fine-tuning-regression-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine-tuning-regression-model\n",
    "<small style=\"display: block; margin-top: -10px\">Obligatory Assignment for DAVE3625 @ OsloMet</small>\n",
    "\n",
    "### Collaborators\n",
    "- Pål André Holdahl (s147098)\n",
    "- Leif Esten Grimstad Kielland (s362057)\n",
    "- Ingrid Lunde (s338231)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tesla stock price predictor\n",
    "\n",
    "\n",
    "\n",
    "## The assignment\n",
    "**Predict stock market price for TESLA.**\n",
    "\n",
    "I want you to make a prediction algorithm which predicts the price of this stock on a specific date.</br>\n",
    "Input will be **date** and output should be **price** of that stock (close value in the data file).</br>\n",
    "You should also show the prediction percentage score.</br></br>\n",
    "Data file: TESLA.csv </br>\n",
    "For updated csv file, please download the data from: https://finance.yahoo.com/quote/TSLA/history?p=TSLA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.21.6)\n",
      "Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (3.5.3)\n",
      "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.3.5)\n",
      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.12.2)\n",
      "Requirement already satisfied: scikit-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.0.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib) (4.38.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/pholdahl/Library/Python/3.7/lib/python/site-packages (from matplotlib) (24.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/pholdahl/Library/Python/3.7/lib/python/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: typing-extensions in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (4.7.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/pholdahl/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn) (1.7.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn) (3.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 24.0 is available.\n",
      "You should consider upgrading via the '/usr/local/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Importing the libraries I think I will need\n",
    "%pip install numpy matplotlib pandas seaborn scikit-learn\n",
    "import numpy as np                                      # For mathematical calculations\n",
    "import matplotlib.pyplot as plt                         # For plotting graphs\n",
    "import matplotlib.dates as mdates                       # For formatting dates\n",
    "import pandas as pd                                     # For data manipulation\n",
    "import seaborn as sns                                   # For plotting graphs\n",
    "from sklearn.linear_model import LinearRegression       # For linear regression\n",
    "from sklearn.model_selection import train_test_split    # For splitting data into training and test sets\n",
    "from sklearn.preprocessing import StandardScaler        # For scaling data\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.169333</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>281494500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>1.719333</td>\n",
       "      <td>2.028000</td>\n",
       "      <td>1.553333</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>257806500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>123282000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>77097000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>103003500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close  Adj Close     Volume\n",
       "0  2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500\n",
       "1  2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500\n",
       "2  2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000\n",
       "3  2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000\n",
       "4  2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the data for Tesla stock\n",
    "tesla_df = pd.read_csv('TSLA.csv')                      # Reading the data from the csv file, using the Date column as index\n",
    "\n",
    "tesla_df.head()                                         # Printing the first 5 rows of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3358 entries, 0 to 3357\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       3358 non-null   object \n",
      " 1   Open       3358 non-null   float64\n",
      " 2   High       3358 non-null   float64\n",
      " 3   Low        3358 non-null   float64\n",
      " 4   Close      3358 non-null   float64\n",
      " 5   Adj Close  3358 non-null   float64\n",
      " 6   Volume     3358 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 183.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking the data types of the columns and if there is any null values\n",
    "tesla_df.info()                                         # Printing the info of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicate values\n",
    "duplicates = tesla_df.duplicated()                      # Checking for duplicates\n",
    "\n",
    "print(duplicates.sum())                                 # Printing the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3358.000000</td>\n",
       "      <td>3358.000000</td>\n",
       "      <td>3358.000000</td>\n",
       "      <td>3358.000000</td>\n",
       "      <td>3358.000000</td>\n",
       "      <td>3.358000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68.414195</td>\n",
       "      <td>69.934683</td>\n",
       "      <td>66.775009</td>\n",
       "      <td>68.389271</td>\n",
       "      <td>68.389271</td>\n",
       "      <td>9.651449e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>100.449599</td>\n",
       "      <td>102.712914</td>\n",
       "      <td>97.955889</td>\n",
       "      <td>100.362548</td>\n",
       "      <td>100.362548</td>\n",
       "      <td>8.047428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.076000</td>\n",
       "      <td>1.108667</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>1.053333</td>\n",
       "      <td>1.053333</td>\n",
       "      <td>1.777500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.197500</td>\n",
       "      <td>10.507333</td>\n",
       "      <td>9.935000</td>\n",
       "      <td>10.163000</td>\n",
       "      <td>10.163000</td>\n",
       "      <td>4.550400e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.803667</td>\n",
       "      <td>17.049667</td>\n",
       "      <td>16.566668</td>\n",
       "      <td>16.812333</td>\n",
       "      <td>16.812333</td>\n",
       "      <td>8.046615e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.503168</td>\n",
       "      <td>71.509165</td>\n",
       "      <td>66.761002</td>\n",
       "      <td>71.074669</td>\n",
       "      <td>71.074669</td>\n",
       "      <td>1.231710e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>411.470001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>9.140820e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  3358.000000  3358.000000  3358.000000  3358.000000  3358.000000   \n",
       "mean     68.414195    69.934683    66.775009    68.389271    68.389271   \n",
       "std     100.449599   102.712914    97.955889   100.362548   100.362548   \n",
       "min       1.076000     1.108667     0.998667     1.053333     1.053333   \n",
       "25%      10.197500    10.507333     9.935000    10.163000    10.163000   \n",
       "50%      16.803667    17.049667    16.566668    16.812333    16.812333   \n",
       "75%      67.503168    71.509165    66.761002    71.074669    71.074669   \n",
       "max     411.470001   414.496674   405.666656   409.970001   409.970001   \n",
       "\n",
       "             Volume  \n",
       "count  3.358000e+03  \n",
       "mean   9.651449e+07  \n",
       "std    8.047428e+07  \n",
       "min    1.777500e+06  \n",
       "25%    4.550400e+07  \n",
       "50%    8.046615e+07  \n",
       "75%    1.231710e+08  \n",
       "max    9.140820e+08  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for outliers\n",
    "tesla_df.describe()                                     # Printing the summary statistics of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using a boxplot to visualize the outliers\n",
    "tesla_df[['Open','High','Low','Close','Adj Close']].plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "- There are 3358 rows.\n",
    "- info() is clearly showing a count of 3358 non-null entries for each column, indicating that there are no null values in the dataframe.\n",
    "- Checking for duplicates shows us that there are no duplicate values in the dataframe.\n",
    "- The boxplot tells us that there are allot of outliers, or that majority of the data might be irrelevant for prediction.\n",
    "\n",
    "We might be better off cutting the first part of the data, to get conzise predictions for todays market.\n",
    "\n",
    "I am also observing that the Date column is of Dtype object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.169333</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>281494500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>1.719333</td>\n",
       "      <td>2.028000</td>\n",
       "      <td>1.553333</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>257806500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>123282000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>77097000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>103003500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Open      High       Low     Close  Adj Close     Volume\n",
       "0 2010-06-29  1.266667  1.666667  1.169333  1.592667   1.592667  281494500\n",
       "1 2010-06-30  1.719333  2.028000  1.553333  1.588667   1.588667  257806500\n",
       "2 2010-07-01  1.666667  1.728000  1.351333  1.464000   1.464000  123282000\n",
       "3 2010-07-02  1.533333  1.540000  1.247333  1.280000   1.280000   77097000\n",
       "4 2010-07-06  1.333333  1.333333  1.055333  1.074000   1.074000  103003500"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Date is of an object type, need it to be of type datetime\n",
    "tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])     # Converting the Date column to datetime format\n",
    "\n",
    "tesla_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the whole dataframe to get a vizual represantation of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting all the data with years on the x-axis and price on the y-axis\n",
    "plt.figure(figsize=(18,5))                          # Setting the size of the plot\n",
    "plt.title('Tesla Stock History')                    # Title of the plot\n",
    "plt.plot(tesla_df['Date'], tesla_df['Close'])       # Plotting the data (Closing price is what we want)\n",
    "plt.xlabel('Year', fontsize=15)                     # Label on the x-axis\n",
    "plt.ylabel('Price', fontsize=15)                    # Label on the y-axis\n",
    "plt.show()                                          # Showing the plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the graph\n",
    "\n",
    "As we can see from the graph, the movement in the stock market from about year 2020 and upwards is a completely different story from the rest of the data.\n",
    "\n",
    "This fits well with the boxplot done earlier with all the outliers.\n",
    "\n",
    "Based on this I suggest that it's better to not use the data from before year 2020, as the data from 2020 untill today seems more relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2394</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>28.299999</td>\n",
       "      <td>28.713333</td>\n",
       "      <td>28.114000</td>\n",
       "      <td>28.684000</td>\n",
       "      <td>28.684000</td>\n",
       "      <td>142981500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>29.366667</td>\n",
       "      <td>30.266666</td>\n",
       "      <td>29.128000</td>\n",
       "      <td>29.534000</td>\n",
       "      <td>29.534000</td>\n",
       "      <td>266677500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>29.364668</td>\n",
       "      <td>30.104000</td>\n",
       "      <td>29.333332</td>\n",
       "      <td>30.102667</td>\n",
       "      <td>30.102667</td>\n",
       "      <td>151995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>30.760000</td>\n",
       "      <td>31.441999</td>\n",
