTEUyzCazFHhc1Dmc+GwCU4Oxqjsck5b4nLsOg0nc1T4nWy2RvlLwnxdPW7AB1QKIcqYWKGrYYbtbgL+AdCBV6SUfziffixXNF2Symmsry/B61T48G0Xs49PDxlFY9K5PEPxLC0VPl48M8rq2gDPnx5lS0s5r/ZFGYgapY1PDcVnHEmdGIgVfOK3dFayfZHcPDMxHM9S5nVS5nUyGJsosfuzI4PYhFFH9zt7ziPNS2d/T3jCk9D+njAjZmGW8+E0rZWXr5N0x+pq7lhdPfuKFrOyrydM13CCk4MJcnkdu00QTudZVRM0y1kKXuwaZVtr+ZRzKsf7Y/RFM5T5nNzcWVkUuW+Ly2e+I/6PAH8A1APjEwViwOdn2K4buFNKmRFCfFMIsVFKeXiefSkqI4ksL5wZpcLnJJ7J47QLbltVdVkjTsUmaKv0cXYkiSJsfPKHr7KhoYQHtzbRWObB41QQAtrdDjRdsrExSC4v6TDDMFsqvOzvCaPYbDTMIkPQVOYthIAOx7M8erCPXZ2VlPmcDEQzvHR2lJYKHzcUaYT1UtcoQ/Est3RWTiuSVRVwcbQ/ykg8x8aGidM9W5pL2d01ittu44411bxyLmwmZvk5PZTgSF+U9fUltFX6OTGQwOdSqA5eWXz+sf4YJwfjbG4qo7liWVUMXZaoms6zJ4fpGk7iUAQSowJaS4WX+hIPAbeDoNuBXRE4FIEuIeC201ltnLu2St8lRn9vd5gLkTSdVUaocn80zbH+GI1lltLpUjBfH/8/Av8ohPg9KeU/X8Z2A+PeqizD2P/nTg1zbiTFM2Ej0sbrtFMdcF/2COUtN9STUXU+8YPD9EXSnBiIc2tnJXWlHj64qw1dShw2GzlNx+1QCqnuAI1lXj58WwdCMGuSV3XQzYdva6c/kuZ7+y4ARhGS+zbV88sTQwxEM3QNJ+momr+y6FAswwumXr0uJW+5YeqHuyN9MaIpFbtN8GJXiNeuu5jB2Vbl422b63EoNvzjBPDcDoV/+eVpcnmd8+E0v3NHJy0VXhyKDeUKonLyms4TRwbRpWQkkZugA2MxNUf7YuzrCbPnXBhdSqQ0BjEbG0u4/bpqfv+uVQgw81qMqgoOxYZdsU24fscIJ3PjqmJpPLS9mX/71VlGEzmeOj7Ee26yfPyLzXxdPW8zX14Y97qAlPL7s2y/CaiSUl4i/yCE+DDwYYDm5oWPRZ9Mpd/FuRHD6DvtNmxCsOdciJe6RnlwW9OsImInBuKMJrLUlri5EElT6nXQF0kTdNspMStETSjwYTP895P9+NPVCw0lcxzpi9JW6aOxzFtor9zvwuWwGZEYZgZrpd/FQDRDwG0vigqlkZiWJ5TMcX3jxBthNJ3js0+cpNzr4J3bmtGkJJnVqCtxT1iv0u/C53LgUARlXieKTaCYx6DK7+JCJF2otzufPis2QbnPYYaCWmqOc6HC78RhsyExXHHJbB6PSyGX1+gJJVlbF6Bimuzosev31GCcoXiWzc2leJwKPpdCMqtRGXDhd9sp8zmJpdXCOV5prPTM4vm6et5k/q8CbgGeMt/fAbwATGv4hRDlGO6gB6f6XEr5ReCLAFu3bl30Kne7OivprPYTdDtIqxqHeiOFOq7JnMbv37Vq2m2H41l+crgfKSXdoRStFT6ayjzct6metgpfUWSdf3y4n5F4loO9ET76mo6CC8rvsvO+na3EM2rhEfquNdWsqw9S7nUWRV00o2q47TbKfQ4y+YmRQ5/56XGeNkd3PpedzU1lhQI349nUWEpt0I3LoRRktse4/8YGhuJZqotQ5UwIwYPbmhhJ5KgNumffwILGMi/v3t5MMpfn+dMj5DWJ02Yjr8FgLMtjh/r59Ztbp91+NJHlx4f7kdLIA3jDxjqj3kVKpa7EjRCC99zUzGgyR511TpaE+bp6fgNACPEksE5K2W++rwO+Ot12Qgg78A3gY5PcPssGIUTBcPpcdqqDboQAKY1R0I8OXmB7azkjiRz1pR4GYxnKvE7SqkY0lWMkkSWczGFXBEPxDEG3H7fDhhxXqbVnNEU2r9FZ7Z9RwAyMZKZEJk+l38lALFMoH+1QbJds659UjMZmE7POEVwOdsVmpuTrKJO67XPZUTUdgVE/dyieNQrVKzaGYhle7Bpla2sZNX4n//6rszRX+Hjopmb6ImliGZXrqgOMxLPsORdiR3vFhAifK8VlV4r6/a9mwokcD+/pRkEQSal4nQrRtIrdZtzowXgK3XMuRG8oRUeVj/bqAPGMSjStsqrKT89oinjGqH/hsts4PZTAoYgJBYzcDuucLCXFqijSOGb0TQYx9Hym4wFgG/AZ02h9XEr5YpH6siBsaCjhY3evpns0yQ8P9LGvJ8y3dvdwU3sFg7EMVQEX0bSKy25D1XT294QRQNBjx+fyc6w/RiyTx2m38cFdbQzFsnxvnyHvcPvqqhlDDYdiGb6zp5e8Zsg9Vwfd1Je6ed26GhrLPFfk+5435v1r8k3ntWurOT4Qx+dUWN9QwslBo0AKUvKXPz7GYCzDjw/1YxPw/OkRhBBk8xqxtKGFNNqW45svdTOayPH4kQH+73u2LP53u4b57989yIGeMMmcxqoqH21Vfsp8TmqDbt61rZlIWiWVy/P3T5ygP5KmtcLPjo5ycnmJLiUveOxE04aO0g1NJZT7XDx6sA8w5rsuR3PKYuEoluH/hRDiceBh8/07MQTbpkRK+fC4dReMZDZPIpsvyqgRjKIQq6r9hcnTjKpd/C9Bzes4bDZUTSKEwOdUyOtQHXBzbtSo7ZrXjB9INm8Ik2lSks3PnGg1Vo5OIguuFSlZslC4vKajKAKPUymIsBU+0yncxJLZfEGsTtXlxeOV19A0HV2CQBJO5go3kGxeK+jqj61vsXDoumQwbjytGsEFecz7NAiBELC2NojNJvC67DSWe9l9ZhQpQZegahqprFaI4klmjXPmd9lprvARTl6U1pjtOrdYPIpi+KWUv2tO7t5qLvqilPIHxWj7Sklk83z9xW4yqsbNHRXTJpRcLiVeJ797Ryf7eyPc0lFBLJPnTdfXMRjLUuox5gMkcHNHBb2hNLeuqmQ0meMtN9RzPpymqdyL12kn4LYTzajk8jqBWUoMNpV7ed26GmMyLOCiP5phU+PSxT97nArZvFEcZnIEx7a2coQwEteubyol4HYwmsixpaWMpnIPvzw+zM0dFURSOT796FECHgfvv6XNcPWkVW5sKaPS7+LZk8PcbsXeLzhPHhvkaF+MUq+DX9vRwqfevJ4vPnsGVTOMek3QDQLeuLGuELa7o6OCd0WbOTeaYG1dkM3NZYwmcoRTOTY1lvDqhRgep0JHlZ98uY6mS+yKjTVWDeNlQ9GKx5oRPDNG8Swm8YxaGDEOxbOzrH153NxZyc1m6n8klSPotk8oCA7GSErV9QmytOMfc8MpleqAu/B6NtbWBcmb7Y2XPlgKEqb/1u+yE59CHnlLSxlj3qe1dRfj99fVlbCuzrhh/eLYIG/cVI8QEE7lJjy9bGosnZCsls1r2G1GOKemSzRdThvtZHF5DMez5HWdaEolm9doqfDx2Qc3k1E1vvDMGaQ03JUdk1w09984MYS30u8qXJ87Oy4OsuyKrWiDLoviMd9wzl9JKXeNE2srfMQSi7TVlXi4qd2YfF0ofZax6lAdVT7+6m0XZZQzqsa3X+4hklZ53boa1tdfOjpfVe2nv6mErKpzY0vpjPtJZPN8++UeklmNN26qveQms9hUB93s7DDmNnZNOrbnRpI8erAPl8PGO7c1XxKxM0ZVwMXZkSRBj53KGYpmnxyM89PDA/jddt60qY4fHewjldN446a6S4yRxeUT9Nh57lSCVdV+fnigj8FYtpD5fdeaGn55YogL4TTffKmbd25rnvKGm85pPPxyD/FMnns21LCm9prSZlyRzDeqZ5f5