Data Analysis with Python/model-evaluation-and-refinement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%205/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Model Evaluation and Refinement\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Evaluate and refine prediction models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<ul>\n",
    "    <li><a href=\"#ref1\">Model Evaluation </a></li>\n",
    "    <li><a href=\"#ref2\">Over-fitting, Under-fitting and Model Selection </a></li>\n",
    "    <li><a href=\"#ref3\">Ridge Regression </a></li>\n",
    "    <li><a href=\"#ref4\">Grid Search</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset was hosted on IBM Cloud object. Click <a href=\"https://cocl.us/DA101EN_object_storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\">HERE</a> for free storage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Import clean data \n",
    "path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'\n",
    "df = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('module_5_auto.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's only use numeric data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>...</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
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       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
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       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>122</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>164</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "   Unnamed: 0  Unnamed: 0.1  symboling  normalized-losses  wheel-base  \\\n",
       "0           0             0          3                122        88.6   \n",
       "1           1             1          3                122        88.6   \n",
       "2           2             2          1                122        94.5   \n",
       "3           3             3          2                164        99.8   \n",
       "4           4             4          2                164        99.4   \n",
       "\n",
       "     length     width  height  curb-weight  engine-size  ...  stroke  \\\n",
       "0  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "1  0.811148  0.890278    48.8         2548          130  ...    2.68   \n",
       "2  0.822681  0.909722    52.4         2823          152  ...    3.47   \n",
       "3  0.848630  0.919444    54.3         2337          109  ...    3.40   \n",
       "4  0.848630  0.922222    54.3         2824          136  ...    3.40   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0        21           27  13495.0   \n",
       "1                9.0       111.0    5000.0        21           27  16500.0   \n",
       "2                9.0       154.0    5000.0        19           26  16500.0   \n",
       "3               10.0       102.0    5500.0        24           30  13950.0   \n",
       "4                8.0       115.0    5500.0        18           22  17450.0   \n",
       "\n",
       "   city-L/100km  diesel  gas  \n",
       "0     11.190476       0    1  \n",
       "1     11.190476       0    1  \n",
       "2     12.368421       0    1  \n",
       "3      9.791667       0    1  \n",
       "4     13.055556       0    1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df._get_numeric_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Libraries for plotting:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "! pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Functions for Plotting</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "\n",
    "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
    "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in dollars)')\n",
    "    plt.ylabel('Proportion of Cars')\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "    \n",
    "    \n",
    "    #training data \n",
    "    #testing data \n",
    "    # lr:  linear regression object \n",
    "    #poly_transform:  polynomial transformation object \n",
    " \n",
    "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
    "\n",
    "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
    "\n",
    "    x=np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "\n",
    "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
    "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
    "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
    "    plt.ylim([-10000, 60000])\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"ref1\">Part 1: Training and Testing</h2>\n",
    "\n",
    "<p>An important step in testing your model is to split your data into training and testing data. We will place the target data <b>price</b> in a separate dataframe <b>y_data</b>:</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = df['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop price data in dataframe **x_data**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=df.drop('price',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we randomly split our data into training and testing data using the function <b>train_test_split</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 21\n",
      "number of training samples: 180\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples :\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b>test_size</b> parameter sets the proportion of data that is split into the testing set. In the above, the testing set is 10% of the total dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #1):</h1>\n",
    "\n",
    "<b>Use the function \"train_test_split\" to split up the dataset such that 40% of the data samples will be utilized for testing. Set the parameter \"random_state\" equal to zero. The output of the function should be the following:  \"x_train1\" , \"x_test1\", \"y_train1\" and  \"y_test1\".</b>\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 81\n",
      "number of training samples: 120\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0) \n",
    "print(\"number of test samples :\", x_test1.shape[0])\n",
    "print(\"number of training samples:\",x_train1.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0) \n",
    "print(\"number of test samples :\", x_test1.shape[0])\n",
    "print(\"number of training samples:\",x_train1.shape[0])\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import <b>LinearRegression</b> from the module <b>linear_model</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a Linear Regression object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lre=LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit the model using the feature \"horsepower\":\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.fit(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the R^2 on the test data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36358755750788263"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.score(x_test[['horsepower']], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the R^2 is much smaller using the test data compared to the training data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6619724197515104"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre.score(x_train[['horsepower']], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #2): </h1>\n",
    "<b> \n",
    "Find the R^2  on the test data using 40% of the dataset for testing.\n",
    "</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7139364665406973"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0)\n",
    "lre.fit(x_train1[['horsepower']],y_train1)\n",
    "lre.score(x_test1[['horsepower']],y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0)\n",
    "lre.fit(x_train1[['horsepower']],y_train1)\n",
    "lre.score(x_test1[['horsepower']],y_test1)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you do not have sufficient testing data; as a result, you may want to perform cross-validation. Let's go over several methods that you can use for cross-validation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Cross-Validation Score</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import <b>model_selection</b> from the module <b>cross_val_score</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We input the object, the feature (\"horsepower\"), and the target data (y_data). The parameter 'cv' determines the number of folds. In this case, it is 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default scoring is R^2. Each element in the array has the average R^2 value for the fold:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7746232 , 0.51716687, 0.74785353, 0.04839605])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rcross"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the average and standard deviation of our estimate:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the folds are 0.522009915042119 and the standard deviation is 0.291183944475603\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use negative squared error as a score by setting the parameter  'scoring' metric to 'neg_mean_squared_error'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20254142.84026702, 43745493.2650517 , 12539630.34014931,\n",
       "       17561927.72247591])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-1 * cross_val_score(lre,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #3): </h1>\n",
    "<b> \n",
    "Calculate the average R^2 using two folds, then find the average R^2 for the second fold utilizing the \"horsepower\" feature: \n",
    "</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5166761697127429"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "Rc=cross_val_score(lre,x_data[['horsepower']], y_data,cv=2)\n",
    "Rc.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "Rc=cross_val_score(lre,x_data[['horsepower']], y_data,cv=2)\n",
    "Rc.mean()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the function 'cross_val_predict' to predict the output. The function splits up the data into the specified number of folds, with one fold for testing and the other folds are used for training. First, import the function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We input the object, the feature <b>\"horsepower\"</b>, and the target data <b>y_data</b>. The parameter 'cv' determines the number of folds. In this case, it is 4. We can produce an output:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14141.63807508, 14141.63807508, 20814.29423473, 12745.03562306,\n",
       "       14762.35027598])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = cross_val_predict(lre,x_data[['horsepower']], y_data,cv=4)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"ref2\">Part 2: Overfitting, Underfitting and Model Selection</h2>\n",
    "\n",
    "<p>It turns out that the test data, sometimes referred to as the \"out of sample data\", is a much better measure of how well your model performs in the real world.  One reason for this is overfitting.\n",
    "\n",
    "Let's go over some examples. It turns out these differences are more apparent in Multiple Linear Regression and Polynomial Regression so we will explore overfitting in that context.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create Multiple Linear Regression objects and train the model using <b>'horsepower'</b>, <b>'curb-weight'</b>, <b>'engine-size'</b> and <b>'highway-mpg'</b> as features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction using training data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7426.6731551 , 28323.75090803, 14213.38819709,  4052.34146983,\n",
       "       34500.19124244])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_train[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction using test data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11349.35089149,  5884.11059106, 11208.6928275 ,  6641.07786278,\n",
       "       15565.79920282])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "yhat_test[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's perform some model evaluation using our training and testing data separately. First, we import the seaborn and matplotlib library for plotting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the distribution of the predicted values of the training data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1: Plot of predicted values using the training data compared to the actual values of the training data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, the model seems to be doing well in learning from the training dataset. But what happens when the model encounters new data from the testing dataset? When the model generates new values from the test data, we see the distribution of the predicted values is much different from the actual target values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A12fZNetzpyKwAxslLYf9reQo9Df0AfYJRlWsPp48Hltuj6N46O/0KezTm7vCjCvr4yiPJVmrQ8e9EBspzVPod9QEe93YHysXyrpNI+fcMaHXo+3YBMrMv7vcXlNzUhO4KnT+M7A5BaND900j57+d1VjJXE6vgaOx5+DZoefxWUBT7LmZX3m9FhdEjq+RzrmjnXMtQgNMG7EBqAL9r3TO1XLODcHq/2/y3qeT9/NkFVAn9PqcIT+veVJAStQlGj51zm3CRgduwV7cL8xh24bAGOwf6M/AM35fX+cHsARkvXMu2wk9OXgDm/T2JzZT/ioA7/0G4HKslno5NjKTuT/z+6HrNc657GpIXwkdezzWPWQ74SV2EeOt/vs2rLZ3JTay3C/TJncCr4V+Zmdmc4ggH8MT2OjL16Hnxy/YpDG89zOxTjdvY49rHf/83ezlvV+ClRH8B/vIdhr7aqlfxmpl1zvnPg69QeuN1ZkuxD5ZeQkbiQRLfhaH7vsa+9nkZTzWMWK5935SptsvB+4OPbbbyX3U9Dbsd7cuFEPm0del2Ijpzdg/zaVYUpnT6/XdWIeYH0PHewg4x3v/e+j+3P7GwhL61OBUrOPSemw0+jMsYQz3GJuA47Hn6wrs7/O/7HuzfB6wyFk5x+DQOfDez8ESxT9Cv9ecSiD+L/SzX4t1rpmMdfTYErr/LqxzzQasXGlklv2zvt68jj03lmMdMH4J42G+jY10v5/lzW+2jy0HhzvnNmPJ2DhgP2yiZsZAR15xZf0bmAU8gv3uVwEtsK4muTkrFMN67G92DdDWZz+RtzTWhvdv7HdaE3vuQt6vqTn5FXve/o11NTnde59RCpfb387W0PYTQo+/U+aDho5xEvbasQbrJnaSt8ng+RLGa3FB5Pgaib1R+gB7XswGvsfe1Gfsd7pzbp1zLrd5Nuudc1uAGdhr6Bne+1dCjyev58l3WCeoP51zGT+v/LzmSQE5n+e8PRERkX9yzv0KPOe9fzXPjUVEpEA0oi4iInlyzh3pnNs/VDIwAGgJfBl0XCIiySwZVgsUEZHoa4R9tF0B67pzuvd+ZbAhiYgkN5W+iIiIiIjEIZW+iIiIiIjEIZW+5KB69eq+Xr16QYchIiIiIkls8uTJf3vva2R3nxL1HNSrV4/U1NSgwxARERGRJOacy3FVbJW+iIiIiIjEISXqIiIiIiJxSIm6iIiIiEgcUo26iIiISDZ27drFsmXL2L59e9ChSBIoU6YMderUoWTJkmHvo0RdREREJBvLli2jYsWK1KtXD+dc0OFIAvPes2bNGpYtW0b9+vXD3k+lLyIiIiLZ2L59O9WqVVOSLoXmnKNatWr5/nRGibqIiIhIDpSkS6QU5LmkRF1EREREJA4pURcRERERiUNK1EVERETi2EcffYRzjjlz5uS57eOPP87WrVsLfK7hw4czZMiQf9y2aNEi6tSpQ3p6+j9uT0lJYeLEidkeZ9GiRTRv3rzAcYTj448/5u677+a+++4jJSWFlJQUihcvvvfrJ598Muxj3X///Xu/3rlzJ926dWP37t3RCDtflKiLiIiIxLF33nmHLl26MGLEiDy3LWyinp169epRt25dfvjhh723zZkzh02bNtGhQ4eInis/HnroIS6//HJuueUWpk2bxrRp0yhbtuzer6+66qqwj5U5US9VqhTHHnss7777bjTCzhe1ZxQRERHJy9VXw7RpkT1mSgo8/nium2zevJkJEyYwduxYTj75ZO68804A9uzZww033MBXX32Fc45LLrkE7z0rVqzg6KOPpnr16owdO5YKFSqwefNmAD744AM+++wzhg8fzqeffsq9997Lzp07qVatGm+99Ra1atXKMY7+/fszYsQIjjzySABGjBhB//79WbRoEeeddx5btmwBYNiwYXTu3Pkf+w4fPpzU1FSGDRsGwEknncR1113HUUcdxddff80dd9zBjh07OOSQQ3j11VepUKECN954I6NGjaJEiRIcf/zxPPzww/84ZlpaGqVLl6Z69erZxrtnzx5uvPFGxo0bx44dO7jiiiu49NJLWblyJWeddRYbN25k9+7dPPvss3z++eds27aNlJQUmjVrxltvvUXfvn256aabOOecc3L9/USbEnURERGROPXxxx/Ts2dPDjvsMKpWrcqUKVNo06YNL7zwAgsXLmTq1KmUKFGCtWvXUrVqVR599FHGjh2bYwKboUuXLvzyyy8453jppZd46KGHeOSRR3Lc/swzz6R169Y89dRTlChRgnfffZf333+fmjVr8s0331CmTBnmzZtH//