       "      <td>30.224001</td>\n",
       "      <td>31.270666</td>\n",
       "      <td>31.270666</td>\n",
       "      <td>268231500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>31.580000</td>\n",
       "      <td>33.232666</td>\n",
       "      <td>31.215334</td>\n",
       "      <td>32.809334</td>\n",
       "      <td>32.809334</td>\n",
       "      <td>467164500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date       Open       High        Low      Close  Adj Close  \\\n",
       "2394 2020-01-02  28.299999  28.713333  28.114000  28.684000  28.684000   \n",
       "2395 2020-01-03  29.366667  30.266666  29.128000  29.534000  29.534000   \n",
       "2396 2020-01-06  29.364668  30.104000  29.333332  30.102667  30.102667   \n",
       "2397 2020-01-07  30.760000  31.441999  30.224001  31.270666  31.270666   \n",
       "2398 2020-01-08  31.580000  33.232666  31.215334  32.809334  32.809334   \n",
       "\n",
       "         Volume  \n",
       "2394  142981500  \n",
       "2395  266677500  \n",
       "2396  151995000  \n",
       "2397  268231500  \n",
       "2398  467164500  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the relevant data:\n",
    "tesla_df = tesla_df[tesla_df['Date'].dt.year >= 2020]       # Getting the data from 2020 and onwards\n",
    "\n",
    "tesla_df.head()                                             # Printing the first 5 rows of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Doing a boxplot to check for outliers\n",
    "tesla_df[['Open','High','Low','Close','Adj Close']].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIiKiDmJnOxERERERERERERFRB7HYTkRERERERERERETUQdpQ70A4c7vdKC4uRmxsLFQqVah3h4iIiIiIiIiIiIgCEAQB9fX1yMzMhFodmh5zFttbUFxcjP79+4d6N4iIiIiIiIiIiIgoCAUFBcjKygrJc7PY3oLY2FgA4l+Q0WgM8d4QERERERERERERUSAmkwn9+/dXarqhwGJ7C+ToGKPRyGI7ERERERERERERUZgLZRw4B6QSEREREREREREREXUQi+1ERERERERERERERB3EYjsRERERERERERERUQex2E5ERERERERERERE1EEsthMRERERERERERERdRCL7UREREREREREREREHcRiOxERERERERERERFRB7HYTkRERERERERERETUQSy2ExERERERERERERF1EIvtREREREREREREREQdxGI7EREREREREREREVEHsdhORERERERERERERNRBLLYTEREREREREREREXUQi+1ERERERGHk9Q0nsPizfXC43KHeFSIiIiIiagNtqHeAiIiIiIhEVocLf1l1GAAwdVASrjijX4j3iIiIiIiIgsXOdiIiIiKiMHG0rF75/qu9xSHcEyIiIiIiaisW24mIiIiIwkRusUn5fv2RcjTanCHcGyIiIiIiagsW24mIiIiIwkRuiafY7haAkxWNIdwbIiIiIiJqCxbbiYiIiIjChHdnOwCcrGwI0Z4QEREREVFbhX2x/S9/+QtUKhX+8Ic/KNdZrVYsWrQISUlJiImJwYIFC1BWVuZzv/z8fMyfPx9RUVFITU3FQw89BKeTy3CJiIiIKPyYrA5sPFqB09VmAMCoDCMA4AQ724mIiIiIeoywLrZv374db7zxBsaNG+dz/f3334+vv/4an3zyCTZs2IDi4mJcddVVyu0ulwvz58+H3W7Hpk2b8O677+Kdd97B448/3t0vgYiIiIioVb97dwcW/mcbKuptAIBJ2fEAgJMV7GwnIiIiIuopwrbY3tDQgBtuuAH//ve/kZCQoFxfV1eHt956Cy+88ALOO+88TJo0CW+//TY2bdqELVu2AABWr16N3NxcLF++HBMmTMBFF12Ep556CsuWLYPdbg/VSyIiIiIiCmjbqWqfy5OyxeNfZrYTEREREfUcYVtsX7RoEebPn48LLrjA5/qdO3fC4XD4XD9y5EgMGDAAmzdvBgBs3rwZY8eORVpamrLNvHnzYDKZcPDgwWaf02azwWQy+XwREREREXUnjVqFsf3iAQCnKhvhdguh3aFOUlxrwcOf7sWhEh5jExEREVHvFJbF9g8//BC7du3C0qVLm9xWWloKvV6P+Ph4n+vT0tJQWlqqbONdaJdvl29rztKlSxEXF6d89e/fv4OvhIiIiIiobeIjdchOioJWrYLF4UKpyRrqXeoUf/hwDz7eUYirXt0U6l0hIiIiIuoSYVdsLygowO9//3usWLECERER3frcixcvRl1dnfJVUFDQrc9PRERERH2P2e70uRwXpYNOo8aAxCgAvSdKZk9hLQDA4nCFdkeIiIiIiLpI2BXbd+7cifLyckycOBFarRZarRYbNmzAyy+/DK1Wi7S0NNjtdtTW1vrcr6ysDOnp6QCA9PR0lJWVNbldvq05BoMBRqPR54uIiIiIqCvJQ1FlCVF6AMDglGgAwMnKXjIktXek4RARERERNSvsiu3nn38+9u/fjz179ihfZ555Jm644Qble51Ohx9++EG5z5EjR5Cfn4/p06cDAKZPn479+/ejvLxc2WbNmjUwGo3Iycnp9tdERERERNQc/2J7fKQOADA4JQZA7+lsF1htJyIiIqJeThvqHfAXGxuLMWPG+FwXHR2NpKQk5fpbb70VDzzwABITE2E0GnHvvfdi+vTpmDZtGgBg7ty5yMnJwU033YTnnnsOpaWleOyxx7Bo0SIYDIZuf01ERERERM0p9yu2x8nF9mS5s72XFNtZayciIiKiXi7siu3BePHFF6FWq7FgwQLYbDbMmzcPr776qnK7RqPBN998g7vuugvTp09HdHQ0br75ZixZsiSEe01ERERE1JR/Z7tGrQLg3dneO2JkWGsnIiIiot6uRxTb169f73M5IiICy5Ytw7Jly5q9T3Z2NlauXNnFe0ZERERE1DH+xXaX1AIuZ7YX1VpgdbgQodN0+751FUEQoFKpQr0bRERERESdKuwy24mIiIiI+pLyeqvPZTluJSlaD2OEFoIA5FX1/CgZwStHxuZ0h3BPiIiIiIi6BovtREREREQh5N/ZnhEXAQBQqVS9Zkiq2y3A7ZUjY7I4QrczRERERERdhMV2IiIiIqIQqmgQi+3XTRmAS8Zl4K7ZQ5Tb5CiZnp7b3mB3+lw2WZ3NbElERERE1HP1iMx2IiIiIqLeqtwkFttvmDoAY/rF+dw2pJd0tteZfTvZ663sbCciIiKi3oed7UREREREIeJyC6hqtAMAUmINTW7PTooCABTUmLt1vzqbya+4zs52IiIiIuqNWGwnIiIiIgqRGrMdLrcAlUociOovUqcB0LaBoodKTHjg4z0oqbN02n52lMniW1xnZzsRERER9UYsthMRERERhYgcIZMUrYdW0/TQXCdd53AJTW4LRBAEPPTpXny2qwj/21nYeTvaQf6d7fXsbCciIiKiXojFdiIiIiKiEJGHoybHNI2QAQCtRgUAcLiC62zffLIKB4pMAIAac/h0j5sszGwnIiIiot6PxXYiIiIiohApN1kBAKnGiIC366XOdmeQxfZ/bzypfO9f4JYJQnBd8p3JP6PdP1aG2qbO7MDJioZQ7wYRERER+WGxnYiIiIgoROTO9pRmO9uDj5E5WlaPdUcqlMv+0S0AkFtswoQla/DyD8fas7vt5l/4b7Sz2N4Rv313O85/YQOKasMnl5+IiIiIWGwnIiIiIgqZinqp2B4buNiua0OMjNzVHmPQAgjcPX79m1tQZ3HghTVH27W/7eVf+DfbXN36/L2J0+XG3oJaCAJwuqox1LtDRERERF5YbCciIiIiCpFyqdie2kyxXa90trdcbC83WfHFniIAwK0zBwEINJTUgdoQ5bjXSZ3t6VJcTgM729utsMYCp1tc6dDAQbNEREREYYXFdiIiIiKiEGmts12rZLa3HCOz5VQ1HC4BY/oZcc6IFABNi+3bTlUr36cZAz9fV5G77DPixWK72cYicXudqvR0szfw50hEREQUVlhsJyIiIiIKkcpWOtvlGBl7K53tNY12AMCAxCgYI3QAmsbIHCv3DNSs7+aOaLnwnxkXCQBoZIxMu530KrZ3998jEREREbWMxXYiIiIiohApbzWzXepsd7fc2V5jFovt8VF6GCPFzPZ6qwNur/sdK/MU2812V1A58J1FHpCaESd2tnNAavudqvT8PbKznYiIiCi8sNhORERERBQCZrtTKZa2Vmx3uQWfwrk/OYs9IUqndLa7Bd+i9vHyep/7dGfet9yBnS4X21kkbrdT7GwnIiIiClssthMRERERhUBlvdiNHqnTIMagDbiNVoqRAQCHu/lOdLmzPSFKjwidBnqteJhvkoqxgiDguFeMjHhb9w1LlTvbM+OlGBk7Y2Ta61SFd2Z7aAbeEhEREVFgLLYTEREREYVAeb0VgNjVrlKpAm6j13gO1x0tDEmtkTrb46P0AKB0t/9wqAwAUFJnRaPdBa1ahaRocZvu6op2uQXU29jZ3hksdheK66zKZXa2ExEREYWXwC00RERERETUpSpayWsHAK3aU4R3tpCxXitntkeKRXZjhBaVDTY8/uVBjM40okEaSDooORoAUNVoV7rNu5p3XI2c2W62u+B2C1CrA59koMDyqhp9LndnFBARERERtY6d7UREREREIVDZIBXbY5ovtmvUKshN7/YWiu1KjEy0WGw/6ZXrveFIBY6ViXntQ1NjEBsh9tuYuqlQK8fVROo0iI/UK9ebHYySaau8St9iez1XCBARERGFFRbbiYiIiIhCoLpRLEInxuib3UalUkGnFg/ZnS3EyNQ2+sbIyAV1ACissSh57cNSY2CUut+7K7O9TuqgN0ZqEaFTQ25mN/sVigVBwJrcMpTUWbplv3oi+SSKHAXEznYiIiKi8MJiOxERERFRCFQ3ip3tiVHNF9sBQCcNSXU009nucLmVDucE6bGWXT9RiaA5XFqPY1KxfWhaLGKlPPfuyvuW42riInVQqVSI1osnAhr8iu1f7S3Gbe/twJwXNnbLfvVEp6Ri+7isOABNf4ZEREREFFosthMRERERhUC1NNQ0IbrlYrtWGpLa3IDUWulxVCqxoA0As4an4McHZwMAjpc34KgUIzMsNQZGOUammzLb5Q56eWhrtEF8frPdN0Zm7aFyACwgexMEAesOl6PcJA5FlYvtY7PiAQD13bQ6gYiIiIiCw2I7EREREVEI1DSKOeuJUs56c3RKsT1wZ7s8HNUYoYPGa+BoVkIkovQa2F1u1FudUKvEAand39kuPo8cXxNl0ABoWlS3McO9iVUHSvGbd7bjxre2wuUWPMX2fp7OdkFoPl6IiIiIiLoXi+1ERERERCFQLRXbE4KMkWkusz2vygzAk+MtU6tVGJ4Wq1zOTopGhE6j5LnXdVFn+8r9JXjyq4OwSJ3rns528XljlM52v2K7s/kBsH3VWz+fAgAcLWvA27+cUv7NyMV2h0vgz42IiIgojGhb34SIiIiIiDpbtdLZ3lqxXeyPsTfT2f7R9gIAwOwRqU1uG5URiz0FtQCAISkxAIDspCgAwL7C2jbvc2vqLA7cvWIXALGo/sd5I5S4GqWzXS93tvt2slvZ2e6juNaCnadrlMsvrT0GAEgzGpASa1Cub7A5EaHTdPv+EREREVFT7GwnIiIiIupmgiCg2hxcsV2rdLY3LbZXNdjw4+EyAMD1Uwc0uX2EV2f7sDSx2H720BSoVcCx8gYUVJvb9wKa8eG2fOX7t34+hY1HK2CS4mrkzHa5s73RP0aGHdo+DpeafC7LQ3AHJUdDo1YpP8fuyt4nIiIiotax2E5ERERE1M3MdhfsUnG5tWK7voUBqQU1FrgFICMuAkNTY5rcPiLdqHw/TLo9LkqHSdkJAID1Ryva9wICcLjceGdTnnLZ4nDhzuU7UVRrAQAYI8XicLwUm1PVYPO5v92r2B7oxEJfU1wrDkU9a0gSVJ4ofgxKlv4epZUCXRUHRERERERtx2I7EREREVE3kyNkDFo1IluJAJE72x3uwJ3tAJAcY2hyGwCMTPfqbE/1fH/mwEQAwInyhjbsdctW7i9BSZ0VyTEG7H9yLtKMBpjtLmw5WQXA09melRAJACiotvjc3+p0eX3PYntpnVhsH5wSjYFJ0cr1ozPFEyhGFtuJiIiIwg6L7URERERE3azGK0JG5d22HICc2e4IUICuVIrtgbvjE6L1mJOThtGZRozwKrw3N6S0vQRBUIZ5LpyejdgInTLEs16OkZGKw/0TxMz4ghrfCBur3VNst9iZ314iFdsz4iLh8Or0v2xCJgAgLrJrB90SERERUdux2E5ERERE1M2qpM72hKiWI2QAQKcWD9md7qYxMpUN4uMkNdPZDgD/Xngmvrl3JvRaz6G/PKTU3ElF7e15NdhXWAeDVo0bpOz4nMw4n23k2JP+iYGL7bVeRWMW24GSOrHzPyMuAnfNHgIAuOWsgcoKAfnnycx2IiIiovChDfUOEBERERH1NTWNwQ1HBQCdVoqRCTggVXyc5mJkZP7d83Kx3b+obbG7oFIBEa1E2/j7908nAQBXTcxSCv85GUafbeQicf9EMUamuNYKp8sNrUYNm9PlU/i3OFhsL/XqbJ8yKBGjM+OU1QKAV7Hd2jmrE4iIiIio49jZTkRERETUzarbUGzXSp3tv/9wD3465jvQtLUYmeZE6sWem0avGBmL3YXpf/kBl73yMwShaRd9c05VNmLtoTIAwK0zByrXy9niMnlAalpsBPQaNVxuQYlK8Y9C6evFdkEQvGJkIqBRqzChfzw0as9JEw5IJSIiIgo/LLYTEREREXUz78z21siZ7QBw01vbUF5vVS5XNbY8ILU5Ubqmne278mtQa3bgaFkD7AG66JuzNrcMggCcPSwZQ72GsGYlRCI+Sqdcljvb1WoV+klDUgtrxKiUWrNvwbizsuR7qjqLQznhkB4XEXAbpdhuZrGdiIiIKFyw2E5ERERE1M2q25LZrvGNgPlkR6HyfZWS2d62zvZAme351Z4MdbMt+M7yWou4D0NSYnyuV6lUGJbquS42wpNgmSEVkEtNYrF941Hfjn1rH+9sl7vaE6P1zUb6sLOdiIiIKPyEZbH9tddew7hx42A0GmE0GjF9+nSsWrVKuX327NlQqVQ+X3feeafPY+Tn52P+/PmIiopCamoqHnroITidfbtDhoiIiIjCgydGRtfKlr6d7QBQVGtRvvfEyLStsz0yQLH9WFmD8n1jGzrLG6XCfIyh6TiooV7Fdq3X65A7+qsbHbA73fjXxpM+97PYg++s743k4ajpxsBd7QBglIrt3x0sxboj5d2yX0RERETUsrAstmdlZeEvf/kLdu7ciR07duC8887D5ZdfjoMHDyrb3HbbbSgpKVG+nnvuOeU2l8uF+fPnw263Y9OmTXj33Xfxzjvv4PHHHw/FyyEiIiIi8lHTKHYjJwST2e7X2V5uErueXW5BKdq3vbNdLIx7Z6MfLatXvjfbg+8sr5cGdEYHKLbPGpYS8D5J0uuuabRj26lqlNfbkByjx4yhSdLz9+0mGbmzPTO+9WI7APzm7e1dvk9ERERE1LqmR8Rh4NJLL/W5/Mwzz+C1117Dli1bMHr0aABAVFQU0tPTA95/9erVyM3Nxdq1a5GWloYJEybgqaeewp/+9Cc8+eST0Ovb9mGEiIiIiKgzVcuZ7UHEyOj9OtvL68Vu9jqLA25pjmkwj+PNEyPjKWofLvUU2xttbelsF7eNMTSNO7lwTDoeuXgkRqb7DkuVTzJUNdqVruzzRqYqhfvNJ6tw1cQsn4GgfUmpVGxvLq8d8MTIyBptzoAnPIiIiIio+4RlZ7s3l8uFDz/8EI2NjZg+fbpy/YoVK5CcnIwxY8Zg8eLFMJs9GZObN2/G2LFjkZaWplw3b948mEwmn+54fzabDSaTyeeLiIiIiKiz1cgxMkF0pPt3tpdJne3ykNXYCK1PREsw5BgZq8MNt1uA3elWImmAtnW2y5EzgQq9KpUKt88aglnDfTvcE7062+Vi+7kjUhEp5ZN/tqsIf/rfvja8ot6luFb8O86Ii2x2G/9iu/ffHxERERGFRti2Puzfvx/Tp0+H1WpFTEwMPv/8c+Tk5AAArr/+emRnZyMzMxP79u3Dn/70Jxw5cgSfffYZAKC0tNSn0A5AuVxaWtrscy5duhR//vOfu+gVEREREREBbregFMqD6Uj3z2yvbLDD5RZQaw5+yKo/ubMdEKNk/IvrbSm2Nyid7cF/tJCL7fsKa1FcZ4VWrcKMYcn4+Xilss2nOwtxxYR+mDksOejH7S3kwbEZbehsr6i3ITspukv3i4iIiIhaFrad7SNGjMCePXuwdetW3HXXXbj55puRm5sLALj99tsxb948jB07FjfccAPee+89fP755zhx4kSHnnPx4sWoq6tTvgoKCjrjpRARERERKbzjX+KDKJRrVL6d7S63gKpGG2rNDukxWh+y6i9C6ym2m+0u1FnsPre3JTO9wdqOYrv0uouluJQzBybAGKFTOttlj36xH1ZH8IX/3qKktvUYmaRoPS4Y5WkwkuOFiIiIiCh0wrbYrtfrMXToUEyaNAlLly7F+PHj8dJLLwXcdurUqQCA48ePAwDS09NRVlbms418ubmcdwAwGAwwGo0+X0REREREnUnOa481aKHXtn44bnV6is3GCLGgXW6yoUYptre9s12tVimFbYvdpRTuZY22NsTI2JqPkWmO/2DYc0ekAoDPzyPWoMXpKjP++eOxoB+3NxAEwTMgtYUYGZVKhTdvPhMXjRE/31Sw2E5EREQUcmFbbPfndrthswU+gNyzZw8AICMjAwAwffp07N+/H+Xl5co2a9asgdFoVKJoiIiIiIhCoUwqpCbHGoLa3jvSpX9iFACgvN6qxMjER7a9sx0AoqWBpmaHs0mxvU2d7e0otif5F9tHisX26kZPh/1frx4HAHhjw0kc8Rre2tuZLE5YpG7+ljrbZSnSvyMW24mIiIhCLyyL7YsXL8bGjRuRl5eH/fv3Y/HixVi/fj1uuOEGnDhxAk899RR27tyJvLw8fPXVV1i4cCFmzZqFcePEA/K5c+ciJycHN910E/bu3Yvvv/8ejz32GBYtWgSDIbgPNUREREREXeFEZSMAYHBycPnaZq8u83SjWHw9XFqv5L4ntCNGBvAMSTXbXcpjyYLtbBcEAY3SyYDYiOCL7d7d+P3iIzEsNQYAUFBjVq6/eGwG5uSkwekWsPizfXDL2Tu9XHGdmNeeGK1HhF+sTiApMSy2ExEREYWLsCy2l5eXY+HChRgxYgTOP/98bN++Hd9//z3mzJkDvV6PtWvXYu7cuRg5ciQefPBBLFiwAF9//bVyf41Gg2+++QYajQbTp0/HjTfeiIULF2LJkiUhfFVERERERMCJ8gYAwOCU4IrtjV5d5hfkiBndK7bko6pB6mxvR4wMAETpxOK4xe5CnaV9ne1WhxsuqQjels52vVatFOdnj0iBSsqlv+3swQCAyydkAgCWXD4aUXoNduXX4kBxXdCP35OVSisf5BMrrVE62xtaLrZb7C7c9t4OfLgtv2M7SERERETNCv6IuBu99dZbzd7Wv39/bNiwodXHyM7OxsqVKztzt4iIiIiIOuRv3x/GO5vyAABDUmKCuo93jMyVZ/TD378/gqJaC77dXwKgfQNSAd/O9qYxMsF1tssRMgAQFUQXtrd0YwTqrQ04T4qQAYDZI1Lx08PnIkOKT8mIi8TwtFjsKahFaZ0V47La9BQ9ktzZnhFEhAzgKbaX11t9rne63NBqPL1V727Ow5rcMqzJLcO1UwZ00t4SERERkbew7GwnIiIiIuptGmxOLFt3Qrk8OMhi+/A0z3YROg0WTh8IAKi3ioXuhPZ2tivFdidqLXbp8cWPB41BdrYrw1H1GqjVqjY9/9NXjMH/uyTHp9gOiLn03kXi5Bjx9VU1+kbd9FZyZ3tGfHDF9qwEMcc/r9IMQRBXGby6/jjGPrkaB4o8qwHypPgiIiIiIuo6LLYTEREREXWDGr9i8ZAgY2T+dOFI/GbGQHxz70wAwI3TBsCg9RzGx7Wzs10utlu8Otsz4yMBAN/uK0F+lbnZ+8raMxxVNnVwEm6dOUiJkGlOUrTYuV3VSkxKb1EiF9vjIoPafnBKNPQaNRpsThTWiF3xz313BBaHC099k6ts5x8VRERERESdj8V2IiIiIqJuUO1VbB+VYURidHAd6fFRejxx6WiM6RcHAEiKMWDBJE+eSns72yP1YoH8VGWjUojtJxXbbU43Llv2c6uPIRfbY9owHLWtkqTO9sqG3t/ZfqCoDp/uLAQQfIyMTqPGUGnA7KESk89tLq+hst7FdpszuJggIiIiImobFtuJiIiIiLpBtVksFmclROKzu85qtaO7JbfOHKR8nxRk0d5fdqIYP/LGxpPYdKIKAJDp1U1da3bA4XK3+BhyjExMOzrbg5UUI3W294EYmXs+2KV8nx5ksR0ARmbEAgAOldT7XO/9T6zGK5dfjiAiIiIios7FYjsRERERUTeQY2QGJUcrw0nba0hKDJ65cgwemDMc/aWieVvdd/4w3Hf+MGjVKqUD2j8n/GRFyznfSoyMvuuK7Upmey+PkalqsCHPK7onOym4mCEAyMkwAhA7251eJ0hU8FTbi2styvdnPr0W7/xyqiO7S0REREQBdN1RMRERERERKeQYmfbGvvi7YWp2h+6v16rxwJzhmDc6DY98th/l9TaMy4rz2eZwqQkj0mObfQyzXYwjiTZ07ORBSzyZ7b27s32/1zDTF68Zr0T6BGOUVGw/XGry6WAXIJ5EMVkdTTLbn/w6F8ZIHa6amAUiIiIi6hzsbCciIiIi6gY1UoxMsFnt3WV0Zhy+vGcmfvnTeUiN9e1s948l8ScX2yO7sLNdzmyvauzdne0HpGL75RMyceUZbSuAj5ROiJyuNqOgxtMdLw++XbmvJOD9Hvh4L5786iDszpbjgoiIiIgoOCy2ExERERF1g+pGsfDZWZ3tnU2tVmF0phGLLxqJc0ekAACOlJpavI/FLsbIROm6sLPda0Cqxd57B3vuOF0DABjbL66VLZtKijEgNdYAQQA2S/n7gHiCp7LBhqWrDje5j1EaavvOpjzc8OYWn/gZIiIiImofFtuJiIiIiLqBnNmeGK0L8Z40T6VS4Y5zhuDuc4cCAA6XBtvZ3nXF9kSvkxPTlv6g5Mv3JjvyqrH+SAVUKuDsYSnteoyRUpTMz8cqletqzA489U0u6iwO5GQYcfawZOW2vy4Yh38vPBNReg2259Vg66nqjr0IIiIiImKxnYiIiIioO1RLMTIJYRYjE4ic015SZ0Wd2dHsdnKxPaoLi+1ajRozh4pF4jqLAyZL8/vTU63Ymg8AuHpiVosZ+S0ZlSHeb/NJT2e7yy3gyz3FUKmAZ68ai3ivExcpsQbMyUnD8DTxfuZevGqAiIiIqLuw2E5ERERE1A2UzvYwjZHxZozQKQM6D7cQJWPphmI7ACz/3VToNCoAQL3V2aXPFQpynv+UQYntfoxR6cZmb1s4LRsT+scjNsKTrS/n8+u14kdC5rYTERERdRyL7URERERE3aCmB3W2A56hm0fKmo+SMTu6fkCqTM66N1l7X2e7Z4VA+3+OozICF9tTYw14cN4IAIDK6/rkWPHnqdeIHwkdzGwnIiIi6jAW24mIiIiIupjbLaBGimNJ7CnFdimW5FBJ88V2ZUBqF3e2A1C6shtsva+z3aqctGj/x7PBKdFK4RzwFNH/fNloGCN00vN4CupyYV9eMWBnsZ2IiIiow7q+BYWIiIiIqI+rtzqVwZ7xUeE7INXbCCmWRI6RcbsFqNUqn226I7NdFiMVjHtjjIwyaFbX/o9nOo0aOo0KcvT6T386F3UWh5LJDgBWZ9NcdjlGhp3tRERERB3HznYiIiIioi4mD0eNMWhh0HZ9YbozjJJiZI6W1uOltccwfslqHPWLlPEUibv+NRmlzvb6XhgjI2ffR3bwpIX3/dOMET6FdgAYkhLT5D46DTPbiYiIiDoLi+1ERERERF2sulHOa+8ZXe0AMDBZjCVptLvw4tqjqLc68a+NJ322sXRC1niwenOMjMXROSsEXrxmAmIMWrx2w8SAt995zmDcOnMQ/nfXdOU6ZrYTERERdR7GyBARERERdbEaqdieGNUz8toBseN5aGoMcktMynVywVtmcXROR3YwYgxyZ3vvK7abpez7jq4QOHtYCg78eV6zt0fptfh/l+T4XOeJkRE69NxERERExM52IiIiIqIuJ8fIJPSQ4aiykem+MSTRfh3s3ZnZHitltpt6WYyM2y0og0u746SFPzlGxsYYGSIiIqIOY7GdiIiIiKiL9cTOdgCYNiTJ57J/XrpF6sjunmJ77+xs9x5a2h0/R386xsgQERERdRoW24mIiIiIulhP7Wy/eGyGz2XvQrcgCDCHIEamoZcV2+XcewCICMHwXCVGhp3tRERERB3GYjsRERERURdTOtt7WLE9xqDFgolZymXvCBeb0w1BivnujgGpRilGxr+7vqeTo3gidGqo1apuf369RnxOOzvbiYiIiDqMxXYiIiIioi5W3SgWiBN6WIwMACy9aixunzUYAGDy6io3e3Vkd3SwZzB6a4yMMmS2G36GgbQWI/PlniKctfQH7Cus7ca9IiIiIuqZWGwnIiIiIupiNeae2dkOiDEjZw9LBgCYLJ6ucrOU127QqqHpho7smN5abFeGzHb96oBA5BgZu1MIePvvP9yD4jor/vjJ3u7cLSIiIqIeicV2IiIiIqIu1lNjZGSxSoSLp9DtKRJ3T0e2vA8Ntt5VbPeOkQkFubO9tRgZpytwMZ6IiIiIPFhsJyIiIiLqYtVKZ7suxHvSPkapq9w7s10uEndX/ElcpPizq2iwobze2i3P2R2sjtB2tuuCHJAqd8ATERERUfN4xERERERE1IWcLjfqLD03sx0AjJGernK3W+xwVort3dTZnp0YhTH9jLA73Xjyq4PK/vx4uAx2r0Kx1eFSIm56gu7+OfqTB6Q2l9kuM4QoU56IiIioJ2GxnYiIiIioC9VZHBAEQKXydGf3NPJwUkEA6qUYl0bpzxhD93Rkq9Uq/HXBOGjUKqzcX4rvDpRg0Ypd+O07O/Dq+uMAxILx+c9vwLx/bES9Vxd+OAv1gFQls721Yjs724mIiIhaxSMmIiIiIqIuJA9HjYvUQavpmYffBq1GKbZe/srPsNhdyutK6MYc+tGZcbjznMEAgP/35UFsOFoBAPh0ZyEAoKTWiqJaCwqqLXh9w4mAj/G/nYV4+ptcpUM/1CxSF353Zd/7UzLbA8TIeHe7s9hORERE1DoeMRERERERdaHqRrHDOrGHRsjIbFIxNq/KjA1Hy5Vie3e/rnvPG4bBKdGoqLcp1w1IjAIAnyz3FVvzm9zXYnfhwU/24s2fT2HLqaqu39kgdHf2vT+52B4oRkaOP/LejrrHdwdKsO1Udcief19hLcpNvWc2AhERUXfhERMRERERUReqbuz+DvCutvFYpXISIb6bi+0ROg0enjfS57r+CWKxvczkKcDXmh0wWR3KAFIA+Pl4pfK9/PciEwRBicbpTkqMTKgy2+UBqa6mnf61Zk+xvbVMd2pdncWBW97ehiVf57a43aESE+5cvgs3/2ebz1Di7vLF7iJc9sovmLr0B3y8o6Dbn5+IiKgnY7GdiIiIiKgLKXErPbyz/dkrxyrfbzxagepGsbCdGN39OfQzhib5XNZKQz69O9sB4MvdRch5/Ds8v/oIAODHw2XKbaV1nm2Pl9fjnL+tx+gnvseXe4q6arcDsoS4s13fQoxMncVzQkLeT2q/3727HeuPVOA/v5yCIDQfY7RqfwkA8UTMyn0l3bV7AACb04W/fS/+fxEEYPXB0m59fiIiop6OxXYiIiIioi4kd1CHoijdma6fOgC5S+ZBp1GhsMaCXfm1AELTsR8b4fuzlAvF3p3tAPC/XUVwC8Cr60/geHkDNp3wRMd4F9vX5JYjv9oMAN0e3XGotB5A09fUXYKNkbE4WGzviJpGO7bn1SiXbQFObshWHfAUuD+R5hF0lw+3FaCo1qJcLqyxtLA1ERER+WOxnYiIiIioC9X0ohiZKL0WZ2YnAgCOlzcACF0W/Q1TByjfy4VL/872vYW1AACXW8Ajn+/H6SqzcluJVx61d1HZZO2+KJmNRyuw8WgFtGoVLhmf0W3P602OkbEHKLZ7x8hYWWzvkJOVjT6Xm4ssOl7egGPlDdCqVVCrgJ2na3CyoqHL9svp9fdutjvxzx+PAwB+O2MQALHY3lIXPhEREflisZ2IiIiIqAspne09PEZGNmt4is/lUJ1EeOLS0Zg+WIyTkTvby6XO9liDFoAYgyHz71j37mz3KbZbuicj2+ly4+lvxezuhdMHYkhKTLc8rz+dFMETKEbGt9jOzPaOOOVXbDc3E8vzvRTbMmNoMs6R/q991EW56U99k4sxT36PI6X12HyiCle9ugmVDTYMSIzC/XOGAQAabE6ffwdERETUsrAstr/22msYN24cjEYjjEYjpk+fjlWrVim3W61WLFq0CElJSYiJicGCBQtQVlbm8xj5+fmYP38+oqKikJqaioceeghOZ/cPPCIiIiKivq3a3Hs62wHg7GHJPpcTQ/S69Fo1rjgjE4CnK1vubJ8wIN5n20HJ0cr3WQmRAHyL7d5DKOu6qdj+3235OFrWgIQoHX5//rBuec5A9C3EyNQyRqbT5PkV2xua6WxfdUDMaL9oTDqumdwfAPDmT6ewyWu4b2fYdLwSb/18ClaHG0tXHcINb27BYSnS6IE5wxEboUNKrAEAo2SIiIjaIiyL7VlZWfjLX/6CnTt3YseOHTjvvPNw+eWX4+DBgwCA+++/H19//TU++eQTbNiwAcXFxbjqqquU+7tcLsyfPx92ux2bNm3Cu+++i3feeQePP/54qF4SEREREfVRcpEtMy4yxHvSOXIyjEiO8RTYQzn41aAVh4r6Z7af0T/eZ7tHLx6FuEgxE31uTrq0rRUut9j6bvKJken6YnudxYEX1hwFIBY246JCl+cvx8g4XL5RIYIg+AzHZIxMxzTtbG9abC+oNuNAkQlqFTAnJw3zRqfjsvGZcLkFvPnzqU7bF6vDhUc+369cXn+kAtJ/BVwwKg2XjhdPYvWXTkwV1JibPAYREREFFpbF9ksvvRQXX3wxhg0bhuHDh+OZZ55BTEwMtmzZgrq6Orz11lt44YUXcN5552HSpEl4++23sWnTJmzZsgUAsHr1auTm5mL58uWYMGECLrroIjz11FNYtmwZ7HZ7iF8dEREREfUVJqsDeVJO+OhMY4j3pnOo1SqcPcwTJRMfBoViu9MNq8OldKVPGpjos92wtBi8eM14zBudhrtmD4FaBTjdAqoaxeK8T7Hd0vWrYTefqEKN2YHspChcN2VA63foQvKAVP8YmfVHK5ROZ0DsbGd2d/v5F9sbbE1PXsgRMlMGJSIpxgCVSqXMJjhaVt9k+/b654/HlN9L3rYsPh9v3nwmNGoxWigrIQoAUMhiOxERUdDCstjuzeVy4cMPP0RjYyOmT5+OnTt3wuFw4IILLlC2GTlyJAYMGIDNmzcDADZv3oyxY8ciLS1N2WbevHkwmUxKd3wgNpsNJpPJ54uIiIiIqL0OFYvHk5lxEb0mRgbwRMnERmiVYm0oyBEoNpcbFfVi4VyvVWPqIN9ie3KMAeeNTMMbN52JlFgDjFKXe52URV3XzZ3tlQ3ivo5Ii4U2hD8/wKvY7nL7FNNX7RfjTBZMzAIg5t/bAuS6U+ssdheOS0NO5WgWc4AYmdUHxWjUi8Z4huUOTRWz/ItqLbA0k/PeFsfK6vHGhpMAgLOGJCnXq1TwWbECAJnxYmd7ca3v4GEiIiJqXtgW2/fv34+YmBgYDAbceeed+Pzzz5GTk4PS0lLo9XrEx8f7bJ+WlobSUrEToLS01KfQLt8u39acpUuXIi4uTvnq379/574oIiIiIupTDkrF9pzMuBDvSec6f1QaRqbH4jIpbiJUvDvb5bz2NKMBEToNovUaZbtoaWCqLF4utktFdpPVU/iUu+S7UlWDuNo2KcbQpc8TDPlnCIjd/oAYIbPhaAUA4JJxnsIvo2Ta5+fjlbA73chKiEROhrjCJVBm+0mp+31SdoJyXVKMAYnReggCcEIq2HfE2kPlcLoFnD0sWcmEB4CkaH2TEz9J0gnCWjNXhxMREQVL2/omoTFixAjs2bMHdXV1+PTTT3HzzTdjw4YNXfqcixcvxgMPPKBcNplMLLgTERERUbsdKhGL7b0lQkYWF6nDd3+YFerd8Cq2u5S89tTYCABARnwkjpcHLk7K+e21ZgcEQWgyFNVkcSBCpwl0104hd7b7dxKHgt6rwGp3uqHTqHG0rAFlJhsidGpMH5IEnUYFh0uA1cHO9mDVNNrx0Y4C1DTasflkFQAxD11egWH261IXBEEpavsPHR6aEoNtjdU4Xt6AMf3EE3c7T1fDbHf5RDoFo8wknpQanRnn8zzJAU78yKtxqs3dMzSYiIioNwjbYrter8fQoUMBAJMmTcL27dvx0ksv4ZprroHdbkdtba1Pd3tZWRnS08VhR+np6di2bZvP45WVlSm3NcdgMMBgCH13CRERERH1DoU1FgDAoOToEO9J76QU211ulJs8ne0AcM7wlGaL7Uavznaz3aUMStVr1bA73TBZHUg1RnTZfstZ8UlhEC2k06iU7x0usZi+4Wg5AGDqoCRE6DSI0GrgcDlhYWd70N78+SSWrTvhc93c0Wn4fFcRgKad7Q02p7KywH/o8JDUGGzLq1Yy9F/58Rj+vlocsPvEpTn4zYxBQe9XRYN8UsrgU2wP9O89MVr8f1LTyM52IiKiYIVtjIw/t9sNm82GSZMmQafT4YcfflBuO3LkCPLz8zF9+nQAwPTp07F//36Ul5cr26xZswZGoxE5OTndvu9ERERE1DeVKQXgrivc9mV6r+GeZfW+ne0Pzh2OhdOz8eHt05rcL86r2C5ntOs0KqRLf0/+ne6drVKKkUmODX2jj0atgkqqt9uVYrsYIXPOcLFrOkKK5OmMzPC+oqRO/L8/bXAi7jtvKF68ZjymD05SIo3Mdt9ie63UPW7QqhGp911VMXmgGCvzxe4iOFxu/GvjSeW2pSsPtynep0JeAWI0ICna8+8v0CoLuehfzWI7ERFR0MKys33x4sW46KKLMGDAANTX1+ODDz7A+vXr8f333yMuLg633norHnjgASQmJsJoNOLee+/F9OnTMW2aeCA9d+5c5OTk4KabbsJzzz2H0tJSPPbYY1i0aBE714mIqFsIggCVStX6hgGUmax4+NN9qLM48NEd02DQdl2UARF1HUEQUCoV29PjWGzvCgbvzHavIiIAROm1WHL5mID3i4/yFNvlwroxQqcU4U2WpnnanamqQe5sD/1nE5VKBb1GDZvTje8PlmHBxH7YfqoGAHDOCLHYHilF6lidLLYHq16aA3DZ+H64fuoA5fpog/izbLR5fpaf7izEHz/ZC6BpVzsAzB+XgWdXHkapyYr3Np+GyeqESgVo1SrYXW4U1liUQaqtkTvbU2IMSJA61wEEjE2SO99ZbCciIgpeWHa2l5eXY+HChRgxYgTOP/98bN++Hd9//z3mzJkDAHjxxRdxySWXYMGCBZg1axbS09Px2WefKffXaDT45ptvoNFoMH36dNx4441YuHAhlixZEqqXREREfUhpnRWTn/kBz6481K77L1qxCxuOVmBPQS0OFJk6ee+IqLvU25xKLnM6O9u7hBwjY/MakCp3trfEp7NdKqzHRepgjBR7keRu99YsXXkIZz/3Y5uLkVXS9uGQ2Q6IPz8A+H9fHMCfv8qF3eVGv/hIDJbijyJ04s/Zys72oJnkkziRvv1tcmd7o1eMjFxoBzw56d4MWg1unp4NAPjHGjE+JishEkNSxAJ7YY056P2S45ZSjRE+J/O16qYNAvK+WBwurmogojaparBh1nPr8PsPd4d6V4i6XVh2tr/11lst3h4REYFly5Zh2bJlzW6TnZ2NlStXdvauERERtWpfYS0qG2z4dl8JHrl4VJvuW9lgw47TNcrlExUNmJSd0Nm7SETdoEyKkYiL1DWJhaDOoQ/Q2S5ntrfEu9gud7bHRupgjPBc3xpBEPCGFOfxzb5iLJw+MKh9drjcSmRIUoChlKH20Y4CAGJXu7xCS+5sZ2Z78ExSZ7v8b0oWrZeK7fbAqycSonQBr79hWjZeWXcc9VKRfnByDHQaFQ6X1iuzIVrTaHOiUSqap/pFGGkCFNtjDVpo1So43QJqzHZE6iODeh4ios93FyG/2oz8ajMWTs/GpOzEUO8SUbcJy852IiKinkz+AF1SZ1GGzQVr04kqn8sn/Ib7bT1ZhV+9vgm5xex4Jwp3cmYzu9q7jpzZ7nQLKKkTC45t7WxfdaAEANAvPgIpUgFS/rtrydEyz+9nOc4mGPKwSbUKiI8MXFjtbheMSoUxwrcPS85rBzwRI2Z2NwdN7myPjWius13sFl+TW+Zze6AYGUCMdFkwKUu5PDglGlkJUQAQdLG9QpprEKXXKPtx1Rn9oFWrcMtZA5tsr1KplO52RskQUVt8d6BU+f7Nn06FcE+Iuh+L7URERJ1MzmF1C0BxbXAfgGW/HKsE4CncnKjwLbbf/PY2bM+rwa3vbu+EPSWirnCwuA7jnvwej31xAACQxrz2LqP3KnLLncTBdbaLBcS9BbX4bFcRAOCOWUOU2JSTfr97/f1r4wnM+8dG5bKczx0MeThqYrQB6gDdxKHw74VnYusjF+Cx+Z7VWGcNSVK+z04Si7o/Havo9n3rqeQoImOkf2e7eOLidFUjLl/2M257b4fP7fHNdLYDwG9nDFK+H5wSg6wEsdM82BiZcmWIsOf/yPO/Ho89T8xFdlJ0wPskScX2GjOL7UQUnJI6i89K3fzq4KOuiHoDFtuJiIg6mXcOa0F18MV2QRDw83Gx2H6z1GF23K+z3eoQO+X9uy4FQWjPrhJRF/jnD8dhsjqVD5fpQRR/qX38B0jrtWqla70l8jZydvqCiVkY3z8eQ6QhkycqGlu8/7MrD/tclmNhglEpDagMl7x2QOxgjtRrcOO0bFwyLgMPzRuBWK/4k6sn9QcAfL23BPVB5tn3ZW63gAZbMzEyUkd5XpXZZ3WEzL84721oagyuOqMfdBoVzhqSpHS2FwTZ2V5majrXQKVSIcbQfLqs3Gnv39nO4w4ias7K/aU+l62MIKM+hsV2IiKiTtbotcy+LZ0c+dVmFNVaoFWrcN2UAcp1NmfLB6gldRZc8MIGLFqxq307TESdxuFyY+sp3zgoeYghdT6dxrczPDXWoOSMt8S7IB+p0+DhC0cAELuFAbHr2NlCDFisX3GyLV2/VY1isT0pjIrtsgidBq9cPxGLzh3qc/3kgQkYkBgFi8Pl061IgTXYnZBr0f4xMt6X5ZUU3myOluPn/var8dj7xFwM8epsP1ZW3+pqDHk7ABgU4Hmbkyh3tnsV201WB2b/fT0e8hrsSkQkW7lfjGe7YkImAE+zEFFfwWI7ERFRJzN7d7YHubQbAH45LhboJg5IwMCkKOi1argFKEP/ADFnVVZnccDqcOGO93fiREUjvt1fglou8yYKqS0nq1AjdTmfPSwZf7pwJH7jFf1AnUulUvlEyfgPfWxOnFdUx92zhyBNytXPMEYgUqeBwyU02y3caHMqQypl/p3t5SYrHv/ygFLc9FYlxcgkRfecFQ8qlQrDpK7/tsaj9UVyXrteq1by7mXjsuJxw9QBePTiUVhx29Qm9zU3MzhVplGrECUNWR2VYcT4rDiY7S7c9NY2ZW5Bcw6Viv8eR2bEBv1aEqLF/yvVXv/GV2zJx+kqMz7ZWRj04xBR31Bca8HO0zVQqYArzugHAK02DhH1Niy2ExERdbJGrw/Kcme7xe5qdajpL1KEzFlDk6BSqZTcYXnZt9stwO70dIYcL6/Ho58fwL7COuW6PQW1nfIaiKh95G6u66YMwPu3TsVds4f4FIOp8xk0np9vWpDDaNNiDRiWGoPRmUbcNmuwcr1arVK6fgMVygEgr0qMmEmM1uMf10wA0LSz/bEvDuC9zadx5aubmtxfzmxPjuk5xXYAyIwXu6hLalsfHtvXmSyBI2QAsVj+zJVjcduswU2GJ+s1avzu7OBPzmnUKrx1y2QMTo5GUa0FC9/a5tOB7u9wqXgcMjLdGPRzJEY17Wxv7v8GEZF8HDQ5O1GZBcHOdupreORPRETUyeQBqQBQWG2G2y3g5re34eKXfwo4XM7udMPtFrDphFhsnzk0GQCUD+GlUrG91uKA0+3JSL32X1vwv12F0KhVGJkudqntzq/tktdERK1zutz4/mAZAGD+2IwQ703f0Z7Odq1GjdX3z8KXi2Y06TyeMCAeALDuSOBhoKcqxWL74ORoZZhljV9nu3zis8HWtEu5qiF8Y2RakhEvviexs711nuGozWehA/CJPIo1aLH78TkYmhp81zkgnrR579YpSDdG4Fh5A15YczTgdvVWhzJHRj5mCEZCdNPMdu/h7Y4W4paIqO/5Viq2zx+XgUjp/ZWZ7dTXsNhORETUybwHpOZXm/HFniJsO1UNAPhwW4HPtptPVGH0E9/h5re3ocbsQLReg/H94wF4OjRLpWGo8lA9mcMlFt4fuXiUkvHOznai0NlyshrVjXYkRusxbXBiqHenz/AptgfZ2Q6IhU6tpunHIflEyXcHSgIWEk9Kw1MHJUcrwyP9I7y8c7n9B0nKQ1nDaUBqMDLjxM724laiSgiotzbf2d4cu8utDE9tq6yEKDwyfxQA4GBxXcBt5GGsaUaDUkAPRqJfsV0QBOX/AOB7zENEvVujzdni//miWgt259dCpQIuGpOOCJ34Hut0Cy3OQSHqbVhsJyIi6mTeMTI1Zgc+312kXP7pWIVSvBEEAf/32T44XAJ+OiZ2tU8dnASdVPyRO9vlGJnKerHYPjQ1BpeNz4Reo8b/XTQSv50xEDmZ4pLwE0EMSCOiriF3c80bnR6wiEtdoz2d7S2ZOigRyTF61Jgd2HSiqsntcmf7oBRPsd0/RibGq2jq3/WudLb3oMx2wCtGpo4xMq2RM9v9h6MGsvSqsVCrgJeundCh5xySIsY1nK4KPCumSFqRIMc6BMv/33hxndVnZkGg1RtE1PuUm6y44IUNOO/59c12qq+SI2QGJiLVGOGzcszqZLGd+o72nTonIiKiZpntvgege726zU1WJ3bn12LKoER8uae4yYfiqyb2U75PU4rtYmGmQirQpMQY8NK1E2BzupWDWPnDcJ3Ft6hDRN1DjJApBcAIme6m9zqx0U8qCHeEVqPGhWPSsXxLPr7ZW4xzhqf43H7SO0ZGGh5pdbhhdbiU38mNXu8Dpyoble5gwJPZ3uNiZOLE96SSWivcbgFqtaqVe/RdnhiZ1jvbr5syAFdM6IdIvabVbVsiF9GrGu0wWR1NuurLpJMk8t9jsPw72095dbUDvtF5RNQ7OV1u3Pvf3crJ1uPlDRjTL67JdkqEjHQcZPA6GW51uHxORBP1Zmy5ISIi6mRyl5ccxWqSlpPLB5xHy+pR2WDDn78+CAC45ayBuGPWYLx/6xRcMi5TeZy0ON/M9gqpsz051gCVSuXTLRInfaCvtzrhcvtGFhBR18uvNqO60Y5InYYRMt3M5RXTMrwNWdQtkX8Xf3+w1GcwtSAIOCWtIBqUHINYgxZaqegsD4UDPN3rAJBX2ehzfzkSrKcNSE2Pi4BKJcadVLUwhJOAcun9Wh4u2pqOFtoBcTWF/G8qP0B3u1wk8x/K2ho5cqbGbBf//Vf5FtsbbDzJT9TbLVt3AlulSExA/Czjr6rBpsRZzh2dBkCMa5M//zC3nfoSFtuJiIg6mVnq8spOjPK5fsogsQB3vLwBT351EDVmB0ZlGPHo/FFYfPEonD3Mt3vSP0ZG7oLPDNCVFufVPVdv5Qdfou5WWCNGNPRPjGSETDfzLix2VgF78sBEpMYaYLI68fNxz6DU6kY7TFYnVCogOykKKpUK/aXf9Q98vBeVDTY4XG6f6JjTXsXJRrsLNql439M623UatRLTwyGpLTteLp6QkaNdusvAJPHf4qnKxia3yccS6W3tbJdOGDhcAhpsTp+TRwDQwM52ol7vo+35ADyrx+QZEN42HquAIACjMozIiPOsMotQhqQyRob6Dn4SICIi6mTy4KCR6Uaf6ycPFIvtX+0txjf7SqBRq/C3q8cpGe3+0oxiUUP+gHxAGnom57N702vViJI642rNLLYTdTc5D7kzYkyobZxdsJpHo1bhYmkZ/Dd7PR3rchEzMy5SKSC8fctkyIkqm05Uocav69u78C53vEfqNIjS97zl9HIBpYRDUlt0TOr6HJ7WOSstgjVAKrbnlpiw6Xilz3Be+e+srTEykXoNIqV/6zWNjibFdg5IJerd6q0OFEsrY66b0h+A53ectw1HxBPT547wbR6Sh6Sys536EhbbiYiIOpEgCMqA1JEZvh+yzxyYAMCTe3rHrMEB8w5l8tJtq8MNs92JQyUmAGj2PnJ3O3PbibpfkdTZ3i+BxfZQiY9qPR+7LS4ZJxbbV+eWKUUCJa/dq2N5YHI0fjtjEADgl2OVSia7rNbrd3JPzWuXySeTims5JLU5VocL+dXiaouhaTHd+tyjM8Xjg9fWn8D1b27FD4fKldtKpWJZWhtjZABPbnupyaoMYpdnJXBAKlHvJq/USYk1KI1DRwIU2/cVik1B0wYn+Vwvn5i2OXt+sd3mdGHziSpGdlKrWGwnIiLqRDanG/Lxl3dne6xBi5wMz+UhKdG47/xhLT5WrEELjdQuuet0LawON6L1GgxKCrwsncV2otDxdLZHtbIldbZxWWKBUS54d5aJAxKQEReBBpsTG46KHXtyZ/ugZN/fwzOHJQMAfj5eqWSyy2rNnuK73Nme1MPy2mVyVzRjZJp3sqIRbkF8T07p5r/nqydl+VzecrIKAOByC0qOvHe8Q7ASpEHAv35jM/Kk2KbR/cRjGna2E/Vux6TImOFpMRiZYYRWrUJhjQW782uUbawOF/KkyLSRfrNTIrS9J0bmjQ0ncd2/t+CxLw6EelcozLHYTkRE1Im8O7xGeB1sJkTrER+lx5CUaGjUKjx39TifAaeBqFQqxEsF9J+kzOCcTCPUcl6BHyOL7UQhw8720Hnr5sl4+bozcPfsIZ36uGq1CvPlKJl9YpTMqYrAxfYpgxKh16hRVGvBjtM1Prd5/06WB4um9NDO9ox4OUaGne3NOS51fg9LjYFKFfj9uqvERerw8IUjlMtlUoG9sMYMp1uARq1CSmzbTwAMSfF06CfH6PGbGQMxPFU8xmGxnajn2XC0Ape/8jNOVjTNXvd3rFzsYh+WGou4SB2uPKMfAOCVH48r2/icZPT7HdMbYmSsDhcEQcB7m08DAP67LR8HiupCvFcUzlhsJyIi6kTycNRInQZZCZFKjq8cCfPBbdPw/R/OxqTsxKAeL06KRfjleCUAzxLxQOTCfC2L7UTdjpntoZMSa8Bl4zO7ZDDtfClK5sdDZRAEodnO9ii9FhOz4wEAH28vAAAMTRULlLUBMtuTontmZ3u/eKmznZntzZL/jts6iLSz3HXOELx4zXgAQF5lI+rMDty5fBcAICfDqKyYa4snLx2NNxeeiQ0Pzcb2Ry/AE5eORrRBnDnAAalEPc9La49ib2EdVu4vaXVbeRjqMCkW687ZQ6BRq/DD4XKsO1wubSMW5EekxTY5yWjo4QNSC6rNOGPJGvzxk30+Jxf/uy0/hHtF4Y7FdiIiok4k57VHG7TQadTKcu1EqWieZozA0NTgB6YlRIlF+oPFLee1A54YmT35tfjDh7uRK92HiLpWrdmuDB/sz872XmWYNOCy0e6CxeHCKWmZ/ODkplncM4eKUTKl0lDrM/rHA/CNkenpme3yexpjZEQnKxpw+3s7cMObW5TBuCaLeBwgrzbrbiqVCmOkE/OnKhtx89vbcKjEhOQYA16+7ox2PWZCtB4X5KQhOylaKaTFGMQCGjvbiXqW6kY7dhfUAghuNayc2S4PfB6SEoNbZ4qxbY99cQBmu1Mptg8LMKciQim298wTc8u3nobF4cL/dhXC4vUavj9Yyux2ahaL7URERJ1I/tAZLX0IHZAo5jfLne1tJXerC9Kx3Jh+xma3lYvt/9tViC/2FOO293bA7uyZXSREPcmnOwvhFoBRGcZ2RTRQ+IrSaSA36R0ta4Dd6YZOowoYFzRzWIrP5YnZ4lBsk9WpfCCv7OmZ7VJne3m9DQ4X318e/GQvVueW4ZfjVbjvw91wuQWYrGLxyhgRmmI7APRPjIJKJUbb7SmoRXyUDst/N6XJioyOkDvbWWwn6ll+OlahfK7wXnkVSIPNqazcG5bqKaT/4YJh6BcfiaJaC15aeww/Sh3uozKafk6J0EoxMj10QKrGr1N/8sAExEXqUNlgx7ZT1SHaKwp3LLYTERF1oka7eCAZrRc/hPZPlDvb21dsl2NkAMCgVWNoStOOEWVbvy66oloL/vPLqXY9LxEFx+50493NeQCAhdOzuz2jmbqWWq1Sfp/vL6wFAGQnRQeM4hjbLw7GCK1y+YwB8cr3Jql7sErqbE/uoZ3tydEG6DVqCAJQZmJue0W9ZxjuT8cq8cKaI8rftTFS29zdulyEToNMaRVCjEGL9347xWdoe2fwxMiw2E7Uk6w/UqF831pnu9zVnhJrQLzXZ5kovRZLLh8NAHhj40kcLq1HlF6DS8dlNnmMiB4eI1Pjd0JiyqBEzM1JAwCsOtB6DA/1TSy2ExERdSL/zvZrJvfH5IEJuHxCv3Y9XoLXge3I9NgWM4ljvYo8E6Uiz8s/HONyf6Iu9P6W0yiotiA5xoDLJzT9kEk9n/z7/Aepc897+LU3jVqFs4YkK98PTo5BjFSQlGdpVDX27Mx2tVqlZJEX17LYLsci3CUN51227gQ+2VkIILSd7QBw+YRMpMYa8J9bJmNcVnynP34Mi+1EPY7LLWDD0eCL7Uo8TGrTZp/zR6XhwtHpyuXrpgzwaRKS+Q9IfWntMcz4y48oqDa3/QWEQJHf56gZQ5JxsTTPZdUBRslQYCy2ExERdSK52B4ldUJOyk7EJ3eehbFZzWettyTeq1t9dAt57QCg12qU71f8bhrOzE6A2e7CM98eatdzE1Hrlm85DQB4YM5w5f899S5yUVHuBpQ72gKZMUwstvdPiIReq1ZWHNX5dbb31Mx2AMiQiu0lHJIKs7Sa7drJ/fHbGYN8bgtVZrvs4QtHYusj52PKoOAGsreV/P/Cv+uTiMLXvsJaVDd65ogE29ku57X7+8uCsbjvvKF4+oox+OPcEQG38c5stzpceHHtURTVWvD6hhPKNm63gF35NXjzp5MorAmvInyR3/5MzE7AjCHJMEZoUVFvw87TNSHaMwpnLLYTERF1IvmDt/whtKPivTpE5IFnzbnijExcNj4T/7ppEiL1Giy5fAzUKuDb/SX4dGchTkuD/Yioc1Q32nGqUvx/NX9sRoj3hrqK9+9zvVaN80c1X2y/fEImLhiVhkXnDgXgifeqNdvhcguoNvf8Ynu/eDGe5Pcf7sGydcdDvDehIwiCMiwvUq/B7872K7ZHhP7kW1fGWo2WZsgcLjWhtI6rHIh6gnXSSeMsae6IKdjO9gCDTwEgPkqPB+aOwI3TshGp1wTcxrvYvtGrq/5YeQMEQcDLPxzDjL/+iKte3YSnvz2Ev6w63LYX1YUEQfBZxZUaa0CETgO9Vo05OWJX/6/f2Ix/bzwZql2kMMViOxERUSdqUDrbAx9wtpV3PmJLw1HF59Ti5evOwFxpSWdOphE3nzUQAPDHT/Zi3j82oqrB1sIjEFFb7CkQu5mGpEQHXDpNvUO0V7F9TKaxxZOpxggd3rz5TPzqzP4APCdM6ywO1JjtEARApWr/HI9wIA9JBYC/fX8khHsSWjanWxkyGKnTICXWAO/adqg727taRlwkzsxOgCAAK/czt5ioJ9hwRIxDk2PvWutsP1YmdrYPSw3c2R4MZUCqw+3zu2JPfi2OlNXjhTVHUVJnhVaahXK4tL7dz9XZaswO5aTqM1eOwZf3zFBuu3isJ0LnmZWHkFfJpibyYLGdiIioE5ntcmZ753a2a9WqZpdwtuT+OcORGitmA1sdbmw8VtHKPYj6roJqM5yu5gd4FdVacPkrP+Pj7QUAgN35tQCAMwYkdMfuUYh4F9eTYtqWtZ4QLRbVi2utSoRMQpS+xfkb4S5DGrzZ11mklWyAWGzXadRI9vr3EerM9u5wkbSih8cWROGvot6GvYV1AIDLxouzpBrtLsz4y48+w55ljTanklceKLM9WBFSA5LJ6sDaQ+XK9XaXG796bTMAccXU9/fPAgCcrmoMmxz0ohrx9SfHGHDD1Gyf97+Zw5KV1WsAsPlkVYefz+0WWjwOpZ6j5x7lERERhaFGm/jhWx6o11HD02Kh16gxfUiSsgyzLYwROnyxaAbOzBaLgRuPVnbKfhH1Nh9vL8DZz63DSz8ca3abl9Yexd7COjz8v334385CvP1LHgDgDGkgMfVOPsX26LZ1pMu/e9cdKUdlgzwcted2tQOeGBmZO0yKIt3NLHU76jVq5eSJfHIbAIyRoY+R6Wo5GeKKu/yq8MpYJqKm5AiX0ZlGDPUqnhfVWvDxjoIm2x+T8tqTYwzKieP2iJBmSq3JLUODzYl0YwRW/G4q9Bo16qUVwZnxERiUFA2DVg2HSwib3Hb5ZEO/hKYnmQ1aDd797RQMlyJ2Np/oeLH92n9vweRn1uLbfVwt1NOx2E5ERNSJ5AGpndXZnmaMwObF5+Gtmye3+zEy4yPxx3ni0KKNRyvgYMcEkY9GmxPPSXEY3l1X/nJLTMr3D36yFw02JyYPTMCVZ/Tr8n2k0PH+fZ7YxoLDHGmY6o68ahyTsm97cl47AKQafbv75SX2fY1FWsnmnVPsfWKmL3S2908UC1CFNZaw6UQlosDWS8X2c0ekQqP2nefgdDX9/yu/Zw1vJq89WHK0pjzX6qKx6ZgxNBmDU6KVbVJiDVCrVRiULF53MkwiWeRie1Z84BVdE/rHY8nlYwB0vLO9oNqMbaeqUWN24J7/7kKt2d76nShssdhORETUieQDyWh953W0JcUYoNd27C174oAEJMfoUdVox+e7izppz4h6h7d/OaV0HR8uNeF4uW9eaFWDDTtP1+BgsafYrlGrcP8Fw/Hf26YhqhP/v1P4iYlof7E9KyEKORlGuAXg4x2FANoeRRNuhqTEKEVWwPO+19dY7OKJa+8ZLd7ft2c1Wk+TERcJrVoFu8uNMhOHpBKFK6fLrXS2zx6R0uT2ioam/3/l7PT2xFh6mzwo0eeyPFDee5VUivS+qBTbK8Kk2C7FyGR6zSrxNz4rHoAY01PTGFyBvKjWgmv/tRkfbM3H3oJaNNicPp3xggDsyKtp/45TyPGTARERUSfq7AGpnUWvVeP2WYPx7MrD+PfGk/i1NLyPqK+rabTjjQ0nAQBqFeAWgAte2Ii/XT0OV03MwusbTuDFNUfhlLo2s5OicPmEfpg9IgUTmdXeJ/hmtre9K33u6DTklpiUlRHJPTxGJkKnwYY/nouRj38Hu9MtzSrp2ScQ2kOe0RKp8y62962P1xq1ClkJkcirMqOg2ozMZro/iSi09hXVoc7iQFykDhP6xze5XS4qe9t5Wiz2ju8f16HnHpLi2xkvHzt5D9tOkSK45G734+UNyv6GUrEcI9PC77ZIvQbpxgiUmqw4VdUYVOTOdwdKseVkNbacrAYA6DSqJquhtuVV4wJpdRz1POxsJyIi6kTyh++YToqR6UwLJmYBAI5XNMDaR5f9E/l7fcMJ1NucGJkei6mDkpTrH/p0Hy7558/42/dHlEL78LQY/OumM/HAnOEstPch0V4nTxOj215UnuP3Ybmnd7YDgFrtKQzIs0o6w9GyeuV9NNzJ8TmRfayb3V//xCgAQH51eGQsE5HHqv0lWLH1NHZJhfPJAxMDDugu9Cu2W+wuHCgSh6memZ3YZPu2evbKsQCA+84bCrUUYeN9ck4utg9KFgvz/92WjwlLVmN7XnWHn7slrc0c8WS2R7W43cBk8fa8IONv6q0On8sOl4AqqSv+12eKn9e2nura105dK/wqAURERD1Yg1R0iArDYntitB4xBi0abE4U1lh8hiMR9UW1Zjve2ZQHAPjThSPhcgvYU1CrFNEOlZgQY9DiyctG4/IJmdAF+IBKvV+MV7dZYlTbu9JzMozoFx+pfGjv6ZntsmiDBpUNgMXROcXxX45X4oY3t2J8Vhy+vGdmpzxmV7JI8Tnene0LJvbD/3YV9qn3V7nYXsBiO1FYcbrcuGvFLgBQ4ii9B7q//ZvJ+OuqwzhcWo/CGgsEQYBKJRbC9xTUwukWkG6MQFaA4aBtdd2U/piUneDzu7FfgGK7d467IABPfHkQK39/doef31+d2YGfj1fiia8O4NLxmXji0tEBt5M721uKkQHE+JstJ6uDLrbXWcRi+5ycNLx6w0QU1liw7nA5tBoVZg9Pxcc7CpFbXAeXW2iSr089Q/hVAoiIiHowT2d7+HW3qVQqDEiMQm6JCfnVjX2qGEAUSG6xCTanGwMSozB7RApUKhVyl8zDjtM1eOeXPETqNfj9+cOUYhL1Td6/zxPbUShXqVSYk5OmnNhJakd3fDiSi8ynq8xYub8U103pj6Gp7c/2/WBbPgBgb2Fdp+xfV5Oz6r07288amoyv75mJAUl953fGALnYHiCGgohCp7zepnxvd4ozJs7wipA5d0QqzhqShJH/7ztYHC5UN9qRFGOAyy3g1fXHAYh563IBviNUKhVGpPu+P2TEeWe2i8XswcnRPtu4hc4fvGx1uHDBixtQIf183v4lTym2HyoxYU1uGe48Zwhcbk+3eVZ8K53tSeJ+n6oK7qRjnVkstp+ZnQCdRo1BydEYNHMQAMDlFqBVq+BwCSgzWRnP1UOx2E5ERNSJ5OX04ZrbKhfbTwd5MEjUm52qEjuQhqbGKB8mVSoVJg9MxOSBHV82Tb2D92f9pHbmrc8d7Sm2J/eaznbxfe79LaexO78W5fU2/PO6M9r9eGZbz4iPkSkxMn7RMWOzOpZv3NP0T2CMDFE4KqlregLM//eTQatBhjECxXVWnKxsRFKMAasPluKnY5WI1Glwz7lDu2z/MuI83eLyiq94v9VjVUEOHG2LygabUmgH4NM5/sDHe3GoxIQ0owGTpPicGIMWxsiWP9cNlE4SnKpsCGof5M72QJn0GrUKmfGRyK/2nYVhtjvD9vMlNcW1sERERJ2oUSoWRIfpwVB2Us/+ULw9rxo7uji/kfoOebmv3JFEFIjd5Va+b28m95SBiUgzGqDXqJVO4J5OHgSeL5283VdYC0DsGpSX3reF3CneU8gxMuE2EL27DWBmO1FYKq61AhDnjgxKjsYVEzIRG9G0uDsqwwgAOChltO+X/rzijH5NutE7U0ZcBFJiDUiVvgKpqLehqsEW8Lb2avA7setyC3C43DhWVo9D0iDznadrfCJkWuvul38PBho0G0itVGyPjwo8ALZ/olhgL6ixwOly46FP9mLME99jTW5ZUI9PoReWxfalS5di8uTJiI2NRWpqKq644gocOXLEZ5vZs2dDpVL5fN15550+2+Tn52P+/PmIiopCamoqHnroITidPatjgoiIeg6XW1A63aLDMEYGgLK0Pb8HdrZbHS786vXNuPr1zT1mgB6Fh/wqM55ffaRJAfCUVGwflNw7ip/UNc4dmYrEaD3OHZHS7sfQatT46Pbp+OTO6Ug1tpz92lPIRWa58/B0lRl1FgeuWPYLzvrLjzheXt/sfXfn1+DRz/fj+dVH8MHWfKw7Uo4yk7Vb9ruzBBqQ2hfJRaGKeptyAoKIQk/ubD9/VBp+fPAc/OPawCuPRvcTu90PFouF5mPlYnf2iLSujZvUatTY+NC5WP/QbJ+hrXLxX/bKuuNwtTLItC0arOJniP6JkZBr6LVmB77aW6xssyu/1jMcNYgYF/lkQY3ZAYfXCfrmyJ3txgCd7YBnxdCJigbctWIXPtlZCLcA7DjNhqOeIizb7jZs2IBFixZh8uTJcDqdeOSRRzB37lzk5uYiOtrTeXTbbbdhyZIlyuWoKM8HJZfLhfnz5yM9PR2bNm1CSUkJFi5cCJ1Oh2effbZbXw8REfUN8gdvwLO8Ptz05A60equnwF5usmFgcnj+jCk0zHYnrA43Er1iPhwuN/7900k8953YtFFca8Xzvx6v3C4X2wcms7OdmmeM0GHT/50Hg7ZjfUq97d9ZoBVcB4vqcLhULLKv2l+Ke88P3BX53HdHsPlkVZfuX1dTMtt1ffu9KC5Sh9gILeqtThTWmDEsres6YYkoeCV14gnMjFY6s0dnisXtA3KxvUz8HT68G/4vBzpZ+dbNZ+KDrfmI0Knx99VH8fYveThR0YiXr53QJGamPeqlzva4SB3qrU7Umh2oMdvx5R5Psf14eQO2nRIL28FkpidE6aFRq+ByC6hutCOthZPq9VYHKqVu/fjIwK9HnhX0xoYT8D7PUN3Q+bE61DXCsrP9u+++wy233ILRo0dj/PjxeOedd5Cfn4+dO3f6bBcVFYX09HTly2j0nAFbvXo1cnNzsXz5ckyYMAEXXXQRnnrqKSxbtgx2O/+BEhFR55O7FLRqVYeLMl0lO1Es9uRXm+HuxC6R7tDoteyzopOXlFLPlV9lxh3v78AZS9ZgyjNrlQ+JALByf4lSaAeA7w+Wwil1HB0rq8eJCsbIUHAidJpOGRLXmwQqksjxAwCga+F9sKpR/B1+3shUnDcyFf4/WmcQnYFdod7qwNrcsqDeH60OxsgAnuHrAFBQ0/NO5BP1ViVSjExmXMvFYrnYfqysHiarQ2nIGdrFne3NyYyPxB/njcA95w3Dy9edgQidGhuPVuDq1zcrv3c7Qu5sjzFokSgV79cfKUd+tRmROo3Syf757iIAQL+E1ovtarVKmeninQfvbVd+De58fyfG/Xk1aqUBqXHNxMhkSc/pFsT9vGpiPwBAdRdk2FPXCM9KgJ+6OvGgLTHRd1DVihUrkJycjDFjxmDx4sUwmz1v7ps3b8bYsWORlpamXDdv3jyYTCYcPHgw4PPYbDaYTCafLyIiomCdloYtZiVEhm1RJiM+Ahq1Cjanu8cVrL0zFnta3AB1DafLjTuW78T3B8tgc7rhdAvYdMLTLbvxaCUAYPrgJETrNWiwObHzdA2cLjcW/mcbAHG1RzBdS0TkK9AKLu9uda26+fdBeaXS788fhv/cMrlJjr25Ewoq7fHgx3vxu/d2YNm6461uK8eZ9fUYGcATeXCyorFdef1E1PnkGBnvQaSB9IuPRHyUDk63gO/2l8ItiFniKTGBc9S702XjM/HZXTOQEmvA8fIGvPXzqQ4/pvx5IsagUzLT39t8GgAwJycNS68aC53G8/4VTIwMAKRIUTKBiu2Pfr4fV726Cd8dLPUZuh7fTIzM6Ewx2ichSocPbpuKC0enA+iagbHUNcK+2O52u/GHP/wBM2bMwJgxY5Trr7/+eixfvhzr1q3D4sWL8f777+PGG29Ubi8tLfUptANQLpeWlgZ8rqVLlyIuLk756t+/fxe8IiIi6q3yKsWTvuEcFaDTqJWDxtM9LLfde3heualnnSigrvH2L3k4VGJCfJQOl4zLACAu/QUAQRCw6YRYbF907lDMkz6orMktw6GSemV59fu3ToGmhaIgEQUWqKN7i1ex3eFqvjtcLrbLebXDUn07KK0hyv5eLQ2fe3X9iVa39cTIsNguz4N5+ttDOOsvP2Ln6ZoQ7xFR31ZmsuKItNKvfytDuVUqldLdvvJACQBgUHJ02DQO5WQa8ejFowAA/+mMYrv0/hMboVWiBwulwaaXjc/ErOEpeGjeCGX7YIvtyTGBi+2VDTas2JoPAPjVpCyf25pbGTU0NQZf3TMDq+8/B+Oy4pX9ZGd7zxH2xfZFixbhwIED+PDDD32uv/322zFv3jyMHTsWN9xwA9577z18/vnnOHGi9QOj5ixevBh1dXXKV0FBQUd3n4iI+pC8qp4RSeGd225zurA7v6ZTBw91Fe8YmbL6pp3tb2w4gTkvbEA5u977hKJaC15cexQAsPiikTh3RCoAsdhudbjwxFcHUVJnhV6rxpkDEzAnR2y6WHOoDNvyxBzOc0ekIDvM/78ShatARQKrwxP/Um91BLyfyy0onYWxEWJ3fEqsb+elOQTFdsGr3TCYAXdWDkhV+Bfz3vzpZIj2hIgA4IXVR2F1uDFxQDxGpreevT5G6qTedFw8Ydpa9Ex3m+fV2V1nDvzeEqx6mydGxjsDPi5Sh1nDxUHoF43JUK7PSmj5ZIVM6Wz3Wzm8WVpxOTI9Fn/71XhlmCqAFk9ojMuKVx6TxfaeJ6yL7ffccw+++eYbrFu3DllZWS1uO3XqVADA8ePikr/09HSUlZX5bCNfTk9PD/gYBoMBRqPR54uIiChYedKwxUFh3NkOeDrQ8qsa8dAn+3Dlq5t6xAdj7xiZr/cU4+lvcrHzdLVy3dJVh3GsvAGvb/C8FpPV0SNOJFDbPfnVQZjtLkwemIBfTeqPoVJnbG6JCQte26QsCb52cn9E6DSYNTwFeo0ap6vM+GCreNvkQYnNPj4RtSzKb0Bqsl/kgPdQa28NXtfLxfbEaN+l9KEotpd6nah1ugXYnc0X3N1uASel93z5NfRl/f0yjXWasC4zEPVqh0tN+Hin2Dj66PycoDrUc6TOdrt0ojG9leiZ7hap1yiFZzlTvr2UzHavznYAuHhsOvTSrJH+iVF46vLRePTiUUH/LOT9K6q14NmVhzDjLz9i04lKJd5wxtBk5bHbKilafOwGm7NTcuup64Xlu6AgCLjnnnvw+eef48cff8SgQYNavc+ePXsAABkZ4hmo6dOnY//+/SgvL1e2WbNmDYxGI3Jycrpkv4mIqG9TOtvDvdju1dn+1d5iAMDrG9q/Mqy7eHe2F9dZ8ebPp/DAx3shCIJPR6I8qHbV/hKc+fRaLFqxq9v3lTrf8fJ6bDpeCUEQsPpgKdbklkGrVuGZK8dCrVZhiFRsr7M4cLDYhMRoPd64aRL+fNloAGK+9FlDkwBAGYw6ZSCL7UTtFW3wdHRH6NQ4MzvB53bvE6TeTFLHu0GrhkErPsaVZ/Tz2cbiCHzftqgzO3DL29uwaMUurNh6GgWtFGjkCCrZiYqGZrYEVh0oxcmKRsQatJgpFVD6Mv/MfZ7kJgqdpSsPQxDE4vEkv9/LzRnTL87ncms576Eg/555ce1R5HcgCrPBJr4HiZ3tnhO9l47P9NnupukDcduswUE/rnzC+YOt+fjXxpMoqrXg1XUnsO2UWGyfPlg8Br1Cer+LDTD3pDnGSK0yB4Xd7T1DWJ6GX7RoET744AN8+eWXiI2NVTLW4+LiEBkZiRMnTuCDDz7AxRdfjKSkJOzbtw/3338/Zs2ahXHjxgEA5s6di5ycHNx000147rnnUFpaisceewyLFi2CwRD6QQ9ERNS7NNicyJMO/AaFeSxFtnSwetqr8NATutACFW5OV5mxu6DWJ7rHLQj4am8x7v9oD1xuATvzmR3b0xXXWnDFsk1osDkxPC1GmTdw26zBGJ4mLo+OMWiRnRSF01VmTBmYiJevO6NJN9KcnDSsP1IBQFySO6F/fLe+DqLeJFLn+SiZEKXH2Kw4fHfQMxuruRiZeiUv11PkGJoai/V/nI0b39qKwhoLLPbWY1xas/ZQmfL//dv9JVCpgDcXnonzR6UF3P5YmW9x/VCJCaMyAq90/nx3EQDg5rMG+sQQ9FX9/Drb5cGMRNS9fj5WiQ1HK6DTqPDwvJFB329QUjSi9BplVVG4dbYD4ueXnadr8OPhcuQWm7DlkfPb9TjeMWZ66fNPaqwBUwcldWj/UrziYRKidKgxO/Dz8UrlunFZ4gmNG6YMgE6tatPqSpVKhYRoPSrqbahutCMzyBx5Cp2w/GT92muvoa6uDrNnz0ZGRoby9dFHHwEA9Ho91q5di7lz52LkyJF48MEHsWDBAnz99dfKY2g0GnzzzTfQaDSYPn06brzxRixcuBBLliwJ1csiIqJe7O2fT8HudGNwcjSyEsL7AEhevljQw4rt/rECcqH0i91FKK71fLD/fHcR/vDhbqWzrrrRDje77Hq0p7/NVT4cHS1rgM3pRnZSFO47b5jPdv+87gw8d/U4fHDb1IAfFC/wKrJNHBAPbQ/4d08Urry7mRusziadkaZmYmTkIrzRL35lYHK0Uqww2zve2V4uDakblhqDgUlREARgVwsnXwtqfDslD5WYmt12f1EtAGD2iJQO72dvIK9QkMkDqImoe32xRzwReO3kAW1aaatWq5DjdXIx3Rh+xXbv+JVSkxW5xc3/jm6JfMI3xqDFnJw0TBmUiEfnj4JG3bGBsFMGJiIzLgLzRqdh9f3n+DR0xEfplPc3tVqFa6cMwJCUmGYeKbAkKfKmip3tPUJYdrZ7LwUPpH///tiwYUOrj5OdnY2VK1d21m4REREFVGdx4N9S5vnvLxgGdQcP1rqanNle2eA5WOvoAWZ38I6ReebKMchKiMLN/9mGb/aVYIpfd4hbAH41KQuf7CyEyy2gxmxHUgxXtvVEh0tNWLm/FCoV8O5vpmDrqSpoVCrcOD27yWDCcVnxGJcV3+xjpRkjMG1wIracrMbvzg5+aTARNSVn/ALiwLmxfsX25jLbTVbf4ajeInXi/2mLVyZtucmK8npbk2J+ayqkYvv5o9IQqdPgxbVHW1x+XyPdNjglGicrGnGopD7gduX1VpSZbFCrfH8G5FFmssLhcveIE/lEvUmpdKLrjAHxbb7v6EwjdpwWT0iGY2d7cqzvcfwr647hn9dNbPNnmAavAalJMQZ8fMf0Ttm/9LgIbFrs6bafOzoNewpqAQDD02KDys5viWdIqq2VLSkc8N2PiIiog976+RRMVjHe4pJxma3fIcSMETokRPkOo9Nqwr/YLh8c//78YbhhajZmDElCcowB1Y12fLS9wGfbfvGR+OuCccrr9D6xQN0vt9iE8vr2dTq+tl6cJ3DxmAzMGp6Ch+aNxANzRyA1tn0fBF+9YRK+XDQD0wZ3bLkwEQHf3DsT/eIjseTy0UiM1qOf19L25mNkpM72SF2T26KkE2jySiZBEHDTW9tw6Ss/Y0dedZPtW1LRIBYkUmINSIyROgJbeC+oNov7NWOImMF+uDRw1+T+wjoAwJCUmCZDYvuym6dnK9+7BbHgTkTdS/5/l9aOzvTRmeIJTZUK7T7G6koTvJop1Cpg5f5S3P/RHjhcbYsd8x6Q2pVmDfOsfBrcCfO84qT3zOZOZFN4YbGdiIioA2oa7fjPz6cAAH+4YHiP6BAHgAF+ufI6teeQoMHm7JQl/J2t0asTBQC0GjUuHS8ORv/pWKXPtmcMiIdarVKGFVU2sAskVE5VNuLSV37GJS//7LM6IRj5VWZ8LQ3xvWv2kE7Zn8RoPcYzq52oU4zpF4df/u88LJw+EIA4kE/WXEGgvqXOdql4bZGK7QeLTThSVg9BAJ5ffbRN+1YhneBLiTUoy++D6WyfNjgJapV4klY+Sdhoc+KRz/fj3L+vx23v7QAAjM1qW6d9b/f/LsnBDw+eg2xp9dyx8uYHzBJR15CL7amxbV/NKXfD90+Igl4bfqXCsVlxWH7rVPz08Ll45fqJ0KpV+GpvMe75YFebCu4Nfp8nuop3LE+g97u2ivQ7GU3hrVP+B+Xm5uL+++/HjBkzMGLECDz88MPKbZs2bcLLL7+M6uq2dSIQERH1BP/+6SQabE6MyjDiwtHprd8hTHhn7QJiFwsgFjjO/ft6zH/551Zj3bpbo3RwGe11cHzlGf0CbnvFBPF6Ftu7V6B/M5tPVMHlFlBeb8Or64+36fHe2HgCbgE4Z3hKmyMkiKj7PTo/BxsfOheAWNAI9DtB7myPNTTtbI/UiR9PLQ4XBEFQTmYDwOaTVShvQ7e0HCOTEmPwWn7fQme7dFtmfISSdSxHybz84zF8sDUfpyob4RbE4XdXT8wKel/6Aq1GjSEpMZg5VFwZ8NWe4hDvEVHfYnW4lJiu1HZ0tg9Li8UbN03CazdO7Oxd6zQzhyWjf2IULh6bgddvnAS9Ro3vD5Zh1YHSFu9X02jHR9vzsWzdceV3fVcX29VqFR6bPwrD02Jw68yORxf6r/yi8Nbhf10vvPAC/u///g9Op/ifWqVSobLSt7vs/vvvh8FgwB133NHRpyMiIgobVQ02vLMpDwDwwJzhYZ/V7i3br9gu5+PuL6pDRb0NFfU2mCxOxEU1LYaEitwVHW3w5HSP7RcHg1YNm1PsaHnr5jNhc7pxQY44CFPOd5SLLtR13t9yGs99dxgv/noCLshJwxsbTuBkRSPsXt1GH20vxINzRgT1f6XcZMUnOwoBAIvOHdpl+01EnStJimxxuQWY7S6fE6RAy5ntciyL2S52kn+2u8jn9hMVjUEXkeQBqSmxBrilon9Lg+VqzOJtCVF6jEo3SrntJqTEGPD2z3kAgKevGIM5OWlIjTV0OH+3t7p6UhZWbM3HqgMlePqKMU3+/omoa5SbxN95ETp1kwHUwZrXgxqHLshJw68nZ2H5lnwcKjHhsvHNR3k++fVBfOl1AlCjViEpuutnOf3u7MGdNiMoWn5/bOMqUQqNDr3zffvtt/jjH/+IQYMG4fnnn8fMmTORmprqs81ZZ52FlJQUfPnllyy2ExFRr/LGxpMw210YlxWHC0altn6HMDI4xTdGptEmFtv3FdYq11U0WMOz2O6VkatSqfDU