f1k+w93csbCCXAd7IwCcGU4Sz6iF2P7RZK4wij89lJjS8NsVG3eumZvg2EA0UxhZnxlOLrnhByZUyRrP2dEkeV2Sz2r0RdLTGv7BWLYg7DacyFLindr4dw0n0KUkllY52h8rHAcjGc0y/PMlklJZUxskq2r0htI4zSic7W3lbGws4dRQHE03kt/CqdyU82VD8QzRtHG9nxlKWoZ/BVAskba/AJ4BXpRSJovR5mKi65KfvjrAhUiKO1ZXs6omwJnhBE8dG6K2xM0bN9ZNSEF/8uggZ0cSCAFnhhOsrQsQcDuIZVR+eKCPbE7j5bOjhJI57lxTzUA0Q2XAxWgiS3O5j3vW15BWNX54oI+MqnHfpnqqAi5e6hrlQG+EjY0lE25aLRVe2qt8xNLqgopa5fM6f/XTY3SPpnjopuYJipnj0XTJY4f6GIplee26mgn1bzc2lHAhnMbjUKgPuvizHxxmMJbhQ7vaURTBvu4w6+tLSGZUHn65B59L4b5NNfzJ9w4RSmT56O2d5PI6h85H2NRYyg1NZQzFs5R4HGxvLSeSUolnVK5vsjReroT9PWFePhtidW2A21dX8+TRAXafCeG229jcWoZNCF6/oYbff3g/fZEU1UE3oUSO9Q1GHshUNJR66Kj2E0nlZn16tVgeFMvH3wU8BPyz6fZ5DnhWSvnDIrW/oIRTuYJ07/6eCKtqAhzoiZDI5jk9lGDEDKEEI1Ll1QtRAHpCKTqq/OTyknhG5dRgnJF4lr5omkhapcTj4Pkzo9yxuponjwywujbIsf4YOzsq6A2lCgJsR/tjvCZQxZ7uMLm8zp5z4QmG36HYppVFKCanhxMc6YsB8PiRgWkN/1DckH8AONAbnmD4K/0u3mvK7L58dpTTQwkAfnZkgJqgm4yqsac7xImBuFl3WOcbu3s4N2K098SRfvwuB3ldsudciJ0dFbxvZ2uh/bduXvjjcDWztztMKqexvyfCzR2VHOiNIpEkchoj8SwbGkp4qStMXyRNv/mkWVvixu9yEJpmxG9XbLz5eqt61kqiKDNkUsqvmIXV78BIzHrA/L8siaZV/mv/BZ48OmhoiXsc1JcaCVpr6gw3yuraADYhqC1xUzbOB+11KrRWGlmo21oNd0eZz8GH/2MPX37uLE4F6kvdZny9jZvMsnPbWssRwjCMvzg2yMnBGOdDKU4PJRiKpfnu3vPUmCJktSVuvrv3PM+fHlnMw0JrhZeGUg9CwM726d1kFT4X1UEXNiFYXRPkzHCCR/b0cuh8xHzqucATRwZor/Qa9XgTOTY0BAm47Rzpi+J1KGxuKiGaVkmrOm/YUEdeM6KENjSU4jfX87uLFntwzXJiIM4je3o50mcMVsYm2zur/XzthbO47TZ0HZyKIJLK8fK5EImsiqpp+N122ip9VAfc1ATdl4jxHeuP8cieXo4PxBb9e1nMj2K5er4MrMNI3HoOeAcT1TqXFXu7Q5w1R5itFV5W1QQMXRldFpKhNjSUsLYueElylBCC+zc3FtbN5XV+6xt76Qml6AmleP36Gu67vgGb+ZnTbiusq+mSZ04OcbA3ynA8i9tho9zn5IUzozSX+2go8/D7d63ihwcu0D2aojeUYlWNvxD9s9C4nXY+984bCv2eDqfdxntuail8ry8/10U8k6cvkmZjQ0nhacDlsLGzoxJNN8TXRhI51tYFSas6qqaxtjaIEMa8wK5VVei6jkOxkcpprK0LkspZcfzz5anjQ2RUjYFohnV1QW7prGRHewVnhhN8Y3c37VV+1tUJ2qr8PHFkkExOoyeU5u51NbxvRwtlppbOVEmCvzg2iKpJhuNZy6+/wijWkKoCUIAIEAJGpJT5GbdYQupKPBw6H8Wh2ApCZmBc3Hu7wzxzYojWSh9PnxiivdLPQDSD22njE/euxWmKUI39EJx2G+1VPg70hnHZFVRdsrtrFK/TTjiVo7bERV8kw4aGEmqCbip8LrpDKWxCUhv0YLNBrd0w7A2lRhZuXYmH7tEUAbed4CxicAvBmNE/3h/jJ4f72dJSxmsmxdS/esG4eW1tLaO+1MOJgTjVATfVATc9oT7cDoW2Ci9feraL4USWtXVBjvVHePbUCDc2lfGGTXU8eXQQt0NhY0Mp4dQIGdV4WkqZFZomC7tZzE5vKMXJwTjr6oPUlXioL3Vz6HyYA71R9vWEqQu6sNtsxLN5BmJpFKCtKkBvOI0Q4HUolHodlHqdBE3xvOmoK/HQE0pRX2qdp8tlqUXeipXAdT+AEGItcA/wSyGEIqVsLEb7xWZtXZC6EjdOu+0SwbR/+eUpklmN/3ixG59L4RfHhvA4FVx2G7VB95QlAbe2lhNNq2RVjSN9MY71x4llVGqDRsZuW6Wf7tEUH9jVRiStUhNwodgEb9xYR2OZF7fTRiKTLyge7uyo4LoaPz5XcdQ0r5R/efo0Q7Ese7rD3NhSVohaGklkefLoIGCU0XvDhjq2tZZT5nWwuytETcCNzSb41ekRuoaT6NKosfrC6VFyeZ3nT4/yrq2N7GivwGm34XPZef/NrWRUjTKfk/X1JYSSuWl1/i2mRkrJjw72kcvrnB1J8qFb27lvUz1PHh2keyRFLKNiVwSKzYaUEr9TIeBxkMjm0XSd166tYVtrOTs6Kgi47bPmSrx1c4N1nlYoxXL13IeRtXsbUIqh0vlcMdpeKEqniVBw2RUGollcZkaq3SaMZCxNJ5ZW+d7eXt50fcOEH4XbYQil2W02UzQNPA6jyIrfNJZjoml+l4Lb/Kwy4CpINLv8Ew38dLK3i0mJ28FQLIvHoUxIRHPZbTgUgapJfC47g7EMT58cZltLmSHqlVFxKjZqTZE7TZcEPQ6cpo6RXREEvS6yed3I8nUpeJxKQdJXsQmqiqDMea0xJhOSy+v4zOstkc2jCIGqGxnlis0G0pAUwSye7lIEuqJgsxlFVwxxtdkHHNZ5WrkUy9VzL4ah/0cpZV+R2lwSNtSXoNgEd66poiboxqHYeOrEIKmMxq9Oj7K3J8JIIsdHXnOxjKKuS5yKjRK3gzduqqPM68TrVMzsXBcDsUxBlnhLSznlPhdep1I0DaGF4k9fv5YXzoywviE44UYXcDt49/Zmwqkc7ZV+/uiRg/RHjVqsH76t3SjKYbNRYZbXG01kuXttDR/e1cZXXjjHA1uayOQ1nHYbdpsgry266vZVywNbm7gQSReut//afwG7IqgOuGkq8+Jz2dF0Qz3VabfhcRoTuFtaS/np4QFODyVmLK5jcXVQNK2esddCiPuklI8Vo91io+mS3lDKKAYxjT6O3S6oMCtu3XZdFT2hFGtrS+iPpon2GyGfk4XJdEnhcbc26C6Efo6N2oOTkpjarqBu7FLgd9u5e/3Fqll9kTQOxUZVwEVK1RiJ52ivlGjSEN/SpUTLS8rMpylNh8Yyjzl3YWN1XQl/8/brAfjVqZHCekYlJ4u5kMzmGY5naSr3Tul/97nsXGdKeoSTOQbjGeIZlaqAm42NpcaNVpfIsVE/UFfqZk1tkOdPjZLXpXU+rgEWIl7u08Cshl8IUW+utw7wL8Zk8JNHBznWH8PnUnj/zW1T+jAVIUhk86RHk3x/3wWcdsGO9nICbqOC1lAsw9u2TJy62LWqkqDHQZnXWTD6VxtH+2I8fmQAmxDcvrqSv3/iJLm8zmtWV/HHd6/hyWODbG8rZ01tAK/bjtepsLYuSInXQSiZu0RU7qb2cjxOG36Xo1BBzGJmVE0vSCOsrg3who11064bTub4xu5uXj1vhHF2VPp5/cZa6oIejg3EaKv0FaJ9NjUaVbLeurmBvkjaKoB+DbAQhn+u4vAh4C5gwVQ8VU0no2p47Ar98QzhpCHWlspp5LSpQxZVTafK76InlIISyOUlTWVeKgMuVtX4UTVZmBCWUhIzBcu2mfH6K4VoWsXvsl8yasxrOilVK0QTja0XSedI5fLYBPSGU+RMid3BWJbGcg9v39JIwGXHZhOsqQ1gNyu0dFT56ai6dP+GmNvKOmZLTV6TJLLG+ChiSoLk8jqD8QwNJW6SOUPSujroZjCWIZvX0aXxBFbud9JgRt+Ml9sYXxylqdxLU7l1E74WWAjD/5G5rCSlzACZ2SpPXSnpnMY3X+omnslztD9GLK1yXY2fmzsqaa7wTuvqcTsUXg1HWV0bYLUZm/z9/RfI6zpIo5rVvRtqWVMb5BfHhjh8IUpDqYcHtjbOWkVruZDI5Pn3X52lOujiXduaC8Y/l9f51kvdhFMqt3RWkszmOdAboa7ETVXAyXOnRrArgvtvbOStmxs4N5LkvTe18OTRQY70xWgo83Bjcxk/PtSP027jXduaJiS/WcwPj1PhnvW1nBtJsqWlDF3X+bMfHKYnlGJVtZ/ecJpcXuee9TVE0iqRVI47V1eT1TTOhzP8t28f5LpqP6/fWMf1Cyj9YbH8KVax9cnLG2H2YuuztD2vYuuhVI54Jk9eN0LbKnxOLkTSvH6Gx2MwngY6qwMIYRj4F06PoOmSSMrQzq8JuukZTbGmNkh3KAUYZRFVTeK0rwzDn9WM0fpQLEta1Qo3wWhaLYjLdY8mSZqjy/5ohmFTLwfgeH+cd2+/eE4eP2pUz7wQTlPuc6JLo+jKYDxjGf4is7YuWMi+TWTyxpMpcOhCFI8Z+vvKuRCd1UZB9DvX1tA1kmAonjMKE+XydIdSluG/xilWsfWpkMxDn3++xdbrgm42NJQwksjyjhsbOTYQ4551tbNu95rrqjh4PsIGU1FzQ2MJfdEMrRXGZFpa1djSYlSY2tVZyZ7uENfVBFaUPrzfZac66KKtwjfhyafS7+SG5lL6Ixl2tFeQzWu8dDZEZ5Wf+jIP5yNp3A6Fu9ZOTObataqSvd1hVtcE6Kz2E0rm8DoV2ist9cyFxO+288ZNdeztDnPX6ioOXogRy6i8a2sTp4cTlHgcNJV7jdrHsSxOxUZTuZftK8wtaVF8ilJsfTliswlet25ussfj2dBQMmFyK+h28I4tU+ehGe6gpZdIvlxcpuTCZIQQ3DEpQ3e8BPTfvn3T5E0AWFMbnJCy/+DWpiL11GI23reztSBi9+bNF5dvGWfcy3xO3rX98p+aLa5eiubjF0K8EVgPFMJapJSfnmF9B/BT4HrgcSHEJ6SULxWrPxYWFhZXK/OVfChW5u4XAC+GOueXMUTaXp5pGymlCry2GPu3sLCwsJg7xXJM3yylfB8QllL+ObATuK5IbVtYWFhYFJFiGf60+T9lJmapwMzhMxYWFhYWS0KxDP9jQohS4O8wdPjPAQ8Xqe15MxTL8OKZUcLJ3FJ3xaKIDMWN8xpa5ufVuv4slhvFmtz9jJQyC3xPCPEYxgRvpkhtzwtdl3x333myqs6pofiEMn4WKxcpJd/fd4F0TuPEQIz339K21F2aEikl39t3gYyqcXIwzq/f3LrUXbKwKNqI/8WxF1LKrJQyOn7ZUiKEob8DICWcHIwTNZOULJY/UkrODCcYik8cRwghCud1pmIhywGbML7HaDLHcDy71N2xsJh35m4t0AB4hBCbuajTE8SI8llyhBA8sLWJc6NJuoaT/PhQP26Hwgd2tc5Jc9xiadndFWJ31yiKTfCem5on1Cl4x5ZGzo4m6axevoliY9ffjw5eYCCa4eGXe/i1HS1WRrPFjMwWrjlf5uvquQd4P9AIfG7c8hjwiXm2XTSCbjtNZV6O90cZSWQp9TjIa5Lxcj2jiSynhxPc1FZxyfbRtIpiE9Pq+1gsHGOyEZouSasaI4kM8XSetio/ZT7ntAY0mc2jajqlXieqphNJqVT4nNhsgtODcSoDrmmL8cxGLq8TTatU+qfefmx/lX4n3aNJYmkVn9OOpkuyqkYylyevS8p9M5c2tJiehS5duNCGd6mZb+bu14CvCSHeLqX8XpH6VHS+u/c8/dEMR/ujdI+mqA64Ucb93kYTWR760m4S2Ty3dlbxN++4mKHaNZzgRwf7UITgwW1Ny754ytXGLZ2V2BVBqdeJpkv+8D8PksvrvGdHM2++fupiIaOJLN9+pRdV07l3fS37eiIMxjKsrg0wGMvws1cH8LkU/v4dN1A+jfGejrwpjRxK5ri+6VL5Yl2XfPuVXkbiWRyK4Dt7eoll8jSVe3DaFNY3Bnn25DCDsSwtFV7eduOyrE561XO1G/bZKNYQ9nkhxL8B9VLK1wsh1gE7pZT/VqT2p0TTJYpNFP6PoZuFUmzmZwOxDLqU9EUylHgcqJpOOKVSZ8ord4+mCnK3p0cSE/YxEM2g6xIdyVAse80Z/snHttht2wQzqpp6nAq3mzISTx0fIpfXkBJODxrnKZe/VF57OJEtyEZfiKQZimfQdElfJE33qCFqlsxq9EXTl234M3m9EEXUF7k0fiGn6YzEs+R1ncPnY6iaTl7TGY3naK3yUeJ2cHYkiddppz+6LOIfLBaA5X5jKZbh/4r592fm+5PAfwILYvillPxg/wW6R1ME3HbimTwd1X7efH09I4ks3917Hinh7VsaqA64cdkV9nWHKPc6OT4Yo7HUS9U4X/GNLWXcuaaaE4MJPnLbxOiQmqCbrpEkTruNmuC1VV/0xTOj7O4apb3Kx5uvry+q7PS5kSSPHuzD57Lzzm1NhRqxM7G5qRSHXSGeVtnZXsFf/+QoB3qj3NxZwX+762K+YGeVn/X1QdKqxva2cn6w/wJH+2Lctaaad9/UxNdf6KapwntFBUf8Lju3rqrk7EiSHe0V/J9JnzsVwcnBGLu7jMLyeR1KPHZubCklnMoTSuV4YEsjJwcTVsETiyWjWIa/Ukr5iBDi4wBSyrwQQitS25eQymmFkduecyFW1wY5M5Qgm9foHk2Szhm7PjeSosLnIqNqrKsv4cmjgwXFyP54hqZxlZ/+4q0bp9zXQCxDR5W/8PpqrbA1FccHYgB0DSfJ5nXcjuJNhp8aSpDXJdG0Sn80PUEMbjpGk1m2NBvKqLGMykGzutS+7siE9eyKrVAyMpUzShVWBVycHU2yrq6Ev55GbG6ubG0tZ+s0CpexTJ5QUkXTJaomcdlt7GivoLHMS3uV8WRS5nXxwNayefXBwmI+FMvwJ4UQFRhSzAghdgDRIrVdIJzI8bsP7yOSzvG6tTV4XXYE8K2Xu2ko8XBqKI4ADvRGEIDNBkf7ovzXfsPHH3Db6R7VKfc5+JufHMPnUtjYUAoYxq0/mqauxE1fJMMtqyp5385W1tQGOD2UQAij/OCB3gj3rK+9Jlw+W1rK2N01Sme1vyhGf39PmP09EXM0nuc/X+nB57Lzlhvq+OQPX2U0keUjr+ngb396nAO9EW5oKuWzD97AE0cHKPE46Kzy8q/PniGvST56Wxu5vM6JwTg3NpeytzvMv/2qi+qAm9+9o4PPPnGSRC7P7925iqZyLwd7w2xvm2is0zmNxw71kc3rvHFj3YSJ4rMjSZ4+MURdiYd71tfM+LTz1PFBzo2k2NAQ5Dt7znP4fISUariacprGjw72YxdQ6nXy+o21nA8n+fmxQTY1lkx7A7GwWEiKZfj/CPgR0C6EeB6owhBqKyo/ebWfc6NJAI4PJPjCr23h7s89g1OxcWoogbBBfySDqknsiuCpY0Nsby/jfDiNYhOEkir1pW7CKZXuUBKkIJXTKfM6ePlsiFKvk309Edoqffzs1QHet7OVCr+LX7+5ldNDCR492AdQMP5XO5saS9nUWFq09nZ3hcioGi92jXJiII5iE2RUja+90M2JAaOQ/Y8P9bGnO4yUkj3dYQ70hhlN5BhN5PjB/gsF3/0je8/jtCuUehycD2d49GBfYb1vvtRD14hxnTx2qI+GUqPge8A98XI/M5zgfNhQG3m1L8qtqy7WiNxzLkQkpRJJqWxpKaMqMLWbT9MlB