79SU1NDeux/f3339x7772MGTOG8uXL89///pdHH32UIUOG8NFHHzFnzhycc6xfv/5f+06YMIE2bdrkeOyXX36ZSpUqMWnSJHbs2MERRxzB8ccfz8iRI+nRowe33HILe/bsYevWrXTt2pVhw4YxLdMbsebNmzNp0qSwHkc0KVEXERERyUseI9/R8s4773D11VcD0K9fP9555x3atGnDmDFjGDx4MCVKWCpXtWrVfB132bJlnHXWWaxcuZKdO3fmuQjP/vvvT7Nmzfj222+pVasWJUuWpHnz5mzYsIEhQ4Ywbdo0ihcvTlpaWtgx/PLLL8yaNYsjjjgCsNrwww8/nP32248yZcpw8cUXc+KJJ3LSSSf9a9+VK1dSo0aNHI/99ddfM336dD744AMANmzYwLx582jfvj0XXXQRu3btom/fvqSkpGS7f/HixSlVqhSbNm2iYsWKYT+mSFOiLiIiIhKH1qxZw3fffcfvv/+Oc449e/bgnOOhhx7Cex9WX+7M22RebOfKK6/k2muv5eSTT2bcuHF7S2pyk1H+UqtWLfr37w/AY489Rq1atfjtt99IT0+nTJky/9qvRIkS/5iImhGH957u3bvzzjvv/GufiRMn8u233zJixAiGDRvGd99994/7y5Yty4YNG3KM1XvPU089RY8ePf513/jx4/n8888577zzuP766zn//POzPcaOHTuyfTyxpMmkIiIiInHogw8+4Pzzz2fx4sUsWrSIpUuXUr9+fX788UeOP/54nnvuub2dSdauXQtAxYoV2bRp095j1KpVi9mzZ5Oens5HH3209/YNGzZw4IEHAvDaa6+FFc9pp53G6NGjeffdd+nXr9/e49SuXZtixYrxxhtvsGfPnn/tV69ePaZNm0Z6ejpLly7d2ymmU6dOTJgwgfnz5wOwdetW0tLS2Lx5Mxs2bOCEE07g8ccf/0dJSoYmTZrs3S87PXr04Nlnn2XXrl2A1bRv2bKFxYsXU7NmTS655BIGDhzIlClTAChZsuTebcHeJNWoUYOSJUuG9bOJFiXqIiIiInHonXfe4ZRTTvnHbaeddhpvv/02F198MQcddBAtW7akVatWvP322wAMGjSIXr16cfTRRwPw4IMPctJJJ3HMMcdQu3btvce58847OeOMM+jatWue9ewZKleuTKdOnahVq9beUpnLL7+c1157jU6dOpGWlkb58uX/td8RRxxB/fr1adGiBdddd93e2vIaNWowfPhw+vfvT8uWLenUqdPebjInnXQSLVu25Mgjj+Sxxx771zG7devG1KlT8d5nG+vFF19M06ZNadOmDc2bN+fSSy9l9+7djBs3jpSUFFq3bs2HH37I0KFD9/7cWrZsuXfy6NixYznhhBPC+rlEk8vpARZ17dq18+FOhhAREZHkM3v2bJo0aRJ0GJKDoUOH0rt3b4477riIH/vUU0/lgQceoFGjRhE9bnbPKefcZO99u+y214i6iIiIiCScm2++OeI948Emtfbt2zfiSXpBKFEXERERkYRTq1YtTj755Igft1SpUjlOMI01JeoiIiIiInFIibqIiIiISBxSoi4iIiIiEoe04JGIJLdt2+DOO2HBAti9G/bssetu3eCGG6CYxitERCQ+6T+UiCSvtWvh+OPhf/+D2bNh4UJYsQKWL4ebb4bTT4ctW4KOUkQkR8WLFyclJYXmzZtzxhlnFKrLyQUXXMAHH3wAWJ/xWbNm5bjtuHHj+Omnn/J9jnr16vH333//67zPP//8P277+OOPc+1TnjnWaPDec8wxx7B48WJSUlJISUlh//3358ADD9z7/c6dO8M6Vtaf1bBhw3j11VcjEqcSdRFJTosXQ5cuMHEivPsuzJwJv/0Gkyfb9eOPwyef2DbLlgUdrYhItsqWLcu0adP4/fffKVWqFM8999w/7s9uJdBwvPTSSzRt2jTH+wuaqGenf//+jBgx4h+3jRgxgv79+0fk+AUxevRoWrVqxcEHH8y0adOYNm0agwcP5pprrtn7falSpcI6Vtaf1UUXXcSTTz4ZkTiVqItI8vntNzj8cFi5Er7+Gs4445/3OwdDh8Jnn1lJTPv2MGlSMLGKSEK4+mo46qjIXq6+On8xdO3alfnz5zNu3DiOPvpozj77bFq0aMGePXu4/vrrad++PS1bttw7eu29Z8iQITRt2pQTTzyRv/76a++xjjrqKDIWdvzyyy9p06YNrVq14thjj2XRokU899xzPPbYY6SkpPDDDz+wevVqTjvtNNq3b0/79u2ZMGECAGvWrOH444+ndevWXHrppdmuFHrccccxZ84cVq5cCcDWrVsZM2YMffv25e6776Z9+/Y0b96cQYMGZbt/5lH61NRUjjrqKAC2bNnCRRddRPv27WndujWffPIJADNnzqRDhw6kpKTQsmVL5s2b969jvvXWW/Tp0yfHn/XkyZM58sgjadu2LT169Ngb+5NPPknTpk1p2bIl/fr1y/ZnVa5cOerVq8fEiRNz/4WGQTXqIpJc5s6Frl2hUiX48Udo1iznbXv1gp9/hpNOgmOOsfKYOnViF6uISJh2797NF198Qc+ePQGYOHEiv//+O/Xr1+eFF16gUqVKTJo0iR07dnDEEUdw/PHHM3XqVObOncuMGTNYtWoVTZs25aKLLvrHcVevXs0ll1zC+PHjqV+/PmvXrqVq1aoMHjyYChUqcN111wFw9tlnc80119ClSxeWLFlCjx49mD17NnfddRddunTh9ttv5/PPP+eFF174V+zFixfn1FNP5b333mPo0KGMGjWKo48+mooVKzJkyBBuv/12AM477zw+++wzevfuHdbP5L777uOYY47hlVdeYf369XTo0IHjjjuO5557jqFDh3LOOeewc+fObD91mDBhwr/KcTLs2rWLK6+8kk8++YQaNWrw7rvvcsstt/DKK6/w4IMPsnDhQkqXLs369eupXLnyv35WAO3ateOHH36gQ4cOYT2WnChRF5Hk8p//2Ij5Tz9B3bp5b9+sGXz3HTRtCtdeC++9F/0YRSThPP54MOfdtm0bKSkpgI2oDxw4kJ9++okOHTpQv359AL7++mumT5++t6Z7w4YNzJs3j/Hjx9O/f3+KFy/OAQccwDHHHPOv4//yyy9069Zt77GqVq2abRxjxoz5R037xo0b2bRpE+PHj2fkyJEAnHjiiVSpUiXb/fv378/111/P0KFDGTFixN4FhcaOHctDDz3E1q1bWbt2Lc2aNQs7Uf/6668ZNWoUDz/8MADbt29nyZIlHH744dx3330sW7aMU089lYYNG/5r37Vr11KxYsVsjzt37lx+//13unfvDlh5Ue3atQFo2bIl55xzDn379qVv3745xlazZk3mzJkT1uPIjRJ1EUke33wDn39uk0fDSdIz1K8Pt9wCt91mpTLHHx+9GEVE8iGjRj2r8uXL7/3ae89TTz1Fjx49/rHN6NGjcc7lenzvfZ7bAKSnp/Pzzz9TtmzZf90Xzv5HHHEEK1eu5LfffuOnn35ixIgRbN++ncsvv5zU1FTq1q3LnXfeyfbt2/+1b4kSJUhPTwf4x/3eez788EMaNWr0j+2bNGlCx44d+fzzz+nRowcvvfTSv96kZByzWDadv7z3NGvWjJ9//vlf933++eeMHz+eUaNGcc899zBz5sxsH+/27duz/Vnll2rURSQ57N5tI+INGsCVV+Z//+uvh0MPhSFDYMeOyMcnIhIlPXr04Nlnn2XXrl0ApKWlsWXLFrp168aIESPYs2cPK1euZOzYsf/a9/DDD+f7779n4cKFgI00A1SsWJFNmzbt3e74449n2LBhe7/PePPQrVs33nrrLQC++OIL1q1bl22MzjnOPPNMBgwYwAknnECZMmX2Jt3Vq1dn8+bNOXZ5qVevHpMnTwbgww8//Mfjfuqpp/bWtU+dOhWAP/74gwYNGnDVVVdx8sknM3369H8ds1GjRvzxxx/Znq9Ro0asXr16b6K+a9cuZs6cSXp6OkuXLuXoo4/moYceYv369WzevPlfPyuw30Hz5s2zPX5+KFEXkeTw8svw++/w0ENQunT+9y9dGoYNg3nzIPQxqohIIrj44otp2rQpbdq0oXnz5lx66aXs3r2bU045hYYNG9KiRQsuu+wyjjzyyH/tW6NGDV544QVOPfVUWrVqxVlnnQVA7969+eijj/ZOkHzyySdJTU2lZcuWNG3adG/3mTvuuIPx48fTpk0bvv76aw466KAc4+zfvz+//fYb/fr1A6By5cpccskltGjRgr59+9K+ffts97vjjjsYOnQoXbt2pXjx4ntvv+2229i1axctW7akefPm3HbbbQC8++67NG/enJSUFObMmbO3zCazE088kXHjxmV7vlKlSvHBBx9www030KpVK1JSUvjpp5/Ys2cP5557Li1atKB169Zcc801VK5c+V8/K7Aa+OOOOy7Hn0W4XHazawXatWvnM2ZDi0ic27ABGjaEJk1g3DirUS+o00+H0aNh1iyoVy9SEYpIApo9ezZNmjQJOgyJgpUrV3L++efzzTffRPzYU6dO5dFHH+WNN974133ZPaecc5O99+2yO1ZUR9Sdcz2dc3Odc/Odczdmc79zzj0Zun+6c65NXvs656o6575xzs0LXVcJ3V7NOTfWObfZOTcs0/blnHOfO+fmOOdmOucejOZjFpEAPPAArF4Njz5auCQd4LHHbLXS/PZNExGRhFG7dm0uueQSNm7cGPFj//3339xzzz0ROVbUEnXnXHHgaaAX0BTo75zL2lm/F9AwdBkEPBvGvjcC33rvGwLfhr4H2A7cBuzrjbPPw977xkBr4AjnXK+IPEgRCd7ChZZcn38+tG1b+OPVrQu3326LIY0fX/jjiUhCU+VB8jrzzDPZb7/9In7c7t27Uy+bT2QL8lyK5oh6B2C+9/4P7/1OYASQtbN8H+B1b34BKjvnauexbx/gtdDXrwF9Abz3W7z3P2IJ+17e+63e+7Ghr3cCUwA1ShZJFhmj6PffH7ljXnklVKkCTz8duWOKSMIpU6YMa9asUbIuhea9Z82aNZQpUyZf+0WzPeOBwNJM3y8DOoaxzYF57FvLe78SwHu/0jlXM9yAnHOVgd7AEzncPwgb2c91MoSIxInt2+Gtt+DUU+HAAyN33LJl4aKL4IknbHXTUP9cESla6tSpw7Jly1i9enXQoUgSKFOmDHXyuaheNBP17ApFs74lzWmbcPbNXzDOlQDeAZ703mfbj8d7/wLwAthk0sKcT0RiYNQoWLcOLrww8scePBgeeQReesn6q4tIkVOyZMm9CwGJBCGapS/LgMwrjtQBVoS5TW77rgqVxxC6/ivMeF4A5nnvHw9zexGJd6++ajXl2ay2V2iHHgo9esDzz1uPdhERkRiLZqI+CWjonKvvnCsF9ANGZdlmFHB+qPtLJ2BDqKwlt31HAQNCXw8APskrEOfcvUAl4OpCPiYRiRfLlsFXX8GAAZCpr25EXX45LF9uI/ciIiIxFrXSF+/9bufcEOAroDjwivd+pnNucOj+54DRwAnAfGArcGFu+4YO/SDwnnNuILAEOCPjnM65RcB+QCnnXF/geGAjcAswB5gSWuZ2mPf+pWg9dhGJgddfB+/hgguid44TT4SDDoJnnrE6eBERkRjSgkc50IJHInHMezjsMJtAmsPKchFz//1wyy0wezY0bhzdc4mISJET2IJHIiJR8eOPMH9+dCaRZjVwIJQsCaHlskVERGJFibqIJJ5XX4UKFeD006N/rlq17DzDh8OWLdE/n4iISIgSdRFJLJs3w3vvwVlnQfnysTnn5ZfDhg0wYkRsziciIoISdRFJNO+/byPbsSh7yXDEEXDIIXZuERGRGFGiLiKJ5a23oGFD6Nw5dud0Dk47Db791hZYEhERiQEl6iKSODZtgvHjoW9fS55j6dRTbeGjzz6L7XlFRKTIUqIuIoljzBjYtQtOOCH2527fHurUgQ8/jP25RUSkSFKiLiKJY/RoqFjRasZjrVgxOOUUWw118+bYn19ERIocJeoikhi8t0T9+OOtr3kQTjsNtm+HL74I5vwiIlKkKFEXkcQwfTqsWAEnnhhcDF26QI0aMHJkcDGIiEiRoURdRBLD6NF23bNncDEUL24TWT/7zEbWRUREokiJuogkhtGjoU0bqF072DhOPdVq1MeMCTYOERFJekrURST+rVsHP/0UTLeXrI45BipVUvmLiIhEnRJ1EYl/X38N6enxkaiXKgW9e8Mnn1irSBERkShRoi4i8W/0aKhaFTp0CDoSc9ppsHatLb4kIiISJUrURSS+padbO8SePW0yZzw4/ngoV06LH4mISFQpUReR+DZ5MqxeHR9lLxnKlYPu3eHLL4OOREREkpgSdRGJb6NHg3PQo0fQkfxT9+6wcCEsWBB0JCIikqSUqItIfBs9Gjp1gurVg47kn7p3t+tvvgk2DhERSVpK1EUkfm3YAJMmWU14vGnYEA46SIm6iIhEjRJ1EYlfP/8M3kO3bkFH8m/O2aj6d9/Bnj1BRyMiIklIibqIxK8ffoASJaBjx6AjyV737rB+PaSmBh2JiIgkISXqIhK/fvgB2rSB8uWDjiR7xx5rI+sqfxERkShQoi4i8WnHDpg4Ebp0CTqSnFWvDq1bK1EXEZGoUKIuIvEpNdWS9a5dg44kd927Wy395s1BRyIiIklGibqIxKcff7TrI44INo68dO8Ou3bB998HHYmIiCQZJeoiEp9++AEaN4YaNYKOJHdHHAFlyqj8RUREIk6JuojEn/R0mDAh/stewJL0bt2UqIuISMQpUReR+DNzprU9jOeJpJl17w6zZsHy5UFHIiIiSUSJuojEn4z69EQYUQc47ji7HjMm2DhERCSpKFEXkfjzww9wwAFQr17QkYSnZUurpVf5i4iIRJASdRGJL95bot61qy0mlAiKFbNR9TFjLH4REZEIUKIuIvFlyRJYtixx6tMzHHUUrFoF8+cHHYmIiCQJJeoiEl9++MGuE6U+PUNGvBnxi4iIFJISdRGJLz/+CPvtB82bBx1J/jRuDNWq7ZsIKyIiUkhK1EUkvvzwgy0iVLx40JHkj3NWrqMRdRERiRAl6iISP9assX7kiVafnqFLF6tR//PPoCMREZEkoERdROLHxIl23blzsHEUVMYbjAkTgo1DRESSghJ1EYkfqalWQtKmTdCRFEybNlC2rMpfREQkIpSoi0j8SE2FRo1sMmkiKlUKOnbUhFIREYkIJeoiEj8mT4Z27YKOonC6doWpU2HTpqAjERGRBKdEXUTiw8qVsHx54ifqXbpAejr88kvQkYiISIJToi4i8WHyZLtO9ET98MOhWDHVqYuISKEpUReR+JCaagluSkrQkRROxYr2GFSnLiIihaREXUTiQ2oqNG0K5csHHUnhdelipS87dwYdiYiIJDAl6iISPO8tUU/0spcMXbvCtm02qVRERKSAlKiLSPCWL4dVq5InUc9Y+Eh16iIiUghK1EUkeKmpdp0sifr++8Ohh6pOXURECkWJuogELzUVSpSAli2DjiRyunSxRN37oCMREZEEpURdRIKXmgrNm0PZskFHEjldusCaNTBnTtCRiIhIglKiLiLByphI2rZt0JFE1uGH2/WvvwYbh4iIJCwl6iISrMWLbeQ5WerTMzRuDPvtp0RdREQKTIm6iAQr2SaSZihWDNq3V6IuIiIFpkRdRIKVmgolS0KLFkFHEnkdO8L06bB1a9CRiIhIAlKiLiLBSk21bi+lSwcdSeR17Ah79sCUKUFHIiIiCUiJuogEJ9lWJM2qQwe7VvmLiIgUgBJ1EQnOggWwYUPyJur77w8HHQQTJwYdiYiIJCAl6iISnGSdSJpZx44aURcRkQJRoi4iwZk2zSaSNm0adCTR07GjtaBctSroSEREJMEoUReR4Eyfbkl6qVJBRxI9HTvatUbVRUQkn5Soi0hwpk+3ji/JrE0bKF5cibqIiOSbEnURCcaaNbB8efIn6uXK2WNUoi4iIvmkRF1EgjF9ul23ahVsHLHQsSNMmgTp6UFHIiIiCUSJuogEIyNRT/YRdbBEfeNGmDMn6EhERCSBKFEXkWBMnw41a0KtWkFHEn2aUCoiIgWgRF1EglEUJpJmaNQIKlVSoi4iIvmiRF1EYm/3bvj996JRnw5QrBi0b69EXURE8kWJuojE3vz5sH170RlRByt/mTEDtm4NOhIREUkQStRFJPaK0kTSDB07wp49MHly0JGIiEiCUKIuIrE3fbotAtSkSdCRxE7GhNKJE4ONQ0REEoYSdRGJvd9+g8aNoXTpoCOJnZo1oU4djaiLiEjYlKiLSOxNn150JpJm1q6dEnUREQmbEnURia3162HJkqJVn56hbVtIS7PFj0RERPKgRF1EYmvGDLsuqok6wNSpwcYhIiIJQYm6iMRWUez4kiEjUU9NDTYOERFJCFFN1J1zPZ1zc51z851zN2Zzv3POPRm6f7pzrk1e+zrnqjrnvnHOzQtdVwndXs05N9Y5t9k5NyzLedo652aEjvWkc85F83GLSC5++w2qVYMDDgg6ktirWRPq1lWduoiIhCVqibpzrjjwNNALaAr0d841zbJZL6Bh6DIIeDaMfW8EvvXeNwS+DX0PsB24Dbgum3CeDR0/41w9I/AQRaQgpk+30fSi+n65bVsl6iIiEpZojqh3AOZ77//w3u8ERgB9smzTB3jdm1+Ays652nns2wd4LfT1a0BfAO/9Fu/9j1jCvlfoePt573/23nvg9Yx9RCTG0tOtRr0olr1k0IRSEREJUzQT9QOBpZm+Xxa6LZxtctu3lvd+JUDoumYYcSzLIw4AnHODnHOpzrnU1atX53FYEcm3P/6ArVuVqANMmRJsHCIiEveimahn97m2D3ObcPaNZBx2o/cveO/bee/b1ahRo4CnE5EcFeWJpBkyEnWVv4iISB6imagvA+pm+r4OsCLMbXLbd1WonCWjrOWvMOKok0ccIhILv/0GxYpBs2ZBRxIcTSgVEZEwRTNRnwQ0dM7Vd86VAvoBo7JsMwo4P9T9pROwIVTOktu+o4ABoa8HAJ/kFkToeJucc51C3V7Oz2sfEYmSGTOgYUMoWzboSIKlCaUiIhKGEtE6sPd+t3NuCPAVUBx4xXs/0zk3OHT/c8Bo4ARgPrAVuDC3fUOHfhB4zzk3EFgCnJFxTufcImA/oJRzri9wvPd+FnAZMBwoC3wRuohIrM2cCc2bBx1F8Nq2hY8/hg0boFKloKMREZE4FbVEHcB7PxpLxjPf9lymrz1wRbj7hm5fAxybwz71crg9FVB2IBKkHTtg/nw466ygIwle5hVKjzoq0FBERCR+aWVSEYmNtDRrz9g063IKRZAmlIqISBiUqItIbMwMVa8pUdeEUhERCYsSdRGJjVmzrONLo0ZBRxIf2raF1NSgoxARkTimRF1EYmPWLDj0UChdOuhI4kPbtjBvnk0oFRERyYYSdRGJjZkzVfaSWbt2dj11arBxiIhI3FKiLiLRt3OnjR4X5YWOstKEUhERyYMSdRGJvrQ02LNHI+qZ1ahhE0pVpy4iIjlQoi4i0Tdrll0rUf+n1q1V+iIiIjlSoi4i0aeOL9lr3do+bdi8OehIREQkDilRF5HomzkTGjSAsmWDjiS+tG4N3sOMGUFHIiIicUiJuohE36xZKnvJTuvWdq3yFxERyYYSdRGJrl27rLxDHV/+rW5dqFpVibqIiGRLibqIRNe8ebB7t0bUs+OcJpSKiEiOlKiLSHSp40vuWre2GvVdu4KORERE4owSdRGJrpkzbeS4ceOgI4lPrVvbglCzZwcdiYiIxBkl6iISXbNmQf36UK5c0JHEJ00oFRGRHChRF5HoUseX3B12mL2JUaIuIiJZKFEXkejZtQvmzlXHl9wULw4tWypRFxGRf1GiLiLRs2CBJesaUc9d69YwbRqkpwcdiYiIxBEl6iISPer4Ep7WrWHjRli4MOhIREQkjihRF5HomTnTrps0CTaOeKcJpSIikg0l6iISPbNmQb16UL580JHEt+bNrVZdibqIiGSiRF1EomfmTJW9hKNMGfs5KVEXEZFMlKiLSHTs2QNpaSp7CVfr1krURUTkH5Soi0h0LF4MO3YoUQ9X69bw5592ERERQYm6iETLnDl23ahRsHEkCk0oFRGRLJSoi0h0ZCTqjRsHG0eiSEmxayXqIiISokRdRKJj7lyoVg2qVw86ksRQqRI0aKBEXURE9lKiLiLRMWeORtPzSxNKRUQkEyXqIhIdc+aoPj2/UlJgwQLYtCnoSEREJA4oUReRyFu3Dv76SyPq+ZVRpz59eqBhiIhIfFCiLiKRN3euXStRz5+MRP233wINQ0RE4oMSdRGJPHV8KZgDD4SqVWHatKAjERGROKBEXUQib84cKFkS6tcPOpLE4pyNqmtEXUREUKIuItEwZw4ceiiUKBF0JImnVSurUd+9O+hIREQkYErURSTy5s5V2UtBpaTA9u0wb17QkYiISMCUqItIZO3aBfPnK1EvKE0oFRGRECXqIhJZf/xhZRtK1AumcWOr79eEUhGRIk+JuohEVkbHFy12VDClSkGzZhpRFxERJeoiEmEZPdSVqBdcq1YaURcRESXqIhJhc+bA/vtD5cpBR5K4UlLgzz9h1aqgIxERkQApUReRyJozR/XphaUJpSIighJ1EYkk7y1RV9lL4bRqZdcqfxERKdKUqItI5Pz9N6xbpxH1wqpSBQ46SCPqIiJFnBJ1EYmcjI4vStQLTxNKRUSKPCXqIhI5StQjJyXFfp7btgUdiYiIBESJuohEzpw5UKaMlW1I4aSkQHo6zJwZdCQiIhIQJeoiEjlz58Jhh0ExvbQUmiaUiogUefpvKiKRo9aMkVO/PlSsqAmlIiJFmBJ1EYmM7dth4UIl6pFSrBi0bKkRdRGRIkyJuohExoIFVlN92GFBR5I8UlJsRD09PehIREQkAErURSQy0tLsWosdRU5KCmzaBIsWBR2JiIgEQIm6iETGvHl23bBhsHEkE00oFREp0pSoi0hkzJsHNWtCpUpBR5I8mje3WnUl6iIiRZISdRGJjLQ0jaZHWtmyVkqkzi8iIkWSEnURiYx58zSRNBpSUjSiLiJSRClRF5HC27QJVq7UiHo0pKTAkiWwbl3QkYiISIwpUReRwps/3641oh55GRNKVf4iIlLkKFEXkcLLaM2oEfXIS0mxa5W/iIgUOUrURaTwMlozHnposHEko1q17KIRdRGRIkeJuogUXloa1KkD5coFHUly0oRSEZEiSYm6iBTevHkqe4mmlBSYNQt27gw6EhERiSEl6iJSeGlpmkgaTa1aWZI+Z07QkYiISAwpUReRwlmzBtau1Yh6NGlCqYhIkaREXUQKJ2MiqUbUo6dhQyhTRhNKRUSKGCXqIlI4GYm6RtSjp0QJaNFCI+oiIkWMEnURKZy0NChWDBo0CDqS5JaSYiPq3gcdiYiIxIgSdREpnHnzoF49KFUq6EiSW6tWNh9g+fKgIxERkRhRoi4ihaOOL7GhCaUiIkWOEnURKTjv1UM9Vlq2tGtNKBURKTKUqItIwf35J2zerBH1WKhYEQ45RCPqIiJFiBJ1ESk4dXyJrYwJpSIiUiQoUReRgktLs2uNqMdGq1Ywfz5s2hR0JCIiEgNK1EWk4ObNs24vBx0UdCRFQ0qKzQuYMSPoSEREJAaUqItIwaWlWd108eJBR1I0tGpl1yp/EREpEqKaqDvnejrn5jrn5jvnbszmfuecezJ0/3TnXJu89nXOVXXOfeOcmxe6rpLpvptC2891zvXIdHt/59yM0Dm+dM5Vj+bjFiky1PElturWhSpVNKFURKSIiFqi7pwrDjwN9AKaAv2dc02zbNYLaBi6DAKeDWPfG4FvvfcNgW9D3xO6vx/QDOgJPOOcK+6cKwE8ARztvW8JTAeGROVBixQl6elWL6369NhxThNKRUSKkGiOqHcA5nvv//De7wRGAH2ybNMHeN2bX4DKzrnaeezbB3gt9PVrQN9Mt4/w3u/w3i8E5oeO40KX8s45B+wHrIj8wxUpYpYuhR07NKIea61awfTpsGdP0JGIiEiURTNRPxBYmun7ZaHbwtkmt31ree9XAoSua+Z2LO/9LuAyYAaWoDcFXs4uYOfcIOdcqnMudfXq1eE8RpGiSx1fgpGSAtu27WuNKSIiSSuaibrL5jYf5jbh7BvW+ZxzJbFEvTVwAFb6clN2B/Dev+C9b+e9b1ejRo08TidSxKmHejBSUuxa5S8iIkkvmon6MqBupu/r8O+Sk5y2yW3fVaHyGELXf+VxrBQA7/0C770H3gM6F+gRicg+8+ZBuXJwwAFBR1K0NGkCJUtqQqmISBEQzUR9EtDQOVffOVcKm+g5Kss2o4DzQ91fOgEbQuUsue07ChgQ+noA8Emm2/s550o75+pjE1QnAsuBps65jCHy7sDsSD9YkSInLc1G0112H2ZJ1JQqBU2bakRdRKQIKBGtA3vvdzvnhgBfAcWBV7z3M51zg0P3PweMBk7AJn5uBS7Mbd/QoR8E3nPODQSWAGeE9pnpnHsPmAXsBq7w3u8BVjjn7gLGO+d2AYuBC6L1uEWKjHnz9pVhSGy1agXffBN0FCIiEmXOqkEkq3bt2vnU1NSgwxCJT7t2QdmycMMNcN99QUdT9Dz2GFx7LaxaBTVr5r29iIjELefcZO99u+zu08qkIpJ/ixZZe0B1fAmGJpSKiBQJStRFJP8yWjOq40swWrWya00oFRFJakrURST/MlozakQ9GFWrQt26GlEXEUlyStRFJP/S0qByZahWLehIiq5WrTSiLiKS5JSoi0j+zZun1oxBS0mBOXNg+/agIxERkShRoi4i+ZeWprKXoKWk2ITemTPz3FRERBKTEnURyZ9t22DpUk0kDZomlIqIJD0l6iKSPwsWgPcaUQ9agwZQoYImlIqIJDEl6iKSPxkdXzSiHqxixaBlS42oi4gkMSXqIpI/6qEeP1JSbERdK0yLiCQlJeoikj/z5tmy9ZUqBR2JpKTAxo22UqyIiCQdJeoikj/q+BI/NKFURCSpKVEXkfzJ6KEuwWve3GrVNaFURCQpKVEXkfBt3Ah//qkR9XhRrpz9LjSiLiKSlJSoi0j45s+3a42ox4+UFCXqIiJJSom6iIQvo+OLRtTjR0oKLF4M69cHHYmIiESYEnURCV9GD/VDDgk2DtknY0Kp6tRFRJKOEnURCV9aGtSta7XREh9SUuxaibqISNLJM1F3zj3knNvPOVfSOfetc+5v59y5sQhOROKMOr7En/33t772qlMXEUk64YyoH++93wicBCwDDgOuj2pUIhKf1EM9PmlCqYhIUgonUS8Zuj4BeMd7vzaK8YhIvFqzBtat04h6PEpJgZkzYdeuoCMREZEICidRH+WcmwO0A751ztUAtkc3LBGJO+r4Er9atYKdO2HOnKAjERGRCMo1UXfOFQM+BQ4H2nnvdwFbgT4xiE1E4klGxxeNqMefjAmlKn8REUkquSbq3vt04BHv/Trv/Z7QbVu893/GJDoRiR9paVC8ONSvH3QkktVhh0Hp0ur8IiKSZMIpffnaOXeac85FPRoRiV/z5kG9elCqVNCRSFYlSkCLFhpRFxFJMiXC2OZaoDyw2zm3HXCA997vF9XIRCS+zJun+vR4lpICH38M3oPGVUREkkKeI+re+4re+2Le+1Le+/1C3ytJFylKvLfSF9Wnx69WreDvv2HFiqAjERGRCAlnRB3nXBWgIVAm4zbv/fhoBSUicebPP2HLFo2ox7PME0oPPDDISEREJELCWZn0YmA88BVwV+j6zuiGJSJxJaM1o0bU41fLlnatCaUiIkkjnMmkQ4H2wGLv/dFAa2B1VKMSkfiS0ZpRI+rxa7/9oEEDTSgVEUki4STq27332wGcc6W993OARtENS0TiSlqadXupWzfoSCQ3KSkaURcRSSLhJOrLnHOVgY+Bb5xznwCarSRSlMybB4cean3UJX61amW/q82bg45EREQiIM/JpN77U0Jf3umcGwtUAr6MalQiEl/U8SUxpKRYh54ZM+Dww4OORkRECinHEXXnXHvnXK/Mt3nvvw992SKqUYlI/NizBxYsUKKeCFq1smuVv4iIJIXcSl/+B8zO5vZZoftEpChYuhR27NBE0kRw0EFQubImlIqIJIncEvVq3vtFWW/03s8HqkUtIhGJLxkdXzSiHv+cs/IXJeoiIkkht0S9bC73lY90ICISpzJ6qGtEPTGkpMD06VayJCIiCS23RH2Mc+4+55zLfKNz7i7gu+iGJSJxY948KF8eatcOOhIJR5s2sG0bzJ0bdCQiIlJIuSXq/wEaAPOdcx+GLvOxHurXxiQ6EQleRseXf75nl3jVpo1dT5kSbBwiIlJoObZn9N5vAfo75xoAzUI3z/Te/xGTyEQkPsybB61bBx2FhKtRIyhTxhL1c88NOhoRESmEcPqo/wEoORcpinbtgoUL4ayzgo5EwlWihLVpnDo16EhERKSQwlmZVESKqoULbVKiOr4kljZtbEQ9PT3oSEREpBByW/CofiwDEZE4pI4vial1a9i40d5oiYhIwsptRP0DAOfctzGKRUTijXqoJ6aMCaUqfxERSWi51agXc87dARzmnPtXlxfv/aPRC0tE4kJaGlSpAtW0xllCad7catWnTIHTTw86GhERKaDcRtT7AduxZL5iNhcRSXbz5qk1YyIqXRqaNVOLRhGRBJdbe8a5wH+dc9O991/EMCYRiRdpaXDkkUFHIQXRpg189hl4rzdaIiIJKpyuLz855x51zqWGLo845ypFPTIRCda2bbB0qerTE1Xr1rB6NaxYEXQkIiJSQOEk6q8Am4AzQ5eNwKvRDEpE4sD8+Xatji+JSSuUiogkvHAS9UO893d47/8IXe4CGkQ7MBEJmDq+JLZWrazkRZ1fREQSVjiJ+jbnXJeMb5xzRwDboheSiMSFjB7qStQTU4UK9mmIRtRFRBJWbu0ZMwwGXs9Ul74OGBC9kEQkLsybB7VqwX77BR1J1O3cCX/9BatWwbp1sGkTbN6873rPHlvkMz39n19nvpQsCWXK5H6pWBGqVrVul5UrQ/HiUX5gbdrAjz9G+SQiIhIteSbq3vvfgFbOuf1C32+MelQiErx585KmPt17mxc7a5aV3s+fbw/vjz/2Jef54RwUK7bv4hzs3m2X/ByjcmVL2jOS96pVoWZNqFt33+Wgg+z9UrFwPv/Mqk0beOcd