5WPwxFcH8dzV43D+qDSf+yRLRZ/KBjtqzXZsOFqBi8dmcGhbJ3G7BbyzKQ8Hiuvw2S6xKLZ862lEGTRYuupwk+0rG2w4WGxqMX7hm33F+PPXubA5XLC73DgzO6HJAFwiCl9Reg00ahVcbgGrc0tx5Rm+HeAtZbbLy+Q/31WE4jor1CrgoXkjselEJX46VomCajOmD2l+3oLLLeCWt7fB5nQrcTUpsQYlYqDO4oDT5YbW7z3A6nAp3YIJ0XqMyojFt/tLsL+oDh9uy4fd5cb5I1Nxw9QBLLK3YkL/eCRG61HdaMfpKjOHyBJ1k7J6T157X/k9NSg5BgCQV9kY8Pbj5fV4duVhrDtSDgA4d0QKhqfFYvLAxLD6jBMMJUbGwc72nqBDxfYXXngB0dHRWLNmDQYPbv5szYQJE3DkyJGOPBUREVFYKa+34r3NeQCA++cM73EHtf4ffuVC9p6CWuW68nobhqbGtvpYu/NrcPeKXbjt7MH47cxBnbqf3hqUznbfw5dfT+6PX52ZFfDvwDtG5pUfj+PNn0/hZEUj7p8zvMv2sy/5am8xlnyT63NdXmUj/t8XB5psO7ZfHPYX1WHdkfKAxXZBEPDCmqP454++UTM3Tstusi0RhS+VSoWUGANKTVbc/9FezBud7jNItMXOdp1YTCiuE4tG/3fRSNw+awiKas346RiQX21u8bmPlzf4zPDQa8UOT7cgxqUJAlBjdiDFL89Y7mrXqFUwRmgxSsra/XZfCQAgPkqH5389vse914eCSqVCQpQO1Y121FkCD8klos6nDEcNw+GmXUUePHqqmWL71a9vRq3Z83vokYtHYVha659twpESI8PO9h6hQ21dO3fuxLRp01ostANAcnIySktbzlAiIiLqSd7ffBpWhxtnDIjH7OEprd8hzAxJifG5bHG44HIL2Ovd2R5E9IrN6cJDn+5DSZ0VK/eXoM7cdR+s5e77QBmLzRVA5M72inobTkoH4p/uLITbHV559D3VB1vzAQBpRgP+dvU4AEBelRknKhqRHKPH878aj4QoHcZnxeH6qQMAAD8eLg/4WJtPVDUptAPA2cOSu2jviairvHL9Gcr3VQ2+0S0mqQAbG9F8ZzsAXDo+E7dJy+/lOSOnWym2Hyiq87mcEiPGvWjUKsRLnfSBctvl6xKi9FCpVEqxXTZjSDLio/QtPjd5xEk/axbbibpPmRQjk2rs+niUcCHP18iramxybO92Cz6FdgDIiI/stn3rbJ6YNXa29wQdKrbb7XbExrZ+Vqi8vBxaLbPaiIio95CXI940LbtHdroFilEprDGjoNqiXK5saD7bdufpavxyvBKvrz+J4+UNAIAdp2swfslqfLarsNP31+UWYJKiB6K8MttbMzRVPKmwp6BW6fgpqrVgWx4Ht3fUsbJ6bMurhkatwlf3zMSvzuzv0y362PwcLJiUha2PXID/3XUWzh0hRi3tLaxFVYCBtV/sEWNorjmzPzReme5JMX3nQyNRb3HmwESkSr8P/Auu9S10tveTCiEj02Px1wVjlffXAYliQcW7s93pcmNPQa0SEQOIc0cA4IJRaZg8MAELp3tWxshDUqsafX//CIKAr/YWS9uIReKMuAhlewCY1kJ0DTUlF9tNLLYTdRt5gHRqH+psz0qIhFatgtXhRqnfAO1DpSafy3GRui4fitqV5M52C2NkeoQOFdsHDRqEvXv3triN3W7Hvn37MHw4l2sTEVF4szmDO3ipbLDhQJF4AHf2sJ7X1S6L9Tvg3HSiyudyc53tTpcbC17bjBve3IoX1x5tcvsDH7d8bNAeW05WwWx3IS5Sh/4JUa3fQTI+Kx5xkTrUWRw4WOw56O6KEwJ9zX+3FQAAzh+ZijRpYOEYKZ7orCFJuHyCOKhKr1VDq1EjPS4CORlGCAKw4WiFz2PZnC6sOiCugrxyYj+8fuMkAMCzV47tltdCRJ2vuYKrktkeoLN9Tk4a3r5lMj6+c7pP9Izc2V7gVWz/x9pjuGLZL3jKK8rqYLFYbL94bDo+ufMs3HHOEOU2eRief6f9vzaexBsbTgIAIqUYG5VKhfsvGKZsM30w50a0hfx3X2tp/qQ9EXUuJUamD3W26zRq9JfeH/xz270HogJiHFhPJr8nNjJGpkfoULH9sssuQ15eHl544YVmt3nuuedQUVGBq666qiNPRURE1KUOl5ow7snVWLryUKvb/nJczIPNyTA2yX3tSf57+zTMHOqJ6Ai22O6/BP8sv44/fScMH/XuVASAz3eLXc/zx2VArw3+8bUaNWYFiPlZub8UFi7DbDerw4X/SScsrpPiYQDg3vOH4aoz+uG5q8cFXPFx7kjx72LdEd9i+8ajlai3OpFmNGDywETMyUnDkacvxHVT+nfhqyCirtRclEhLne1ajRrnjkxtUogfkCQWU6obxWHXAPDKOjF26r3NpwGIK6Dkk6pj+jWdCyE/xmGvbsdDJSb89TuvYc5ev7dunJaNRy4eiT/OHR7U/BLyYIwMUfeTY2TkBoi+YpAUJXPSq9hebrLi3U15PtvZnb6fLXoaJbOdn196hA59Gn744YfRr18/PPTQQ7jmmmvw4YcfAgDKysrw+eefY+HChXjiiScwaNAg3HPPPZ2yw0RERF1hd34tbE43vt1f0uq2cmzKGQPiu3ivutaYfnFY/rupyrL9TdJJhDH9xO7kigBRH4A4OFUWrdfguavH+SzLDFRACVZJnQUXvLABwx9bhdc3nAAAWOwurJL+Xq48o1+bH/Psob6Z35lxEWiwObE6t3fMk6mzOJRO0e6ycn8J6iwO9IuPxCyv1R0TByTghWsmIKuZ1QdylMzGoxWotzqUopcc4XDJuEwlQsag1fTIiCYiEjVXcDW10NnenBiDVnmvOlxaH3CbU5WNMNtdiNCpm8wlAYApA8Xu9K0nxRgxm9OF+z/aA7cgdjyOyjDi7tmeTniVSoXbZw3BPecNa/JY1DIW24m6X1m9FCPThzrbAWBgkpTb7lVsX3OoDDanG+P7xyvX2XpJsZ0xMj1Dh4rtCQkJWLt2LUaPHo1PPvkEN9xwAwDgu+++w9VXX43ly5dj1KhR+O6774LKdiciIgoVuVu7sMaCmgDD07yV1okHsxlxvaNzJFrKQK+SXvcFo9IAAJXNdLbLHe9qFfDVvTORlRDlk23bkWL727/k4Xh5AwQBWH1QLIavOVSGRrsLWQmRmDQgoc2POTLDcwwSF6nD1WeK3dKf7Spq936Gi1qzHXNe2IAL//FTmzr1j5c3wGxv/zLU/24TB6NeO9k3X701ZwxIQHyUGOtz9nPrcOE/fsL3B0uxNrcMgDgQkYh6h0AFV4fLDatDLHgYI9v2XiEPLT1UYgp4uzwcNSfDGPD30lQpCmZvYS2sDhdeXHMMh0vrkRitx5r7z8Gq35+NeaPT27RPFJhR+bvvnLiDOrMD+VVmCAKHmxM1p6LPdrZLMTJVnmK7vMrprCFJyonWX52Z1f0714k8MTIstvcEHZ4OMHz4cOzZswdff/01Vq9ejby8PLjdbmRlZWHOnDlYsGABNJrgB5kRERGFgrwsHQAOFNe1mMVeJhWbU3vJwWy0V1e6TqPCrOEp+MfaY812tsvF9lnDU5TuQbfXB2CDtn3v+1aHC5/sKFAu78qvxcOf7sXeArGAcuUZ/aBuQ2FXJg9JBcQTBFee0Q8v/3AMPx2rQHm9tUcPknp1/QllpcHq3FJcPqH1zv9fjlfihje34qqJ/fDCrye0+TmLay3YnlcDtQr49eS2xbxo1CrMGpaCr/YWo9YsFuDueH8nACA7KQrjs5pGPxBRz2QMUGyXI2QAtHlQXU5GLNYeKsPhksCd7XKxfWyACBlAzH1PMxpQZrLh3xtP4o2N4uqpZ68c26Mj4cJRZ3a2u90CrnztF5ysaMSQlGicOyIVeq0aF4/NCBgXRNQXNdqcqJeyvPtesV08zveOkZGL7aMzjbjt7MFYf6QcF43JCMn+dRals70DzTLUfTplFK9arcbll1+Oyy+/vDMejoiIqNtVN3o+EO4vaqXYLnW2p/eSg1nvgsfIdCOyEsSl+lUNNrjcQpMOQbkInxLjKU54Z6w3tHNwz6oDJagxOxAfpVMKsR/v8AwyvaIdETIAfIbs1ZgdGJQcjbH94rC/qA4/H6vEVRN7VqdLSZ0FPx2txPQhSXjHK49yyde5yIiLREKUDoOSo6FtJjtf7uj/+Vhlu57/h0NiF/qk7IR2faAb08+oxMZ4u3RcJmNjiHqRwMV28fsovabZ31HNGSl3tpcG7mzfLxXbRzdTgFWpVJg6KAlf7S3G82vE4d4LJmbhwjHsZu9snVls319Uh5MVYhHtREUjTlScAiCuUFjxu2kdfnyi3kBuvIjSa9p8IrOnG5jsGaDtlD6PHJZWQOVkGJEYre9xx/qBKJntDhcEQeAxc5jr+AQzIiKiXqDGu7Nd+sDenFKTVGzvJTEyk7I90SzjsuKQGKWHSgW4habDUAFx6BDgmwnpcHk6203tzA9fvkWMJrl1xiCk+nUZJscYAmbwttfMYWKO+8/H21dwDqWHPtmHh/+3D2c/tw52pxsj02MRpdegqtGOX7+xGXNe3IiHPt0X8L5Olxs/HBaL5eX1NlQ2s3qhJWsOlQPwxA21ldyB5I8RMkS9i3/BVRAEmCzND0dtjRwjc6S03ucEr0GrhtstIFfqZGyusx0ApgxKVL6PjdDiicty2rwf1Dr5797UCcX2Hw+L7znnjkjBS9dOUBod5Eg/IgLKpGPzvtbVDgCZcZHQa9VwuAQU1VpwqrIRNqcbUXqNkufeG0RJJ1EEAUocG4WvDhXbP/vsM0ycOBE//PBDs9usXbsWEydOxJdfftmRpyIiIupS3sX2/S0U260Ol1I46C0HtDdOy1a+H5EeC61GjSQpg73CL7d9R1413t18GoBvZ/uCiZ6u8wabE25323JVD5WYsPN0DbRqFa6Z0h96re8hyu8v6NiAul9NypL2U/xzpjQ09edjlXC63CiqtaCxnR353c3/BMGzV43FF4tm4NdeWZSf7y7ChqMVTe67La9aWTUANJ993JI9+TUA0OLqj5YMSm76wWdYagxGpHO+D1Fv4l1s33i0ApOf+QGvrDsGoG3DUWUDEqMQqdPA5nRjb0Gtcr1eq8bpajPqbU7otWqf6DB/U72K7XNy0tq1H9S6uKjO6Wxfk1uGl34Q/81cNDYDl0/oh3d/OwWAuFKNiERysd2/WaUvUKtVGJgkdrefqmxUImRGZRjbFT8ZriJ1npjOjsxdou7RoWL722+/jdOnT2PmzJnNbnP22WcjLy8P//nPfzryVERERF3KeyhqQbXFJ8Pdm3wwG6FTw9iBQaDhJDnGgCcvzcHMoclK5neyVEj373xe9MEu5fsUr6zzB+eOwF+uGgtA7LiQcyPXHS5XBp22ZPkWsYA/b3Q6UmMjfP4+Xr9xEm6YMqA9L03x58tH45Xrz8CTUhfjpOwEROs1KK+3Ye6LG3Hu39bj3v/u7tBzdAf/4XBzc9IwcUAChqfF4rmrx+PQkguxcLp48uT/fXEAVofvEKXVB8t8LsudoMGy2F0wSZnLWYmRbd19AGLBzN+ic4e267GIKHzJxfYT5Q1Y9MEuVDbY8L30O6g9ne0atUo5KffL8SrlervTraxIG5VhhK6FeBrvQvxVZ/T8WIFw5X2iZfOJKuwtqPVZjRAMi92Fhz/dC0A8SSvH/SREi49dY7bD1cYT+0S91fHyBgBAv/j2HZv1dHIH+6nKRuSWePLaexONWoUInfj+ZrZzSGq461Cxfe/evRg/fjwMhubPnhkMBkyYMAF79uzpyFMRERF1KTkuRacROyAOFAUuQpZ65bX3pqy8W2YMwvLfTVU+IMvD4vw728tMnsveA+UidBpcO2UADFJHusniwP7COvz23e24/f2dOFnREPB5X99wApf+82es2CpGyNwwTSyqPysV7h+YMxwXjknvcGdKlF6LS8ZlIlbqYozQafDiNROg06hwsrIRdpcbPx4uh90Z3ssyvbvS+8VHYvHFo3xuj9Rr8PCFI5ERF4H8ajP++eMx5TZBELAmVyx0TegfD6Dtne3l9Z6TTbHtzAT1X7Xw2g0TcfkERsgQ9Tby+0lxndVnMCoA5XdxW43KkIvtnhU+Nqcbe6RO97H9Wi6uqFQqfHPvTLx+4yQlTow6X3ykuDrO5RZw3b+34PJlv+CBj8XCuTNA0f1wqQkF1Waf6z7dVYgaswPpxgis+v3ZyiqEhCjxsQWhczLhiXqD9UfE1YzThySFeE9CY1CKWGzPq2zEwWJpfkcvK7YDnjlULLaHvw4V28vLy5GZ2fqHo4yMDJSXl3fkqYiIiLqM0+VWunXPzBaXmMtRMoIgoKDajHVHyvHmTyfx4lpxqFpvyWtvjhwRU+HX2Z7p9boDxX54D8R7/KsDkBuxP91Z2GRbAPjLqsPKz3pISjSmDxY/JFw2PhPr/zgb93Rhx/Pc0em4eGyGz3WHmxm8Fy7ypWJEaqwBv/zfeQEjWWIMWjx52WgAwL82nsSxsnoAwMFiE4pqLYjUaXD7rMEAoHT/BEsewJXWSSebzhuZiovGZvSqE1dEJJKL7UDTk2zt6WwHPLnt2/Kqfa7fekrsdB+T2Xxeu2xMvzgORe1ikXoN7jtvKMb0MyJbinfYerIKn+4sRM4T32NtrmeV1Z6CWlz4j59w41tbfR5jvZTV/psZAxHhFZ+g06iVfz+B5soQ9TUV9TblWPqcEe2L+OvpBkmd7Se9YmRyMlp/P+hp5CgZxsiEvw4V2+Pj45Gfn9/qdgUFBYiJ6byhZkRERJ2p1qszSu50k5ek371iF85+bh1+8/Z2PP3tIWw5WQ29Vq0UK3ur5jrbHdKS7a/umeFTSJHJ0Tpv/5KH3fm1yvWf7y5qsq1/JMoNU7OVoqtKpcLA5Oguz1r0zqsH4JMDHI4KasRie/8AUSze5o1OxwWj0uBwCXj08wNwuwUlzuec4Sk4Y0A8AOBERWOTqJl3N+Xhg62Bj+86KxP07VsmY/rgJDx56egOPQ4Rha90YwT0WjUidGr8dcFYn9uMAd4/giEX2/3Jq9HGtDAclbrXA3NH4Jt7z8Y394qRs+X1Nvzxk72wO9148uuDAACHy43Fn+0HAJyuMvtEzcgn+wMNR0+U5srUNBP5R9SXyDN6xvQzIjW2dzcDNUduPtl8ogq1Zge0ahWGp/e+GmS0QSy2W9jZHvY6FDY7ZcoUrFq1Cvv378fYsWMDbrN//35s3rwZc+bM6chTERERdRk5HzwuUqfEa+wvqoPV4cJqqftqWGoMhqXFYGhKDC4Zn4nhab17mGNzxXY5ZiVKr2lyH8ATDfC/XWIn+33nDcXLPx5HSZ0VZrtTWf4I+C7/HpAYhQWTuj8/d/LARLy58Eys2Hoa645UYG9hHW7q9r0IXkG1BQDQP6H1TM4/Xz4am05UYlteNTadqFL+Lc8dnYZ0YwQSonSoMTtwrKwBY7PipMc344mvxCLIkJRoTJVWGmw6Xol//3QSg6WiR0c/zJ07MhXnjkzt0GMQUXiLi9Lhf3eehWiDxud3P9D+zvaWBinrNepe/97cE8VG6JAaa1BWRgGeYvkDH+/1iTMzWRxIkmfGSNsnBzi5mxClx+kqMzvbiQCsOyKuAjl3RN89rpKL7U6pKWhoagwM2sCfVXqyGCnC0WRlhFa461Bn+9133w2Xy4X58+fj008/bXL7p59+ivnz58PtduPuu+/uyFMRERF1mRopBzshSqcsQc+vNmN7XjVcbgGJ0Xqsvn8WXr1hEh6YO6JPfJiXi+3+A1LlYrte01yx3VNAGZISjXvOG6Yseays9/1QLBfy4yJ12PjwuQE75bvDBTlpuGGq2OG+r7A2JPsQrKJasbM9K6HlznZAzHSfm5MGAHjiqwM4XFoPrVqF80amQqVSKR2iuSV1yn32eHX2P/VtLgRBQE2jHff+dzfWHanAWz+fAgCkGjvW2U5EfcPYrDgMTolp8vs9UcrdbitjhA5ZzZxsHJEe2ySuhsKDf3d6ZlwkTFYHvt5b7HO9fBJeEARUNojHDMkxTf+tKJ3tLLZTH+d0ufGT1Nk+u49GyADi55b4KM/7TE4vzGsHgAxpAG5RrTXEe0Kt6dDRyIUXXoj7778fhYWFuOaaa5CUlITJkydj8uTJSEpKwjXXXIPCwkLce++9uOSSSzprn4mIiNrE5Rbw7b4SVPkVjmVyQTkpxoC4KB0GSBEdH24vACAO2OlrmdJKZrt/jIy0xLu5gkZyjKcI+/QVY6HXqj1d8g2+B4byY6d0MJKkM4zrL55kOV7egEZb+OYgViiZ6cH9zORIhRMVjQCARecORbxU5JK72V/+4TgKpXga7xidA0UmfH+wDM+sPIQqv4JGX12mTETtE6FTKwPIgY6dsBuW6incGrzei8a0MhyVQmdwiu98kUa7EwcKxRO9/eIjlRMocqxfvc0Ju3S84X1cIZOHpFYzRob6uP9uL4DJ6pRW5yaEendCRqVSKXOfAGB0EPM7eqIsqdguH7cDwP7COpTXs/gebjp86v/555/He++9hxEjRqCmpgY7d+7Ezp07UVNTg5EjR+Ldd9/FP/7xj07YVSIiovb589cHseiDXfjz17kBb5eL7XL3lFyE/HZfCYDe2x3RkuTYpgNS3W5BWZ7ZXLH9rtlD8LuZg/DdH87G9CHiQW9zkTQVfj/3UEqNjUBmXATcgievPxy19QTFuKx45ft+8ZG49zzPwNlbZwzC4ORoFNVa8Pzqo2i0ObH9dA0AIEMahPv4lwcCDrftaGY7EfUtKpXKp7s9rQMn7Lzff0Z7vT8zrz18DU317WyvtzqxR1pJNqF/vPJvQ+5slyNkYgxan+GossRocfutJ6vh9Mp5J+pLtpyswp+l6L87zxkCTRfPOQp3M4YmK9+P7qWf3eQTk0U1YqzkvsJaXPrKz7juX1tCuVsUQKess7vxxhuRm5uLoqIibNmyBVu2bEFRUREOHjyIm24K5+RTIiLq7QRBwHubTwMAvvJbriyr9Ctgnu11sAb03u6Ilsid7bVmhxIdY/f6QNtcsX14WiweuyQHI9M9B7nNdcl7Csfh0SUtF6b3hnGUjGdZfXDFbu8PG7OGp0Cr8fy9pRoj8NK1ZwAAvthThLFPfq90tr9y/RmIMWiVjN0bpw1AtFdO/8Bk3y5FIqLWGCM8xfaOdLZfPqEfAGBgUpRPcemsIcnN3YVC7LLxmbhiQiaumzIAAFBvdWBfgXhie1xWnFJsf3dTHvIqG1uMkAGABClGZsPRCryzKa+L95785VeZUW5iJ20oFVSbcdfynXC6BVwyLgN3njM41LsUcjO9Pr81N0y7p+uXIHe2i8V2uTHsREUjY7XCTKeG2mVkZGDKlCmYMmUKMjIyOvOhiYiI2uVgsWfwlv8y5iOl9didX+PVYS1++L9AyrmW+Rff+4K4SJ2y5L+qUfz52Jz/v737DpOqvP8+/pmZ7b2wlS0sIL33FRRRFNBYSWLBjhoVEo2JSUyMJYkaExONPmjsGGOL/hQVuwiC9N47C0vZ3nuZOc8fszO7wy6wy5aZWd6v69orU86cc894cpj5nO/53k3Cdkvrv0I4TmL87avdLlXjjs89ppXBcWdztJLZfKT7VLYH+/toUt8eCvH3afGH2NCkcE3pHyPDkGyGvfr99nPSNColUrdO7CXJ3rLmN9MH6KpR9glsf3Zub41KieiQ9wPgzNH0JG17TrJePDRB828Zq/d+lq5tRxv/je8Vfeq5LOAe0SH+euaakbp+gj1sL62ud57YHp4c4ey1vGR3ni6ft1zL9+VLOvGJ5abtItYdLOrEkeN4hRW1OvfvizX570tkGIa7h3PG+vk7G1VUWaehPcP19x8PP+PaXbakV49g/fWqoXr66uFumweqsznmbDpabA/bM/IrnM+tOVjoljGhZac3DTwAAF5ixf58522brfFHQXWdVT99caVKquqcM9g7ftT1CPHX+LQorc4o1J2T+zgrqM4kZrNJPUL8lVVSrbyyGiWEBzor3CW59N49FUcwXFZdr8v+3w868MQlkjyrZ7skDW+obPfUSVIraupVVWeV1PrKdkl65aYxqqq1nnA//te1I7Ups1j940MVF9YYgN09pa98LWadPzBWYQG+evSywfrlhf2cE9MBQFs0/TckLKB9P0PP6x8ryT5R5tHiKkUG+RI2eQHH1Q0F5TWyGZLZJA3tGe4SjJVU1elfi/ZKOvG/dSNTIvXv60fpzv9u0JHiyhaXQedYk2EP9KrqrKqotSrEn0ipq5VU1TkntH/h+lEK9GveaulMdU3D1TPdVc+Gnu0lVXX6YmuW1h9qPNm4JqNQ0wbHu2toOE6bjoy33nqrTCaTHn/8ccXFxenWW29t9WtNJpNeffXVNg8QAID22Hy4sUrZ0QtUsofwjvuOqoCmP+qe+slwLd+Xrx+PTuqikXqepmG71NhGxs/H3KZQo2l/b5thb+1jMpmc6/WEnu1SY6/+w4VVKqyo9bhQ2TG3QKCvRcFt+HEb4GtpseetQ1iAr87tF9Pi635+wVnO+2azyeM+EwDeo7rhZKGkDgvGn756hP61aI8evnRwh6wPncsRtjtqH/rGhijY30fhgY3/tphMkqNgukfoif/NSeth7wN/uLCqcwaLFu3PK3feLqqoJWx3gwMN/w3iwvydlc44MwT7+ygyyFdFlXW6660NLs9tzOQqH0/SpiPj/PnzZTKZ9Nvf/lZxcXGaP39+q19L2A4AcAdH5Ydkv2zZEfR+uzO32bJNK6yTo4K6fXXEqRw/samjKtG/DS1kJDWrqC6oqHUG+ZIUH+4ZPdvDAnzVOyZYB/IqtPlIsaY0VE62lmEYeuH7/Qrx99GN6b06fHyediUAALRFdX3HT2Q5Li1Kb902ocPXi84RctwVDY4ryppWtv9m2gC9tzZTBwsqlXySINExUWBJVZ1Kq+tc5gRA59napNVeQUWtkqMIe7va/jx7kVCfmJBTLInuaO75Z+n9dYfl52OWv49ZvXuE6L11h11aysD92hS2L168WJKUkpLich8AAE+UV1bj7GknSVabob9/tVs/O7ePFu3Maba8p/QO9xTHT2zqCNtPNDnqiTgueXQ4VFCh6GA/HSmyX/rtSVU5w5Mi7GH74baH7btzyvS3L3dLsl8WPzIlskPHll/uWVcCAEBb1DSpbMeZyWI2KdjPoopa+74wLDlCkmvYPrZXpH48Oklf78jWZcMTT7iuYH8fRQX7qbCiVkcKqzQokbC9K2xtMvdOYUXNSZZEZ3FUthO2n5lmT0rT7ElpzvuVtfV6b91hFVXWqaii9oxsf+qJ2hS2T548+aT3AQDwJI7e231jQ5RZUKlaq03PL9mvr7ZnK6e0RkF+Fl0wME6fbj4m6eSXK5+JHBXUjpD3dMP2IT3D9czVI/TAh1tVVWfVoYJK9YoOVnWdfX2JEZ5R2S5Jo1Ij9dHGo86epG2xYl+B8/Z1L69WUmSgEiIClRoVpLnn93Xph95aazIKVVZdp/G9o6lsB+DVHrtyqO59b5N+0aQ9Fc48YYG+zrB9RENle1CTntMp0UGKCfXXrPGpp1xXcmSgPWwvqtSgxLBOGe+ZznFFqCTV1Ftdilhunb9OD14yULed03wCdocDeeWKDPLr0gCwoqZe/j5m+bTxSkxv4Wjl0zsm2M0jgScI8vNRQniAskqqdSC/QqMJ2z1Cu44+V111lebMmdNRYwEAoENtbmghMyI5QmFNqqYcl1+ec1YPnXtWD+fjQX70nWzKUUGd5wjbrfYfx20N2yXpipE9dcVIe4XawYJKHSmy/1iLC/OXv4/nTOyU3jtakrT+UJFq6ttWhblif2PYXlVn1d7cci3dk6c3Vx3Sa8sz2jyWnVmluvqllZr9xjqNePRr/eObPZKknhGecyUAALTWFSN7as0fLtAvpxK2n8lCG1rJ+FnM6h8fKknOyb+ltl1l6Lgy7lABk6R2hq+3Z2vkn7/Rd7vsV4M2nfvI4R9f71FFTX2Lr9+fV65pzyzVj/+9QnXWjm8j1ZLS6jpNfPI7XfPSqi7ZnjvszaWyHa4cJ15mvrBCX23PdvNoILUzbP/8889VUFBw6gUBAHCDTQ19JYcnRyg8sHmQfsHAOF0xsqeuHZesh340qKuH5/EcldiO3uo1jsr206wUSomyfxFcsS/fWZVzfIsZd+sTE6yYUH/V1Nu0MbO41a+z2QytzrB/J/rfz9L17X2T9d/Z43VtQ9//PdllrVrPx5uO6v73N6uipl6vLMuQYUgBvmbV2wwVV9YpKTJQs89JO/WKAMADxYYGdNjkqPBOoQ291QclhjlP3l80KE7hgb66ZFhCm/YPRzX7qgNkEp3hjjfXq7iyTnPe2ihJKqlsHrZX1Vn15baWw72vtmerzmpof16F/rfucKeO1eFAXoWKK+u07lCRckuru2SbXSmntFoH8ipkMtmvHAUkqUeTk5SPf77TjSOBQ7tK+NLS0lRRQRN+AIDnMQyjsbI9KUKBfq7V0yaTdP6AWPlazHriqmFuGKHnc0x6dbjQXoV+um