3uNMc7DL/fQNZQgY/ZxPHkJkbRKuc/FU8eH8bnsvHhm1DL8FktCsRK4jmLUzn0FGAS+hOHnLwq6hJ8e7kdi1Lu1CcEtnUbY5UaziHdDmQe33U59qQchjAneRFbl8VcH8bmM0WqJx/jhVwfcZLIaEklzuZdyr7NQa3Stqa+/rj44oQ+1JW4CbqNGbUeVD4vLZ5UZb99R5WdjY5BoOk9a1blnfRW9oRTHB2JUBVzUlhhGtrbERXuVH5sQBD0OHtrWiN0mEEJwS2dlwQW3ri7I1pYyhIAyr4PXrq3G51Kw2wQ72ipoqzTOV8ekeP+GUg8ep4JDEbRW+Dh8Psp/7b9AbyhVyA2oCrgo9Tqm/U6KTVBf6iaZzeN12tGl4d6ZiqDHTonHUbhmV9Us3/wDi6ubYo34/wMjdv+vzPcPAV8HHihG46lcnuPmiPCv7t9IZ5WfMjMa47MP3sBQLE2Fz0lK1TneH+OpY0OcHUmwpzuC025jU0MJn3rLejqrgwzF0hzqjbC3J4piE7zp+jraq/woApI5jYDbQTSVo2RSjLffZec3bmlD0+WKqra1nHjtuhp2rarE7VD43BMnWFXjQwGePjECgNdpZ8+5CM/8jzs5PRSjs9q4+bZW+LDbBDabYPfHK4mlc7SaRn8olqY66AHgNaur8NgV7HYbX3jvVnJ5Hb/bjpRyyjmKMp+TD+1qQ2KM3L+3zwgKiKZVfv3mVtbVB3HYbNhmiWp6cGsT33q5h6FYlttWVfN/H6rn8WPDPPJyDyeHUzhtxgDlWx/eWWgvo2pFnTOxsLgcimX4N0gp1417/0shxNEitY1DsSEE2ISgsdxTMPpjjP3wA4pCbYkHt9NOddCD2xlHSklnTaBgRKqDHmpLczj74jgUQaXfhUMxDHnAbfyfbPTHUGzCSriZJ2PGrq3Sz0tnQzgUwZbmcp44OoSq6XRUG6PzsfMFTLjRlvudE0Iwx849QMDtmLDN2HZCiGmNrN0894qQlHmdhJI5qk23zlwzu4UQNJR6GIplKfM7qS71sa4+R0dNgO5wBoci2NxUNqE9y+gvLMs9nHKpEVJedjnbSxsR4hvA56WUu833NwG/Y2r0F6P9YaB7ns1UAiNF6M5itr1Q7d6IoaK6kMdkJTGf4zB2LBeC5XB+FrsPC3k858LVdsxbpJRVkxcWy/AfA1YDPeaiZuAEkAeklHJ+8XNFQAixR0q5dSW1vZB9Xoz2VwrL9Tgsh34thz4sJsvh+y5GH4rl6rm3SO1YWFhYWCwwRTH8Usr5umEsLCwsLBaJayk85YsrsO2F7PNitL9SWK7HYTn0azn0YTFZDt93wftQFB+/hYWFhcXK4Voa8VtYWFhYYBl+CwsLi2sOy/BbWFhYXGNcE4ZfCLFBCPEuIcS2IrRVZ/4XQoi3CiE+brY93/rFbxZCLGidYiHEeiHEmknLblrIfa4EhBC/s9R9WG4IIfxCiEYhxDUnKFQMO3GZ+1v03+VVO7krhPiZlPJeIcQfAHcBPwZuAc5LKT8+j3afklLeKYT4RyANPAXcAGyVUj44j3b7MLKTBzEE734kpQxfaXtTtP9ZoAZQMTIDPyClHB77PsXaz3JHCPEcpnw4MKa/sR54VUp529L0CoQQfyCl/D9CiOuBf8boox34Uynlc4vYjzuB/w9DeysGBIEA8FdSyp8vVj8WAyHEVANfAfxMSvm6RerDkvwur+bq4WOCLvcDd0gpdeALQohfzbPdMc3d9VLK15qvnxBC/HKe7Z6QUt4hhGgD3gb8QAiRBX4opfy/82wbYNuYYRNCbAK+I4T4WBHaXWl8H7ge+KqU8mkAIcRPpZSvX9JewZuB/wP8HcaP/7QQohL4IcaAZbH4NHC3lDI1tkAI4QOeAK4qww8kgN0Yxn78YGAxlQaW5Hd5NRv+dUKI/wA6ABfG6BxgvhVUviaE+DLQa2oUPYNxoeyZZ7sASCnPAp8FPiuEqAHeUox2AUUI4ZRS5qSUh4QQ9wPfwBjtXjNIKf9BCOEEPiiE+CjwraXuk0m5Odoul1KeBpBSjgghFvuRPItxPe8et2wjcDUWCD4G3C+lnFA0Sgjx5CL2YUl+l1ezq6dl3Ns+KaVq+itvlVL+dJ5t1wP3YDyiRYEXpJQH59nmPVLKx+fTxiztbwfOSSmHxi1TgAeklN9eqP0uZ8x5mV8DVksp/3SJ+/K/xr39RyllRAgRAP5OSvnRRexHHfCnGMbehvGEe8jsx4XF6sdiYH7XUSllbtJyu5Qyv0h9WJLf5VVr+C0sLCwspuaaiOqxsFipmEEES44Q4p+Wug8WxcMa8VtYLBOEEOsBTUp5fNyyHWN1LpagPxuADcAZKeUrS9EHi4XBGvFfBkKI24UQj03z2dNCiKJqaAshSoUQvz2X/a9k5vO9hBCfFkK8dorlhTbN1zeP++yrQoh3XHmPi48Z1vdx4E+EEI8KIcaKZ/zVDJstRD9+Zv7/A+CvgVLg94UQi9qPxUAI8X5zvm629a74ehFCvDBbm0KIPxifwyOESFzJvi6Hqzmq52qgFPhtoBjhnFclUspPzmG12zFC96b8ES4Tlku47UKFQS9H3g+8CvQt1A6klDfPvhZ/gBHJk5plvaJx1Y34hRA+IcSPhRAHhRCvCiHeKYTYIoR4RgixVwjxuLiYffu0EOIfhRAHzHW3m8u3CyFeFELsF0K8IIRYfZl9uNvcfp8Q4jtj2Y9CiHNCiD83lx8WZraeEKJKCPGkEOKIEOLLQohuM4b7b4AOs39/ZzbvF0J8VwhxXAjxTSHEohQBXorjKoTYJoT4vvn6LUKItBDCKYRwCyG6zOXjR073msdlH0YuBEKIVuCjwB+a/bnVbP42sw9dy2T0rwgjzBQp5SEMw/vnLH647eQw6DHmGwa94AghWsf9Lo6ZvxPvVNepec63At80rwuPEOKTQohXzGv2i7P9toQQ/yKEeLP5+gdCiH83X39ACPG/zdcJ878QQnxeCHFCCPFzoNpc/vtAPUad8l+Oa/t/m7+13cII6y4uUsqr6g94O/Clce9LMEZ6Veb7dwL/br5+emxd4DaM7E0wshXt5uvXAt8zX98OPDbNfp/GuJAqgWcBn7n8T4BPmq/PAb9nvv5t4Mvm688DHzdf34uRTFIJtI71adz+o0Ajxk37RWDX1XpcMZ5Iu8zXfw+8gpHM9BrgYXP5V4F3YBimXmAVRhLOI2NtAp8CPjau3a8C3zGP4Trg9DK4brcD1ZOWKcC7FrkfLeP+HOYyP/D6pT5Gc+h7q/nbucV8/+/A/5jlOt06bvvyca+/Drxp/DU2xf7ehRHmCvAysNt8/RXgHvN1wvz/NuBJ85zWA5GxNjHsQuW4duW4fX8G+J/FPlZXo6vnMEby098CjwFhjAmqJ80buAL0j1v/YQAp5bNCiKAQohQjRf1rQohVGCfBcRn734FhTJ439+fEMNBjfN/8vxdzVArswhjhIaX8mRBiJqmGl6WU5wGEEAcwLvbFeAxf9OMqpcwLIc4IIdZiGMbPYdxIFGCyjMEa4KyU8hSAMJLrPjxD8/8lDTfG0QUZUV0mUsqXp1imAYuaYyGnqKYnpUwA88p9WUR6pZTPm6+/AXyCma/T8dwhhPhjwAuUA0eAR2fY13PAHwgh1gFHgTLzqXcn8PuT1r0NY7CiAX1CiKdmaDeH8RsDw04UXT7iqjP8UsqTQogbgTcAf4mhpXNESrlzuk2meP8XwC+llPebroKnJ28khHgcI4Frj5TyQ+M/Ap6UUr57mv1lzf8aV3b8s+NeX2kbl80SHtdngddjaJn8HGP0pWCM5ObD+OO4KO4yi0Vh8nUXZ+brFAAhhBtjLm2rlLJXCPEpJrm3hCGc9q/m209KKX9kDmjuxbhOy4EHMUb58Xl8B1Waw30W6Dd+Nfr464GUlPIbGLonNwFVQoid5ucOYYTNjfFOc/kuICqN9O0SYCxL8f1T7UdKeY+U8oZJRh+MVPdbhBCdZrs+IcR1s3T7eYwLBiHE3UCZuTyOMUpecpbwuD6HMfn1opRyGKgAVmNMyo3nONAqhOgw34+/8S6b42ix4DSPXZPAQxi/x+mu0/HXxZiRHxHGnNwl8z5SypfMa/MGKeWPzMW7Ma7PZzGu1Y9x6dMo5ufvFEIo5lPBHeM+W/Tr86oz/Bip5i+bbpD/BXwS4yT+rRDiIHAAGD/TnhFC7Ae+AHzQXPYZ4K/N5Zd1tzWN0/uBh4UQhzDcPGtm3MiYxLtbCPEq8AAwAMSllKMYLqNXxcXJ3aViqY7rSxhPAM+a7w8Bh8eNiACQUmYwXDs/Nid3h8Z9/Chw/6TJ3RWDmGfYoTnpOflGWYx+Lccw2RPA7wghjmEMoP6Z6a/Tr2JELB3AeAL8EsaA4nGM+aS58BzGvNVpYB/GqH8qw/8D4BSGS+g/mOj+/SLwMzF/occ5c00ncAkhnsaY9CuKwNo8+uHCSNzJmyOT/yelvGEp+zQflstxvVqY6/EUQnwVY0L7u5OWt5rLNxS5X5/CcGv8/Uz7XywW6ntejVyNI/6VSDPwijki+SfgN5e4PxYLiFjksMNJ+1aEEH9nbn9ICPERc/ntwgjDvSRUWAjxBnPZXiHEPwkhHhMrJ0zWYioWI8zK+rP+rL+Lfyx+2GErF0NqP4wZHogRp78HaGOaUGEuhsm2mds8zAoKk7X+pv676qJ6LCxWCIsZdjieu4FN40bjJRi5DzmmDhVOYORSnDXXf5gVFCZrMTWW4bewWBoWLewQY0K88DFGEuHjk7a5neKEClthsisAy8dvYbE0LHbY4RiPA78lhHCY+7lOGKUVp+ME0G769MEM052iXxYrCMvwW1gsDYsddjjGlzFCCveZIZ7/ygwjeyllGkNe5GdCiL0Yxn6sVOGKDpO9lrmmwzktLJaClRZ2KITwSykTZpTPvwCnpJT/sNT9srhyrBG/hYXFbPym+bRxBGMy+F9nXt1iuWON+C0sLCyuMawRv4WFhcU1hmX4LSwsLK4xLMNvYWFhcY1hGX4LCwuLawzL8FtYWFhcY1iG38LCwuIa4/8H2kYIy/iNdJAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas.plotting import scatter_matrix\n",
+    "scatter_matrix(dataset)\n",
+    "\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2><font color=\"blue\"> Learning and evaluating a Decision Tree classifier </font></h2>\n",
+    "\n",
+    "<p>We will use the scikit-learn library to build a decision tree model. We will be using the iris dataset to build a decision tree classifier. The task is to predict the class of the iris plant based on the attributes.</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Extracting attributes & labels </font> </h3>\n",
+    "<p>We have already imported the iris data in the variable 'dataset'. We will now extract the attribute data(X) and the corresponding labels(y).</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5.1 3.5 1.4 0.2]\n",
+      " [4.9 3.0 1.4 0.2]\n",
+      " [4.7 3.2 1.3 0.2]\n",
+      " [4.6 3.1 1.5 0.2]\n",
+      " [5.0 3.6 1.4 0.2]\n",
+      " [5.4 3.9 1.7 0.4]\n",
+      " [4.6 3.4 1.4 0.3]\n",
+      " [5.0 3.4 1.5 0.2]\n",
+      " [4.4 2.9 1.4 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [5.4 3.7 1.5 0.2]\n",
+      " [4.8 3.4 1.6 0.2]\n",
+      " [4.8 3.0 1.4 0.1]\n",
+      " [4.3 3.0 1.1 0.1]\n",
+      " [5.8 4.0 1.2 0.2]\n",
+      " [5.7 4.4 1.5 0.4]\n",
+      " [5.4 3.9 1.3 0.4]\n",
+      " [5.1 3.5 1.4 0.3]\n",
+      " [5.7 3.8 1.7 0.3]\n",
+      " [5.1 3.8 1.5 0.3]\n",
+      " [5.4 3.4 1.7 0.2]\n",
+      " [5.1 3.7 1.5 0.4]\n",
+      " [4.6 3.6 1.0 0.2]\n",
+      " [5.1 3.3 1.7 0.5]\n",
+      " [4.8 3.4 1.9 0.2]\n",
+      " [5.0 3.0 1.6 0.2]\n",
+      " [5.0 3.4 1.6 0.4]\n",
+      " [5.2 3.5 1.5 0.2]\n",
+      " [5.2 3.4 1.4 0.2]\n",
+      " [4.7 3.2 1.6 0.2]\n",
+      " [4.8 3.1 1.6 0.2]\n",
+      " [5.4 3.4 1.5 0.4]\n",
+      " [5.2 4.1 1.5 0.1]\n",
+      " [5.5 4.2 1.4 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [5.0 3.2 1.2 0.2]\n",
+      " [5.5 3.5 1.3 0.2]\n",
+      " [4.9 3.1 1.5 0.1]\n",
+      " [4.4 3.0 1.3 0.2]\n",
+      " [5.1 3.4 1.5 0.2]\n",
+      " [5.0 3.5 1.3 0.3]\n",
+      " [4.5 2.3 1.3 0.3]\n",
+      " [4.4 3.2 1.3 0.2]\n",
+      " [5.0 3.5 1.6 0.6]\n",
+      " [5.1 3.8 1.9 0.4]\n",
+      " [4.8 3.0 1.4 0.3]\n",
+      " [5.1 3.8 1.6 0.2]\n",
+      " [4.6 3.2 1.4 0.2]\n",
+      " [5.3 3.7 1.5 0.2]\n",
+      " [5.0 3.3 1.4 0.2]\n",
+      " [7.0 3.2 4.7 1.4]\n",
+      " [6.4 3.2 4.5 1.5]\n",
+      " [6.9 3.1 4.9 1.5]\n",
+      " [5.5 2.3 4.0 1.3]\n",
+      " [6.5 2.8 4.6 1.5]\n",
+      " [5.7 2.8 4.5 1.3]\n",
+      " [6.3 3.3 4.7 1.6]\n",
+      " [4.9 2.4 3.3 1.0]\n",
+      " [6.6 2.9 4.6 1.3]\n",
+      " [5.2 2.7 3.9 1.4]\n",
+      " [5.0 2.0 3.5 1.0]\n",
+      " [5.9 3.0 4.2 1.5]\n",
+      " [6.0 2.2 4.0 1.0]\n",
+      " [6.1 2.9 4.7 1.4]\n",