+PtvqF69AAcQEZEghTOiDihBFyly0tLgxBODjqJAtmyBCRNg4sR9l1Wr9t1fvrxV9DRtCscea4lwrVqWJFerZiPfFSrYdfnyNlqeNTHPzu7dsGMHbN/+78u2bTZCv2YNrF1r15m//usvmDMH/vzTts2sXDmL97DD7NKoEaSkQOPGFluOWre266lToXv3wvxIRUQkAGEn6iJShGzcaJltAo2oL11qbcM//RS++84SZoAmTaBnT+jQAVq0sIS3Vq3otBYvUcIu5csX/Bje2wj/kiX2mJYsgQUL7H3TtGkwcqSV34Cta9S8uSXt7dtDly72ePeOvmck6lOmKFEXEUlAStRF5N8SpOPLpk3w1lvw0kswebLddsghcNll0KsXdOwIlRJs1QfnrASmalVLwLPaudN+Pb/9ZgPl06bBxx/Dyy/b/VWrwhFHWNLevXtVUg6uh1PnFxGRhBRWou6c6wzUy7y99/71KMUkIkHLSNTjdER9+nR49ll4802b7JmSAv/9L/TubeUgybwQZ6lS0KyZXc4+227z3kbdf/zRLj/8YJ8s3HAD7F96Cj1Gj6XXuzaoXrVqsPGLiEj48kzUnXNvAIcA04DQB654QIm6SLLKaM14yCHBxpFFaircdBOMGWNdVM46y0bPO3RI7uQ8L87BoYfa5YIL7LY//4SvvoIvH1nBpzOO5LV+VpZz7LFw5pnQt6+SdhGReBfOiHo7oKn33kc7GBGJE/PmWbuRsmWDjgSwcG69Fd57z5qXPPQQDByoRDM3++8PAwbAgJpL2HNCSyY+nconS1rz3nv2s7v0Ukvazz0XTj3VJqyKiEh8Cafh1+/A/tEORETiSFpaXNSnr1sHV1xh3Vk+/xxuu81KPK6/Xkl62Nq2pTjpHL7tOx580H5+qalw7bUwdy6cdx7Urg2DB8OkSVZGIyIi8SGcRL06MMs595VzblTGJdqBiUhAvI+LRP3rr61Ly/PPw6BB1vv87ruLxPpLkZXRmD01FbAymbZtraZ/wQIYOxb69IHXX7cSolat4IUXYOvWgOMWEZGwEvU7gb7A/cAjmS4ikozWrIH16wObSLpli42i9+hhSfkvv8DTT1sphxRQu3b72uJkUqwYHHWUJekrV9qbouLFrSymTh34v/+DxYtjH66IiJg8E3Xv/ffAHKBi6DI7dJuIJKMAWzP+8ot1cHn2WbjmGsst27WLeRjJp107+72uX5/jJpUq2ScXU6ZY15jjjoNHH4UGDWzyqTo8iojEXp6JunPuTGAicAZwJvCrc+70aAcmIgHJ6PgS4xH111+Hbt1g1y4rx3j00biZy5r42ra16ylT8tzUOevB/t578McfcN111j2mTRvrTT9+vOrYRURiJZzSl1uA9t77Ad7784EOwG3RDUtEAjNvntU/1K8fk9Olp9sk0QEDoGtXG7k98siYnLroyEjUsyl/yc1BB1kt+5IlcP/9tvuRR9rvacwYJewiItEWTqJezHv/V6bv14S5n4gkorQ0S9JLloz6qbZts0V77r3XWgZ++SVUqRL10xY91atDvXp7J5TmV6VK1r9+0SJ46imrW+/eHY4+2spkREQkOsJJuL8MdXy5wDl3AfA5MDq6YYlIYObNi0l9+urVcMwx8O671hf9xRdj8t6g6GrXrsCJeoZy5WDIEHuKPPmktXfs1s0m/k6aFKE4RURkr3Amk14PvAC0BFoBL3jvb4h2YCISAO8tC4tyffqaNbbYzrRp8OGH1he9KK8sGhNt21rR+bp1hT5UmTJw5ZXW3vHhh630vUMHOOccdYkREYmksEpYvPcfeu+v9d5f473/KNpBiUhAVq60/ohRHFFfv95GYNPSYNQoWxVTYiCjfU4+69RzU64c/Oc/lv/feiuMHAmNGlmZzMaNETuNiEiRlWOi7pz7MXS9yTm3MdNlk3NOL8EiySjKHV82bbLOIdOnwwcfWJ2zxEjGhNJClr9kp2JFuOcee/qceSY8+CAceig89xzs3h3x04mIFBk5Jure+y6h64re+/0yXSp677U2oEgyimIP9S1b4MQTrZZ5xAg46aSIn0JyU6WKNUWP4Ih6VnXrWpvNSZOgSRO47DJb6fSLL9QhRkSkIMLpo/5GOLeJSBJIS4PSpS3jiqCdO6FvX5gwAd58U+UugYnAhNJwTzNunJXC7NwJJ5xg5U4zZkT91CIiSSWcGvVmmb9xzpUA2kYnHBEJ1Lx5cMgh1kc9goYOtb7bL74I/fpF9NCSH+3aWY/FNWuifirn4JRTYOZMeOwxe3+QkmLPhQ0bon56EZGkkFuN+k3OuU1Ay8z16cAq4JOYRSgisZOWFvH69Oees8v118NFF0X00JJfBVz4qDBKlYKrr4b582HwYOvD3qgRvPGGymFERPKSW436A0Al4PUs9enVvPc3xS5EEYmJPXus314E69O//97a+PXqBQ88ELHDSkG1aWPXMSh/yapqVXj6aatfr1cPzj/ferCrHEZEJGe5lr5479Ox3ukikuyWLLGC4giNqC9aBKefbpU077wT8WoaKYjKle2NWACJeoa2beGnn+Cll2D2bGjdGq65Ru0cRUSyE06N+i/OufYFObhzrqdzbq5zbr5z7sZs7nfOuSdD9093zrXJa1/nXFXn3DfOuXmh6yqZ7rsptP1c51yPTLeXcs694JxLc87Ncc6dVpDHI5LUItjxZcsW6NMHdu2yXumVKhX6kBIpbdvGtPQlO8WKwcCBVml1ySXwxBNWDvP22yqHERHJLJxE/WjgZ+fcglAyPcM5Nz2vnZxzxYGngV5AU6C/c65pls16AQ1Dl0HAs2HseyPwrfe+IfBt6HtC9/fDJr/2BJ4JHQfgFuAv7/1hoeN9H8bjFilaIthDffBg+P13a8MY5UVOJb/atbNPT/76K+hIqFoVnn0Wfv0V6tSxlU2PPtpG2kVEJLxEvRdwCHAM0Bs4KXSdlw7AfO/9H977ncAIoE+WbfpgNfDee/8LUNk5VzuPffsAr4W+fg3om+n2Ed77Hd77hcD80HEALgIeACvn8d7/HUb8IkXLvHlQoQLsv3+hDjNihLVgvP126NkzQrFJ5GSsUBpg+UtW7dvDL7/A88/bYlitWsEdd8D27UFHJiISrDwTde/9YqAylpz3BiqHbsvLgcDSTN8vC90Wzja57VvLe78yFNtKoGZux3LOVQ59f49zbopz7n3nXK3sAnbODXLOpTrnUlevXh3GQxRJImlpVvbiXIEPsXSpLXLTqRPccksEY5PIadPGfseTJgUdyT8ULw6DBsGcOba66d13W8I+blzQkYmIBCecBY+GAm9hCXFN4E3n3JVhHDu7//ZZqw9z2iacfcM9XwmgDjDBe98G+Bl4OLsDeO9f8N638963q1GjRh6nE0ky8+YVqj49PR0GDLC69DffhBIlIhibRE7FitCsGUycGHQk2apZ054/X35pz6Wjj7a2njFo/S4iEnfCKX0ZCHT03t/uvb8d6ARcEsZ+y4DMyxvWAVaEuU1u+64KlccQus4otMxpnzXAVuCj0O3vA20QkX127rQ2LYUoKH/sMRg7Fp580jq9SBzr0MEKw+N45maPHjbP4YYb4PXXoUkTS+DjOGQRkYgLJ1F3wJ5M3+8h+9HrrCYBDZ1z9Z1zpbCJnqOybDMKOD/U/aUTsCFUzpLbvqOAAaGvB7Bv8aVRQD/nXGnnXH1sgupE770HPgWOCm13LDArjPhFio4//rA+6gUcUf/tN7j5ZluJ8sILIxybRF6HDjZEvXBh0JHkqlw5ePBBmDIFGjSA886zBH7BgqAjExGJjXAS9VeBX51zdzrn7gJ+AV7Oayfv/W5gCPAVMBt4z3s/0zk32Dk3OLTZaOAPbOLni8Dlue0b2udBoLtzbh7QPfQ9ofvfw5LwL4ErvPcZbzBuAO4Mdas5D/hPGI9bpOjI6PjSqFG+d92+3bp1VK0KL7xQqBJ3iZWOHe3611+DjSNMLVvChAm2qukvv0Dz5pbA79oVdGQiItHlfBifI4b6m3cJffuD935qVKOKA+3atfOpcdQVQSSq/vc/+L//g7VroUqVvLfP5M474a67YPRoW4FUEsCuXdbc/tJLrWYpgSxfbqvdfvQRtGhhbw47dQo6KhGRgnPOTfbet8vuvnBG1Pceh5wneopIIktLgxo18p2kz5sHDzwA/fsrSU8oJUta95c4nVCamwMPhJEj4eOPYd066NwZrrgCNmwIOjIRkcgLp+vL7Vi/8ipAdeBV59yt0Q5MRGJo7tx8l714D5dfDmXLwqOPRikuiZ6OHa34O0HrR/r0gVmzbHT92WehaVNbBVdEJJmEM6LeH2jvvb/Te38H1vXlnOiGJSIxlZaW744vI0bAmDFw332FXiNJgtChg00wmDEj6EgKrGJFeOIJq1uvVs2S97PPBi2DISLJIpxEfRFQJtP3pQHNuRdJFhs2wKpV+RpRX78err3WFrkcPDjPzSUeZUwoTcDyl6w6dLCFVu+6Cz74wEbXR4xQK0cRSXzhJOo7gJnOueHOuVeB34HNzrknnXNPRjc8EYm6uXPtOh8j6rfeCn/9Bc89ZytKSgI6+GCbl5AgnV/yUqoU3H67VfPUr2/zJvr2hRVZV+8QEUkg4awd+BH7FgsCGBedUEQkEPlszThpEjzzDAwZAm3bRjEuiS7nbCg6CUbUM2veHH7+GR5/3N5QNm0Kjzxiq5uqdaiIJJo8R9S9968B7wCTQ5e3vfevZVyiHaCIRNncuVCsWFjLiaanW4eNWrXgnntiEJtEV8eOMHs2bNwYdCQRVbw4/Oc/Vn6fkgIXXwzHHx/36zuJiPxLOF1fjgLmAU8DzwBpzrlu0Q1LRGImLc1qBUqVynPTd9+1EfUHH7Q23JLgOnSwQu4kXTPi0EPhu++sK0zGQknPPafadRFJHOHUqD8CHO+9P9J73w3oASTWChkikrMwWzPu2AE33wytWsG558YgLom+9u3tOsnKXzIrVswmPM+cCUccAZddBiecoNp1EUkM4STqJb33czO+8d6nASWjF5KIxEx6etitGZ9+GhYtskVMNYE0SVStCg0bJs2E0twcdBB8+SUMGwbff2+j6+++G3RUIiK5CydRn+yce9k5d1To8iJWqy4iiW75cti2Lc8R9XXr4N57oUcP6N49RrFJbCThhNKcFCtmcyymTbP3pv36Wd/1tWuDjkxEJHvhJOqDgZnAVcBQYFboNhFJdGG2ZrzvPuud/tBD0Q9JYqxjR6sDWb486Ehi5rDD4McfbUL0++9Dixbw1VdBRyUi8m+5JurOuWLAZO/9o977U733p3jvH/Pe74hRfCISTWG0Zly0CJ56CgYMgJYtYxOWxFCHDnZdBMpfMitRwto3/vorVK4MPXvaaPuWLUFHJiKyT66Juvc+HfjNOXdQjOIRkViaOxfKl4cDDshxk1tusZp0tWNMUq1aQcmSRS5Rz9CmDUyebCvtPvustXOcNCnoqERETDilL7WxlUm/dc6NyrhEOzARiYGMiaQ5rAQzZQq8/TZccw3UqRPj2CQ2ypSxbPXnn4OOJDBlytiiSGPHws6d0LkzPPywzbUWEQlSOCuT3hX1KEQkGHPnWo1yDu64wxqD3HBDDGOS2Dv8cGswvmuXja4XUUceaRNNBw6E66+Hb7+F116DmjWDjkxEiqocR9Sdc2Wcc1cDZwCNgQne++8zLrEKUESiZPt2K0DPYSJpaip89pmt8LjffrENTWKsc2d7PkybFnQkgatSBT78EJ55xkbYW7WyhF1EJAi5lb68BrQDZgC9sIWPRCRZLFhgSzTmMJH0rrtsNH3IkBjHJbF3+OF2/dNPwcYRJ5yzhZEmTrSJpt27W+cjlcKISKzllqg39d6f671/Hjgd6BqjmEQkFnJpzTh5so2mX3utRtOLhDp1oG7dIl2nnp2WLW1iab9+1iGmTx9bU0BEJFZyS9R3ZXzhvd8dg1hEJJYyWjNmk6jfdZeVAFx5ZYxjkuB07qwR9WxUqABvvWUtSr/6Ctq2halTg45KRIqK3BL1Vs65jaHLJqBlxtfOuY2xClBEomTuXNh//38NmU+eDJ9+qtr0Iufww2HpUli2LOhI4o5zVgL2/ffWFebww+H114OOSkSKghwTde99ce/9fqFLRe99iUxf69+3SKJLS8u2Pv3uuzWaXiR17mzXKn/J0eGH22h65862ANj118OePUFHJSLJLJw+6iKSjObO/VeiPmUKjBql2vQiKSUFypZV+UseatSwEpgrrrBe6yefDBv1GbOIRIkSdZGiaM0au2SpT1dtehFWsiS0a6cR9TCULAnDhtlKpl9/DZ06wfz5QUclIslIibpIUZQxkTTTiPrvv9to+tVXQ6VKwYQlAevc2T5W2bYt6EgSwuDBlqivWmXrhv34Y9ARiUiyUaIuUhRl05rx4YehXDn7SF+KqM6dbXXSyZODjiRhHH209VuvXh2OO84WSxIRiRQl6iJF0ezZ9vl9gwaANfp46y24+GKoVi3g2CQ4nTrZtcpf8uWQQ2DCBGjTBs44A558MuiIRCRZKFEXKYrmzIGGDaFECQAef9wWKb3mmmDDkoDVrAmHHqoJpQVQvTqMGWOLIg0dah1htJKpiBSWEnWRomjOHGjSBID16+GFF+DMM6FevUCjknhw+OGWqHsfdCQJp1w5+OADuPxyKyU7/3yrJBIRKSgl6iJFzc6dsGABNG4MwPPPw6ZNNgIoQufO8NdfsHBh0JEkpOLFrSPMffdZOdlZZ8GOHUFHJSKJSom6SFGzYIGt0tK4MTt2WNlL9+7QunXQgUlcyFj4SOUvBeYc3HwzPPEEfPQR9O0LW7cGHZWIJCIl6iJFzezZdt24MW+9BX/+qdF0yaRZM6hYURNKI+Cqq+Cll2yBpBNPtE+uRETyQ4m6SFEzZw4A6Yc15n//swUpjzsu2JAkjhQvbk3BJ0wIOpKkMHCglcD88IN9crV+fdARiUgiUaIuUtTMmQN16vDZuArMmQP/93/2Ub3IXl27wvTpyiojpH9/668+ZQr06qWRdREJnxJ1kaJmzhxo3JjHHoODDrK+zyL/0K2bdX3RqHrE9OkD778PqalWBrNlS9ARiUgiUKIuUpR4D3PmML36MYwbB0OG7G2lLrJPx462INb48UFHklT69LEymAkT4OSTYdu2oCMSkXinRF2kKFmxAjZt4qklfShb1upnRf6lbFlo394KqyWizjwTXnsNxo6F005T60YRyZ0SdZGiZM4c1lCVN1Mbcd55ULVq0AFJ3OrWDSZNUl/BKDj3XFtk7IsvoF8/2L076IhEJF4pURcpSubM4SUuZvvO4lx5ZdDBSFzr2tUyyF9+CTqSpHTxxfDUU/Dxx3DFFVoIVkSyp0RdpAjZPXMuT7shHHOMp3nzoKORuHbEEdYOSOUvUTNkiC2M9MILcPfdQUcjIvFI08hEipBPfqzGUl+XJzWaLnmpVMma7GtCaVTde69NHbnzTqhdGwYNCjoiEYknGlEXKUKenNuDg8uvpnfvoCORhNCtm61QunNn0JEkLedsRL1XL7jsMhg1KuiIRCSeKFEXKSKmTdjC+J2dGNJtOsWLBx2NJISuXa2H4JQpQUeS1EqWtB7rbdvCWWfZeyMREVCiLlJkPPXfrZRjCwP7q3mzhKlrV7tW+UvUlS8Pn38OBx4IffvCkiVBRyQi8UCJukgR8Pff8NaXVTmPN6jS/tCgw5FEUbMmNG6sRD1GatSATz+F7dttQaTNm4OOSESCpkRdpAgYPhx27CrOFcWfh0MOCTocSSRdu8KPP8KePUFHUiQ0aQLvvgszZsB550F6etARiUiQlKiLJLn0dHj+eehSbRYtDt1mBbEi4erWDTZsgN9/DzqSIqNnT3jkEeuxftttQUcjIkFSoi6S5L77DubPh8Glh9twnUh+dOtm1yp/iamhQ21RpPvvh7ffDjoaEQmKEnWRJPfss1Ctmue0v561emOR/DjoILto4aOYcg6eftreJ110kRrviBRVStRFktiKFfDJJ3BR33WU2b1ZiboUTLduNqKude5jqlQp+OADm2R6+umwbl3QEYlIrClRF0liL79scwAHtZ9qNyhRl4I48khYtQrmzAk6kiKnRg3rsb5sGZx/viaXihQ1StRFktTu3bbiYffucOj6VLtRiboUxDHH2PV33wUbRxHVqRM8+ih89hk8+GDQ0YhILClRF0lSo0fbKNxllwGzZ0Pt2lCpUtBhSSJq0ADq1YNvvw06kiLriiugf3/rAjNmTNDRiEisKFEXSVLPPQcHHAAnnQTMnAnNmgUdkiSyY46BcePUTz0gztknZI0bW8K+bFnQEYlILChRF0lCCxfCl19ae7eSxdNh1ixo2jTosCSRHXuszWacNi3oSIqsChVg5EhbubRfPytvE5HkpkRdJAm98IKNwF1yCbBkCWzdqhF1KZyjj7Zr1akHqlEjW8BswgS4996goxGRaFOiLpJkdu2CV1+1kpc6dbCyF1CiLoVTu7Z9KqM69cCdfbZ1gLnnHrW3F0l2StRFksxnn1knvUsuCd2Qkair9EUK65hjLDPcuTPoSIq8YcOgfn045xz1VxdJZkrURZLMSy/ZJNKePUM3zJplo6FVqgQalySBY4+1Mqpffw06kiKvYkV45x1YuRIGDdJaVCLJSom6SBJZutQmkV54IZQoEbpRHV8kUo48EooVU516nGjf3urUP/jAFjcTkeSjRF0kiQwfbisXXnRR6IZ0dXyRCKpSBdq0UZ16HLn+evug46qrYO7coKMRkUhToi6SJNLTbVTtuONsfRpAHV8k8o45Bn75BbZsCToSwT7geP11KFMGLrhALRtFko0SdZEkMWYMLF5svdP3UscXibRjjrHWQj/+GHQkEnLAAfD00/b+6eGHg45GRCJJibpIknjpJahaFfr2zXSjOr5IpHXpAiVLqk49zvTrB6efDrffDjNmBB2NiESKEnWRJLB6NXz8sfVWLl060x3q+CKRVr48dOqkOvU44xw884z9qZ9/vjpoiiQLJeoiSeCNN6waYeDALHeo44tEw7HHwpQpauAdZ2rUsFVLp02D++4LOhoRiQQl6iIJznt48UUb5GzePNMd6vgi0XLssfbEU/lL3OnbF847zxL11NSgoxGRwlKiLpLgfvoJ5szJtBJpBnV8kWjp2BH228+a9kvceeIJ2H9/GDAAduwIOhoRKQwl6iIJ7tVXrWz4zDOz3KGOLxItJUtC9+6WqGtJzLhTpYqVwMyaBQ8+GHQ0IlIYStRFEtiWLfDee3DGGVChQpY71fFFoqlnT1i2bN/zTOLKiSdC//5WAjNrVtDRiEhBKVEXSWAjR8KmTbbQyb/MnKmOLxI9PXvatcpf4tYTT1iF0sUXw549QUcjIgWhRF0kgQ0fbquQdu2azZ2zZqnsRaKnTh2bvaxEPW7VqAGPPQY//wzPPht0NCJSEErURRLUokXWdGPAAFtG/B/U8UVioWdP+OEH2Lw56EgkB+eeCz16wE032fxyEUksStRFEtTrr9v1gAHZ3Ll4sTq+SPT17Gkr64wdG3QkkgPn4LnnbM7vZZdp7q9IolGiLpKA0tOt7OWYY+Dgg7PZIGP2mBJ1iaYuXazlkMpf4lq9ejapdPRoGDEi6GhEJD+imqg753o65+Y65+Y7527M5n7nnHsydP9051ybvPZ1zlV1zn3jnJsXuq6S6b6bQtvPdc71yOZ8o5xzv0fjsYrE0g8/wMKFcOGFOWygji8SC6VL27vFL77QUG2cGzIE2rWDa6+FDRuCjkZEwhW1RN05Vxx4GugFNAX6O+eyZg29gIahyyDg2TD2vRH41nvfEPg29D2h+/sBzYCewDOh42TEcyqgQkpJCq++ChUrwqmn5rCBOr5IrPTsae8a580LOhLJRfHiNqF01Sq47bagoxGRcEVzRL0DMN97/4f3ficwAuiTZZs+wOve/AJUds7VzmPfPsBroa9fA/pmun2E936H934hMD90HJxzFYBrgXuj8DhFYmrzZvjgAzjrLChXLoeNZs5U2YvERkabxi++CDYOyVO7dnD55fD00zBlStDRiEg4opmoHwgszfT9stBt4WyT2761vPcrAULXNcM43z3AI8DW3AJ2zg1yzqU651JXr16d26YigXn/fVvoKNve6WANk2fOhBYtYhmWFFUNGsBhh6lOPUHce6+1bbzsMvVWF0kE0UzUXTa3ZS1izGmbcPYN63zOuRTgUO/9R3nsj/f+Be99O+99uxo1auS1uUgghg+Hhg2hc+ccNpg/H7Zvh5YtYxmWFGU9e8K4cbBtW9CRSB4qV4ZHHoGJE+Gll4KORkTyEs1EfRlQN9P3dYAVYW6T276rQuUxhK7/yuNYhwNtnXOLgB+Bw5xz4wr0iEQCtmABjB9vo+kuu7emANOn27USdYmVXr3szeH33wcdiYTh7LPh6KPhxhvhr7/y3l5EghPNRH0S0NA5V985Vwqb6DkqyzajgPND3V86ARtC5Sy57TsKyOgcPQD4JNPt/ZxzpZ1z9bEJqhO998967w/w3tcDugBp3vujovGARaLttdcsQT///Fw2mj7dVkBSxxeJlSOPhDJl4PPPg45EwuAcPPOMldBdf33Q0YhIbqKWqHvvdwNDgK+A2cB73vuZzrnBzrnBoc1GA39gEz9fBC7Pbd/QPg8C3Z1z84Duoe8J3f8eMAv4ErjCe68KPEka6emWqHfvbqu352j6dGjUyBInkVgoWxaOPx4++URtGhNE48aWpL/+OkyYEHQ0IpIT5/Wimq127dr51NTUoMMQ2evbb+G44+Cdd6Bfv1w2rF8fOnbUyiYSW6+8AgMHWjuR1q2DjkbCsGWLJew1a1rNevHiee8jIpHnnJvsvW+X3X1amVQkQQwfDpUqQZ+sTU4z27gRFi1SfbrE3kknWU3Fxx8HHYmEqXx5ePhhe2/1yitBRyMi2VGiLpIANmyADz+E/v2tyiBHv4cW3lWiLrFWsyYccYSVv0jCOPNM6NYNbr4Z1q0LOhoRyUqJukgCeP9963yXY+/0DOr4IkHq0wd++80+1ZGE4Bw8+SSsXQt33BF0NCKSlRJ1kQTw6qvQpAl06JDHhtOnW31M3bp5bCgSBRl1WRpVTyitWsHgwdYJZsaMoKMRkcyUqIvEubQ0+OmnPHqnZ5g+3UbT89xQJAoaNrS2oErUE87dd9t7/KFD1bhHJJ4oUReJc8OHW1v0887LY0PvLVFv0SIWYYlkr29fW5Vr7dqgI5F8qFYN7r0Xxo6FDz4IOhoRyaBEXSSO7dljfY579oTatfPYePFi2LRJ9ekSrD597ImrxY8SzqBB9vLxf/9nC82KSPCUqIvEsTFjYPlyuPDCMDbWRFKJB+3awQEHqPwlARUvDo88YnOBn3oq6GhEBJSoi8S14cOhalXo3TuMjTNmgTVvHs2QRHJXrBicfDJ8+aWGZRPQccfBiSdaGczq1UFHIyJK1EXi1Lp18NFHcPbZULp0GDtMnw4NGkDFilGPTSRXffrYspfffht0JFIA//uf/fruuivoSEREibpInHr3XdixI4ze6RkyOr6IBO3oo+0No1YpTUhNmli9+nPPwZw5QUcjUrQpUReJU6++ag1c2rQJY+Nt26yPoxJ1iQelS1v9xMcfw65dQUcjBXDnnVCunE0sFZHgKFEXiUOzZsHEiWH2Ts/YIT1dibrEj3794O+/4bvvgo5ECqBmTbjlFvj0U/0KRYKkRF0kDg0fDiVKwLnnhrmDOr5IvOnZ01bQGTEi6EikgIYOhYMPhv/8xzpuikjsKVEXiTO7d8Mbb8AJJ9ioVlimT7fPqRs0iGpsImErXRpOOQVGjrTJFpJwypSBBx6AadPsNUlEYk+Jukic+eor+PPPMHunZ5g+3doyFi8etbhE8q1fP9i40Vo1SkLq1w86dLAymC1bgo5GpOhRoi4SZ4YPh+rVbUQ9LN6r44vEp2OOsSezyl8SlnPw6KOwYgU8/HDQ0YgUPUrUReLImjUwapTVppcqFeZOK1fapD0l6hJvSpaE00+3J7WGYxPWEUfYr/GhhyxhF5HYUaIuEkfeeQd27sxH73SAKVPsOqw+jiIx1q8fbN0Kn30WdCRSCA8+aJ02b7st6EhEihYl6iJxZPhwaN0aWrXKx05Tptjn0/naSSRGunSBAw5Q+UuCO+QQuPJKW99h2rSgoxEpOpSoi8SJGTNg8uR8jqaDJeqNGkGFCtEIS6RwiheHM8+E0aNhw4ago5FCuPVWqFIFrrvOpsaISPQpUReJE6+8YnXp55yTzx2nTFHZi8S3fv2spuvjj4OORAqhShW44w749lt73yUi0adEXSQO7NwJb74JJ58M1arlY8fVq2HpUiXqEt86dIB69VT+kgQuuwwaNoTrr7c1H0QkupSoi8SBzz6zxi0XXZTPHadOtWsl6hLPnIP+/eGbb2yRAElYJUvaIkizZ8PrrwcdjUjyU6IuEgdeeQUOPBCOPz6fO2Z0fGndOuIxiUTUgAG2Dr2yu4R36qn2Icntt8O2bUFHI5LclKiLBGzFCvjiCzj//AIsLDplCjRoAJUrRyM0kchp1Mgacr/8smYiJjjnrKf68uXw1FNBRyOS3JSoiwTsjTcgPR0uvLAAO2siqSSSgQMhLQ1++inoSKSQjjzSVk9+4AFYty7oaESSlxJ1kQB5b2UvXbrYBK18Wb8eFixQoi6J44wzrI3oyy8HHYlEwAMPWMfNBx8MOhKR5KVEXSRAP/9sA4z5nkQK+yaStm0b0ZhEoqZCBTjrLHjvPdi0KehopJBatoRzz4UnnrDmUyISeUrURQL0yitQvrwNNOabJpJKIho4ELZssWRdEt7dd9sng3feGXQkIslJibpIQLZsgXfftUUbC7So6JQpULcu1KgR8dhEoqZTJ2jcWOUvSaJePbjiChg+HGbNCjoakeSjRF0kIB98AJs3F7DsBTSRVBKTczaq/vPP1oxbEt7NN9tgw803Bx2JSPJRoi4SkFdesQmkRxxRgJ03b4a5c5WoS2I67zwoUcL+CCThVa8O//d/8MknMGFC0NGIJBcl6iIBmD8fxo+3lozOFeAAv/1mhaFK1CUR1aoFJ51kix/t2hV0NBIBV18N++8PN9ygNvkikaREXSQAw4dDsWK2yFGBZEwkVaIuiWrgQPjrLxg5MuhIJALKl7cJpRMmwKefBh2NSPJQoi4SY3v2WKLeowcceGABDzJlio1K1q4dydBEYqdXL1tV94kngo5EIuSii+Cww+Cmm+x1TkQKT4m6SIyNGWNLbxd4Einsm0haoLoZkThQvDhcdZVNKv3116CjkQgoWRLuv9+6v7z+etDRiCQHJeoiMfbKK1CtGvTuXcADbN8OM2eq7EUS30UXwX77aVQ9iZx6KnToALffDtu2BR2NSOJToi4SQ2vXwscfwznnQOnSBTzIjBn2ubISdUl0FStarfr778OyZUFHIxHgHPz3v/brHDYs6GhEEp8SdZEYevtt2LmzkGUvEyfadbt2EYlJJFBXXgnp6fDMM0FHIhFy1FE2BeGBB2DduqCjEUlsStRFYuiVV2wgvFWrQhzkl19sEmnduhGLSyQw9etDnz7w/POwdWvQ0UiEPPAArF9vo+siUnBK1EViZNo0mDrVeqcXyq+/QseOmkgqyePqq60u7M03g45EIqRVKzj3XJt+oKomkYJToi4SI6++CqVKwdlnF+Iga9bAvHmWqIski65doXVrePxxrZaTRO6+26qa7rwz6EhEEpcSdZEY2LHDBgv79oWqVQtxoIz69E6dIhGWSHxwzkbVZ8+Gr74KOhqJkHr14PLLbZBi9uygoxFJTErURWLgk0/sk/2IlL0UK6aJpJJ8zjrLVgC7916NqieRW26xVUtvvjnoSEQSkxJ1kRh48UU4+GDo3r2QB/rlF2jeHCpUiEhcInGjdGnL5iZMsFXBJClUrw433GBtaX/6KehoRBKPEnWRKFuwwPKOgQNtMcYCS0+30hfVp0uyGjjQuhndfrtG1ZPI1VfD/vtbwq5fq0j+KFEXibKXX7ZqlUL1TgebRLpunRJ1SV6lS1utxC+/wJdfBh2NREj58nDHHfDjj/DZZ0FHI5JYlKiLRNGuXdY7/cQTrfy2UH791a41kVSS2YUXWp3YHXdo+DWJDBwIhx0GN95oCyuLSHiUqItE0aefwqpVMGhQBA7266+25HrjxhE4mEicKlUKbrsNJk2Czz8POhqJkJIl4f77YdYseO21oKMRSRzOa8QiW+3atfOpqalBhyEJrlcv+P13WLgQSpQo5MHatoUqVTTRTpLfrl32hrRyZUhN1eJeScJ7OPxwWwApLQ3KlQs6IpH44Jyb7L3Ptp2bRtRFomTRImsJfdFFEUjSt26F6dNVny5FQ8mSNqo+ZQqMGhV0NBIhzsFDD8Hy5fDUU0FHI5IYlKiLRMnLL9v1wIERONiUKbB7txJ1KTrOPRcaNoSbboKdO4OORiKkWzc46SR44AFbaFlEcqdEXSQKdu+2SaS9esFBB0XggBkTSZWoS1FRogQ88ogtafnkk0FHIxH0wAOwaZPVrItI7pSoi0TB6NGwYgVcckmEDvjrr7Yed61aETqgSALo3duGX++6y/6gJCk0bw4DBsCwYbB4cdDRiMQ3JeoiUfDii1C7trVljIhfflFbRimannjCJpded13QkUgE3X23rS9x221BRyIS35Soi0TYokXWVe6ii2xOXKGtWAFLl6rsRYqmBg1sSct33oGxY4OORiKkTh0YOhTefBN++y3oaETilxJ1kQh74QXrbhCR3umg+nSRG2+00q8hQ2x0XZLCDTdYB84bbww6EpH4pURdJIJ27ICXXrLS2ohMIgX4+Wcbmm/dOkIHFEkwZctaCcysWerrl0SqVIFbboEvv4Tvvgs6GpH4pERdJII+/BBWr4bLL4/gQX/4ATp0gDJlInhQkQTTu7dN+rj9dlstR5LCFVfYoMb//R+kpwcdjUj8UaIuEkHPPAOHHgrHHRehA27ZYiszdusWoQOKJCjn4LnnoHRp6NfPPr6ShFemDNxzD0yeDO+/H3Q0IvFHibpIhPz2G0yYAJddZt0MIuLnn60p+5FHRuiAIgmsTh1boGDqVFsISZLCOedAy5Zw881a20okKyXqIhHy7LM2OnTBBRE86PjxlvV37hzBg4oksD59rF7iscdswQJJeMWLw3//C3/8Ac8/H3Q0IvHFee+DjiEutWvXzqempgYdhiSIDRvgwAPhzDNtwC9ijjrKyl8mTYrgQUUS3PbtNm/jzz/to6zatYOOSArJeysZnD4dFiyA/fYLOiKR2HHOTfbet8vuPo2oi0TAG29YPh3RSaQ7dthCR6pPF/mnMmXg3Xdh82Y47zzYsyfoiKSQnLNR9b//hv/9L+hoROKHEnWRQvLeJpG2a2eXiJk0yZJ1Jeoi/9akibVq/PZbK4XRp8MJr107myf8yCOwfHnQ0YjEByXqIoX0/fcwe3aER9MzDgzQpUuEDyySJAYOtFVznn/e2jZKwrv/fvuA5NZbg45EJD4oURcppKeesoU7zjorwgcePx5atIBq1SJ8YJEk8sADlrDfey88/njQ0Ugh1a8PQ4fCa6/BtGlBRyMSvBJBByCSyBYtgo8/tsU6ypWL4IF377ZejxFtISOShDL6q69bB9dcY29szzsv6KgKLz3d6j/WrbPZ6hs32qVUKahYcd/lwANtpCCJ3HyzTcq/7jr45hv7FYsUVUrURQph2DD7JxLxspepU212qurTRfJWogS89ZatXHrhhfZHee65QUcVvo0b7Y35xIlWRzdnDsyda91twlGzJjRubJfmzW3dhebNI7igQ2xVrgx33AFXXWUdOE88MeiIRIKj9ow5UHtGycvmzbb+Ss+eMGJEhA/+yCM2nLRihVrPiYRr0ybo3dvmd1x3HTz4oDXpjjc7dtgk2DFjLNZp02wE3Tmr/Wjc2CbLNmwI1atbr8JKlWwEfdcue5ybNlmCv3SpJfazZ9tl7Vo7R7VqlrAfcwycfDLUrRvoQ86vnTvtvUaJEtaysYSGFSWJ5daeUU99kQJ67TX7RPrqq6Nw8O+/t3/SStJFwlexotVKXH01PPwwzJgB77wTH6UhmzfDl1/CyJHw2WeWaJcpA506wW232adnnToVvoZu8WIYN84uY8fa+YYMgY4d4bTT7NKgQSQeUVSVKgUPPQSnnAIvvQSDBwcdkUgwNKKeA42oS27S023Qq0oV+PXXKBy8WjX7h/rSSxE+uEgR8eKL1rbx4IPhgw+gVavYx7BunSXlI0dakr59u42Q9+kDp55qo91lykQ3hrQ0O/8HH8DkyXbb4YfbBNyzzoIKFaJ7/kLw3tZ8mz0b5s/XIkiSvLTgkUiEffklzJsXpdH033+H9etVny5SGJdcYiPKmzZBmzb2/cqV0T/vn39au8gePax2/PzzbU2EjHhWrrQ34CecEP0kHeCww+DGGyE1Ff74w1YVWrcOLr7YPrG7+GL4+ee47EPvnFUBrl5tbRtFiiKNqOdAI+qSmx49LJ9etAhKlozwwYcNgyuvtIMffHCEDy5SxKxZY60bn37a6imuv97q18uXj8zxvbd37Z9/biPXEybYbYceap+KnXqqreQTTxM7vbfk/OWXbYXXLVusJn7gQOuYU7Nm0BH+w4ABNg9o1iw45JCgoxGJvMBG1J1zPZ1zc51z851zN2Zzv3POPRm6f7pzrk1e+zrnqjrnvnHOzQtdV8l0302h7ec653qEbivnnPvcOTfHOTfTOfdgNB+zJL9Zs+Drr+1T9Ygn6WD16QcdpCRdJBKqVYPHHrP6iRNOgDvvtJaG554LH30EW7fm/5h//QUffgiDBtnkz0aN4NprbfT+zjutNj4tzSazdugQX0k62FB1586WqGeM8FeqZG9gDjzQ3mB8+aWtPBQHHnjAXmuvvz7oSERiL2oj6s654kAa0B1YBkwC+nvvZ2Xa5gTgSuAEoCPwhPe+Y277OuceAtZ67x8MJfBVvPc3OOeaAu8AHYADgDHAYUBpoKP3fqxzrhTwLXC/9/6L3OLXiLrkZPBgm0i6dKmVm0bUnj02mtW7NwwfHuGDiwg//2z16598Yh1SypaFY4+1EpGDDrLLgQfaWgYbN+7rrpKWBr/9Zpc//7RjVaxo+/boAccfnxCTNHM1a5Yl76+/Dn//bZ1iLrrILgcdFGho991nq5V+9x0cfXSgoYhEXG4j6tFM1A8H7vTeZ4xs3wTgvX8g0zbPA+O89++Evp8LHAXUy2nfjG289yudc7VD+zfKenzn3FehY/ycJa4ngN+99y/mFr8SdcnO33/b/6uzz47SPM+JE607w9tvQ//+UTiBiADW5nD8eCtX+fZbWLIEtm3LefuSJaFZM5uU2qqVjZR36BClj9UCtnOnvZF56SXrogPW6vGcc+D0063ReYxt22bVOZUr25zYAnXd3LPHJvSWLq1+jxJXgmrPeCCwNNP3y7BR87y2OTCPfWt571cChJL1jGK6A4FfsjnWXs65ykBv4InsAnbODQIGARwU8OiBxKenn7Z/GP/5T5RO8NVX9rF09+5ROoGIAJZgH3usXcDqttessYR9+XKrZ99vv30rgB5wQHIm5dkpVQrOOMMuixbZR4hvvWUTYq+4Ak46yWrve/a00qIYKFsW/vc/OPNMG/QfNCjLBps3Wz/5BQts0uzChXZZtcom569fb5+OZChRwibzli9v5UuHHbbv0rmzfaoiEgeimahnt+hv1uH7nLYJZ998nc85VwIrjXnSe/9Hdgfw3r8AvAA2op7H+aSI2bIFnnrK1g5p0iRKJ/nqK+tQEfGaGhHJlXP2d1e9uv0NiqlXz5YJvf126xzz1ls2AXXkSKu9P+IIK9U77jho0SKqI9Wnnw5dO+/m1hs9Z+1+j0qLfoOZM61kZ9Gif25cvbol4A0aWB/dypXtUrasLTi1fbtdNm60xP6776zkJ0OLFvZGpFcve4ylSkXtcYnkJpqJ+jIg81JodYAVYW5TKpd9VznnamcqffkrzPO9AMzz3j+e/4ciAq+8YgNu//d/UTrBhg3wyy9www1ROoGISAE5B+3b2+XRR63l5Gefwaef7ntRLF/e7j/8cGjb1jrfHHJI/nu1795tI+ELFtgk4FmzYPZs3KxZPL68Ju1I5Z4rVvJwqSdsQYtOnaxjTdOmtlBc/foF6w+/ZYuNyn/3nU2mffxxG8avWRMuvdQmKB1wQP6PK1II0axRL4FNCD0WWI5NCD3bez8z0zYnAkPYN5n0Se99h9z2dc79D1iTaTJpVe/9/znnmgFvs28y6bdAQ+/9HufcvUAT4AzvfXo48atGXTLbvdv+59SpAz/+GKWTfPSRfZz8/ffqoS4iiWPZMvjhB5uo+/PPMG2avWhmqFnTulhVqmQJdIUKtgLrrl02qr1tm11Wr4YVKyxJz5yblC9vH2M2bQpNmnDxt/15bdxBzJi6h8bNozjeuHkzjBljtTaff26F8aedZgtodOoUvfNKkRPIZNLQiU8AHgeKA6947+9zzg0G8N4/55xzwDCgJ7AVuNB7n5rTvqHbqwHvAQcBS7Dke23ovluAi4DdwNXe+y+cc3Wwevc5wI5QaMO897lOBVSiLpm9/bbNo/rkEyt9iYrBg+1j5bVri04trIgkn61b99WLZ1yWLLEa8c2b7bJli73OlS2771K9uo1YH3CA1YgffLAl6HXr2oh+yF9/WSl5+/bWKtdlV/gaaQsWwLPPWtK+fr3V7z/0kJUGiRRSYIl6IlOiLhm8h9atrRHC779HqSWy91ZL2bKlvRsQEZEcPfUUXHUVfPCBDXLHzJYttlzqf/9rXWT+8x+46aaCldqIhAS24JFIMvj6a2udfP31UVy3ZP58mwzVo0eUTiAikjwuu8zGNa65pmBrVhVY+fI2sXbuXJvdev/9Nrw/enQMg5CiRIm6SB7++1/7FPacc6J4kq++smsl6iIieSpRAoYNs4Xn7r8/gADq1IE337Sa/OrV4cQTYehQq7kXiSAl6iK5mDQJxo61UZuoduf66ivrjnDIIVE8iYhI8ujaFc491xqzzJ8fUBCdOtlCdVddBU8+aYtgzZyZ934iYVKiLpKLBx6wRgWXXBLFk+zcae8Gjj8+iicREUk+Dz1kgyhXXx1gEGXKwBNPWGeYVaugXbt/9mQXKQQl6iI5mD7dOiYOHWoLFEbNTz/ZBCWVvYiI5Evt2nDnnZYjjxoVcDAnnGD/ODp3hgED4JZbID2sjtAiOVKiLpKDu++2BD3qIzVffWUFl0cfHeUTiYgkn6uugmbN4MorbcwjULVq2WJJl1xixfNnnWU94kUKSIm6SDZmzIAPP7TR9CpVonyyr76yEZioDtuLiCSnkiXhueesVfuddwYdDRbQ88/Dww/bP5KjjoI//ww6KklQStRFsnH33VCxYgxG05cvh6lTVfYiIlIIXbrAxRfDY49ZO93AOWc91keOtAU4jjgCFi8OOipJQErURbL4/XdbRGPoUKhaNcon+/hjuz7llCifSEQkuf33v/aafemlthZRXOjbF777zlac7tYtwPY0kqiUqItkcc89Npp+zTUxONlHH0HjxrZMtoiIFFjVqvDoo/Drr/DCC0FHk0nHjpasb9liyfqcOUFHJAlEibpIJjNnwvvv2+SkqI+mr1kD48bBqadG+UQiIkXDOefAscfCjTfCypVBR5NJ69b2ep+eDkceaROhRMKgRF0kk3vusRWiYzKa/umn9vmsyl5ERCLCOXj2WdixI+De6tlp3hy+/94mmx59NMyaFXREkgCUqIuEzJwJ771no+nVqsXghB99BHXrQtu2MTiZiEjR0LChtTB/7z0bD4krjRrZyHrJktC9OyxcGHREEueUqIuE3HSTdUi89toYnGzzZmvLeMopNgQkIiIRc8MN0KIFDB4M69cHHU0Whx4KX39t/dWPOw5WrAg6IoljStRFgB9+sJGXG2+M0Wj6l1/aZ7OqTxcRibhSpeDVV2HVKuuSGHdatLD/A3/9ZSPrf/8ddEQSp5SoS5HnvY2+HHCAlb3ExMiRUKOGNf8VEZGIa9sWrr8eXnnFBrDjTocONkK0YAH06gWbNgUdkcQhJepS5H3yCfz8M9x1F5QrF4MT7tgBn30GJ58MxYvH4IQiIkXTHXdYWfgll8RpHnzUUbZwx9SpcMYZsGtX0BFJnFGiLkXa7t1Wm964MVxwQYxO+t139h9DZS8iIlFVpoyNqC9daqWNcemkk+C552ze0uDB9jGvSIgSdSnSXn3V1p548EEoUSJGJx050lZUOvbYGJ1QRKTo6tzZWjU+84w1XIlLF18Mt91m7yruuSfoaCSOOK93btlq166dT01NDToMiaKtW23yff368OOPMWq+smcP1K5tSfo778TghCIisnUrtGxpL8G//WYdvuKO93DhhfDaa5awX3hh0BFJjDjnJnvv22V3n0bUpch64glbue6//41hh8Tx42H1ai1yJCISQ+XKweuvw5IlMHRo0NHkwDl44QVr2ThoEHzzTdARSRxQoi5F0sqVcP/9Np8zpo1XXnvNhnJ6947hSUVEpHNnm5M0fLhVIMalUqXgww+hSRM4/XStXipK1KVouuEG2LkTHnkkhifdvNlm9591FpQtG8MTi4gIWBeYtm1twHrlyqCjycF++1lnsLJl4cQTrde6FFlK1KXImTAB3ngDrrvOatRj5sMPYcsWGDAghicVEZEMJUvCm29azfrAgXHcYOWgg2DUKPjzT+jbF7ZvDzoiCYgSdSlS9uyBIUOgTh24+eYYn3z4cHtn0LlzjE8sIiIZGjeG//0PvvgCnn026Ghy0aGDjSr9/DNcdFEcv6uQaFKiLkXKiy/CtGnw8MNQvnwMT7xokfUFGzAghjNXRUQkO5dfDj162Cers2cHHU0uTj8dHnjAuoTddVfQ0UgAlKhLkbFmDdxyiy0Ed+aZMT75669bgn7++TE+sYiIZOWcraNRoYL9P9i6NeiIcnHDDdaq8a674K23go5GYkyJuhQZt94KGzbAU0/FeFDbe+v2cvTRVncoIiKBq13b6tVnzoSrrgo6mlw4ZyuXHnmklcBMmBB0RBJDStSlSJgyBZ5/Hq64Apo3j/HJf/wR/vgDLrggxicWEZHcHH+8tWx8+WVL2uNWqVLWU/Lgg21y6YIFQUckMaJEXZLerl22OnPNmgGV+L32mn2+euqpAZxcRERyc9dd0LUrDB4Mc+YEHU0uqlaFzz+H9HQ46SRYvz7oiCQGlKhL0nv4YZg6FZ55BipXjvHJt26F996DM86I8exVEREJR4kSNlezbFmrV9+2LeiIctGwoY2sL1hgE0137Qo6IokyJeqS1GbPhjvvtNezQAa0R46ETZtU9iIiEscOPNA6Ic6YEef16mC16i++CN9+a/WcatuY1JSoS9Las8cWtKhQAYYNCyAA7+GJJ6BRI+jSJYAAREQkXD172voaL71kc5ri2oABFuyLL8KjjwYdjURRiaADEImWYcNsnYg33oBatQIIYMIESE21FTWK6T2xiEi8u/tuK5UcMgSaNYvzMZZ77oG0NLj+ejjkEJtkKknHeX1kkq127dr51NTUoMOQAvrjD2jRwnqmf/ZZQGsMnXaaLXK0dCmUKxdAACIikl/r19uioBs2wOTJtpJ13Nq2zf7R/f47/PADtGkTdERSAM65yd77dtndp2E+STrp6dblpXhxaz0bSJK+cCF8/DFceqmSdBGRBFK5MnzyieXAp5wS55NLy5a1YKtXh969YfnyoCOSCFOiLknnkUdg7Fi7rls3oCCefNLKXa64IqAARESkoJo0sb7qqak23hLXxQf7728fHW/aZMn65s1BRyQRpERdksrEiTa/5rTTbFQ9EBs22Gykfv2slYCIiCSck0+2mvU33oD//S/oaPLQogW8+y789hucc451U5CkoERdksbGjdC/PxxwgE2ED6TkBWyJu82b4ZprAgpAREQi4ZZbbMzlhhtgxIigo8lDr17WaWzUKPi//ws6GokQdX2RpOA9XHYZLFoE48dDlSoBBbJ7t5W9dOumST0iIgmuWDEYPhxWrLCOiLVrWxvzuDVkiHWCefRROOwwq9uRhKYRdUkKr78Ob79tixsdcUSAgXz0ESxerNF0EZEkUbq09QbI6IA4a1bQEeXh0UfhhBNsjtQ33wQdjRSS2jPmQO0ZE0damg1et28PY8ZYt5dApKdbEBs2wNy5AQYiIiKRtngxdOoEpUrZGh0HHBB0RLnYtMlGrZYsgZ9+gqZNg45IcqH2jJK0Nm609lllytgM/UBz4w8+gClT4I47lKSLiCSZgw+Gzz+HNWtswHr9+qAjykXFitYJpkwZOOkkWL066IikgJSoS8LaswfOPdcGr997L+AGK7t2wa23QvPmcPbZAQYiIiLR0qaNjcnMmmVzNzdtCjqiXBx0kE0sXbnSana2bw86IikAJeqSsG67DT791Ca5H3NMwMG8+irMmwcPPKDRdBGRJNazp3VCnDTJBqu3bg06olx06GD9JX/6CS66KM4bwkt2lKhLQnrnHcuJBw2Cyy8POJitW/fNYj3xxICDERGRaDvlFMt/f/ghAQarTz8d7r/f/nHeemvQ0Ug+qT2jJJxJk2xgoFs3eOqpAPulZ3jqKfto8b334iAYERGJhf79YccOuPBCOOMM+PBDm2gal268ERYutIS9cmW4/vqgI5IwKVGXhLJ0qY1e1KpldYKBvyiuWwcPPmgj6V26BByMiIjE0gUX2Gj6ZZdZsv7uuzZ/M+44B88+ax0Y/u//YL/91GM9QShRl4SxejV0726Lfv7wA9SoEXREwEMPWTvG++8POhIREQnA4MHWnfeKK2zM5uOPrelK3Cle3Op1Nm+2dxYVK6r5QQJQjbokhA0bbALP4sXWcaply6Ajwhq4P/YYnHNOnAQkIiJBuPxyW3jv++9tQGnt2qAjykHJkvD++7a86vnnwyefBB2R5EGJusS9bdvg5JNh+nSrAezaNeiIsOGTiy+GsmVtVF1ERIq0886zksypUy0PXrky6IhyULastW1s29bqdZSsxzUl6hLXdu2y15EffrBP7E44IeiIQp5/3oJ69FGoXTvoaEREJA707QujR9u8za5drWtvXKpYEb76Clq3tq4wI0cGHZHkQIm6xK1du2xBo88/h2eegX79go4oZMkSm4xz3HE2k0hERCTk2GNhzBhbubRjRxg7NuiIclC5Mnz9NbRvD2eeaZ3LJO4oUZe4tG2b9al97z343/9ssk5c8H7fzKEXXlA7RhER+ZdOnWDiRNh/fzj+eHjxxaAjykGlSjayfvjh1m/y7beDjkiyUKIucWfjRps4Onq0dZO67rqgI8rk7bfhiy+sy0v9+kFHIyIicapBA/j5ZxthHzQI/vMf2LMn6KiyUbGi/V/r1s0+xn766aAjkkyUqEtc+ftvOOYYW+34rbfiaCQdYMUKGDrUhkqGDAk6GhERiXOVKlmnsiuvtClNvXvb/7m4U6GC1Zn27m3/3265xT5BlsApUZe4sWSJvaGfOdP60PbvH3REmezYAaedZitbvPKK9aMVERHJQ4kS8OST9gnxt9/a/M0ffww6qmyUK2et1QYNsk+NL7rIJotJoJSoS1wYPx7atYPly61c7sQTg44oE+9thOGXX2D4cGjSJOiIREQkwQwebKUwpUvDUUfZotbp6UFHlUWJEvDcc3DXXfb/7uSTYdOmoKMq0pSoS6C8t3K4Y4+FqlVt8k23bkFHlcXzz8NLL8HNN1sbKxERkQJo0wamTLEPaG+6yQalVq0KOqosnIPbb7cZsN98Y+WecdtnMvkpUZfA7NgBl1xig9U9e8Kvv0KjRkFHlcWPP8JVV0GvXnD33UFHIyIiCW6//WDECCuFGTsWmjWzPgVxVxJ+8cXWvnHVKmvh+MUXQUdUJClRl0DMn28rt738ss1Z+eQTm3QTV5YutRH0gw+2V1HVpYuISAQ4Z6UwU6dCw4Zwzjm2WFLcrWZ6zDGQmgr16tnw//33x+E7iuSmRF1iyntrP56SAnPn2nLL994LxeLtmbh0KRx9NGzdajNbK1cOOiIREUkyTZrYB7ePPGKD102bwquvxlnter161ortrLNsZO3kk+Gvv4KOqsiIt/RIktiff1rnp0svtbUVZsywOr24s3SpzfRZvdpeOZs1CzoiERFJUsWLw7XXwvTp0KKFNVs54gibsxU3ypWzT5Yff9zq1lu2VClMjChRl6jz3nqiN29uramefNI6u9SpE3Rk2ViyxJL0v/+2JL1Tp6AjEhGRIqBhQxg3Dl57DRYtgo4dYcAAW8IjLjhna4lMmgQ1asAJJ9gcrm3bgo4sqSlRl6iaNm3fYmcNGths9yuvjMNSF4DFiy1JX7PGRgw6dgw6IhERKUKKFYPzz4e0NOsKM2IEHHYY3HEHrF8fdHQhLVpYsj50KDz1lNWyfvdd0FElrXhMlyQJrFkDl18ObdvCnDnW5emXX+K4Bfm4cdChA6xda0l6hw5BRyQiIkVUxYo2b3P2bOuKdvfdVip+111xkrCXKbOvDGbPHuuxfO65cdhrMvEpUZeI2rAB7rvPPsJ74QVrvZiWZl2e4nIU3XubxXPccVClik2Yad8+6KhERERo0MCaLkydag1Y7rwT6te3hH316qCjw/53zpgBt90G771nPZafeUYrmkZQPKZOkoDWr9/3jv/WW6FzZ3theeIJy3/j0qZNcOaZcN111hdr4kSbci8iIhJHUlJg5Ej7v3r00Zaw160LAwfCb78FHFzZspYAzJhhH6NfcYX9Lx0xIs7a1yQmJepSKEuW2IKd9epZDV23btZy9bPPrIwtbn31lS0RN3Ik/O9/8P77tgqFiIhInMpI2GfOhAsvtFw4JcWmV737LmzfHmBwjRrBmDEwapQl7/37W+I+erR6rxeCEnXJt/R0+PJLa6Vavz7897/2kdyUKbZwUdu2QUeYi4xFjHr2tBns331nI+rOBR2ZiIhIWJo2tZVNly2zsaZFi6BfP9h/f2uBPGFCQLmxc9aHeepUePNN2LjRFkpKSYHhw21JcskX5/UuJ1vt2rXzqampQYcRV2bNsnfsb74Jf/wBNWta7fmgQbZ4Z1zbvBmGDYN77rFXr1tvhf/8B0qXDjoyERGRQtmzB8aOtdaOI0faWn2HHAJnnGGVne3bBzRPbOdOSxoeewx+/x1q1bLSmIsvhtq1AwgoPjnnJnvv22V7nxL17ClRt3w2Lc2qQt591/7GnLOP2AYNglNPhVKlgo4yD6tWWfuoZ56BdeugTx+bqV6vXtCRiYiIRNymTZasv/mmNTTbvdty4pNPtsHuI4+EChViHJT3tpDKo4/aQknFisHxx1svyj59bEGlIkyJegEU1UR940arBvnySyvjXrTIbj/iCFs9+PTTE+BNsPc2MfTll+H11+0d/SmnwPXXawEjEREpMtatsxLxjz+2/HjLFihRwv4VHnusXTp0iPGHy2lp9r/5jTdsolvFipas9+4NPXpApUoxDCY+KFEvgKKSqC9fbh0JMy5Tpti77woVrO68Z0846SSbXR7XvLfg333XWkQtXmx9XgcMsBKXhg2DjlBERCQw27bBjz/awPa338Lkyfavs1QpKyHv1MkuHTrY/LOol8qkp8P48Za0jxplC7CUKAFdu0KvXtadok0bKFkyyoEET4l6ASRbor5rF8yfz/+3d/cxclX3Gce/D3bt9Rsv6wVkbBKcsI5FYsxLtODgIFoHCP7HpEoqV1WgDgpyGgpphRKiqClqJeREbVWiqKE0ccFVC4SQF5cGkqYJhqjg5aV+5cUv2IqNDYuxi23MOtn1r3+cM9npMDNrm5mdO+vnIx3dO+ee+zL3+K5/c+6557Ju3VBasyY9iAIppu3pSS3nV18N8+YVvFtLROo0v2pVutBXrYJXX00X+VVXpeb/RYtOyF/mZmZmw9m3L/3X+eSTsHp1etnooUNp2eTJaeS2OXPg/PPTywq7u2H69CYF8IOD6a2IDz+c0oYNKX/ChPTrYf789Gti7twR+hUxshyoH4d2C9QHBlKc+sorKfjesQO2bk3B+ebNqQvL4GAqO3YszJ6dLr6enjTm+dy5BQ7M+/vTX5ONG1NwXpru35+WT5+eOt0tWJCemunsbOnhmpmZtZuBgfTfa29vGhJ9/fo0Rvu+fUNlOjrg3HNTeu97093297wnTc86C7q6GtTdfPfuNHTNE0+k2wBr1gyNyT5pUvoF0d2d3ghVSjNmwOmnp+VtpmWBuqSPA3cCY4BvR8SyiuXKyxcCh4A/jojn6q0rqRN4ADgH2A78QUTsy8u+DNwADAI3R8RPcv7FwD3ABODHwC0xzBdvRaD+6qvQ15f6kL31VhqopNr8/v2wZ89Qev31tF7lewVOPjn9Oy5dVLNmpYB89uw2G+xkz5508UGafvCDKV18cQrQZ8708IpmZmYNFgG7dsGLL6ZGv82bUwPgli2pe/nBg+9cp6MjBexdXTB16v+fnnZaaiSvljo6hqYnnZSSlNK4gUOc3rcxdQdYuzb9iti6NbVMVoZzEyemYek6O1PQPnFimnZ0pI2Vyh85km4hHDiQ0sGD6Xm2O+5o/omt0JJAXdIYYBNwJbATeBr4w4h4vqzMQuBPSYH6JcCdEXFJvXUlfR3YGxHLJN0GnBYRX5J0HnAf0AOcBfwMmBURg5J6gVuAp0iB+jci4pF6x9+KQH3JkjTMaD3jxqXnLrq6UsxauhimTUsNyzNmDE2nTh1F8esvf5leplAK2M3MzKxlIuDNN1PAvmNHagR/443Utlaals/v23f8Y7vPmZNi9Hc4fDgdwMsvpwPo6xtKe/emQLzUyln+NqhScDRpUgqqpkxJ/X2uvBI++9njO8h3oV6gPraJ++0BtkTEy/kg7gcWAc+XlVkErMit209JOlXSNFJrea11FwFX5PXvBR4DvpTz74+Iw8A2SVuAHknbgZMj4sm8rRXAtUDdQL0Vli5N7wWYPDn92ylNy+fHNrPGimz+/FYfgZmZmWUSnHpqSuefP3z5wcEU2L/9dkr9/UPz5Xn9/amx+8iRFNhHpJb4qsaPT10HRvGAEc0M+6YDO8o+7yS1mg9XZvow654ZEbsBImK3pDPKtvVUlW39Js9X5r+DpBuBG/PHg5JeqvXlrCG6gD2tPghzPRSE66H1XAfF4HooBtfDyKn52shmBurVOl1U3vSoVeZo1j3a/R31tiLibuDuYfZjDSLpmVq3emzkuB6KwfXQeq6DYnA9FIProRiaOb7NTqB89O0ZwK6jLFNv3ddy9xjytO8otjVjmOMwMzMzMyuUZgbqTwPdkmZKGgcsBlZWlFkJXKfkUuDN3K2l3rorgevz/PXAj8ryF0saL2km0A305u0dkHRpHmXmurJ1zMzMzMwKqWldXyJiQNJNwE9IQywuj4iNkpbm5XeRRmBZCGwhDc+4pN66edPLgO9KugH4FfCpvM5GSd8lPXA6AHw+IvLI4XyOoeEZH6GAD5KeoNzNqBhcD8Xgemg910ExuB6KwfVQAH7hkZmZmZlZAY2ud7CamZmZmY0SDtTNzMzMzArIgbq1hKSPS3pJ0pb8hllrIEnbJa2XtEbSMzmvU9J/Stqcp6eVlf9yrouXJF1dln9x3s4WSd/ID2RbDZKWS+qTtKEsr2HnPT8s/0DOXy3pnBH9gm2iRj3cLumVfE2syW/GLi1zPTSYpLMl/ULSC5I2Srol5/t6GEF16sHXQ7uICCenEU2kB4S3Au8DxgFrgfNafVyjKQHbga6KvK8Dt+X524Cv5fnzch2MB2bmuhmTl/UC80jvI3gEuKbV363ICbgcuAjY0IzzDvwJcFeeXww80OrvXMRUox5uB26tUtb10Jw6mAZclOenAJvyufb1UIx68PXQJskt6tYKPcCWiHg5In4N3A8savExnQgWAffm+XuBa8vy74+IwxGxjTQKU4/SewpOjognI/0FXlG2jlUREY8DeyuyG3ney7f1PWCB73K8U416qMX10AQRsTsinsvzB4AXSG8F9/UwgurUQy2uh4JxoG6tMB3YUfZ5J/X/cNixC+Cnkp6VdGPOOzPSewXI0zNyfq36mJ7nK/Pt2DTyvP92nYgYAN4EpjbtyEefmySty11jSl0uXA9NlrtCXAisxtdDy1TUA/h6aAsO1K0Vqv3S9jihjXVZRFwEXAN8XtLldcrWqg/XU3Mdz3l3nRy/bwHvBy4AdgN/m/NdD00kaTLwEPCFiNhfr2iVPNdDg1SpB18PbcKBurXCTuDsss8zgF0tOpZRKSJ25Wkf8ANSd6PX8u1L8rQvF69VHzvzfGW+HZtGnvffriNpLHAKR9/F44QWEa9FxGBEHAH+iXRNgOuhaST9Dik4/NeI+H7O9vUwwqrVg6+H9uFA3VrhaaBb0kxJ40gPn6xs8TGNGpImSZpSmgeuAjaQzvH1udj1wI/y/EpgcX5yfybQDfTm29IHJF2a+xteV7aOHb1GnvfybX0S+HnuL2rDKAWH2SdI1wS4Hpoin7PvAC9ExN+VLfL1MIJq1YOvhzbS6qdZnU7MBCwkPX2+FfhKq49nNCXSaDprc9pYOr+kPoP/BWzO086ydb6S6+IlykZ2AT5M+gO+Ffgm+W3GTjXP/X2k28i/IbUy3dDI8w50AA+SHvDqBd7X6u9cxFSjHv4FWA+sIwUW01wPTa2D+aTuD+uANTkt9PVQmHrw9dAmqXSSzczMzMysQNz1xczMzMysgByom5mZmZkVkAN1MzMzM7MCcqBuZmZmZlZADtTNzMzMzArIgbqZWRuRNChpjaQNkh6UNLFGuf9u0P6ulfTVPL9U0nXvYlv3SPrkMGUek/ThPL9dUtfx7q9smzdJWvJut2NmNtIcqJuZtZe3I+KCiPgQ8GtgaflCSWMAIuIjDdrfF4F/yNu8KyJWNGi7DVf67lUsB24eyWMxM2sEB+pmZu3rCeBcSVdI+oWkfyO9xARJB0uFJH1R0npJayUty3nvl/SopGclPSFpduXGJc0CDkfEnvz5dkm35vnHJH1NUq+kTZI+WmV9SfqmpOcl/QdwRtmyBZL+Jx/Xcknj631RST/Mx7pR0o1l+Qcl/ZWk1cA8Scvy/tZJ+huAiDgEbJfUU2v7ZmZFNLbVB2BmZsdO0ljgGuDRnNUDfCgitlWUuwa4FrgkIg5J6syL7gaWRsRmSZeQWs1/r2I3lwHP1TmMsRHRI2kh8JfAxyqWfwL4ADAHOBN4HlguqQO4B1gQEZskrQA+B/x9nX19JiL2SpoAPC3poYh4A5gEbIiIr+bv9h1gdkSEpFPL1n8G+CjpzYlmZm3BLepmZu1lgqQ1pMDzV6TAFKC3MkjPPgb8c25VJge7k4GPAA/mbf0jMK3KutOA1+scy/fz9FngnCrLLwfui4jBiNgF/DznfwDYFhGb8ud7c9l6bpa0FngKOBvozvmDwEN5fj/QD3xb0u8Dh8rW7wPOGmYfZmaF4hZ1M7P28nZEXFCeIQngrRrlBURF3knA/1Zup9q+gFPqLD+cp4PU/v+kct+lYzpqkq4g/eCYl+8KPAZ05MX9ETEIEBEDuXvLAmAxcBNDdwk6SN/HzKxtuEXdzGx0+ynwmdLoMJI6I2I/sE3Sp3KeJM2tsu4LwLnvYt+PA4sljZE0DfjdnP8icI6k0rY/Dayqs51TgH05SJ8NXFqtUL5TcEpE/Bj4AnBB2eJZwIbj/SJmZq3gQN3MbBSLiEeBlcAzuZvLrXnRHwE35O4kG4FFVVZ/HLhQucn+OPwA2Ex6wPVb5GA8IvqBJaSuN+uBI8BddbbzKDBW0jrgr0ndX6qZAjycy60C/qxs2WXAz47ze5iZtYQiqt2VNDMzA0l3Av8eEW0b5Eq6EPjziPh0q4/FzOxYuEXdzMzquQOo+lKlNtIF/EWrD8LM7Fi5Rd3MzMzMrIDcom5mZmZmVkAO1M3MzMzMCsiBupmZmZlZATlQNzMzMzMrIAfqZmZmZmYF9H91wsukZSMM1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2: Plot of predicted value using the test data compared to the actual values of the test data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Comparing Figure 1 and Figure 2, it is evident that the distribution of the test data in Figure 1 is much better at fitting the data. This difference in Figure 2 is apparent in the range of 5000 to 15,000. This is where the shape of the distribution is extremely different. Let's see if polynomial regression also exhibits a drop in the prediction accuracy when analysing the test dataset.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Overfitting</h4>\n",