1kHCb3i9E/vt6tdYeKtO2Y/USIJ/Vrl+wTuo9Li9JnW7K0MbNYExoq3U/lQH65yqrrFeBr1qiUCPlYzOobGyIfi0nvrMnUvoaTCydisxl66uvden7JfknSsKRwfbL5qCTp3TvSFRnkq02HizWxbw+XL9QAAHgTR2X7iIZ+7ZIUHeKvNX+4oM0n86f0j9Xfv9qt5fvzVV1nVYCv51wp153UNlSlF7dQ2S5JH248opmjk5o9/sPefOftV5Zl6LpxKZ1+sq2gvLGP/NqDRbpkWEKnbq+rfb8nT5I0rGe4omgXggYTekfr4032tqiHCipVUlmn8CDmsXCndlW2X3vttfr++++Vnc1lCgAAz3KooFIlVXXy87FfplxW7XqJ64jkCELLU0iKtFed55fXqKrW2u6wfVBimN6cPV6RQb7Ofu2ObXiSXtH2EwBZJVWnWLLRpsP2kwdDe4a79AjtG2u/xPdIUZWqW5gc0Goz9OGGI7rhtdXOoF2S3lqdqTqrodToII1IjlBqdLAuH9GTfRYA4NWGNlTjThngOgm5v4+lzUHswIRQxYX5q7rOptWnMdcKTsxqM5y3IxpaMbZU2S7Z2+gd/52poqZeaw82/jfJyK/Q9mOlnTBSVwUVtc7bTbffXTjC9sn9Ytw8EniSmaOS9LeZw5wtQJfsyXXziNCusP2BBx7QOeeco8mTJ+ujjz5SXV3LB9+2euKJJzR27FiFhoYqNjZWV1xxhXbv3u2yTHV1tebMmaPo6GiFhIRo5syZysnJcVkmMzNTl1xyiYKCghQbG6v7779f9fUt9xMDAHQvmxsmRx3ccJny8X0mpw6Mc8OovEt4oK9C/e0VaEeLK52VTafbRkaS0vtE6+M5k9Q/zt6n1RMvgU0It58AcLTPOZXiylq9uyZTkjSsYbI3h+hgP0UE+cow7Jc2H+/DDUd03/82a/m+AvmYTTqnYQ6BXQ1tZ9JbWVkPAIA3uO/Cflr34NQOCQtNJpOm9LeH9ot3ES51pMOFjX3w/RuKLFqqbE/rESzDkBZstFfVVtbWK7OgUqsOFKjOaig5KlDTB8dLkj7bmtXp4y7sxmF7RU29vttp38/PO+5kFc5sfj5m/XRssn4yJlmS9O1Ojofu1q42Mv3795fNZtPhw4f14x//WCaTSbGxsQoICGi2rMlk0v79+1tYS3Pff/+95syZo7Fjx6q+vl6///3vddFFF2nHjh0KDrb3e/3lL3+pzz77TO+//77Cw8M1d+5cXXXVVVq+fLkkyWq16pJLLlF8fLxWrFihrKws3XjjjfL19dXjjz/enrcNAPACmxpayAxvCD+Lm1TjzJ6UppvP7tX1g/IyJpNJSVFB2plVqsOFVe2ubHdIiQ7Sx3Mnal9uuQY39Fv1JAnh9u8x2a0M2297Y53WHSqSZG//0pTJZFKfmBCtP1SkfXnlGpQYpi1HivXQx9sV6GvR0eLGSrD/3jZeNsPQsiaXXaf3IWwHAHQfJpOpQ6/SOq9/rN5de1hLdudKGtxh6z3T7clpnGsmv6JWhmE4C1eC/CyqrLVfrfezc3vrdx9u1Ycbjui6cSm68oXlOlRQqbMbvr9M7hejYT0j9OX2bG07WtLp424atu/MKlVZdZ1zngBv982OHFXVWZUaHaSRTdowAQ5TB8bqhSX7tWR3ruqsNvm2o0AK7dOusP3gwYMu9w3D6JCWMl9++aXL/fnz5ys2Nlbr16/Xueeeq5KSEr366qt6++23df7550uSXn/9dQ0cOFCrVq3ShAkT9PXXX2vHjh369ttvFRcXpxEjRujPf/6zfvvb3+qRRx6Rnx/9rQCgO3P0ax+ebA8/50zpo3mL9+sno5P0RyZDbbWkyEB72F5U6axo929n2C5JAb4Wj6xql6T4cMfEsKduI7M3p8wZtEvSuLSoZsv0ig7W+kNFysir0D+/3q15S/a7XJ4tSf93V7pGp0Ypr6zG5fGz+/Q4nbcAAMAZYWLfaPlaTDpYUKmM/Aql9Qh295C6hV1NJnavrbeptKpeJZX2IPuy4YnKK6vRqNRIXTwsQQ9/sl17c8v10xdXOq/icxQOnHtWjLNIo6C8Vp2t6TZshrQhs7hbtFwxDENvrT4kSbpiRE8mmkaLRiRHKjrYTwUVtVqbUaiz+/I7wl3a9WvZZrO16e90lZTYz4BGRdl/wK5fv151dXWaOnWqc5kBAwYoJSVFK1eulCStXLlSQ4cOVVxcY5uAadOmqbS0VNu3b29xOzU1NSotLXX5AwB4n4qaem1r6AvpqGy/54J+evu28frzFUPcODLvkxzpmCS1SRuZDgjbPZmjjUx+ea1q6pv3WW/KMRnR8KRwLfrVZOdrm4oJtVfwPf3tHj373T5ZbYYm9HYN5R3tZxy9FiVp+uB452sBAEBzoQG+GtvL/m8qrWQ6zobMIpf7eeXVzsr2mFB/vXrzWM2Z0ldhAb66cJA9c9ndpBpeknzMJp3dt4eig+3fZZpWnXeWwgp70YLju+rabtLLf8X+Aq09WCQ/H7OuHZfi7uHAQ1nMJud8GLSScS+P/7Vss9l07733auLEiRoyxB6QZGdny8/PTxERES7LxsXFOSvrs7OzXYJ2x/OO51ryxBNPKDw83PmXnJzcwe8GANAVXvshQ7X1NvWKDlKvaHuFk5+PWWf37aEAX4ubR+dd0mLsn9/OrLLGNjLd/JLEyCBf54+03NKaEy5nGIY+3nxUkjT7nN7qExPS4nLHB+ZPXDVU796Rrk/nTlJ4oK9uPruX8zJPk8mk304foEl9e+jxq4Z2xNsBAKBbc/Zt30241BFsNkMbM4tdHsstrXH2bA8PdG3LMnNUUovrGdMrUiH+PopuKCTILq3W0j15qreefiFmUUXtSV/vCPTPbZgDZ0036NtuGIae+XaPJOm6cSnOKzCBlkwdaD8eLtqVI8MwTrE0Ostp/Vr+/PPPdccdd2jGjBm64oor9NBDDykjI6OjxyZJmjNnjrZt26Z33323U9bf1AMPPKCSkhLn3+HDhzt9mwCAjmUYhl5fcVCS9MsL+8ls5jLL9hjf0BZl3aFCldfYJxnv7pXtJpPJ2bd9V3aZyyRhTW3ILNbhwioF+Vl04Ukm3D0+bHdMFDY0KVwb/nihHrnMtcfsXef10X9vG6+oYFreAQBwKlMG2NuErD5QqMraejePxvtlFFSopKpOAb5mje0VKUnKKWusbD8+bHdM7i5Jof6NnYrPbWjf0vT7zI2vrdGv3998WiHgseIqjXnsW93w6poTLlPQELZPa/iutflw8SmvUvR0Tava7zqvj7uHAw93zlkx8rOYdaigUvtyy909nDNWm38tz5o1S5deeqleffVVffXVV/rkk0/02GOPafDgwfrkk086dHBz587VwoULtXjxYiUlNZ4tjY+PV21trYqLi12Wz8nJUXx8vHOZnJycZs87nmuJv7+/wsLCXP4AAJ7NMAyXL+zFlXXOqhbHF22cvrNiQ9QjxE/VdTatPmCvDuruYbskxYfZw/bb/7NO5/9jidY0uQy53mrT93vy9L+19pPy0wbHK9DvxFdMNG0NE+BrVkRQ449UCyeDAABolz4xIUoMD1Ct1abNh0tUZ7XpkU+2a+GWY+4emlf6doc9NxnWM8J51d5jn+3Srix7m5jjw3Yfi1m/v3iAfMwmvXzTGGe47rjiIMDXopAmIfyCTcf07tq2FzZ+uS1bVpuhlQcKZLO1HNY7fgOM6RWl6GA/1dTbumRi1s5yfFV7XBhV7Ti5YH8fpfeJltkkbTnivfu+t2vTr+VXX31V77zzjiwWi26++WY9++yzeuyxxzRhwgRVV1frxhtvdPZXbw/DMDR37lx99NFH+u6775SWluby/OjRo+Xr66tFixY5H9u9e7cyMzOVnp4uSUpPT9fWrVuVm9t4Kdk333yjsLAwDRrExHgA0B2s2Jev/g9+qee+2ydJWrQzR7NeWS1J6hHiT8uYDmAymZTeMEnnukMNYbul+3+uTSdYq7Mauu9/m1RWba/o+mjjUd302hq9t87+Q/GyEYknXVdsk8r2xIhAJrUCAKADmUwmDUq0T7q+J6dM3+/O0/wVB3Xfe5uVkV+hOqtNmw8Xt7ma+psdOXr4422a+/YGfbTxSGcM3eNU1Vr18jJ714Ifj0nS3PP7qn9cqPLLa5RdWi1JighqfuXd7ef01t7HZmhC72i9fOMY/fv6URqY0Fi8ePzVeg9/sl3bj7UtOwoNaAzsHWNpKresWpW19ir26BA/jWmoyl97sKjZst5i+T6q2tF2D106SOsevFAzR7fc4gmdr01h+xtvvCGz2awvvvhCr776qubOnasHHnhAy5cv10033aSysjJ9+OGH7R7UnDlz9N///ldvv/22QkNDlZ2drezsbFVVVUmSwsPDNXv2bN13331avHix1q9fr1tuuUXp6emaMGGCJOmiiy7SoEGDdMMNN2jz5s366quv9OCDD2rOnDny92eyMQDwdjabob98tlO1VpvmLd6n3NJqzX5jnXZk2SdGTYpsPlElTs/ZfaIl2UNn6cyobD8rLtTl/pGiKj366Q5J9sqqpib17aGTiQlprELqEcx3EAAAOlr/eHsF9q7sMm1rCHFrrTY9+ul2/XHBNl0+b7kWbDra6vXVW22a+/YGvbHykBZuydIfPtqmunb0GvcW8xbvU355jZIiA3XlyJ5KigzSgjkTdfWYxvnsYluYvN1kMjmLCUanRmr6kASX55tWw49KiVBtvU1z3tqg6rrWt3ipqGlsEbQ/r3l7jD8u2CZJGpQQplB/H41Ls39//WxLllf2rqaqHaerT0wI7SjdrE2/lrdu3aoJEyboggsuaPbc73//exmGoa1bt7Z7UC+88IJKSkp03nnnKSEhwfn33nvvOZd5+umn9aMf/UgzZ87Uueeeq/j4eJeg32KxaOHChbJYLEpPT9f111+vG2+8UX/605/aPT4AgPt9vSPbGazX1Nuc1e0OPQnbO0x672iX+2dE2B7bONlpQniATCbpg/VHdO7fFmvZ3nznc00nNz2RsMDGSixfH6raAQDoaP0aTpLvySnT9mOlzseX7M5ztix5+pu9rV5faXW9auobw/XKWqvLerujPTll+vf3+yVJD14y0Pn9JtDPoid/PEwv3zhGj185VL2aXP3XWk176b9601j1CPHXwYJKrTxQ0Op1OCZolaR31x52WWdJZZ2+2m5vf/OPnw6XyWTSFSMS5e9j1tajJW3ajqfYkFmsdYeK5O9j1t1UtQNexefUizQqLS1Vnz4t/5/c8Xhpafv/AWrNWceAgADNmzdP8+bNO+Eyqamp+vzzz9s9HgCAZ7HZDOcPpgm9o7TqQKHeXHXIZZmkCML2jpIaHaTE8AAdK7Ffsutn6f6B8VlxjWH7ef1jFRHkqxeW7Fdmk8lSZwyJ16+n9T/lupq2jfExd/8TFQAAdLUB8faWJXuyyxTWUEU9Pi1Kq5vMuZIY0frK4KJKe+/vUH8fje8drW935mhNRoFGJEd03KA9iM1m6IEPt6reZmjqwLgW5z26cNCJJ4M/ldLqxmA8MthP43tH6bMtWdqbU+bs7X4qxZWNYftnW7JUb7Xp1olpemnpAS3aZW8fnBod5GxfEx3ir6tG9dQ7aw7ry23ZOrvPya9E9DS7su3Z2sS+PRRLVTvgVdr0i88wDFlO0KfV3PDj0Wbr/pdWAQDca+PhIu3OKVOov49evGGMhrfww4fK9o7TtG+7dGZUtsc3+VHjazHpl1P7uVxCLUm/mT7AZcKv1ohp4dJrAADQPmk9gmUxm1RWU6+jxfb2s89eO1KJ4Y3/nueW1bR6fY5gNyLYV+PToiTJZbL07uadtZlaf6hIwX4W/enywR0+v8zx35f6xdqvRNid3bwdzImUNqlsl6Svtufo6pdWOYN2Sc1OhoxMtvdtz8ivaMtwPcKxhv2Y1piA9+n+v5YBAN2O44v5qNRIhQf66t4Lzmq2zKlae6BtHH3bJcnvDPhsTSaTJvS2/7j+6Zhk+fmY9eSPhymsyeRcbamQe/zKoRqUEKZfX3TqSngAANA2fj5mRQY19gWPCfVXXFiAnvrJcIU2BL1HCqtks7Wud3dxQ2V7RKCfxjUJ21v7em9SUF6jJ7/YJUn61UX9ldgJV4c+9ZPhSokK0r+vHy1J6tdwBeHe3LJWr6NpGxlH8YKfj9ll7pyhPcNdXpMaHSRJOlRQKW9QUF7jPFmUVWy/ojQhnLAd8DZt/rX8xhtvyGKxtPhnMplO+LyPT9sqvwAAOJF9ufawvW9DX+3z+sdoeFLjl+sQfx9NHXj6l7qiufSmYbtPy1e5dTev3TxW3/1qsoY0+eHWdLIh/zZ8DteNT9Hn95yj+HAuAwYAoDNEBDX+Gx3VcPvsvj206eGL5GM2qdZqU05ZdavW5axsD/LV4MQwBflZVFpdr905rQ+HvcU/vtmj0up6DUoI001n9+qUbYxOjdTS30zR9CH29jT94u2V7Xtzytt8AuTf14/W8t+er39fP1o//HaKXrt5rHOZsb2iXF7j6C9/pKhStfWe3YWh3mrTVS+s0EX//F45pdXO0L0txR0APEObE/DTncXZG2d/BgB4JkcVjGMSS5PJpJdvHKNFu3I1c1SS6m02BflxkrcjJUYEKq1HsDLyK+R/BrSRkaQgPx/1jglxeWx8WrQOekl1FAAAZ5Kmle2RwY23LWaTkiIDdbCgUpkFla2qFHb0bI8M8pOPxazRqZFatjdfazIKnT3BuwPDMPTppmOS7JOiWsxdMy9PalSQ/CxmVdVZdaSoSikNFegn46hsjwjylZ+P2RncS9LCn0/S4cLKZq0lY0P9FehradhOpcv3uk2HixUV5Oey7WV787Qps1h3TO7dpqKKjrAmo9BZgf/ZlixlNcyV1BlXGgDoXG1KIujHDgDwBPuPq2yXpNiwAF07LkWS5EeXtE5x93l99NbqTJcq9zPNAxcPUGWdVT8eneTuoQAAgCZcKtubXIkmSSnRwTpYUKk9ueUa3/vU32NKmgS7kn2yVUfY3lnV3+6QX16rspp6mUzS6F6RXbZdH4tZvWOCtSu7THtyyloVtpdUuv43aWpIz3CXKxEdTCaTUqODtCu7TBn5Fc6wff2hQs18YaV6RgTq6rHJGpUSqU83H9N76w5Lsren+dnkPu15i222cGuW8/anW44pq8Re2Z7AVZGA16HsDwDgVcpr6nWsodKjadiOzveTMcn6yXGThJ5pIoL89Ny1I909DAAAcJyIwMYQtmnwLkljUiO1dE+eVu7P1w0TUk+5Lkdlu2M949LsAf3qjEIZhtHhE4i6i2Pi0J4RgV1eyd0/PlS7ssu0O6dMUwedvP2jYRiNle2Bfidd9ni9ou2h/uw31mna4DilRgdr+b58SdLR4ir985s9zV7z4YajuuPc3l3237neatNX27Kd9zdmFkuSzCYpLoywHfA2lP4BALzK0SJ7lUdEkG+zH1IAAAA4M0UGN+/Z7jCxYRLNFfsLZD2uR3hFTb2q66wujzl7tjcE+MOSwuXnY1Z+eY0zoO4ODja8l7SG3uZdqV+co2+7ax98m81QfnmN5i3ep6n//F5ZJVWqqLU6/7u1VNl+MteMS3ZOcP/V9hy9tPSAth8rbbZcclSgXr5xjPx8zNqdU6Zd2V3Xn39NRqEKKmoVEeSrUSkRzsdjQwPkayG2A7wNle0AAK+SX14jSYoJ8XfzSAAAAOApmoawxweyw5PCFervo+LKOm07WuLs7b0zq1Q/eu4HGYahnpGBsphMqrMazskpHb3fA3wtGpEcoTUZhVqTUdhsThdvlVFgD9t7RXd92O6Ye2lPTrnL42+uOqSHP9nuvP/vJfs1ZUCsJHt7lwDftlXgn9c/VisfuEBrDhbqUH6FDhXae/cv2pXrXOb6CSn6/cUDFeTno3PP6qFvd+Zqye68LuvP72ghM31wvAYmhGlDQ2X7xUMTumT7ADoWp8gAAF7FEbb3IGwHAABAg8iT9Gz3sZh1Tj97dfuX2xvbdaw/VCSrzZDNkA4XVulgQaUzaJdcW5aMT4uSJH2/J69Txu8OGXnuq2zvH2+vbN+XV+5ytUHToF2Slu3L191vbZAkXXKa4XOwv4+m9I/VzRPT9PClg/XqzWN1U3pjO6HrJ6QqyM9eizqp4SqIH/Z1zX/npi1kLh6aoIuHJig0wEep0UG676J+XTIGAB2LsB0A4FXyyhrC9lDCdgAAANg17dke2UKrwUuGJkqSPtuSJcOwh7vFDb3ZLx4ar/fvTNcHd6br8SuHOl8T1mSd04fES5IW7cx1vs7bHXRUtvc49QSlHS0pMkg+ZpNq623KLatu8nigy3IH8ipUWWvVpL499MRVQ49fzWnzb1Ih36fJlQqTzoqRJK09WNSsvVBnaNpCJr1PtGJC/fX9/VO08OeTFOJPMwrAGxG2AwC8Sn65/cdNdDD92gEAAGDXdC6fyBa+J04ZEKNAX4syCyud/biLGnqzJ0cFaWyvKI3pFaVrxyVrcr8YJYQHOKuvJWlwYrgGJYSp1mrTp5uPKbe0WpsOF3fum+pkjrmQkiO7Pmy3mE3OyT+zShrD9pp6W7Nlx/WK0ks3jm5zC5mTmdFw8qR3TLBLX/Q+McGKDPJVbb1N+/PKT/TyDtO0hYxjHFHBfgoNaFtvegCeg9NkAACvUuDo2U5lOwAAABo4+qtLzSdIlaQgPx+NS4vS93vytOpAgQYmhKmowl7E0bQS3mQy6bWbx8psst9uasaQeO3IKtWGzGJ9uiVLaw8W6qO7J2pEQw94T2YYhjZkFqtvbIjCA31VUlWnspp6SVLP46rJu0p8eICOFlcpuyFsr623OVtG/uWKIQr2t2j9oSL9dvoAZ5uXjjIyJVILfz5JCeEBLo+bTCZFBvupqLJOZdX1HbrN4x3fQgZA90BlOwDAqzT2bKeyHQAAAHbBTcLYiOCWq4LH97b3XV+TUShJKqp0hO2uy1vMpmZBuyQNaJgwc9vREm3MLJJhSIt25rR/8F1gxf4CzXxhhX73f1skNVa1RwX7dXiQ3VqOoPtYQ5/8nNJqGYbk72PWrPEpunJkkv5yxdBOq/Ie0jNc0S3MAxXa0L6lvJPD9uNbyADoHgjbAQBexdFGhglSAQAA4NAzIlAjkiM0oXeUMyw9nmOS0zUZhTIMw9lGJqKFSviW9I+zt5XZm1uuOqu97/uyvfntHXqXcPRnX7I7TzX1VudEsD0j3FPVLjWG7Y7KdseYEsIDWjzZ0VUc4X5ZTV2nbqelFjIAvB9tZAAAXqWxsp2wHQAAAHZms0kf3X22pObtXxyG9oxQgK9ZBRW12p9X7pzoNKqVcwElRQYqyM+iytrGiTO3HClWSVWdwgM9u8e2oyVKVZ1VGzOLdaSoUlLzCUm7Uny4fdtZpfawPavEEba7b0ySnBOTdmZlOy1kgO6LU2cAAK9hGIYKHJXt9GwHAABAEyZTy+1fHPx8zBqVEilJWnWgUIUVLbeRORGz2aSz4kJdHrMZ0qoDBac54q5TVt1Ypf3D3nxnGxlPqGz/bEuWHlywVX9euNP+eETAyV7W6UID7GF7aSeG7bSQAbovwnYAgNc4VFCpWqtNvhYTPdsBAADQZuMaWsms3F/gDFNb20ZGks7p28N52xHK/uAFrWRKqxqD4x/25Ttbtrizsr3p5KT/XZWpwopaRQf76cejk9w2JqlJG5lODNtX7LefoLlgQBwtZIBuhjYyAACv8f2ePEnSmNQo+ftY3DwaAAAAeBtH2P7NjsaJTSPa0ALm5xf0VaCfRRszizV1YKx+9+FWLd/n+WF708r2LUeKlV9uD9lTooPcNST17hEifx+z6qw2XTGypy4f0VMT+0TLx83hc0jDSZTy0+zZXltv039WHtS6g0V6+LJBeujj7crIr9CCOROdLWocPfQHxIeebFUAvBBhOwDAayxtCNvP7Rfj5pEAAADAG41KiZSvxaRaq02SFBbg06Zw19/HojlT+kqSSqvr9IcF23Qgv0JHi6vc2pLlVJpWadsM6UhDG5nePULcNSSFB/nq2/smy9/XrNhQ97aOaSqsIWw/ncr273bl6M8Ldyoj3x6mV9VZnQVDy/bkaUZDf/bMQnvPfHee7ADQObhWBQDgFWrqrc7LLScTtgMAAOA0BPhaNDwpwnk/spWTo7YkLMBXw5PCJUnLPbyVTGlDZXtsk3mPfC0mt7aRkaTkqCCPCtql058g9fs9ebp1/jpl5Fc41+EI2iVp4+Fi5+1DBfawPZWwHeh2CNsBAF5h/cEiVdVZFRPqr4EJXG4JAACA0+NoJSNJQ3uGt2tdkxp6uC/z8FYyjirtGUPinY+lRge7vWWLJzrdnu3bjpZIkib0jtLy353f7EqH1Q0T6ZZU1qmkyn7yIyWKsB3objiqAgC8gqMq5NyzYmQymdw8GgAAAHirsU3C9rvO69OudU06y37F5Yp9+TIMo13r6kyO4PjCQfGymO3fpXv3CHbnkDyWo2d7aXXberZnldhb84xJjVJ4oK8ev2qoIoMa5wPYfKREd/13vZbsyZUkxYT6K8iP7s5Ad8P/qwHAjeqsNj3z7R6de1aMxveOdvdwPNKnm4/p39/v177ccknSuf16uHlEAAAA8GYT+/TQZcMT1TsmWIMT21fZPqyhjUxBRa0KK2oVHeJ/ile4hyM4TowI0PCkcG3ILFbvGPf1a/dkoc4JUttW2Z5VXC1JSoiwt8WZ3C9Gy393vsqr6/WflYf0/JJ9+mJbtr7Yli1JSqWqHeiWqGwHADd6c+UhzVu8X1e/tMrdQ/FYP39no7YfK1VNvU0mk3TOWfRrBwAAwOnz8zHr2WtH6t6p/dq9rgBfi7NdyMGCinavrzPYbIYzOA4N8NWdk/uob2yIrhzZ080j80yOCVKPFFXp5aUHZLU1v2KhpasYjpXYw/bE8Mb2MUF+PooNC9Cvp/XXF/ecqwsGxDqfO39gbLN1APB+VLYDgBttajJJDpo7vppkaM9wRbVjEisAAACgo/XqEaSjxVXKyK/U6NSoU7+gi5XX1suRDYcG+OiiwfG6aHD8yV90Bgvxb2z98tjnO7Vif76evXaks5f7//tur+avOKRHLxusS4YlOJfNbmgj46hsP17/+FC9evNYZRZUytfHpIRw905OC6BzUNkOAG5UVWd13i6pbFtPwDPBxswil/tnxTIxKgAAADxLr2h77/OM/HI3j6Rljn7tfj5mBfha3Dwaz+doI+OweHeefvzCSh0pqpRhGPrvqkzll9doztsb9M9v9shmM1RVa1VRw++5U4XoKdFBBO1AN0bYDgBudKSoynn7cFGlG0fimdYebAzbQ/19dPeU9k1gBQAAAHS0tIaJRg/me+b3+dIqewgcFkBzg9YI8rMoOSpQJpP04g2jFRvqr905Zfr5Oxt1uLBK2aXVzmWfXbRXv3h3o441VLUH+1n4nIEzHEcAAHATq83QgbzG6pfDhZUa0rN9EzR1N0cbTkb8Znp/3X1eXzePBgAAAGjOEbYfyPfMnu2OynZHGxScnMlk0sdzJqnOalNcWIAGJ4bpwn8u1cbMYv3lsx2SpFEpEbpmXIoe/GibFm7J0sItWZKk+PAAmUwmdw4fgJtR2Q4AbnKkqFI19TbnfSrbmytpqMIJD+SHAQAAADxTvzh7q8O9OWWqqrWeYumuV1Zt/059fHsUnFhUsJ/iwuy915Mig3Rjeqok6esdOZKksWlR+umYZP3+4gEur7vp7F5dOk4AnocjLQC4yb5c156OhwurTrDkmauUsB0AAAAeLikyUAnhAcoqqdbGw0U6u08Pdw/JhaOyPYzK9tM2OjXS5f7QhiuSb0zvpYKKWpVW1emqUUkanhzhhtEB8CRUtgOAm+w9Lmw/QmV7M1S2AwAAwNOZTCaN7RUlSbru5dV6c+VB9w7oOKVUtrebo1WQQ5+YEEmS2WzSry7qr0cvH0LQDkASYTsAuI2jst1RJZFVUn2yxc9IhO0AAADwBuPSopy3/7PykBtH0lxjz3bC9tOVHBXkcv/48B0AHAjbAcBNHGH7pL72y0xzSgnbj0fYDgAAAG9w8dAE+VnsEUthRa2bR+PKUdlOG5nTF+BrOel9AHAgbAcANzAMwxm2n9vPHrYXVdapus6zJlQyDENfb89WblnXnwiorbepquHzIGwHAACAJ4sK9tPaP0yVJBVU1Kqytt7NI2pUWuWobOc7NQB0No8M25cuXapLL71UiYmJMplMWrBggcvzN998s0wmk8vf9OnTXZYpLCzUrFmzFBYWpoiICM2ePVvl5a79kQHAXXJKa1ReUy+L2aShPSMU2FAZke1hrWQ+2nhUd7y5XrNeXt3l23ZUtUv8MAAAAIDnCw/yVVhDq5bDhVVuHk2jMnq2d4jBiWGSpFEpEe4dCACP5pFhe0VFhYYPH6558+adcJnp06crKyvL+ffOO++4PD9r1ixt375d33zzjRYuXKilS5fqjjvu6OyhA0Cr7M0tkySlRgfJz8eshPAASZ7Xt/3DDUcl2Sdzra23dem2HWF7aICPLGZTl24bAAAAOB2p0fZe3ocKKtw8kkb0bO8Y/75+tG6Z2EvPXTfK