+      " [5.6 2.9 3.6 1.3]\n",
+      " [6.7 3.1 4.4 1.4]\n",
+      " [5.6 3.0 4.5 1.5]\n",
+      " [5.8 2.7 4.1 1.0]\n",
+      " [6.2 2.2 4.5 1.5]\n",
+      " [5.6 2.5 3.9 1.1]\n",
+      " [5.9 3.2 4.8 1.8]\n",
+      " [6.1 2.8 4.0 1.3]\n",
+      " [6.3 2.5 4.9 1.5]\n",
+      " [6.1 2.8 4.7 1.2]\n",
+      " [6.4 2.9 4.3 1.3]\n",
+      " [6.6 3.0 4.4 1.4]\n",
+      " [6.8 2.8 4.8 1.4]\n",
+      " [6.7 3.0 5.0 1.7]\n",
+      " [6.0 2.9 4.5 1.5]\n",
+      " [5.7 2.6 3.5 1.0]\n",
+      " [5.5 2.4 3.8 1.1]\n",
+      " [5.5 2.4 3.7 1.0]\n",
+      " [5.8 2.7 3.9 1.2]\n",
+      " [6.0 2.7 5.1 1.6]\n",
+      " [5.4 3.0 4.5 1.5]\n",
+      " [6.0 3.4 4.5 1.6]\n",
+      " [6.7 3.1 4.7 1.5]\n",
+      " [6.3 2.3 4.4 1.3]\n",
+      " [5.6 3.0 4.1 1.3]\n",
+      " [5.5 2.5 4.0 1.3]\n",
+      " [5.5 2.6 4.4 1.2]\n",
+      " [6.1 3.0 4.6 1.4]\n",
+      " [5.8 2.6 4.0 1.2]\n",
+      " [5.0 2.3 3.3 1.0]\n",
+      " [5.6 2.7 4.2 1.3]\n",
+      " [5.7 3.0 4.2 1.2]\n",
+      " [5.7 2.9 4.2 1.3]\n",
+      " [6.2 2.9 4.3 1.3]\n",
+      " [5.1 2.5 3.0 1.1]\n",
+      " [5.7 2.8 4.1 1.3]\n",
+      " [6.3 3.3 6.0 2.5]\n",
+      " [5.8 2.7 5.1 1.9]\n",
+      " [7.1 3.0 5.9 2.1]\n",
+      " [6.3 2.9 5.6 1.8]\n",
+      " [6.5 3.0 5.8 2.2]\n",
+      " [7.6 3.0 6.6 2.1]\n",
+      " [4.9 2.5 4.5 1.7]\n",
+      " [7.3 2.9 6.3 1.8]\n",
+      " [6.7 2.5 5.8 1.8]\n",
+      " [7.2 3.6 6.1 2.5]\n",
+      " [6.5 3.2 5.1 2.0]\n",
+      " [6.4 2.7 5.3 1.9]\n",
+      " [6.8 3.0 5.5 2.1]\n",
+      " [5.7 2.5 5.0 2.0]\n",
+      " [5.8 2.8 5.1 2.4]\n",
+      " [6.4 3.2 5.3 2.3]\n",
+      " [6.5 3.0 5.5 1.8]\n",
+      " [7.7 3.8 6.7 2.2]\n",
+      " [7.7 2.6 6.9 2.3]\n",
+      " [6.0 2.2 5.0 1.5]\n",
+      " [6.9 3.2 5.7 2.3]\n",
+      " [5.6 2.8 4.9 2.0]\n",
+      " [7.7 2.8 6.7 2.0]\n",
+      " [6.3 2.7 4.9 1.8]\n",
+      " [6.7 3.3 5.7 2.1]\n",
+      " [7.2 3.2 6.0 1.8]\n",
+      " [6.2 2.8 4.8 1.8]\n",
+      " [6.1 3.0 4.9 1.8]\n",
+      " [6.4 2.8 5.6 2.1]\n",
+      " [7.2 3.0 5.8 1.6]\n",
+      " [7.4 2.8 6.1 1.9]\n",
+      " [7.9 3.8 6.4 2.0]\n",
+      " [6.4 2.8 5.6 2.2]\n",
+      " [6.3 2.8 5.1 1.5]\n",
+      " [6.1 2.6 5.6 1.4]\n",
+      " [7.7 3.0 6.1 2.3]\n",
+      " [6.3 3.4 5.6 2.4]\n",
+      " [6.4 3.1 5.5 1.8]\n",
+      " [6.0 3.0 4.8 1.8]\n",
+      " [6.9 3.1 5.4 2.1]\n",
+      " [6.7 3.1 5.6 2.4]\n",
+      " [6.9 3.1 5.1 2.3]\n",
+      " [5.8 2.7 5.1 1.9]\n",
+      " [6.8 3.2 5.9 2.3]\n",
+      " [6.7 3.3 5.7 2.5]\n",
+      " [6.7 3.0 5.2 2.3]\n",
+      " [6.3 2.5 5.0 1.9]\n",
+      " [6.5 3.0 5.2 2.0]\n",
+      " [6.2 3.4 5.4 2.3]\n",
+      " [5.9 3.0 5.1 1.8]]\n",
+      "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting data attributes\n",
+    "X = dataset.values[:,0:4]\n",
+    "print (X)\n",
+    "# Extracting target/ class labels\n",
+    "y = dataset.values[:,4]\n",
+    "print (y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Creating train and test datasets</font> </h3>\n",
+    "<p>We need to know that the model we created is good.</p>\n",
+    "\n",
+    "<p>We split the loaded dataset into two partitions, <b>75%</b> of which we will use to train, evaluate and select among our models, and <b>25%</b> that we will hold back as a test dataset.</p>\n",
+    "\n",
+    "<p>We have to use <b>'train_test_split'</b> function from the package <b>sklearn</b></p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 47, test_size = 0.25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now have training data in the <b>X_train</b> and <b>Y_train</b> for preparing models and a <b>X_validation</b> and <b>Y_validation</b> sets that we can use later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Building a  model </font> </h3>\n",