    "<p>Overfitting occurs when the model fits the noise, but not the underlying process. Therefore, when testing your model using the test set, your model does not perform as well since it is modelling noise, not the underlying process that generated the relationship. Let's create a degree 5 polynomial model.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use 55 percent of the data for training and the rest for testing:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform a degree 5 polynomial transformation on the feature <b>'horsepower'</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=5, include_bias=True, interaction_only=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = PolynomialFeatures(degree=5)\n",
    "x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "pr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a Linear Regression model \"poly\" and train it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = LinearRegression()\n",
    "poly.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the output of our model using the method \"predict.\" We assign the values to \"yhat\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6728.68465468,  7308.01690973, 12213.81302023, 18893.19052853,\n",
       "       19995.88231726])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = poly.predict(x_test_pr)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take the first five predicted values and compare it to the actual targets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values: [ 6728.68465468  7308.01690973 12213.81302023 18893.19052853]\n",
      "True values: [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values:\", yhat[0:4])\n",
    "print(\"True values:\", y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the function \"PollyPlot\" that we defined at the beginning of the lab to display the training data, testing data, and the predicted function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly,pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3: A polynomial regression model where red dots represent training data, green dots represent test data, and the blue line represents the model prediction.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the estimated function appears to track the data but around 200 horsepower, the function begins to diverge from the data points.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R^2 of the training data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.556771690250259"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.score(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R^2 of the test data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.871506261647205"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the R^2 for the training data is 0.5567 while the R^2 on the test data was -29.87.  The lower the R^2, the worse the model. A negative R^2 is a sign of overfitting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the R^2 changes on the test data for different order polynomials and then plot the results:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3, 0.75, 'Maximum R^2 ')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rsqu_test = []\n",
    "\n",
    "order = [1, 2, 3, 4]\n",
    "for n in order:\n",
    "    pr = PolynomialFeatures(degree=n)\n",
    "    \n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    \n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])    \n",
    "    \n",
    "    lr.fit(x_train_pr, y_train)\n",
    "    \n",
    "    Rsqu_test.append(lr.score(x_test_pr, y_test))\n",
    "\n",
    "plt.plot(order, Rsqu_test)\n",
    "plt.xlabel('order')\n",
    "plt.ylabel('R^2')\n",
    "plt.title('R^2 Using Test Data')\n",
    "plt.text(3, 0.75, 'Maximum R^2 ')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the R^2 gradually increases until an order three polynomial is used. Then, the R^2 dramatically decreases at an order four polynomial.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function will be used in the next section. Please run the cell below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(order, test_data):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n",
    "    pr = PolynomialFeatures(degree=order)\n",
    "    x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
    "    x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
    "    poly = LinearRegression()\n",
    "    poly.fit(x_train_pr,y_train)\n",
    "    PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train,y_test, poly, pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following interface allows you to experiment with different polynomial orders and different amounts of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a7bf35d106c44c7b0fb6982ae5c3d75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='order', max=6), FloatSlider(value=0.45, description='tes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(order, test_data)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4a):</h1>\n",
    "\n",
    "<b>We can perform polynomial transformations with more than one feature. Create a \"PolynomialFeatures\" object \"pr1\" of degree two.</b>\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "pr1=PolynomialFeatures(degree=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "pr1=PolynomialFeatures(degree=2)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4b): </h1>\n",
    "\n",
    "<b> \n",
    " Transform the training and testing samples for the features 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg'. Hint: use the method \"fit_transform\".</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "x_train_pr1=pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "\n",
    "x_test_pr1=pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "x_train_pr1=pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "\n",
    "x_test_pr1=pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The answer is below:\n",
    "\n",
    "x_train_pr1=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "x_test_pr1=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
    "\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4c): </h1>\n",
    "<b> \n",
    "How many dimensions does the new feature have? Hint: use the attribute \"shape\".\n",
    "</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110, 15)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "x_train_pr1.shape #there are now 15 features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "x_train_pr1.shape #there are now 15 features\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4d): </h1>\n",
    "\n",
    "<b> \n",
    "Create a linear regression model \"poly1\". Train the object using the method \"fit\" using the polynomial features.</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "poly1=LinearRegression().fit(x_train_pr1,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "poly1=LinearRegression().fit(x_train_pr1,y_train)\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4e): </h1>\n",
    "<b>Use the method  \"predict\" to predict an output on the polynomial features, then use the function \"DistributionPlot\" to display the distribution of the predicted test output vs. the actual test data.</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mxf2NReqX0G2uCD3mx1Pyr+U/BTo6584O3e8051w/51xn51xlZ/1o64S+Zg/HCfac1ncRtmVzzlVxzvXBktA1wIuhq2oRmkzkrK77hnw3zf/aqQVsw0YDq2N/K4UK/Q29i43U18PqnSnmvhV1P1JCf6dPYd9S3B1hXPnvRw0ssVkR2u/fsRHBYoWeo87Y+0YTrBQl/zadnHOHhN6PtmKT0PI+d0W9pxamEXBV6PinYjXin4eum0ThfzsrsHKswt4DP8deg2eEXsd/A7pgr82SKu69uDQKfY90zh3snOseGtRZjw36lOp/pXOusXPuCqye+2bvfS7Fv06WAS1C789hJXnPk1KK++Q4lEzsD7zjnJsE/JfQV53OuZOc9dbMf/oqzy5aee/7Yi+gJ5xzJZr0lEA+cc5twD4F34q9of69kG07AN9g/7R+AZ7xO/uuPoj901/rnCtwUkQhXsVKYpZiM3yvAvDerwMux2pj/8RGIPL2T30ndL7KOVdQTeCI0L5/wLoebCWyZCpqvNXz3o7VamZiI6in59nkLuDl0GN2WgG7CPI+/B82yjAq9Pr4FZt4g/d+Otah4w3sfq1h1+dmB+/9Iuwr6uuwb3YmsbM29gWs9nGtc+7D0IeiY7G6wfnYNwjPYyNuYAnHwtB1o7DHpjg/YDPd//Tej89z+eXAPaH7dgdFjw7ejj13a0Ix5B1lXIyNDN6C/aNajCVyhb1H3oN1tvgptL+HgTO99+G5EkX9jUUkNDp+EtYpZi026voplqRFuo8NwBHY6/Uv7O/zX+z8gHo2sMBZqcCloWPgvU/HkrN5oee1sK/Xbww99quxjhu/Y50INoWuvxvruLEOK4V5P9/t87/fvIK9Nv7EZu7/GsHdfAMb0X0n3wfOAu9bIfo75zZiCdAYoDY22S08uFBcXPn/BmYAj2LP/TKgO9aNoSh/C8WwFvubXQX08QVPhqyCtexciT2njbDXLhT/nlqYcdjrdiXWjeEU7324zKqov53Noe3Hhu7/fnl3GtrHMdh7xyqsC9Ix3ibUlkgE78WlUeh7JPbh5F3sdTET+B77IB2+3SnOuTXOuaLmTax1zm0CpmLvoad670eE7k9xr5PvsA42S51z4cerJO95UkrOFzsPq/w5K67/1HvfzTlXG5jlvS9zr0fn3Euh/b5b1n2JiJQ359w44Dnv/YvFbiwiIqUS9yPHoZmh80Nf84TbV0XUjSD0tW2V0M8NgAHYp34RkbjnnDvIOdck9HX0uUAP4Mug4xIRSWZxlxw7597EvmLo5Jxb4py7AJtdfYFzbjL2FcPxRe0jj87AhNDtRgMPhb7GEBFJBJ2wNoPrsK+lT/HeZwYbkohIcovLsgoRERERkSDE3cixiIiIiEhQyrJCV9Q1aNDAt2nTJugwRERERCSJ/f777yu99w0Lui6ukuM2bdowYcKEoMMQERERkSTmnCt0dVaVVYiIiIiIhCg5FhEREREJUXIsIiIiIhISVzXHIiIiUrFlZWWxZMkStm7dGnQokgSqVq1KixYtSEtLi/g2So5FREQkbixZsoRatWrRpk0bnHNBhyMJzHvPqlWrWLJkCW3bto34diqrEBERkbixdetW6tevr8RYysw5R/369Uv8LYSSYxEREYkrSowlWkrzWlJyLCIiIiISouRYRERERCREybGIiIhIPh988AHOOdLT04vd9oknnmDz5s2lPtZLL73EFVdcsctlCxYsoEWLFuTm5u5yea9evfjtt98K3M+CBQvo1q1bqeOIxIcffsg999zD/fffT69evejVqxcpKSk7fn7yyScj3tcDDzyw4+ft27dz4IEHkp2dHYuwS0TJsYiIiEg+b775JgMHDmTkyJHFblvW5Lggbdq0oWXLlvz44487LktPT2fDhg3ss88+UT1WSTz88MNcfvnl3HrrrUyaNIlJkyZRrVq1HT9fddVVEe8rb3JcuXJlDj30UN56661YhF0iauUmIiIi8enqq2HSpOjus1cveOKJIjfZuHEjY8eOZfTo0Rx33HHcddddAOTk5PDPf/6Tr776CuccF110Ed57/vrrLw4++GAaNGjA6NGjqVmzJhs3bgTg3Xff5dNPP+Wll17ik08+4b777mP79u3Ur1+f119/ncaNGxcax9ChQxk5ciQHHXQQACNHjmTo0KEsWLCAs88+m02bNgHw9NNPs//+++9y25deeokJEybw9NNPA3DMMcdw/fXXM2jQIEaNGsWdd97Jtm3baN++PS+++CI1a9bkpptu4uOPPyY1NZUjjjiCRx55ZJd9zp49mypVqtCgQYMC483JyeGmm25izJgxbNu2jX/84x9ccsklZGZm8re//Y3169eTnZ3Ns88+y2effcaWLVvo1asXXbt25fXXX+eEE07g5ptv5swzzyzy+Yk1JcciIiIieXz44YcceeSRdOzYkXr16vHHH3/Qu3dvhg8fzvz585k4cSKpqamsXr2aevXq8dhjjzF69OhCk8awgQMH8uuvv+Kc4/nnn+fhhx/m0UcfLXT70047jb333punnnqK1NRU3nrrLd555x0aNWrE119/TdWqVcnIyGDo0KFMmDAhovu2cuVK7rvvPr755htq1KjBv/71Lx577DGuuOIKPvjgA9LT03HOsXbt2t1uO3bsWHr37l3ovl944QXq1KnD+PHj2bZtGwMGDOCII47g/fffZ/Dgwdx6663k5OSwefNmDjjgAJ5++mkm5fnw061bN8aPHx/R/YglJcciIiISn4oZ4Y2VN998k6uvvhqA008/nTfffJPevXvzzTffcOmll5KaaulTvXr1SrTfJUuW8Le//Y3MzEy2b99e7MIUTZo0oWvXrnz77bc0btyYtLQ0unXrxrp167jiiiuYNGkSKSkpzJ49O+IYfv31V2bMmMGAAQMAq/Xt378/tWvXpmrVqlx44YUcffTRHHPMMbvdNjMzk4YNGxa671GjRjFlyhTeffddANatW0dGRgb9+vXj/PPPJysrixNOOIFevXoVePuUlBQqV67Mhg0bqFWrVsT3KdqUHIuIiIiErFq1iu+++45p06bhnCMnJwfnHA8//DDe+4j65ubdJu8CFFdeeSXXXnstxx13HGPGjNlRrlGUcGlF48aNGTp0KACPP/44jRs3ZvLkyeTm5lK1atXdbpeamrrLZL5wHN57Dj/8cN58883dbvPbb7/x7bffMnLkSJ5++mm+++67Xa6vVq0a69atKzRW7z1PPfUUgwcP3u26H374gc8++4yzzz6bG264gXPOOafAfWzbtq3A+1OeNCFPREREJOTdd9/lnHPOYeHChSxYsIDFixfTtm1bfvrpJ4444giee+65HR0VVq9eDUCtWrXYsGHDjn00btyYmTNnkpubywcffLDj8nXr1tG8eXMAXn755YjiOfnkk/n888956623OP3003fsp2nTplSqVIlXX32VnJyc3W7Xpk0bJk2aRG5uLosXL97R4WK//fZj7NixzJkzB4DNmzcze/ZsNm7cyLp16xgyZAhPPPHELuUOYZ07d95xu4IMHjyYZ599lqysLMBqlDdt2sTChQtp1KgRF110ERdccAF//PEHAGlpaTu2Bftg0rBhQ9LS0iJ6bGJFybGIiIhIyJtvvsmJJ564y2Unn3wyb7zxBhdeeCGtWrWiR48e9OzZkzfeeAOAiy++mKOOOoqDDz4YgIceeohjjjmGQw45hKZNm+7Yz1133cWpp57KAQccUGx9cljdunXZb7/9aNy48Y4yjMsvv5yXX36Z/fbbj9mzZ1OjRo3dbjdgwADatm1L9+7duf7663fUCjds2JCXXnqJoUOH0qNHD/bbb78dXTCOOeYYevTowUEHHcTjjz++2z4PPPBAJk6ciPe+wFgvvPBCunTpQu/evenWrRuXXHIJ2dnZjBkzhl69erH33nvz3nvvMWzYsB2PW48ePXZMwBs9ejRDhgyJ6HGJJVfYHQxC3759faQF5SIiIpJ8Zs6cSefOnYMOQwoxbNgwjj32WA477LCo7/ukk07iwQcfpFOnTlHdb0GvKefc7977vgVtr5FjEREREYnILbfcEvWezmATA0844YSoJ8aloeRYRERERCLSuHFjjjvuuKjvt3LlyoVO0itvSo5FREREREKUHIuIiIiIhCg5FhEREREJUXIskscvv8Cll8KSJUFHIiIiIkFQciwCLF4MZ5wB++8P//0vHHccbNoUdFQiIhKElJQUevXqRbdu3Tj11FPL1J3hvPPO27Gc8oUXXsiMGTMK3XbMmDH8/PPPJT5GmzZtWLly5W7H/e9//7vLZR9++GGRfYTzxhoL3nsOOeQQFi5cSK9evejVqxdNmjShefPmO37fvn17RPvK/1g9/fTTvPjii1GJU8mxVGibN8Ndd0GnTvDBB3DbbfDWWzBpEpx3HuRZeVNERCqIatWqMWnSJKZNm0blypV57rnndrm+oBXpIvH888/TpUuXQq8vbXJckPCy03mNHDlyxxLUQfj888/p2bMnrVu3ZtKkSUyaNIlLL72Ua665ZsfvlStXjmhf+R+r888/nyeffDIqcSo5lgpt6FC4+2449lhIT4d774XTToOHH4Z337XfRUQkGFdfDYMGRfd09dUli+GAAw5gzpw5jBkzhoMPPpgzzjiD7t27k5OTww033EC/fv3o0aPHjlFa7z1XXHEFXbp04eijj2b58uU79jVo0CDCi519+eWX9O7dm549e3LooYeyYMECnnvuOR5//HF69erFjz/+yIoVKzj55JPp168f/fr1Y+zYsYAts3zEEUew9957c8kllxS4Yt1hhx1Geno6mZmZgC0T/c0333DCCSdwzz330K9fP7p168bFF19c4O3zjkZPmDCBQYMGAbBp0ybOP/98+vXrx957781HH30EwPTp09lnn33o1asXPXr0ICMjY7d9vv766xx//PGFPta///47Bx10EH369GHw4ME7Yn/yySfp0qULPXr04PTTTy/wsapevTpt2rTZsUx2WaSWeQ8iCWr+fPjkE7j1Vrjvvl2vu+46mD7dRpW7dIFTTw0kRBERCVB2djZffPEFRx55JAC//fYb06ZNo23btgwfPpw6deowfvx4tm3bxoABAzjiiCOYOHEis2bNYurUqSxbtowuXbpw/vnn77LfFStWcNFFF/HDDz/Qtm1bVq9eTb169bj00kupWbMm119/PQBnnHEG11xzDQMHDmTRokUMHjyYmTNncvfddzNw4EDuuOMOPvvsM4YPH75b7CkpKZx00km8/fbbDBs2jI8//piDDz6YWrVqccUVV3DHHXcAcPbZZ/Ppp59y7LHHRvSY3H///RxyyCGMGDGCtWvXss8++3DYYYfx3HPPMWzYMM4880y2b99e4Oj62LFjdyv1CMvKyuLKK6/ko48+omHDhrz11lvceuutjBgxgoceeoj58+dTpUoV1q5dS926dXd7rAD69u3Ljz/+yD777BPRfSmMkmOpsF54AZyDSy7Z/Trn4LnnYPZsOPdcaNcO+vQp/xhFRCqyJ54I5rhbtmyhV69egI0cX3DBBfz888/ss88+tG3bFoBRo0YxZcqUHTW669atIyMjgx9++IGhQ4eSkpJCs2bNOOSQQ3bb/6+//sqBBx64Y1/16tUrMI5vvvlmlxrl9evXs2HDBn744Qfef/99AI4++mj22GOPAm8/dOhQbrjhBoYNG8bIkSN3LLIxevRoHn74YTZv3szq1avp2rVrxMnxqFGj+Pjjj3nkkUcA2Lp1K4sWLaJ///7cf//9LFmyhJNOOokOHTrsdtvVq1dTq1atAvc7a9Yspk2bxuGHHw5Y6UrTpk0B6NGjB2eeeSYnnHACJ5xwQqGxNWrUiPT09IjuR1GUHEuFlJUFI0bAkCHQsmXB21SpYnXIfftaB4vx48s3RhERCUa45ji/GjVq7PjZe89TTz3F4MGDd9nm888/xzlX5P6998VuA5Cbm8svv/xCtWrVdrsuktsPGDCAzMxMJk+ezM8//8zIkSPZunUrl19+ORMmTKBly5bcddddbN26dbfbpqamkhuaeJP3eu8977333m7LPHfu3Jl9992Xzz77jMGDB/P888/v9sEgvM9KlXav6vXe07VrV3755Zfdrvvss8/44Ycf+Pjjj7n33nuZPn16gfd369atBT5WJaWaY6mQPvsMMjPh4ouL3q5RI7j2WpgwAYqYYCwiIhXM4MGDefbZZ8nKygJg9uzZbNq0iQMPPJCRI0eSk5NDZmYmo0eP3u22/fv35/vvv2f+/PmAjagC1KpViw0bNuzY7ogjjuDpp5/e8Xs4YT/wwAN5/fXXAfjiiy9Ys2ZNgTE65zjttNM499xzGTJkCFWrVt2R6DZo0ICNGzcW2p2iTZs2/P777wC89957u9zvp556aked8sSJEwGYN28e7dq146qrruK4445jypQpu+2zU6dOzJs3r8DjderUiRUrVuxIjrOyspg+fTq5ubksXryYgw8+mIcffpi1a9eycePG3R4rsOegW7duBe6/JJQcS4U0fDg0bw5HHVX8tkOHQkoKvPpq7OMSEZHEcOGFF9KlSxd69+5Nt27duOSSS8jOzubEE0+kQ4cOdO/encsuu4yDDjpot9s2bNiQ4cOHc9JJJ9GzZ0/+9re/AXDsscfywQcf7Jhk9uSTTzJhwgR69OhBly5ddnTNuPPOO/nhhx/o3bs3o0aNolWrVoXGOXToUCZPnszpp58OQN26dbnooovo3r07J5xwAv369SvwdnfeeSfDhg3jgAMOICUlZcflt99+O1lZWfTo0YNu3bpx++23A/DWW2/RrVs3evXqRXp6+o4SjryOPvpoxowZU+DxKleuzLvvvss///lPevbsSa9evfj555/JycnhrLPOonv37uy9995cc8011K1bd7fHCqym+bDDDiv0sYiUK2iGYlD69u3rw7M4RWJl4UJo29batt1zT2S3OfpomDLFblvAt0EiIhIlM2fOpHPnzkGHITGQmZnJOeecw9dffx31fU+cOJHHHnuMVwsYySroNeWc+91737egfenfvFQ4L7xg5xdcEPltzj7bVs0r5AOviIiIFKNp06ZcdNFFrF+/Pur7XrlyJfdGqf+qJuRJhZKdbcnxkUdC69aR3+7446F2bSutKGDisYiIRFGkE9Yk8Zx22mkx2W+4y0V+pamQ0MixVCiffw5//VX8RLz8qlWDU06xhUHKsIqoiIgUo2rVqqxatapUSY1IXt57Vq1aRdWqVUt0O40cS4UyfDg0bWo1xCV1zjnW/u3DD+GMM6IemoiIAC1atGDJkiWsWLEi6FAkCVStWpUWLVqU6DZKjqXC+PNP+OILuPlmSEsr+e0POABatbLSCiXHIiKxkZaWtmNxDJEgqKxCKowxYyA3F04+uXS3r1TJJuaNGmU9kkVERCT5KDmWCuOXX6BGDejevfT7OPtsS7DffDN6cYmIiEj8UHIsFcYvv8A++0BqGYqJOnWCfv3glVeiF5eIiIjEDyXHUiFs2gSTJ0P//mXf1znn2L60nLSIiEjyUXIsFcKECZCTE53k+Nhj7fybb8q+LxEREYkvSo6lQvjlFzvfb7+y76t1a1t+evTosu9LRERE4ouSY6kQfvkFOnSABg2is79Bg+D7721ynoiIiCQPJceS9Ly35DgaJRVhBx8Ma9bA1KnR26eIiIgET8mxJL1582DFiugmxwcdZOcqrRAREUkuSo4l6YXrjaOZHLdqBe3a2cIiIiIikjyUHEvS++UXqFkTunWL7n4PPtjqjnNyortfERERCY6SY0l64cU/UlKiu99Bg2DtWpgyJbr7FRERkeAoOZaktmmTJa/RLKkIGzTIzlVaISIikjyUHEtSGz8+eot/5NeiBey5pybliYiIJBMlx5LUorn4R0EGDYIfflDdsYiISLJQcixJ7ZdfoGNHqF8/Nvs/+GBYtw4mTYrN/kVERKR8KTmWpBWLxT/yU92xiIhIclFyLElr7lxYuTK2yXGzZjYyrbpjERGR5KDkWJJWLBb/KMigQfDjj5CdHdvjiIiISOwpOZak9csvUKsWdO0a2+MMGgTr16vuWEREJBkoOZakNXUq9OwZ/cU/8gvXHau0QkREJPEpOZakNXMmdO4c++M0bQqdOllLNxEREUlsSo4lKa1cCatWlU9yDLDvvrbgiPflczwRERGJDSXHkpTS0+18r73K53h9+8KyZfDnn+VzPBEREYkNJceSlMo7Oe7Xz84nTCif44mIiEhsKDmWpJSeDlWrQqtW5XO88MS/8ePL53giIiISG0qOJSnNnGmLc8S6U0VYtWrQrZtGjkVERBKdkmNJSunp5TcZL6xfP0uONSlPREQkcSk5lqSzdSvMn19+9cZhffvC6tV2bBEREUlMSo4l6WRk2OhtEMkxqLRCREQkkSk5lqQzc6adl3dy3L07VK6s5FhERCSRKTmWpJOeDs7ZhLzyVLmyda1QxwoREZHEpeRYkk56OrRuDdWrl/+x+/aF33+H3NzyP7aIiIiUnZJjSTrp6eVfUhHWrx9s2GB1zyIiIpJ4lBxLUsnNhVmzgkuOw5PyVFohIiKSmJQcS1JZvBg2by7/HsdhnTvbgiCalCciIpKYlBxLUklPt/OgRo5TU6F3byXHIiIiiUrJsSSVoJNjsNKKP/6A7OzgYhAREZHSUXIsSSU9HfbYAxo2DC6Gvn1hy5ad/ZZFREQkcSg5lqQS7lThXHAx9Otn5yqtEBERSTxKjiWpzJwZ3GS8sA4doHZtdawQERFJREqOJWmsWQPLlgVbbwxQqRL06aORYxERkUQU0+TYOXeNc266c26ac+5N51zVWB5PKrZZs+w86OQYrO548mTYvj3oSERERKQkYpYcO+eaA1cBfb333YAU4PRYHU8kHjpVhPXta4nx9OlBRyIiIiIlEeuyilSgmnMuFagO/BXj40kFlp4OlStD27ZBRwI9e9r55MnBxiEiIiIlE7Pk2Hv/J/AIsAjIBNZ570fl3845d7FzboJzbsKKFStiFY5UADNn2mS41NSgI4E997SV8qZMCToSERERKYlYllXsARwPtAWaATWcc2fl3857P9x739d737dhkM1pJeGF27jFg5QU6NZNI8ciIiKJJpZlFYcB8733K7z3WcD7wP4xPJ5UYNu3w9y58ZMcA/ToYcmx90FHIiIiIpGKZXK8CNjPOVfdOeeAQwGtGSYxMW8e5ORAp05BR7JTz56wahVkZgYdiYiIiEQqljXH44B3gT+AqaFjDY/V8aRiW7DAztu1CzSMXfToYeeqOxYREUkcMe1W4b2/03u/l/e+m/f+bO/9tlgeTyqucHLcpk2QUewqnByr7lhERCRxaIU8SQoLFkBaGjRtGnQkO+2xB7RsqZFjERGRRKLkWJLCggXQurUt3RxPevbUyLGIiEgiibNUQqR0FiyIr5KKsB49rMXcNhUUiYiIJAQlx5IU4jU57tnTumjMmBF0JCIiIhIJJceS8LZsgWXL4jM5VscKERGRxKLkWBLewoV2Ho/JcYcOULWqkmMREZFEoeRYEl48tnEL0zLSIiIiiUXJsSS8eE6OYWfHCi0jLSIiEv+UHEvCi8cex3n16AErV8LSpUFHIiIiIsVRciwJb+HC+OxxHNazp52r7lhERCT+xWk6IRK5eG3jFqZlpEVERBKHkmNJePGeHGsZaRERkcSh5FgS2pYtVssbz8kx2OixRo5FRETin5JjSWiLFtl5vCfHPXtqGWkREZFEoORYElq8t3EL69EDsrNh5sygIxEREZGiKDmWhJYoybE6VoiIiCQGJceS0OK9x3HYnnvaMtKqOxYREYlvSo4loS1YAK1axW+P47DUVOjaFaZODToSERERKUqcpxQiRYv3Nm55de0K06cHHYWIiIgURcmxJLRES47/+gvWrAk6EhERESmMkmNJWInS4zisWzc71+ixiIhI/FJyLAkrUXoch3XtaudKjkVEROKXkmNJWInSxi2sVSuoWVPJsYiISDxTciwJK9GSY+ds9HjatKAjERERkcIoOZaElSg9jvNSxwoREZH4puRYEla4x3FKStCRRK5rV1i+HFasCDoSERERKYiSY0lYidTGLUwdK0REROKbkmNJWImYHKtjhYiISHxTciwJKdF6HIc1awZ16ig5FhERiVdKjiUhJVqP4zDnrLRCHStERETik5JjSUiJ1sYtr3DHCu+DjkRERETyU3IsCWnhQjtP1OR49WpYtizoSERERCQ/JceSkBKxx3FYuGOFSitERETij5JjSUgLF0KLFonV4zhMHStERETil5JjSUhLlkDLlkFHUTqNGkH9+kqORURE4pGSY0lIf/4JzZsHHUXpqGOFiIhI/FJyLAnHexs5btEi6EhKTx0rRERE4pOSY0k4q1bBtm2JO3IMlhyvX28j4CIiIhI/lBxLwgknlIk8cqyOFSIiIvFJybEknCVL7DzRR45Bk/JERETijZJjSTjJMHJcvz40bqyRYxERkXij5FgSzpIlUKkSNGkSdCRl062bRo5FRETijZJjSTh//mmJcWpq0JGUTdeuMGMG5OYGHYmIiIiEJXh6IRVR1Nq4bdsGc+dCejps2gSnn25rUpeTrl3tsAsXQtu25XZYERERKYKSY0k4S5ZAp06lvPGGDXDZZTBuHMybt+uw7TvvwNtvQ9WqUYmzOOGOFdOnKzkWERGJFyqrkITz55+lHDn2Hi69FN58E3r1gltugddegwkT4Kmn4JNP4JhjYOPGaIdcoC5d7Fx1xyIiIvFDI8eSUDZuhHXrStnG7aWX4I034J574Pbbd72uTx+oUwfOOw+OOAI+/xzq1i17wEWoW9fuhzpWiIiIxA+NHEtCKXUbt5kz4Yor4OCDbcS4IGefbaUVEybYditWlCnWSKhjhYiISHxRciwJpVQLgGzZAn/7G1SvbmUUKSmFb3vSSVZeMWsWHHoobN9epniL07Wr5e05OTE9jIiIiERIybEklFKNHF93HUydCq+8As2aFb/94MFWfjF1Kjz/fKnijFTXrrB1q80NFBERkeApOZaEUuKR4/feg2efheuvh6OOivxAxx8PBxwA995r/dZiJG/HChEREQmekmNJKEuWQL16UK1aBBtv22Zt2/r1g/vvL9mBnIMHH4SlS62TRYyoY4WIiEh8UXIsCaVEbdw+/tgm1d17L1SuXPKDDRgARx8N//oXrFlT8ttHoGZNaN1aHStERETihZJjSShLlpSgpOKFF6BlSzjssNIf8P77Ye1a+Pe/S7+PYqhjhYiISPxQciwJJeKR48WLYdQo61tcVHeK4vTsCUOHwv/9n5VYxEDXrtYcIysrJrsXERGRElByLAlj+3ZYtizCkeOXXrIV8f7+97If+J577OD33Vf2fRWga1fb/Zw5Mdm9iIiIlICSY0kYmZl2XuzIcW4ujBhhfYrbti37gffcEy64AIYPh/nzy76/fNSxQkREJH4oOZaEEXEbt9GjYcECOP/86B389tutPOPuu6O3z5C99rLmGEqORUREgqfkWBJGxAuAjBgBdevCiSdG7+DNm1uyPXJk1DtXVK8O7dqpY4WIiEg8UHIsCSM8clxkcrxmjS38ceaZETZDLoHzz7feySNHRne/qGOFiIhIvFByLAljyRIbZa1Tp4iN3njDEtgLLoh+AL17Q/fu8OKLUd91166QkWGhi4iISHCUHEvCCLdxc66IjUaMgF69YO+9ox+Ac9b9Yvz4qA/zdu0K2dkwe3ZUdysiIiIlpORYEkaxC4BMmgR//BGbUeOws86C1FRrFRdF6lghIiISH5QcS8IodgGQESOgShU444zYBdGwIRxzDLz6alRX7ejUyZphaFKeiIhIsJQcS0LIzbXkuMiR408/hcGDoV692AZz3nm2GsmXX0Ztl1WqWDtljRyLiIgES8mxJIQVK6wmt9CR43nzbIGOww+PfTBDhkCjRlGfmKeOFSIiIsFTciwJodgFQL75xs4POyz2waSlWe3xJ59Y1h4lXbvaEtJbtkRtlyIiIlJCSo4lIRTb4/jbb6FZMyveLQ9//7sNZb/xRtR22bUreA/p6VHbpYiIiJSQkmNJCEWujpeba8nxYYcV0+ctirp1g759o1paoY4VIiIiwVNyLAlhyRLroNaoUQFXTpkCq1aVT0lFXn//O0yeDBMnRmV3HTpYxYY6VoiIiARHybEkhD//tKqJSgW9YsP1xoceWq4xcfrpULkyvPJKVHaXlmZVIRo5FhERCY6SY0kIRS4A8s030LmzZc/lqV49OOII+OADKxaOgq5dlRyLiIgEScmxJIRCFwDZtg1+/LH8SyrCTjgBFi601fmioGtX60i3cWNUdiciIiIlpORY4p73RYwc//orbN5c/iUVYccdZ7UeH34Yld2FJ+XNnBmV3YmIiEgJKTmWuLd+PWzaVEhy/O23lpwOGlTeYZmGDWHAACutiIKuXe1ck/JERESCoeRY4t5ff9l5gcnxN99Av35Qp065xrSLE0+EqVNh7twy76p9e1tKWnXHIiIiwVByLHEvM9POmzbNd8X69fDbb8HVG4edcIKdR6G0IiXF5hZq5FhERCQYSo4l7oWT4yZN8l3x/feQkxN8cty2LfTsGdW6Y40ci4iIBEPJscS9QkeOv/kGqlWD/v3LPabdnHgijB0Ly5aVeVddu9oExLVryx6WiIiIlIySY4l7S5daDly7dr4rvv0WDjjAinSDdsIJ1lbj44/LvKvwpLwZM8q8KxERESkhJccS9zIzbdTYuXwXTp8efElFWI8eVl4RhdKKcDs3lVaIiIiUPyXHEvcyMwuoN/72WzsPqr9xfs7Z6PE339hEwTJo3RqqV9ekPBERkSAoOZa4Fx453sXYsVZn0atXECEV7MQTYft2+PLLMu2mUiUtIy0iIhIUJccS95YuLSA5Hj8e+va1TDJe7L+/LQoShQVBunbVyLGIiEgQ4iizENndli3WtWGX5HjbNpgyxRb/iCcpKbac9GefWYxl0K2bNb5YuTJKsYmIiEhElBxLXFu61M53qTmeMgWysuIvOQYrrdiwAUaPLtNuwh0rVFohIiJSvpQcS1wrsMfx+PF23rdvucdTrEMPtb5zn39ept2oY4WIiEgwlBxLXAuPHO+WHDdsCK1aBRJTkapWhYMPhi++KNNumje3+YaqOxYRESlfSo4lrhW4dPSECVZSsUvj4zgyZAjMmQMZGaXehXPqWCEiIhIEJccS1zIzrSFFw4ahCzZtsqXj4rHeOOyoo+y8jKPH3bpZcux9FGISERGRiCg5lri2dCk0bmyNIAD44w/IzY3PeuOwdu2gY8cyJ8ddu8KqVda1QkRERMqHkmOJa7stADJhgp3H88gx2Ojx6NGweXOpd6FJeSIiIuVPybHEtd2Wjh4/Hlq2tOHkeDZkiPU6HjOm1LsIt3PTpDwREZHyo+RY4tpuI8fhlfHi3YEHQvXqZSqtaNwY6tfXyLGIiEh5UnIscSsnB5Yvz5Mcr1ljXSDivaQCdrZ0+/zzUs+oC3es0MixiIhI+VFyLHFrxQqbe7cjOf79dztPhOQYrLRi3rwytXQLt3NTxwoREZHyoeRY4tZuPY7DK+P16RNIPCUWhZZu3brB+vWwZEmUYhIREZEiKTmWuLXb0tHjx8Oee8IeewQWU4m0bQudOpVpKenu3e186tQoxSQiIiJFUnIscWu3paPDK+MlkqOOgu+/L3VLt3A7NyXHIiIi5UPJscStXcoqli2DxYsTLzkOt3QbPbpUN99jD2jRQsmxiIhIeVFyLHErMxPq1rXGDzvqjROhjVte4ZZuZSytUHIsIiJSPpQcS9zapcfxhAlQqRL07h1oTCVWpQoccohNyitly4nu3WHmTMjKinJsIiIishslxxK3li7NNxmvSxeoUSPQmEplyBCYPx9mzSrVzbt3t8R49uwoxyUiIiK7UXIscWvHyLH3ibMyXkEGD7bzr74q1c179LDzKVOiFI+IiIgUSsmxxCXvLTlu0gSbiLdiReJNxgtr1w46dCh1crzXXpCaqrpjERGR8qDkWOLSunWwdWto5Di8Ml6ijhwDHHkkjBljd6qEKle2dslKjkVERGIvpsmxc66uc+5d51y6c26mc65/LI8nyWOXHsdTp4JztpZyoho8GLZsgR9/LNXN1bFCRESkfMR65Pj/gC+993sBPYGZMT6eJIldVsebNs1KExJxMl7YoEE2BFzK0oru3WHhQltKWkRERGInZsmxc642cCDwAoD3frv3fm2sjifJZZcFQKZO3bmOcqKqUQMOOAC+/LJUNw/f/WnTohiTiIiI7CaWI8ftgBXAi865ic65551zuw39Oecuds5NcM5NWLFiRQzDkUSyY+R4j62QkbFzHeVEduSRMH06LFlS4puGk2OVVoiIiMRWLJPjVKA38Kz3fm9gE3BT/o2898O99329930bNmwYw3AkkSxdaivj1clMh5ycxB85hjK1dGvdGmrVUnIsIiISa7FMjpcAS7z340K/v4slyyLFCvc4dtNDdQTJkBx36wbNmpUqOXbObq7kWEREJLZilhx775cCi51znUIXHQrMiNXxJLns6HE8dapNZNtzz6BDKjvnbPT4668hO7vENw93rCjlKtQiIiISgVh3q7gSeN05NwXoBTwQ4+NJktixOt60adC5M6SlBR1SdBx5JKxdayv+lVCPHrBmDfz1V/TDEhERERPT5Nh7PylUT9zDe3+C935NLI8nyWPp0jw9jpNhMl7YYYdBpUql6lqhSXkiIiKxpxXyJO5s3WojpE3rbrGlo5Oh3jisXj3YZ59S1R0rORYREYk9JccSd8Kr4zXJCbU8S6aRY7C6499+g1WrSnSzPfaA5s2VHIuIiMSSkmOJOzt6HG/IsB+SaeQYrO7Ye/jmmxLfVMtIi4iIxJaSY4k74ZHjpiumQO3a0LJlsAFFW79+NgxcyrrjmTNL1exCREREIqDkWOLOjpHjReOspMK5YAOKtpQUOPxwqzsuYV+27t1h2zZbNFBERESiT8mxxJ3MTKhUydNw1k/JV28cNniw3dES1khoUp6IiEhsKTmWuJOZCY0a5JKyZmXy1RuHlXIp6c6dbeB5ypQYxCQiIiJKjiX+LF0KTWtvsl+SdeS4eXO7byWsO65SBfbaS8mxiIhIrCg5lriTmQlNU1faL8maHIN1rfjpJ9i4sUQ369ULJk2KSUQiIiIVnpJjiTuZmaEex02aQIMGQYcTO4MHw/btMGZMiW7Wq5etjVLCNskiIiISASXHEldycmDZslCP42StNw4bOBCqVy9x3XGvXnY+eXL0QxIREanolBxLXFm5EnJzoemqacldUgFQtSoMGlTiuuOePe1cpRUiIiLRp+RY4sqOHsdZC5N/5Bis7njOHJg7N+KbNGwIzZpp5FhERCQWlBxLXAknx01Ymvwjx1Dqlm6alCciIhIbSo4lruwYOWYpdOkSbDDloUMHaNu2VMnxjBm2Wp6IiIhEj5JjiStLl9p507ZVoUaNYIMpD87Z6PF331nnigj17AnZ2ZYgi4iISPQoOZa4kpkJdSuto2rPTkGHUn6OPNJ6Hf/8c8Q3UccKERGR2FByLHElc0kOTXL/qhj1xmEHHwypqSUqrWjf3gbWVXcsIiISXUqOJa5kLthKUzIrRr1xWO3aMGBAiVq6paRAjx5KjkVERKJNybHElaWZ3pLjzp2DDqV8DR5smW646DoCPXvaTbyPWVQiIiIVjpJjiRveQ+bqKpYcd+wYdDjl68gj7XzUqIhv0qsXrFsHCxfGJiQREZGKSMmxxI3162FLVhpN6m6zZZUrkp49oVGjEpVWaFKeiIhI9Ck5lrixo8dxy9RgAwlCpUpw1FE2KS87O6KbdO9uN1PdsYiISPQoOZa4sTTTimebdqgZcCQBOeYYWL0afv01os2rV7c1RJQci4iIRE+xybFz7mHnXG3nXJpz7lvn3Ern3FnlEZxULJnTVwPQtFv9gCMJyOGHW0u3Tz+N+CZaRlpERCS6Ihk5PsJ7vx44BlgCdARuiGlUUiFlTlsFQJM+zQOOJCB16sCBB5Y4OV6wANaujVVQIiIiFUskyXFa6HwI8Kb3fnUM45EKLHPORqqwlbp99ww6lOAccwxMnw7z50e0eXhS3pQpsQtJRESkIokkOf7YOZcO9AW+dc41BLbGNiypiJYuzqapW4Zr2iToUIJzzDF2/tlnEW3es6edq7RCREQkOopMjp1zlYBPgP5AX+99FrAZOL4cYpMKJnNFKk1rrAPngg4lOB06WI/nCEsrmjSxDnBKjkVERKKjyOTYe58LPOq9X+O9zwldtsl7H/kyXiIRytxQgyb1tgcdRvCOOQZGj4aNG4vd1DlNyhMREYmmSMoqRjnnTnauIg/nScxt2EBmdkOaNtXLjGOOge3b4dtvI9q8Vy8rU96uzxUiIiJlFklyfC3wDrDNObfeObfBObc+xnFJBbNt6mzWUI+m7aoFHUrwBg6E2rUjLq3o3dsS4+nTYxyXiIhIBVBscuy9r+W9r+S9r+y9rx36vXZ5BCcVx9LfFgHQtEvdYAOJB2lpMHiwTcrLzS128z597Pz332Mcl4iISAUQ0Qp5zrk9nHP7OOcODJ9iHZhULJmTlwHQpFvDgCOJE8ccY+tpT5xY7Kbt21uL5AkTyiEuERGRJBfJCnkXAj8AXwF3h87vim1YUtFkplulTtNWacVsWUEcdZTNtougtMI5Gz1WciwiIlJ2kYwcDwP6AQu99wcDewMrYhqVVDhLF24DoGnTgAOJFw0bwn77RVx33LevLQSybVuM4xIREUlykSTHW733WwGcc1W89+lAp9iGJRVKTg6ZyypRyeXSqFHQwcSRY46x4eDMzGI37dsXsrJg2rRyiEtERCSJRZIcL3HO1QU+BL52zn0E/BXLoKSCWbCAzNxGNKy1lZSUoIOJIyVYLU+T8kRERKIjkm4VJ3rv13rv7wJuB14ATohxXFKRpKeTSVOaNi6+M0OF0r07tG4NH31U7KZt28Iee6juWEREpKwKTY6dc/2cc0flvcx7/33ox+4xjUoqlvR0ltKEpq0rBx1JfHEOTjwRRo2CDRuK3VST8kRERMquqJHjfwMzC7h8Rug6kehITyezUnOatlJyvJuTT7YVPiIorejb12qOt24th7hERESSVFHJcX3v/YL8F3rv5wD1YxaRVDg5M2ezLLchTZoEHUkc6t8fGjeG998vdtPwpLypU8shLhERkSRVVHJc1Dq+NaIdiFRcq2YuJ4dUtXErSEqKlVZ8/jls2VLkppqUJyIiUnZFJcffOOfud865vBc65+4GvottWFJhrFxJ5morp1ByXIiTToJNm6z2uAitW0P9+qo7FhERKYuikuPrgHbAHOfce6HTHKzH8bXlEp0kv1mzyMSyYiXHhRg0yFpRFFNaoUl5IiIiZZda2BXe+03AUOdcO6Br6OLp3vt55RKZVAyhNm6Aao4Lk5YGxx1nLd22b4fKhU9c7NsXHn7YKjCqFVUYJSIiIgWKpM/xPO/9J6GTEmOJrvR0lqa0ADRyXKSTToK1a2HMmCI369MHsrNtKWkREREpuUhWyBOJnfR0MuvsRZ06Guks0hFHQI0axZZW9O1r55qUJyIiUjpFLQLStjwDkQpq1iwyq7XTqHFxqlaFo4+GDz6AnJxCN2vZEho2VN2xiIhIaRU1cvwugHPu23KKRSqarCyYN49M11T1xpE4+WRYvhx+/rnQTTQpT0REpGwKnZAHVHLO3Ql0dM7t1p3Ce/9Y7MKSCmH+fMjJYen2euyjkePiHXUUVKkC770HBxxQ6GZ9+8LXX8PmzVC9ejnGJyIikgSKGjk+HdiKJdC1CjiJlE1GBh7IXF9DZRWRqFULBg+2umPvC92sTx+rvJg8uRxjExERSRJFtXKbBfzLOTfFe/9FOcYkFUVGBhuoxeatKSqriNRJJ8HHH1vdRL9+BW4SnpQ3YYKtPi0iIiKRi6Rbxc/OuceccxNCp0edc3ViHpkkv9mzyazVCVAbt4gdd5z1OX7zzUI3ad7cHs9x48oxLhERkSQRSXI8AtgAnBY6rQdejGVQUkFkZLC0WW9AyXHE9tjDula8+WahXSucg/32g19+KefYREREkkAkyXF77/2docVA5nnv78aWlRYpm4wMMut3A5Qcl8iZZ8LSpfDdd4Vu0r8/zJtnzS1EREQkcpEkx1uccwPDvzjnBgBbYheSVAhbt8KiRWTW7ABo6egSOfpoqFMHXn+90E3Ctca//lpOMYmIiCSJSJLjS4H/OOcWOOcWAE8Dl8Q0Kkl+c+eC92SmtaJKFasWkAhVrQqnnGIt3TZvLnCTPn0gNVWlFSIiIiVVbHLsvZ/sve8J9AB6eO/39t5PiX1oktQyMgBYmtuIJk2sTlZK4MwzYeNG+OSTAq+uVg169VJyLCIiUlKRjBwD4L1f771fH8tgpAIJJceZW+qo3rg0DjoIWrSA114rdJP+/WH8eMjOLse4REREElzEybFIVM2eDQ0bkrkiTfXGpVGpEgwdCl9+CStXFrhJ//5WdTF1ajnHJiIiksCUHEswMjKgQwcyM9WpotTOOsuGhd9+u8Crw5PyVFohIiISuYiSY+fc/s65M5xz54RPsQ5MklxGBtvadWb1aiXHpdajB3TrVmjXitatoXFjJcciIiIlUWxy7Jx7FXgEGAj0C536xjguSWYbN8Jff7GsaS9AyXGZnHkm/PyzNTXOxzkbPVZyLCIiErlIRo77AgO895d7768Mna6KdWCSxObMASCzbmdAPY7L5Iwz7PyNNwq8un9/65q3YkU5xiQiIpLAIkmOpwFKXyR6wp0qqtlCixo5LoNWreDAA620wvvdrtZiICIiIiUTSXLcAJjhnPvKOfdx+BTrwCSJzZ4NwNJKlhUrOS6js86C9HQYN263q7QYiIiISMmkRrDNXbEOQiqYjAxo1ozM1VVxDho1CjqgBHf66XDttTB8OOy33y5XVa8OPXsqORYREYlUJCvkfQ+kA7VCp5mhy0RKJ08bt4YNbWRTyqBWLas9HjkS1q7d7WotBiIiIhK5SLpVnAb8BpwKnAaMc86dEuvAJInNng0dO6rHcTRdfDFs2VLginn9+8OmTTBtWgBxiYiIJJhIao5vBfp578/13p8D7APcHtuwJGmtXWsrunXowNKlSo6jpk8fOw0fvtvEPC0GIiIiErlIkuNK3vvleX5fFeHtRHYX6lSh1fFi4JJLbK3ofK0p2rSxum4lxyIiIsWLJMn9MtSp4jzn3HnAZ8DnsQ1LklaoU0Xunh1Ztkw9jqPq9NOhZk347393uViLgYiIiEQukgl5NwDDgR5AT2C49/6fsQ5MklRGBjjHytrtyM7WyHFU1aplK+a99RasWbPLVQMG2NorS5cGFJuIiEiCiKg8wnv/nvf+Wu/9Nd77D2IdlCSxjAxo1Yqla6sCSo6j7pJLYOvW3SbmHXSQnf/wQwAxiYiIJJBCk2Pn3E+h8w3OufV5Thucc+vLL0RJKhkZOzpVgJLjqNt7b+jXz0or8kzM693bKi7GjAkuNBERkURQaHLsvR8YOq/lva+d51TLe1+7/EKUpOG91RyHJuOBao5j4uKLYfp0+PnnHRelpsLAgfC9OpSLiIgUKZI+x69GcplIsVauhHXrdkmONXIcA6efbvXHw4fvcvFBB8GMGbB8eSG3ExERkYhqjrvm/cU5lwr0iU04ktTCbdw6dmTpUqhd25Y3liirWRPOPtsm5i1btuNi1R2LiIgUr6ia45udcxuAHnnrjYFlwEflFqEkj1AbN/U4LgfDhsH27fCf/+y4qG9f+zCi0goREZHCFVVz/CBQB3glX71xfe/9zeUXoiSNjAxISYE2bcjMVL1xTHXsCMceC888A5s3A5CWZi3dNClPRESkcEWWVXjvc7HexiJll5EB7dpBWppGjsvD9dfDqlXwyis7Lho0CKZNs/JvERER2V0kNce/Ouf6xTwSSX6hThVgi1EoOY6xgQOtrdtjj0FuLqC6YxERkeJEkhwfDPzinJvrnJvinJvqnJsS68AkyXhvS7R16MCGDbBpk5LjmHMOrrvORuw/+QSwXLlaNdUdi4iIFCY1gm2OinkUkvwyMy0jzrMAiGqOy8HJJ0Pr1vDII3D88VSuDPvvr7pjERGRwhQ7cuy9XwjUBY4NneqGLhOJXL5OFaCR43KRmgpXXw0//QTjxgFWWjF1KqxeHWxoIiIi8SiSRUCGAa8DjUKn15xzV8Y6MEky4R7HHTqwdKn9qOS4nFxwAdSpA48+CtikPO9VdywiIlKQSGqOLwD29d7f4b2/A9gPuCi2YUnSyciAKlWgZUuNHJe3WrXgkkvgvfdg/nz22QeqVlXdsYiISEEiSY4dkJPn95zQZSKRmz0b2reHlBQyM6FyZdhjj6CDqkCuvBIqVYInnqBKFejfX8mxiIhIQSJJjl8Exjnn7nLO3Q38CrwQ27Ak6WRk7GjjFl4AxOkjVvlp0QLOOguGD4elSznoIJg0CdasCTowERGR+BLJhLzHgL8Dq4FVwN+990/EOC5JJrm5MHeuehwH7dZbISsLHn6Ygw6yuuMffww6KBERkfgSychxmAM8KqmQklq8GLZtsyWNQavjBWXPPeHss+HZZ9mvzVKqVoVvvw06KBERkfgSSbeKO4CXgT2ABsCLzrnbYh2YJJE8bdxgZ1mFBCA0elz1yYcZNAi+/DLogEREROJLJCPHQ4F+3vu7vPd3Yt0qzoxtWJJU8rRx274dVq3SyHFg9tzTao+ffZYj91/P7Nkwf37QQYmIiMSPSJLjBUDVPL9XAebGJBpJThkZUL06NGvGsmV2kZLjAN12G2RlceS8ZwD46quA4xEREYkjkSTH24DpzrmXnHMvAtOAjc65J51zT8Y2PEkKs2dbSYVz6nEcD0Kjxx1H3kPrFjkqrRAREckjNYJtPgidwsbEJhRJWhkZ0LMnwI7kWDXHAbvtNtxrr3FkvXG8/u3+bN9uvadFREQqumKTY+/9y865ykDH0EWzvPdZsQ1LkkZWlhW1nnoqgEaO40Vo9PjIN/+P/27fn19+gYMOCjooERGR4EXSrWIQkAH8B3gGmO2cOzC2YUnSWLAAsrN36XHsHDRqFGxYAtx2G4dkjyK1kkorREREwiKpOX4UOMJ7f5D3/kBgMPB4bMOSpJGnUwXAX39ZYpyWFmBMYvbck9rnn8IA/xNffrwt6GhERETiQiTJcZr3flb4F+/9bECpjUQmnByHFgD56y9o1izAeGRXd93F4JRvmDSjyo6SFxERkYoskuT4d+fcC865QaHT/4DfYx2YJInZs6FOHWjQAFByHHeaN+fIsxoCMOr5RQEHIyIiErxIkuNLgenAVcAwYEboMpHiZWTsaOMGSo7jUc9HzqaxW8ZXz84LOhQREZHAFdmtwjlXCfjde98NeKx8QpKkkpEB++8PWOOK5cuVHMebSvX3YHDvaXz2ezdyvvuelEPUtkJERCquIkeOvfe5wGTnXKtyikeSybZtsHDhjsl4y5aB90qO49GRV3ZkFQ34/aqX7UkSERGpoCIpq2iKrZD3rXPu4/Ap1oFJEpg71xKtPJ0qQMlxPDr86Mo45/lyegv44IPibyAiIpKkIlkh7+6yHMA5lwJMAP703h9Tln1JgimgUwUoOY5HDRpA3z7w5fQTueOWoXDccZAayduDiIhIcil05Ng5V9U5dzVwKrAXMNZ7/334VIJjDANmli1MSUgF9DgGJcfxasjRjl+39mLZrDXwwgtBhyMiIhKIosoqXgb6AlOBo7DFQErEOdcCOBp4vlTRSWKbPduGJOvWBSw5TkmBhg2DDUsKdtJJ4L3j4z2vgzvvhA0bgg5JRESk3BWVHHfx3p/lvf8vcApwQCn2/wRwI5Bb2AbOuYudcxOccxNWrFhRikNI3MrI2FFSAZYcN2liCbLEn+7doV07eL/BxTZ78t//DjokERGRcldUcpwV/sF7n13SHTvnjgGWe++LXDDEez/ce9/Xe9+3oYYUk0u4x3GIehzHN+ds9Pjb3+uy9sS/wyOPwJ9/Bh2WiIhIuSoqOe7pnFsfOm0AeoR/ds6tj2DfA4DjnHMLgJHAIc6516IQsySCTZsssVJynFBOOsn6UX92wEOQkwN33BF0SCIiIuWq0OTYe5/iva8dOtXy3qfm+bl2cTv23t/svW/hvW8DnA58570/K4qxSzybM8fO85VVKDmOb/vuC02bwgdjG8EVV8CLL8KUKUGHJSIiUm4i6XMsUnL5OlVs2warVik5jneVKsGJJ8IXX8Dma261yZQ33hh0WCIiIuWmXJJj7/0Y9TiuYGbPtvM99wQgM9N+VXIc/046CTZvhlET6sFtt8FXX8GoUUGHJSIiUi40ciyxkZFhmXDNmoB6HCeSAw+EevXg/feBf/wD2raFG26wGmQREZEkp+RYYqOAThWg5DgRpKXZAnmffALbXRV46CGrO37llaBDExERiTklxxIbs2crOU5gJ50Ea9fCmDHAqafaTL3bbrN6CxERkSSm5Fiib906WLFit04VaWlQv36AcUnEDj8catQIlVY4Zz2P//oLHnss6NBERERiSsmxRF++ThWws42bcwHFJCVStSocfTR8+GGo1HjgQGtj8a9/2ep5IiIiSUrJsURfEcmxJI4TT7Q8+OefQxc89BBs3Qp33RVkWCIiIjGl5Fiib/ZsGyJu337HRUqOE8/RR9sI8siRoQs6doTLLoP//Q9mzgw0NhERkVhRcizRl5EBrVpZZhWi5Djx1KoFJ5xgyfH27aEL77jDipH/+c8gQxMREYkZJccSffnauG3aZHP0lBwnnnPOgdWr4fPPQxc0aAC33GJ93saMCTI0ERGRmFByLNHl/W5t3LQ6XuI6/HBo3Dhfi+OrroKWLeH66+35FhERSSJKjiW6Vq2yBrn52riBkuNElJoKZ5wBn35qI8gAVKsG994Lv/8O770XaHwiIiLRpuRYoquQThWg5DhRnXMOZGXBW2/lufCss6BLF1sYJDs7sNhERESiTcmxRNfs2Xau5Dhp9OwJ3bvnK61ISYH774dZs+DllwOLTUREJNqUHEt0ZWRY4tS27Y6L/vrLvomvUyfAuKTUnIOzz4Zff9352QeA44+3ZaXvusv6H4uIiCQBJccSXRkZlhinpe24SKvjJb4zz4RKleC11/Jc6Bw88AAsWQLPPhtYbCIiItGk5FiiK1+nClCP42TQrBkceii8+irk5ua54pBD4LDDLElevz6w+ERERKJFybFEj/c2cpynUwUoOU4W55wDCxbATz/lu+KBB2DlSnjssSDCEhERiSolxxI9S5faih95Ro69V3KcLE480RbHe/XVfFf06wcnnwyPPgorVgQSm4iISLQoOZboKaBTxYYNli8rOU58NWrAKadYS7cNG/Jdee+9sHkzPPhgILGJiIhEi5JjiZ5wj2MtAJK0Lr3UEuPXX893RefO1tLi2WftGwQREZEEpeRYoicjAypXtqWFQ5QcJ5d994W994Znnilg5ehbb4Xt2+GRRwKJTUREJBqUHEv0ZGRA+/bW5zhEyXFycQ4uvxymToWxY/Nd2aGD9Xx75hlYvjyQ+ERERMpKybFEz+zZBXaqAGjaNIB4JCaGDrUFXZ55poArb7sNtm3T6LGIiCQsJccSHbm5MHdugT2Oa9WykySHGjXg73+Hd9+FZcvyXdmxo2XP//mPOleIiEhCUnIs0bFkiS0hrAVAKoRLL4WsLHjhhQKuvO022LLFWruJiIgkGCXHEh3hNm5aAKRC6NTJFsZ77jnIycl35V57wemnw9NP2+IgIiIiCUTJsURHuI2bRo4rjMsvh8WL4bPPCrjy9tut77FWzRMRkQSj5FiiIyMDqlffJRMOr46nyXjJ6dhjoUULKy/eTefOcNpp8NRTsGpVuccmIiJSWkqOJTpmz7ZRY+d2XLRqlTUuaNEiwLgkZlJT4ZJLYNSonV8c7OL22215xP/7v3KPTUREpLSUHEt0ZGTsVlKxZImdKzlOXhdeaOu+PPFEAVd27QrHH2+1xxs3lndoIiIipaLkWMouOxvmzdstOV682M7zLJgnSaZJE1s1esSIQjq33XgjrFljG4iIiCQAJcdSdgsWWIKcr1OFRo4rhhtusPKZp58u4Mr+/eGAA6ytW1ZWuccmIiJSUkqOpewK6VSxeLHVpTZuHEBMUm46ddpZPbFpUwEb3HgjLFoEb79d7rGJiIiUlJJjKbv0dDvv1GmXi5csseYVKSkBxCTl6sYbYfXqQhYFGTIEunSBhx+2FiYiIiJxTMmxlN2sWVC/PjRosMvFixer3rii6N8fBg4spHqiUiXLnqdMga++CiQ+ERGRSCk5lrJLT99t1BgsOVa9ccURrp54550Crhw61F4MDz9c7nGJiIiUhJJjKbtZs2zJ4Dy8t7IKjRxXHEcfXUT1ROXKcM01MHo0jB8fSHwiIiKRUHIsZbNuHSxdutvI8cqVWgCkoqlUyTpXTJ5sC4Ps5qKLoG5djR6LiEhcU3IsZTNrlp3nGzkOt3HTyHHFcsYZNgnzoYcKuLJWLbj8cnjvPZgzp9xjExERiYSSYymbcHKcb+RYC4BUTJUr2+jxmDHwww8FbHDlldbf78knyzs0ERGRiCg5lrJJT7dkp127XS7WAiAV1yWX2Mp5d91VwJVNmsDpp8OLL1pJjoiISJxRcixlM2sWtG8PaWm7XKwFQCquatXgppts7t333xewwbBhsHGjJcgiIiJxRsmxlE0hbdyWLIHmzW2SllQ8F18MTZvCnXcWcGWfPrD//vDUU5CTU+6xiYiIFEWpi5ReTo4tHZ1vMh5oAZCKLjx6/P33Vn+8m2HDYN48+Oyz8g5NRESkSEqOpfQWLIDt2wsdOVa9ccV20UU7R49363t84on2Avm//wskNhERkcIoOZbSK6SNmxYAEbDR45tvtq4Vu40ep6XBP/4B330HU6cGEZ6IiEiBlBxL6aWn23m+keMVK7QAiJiLLrK+xwWOHl90kWXQausmIiJxRMmxlN6sWVC/vp3y0AIgEla1qo0e//gjfPttvivr14ezzoLXXrMlFUVEROKAkmMpvVmzCp2MBxo5FnPRRdCqlSXJu40eX3UVbN0K//tfILGJiIjkp+RYSq+INm6gkWMxVarAPffAhAnw7rv5ruzWDQ49FP7zH8jKCiQ+ERGRvJQcS+msXQvLlhU6cpyWBo0alX9YEp/OOgu6doVbby0gB77qKvjzT/jkk0BiExERyUvJsZROuFOFFgCRCKSkwAMPWFvs3RbGO/po+5rh2WcDiU1ERCQvpS9SOoW0cQMbOVa9seR37LEwYADcdRds3pznipQUW1Lvm28sexYREQmQkmMpnfR0SE2Ftm13u0o9jqUgzsFDD0FmZgHd2y680F5Pzz0XSGwiIiJhSo6ldGbNgvbtrbg4j9xcrY4nhRs4EI45xpLk1avzXNGkCZx0ktVcbNkSWHwiIiJKjqV00tMLLKlYudJWlNbIsRTmgQdg/XpLkHdx2WWwZg289VYgcYmIiICSYymNnByYM6fAyXjqcSzF6d7dulc89dTOtn8AHHQQdO6siXkiIhIoJcdScgsW2PBwEQuAaORYinLPPVaCc/fdeS50Di69FH77DX7/PbDYRESkYlNyLCWXnm7nWgBESqlNG6uiGDFi58sJgHPOgerVNXosIiKBUXIsJVdEj+PwAiANG5ZzTJJwbr0VatSw8x3q1oUzzoA33rCFZkRERMqZkmMpufR0aNAA6tff7apwpwotACLFadgQrr8e3n8fxo3Lc8Vll1nHildeCSw2ERGpuJTCSMnNmlXgqDFoARApmWuusST5ppvA+9CFvXvDPvtYz+MdF4qIiJQPJcdScoW0cQMtACIlU6sW3H47jBkDo0blueKSS2DmTPj556BCExGRCkrJsZTMmjWwfHmBI8daAERK45JLbKHFm26y1xAAp51mmfP//hdobCIiUvEoOZaSmT7dzrt23e2qFSsgK0sjx1IylSvDvffCpEl51v+oWROGDoW339bEPBERKVdKjqVkZsyw8y5ddrtKC4BIaQ0dCj16WIlFVlbowosusol5b7wRaGwiIlKxKDmWkpk+3fpvtWq121XqcSylVamSLSs9d671PgagTx/o1Quefz7I0EREpIJRciwlM326jRoX0KtNI8dSFkOGwIABtmre5s3YinkXXQQTJ2rFPBERKTdKjqVkpk8vsN4YYOFCqFJFC4BI6TgHDz0EmZnw9NOhC884A6pV08Q8EREpN0qOJXKrV8PSpQXWGwMsWGDLAmsBECmtgQNtBPnBB60xCnXrwqmnWt3xxo1BhyciIhWA0hiJXHgyXiEjx/PnW3IsUhYPPGANKv7979AFF10EGzZY5woREZEYU3IskSuijRvYyHHbtuUXjiSnnj2te8X//Z+VWDBggC06o4l5IiJSDpQcS+SmT7f+swV0qli/3qoulBxLNNxzD2zfDvfdhxUjX3gh/PLLzg9oIiIiMaLkWCI3fTp07mzJSj4LFti5yiokGvbc06ophg+HefOAc86BtDRNzBMRkZhTciyRK6JTxfz5dq6RY4mW22+3fPiOO7AWKCeeCK++Clu3Bh2aiIgkMSXHEplVq2DZsiLrjUEjxxI9TZvCsGHWqGLKFKy0YvVq+OCDoEMTEZEkpuRYIhNBp4oaNaBBg3KMSZLejTdCnTpw663AoYfaVxMqrRARkRhSciyRCU+EKqbHcQHlyCKltsce8M9/wqefwthfKsEFF8Do0TBnTtChiYhIklJyLJEpolMF2Mix6o0lFq66ykosbroJ/Lnn2SozausmIiIxouRYIjN9uo0aFzA07P3OkWORaKte3Sbn/fQTfDGlORx9NLz0EmRlBR2aiIgkISXHEpkZMwqtN16zxvoca+RYYuXCC6F9e7jlFsi94CKbHPrpp0GHJSIiSUjJsRSvmE4VauMmsZaWZguDTJ4M724eAs2aaWKeiIjEhJJjKV4Ek/FAZRUSW3/7m70E774vhZxzz4cvv4RFi4IOS0REkoySYyleODnWyLEEKCXFFgSZMQPeaXKlFbu/+GLQYYmISJJRcizFmz4datWCli0LvHrBAutFW7duuUYlFdCpp9pntLufbUTOYYPhhRcgJyfosEREJIkoOZbizZhRaKcKUBs3KT+VKsGdd0J6OrzV+S5YvBhGjQo6LBERSSJKjqV44TZuhVAbNylPJ58M3brBPV/tS06DxpqYJyIiUaXkWIq2ciUsX15ovXG4x7FGjqW8hEePZ812jNznMfj4Y8jMDDosERFJEkqOpWjFTMZbsQI2b9bIsZSvk06CHj3gnhmnkJ2DJuaJiEjUKDmWoqlThcSh8Ojx7AWVeXOve2w56dzcoMMSEZEkoORYijZjhnWqaNGiwKvDPY6VHEt5O+EE6NkT7lt3BTnzF8K33wYdkoiIJAElx1K08GS8IjpVgMoqpPxVqgS33gqzM2vzXo1zNTFPRESiQsmxFM57mDrVWgMUYsECaNAAatYsv7BEwk46CTp1gvur3Yd//wNb5lxERKQMlBxL4f78E1atgl69Ct1k/nyNGktwUlLglltgyspmfJpzJLz8ctAhiYhIglNyLIWbNMnOi0iO1cZNgjZ0qH1Au7/WQ/jh/7NvPEREREpJybEUbvJkO+/Ro8Crc3O1AIgELy0NbroJxm3oyrdzW8OYMUGHJCIiCUzJsRRu0iRo3x5q1y7w6qVLYft2jRxL8M47D5o19dyfeicMHx50OCIiksCUHEvhJk2yXlmFUKcKiRdVqsANNzrGZB/A2HczbWVHERGRUlByLAXbsAHmzCm23hg0cizx4aKLoMEe2dyffSO88krQ4YiISIJSciwFmzLFzovpVAHQunXswxEpTo0acO0NqXzBECY+9ZMm5omISKkoOZaChSfjFTNy3KQJVKtWLhGJFOuyy6BW1e08vOBU+OmnoMMREZEEpORYCjZpEtSrV+iy0WAjxyqpkHhSty5ceim8zWnMf+yDoMMREZEEpORYCjZpko0aF7JsNGgBEIlPw66vTEolz6Mfd4DVq4MOR0REEoySY9lddrYtG11Ep4qsLFi0CNq1K8e4RCLQvDmcfew6RuSey4rn3gs6HBERSTBKjmV3s2fD1q1F1hvPmwc5OdCxY/mFJRKp6x+szxaq8/QT2ZqYJyIiJRKz5Ng519I5N9o5N9M5N905NyxWx5Ioi2Ay3uzZdt6pU+zDESmpzp3h+J4LeHrFaWwa/VvQ4YiISAKJ5chxNnCd974zsB/wD+dclxgeT6Jl0iSoXBn22qvQTWbNsnONHEu8uvGRhqymPi/cPCfoUEREJIHELDn23md67/8I/bwBmAk0j9XxJIomTYKuXS1BLsTs2dCwIeyxR/mFJVIS+x9Wg4GNM3h0/AFkrVwXdDgiIpIgyqXm2DnXBtgbGFfAdRc75yY45yasWLGiPMKRongPEycWORkPbORYo8YS7/75T1jkW/H29SqtEBGRyMQ8OXbO1QTeA6723q/Pf733frj3vq/3vm/Dhg1jHY4UZ+lSWLGiyHpjsJFj1RtLvBty1Z50qTqXR95qic/VxDwRESleTJNj51walhi/7r1/P5bHkiiJYDLe+vWWQ2vkWOJdpRTHtacsZtLWvfj+2RlBhyMiIgkglt0qHPACMNN7/1isjiNRNmmSnRdRVhHuVKHkWBLBmY/3paFbwWMPbg06FBERSQCxHDkeAJwNHOKcmxQ6DYnh8SQaJk2yZe/q1i10k3CnCpVVSCKo2qAml/ebwCd/9mH2zyuDDkdEROJcLLtV/OS9d977Ht77XqHT57E6nkRJeNnoIsyeDZUqQfv25RKRSJld9lgHqrCVJ65ZEHQoIiIS57RCnuy0aZNlvhF0qmjTBqpUKZ+wRMqq8YA9OavZaF4a35VVy7KDDkdEROKYkmPZado0a+UWwcix6o0l0Vxzc1W2+Gr895r0oEMREZE4puRYdgpPxisiOfZebdwkMXW97EAGV/2ep95ryrZtQUcjIiLxSsmx7DRxItSpA61bF7rJX39Z9YVGjiXhpKRw7RlLWbq9Pm89uiToaEREJE4pOZadxo+HPn3AuUI3UacKSWSHP3QoXd10HnvMvgURERHJT8mxmC1bYMoU2HffIjdTj2NJZK5hA67t/yuTV7Vg9Kebgg5HRETikJJjMX/8AdnZxSbHs2ZB9erQvHk5xSUSZWc83ItGLOOxm1cEHYqIiMQhJcdixo2z8whGjjt0sD7HIomo6oA+/KP5R3w2vQ2zZuYGHY6IiMQZpThixo2DVq2gSZMiN5s1S/XGkvguu72BLQpy3eKgQxERkTij5FjMuHHFjhpv3w7z56veWBJfw/OP5Zwa7/PyV41ZqRWlRUQkDyXHAsuWwcKFxSbHc+dCbq5GjiUJpKVx9WXb2JJblf/e+VfQ0YiISBxRciwlqjcGjRxLcuhy64kclfIVT79YXYuCiIjIDkqOxZLjlBTo3bvIzcI9jpUcS1KoW5drjp3L0i11GfnsmqCjERGROKHkWCw57tHDerQVYfZsaNQI6tYtn7BEYu2wR4+iG1N57IGtWhREREQAJceSm2sr4xVTUgHqVCHJx7Vry7V9f2DKiqZ899mWoMMREZE4oOS4oktPh/XrI0qOZ89WSYUknzMe6UNjlvLoP5cFHYqIiMQBJccVXYST8dauheXLNXIsyafKQftxRcuP+WJGG2ZMzQk6HBERCZiS44pu3DioU6fYrFedKiSZXXpXE6qxmcev1aIgIiIVnZLjim7cOOjXr9j1oNWpQpJZg3OGcG6tD3j126YsW6qZeSIiFZmS44ps82aYOjWieuPp0yEtDdq3L4e4RMpbairXXFeJ7T6NZ26cH3Q0IiISICXHFdnvv0NOTkTJ8aRJ0KULVK4c+7BEgtDxnydybNWveWZkPbaocYWISIWl5Lgi++03O48gOZ48GXr2jHE8IkGqWpXrLljHyqy6vHLn3KCjERGRgCg5rsjGjYM2bWxljyIsWwZLl0KvXuUSlUhgDnjoaPqmTuSxZ6qQmxt0NCIiEgQlxxXZuHERjxqDRo4l+bmaNbju5IXM3tSCz57U6LGISEWk5LiiWroUFi1SciySzyn/OZhWbjGP3r816FBERCQASo4rqvDiH/vsU+ymkydDixZQv36MYxKJA6n16zDs0Gl8v7Irv78zL+hwRESknCk5rqh++slaT/TuXeymmownFc2Fw/ehNut49MalQYciIiLlTMlxRTV6NOy3H1SrVuRmW7fCzJlKjqViqd22Phf1mcjbC/Zh0Y8Lgw5HRETKkZLjimjtWpg4EQ4+uNhNZ8ywVshKjqWiuerZzgA8eemMgCMREZHypOS4IvrhB8jNjSg5Dk/GUxs3qWha9WvMqXtN5X8z9mf9uJlBhyMiIuVEyXFFNHo0VK1qZRXFmDwZqlfXstFSMV33VFvWU4fnL/w16FBERKScKDmuiEaPhv33hypVit100iTo3h1SUmIflki86XtYXQ5svZD/m3YI2WPHBR2OiIiUAyXHFc2qVTYcHEFJhffqVCFy3b8asojWvHvJ10GHIiIi5UDJcUXz/fd2PmhQsZsuXmxz91RvLBXZMadWp2PDNTw6fTD+62+CDkdERGJMyXFFM3q0FRFHuPgHaORYKrZKleCaO2oygX78eNU79pWKiIgkLSXHFc3o0TBggC0AUoxJk+y8e/fYhiQS7845P40GNbfyr/Tj4IMPgg5HRERiSMlxRbJ8OUyfHlG9MdjI8Z57Qq1aMY5LJM5Vrw5X35jG5xzNxOtfh+zsoEMSEZEYUXJckYTrjUuQHKukQsT848oUalfP4oH5p8PzzwcdjoiIxIiS44pk9GioWRP69Cl20w0bYO5cJcciYXXrwhXDUnmPk5l508uwenXQIYmISAwoOa5IRo+GAw6AtLRiN5061eYdKTkW2enqaxxVq8JD6y6DO+8MOhwREYkBJccVRWYmpKeXqKQC1MZNJK+GDeHiSyrxujuT+c98YZ8iRUQkqSg5rijGjLHzEiTHdetCy5Yxi0gkIV1/PVRKrcTDqbfAsGFq7SYikmSUHFcUo0dD7dqw994RbT5pkpVUOBfbsEQSTYsWcN55jhG55/LX6HR4//2gQxIRkShSclxRjB4NBx4IKSnFbrp9O0yZEnEeLVLh/POfkJ1biUcbPAjXXQdbtgQdkoiIRImS44pgyRKYMyfikoqJE+1//cCBMY5LJEG1bw9Dhzqe23gWyxduhn//O+iQREQkSpQcVwRffGHnhx8e0eY//WTnAwbEKB6RJHD77bB1ewoPdngRHnwQMjKCDklERKJAyXFF8NFH0KYNdOsW0eY//WQr4zVpEtuwRBJZp05w7rnw7KIhLE5rBxdeCLm5QYclIiJlpOQ42W3cCN98A8cfH9HsOu8tOVZJhUjx7rwTcnMd9+79HvzwA/z3v0GHJCIiZaTkONl9/TVs22bJcQRmzYKVK5Uci0SidWu49FIYMbYTc/Y/B268ERYtCjosEREpAyXHye6jj6xhcYTZbrjeWMmxSGRuuQUqV3bc2eA/9tXLJZeo97GISAJTcpzMsrPh00/h6KMjWjIaLDlu0AA6doxxbCJJokkTWwvkzU9qMvXK4fDll/Dqq0GHJSIipaTkOJn98gusWhVxSQXsrDfW4h8ikbvhBltj5/YZQ63Ny9VXw9KlQYclIiKloOQ4mX30kY0YDx4c0eaZmTB3rkoqREqqXj1bVvqjjx3jrnodNm+2YmSVV4iIJJzUoAOQGPHekuNDDrEhrQiMHWvnSo5FSm7YMHjqKbj2/1rz0/0P4K6/Dp55Bv7xj6BDk2jatAlmz7Zv5TZvtt83b4atW6FOHWjUaOepYcOIS9pEJH4oOU5W6em2Kt6110Z8k59+gmrVtGy0SGnUqmVrgVxwAbxx6dWcOeRb+/sbMAB69Qo6PCmNP/+Eb7+F33+399T09JJ1I0lNhZ49oX9/2G8/O2/bVnVrInHO+Tj62q9v375+woQJQYeRHB56CG6+GRYvhhYtIrpJ3772D3706BjHJpKkcnNh333hr79g1tiV1BzQE2rWtOSqZs2gw5PibNgAo0ZZQvzdd9bbEqBGDdhrr11PjRvb5dWr23mVKrBuHSxbBsuX2/miRTBuHPz2m40wg70fn346nHUW9OihRFkkIM653733fQu8TslxkurfH7KyIMLHc8MG6/h2yy1w772xDU0kmf36q/353XwzPDD4eyttOvNMeOWVoEOTgmzdCl98AW++CZ98Yr/XqAEHHWTP3aGHWhJbqQxTdLKzYfp0myT9+ed2vOxsW7X0zDPh7LOhefPo3ScRKVZRybEm5CWjpUtttKIEXSrGjbNRL9Ubi5TNfvvBOefAo4/CnOYHwR13WGu3l18OOjTJa9w4q4Fp0gROOgnGjLHfv/8e1qyBzz6D666zkpiyJMaws7zi0kvh449t9vMzz9h8kJtvtlKLSy+FBQuicMdEpKyUHCejTz+1CXnHHRfxTX76yd7/+/ePYVwiFcRDD0HlyqGS/9tug0GD4PLLYebMoEOr2LZutRH8ffaxTzFvv22DCF9+abUwTz8NBx4Y+0l0DRrAZZfZLOg5c+DCC+HFF6FDB0vQ58yJ7fFFpEhKjpPRRx/ZurY9ekR8k59+ss0jbGwhIkVo2tQGjD/5BL78OgVef92+qj/hBFi9OujwKp7ly+1DSqtWcO65Vkf29NOWEL/8srW7TA1ofnr79jaKPHeuJcxvvAGdOtnPa9YEE5NIBafkONmsXw/ffGOjIRFO9MjKsjpJlVSIRM+wYTYQOGwYbK3XDN5/3742P+UU2L496PAqhiVL7Alo0wYeeAD23x++/hpmzLAWe7VqBR3hTi1awJNPwrx5Ftvw4ZYkv/KK+mWLlDMlx8nmrbfsq8Mzzoj4JpMn20RqJcci0VO5MvznP9YS9/bbsT+w55+3djCXX66EJ5bmzYOLL4Z27WxU9m9/s5KWDz+Eww6L7w4RTZtakvz777DnnjbSPWiQTegTkXKh5DjZjBgBXbpYTV2Evv3Wzg84IEYxiVRQhx8Ol1xik/PGjsW6Etx6K7zwAjz2WNDhJZ8//7SJbeER14susvrdF1+0yxJJr15W7/a//8G0afb7ffdBTk7QkYkkPbVySyYzZkDXrvDIIzbLOkL77w/bttlAhYhE14YN1qigUiX7lqZGtVwbyXzvPfjggxJ1lZFCrFxpsyCfftra7lxyifWlbNo06MiiY+VKuOoqazd3wAHW/aR166CjEkloauVWUbz4ok0qOeusiG+ybJnVG+v/s0hs1Kplf5pz58I//4llyS+/bKvunHEGjB8fdIiJa9MmuPtua4X2+OMwdKjVsTz1VPIkxmDdLd54w5LiSZPs09bIkUFHJZK0lBwni6ws+xrxmGNs5aYIffKJlT4qORaJnYMOgquvthrkb7/FVlX76CP7Wz3iCPjjj6BDTCw5OVa/3aED3HWXdZuYNs0+hbRpE3R0sXPWWZYcd+5sHwTOO2/nynsiEjVKjpPF559bu6Lzzy/RzT780P6XlKDrm4iUwgMPQMeO9ie6fj02svndd9Y/8fDDreZCiua9rS7Xq5fVE7dpY8Xc775rCWNF0K4d/Pij9Qp85RVrTj93btBRiSQVJcfJYsQIW+npqKMivsnGjSXu+iYipVStmlVTLFli6zx4jyV3o0fbSPKhh8LUqUGHGb8mTbJR9iFDYMsWS4jHjrVJExVNaqqVk3z5pb2g+va1Dw0iEhVKjpPB0qW21Ok555Sokf2oUTYRTyUVIuVjv/3gX/+yvO6BB0IXtmtnCXKVKpYgz5gRaIxxZ8kSKx/o3dvKT554wh6jk0/Wp/ojjrCZ1K1bw9FHWzeL3NygoxJJeEqOk8Frr1kN3t//XqKbffQR7LGHWriJlKfrroMzz7QF2z75JHThnntagpySAoccohpksNqT226zWpQ334Trr7fygWHDrIm0mLZt4eefbXLn7bfbh4aNG4OOSiShKTlOdN5bScX++8Nee0V8s+xs+PRTG2wIatVUkYrIOWtd27u3JckzZ4au6Nhx5wjyAQfAxx8HGmdgtm2z0eH27eH+++HEE2HWLHj4YahbN+jo4lP16tbJ4okn7HVzwAE24i4ipaLkONGNG2f/XUs4EW/sWFi9WiUVIkGoVs0mw1arZn+Da9eGrthrL/ub7toVTjjBkp046kUfUzk5luB16gTXXGOT7iZMgNdfT+4OFNHinI2qf/aZjbDvs489fiJSYkqOE90LL9iowWmnlehmH31k30wOHhyjuESkSC1b2jogCxbYmiDbtoWuaNIExoyBk06yJPGKK+yrnmTlvXXb6d3b5k3Ur28TIr7+Gvr0CTq6xHPkkVZmUaUKHHigvchEpESUHCeypUut3njoUFtpIELeW3J86KElupmIRNnAgfDcc5YLnnxyngS5enV4+2248UZ45hmrf1q2LNBYY2LcODj4YLt/GzdabfH48dbaTkqvWzd7bHv1glNOgQcfrDjfQESZ9/ant+PbHakQlBwnskcfhe3bQ8tuRW7aNJg3z761FZFgnX8+PPusfRu+S4JcqZK1tnj+efjhB0t4PvwwyFCjJz3d7ux++1lZ2NNP2/npp9v9lrJr1Mj6aJ9xhi2l/fe/53lxSUG8h19+sbmfxx8P3btDzZr2Zc4ee9hChf372xccDz0ECxcGHbHEivNx9Gmyb9++foJqpCKzcqXV4R1/vNXklcB999mk5r/+Sq4VVkUS2X//C5deaoOo771n34rvMGOGrY42caJl0088kZhf+0yZYpPs3nkHatSAG26w0pFEvC+Jwnu49164806bqPf++5blyQ6rV1u5+//+B9OnQ9Wqtvhiu3Z2atvWxqEyMnaeFi+2Mu8hQ+zv9qijrNmMJA7n3O/e+74FXum9j5tTnz59vETollu8d8776dNLfNPevb3fd98YxCQiZfLf/3oP3g8Z4v2WLfmu3LbN/u4rVfK+bVvvR48OIsTS+fVX74891u5crVre33ST98uWBR1VxfLmm95XqeJ9+/bez5wZdDRxYcUK7y++2B4W8H6ffbz/3/+837Ch+NsuXOj97bd737Sp3bZlS++fesr77OzYxy3RAUzwheSj+v4qEa1ZA089ZV9LdulSopuOH28tVM88M0axiUipXXyxjSB//rnNpVq0KM+VlSvbqOsPP9jvBx8Mxx0Xv4uGZGXZaieDBln5xNixtqrbwoVWA9uoUdARViynn24TPTdssNqAb78NOqLAeA8vvmjNYUaMsIqTSZOsTPvCC62UojitWsE999jL+b33bIT5yivtpT5xYszvgsSYkuNE9NRT9gZ3220lvul//mN/+OeeG4O4RKTMLr7YvvlOT7dmDbvlMAMG2MSB+++H77+3wsgLL4Q//wwk3t0sW2a1W23bwqmnWvbw739bW4477rDiTQnGfvvBb79BixbWqmj48KAjKnfTp8NBB1l10l572WDRs89Cz56l219amjWWGT3a5pMuXmyreV93ndZiSWiFDSkHcVJZRQTWrfN+jz28P+64Et90+XL7+ujyy2MQl4hE1axZ3nfpYlUUDz7ofW5uARutWOH9sGHep6V5X62a95dd5v0ff5R3qN5v2uT9W295f8IJFgt4f8QR3n/8sb5njkfr1nl/1FH2PF1zTYV4jnJzvX/iCXt51qvn/fPPe5+TE/3jrFnj/SWX2EPbqpX3v/wS/WNIdKCyiiTyzDNWVnH77SW+6Qsv2GTlf/wjBnGJSFR17Ghf8556Ktx8Mxx7rA2+7qJBA5ucl55uG44YYf2C+/a1HnHr18cuwA0bbDW2M8+0Eom//c0C/sc/rPPEV19Z0JqlFH9q17bn7qqr4PHHrXXRhg1BRxUzGzdax9Orr7aJc+npcMEFsWmMUreu/emNHWsv/QMPtJHpOOp9IBFQt4pEsmmTdajo2xe++KJEN83OttVY99yzQpeaiSQc762S6uabITfXzm+80WbU72b1aute87//wdSptgTfAQfY98gHHQT9+lntcmls2mT/8UePttrV8eNtVbt69ayX7umnWyagZDixPPOMJcldu1rC3Lp10BFFVXq6lT3MmmWVSDfeWH7dAlevtiYzX3xhpYzPPmt/khIfiupWoeQ4kfz73/aXPXYs7L9/iW764Ydw4olWy3jiibEJT0RiZ/Fi67/69ts2+eeJJ+CYY6yd1G68t+T1tdcsmZ02zS6vVs0+XLdpA82b26lFCxvu2r7dvloKn2dm7t67CiA11ZLsgw+204EHlj7hlvgwapStspqaCiNHwmGHBR1RVLz3Hpx3nr3s33zTFr4qb7m51knv7rutrvn9960cX4Kn5DgZLFpknSkOOshWCyihww6D2bNt8Y/U1BjEJyLl4rvvbFb8jBmW5157rQ3cpqUVcaOVK+HHH20C34QJsGSJTeArblnqevWsvqNDBzvts49NCIxkOr8klowMGzmZOdO6idxwQyGfvOKf9/DIIzaWtO++1jSlRYtgY/r8c6tASkuzAfr99gs2HlFynPi8tyGi77+3qbYl/Npr5kzLq++/3xZKEpHElpVlcwgef9w+9LZoAVdcARddZPlsRHJzYcUKS5LXrbNVRypX3nneoAHUrx/T+yFxZuNGK8Z9+237xDViRMIt0JKdDcOGWbXIqafCyy/HTynD7Nm2yM+SJfDKKxafBEfJcaIbOdJmEzz+uM0oKKErr7SOPYsXq7WoSDLJzbURqccftxHltDT7lujkk23xTC2EJiXmPTz6KPzzn9br7K23bOnyBLBpk5W+f/qpDXw/9FD8rUa+cqXNfxw71uK78caEHaBPeEqOE9mqVdC5s9UI/vJLiSe7rF9vZYUnnmifVEUkOU2ebCXG770H8+fbW8VBB8Hhh1tZcN++Kg2WEvj2W6sDWLvWahT+8Y+4zuKWLrUvWCdOtAmsl18edESF27rVFh4ZOdK+7fnPf4opi5KYKCo5VvVpvLv+emvd9s03pZoF/n//Z9+UXXFFDGITkbjRs6edHn7YEoT33oOPPrLuFmBfLffvb6e997ZT27Zxne9IkA49FKZMsSzuyivhyy+tzCIOv36cOdNatK1YYZPPjz026IiKVrWqNZVp1w4eeMBaNL7zDtSpE3RkEqaR43j2zTc27HPLLVYwXEILFtig87HHWgmZiFQ8K1bATz/ZqtPff2/5Tk6OXVe7tiXU3brZe0WXLnZq0kRJs4R4D08/bXUKdevaustHHRV0VDt8/72VKVSpYuUUfQscB4xfI0bAJZdYBctnn9my1FI+VFaRiDZvtmVhU1Pt+9ICm5oW7eST7cP+zJn6gxMRs3WrdXabOBEmTbLTjBn27XlY3bo7E+UuXXYmzi1bKmmusKZOhTPOsBfP0KHw2GP2KSpAr79uA9t77mm1923aBBpOqX37rf2/rlbNEvw+fYKOqGJQcpxovIdzzrECwjFjrHCwhEaNgsGD1aFCRIrnvdVszpxpiXLe04oVO7erWXNnoty9uy3Gt/felkxLBbB1q80ie/BBqF7dfr7oonKf9ea9lSPcdpv9e/zgA9hjj3INIepmzIAhQ+zv7Y03bEKtxJaS40Rz991w111w331w660lvvn27faPKzfXPuRXqRL9EEWkYlixwpLmvInz9Om2RkhY27Y22tW/v61PtPfeet9JarNmwaWX2uBN//7WN61Xr3I59NatcOGFNmp81lnw/PPJ81pbtszKICdMsA40V12lb2piSclxInntNTj7bFtr8sUXS/WX8a9/wU032ddMcVQaJiJJZMUKK8344w87jR9v8xzAkpW+fa1LxuGHW8KcLAmMhHgPr74K111n/clOPdUGdjp3jtkhly2z+uJff7VvRW++OfmSx82bLen/4AObSP/441q4K1aUHCeKH37Y+Z/kq69K1XdpyRIr7D/sMJu1KyJSXjIzrePkzz9bH9fx423yX7VqOxPlww+3b7aSLampsNassfrjJ56wzO7MM+HOO6F9+6geZvJkG1Vdtcpy8pNOiuru40purrWZfuQRWzRk5EgtShkLSo4TwezZ9vVUw4b236UUBVTeW1H/55/bV6Bav11EgrR+vXUT+PprO6Wn2+WNG9sH+MMOgyOOgGbNgo1TomDlSusj+PTTVtt36qnWhuGgg8r8Sej1121Xe+xhSy/vvXeUYo5zzz5ro8fdu9sS2HvuGXREyUXJcbxbsgQOPtimi//6a6k/cd96q01S+Pe/rT2yiEg8WbLEOlR+/bWdL19ul3fvDkceaZOIBw5UCUZCy8y0f0Ivvmj/0zp1ssz23HNLsLa52bzZWiyPGAEHHGCL9TVtGpuw49WXX1qTkOxsq68+7bSgI0oeSo7j2e+/w3HHwYYN9lew//6l2s3//gcXX2wTFYYP11eWIhLfcnOtO9ioUfbW9+OPkJVlTRAOOcQS5SOPjO/RMu/hr79g0SKrwQ6fVq+2+5Kbu/NUpYrlhvXr26lhQ7tvLVok6fv15s22ssV//2vfhlaubE/s0UfbUnbF9F2bPt0SwZkzbeDnzjsrbu3twoW2LPavv9o8yMcfL1V3V8lHyXG8+vBDq89q0MCaG3bvXqrdfPmlvdccdhh88omWoRSRxLNxozU/+PJLm3IxZ45d3q6d5VQDB9qpXbvyTybXrbPKt9mzrVFD+OfZs2HTpt23r1LFcsGUFOtyVqkSbNlS8LbVq9vgaqdO0KOHVdf17ZtkNaZTpsBLL9n/uYwMu6xLF5sx3q+ftTpp1w4qVSI31wZ4rr3WFql57TX731bRZWXZh4R//9sW7nnjDXsIpfSUHMcb7+HRR+HGG+2N4aOPSt1MfdIk+7qpfXsbealVK7qhiogEYc4cS5K/+spW+Fuzxi5v3Ni+YOvRw8YTune397+UlLIdb9MmGwHOyNiZAIfPly3buV2lSjbo2bGjJbQdO9rvDRvuPNWoUfAxtm2zCWWrVllJSUaG1WHPmmXn4W4flSrtTJSPOMI+HNSuXbb7Fzdmz7al4D77zP5pbd9ul9euzbSOJ3Hxktv5ZWk7Du+WySv3LqRJz8b2oFavHp1+yrm5lmlmZVmtQlaWXZ6aai+ilBT7uXLluBzS/+wzq1BZv966Ut1yi0aRS0vJcTxZtw6uucbqsU49FV5+2aZyl0J6Ohx6qL1f/PorNG8e5VhFROJAbq59vf7TT3b67TdLnnNz7fqqVW0V0GbNrCa1WTP7Qi4tbecpNdW+6V+/3t6G16+3EohFi+xr61Wrdj1mw4a7JsDh8/btY1cTvWoVjBtn7+e//GLnGzda7Pvvb2UmRx5pI4flvO5GbGzfDtOns/mXydz7fFMemXQodVjHY/4azuZVdktNa9SwEaAaNexBcW7nCSzZDSe8eZPfvJdFmvNUqmQJec2adryaNXfWwzRoYKdGjawupkULWz6yQYNySaiXL7eR9ddft9fkc8/ZtCUpGSXH8cB7eyVff729sm+91XpClvId7s03bWGiatVs6ckePaIcr4hIHNuyxRYkmTrVFjtavNjqfzMz4c8/bbGIwlSrBnXqWK7TqhW0bm3nrVpZHXDHjvGx4tr27ZYkh0tNJk60yxs33lmTffjhlpMloqws+7d4zz0wf74tBf3ww9Cgygabvbl4sZ1Wr7ZPCXlP2dn2fzXvKfwpKO8nokh+Bus5mJ2983zLFjvOpk12Wr/e4lixwjpzhL/KyKtKld2/VgifN24c9cT566+tBnnePFtU9+67E3cJ7SAoOQ7atGnwj39YH+N+/Ww1ob4FPh/F2rrVBp6fe87q70aO1IixiEhe3ltuEx4sDA8eVq9u5QmJOi9j6dKdExhHjbKRZufs30l4VHmffeJ/4lpWFrzyii3kMX++tWZ77DEYNCjoyEogO9sGuv780xL4cDI/b56VjmRkWB1NWO3aliSHE+YuXaBrV/s0VoYX5ObNcO+99vjl5sIFF1ipRatWUbiPSU7JcVBmzoQnn7RWEnXq2Dr0F1xQ6tHiOXPgb3+z1ahuvNFWl07UN3kRESm9nBxrdhQeVf71V0uO6ta1CWzh1ngtWgQd6U5Ll9pEsieftFKWvn3hjjtsQnkclveWTU6OJcv5C9hnzbJanrC0NFu5q2vXnadu3WyCYgkK6ZcsgQcftHTDOetcdc018d3tJWhKjstTdrZNsPvPf2D0aCvq//vfLZMt5Xdfs2dbXv3qq1Zu9fLLtlKQiIgI2Lf833xjifKXX9qAJliudcQRNnF7//3t2/3ytHmz/Ut85RUb7c7NtYmGt91mzSqSLimOxObNNmlo2jTrWTd9uv28cOHObapWtaS5W7ddk+bWrYscYFu0yEbkR4ywdGTQIBuTO/nkUk9vSlpKjmNt2zabJfLll1bnsGSJfadx6aX2qmzUqFS7nTrVFvV4+23LsS++2EaMVUYhIiKF8d7yrfCo8o8/7vyGv317GDDAuqeFu31Es2Z561abMPn993b6+WcrcWnVCs46C84+23I+KcCGDfaNczhZDifOS5bs3KZ69Z0lGeHEuWNHe4DzfJX85582kDZiBMyda19en3YaDBliE/nV2UrJcfRt3Wov3HHj7J3nu++sYD/c5PzSS+17ohL2FsrKsjeSzz+3di3Tp9sE2X/8w74eKe9P/CIikvi2bbNyvLFj7X/M2LE7VycE6yTapYvlVy1b7mzAsMceNtpYvbqdUlPtX114ntqGDTZSOWeOJWBz51put22bjQh3726rR590Ehx4YJJ02AjC2rU2+zTvKPP06VanElapkj2B7drtcspt3Zbvl3bihXdr89FHbkf3k4EDrfRm//2hV6+KmSwHlhw7544E/g9IAZ733j9U1PZxlRzn5NgLb9EiO82fb0O5kyZZzVBOjm3Xrp19N3Tkkfb9RYSd2zdtsm9Vwq/16dPtDWvtWnvhHnig5delWHFTRESkUN7bv7dp0+zf2tSp9v9o8WLr9hFukReptDT7V9i+PXTubP+/Bg7U/66YW7XKkuY5c2wiYPg0f/6uzbkBatZke5NW/Fz1EL7MOpQvVvZlyqqdBekdW22hd69cuu2dRtsOabRp62jb1gblkvVDTSDJsXMuBZgNHA4sAcYDQ733Mwq7TSDJ8TPPwOTJ1qIlfFq1yt4hsrN33bZVK2sw2bOnfdTae29o27bYoqn/+z8rP16+fOdpw4ad11eubJNX+/a1lTUPPzyJGr6LiEjCyM62f39Lllj3ss2b7RTu/lGjxs62vzVq7BxpLusiLBJlGzfaqjLhhHnBAvtEtGyZnS9dyrK1lfmdPvxBb/6gN7/Th0W03mU3ld12GqSto36VjdSruoV6NbZRq3oOaWlQubKncprlMOcNyKBL69A36OFTaurO9dO933U99by/9+pleVU5Kyo5jmXDl32AOd77eaEgRgLHA4Umx4H44gsrkAovet+ypT1JzZrt3gCzlN87zJ1rH+QaNrQ2Ow0b2qexTp12dnKJ99Y7IiKS/FJT7d9gy5ZBRyJlUrOm1SR361boJo23bWPIsmUM2ZEwj2LT0g0sXFyJBZlVWLC8OgvX1GLVxqqs2lKN1RtrkLGmFhtyqpPlU9lOZbJIYzuVOXjMHXThy9LFes89gSTHRYnlyPEpwJHe+wtDv58N7Ou9vyLfdhcDF4d+7QTMiklAiaUBsDLoIKRYep4Sh56rxKDnKTHoeUoMep6K1tp737CgK2I5XllQrcFumbj3fjgwPIZxJBzn3ITChvolfuh5Shx6rhKDnqfEoOcpMeh5Kr1YllkvAfJ+MdMC+CuGxxMRERERKZNYJsfjgQ7OubbOucrA6cDHMTyeiIiIiEiZxKyswnuf7Zy7AvgKa+U2wns/PVbHSzIqM0kMep4Sh56rxKDnKTHoeUoMep5KKa4WARERERERCVKStnYWERERESk5JcciIiIiIiFKjuOMc+5I59ws59wc59xNQcdTETjnRjjnljvnpuW5rJ5z7mvnXEbofI88190cen5mOecG57m8j3Nuaui6J52zpROdc1Wcc2+FLh/nnGtTrncwCTjnWjrnRjvnZjrnpjvnhoUu1/MUR5xzVZ1zvznnJoeep7tDl+t5ikPOuRTn3ETn3Keh3/U8xSHn3ILQYzzJOTchdJmeqxhSchxHnC25/R/gKKALMNQ51yXYqCqEl4Aj8112E/Ct974D8G3od0LPx+lA19Btngk9bwDPYgvadAidwvu8AFjjvd8TeBz4V8zuSfLKBq7z3ncG9gP+EXou9DzFl23AId77nkAv4Ejn3H7oeYpXw4CZeX7X8xS/Dvbe98rTt1jPVQwpOY4vO5bc9t5vB8JLbksMee9/AFbnu/h44OXQzy8DJ+S5fKT3fpv3fj4wB9jHOdcUqO29/8XbLNdX8t0mvK93gUPDn9glMt77TO/9H6GfN2D/0Juj5ymueLMx9Gta6OTR8xR3nHMtgKOB5/NcrOcpcei5iiElx/GlObA4z+9LQpdJ+Wvsvc8ES8yARqHLC3uOmod+zn/5Lrfx3mcD64D6MYs8yYW+8tsbGIeep7gT+qp+ErAc+Np7r+cpPj0B3Ajk5rlMz1N88sAo59zvzrmLQ5fpuYqhWC4fLSUX0ZLbEqjCnqOinjs9r1HinKsJvAdc7b1fX8Tghp6ngHjvc4Bezrm6wAfOuW5FbK7nKQDOuWOA5d77351zgyK5SQGX6XkqPwO893855xoBXzvn0ovYVs9VFGjkOL5oye34sSz0NRSh8+Whywt7jpaEfs5/+S63cc6lAnXYvYxDiuGcS8MS49e99++HLtbzFKe892uBMVhdo56n+DIAOM45twAr3zvEOfcaep7ikvf+r9D5cuADrARTz1UMKTmOL1pyO358DJwb+vlc4KM8l58emt3bFpvU8Fvoa60Nzrn9QrVa5+S7TXhfpwDfea2+UyKhx/QFYKb3/rE8V+l5iiPOuYahEWOcc9WAw4B09DzFFe/9zd77Ft77Ntj/me+892eh5ynuOOdqOOdqhX8GjgCmoecqtrz3OsXRCRgCzAbmArcGHU9FOAFvAplAFvYJ+gKs3upbICN0Xi/P9reGnp9ZwFF5Lu+LvWnNBZ5m5wqUVYF3sIkRvwHtgr7PiXYCBmJf800BJoVOQ/Q8xdcJ6AFMDD1P04A7QpfreYrTEzAI+FTPU3yegHbA5NBpejgv0HMV25OWjxYRERERCVFZhYiIiIhIiJJjEREREZEQJcciIiIiIiFKjkVEREREQpQci4iIiIiEKDkWESmGcy7HOTfJOTfNOfeOc656Idv9HKXjneCcuyP086XOuXPKsK+XnHOnFLPNGOdc39DPC5xzDUp7vDz7vMI59/ey7kdEpLwpORYRKd4W730v7303YDtwad4rnXMpAN77/aN0vBuBZ0L7fM57/0qU9ht14ftegBHAVeUZi4hINCg5FhEpmR+BPZ1zg5xzo51zbwBTAZxzG8MbOedudM5Ndc5Nds49FLqsvXPuS+fc7865H51ze+XfuXOuI7DNe78y9PtdzrnrQz+Pcc79yzn3m3NutnPugAJu75xzTzvnZjjnPgMa5bnuUOfcxFBcI5xzVYq6o865D0OxTnfOXZzn8o3OuXucc+OA/s65h0LHm+KcewTAe78ZWOCc2yfyh1ZEJHipQQcgIpIonHOpwFHAl6GL9gG6ee/n59vuKOAEYF/v/WbnXL3QVcOBS733Gc65fbHR4UPyHWYA8EcRYaR67/dxzg0B7sSWaM7rRKAT0B1oDMwARjjnqgIvAYd672c7514BLgOeKOJY53vvV4eWgh7vnHvPe78KqAFM897fEbpvLwB7ee99ePnokAnAAdiqWyIiCUEjxyIixavmnJuEJXuLsGQQ4Lf8iXHIYcCLodFTQglmTWB/4J3Qvv4LNC3gtk2BFUXE8n7o/HegTQHXHwi86b3P8d7/BXwXurwTMN97Pzv0+8uhbYtylXNuMvAr0BLoELo8B3gv9PN6YCvwvHPuJGBzntsvB5oVcwwRkbiikWMRkeJt8d73ynuBcw5gUyHbO8Dnu6wS/9/e/bNUGUcBHP8ebHCJC9LS5iDhZm5FS+ELaAqEcKhWBxPXJsG9qd5FUCIiDensVIlCQy8hcbpBehqec1Hk6ZI34enC97NceM7v+f1ZLodzz/NcOL48T9taQG9I/Gd9nvLn7/DLaw/29Nci4iFNkn+/qt+fgMkK9zPzFCAzf1XrxAKwCCxzXg2fpDmPJI0NK8eSdP12gOeDt1pExFRmngDfI+JJXYuImGu59xCY+Ye194DFiJiIiNvAo7p+BExHxGDuJWB3yDw94EclxrPAvbZBVRHvZeYWsALcvRC+A3wd9SCS1AWTY0m6Zpm5DbwH9quFYq1CT4EX1apwADxuuX0PmI8qTY/gHfCN5iHBN1QCnJl94BlNW8cX4Ax4O2SebeBGRHwG1mlaK9rcBDZr3C7w8kLsAfBxxHNIUicis+3XN0lSVyLiNfAhM8c2sYyIeWA1M5e63oskXYWVY0n6/2wArX80MkZuAa+63oQkXZWVY0mSJKlYOZYkSZKKybEkSZJUTI4lSZKkYnIsSZIkFZNjSZIkqfwG8zQDqPCBOSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "yhat_test1=poly1.predict(x_test_pr1)\n",
    "\n",
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "\n",
    "DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "yhat_test1=poly1.predict(x_test_pr1)\n",
    "\n",
    "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
    "\n",
    "DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #4f): </h1>\n",
    "\n",
    "<b>Using the distribution plot above, describe (in words) the two regions where the predicted prices are less accurate than the actual prices.</b>\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "#The predicted value is higher than actual value for cars where the price $10,000 range, conversely the predicted price is lower than the price cost in the $30,000 to $40,000 range. As such the model is not as accurate in these ranges.\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"ref3\">Part 3: Ridge Regression</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will review Ridge Regression and see how the parameter alpha changes the model. Just a note, here our test data will be used as validation data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's perform a degree two polynomial transformation on our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n",
    "x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import  <b>Ridge</b>  from the module <b>linear models</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a Ridge regression object, setting the regularization parameter (alpha) to 0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "RigeModel=Ridge(alpha=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like regular regression, you can fit the model using the method <b>fit</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RigeModel.fit(x_train_pr, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you can obtain a prediction:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = RigeModel.predict(x_test_pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the first five predicted samples to our test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: [ 6570.82441941  9636.2489147  20949.92322737 19403.60313256]\n",
      "test set : [ 6295. 10698. 13860. 13499.]\n"
     ]
    }
   ],
   "source": [
    "print('predicted:', yhat[0:4])\n",
    "print('test set :', y_test[0:4].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select the value of alpha that minimizes the test error. To do so, we can use a for loop. We have also created a progress bar to see how many iterations we have completed so far.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [02:12<00:00,  7.52it/s, Test Score=0.564, Train Score=0.859]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "Rsqu_test = []\n",
    "Rsqu_train = []\n",
    "dummy1 = []\n",
    "Alpha = 10 * np.array(range(0,1000))\n",
    "pbar = tqdm(Alpha)\n",
    "\n",
    "for alpha in pbar:\n",
    "    RigeModel = Ridge(alpha=alpha) \n",
    "    RigeModel.fit(x_train_pr, y_train)\n",
    "    test_score, train_score = RigeModel.score(x_test_pr, y_test), RigeModel.score(x_train_pr, y_train)\n",
    "    \n",
    "    pbar.set_postfix({\"Test Score\": test_score, \"Train Score\": train_score})\n",
    "\n",
    "    Rsqu_test.append(test_score)\n",
    "    Rsqu_train.append(train_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot out the value of R^2 for different alphas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff1cfe3e6a0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 12\n",
    "height = 10\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.plot(Alpha,Rsqu_test, label='validation data  ')\n",
    "plt.plot(Alpha,Rsqu_train, 'r', label='training Data ')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 4**: The blue line represents the R^2 of the validation data, and the red line represents the R^2 of the training data. The x-axis represents the different values of Alpha.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the model is built and tested on the same data, so the training and test data are the same.\n",
    "\n",
    "The red line in Figure 4 represents the R^2 of the training data. As alpha increases the R^2 decreases. Therefore, as alpha increases, the model performs worse on the training data\n",
    "\n",
    "The blue line represents the R^2 on the validation data. As the value for alpha increases, the R^2 increases and converges at a point.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #5): </h1>\n",
    "\n",
    "Perform Ridge regression. Calculate the R^2 using the polynomial features, use the training data to train the model and use the test data to test the model. The parameter alpha should be set to 10.\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5418576440207993"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "RigeModel = Ridge(alpha=10) \n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "RigeModel = Ridge(alpha=10) \n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_test_pr, y_test)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"ref4\">Part 4: Grid Search</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The term alpha is a hyperparameter. Sklearn has the class <b>GridSearchCV</b> to make the process of finding the best hyperparameter simpler.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import <b>GridSearchCV</b> from  the module <b>model_selection</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a dictionary of parameter values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
    "parameters1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Ridge regression object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RR=Ridge()\n",
    "RR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a ridge grid search object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grid1 = GridSearchCV(RR, parameters1,cv=4, iid=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to avoid a deprecation warning due to the iid parameter, we set the value of iid to \"None\".\n",
    "\n",
    "Fit the model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise-deprecating',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid=None, n_jobs=None,\n",
       "       param_grid=[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object finds the best parameter values on the validation data. We can obtain the estimator with the best parameters and assign it to the variable BestRR as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=10000, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR=Grid1.best_estimator_\n",
    "BestRR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now test our model on the test data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8411649831036152"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
    "<h1> Question  #6): </h1>\n",
    "Perform a grid search for the alpha parameter and the normalization parameter, then find the best values of the parameters:\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=True, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute \n",
    "parameters2= [{'alpha': [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],'normalize':[True,False]} ]\n",
    "Grid2 = GridSearchCV(Ridge(), parameters2,cv=4)\n",
    "Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']],y_data)\n",
    "Grid2.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "parameters2= [{'alpha': [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],'normalize':[True,False]} ]\n",
    "Grid2 = GridSearchCV(Ridge(), parameters2,cv=4)\n",
    "Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']],y_data)\n",
    "Grid2.best_estimator_\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Mahdi Noorian PhD</a>\n",
    "\n",
    "Bahare Talayian\n",
    "\n",
    "Eric Xiao\n",
    "\n",
    "Steven Dong\n",
    "\n",
    "Parizad\n",
    "\n",
    "Hima Vasudevan\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/fiorellawever/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Fiorella Wenver</a>\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/yi-leng-yao-84451275/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\" >Yi Yao</a>.\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                  |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------- |\n",
    "| 2020-10-30        | 2.3     | Lakshmi    | Changed URL of csv                  |\n",
    "| 2020-10-05        | 2.2     | Lakshmi    | Removed unused library imports      |\n",
    "| 2020-09-14        | 2.1     | Lakshmi    | Made changes in OverFitting section |\n",
    "| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab  |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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