3UMB4ME88kg7Y8YMzZgx46TL+Pv7Kz4+vsXndu7cqS+//FJr167VmDFjJEnPPfecLr74Yj311FNKTEzs8DEDQFs4WsicFWufxT4+PEAH8iuUXeo5FTCSXC5/3XasRKNSIrts246wPSKIqnYAAAB4h5SoIG09WqIdWaU6r3+s/HzcX+PoqGwPozVjuyRHBenhSwe7exgAPJz7j/qnacmSJYqNjVX//v111113qaCgwPncypUrFRER4QzaJWnq1Kkym81avfrErRBqampUWlrq8gcAHS2rpEqfbcmSJPVtErbbn2tdZfvHm47qoqe/156cMudj+/PKddNra/TaDxknfe2inTnaeqTklNuot9q0I6vxOLgmo7BVY+sopUyOCgAAAC+TEh0kSXrm273q/8cvdNHT37u9VWQple0A0GW8MmyfPn26/vOf/2jRokV68skn9f3332vGjBmyWu0T6WVnZys2NtblNT4+PoqKilJ2dvYJ1/vEE08oPDzc+ZecnNyp7wPAmWnWy6u17lCRJOms2FBJUnyYPWxv7Rfxe97dpD055bru5VWS7AH6Ff9vub7fk6cXl+4/4et2ZZdq9hvrdOn/++GU29h6tETVdY2tY77fndeqsXWUEsJ2AAAAeJkrRvTUwIQw+fuYZRjSnpxyLd+X79YxOSvbmQcJADqdV57WvOaaa5y3hw4dqmHDhqlPnz5asmSJLrjggtNe7wMPPKD77rvPeb+0tJTAHUCHyi+v0YH8xv6Njsr26BB/SVJBRW0b11ereYv36amvd8sw7I/lltWott7W4iWrmzKLnbdPtIxkbx/z0MfbJUnDksK15UiJVmcUqKC8xjnWzkbYDgAAAG/TPz5UX9xzjgzD0Nx3NuqzLVkqbON3/I7m6NlO2A4Anc8rK9uP17t3b/Xo0UP79u2TJMXHxys3N9dlmfr6ehUWFp6wz7tk7wMfFhbm8gcAHWl3dmPbl+hgP2fYHtnQl7y4snVfxJv2Mf/7V/ag/brxKc4KmhNVyDcN8wsqalpcpriyVte/slpbj5YoPNBXL90wRkN6hslmSB+sP9Kq8Z2IYRj6enu2ilrxg6O4krAdAAAA3slkMik29PQKajpSndWmylp7FwDayABA5+sWYfuRI0dUUFCghIQESVJ6erqKi4u1fv165zLfffedbDabxo8f765hAoB2NYTtY1Ij9cW95yjA1yJJigzykyQVVdSdch019VZnEO0Q6GvRXy4fop4RgZKko8UtT7S6P6/ceTu/rPmX/pzSal394iptyCxWWICPXrt5rOLDAzRzVJIk6a9f7mpX4L5wS5bueHO9rnlp1SmXzS+3nwyIDu6aSnoAAACgI0UH27/jF56gyKUrlDdUtUtSCGE7AHQ6jwzby8vLtWnTJm3atEmSlJGRoU2bNikzM1Pl5eW6//77tWrVKh08eFCLFi3S5Zdfrr59+2ratGmSpIEDB2r69Om6/fbbtWbNGi1fvlxz587VNddco8TERDe+MwBnut3Z9glHz+7bQ7GhAc7HI9pQ2Z5b2vzLep/YYJnNJvWMPFXY3tjCxhFmOxiGodv/s067c8oUG+qv9+88W6NTIyVJN6X30qzxKTIM6f4PNuu9tZmnHGdLPt18TJK0O6dMWSUtj9HBUXnfI8TvtLYFAAAAuFNUQ9GIu9rI7Mst1zPf7pEkBflZ5GvxyAgIALoVjzzSrlu3TiNHjtTIkSMlSffdd59Gjhyphx56SBaLRVu2bNFll12mfv36afbs2Ro9erSWLVsmf//G6se33npLAwYM0AUXXKCLL75YkyZN0ksvveSutwQAkqTdOfbK8gHxoS6PRzVUvRRVnrqyPbesedjeN8bejiYx3B62H2shbDcMQwdyGyvb845bz9ajJdpypET+Pmb9311nq3+TMZrNJv3liiG6KT1VhiH99v+2auuRklOO9XiOS1gl6ZVlGSdd1lF53yOUynYAAAB4n+iGohF3tJGprbfpJ/9eoTdWHpJkn4cJAND5PPIaovPOO0+GY6a/Fnz11VenXEdUVJTefvvtjhwWALTb4cJKSVKv6GCXxyMa2shU1VlVXWd1tpdpSW6pvR97bKi/M3hP62EP252V7UXNw/b9eRUqq2m8jDSvvEYH8sr1+vKDOrdfjB74cIskadrgeCVHBTV7vclk0iOXDdaxkmp9syNH768/rCW7cxUR7KcbJqRKsn+pX3WgQOPSopq9h8raeu3MKnXef/WHDAX5WXTfhf1kMpmabc9Red+jiyZkBQAAADqSo41MQXnXh+1rDxY6C3ke+tEgXTc+pcvHAABnIo8M2wGgO6quszovIXX0VncIC/CRxWyS1WaouLJO8eEnCdsbAvaRKRH6anuOpMb+i4kN6z12XIuWwopaPb94n8tju7LL9PbqTB0trtKbqw45H79mbPIJt20ymfTTMcn6ZkeO/rOy8TWxof4anRqpX7+/WUt25+l3Mwbozsl9nM8XlNdo9F++dd6/54Kz9K9Fe/Xcd/tUXWfV7y8e2CxwzyNsBwAAgBeLcvZs7/qw/btduZKkn4xO0q2T0rp8+wBwpvLINjIA0B05WrsE+1kUFuh6rtNkMiki0N63/e3Vh2S1nfjqnpyGyvaE8EBdOjxREUG+unyEfT4KR3/zptUzZdV1uvS5H/ThxqOSpNRoe9X6p5uPNevt/tiVQ3R23x4nfR/n9uuhsOMmV/rZm+s15i/fasnuPEnSF9uyXZ7fcrSx5UxUsJ9+eWE/PXLpIEnSy8sytHh3ruqtNucy1XVWlTVM5hRD2A4AAAAvFN3Qs728pl419dZTLN2xFu+2h+1TBsR26XYB4ExH2A4AXSSrpCEkjwhssW2KY5LUZ7/bp7dWH2r2vMOhAnsrmoTwAD17zQit+f1UZ/V3eENgX1LV2Pv9yS93OUN1i9mkq5tUrjuWd7hu3KkvL/X3sei1m8fqgRkD9N4dEzS0Z7gCj2sZU17t2nv+SEP7HEl6YMYASdLNE9N0bcP2bp2/TpOeXOxskePoa+lnMTc7MQEAAAB4g7BAH/mY7d/7u7K6/VBBhQ7kVcjHbNKks05eSAMA6FiE7QDQRRyBd+JxLWQcwpoE359sOnbC9Ww/Zq8SH5wYLpPJJD+fxkO5o/e7I2xfdaBA/12VKUl68JKBeveOCRrbK0qS5Gsx6cUbRjtf2ycmuMWTAC0Z0ytKP5vcR+N7R+vTn0/Szj9PV8YTF2vp/VMkSRn5Fc7gXJION/SQv2ViL/1kTGPYP3VgY6VNdmm1/vH1HklSfkOrnOgQv1aPCQAAAPAkJpPJ2Urmnnc2yXaSq1c70uKGFjJjekUqLMD3FEsDADoSYTsAdJIDeeU6/x9L9PjnO1VntTnbyCSGB7S4fNNJTY+vOHcoq67TwYbK9sGJYc2ed7yuvKZe5TX1+t3/2Sc9vXZcsm47p7fG9orS6JRI/WZ6f71xyzhN6B2tl28co76xIfrXNSNP/83K/mMiOSpQwX4W2Qxp3OOLtPWI/cSAY2LY5EjXiVcn9I52uf/eusN6/POd+mD9EUn0awcAAIB3G5tmL3RZc7BQ+/PKu2SbixtaO07pTwsZAOhqXJsPAJ3knTWZOpBXoZfyDmjz4WIF+tlbrZyost3Ro1ySjhRVtbjMjmOl9nWEByiyoUqmqaa91J/5Zo8OFlQqLsxfD1w80Pm42WzS3ef1dd6/cFCcLhwU14Z3dmImk0kVtY39KJfuzdPQpHDn+0mKdH3vwf4+mjU+RUt252lkSoQWbsnSS0sPOJ+PDSVsBwAAgPd69pqR+mpbtupthvLLa3VWx3ztPqGqWqtWHiiQJJ1Pv3YA6HKE7QDQCQzD0Nc7ciRJZpO0OqPQ+Vz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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data with years on the x-axis and price on the y-axis\n",
    "plt.figure(figsize=(18,5))                          # Setting the size of the plot\n",
    "plt.title('Tesla Stock History')                    # Title of the plot\n",
    "plt.plot(tesla_df['Date'], tesla_df['Close'])       # Plotting the data (Closing price is what we want)\n",
    "plt.xlabel('Year', fontsize=15)                     # Label on the x-axis\n",
    "plt.ylabel('Price', fontsize=15)                    # Label on the y-axis\n",
    "plt.show()                                          # Showing the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "- That looks much more concise.\n",
    "- There are no outliers present in the new boxplot.\n",
    "\n",
    "The data is now ready to make some prediction models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression model\n",
    "- As I've learned, a regression model is a wise choice when it comes to continuous values, such as stock market prices\n",
    "- First we will try a simple regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2394</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>28.299999</td>\n",
       "      <td>28.713333</td>\n",
       "      <td>28.114000</td>\n",
       "      <td>28.684000</td>\n",
       "      <td>28.684000</td>\n",
       "      <td>142981500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>29.366667</td>\n",
       "      <td>30.266666</td>\n",
       "      <td>29.128000</td>\n",
       "      <td>29.534000</td>\n",
       "      <td>29.534000</td>\n",
       "      <td>266677500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>29.364668</td>\n",
       "      <td>30.104000</td>\n",
       "      <td>29.333332</td>\n",
       "      <td>30.102667</td>\n",
       "      <td>30.102667</td>\n",
       "      <td>151995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>30.760000</td>\n",
       "      <td>31.441999</td>\n",
       "      <td>30.224001</td>\n",
       "      <td>31.270666</td>\n",
       "      <td>31.270666</td>\n",
       "      <td>268231500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>31.580000</td>\n",
       "      <td>33.232666</td>\n",
       "      <td>31.215334</td>\n",
       "      <td>32.809334</td>\n",
       "      <td>32.809334</td>\n",
       "      <td>467164500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date       Open       High        Low      Close  Adj Close  \\\n",
       "2394 2020-01-02  28.299999  28.713333  28.114000  28.684000  28.684000   \n",
       "2395 2020-01-03  29.366667  30.266666  29.128000  29.534000  29.534000   \n",
       "2396 2020-01-06  29.364668  30.104000  29.333332  30.102667  30.102667   \n",
       "2397 2020-01-07  30.760000  31.441999  30.224001  31.270666  31.270666   \n",
       "2398 2020-01-08  31.580000  33.232666  31.215334  32.809334  32.809334   \n",
       "\n",
       "         Volume  \n",
       "2394  142981500  \n",
       "2395  266677500  \n",
       "2396  151995000  \n",
       "2397  268231500  \n",
       "2398  467164500  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting data into train and test sets\n",
    "X = np.array(tesla_df.index).reshape(-1, 1)         # Reshaping the data to be used in the model\n",
    "y = tesla_df['Close']                               # Getting the target variable (Closing price)\n",
    "\n",
    "# Splitting the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)    # Using 20% of the data for testing\n",
    "\n",
    "\n",
    "# For feature scaling\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "# scaler.transform(X_train)\n",
    "# #feature scaling\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Since I am not getting the feature scaling to work properly, I will not use it for now\n",
    "\n",
    "tesla_df.head()                                     # Printing the first 5 rows of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a linear regression model\n",
    "regression = LinearRegression()\n",
    "\n",
    "# Fitting the model\n",
    "regression.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function for plotting the prediction against actual (Since I will be using it more than once)\n",
    "def plot_predictions(dataframe, y, test_dates, y_pred, title=\"Stock Prices: Actual vs. Predicted\"):\n",
    "    \n",
    "    # Create the plot\n",
    "    plt.figure(figsize=(12,6))\n",
    "\n",
    "    # Plot all actual values\n",
    "    plt.plot(dataframe['Date'], y, color='blue', label='Actual', alpha=0.5)\n",
    "    \n",
    "    # Plot predicted values for test dates\n",
    "    plt.scatter(test_dates, y_pred, color='red', label='Predicted', alpha=0.7, s=5)\n",
    "\n",
    "    # Setting labels and title\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the predictions\n",
    "y_pred = regression.predict(X_test)\n",
    "\n",
    "# Getting the dates for the plot\n",
    "test_dates = tesla_df.loc[X_test[:, 0], 'Date'].values\n",
    "\n",
    "# Plotting the predictions\n",
    "plot_predictions(tesla_df, y, test_dates, y_pred, 'Stock Prices of Tesla: Actual vs. Predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the plot of the Simple Linear regression plot\n",
    "- As we can see from the plot the predicted values form a line with a slope.\n",
    "- It does not even try to follow the spikes of the actual stock market prices over time, which is no surprise, since it is a simple linear regression model.\n",
    "- It only generates a slope that is a somewhat average of all the actuall values.\n",
    "- Therefore it is not very precise.\n",
    "\n",
    "### Lets measure how precise it is\n",
    "We will calculate the following:\n",
    "- Mean Absolute Error (MAE)\n",
    "- Mean Squared Error (MSE)\n",
    "- Root Mean Squared Error (RMSE)\n",
    "- R-squared (Coefficient of Determination)\n",
    "- Mean Absolute Percentage Error (MAPE)\n",
    "\n",
    "For MAE, MSE, RMSE, and MAPE: We're aiming for values as close to 0 as possible.\n",
    "\n",
    "For R-squared: We want values closer to 1, indicating a better fit of the model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 62.73\n",
      "Mean Squared Error (MSE): 5600.31\n",
      "Root Mean Squared Error (RMSE): 74.84\n",
      "R-squared: 0.21\n",
      "Mean Absolute Percentage Error (MAPE): 47.75%\n"
     ]
    }
   ],
   "source": [
    "# Importing the metrics I will be using\n",
    "from sklearn.metrics import mean_absolute_error                 # For MAE\n",
    "from sklearn.metrics import mean_squared_error                  # For MSE\n",
    "from sklearn.metrics import r2_score                            # For R2\n",
    "\n",
    "# Creating a function for calculating MAPE\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "\n",
    "# Printing the results\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
    "print(f\"R-squared: {r2:.2f}\")\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the metrics of the Linear Regression model\n",
    "- The model is not precise at all!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Regression model\n",
    "- Lets try a polynomial regression model, and see if it is a better fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing PylynomialFeatures\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the polynomial features\n",
    "poly = PolynomialFeatures(degree=5)             # Setting the degree to 5 (I tried different degrees, and 5 gave me good results)\n",
    "X_train_poly = poly.fit_transform(X_train)      # Transforming the training data\n",
    "X_test_poly = poly.fit_transform(X_test)        # Transforming the testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a polynomial regression model\n",
    "polyregression = LinearRegression()             # Creating the model\n",
    "polyregression.fit(X_train_poly, y_train)       # Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the predictions\n",
    "y_pred = polyregression.predict(X_test_poly)\n",
    "\n",
    "# Plotting the predictions\n",
    "plot_predictions(tesla_df, y, test_dates, y_pred, 'Stock Prices of Tesla: Actual vs. Predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the plot of the Polynomial Regression plot\n",
    "- Here the plots create a curvature line which is trying somewhat to follow the trends in the stock market.\n",
    "- It seems to be much more precise than the linear model.\n",
    "\n",
    "\n",
    "### Lets measure how precise it is\n",
    "We will calculate the following:\n",
    "- Mean Absolute Error (MAE)\n",