+    "<p>We want to  import the <b>DecisionTreeClassifier</b> function from the <b>sklearn</b> library. Next, we will set the 'criterion' to 'entropy', which sets the measure for splitting the attribute to information gain.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "DTree = DecisionTreeClassifier(criterion = 'entropy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Training a  model </font> </h3>\n",
+    "\n",
+    "<p>Next, we will fit the classifier on the train attributes and labels.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"â–¸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"â–¾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(criterion='entropy')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DTree.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Make predictions </font> </h3>\n",
+    "<p>Now, we will use the trained classifier/ model to predict the labels of the test set.</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred =  DTree.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3> <font color=\"blue\"> Evaluate a model </font> </h3>\n",
+    "<p>We can evaluate the predictions of the model by comparing them to the expected results, then calculate the overall classification accuracy, as well as a confusion matrix or a classification report.</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1. Accuracy\n",
+    "\n",
+    "<p> First, we have to import the package for 'accuracy_score'</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy Score on train data: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy Score on train data:', accuracy_score(y_true=y_train, y_pred=DTree.predict(X_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2. Confusion Matrix\n",
+    "\n",
+    "<p> Similarly, we can import and use the package for 'confusion_matrix'</p>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrox of train data:  [[35  0  0]\n",
+      " [ 0 42  0]\n",
+      " [ 0  0 35]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "print('Confusion Matrox of train data: ', confusion_matrix(y_true=y_train, y_pred=DTree.predict(X_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 3. Classification Report\n",
+    "<p>The classification report displays the precision, recall, F1, and support scores for the model.</p>\n",
+    "<p> This is implemented in the package  'classification_report'</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00        35\n",
+      "Iris-versicolor       1.00      1.00      1.00        42\n",
+      " Iris-virginica       1.00      1.00      1.00        35\n",
+      "\n",
+      "       accuracy                           1.00       112\n",
+      "      macro avg       1.00      1.00      1.00       112\n",
+      "   weighted avg       1.00      1.00      1.00       112\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_true=y_train, y_pred=DTree.predict(X_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3><font color=\"red\">Exercise 1 </h3>\n",
+    "    <p>As you probably noticed we only evaluated the model on the training data. Modify the code above to evaluate the model on the test dataset and generate accuracy value, confusion matrix and classification report. Use the code cell below for this purpose</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy Score on test data: 0.9736842105263158\n",
+      "Confusion Matrix of test data:  [[15  0  0]\n",
+      " [ 0  8  0]\n",
+      " [ 0  1 14]]\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00        15\n",
+      "Iris-versicolor       0.89      1.00      0.94         8\n",
+      " Iris-virginica       1.00      0.93      0.97        15\n",
+      "\n",
+      "       accuracy                           0.97        38\n",
+      "      macro avg       0.96      0.98      0.97        38\n",
+      "   weighted avg       0.98      0.97      0.97        38\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Answer to Exercise 1\n",
+    "# Evaluate the model on the test dataset\n",
+    "\n",
+    "print('Accuracy Score on test data:', accuracy_score(y_true=y_test, y_pred=DTree.predict(X_test)))\n",
+    "print('Confusion Matrix of test data: ', confusion_matrix(y_true=y_test, y_pred=DTree.predict(X_test)))\n",
+    "print(classification_report(y_true=y_test, y_pred=DTree.predict(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3><font color=\"red\">Exercise 2 </h3>\n",
+    "    <p>Compare the results from evaluating the model on the training and test data and explain your observations. Use the markdown cell below to provide your answer to Excercise 2</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Answer to Exercise 2 </h3>\n",
+    "    <p>Use this space to provide your answer to Exercise 2\n",
+    "</p>\n",
+    "\n",
+    "Accuracy of 1.0 in training data indicates the perfect fit and generalization and the 0.97 on test data can be a measure of excellent prediction. However it points out every possibilty of overfitting as the model is too complex and is tracing the train data exactly as such. \n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3><font color=\"red\">Exercise 3 </h3>\n",
+    "    <p>Try to improve the accuracy of the model on the test dataset by tuning the parameters of the decision tree learning algorithm. One of those parameters is <b>'min_samples_split'</b>, which is the minimum number of samples required to split an internal node. Its default value is equal to 2 because we cannot split on a node containing only one example/ sample. More details about 'min_samples_split' and other parameters can be found from <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">here</a></p>\n",
+    "    \n",
+    "<p>Use the code cell below to write your code for Excercises 3</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters: {'min_samples_split': 14, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 5, 'criterion': 'entropy'}\n",
+      "Accuracy Score on test data: 0.9473684210526315\n",
+      "Confusion Matrox of test data:  [[15  0  0]\n",
+      " [ 0  8  0]\n",
+      " [ 0  2 13]]\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "    Iris-setosa       1.00      1.00      1.00        15\n",
+      "Iris-versicolor       0.80      1.00      0.89         8\n",
+      " Iris-virginica       1.00      0.87      0.93        15\n",
+      "\n",
+      "       accuracy                           0.95        38\n",
+      "      macro avg       0.93      0.96      0.94        38\n",
+      "   weighted avg       0.96      0.95      0.95        38\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Answer to Exercise 3\n",
+    "# tune the parameters of the decision tree to increase its accuracy on the test data\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "\n",
+    "DTree = DecisionTreeClassifier()\n",
+    "\n",
+    "hyper_p = {\n",
+    "    'max_depth': [2,3,4,5,10,15],\n",
+    "    'min_samples_split': [2,6,10,14,15,20,25,30],\n",
+    "    'min_samples_leaf':[2,3,5,7,9,11],\n",
+    "    'criterion': ['gini', 'entropy'],\n",
+    "    'max_features': ['auto', 'sqrt', 'log2']\n",
+    "}\n",
+    "\n",
+    "DTclass = RandomizedSearchCV(DTree, param_distributions=hyper_p, n_iter=100, cv=5, n_jobs=-1)\n",
+    "\n",
+    "DTclass.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "print(f\"Best parameters: {DTclass.best_params_}\")\n",
+    "\n",
+    "print('Accuracy Score on test data:', accuracy_score(y_true=y_test, y_pred=DTclass.predict(X_test)))\n",
+    "print('Confusion Matrox of test data: ', confusion_matrix(y_true=y_test, y_pred=DTclass.predict(X_test)))\n",
+    "print(classification_report(y_true=y_test, y_pred=DTclass.predict(X_test)))\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3><font color=\"red\">Save your notebook after completing the excercises and submit it to SurreyLearn (Assignments -> Lab Exercises - Week 1) as a python notebook file in ipynb formt. </h3>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}