    "- Mean Squared Error (MSE)\n",
    "- Root Mean Squared Error (RMSE)\n",
    "- R-squared (Coefficient of Determination)\n",
    "- Mean Absolute Percentage Error (MAPE)\n",
    "\n",
    "For MAE, MSE, RMSE, and MAPE: We're aiming for values as close to 0 as possible.\n",
    "\n",
    "For R-squared: We want values closer to 1, indicating a better fit of the model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 32.10\n",
      "Mean Squared Error (MSE): 1696.63\n",
      "Root Mean Squared Error (RMSE): 41.19\n",
      "R-squared: 0.76\n",
      "Mean Absolute Percentage Error (MAPE): 17.27%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "\n",
    "# Printing the results\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
    "print(f\"R-squared: {r2:.2f}\")\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the metrics of the Polynomial Regression model\n",
    "- It comes as no suprise that the Polynomial model is much more precise than the linear model.\n",
    "- But there are still potential for improvement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regression combined with Polynomial Regression\n",
    "- I've read several articles online that combining Ridge Regression with Polynomial could make it more precise, so that is what I will be trying now.\n",
    "- When looking back at the other models, we're using all the features of the test set, which might not be ideal, since we then need to use the same amount of features when doing the predictions.\n",
    "- On that note, I will be trying to use only the Date and the Open market price. \n",
    "- This will make it so that the model should be able to predict closing stock market prices based on only the date and the opening market price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Ridge\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train and test sets\n",
    "X = np.array([tesla_df['Date'], tesla_df['Open']]).T       # Reshaping the data to be used in the model\n",
    "y = tesla_df['Close']                                      # Getting the target variable (Closing price)\n",
    "\n",
    "# Splitting the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Transforming input features\n",
    "poly = PolynomialFeatures(degree=5)                         # Creating a polynomial object with degree 5\n",
    "X_train_poly = poly.fit_transform(X_train_scaled)                  # Transforming the training data\n",
    "X_test_poly = poly.fit_transform(X_test_scaled)                    # Transforming the testing data\n",
    "# print(X_test)\n",
    "# print(X_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Ridge regression model\n",
    "ridgeRegression = Ridge(alpha=0.1)                          # Creating the model\n",
    "ridgeRegression.fit(X_train_poly, y_train)                  # Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_dates: DatetimeIndex(['2023-01-09', '2023-07-10', '2023-09-25', '2023-08-02',\n",
      "               '2022-06-22', '2021-01-12', '2020-11-11', '2023-02-07',\n",
      "               '2020-05-06', '2023-09-26'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "y_pred: [120.58151584 276.11615118 243.28721453 256.71826913 235.15720614\n",
      " 276.91234917 140.55056263 196.6796931   51.17922142 242.8523536 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the predictions\n",
    "y_pred = ridgeRegression.predict(X_test_poly)\n",
    "\n",
    "# Convert Unix timestamps in X_test back to datetime objects\n",
    "test_dates = pd.to_datetime(X_test[:, 0], unit='ns')\n",
    "print(\"test_dates:\", test_dates[:10])  # Print first 10 test dates\n",
    "print(\"y_pred:\", y_pred[:10])           # Print first 10 predictions\n",
    "\n",
    "# Plot actual vs. predicted stock prices\n",
    "plot_predictions(tesla_df, y, test_dates, y_pred, 'Stock Prices of Tesla: Actual vs. Predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the plot of the Polynomial Regression plot\n",
    "- Here the pot seems to try to follow the highs and lows of the actual stock market value.\n",
    "- If not allways on point, at a glance it looks much more precise than any of the previous models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets measure how precise it is\n",
    "We will calculate the following:\n",
    "- Mean Absolute Error (MAE)\n",
    "- Mean Squared Error (MSE)\n",
    "- Root Mean Squared Error (RMSE)\n",
    "- R-squared (Coefficient of Determination)\n",
    "- Mean Absolute Percentage Error (MAPE)\n",
    "\n",
    "For MAE, MSE, RMSE, and MAPE: We're aiming for values as close to 0 as possible.\n",
    "\n",
    "For R-squared: We want values closer to 1, indicating a better fit of the model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 4.94\n",
      "Mean Squared Error (MSE): 44.38\n",
      "Root Mean Squared Error (RMSE): 6.66\n",
      "R-squared: 0.99\n",
      "Mean Absolute Percentage Error (MAPE): 2.60%\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
    "print(f\"R-squared: {r2:.2f}\")\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the metrics of the Ridge Polynomial regression model\n",
    "- These metrics are much more precise than the previous models.\n",
    "- There are still some potential for improvements, but I think this is good enough for this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A function to predict the Tesla stock price\n",
    "- As described in the assignment what follows is a function to predict Teslas stock market prices.\n",
    "- It only works on historical dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a function for predicting the price for a given date\n",
    "def predict_and_actual_price_for_date(date, dataframe, poly_transformer, model):\n",
    "    # Convert the given date to Unix timestamp (in nanoseconds since the Unix epoch)\n",
    "    date_unix_timestamp = date.timestamp() * 1e9\n",
    "    \n",
    "    # Check if date doesn't exists in the dataframe\n",
    "    if len(dataframe[dataframe['Date'] == date]) == 0:\n",
    "        return None, None\n",
    "    \n",
    "    # Get the 'Open' price for that date\n",
    "    feature_for_date = dataframe[dataframe['Date'] == date]['Open'].values[0]\n",
    "    \n",
    "    # Prepare input data for the model\n",
    "    input_data = [[date_unix_timestamp, feature_for_date]]\n",
    "    \n",
    "    # Normalize the input data\n",
    "    input_data_scaled = scaler.transform(input_data)\n",
    "    \n",
    "    # Apply polynomial transformation\n",
    "    input_data_poly = poly_transformer.transform(input_data_scaled)\n",
    "    \n",
    "    # Make prediction\n",
    "    predicted_price = model.predict(input_data_poly)[0]\n",
    "    \n",
    "    # Retrieve the actual price (close price) for that date\n",
    "    actual_price = dataframe[dataframe['Date'] == date]['Close'].values[0]\n",
    "    \n",
    "    # Return the predicted price and actual price\n",
    "    return predicted_price, actual_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-05-01\n",
      "Predicted Price: 49.62501056507574\n",
      "Actual Price: 46.754665\n",
      "Relative Error: 6.14%\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-02\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-03\n",
      "-------------------------------\n",
      "2020-05-04\n",
      "Predicted Price: 46.95584178952177\n",
      "Actual Price: 50.745998\n",
      "Relative Error: 7.47%\n",
      "-------------------------------\n",
      "2020-05-05\n",
      "Predicted Price: 51.84589354680659\n",
      "Actual Price: 51.214001\n",
      "Relative Error: 1.23%\n",
      "-------------------------------\n",
      "2020-05-06\n",
      "Predicted Price: 51.17318235837655\n",
      "Actual Price: 52.172001\n",
      "Relative Error: 1.91%\n",
      "-------------------------------\n",
      "2020-05-07\n",
      "Predicted Price: 51.285556827380105\n",
      "Actual Price: 52.002666\n",
      "Relative Error: 1.38%\n",
      "-------------------------------\n",
      "2020-05-08\n",
      "Predicted Price: 52.278310599804996\n",
      "Actual Price: 54.627998\n",
      "Relative Error: 4.30%\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-09\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-10\n",
      "-------------------------------\n",
      "2020-05-11\n",
      "Predicted Price: 52.31975172849596\n",
      "Actual Price: 54.085999\n",
      "Relative Error: 3.27%\n",
      "-------------------------------\n",
      "2020-05-12\n",
      "Predicted Price: 54.429518567584665\n",
      "Actual Price: 53.960667\n",
      "Relative Error: 0.87%\n",
      "-------------------------------\n",
      "2020-05-13\n",
      "Predicted Price: 54.153436357953495\n",
      "Actual Price: 52.730667\n",
      "Relative Error: 2.70%\n",
      "-------------------------------\n",
      "2020-05-14\n",
      "Predicted Price: 51.98717455279811\n",
      "Actual Price: 53.555332\n",
      "Relative Error: 2.93%\n",
      "-------------------------------\n",
      "2020-05-15\n",
      "Predicted Price: 52.62885612538818\n",
      "Actual Price: 53.278\n",
      "Relative Error: 1.22%\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-16\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-17\n",
      "-------------------------------\n",
      "2020-05-18\n",
      "Predicted Price: 54.91835950216486\n",
      "Actual Price: 54.242001\n",
      "Relative Error: 1.25%\n",
      "-------------------------------\n",
      "2020-05-19\n",
      "Predicted Price: 54.306535622345365\n",
      "Actual Price: 53.867332\n",
      "Relative Error: 0.82%\n",
      "-------------------------------\n",
      "2020-05-20\n",
      "Predicted Price: 54.67959014439461\n",
      "Actual Price: 54.370667\n",
      "Relative Error: 0.57%\n",
      "-------------------------------\n",
      "2020-05-21\n",
      "Predicted Price: 54.51897530764725\n",
      "Actual Price: 55.173332\n",
      "Relative Error: 1.19%\n",
      "-------------------------------\n",
      "2020-05-22\n",
      "Predicted Price: 54.93796956970323\n",
      "Actual Price: 54.458668\n",
      "Relative Error: 0.88%\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-23\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-24\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-25\n",
      "-------------------------------\n",
      "2020-05-26\n",
      "Predicted Price: 55.94956209508763\n",
      "Actual Price: 54.591331\n",
      "Relative Error: 2.49%\n",
      "-------------------------------\n",
      "2020-05-27\n",
      "Predicted Price: 55.30957122770988\n",
      "Actual Price: 54.681999\n",
      "Relative Error: 1.15%\n",
      "-------------------------------\n",
      "2020-05-28\n",
      "Predicted Price: 55.01464506276892\n",
      "Actual Price: 53.720669\n",
      "Relative Error: 2.41%\n",
      "-------------------------------\n",
      "2020-05-29\n",
      "Predicted Price: 54.861289258533674\n",
      "Actual Price: 55.666668\n",
      "Relative Error: 1.45%\n",
      "-------------------------------\n",
      "No data available for date: 2020-05-30\n",
      "-------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Importing datetime\n",
    "import datetime\n",
    "\n",
    "# Define the starting date\n",
    "start_date = datetime.datetime(2020, 5, 1)\n",
    "\n",
    "# Loop through a range of 30 days\n",
    "for i in range(30):\n",
    "    \n",
    "    # Adding i days to the starting date\n",
    "    current_date = start_date + datetime.timedelta(days=i)\n",
    "    \n",
    "    # Get the predicted and actual price for the current date\n",
    "    predicted, actual = predict_and_actual_price_for_date(current_date, tesla_df, poly, ridgeRegression)\n",
    "    \n",
    "    # Print the results\n",
    "    if predicted and actual:\n",
    "        relative_error = (abs(actual - predicted) / actual) * 100\n",
    "        print(current_date.strftime('%Y-%m-%d'))\n",
    "        print(f\"Predicted Price: {predicted}\")\n",
    "        print(f\"Actual Price: {actual}\")\n",
    "        print(f\"Relative Error: {relative_error:.2f}%\")\n",
    "        print(\"-------------------------------\")\n",
    "    else:\n",
    "        print(f\"No data available for date: {current_date.strftime('%Y-%m-%d')}\")\n",
    "        print(\"-------------------------------\")\n"
   ]
  }
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