Salary_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction LearnML "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing all the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the .csv file for the input data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "    }\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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Salary_Data.csv')\n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0\n",
       "5              2.9  56642.0\n",
       "6              3.0  60150.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.head() is a method to print only n number of values with 5 being default \n",
    "data.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the Scatterplot with Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data into two arrays x - independant variable , y - dependant variable\n",
    "x = data['YearsExperience']\n",
    "y = data['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the scatterplot with the values from the data set \n",
    "plt.figure(figsize=(20,12))                 #to set the size of the graph (optional)\n",
    "plt.scatter(x,y)                            #built-in function in matplotlib to visualize scatterplot\n",
    "plt.title(\"Salary Prediction\")\n",
    "plt.xlabel(\"Years of experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()                                  #starts the event on loop and visualises all data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_model = LinearRegression()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshapes the arrays to fit in the graph accodringly \n",
    "x = np.array(x).reshape((len(x), 1))\n",
    "#to minimize the residual sum of squares between the observed targets in the dataset, \n",
    "#and the targets predicted by the linear approximation\n",
    "regression_model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 36187.15875227,  38077.15121656,  39967.14368085,  44692.12484158,\n",
       "        46582.11730587,  53197.09093089,  54142.08716303,  56032.07962732,\n",
       "        56032.07962732,  60757.06078805,  62647.05325234,  63592.04948449,\n",
       "        63592.04948449,  64537.04571663,  68317.03064522,  72097.0155738 ,\n",
       "        73987.00803809,  75877.00050238,  81546.97789525,  82491.9741274 ,\n",
       "        90051.94398456,  92886.932681  , 100446.90253816, 103281.8912346 ,\n",
       "       108006.87239533, 110841.86109176, 115566.84225249, 116511.83848464,\n",
       "       123126.81210966, 125016.80457395])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#determining the value for the dependant variable using the .predict() function from Linear regression in sklearn \n",
    "y_predicted = regression_model.predict(x)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaulation for Cost Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope:  [9449.96232146]\n",
      "Intercept:  25792.20019866871\n",
      "Root mean squared error:  31270951.722280968\n",
      "R2 score:  0.9569566641435086\n"
     ]
    }
   ],
   "source": [
    "#calculate the cost function (MSE) and \n",
    "mse = mean_squared_error(y, y_predicted)\n",
    "r2 = r2_score(y, y_predicted)\n",
    "print('Slope: ',regression_model.coef_)\n",
    "print('Intercept: ', regression_model.intercept_)\n",
    "print('Root mean squared error: ', mse)\n",
    "print('R2 score: ',r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Graph with the best fit line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k1YM+4FJgI+CLQA/5vLL7gd2xkBJYSkmSJEmSpGEzCzgP2ADYofT5BcC/gG8Bo4uLpopjKSVJkiRJkhbT68CZQDOwM7AUcBlwN/B13D1I82IpJUmSJEmSFtGrwEnAuuRNy1cErgI6gS9j7aAFsaqUJEmSJElD9DJwOtAOPAF8BDgD2BKIAnOpmlhKSZIkSZJUw6Z2dtPe0cWMnl7GNDXS2tLMhHFjF/HZXiLPjDoGeAb4JHAhsDmWURoqSylJkiRJkmrU1M5u2qZMp3dmHwDdPb20TZkOMMRi6nngl8BxpdtbAYeQZ0hJi8bFnZIkSZIk1aj2jq43Cql+vTP7aO/oGuQzPAscDKwN/BT4OHA7cB0WUlpczpSSJEmSJKlGzejpHdL4/3kSOAo4BegFvkQup94/nPFU55wpJUmSJElSjRrT1DikcXgc2AtYBzgW2Ba4B7gMCykNN0spSZIkSZJqVGtLM42jG+YYaxzdQGtL81yPfAj4LvAO8uyorwFdwPnAe8qQVPXI5XuSJEmSJNWo/s3M53/63r+ByeTyqQH4NnAAsFYRcVVnLKUkSZIkSaphE8aNncdJe/cAhwKXAksBewL7A0M5kU9aPJZSkiRJkiTVjU5gEjAFWJZcRO0LrFZkKNUpSylJkiRJkmre7cBE4FrgrcCPgb2BlYoMpTpnKSVJkiRJUs26hVxG/R5YsXT7B0BTgZmkzFJKkiRJkqSakoAbyAXUn4BVgSOB3YHlCswlzclSSpIkSZKkmpCA68hl1N/Im5YfTz5Rb5kCc0nzNqroAJIkSZIkaXHMBn4NjAe2Bp4ATgH+A+yFhZQqlaWUJEmSJElVqQ+4FHg/sC3wInA2cD95qd5SxUWTBsFSSpIkSZKkqjILOA/YANiBXE5dANwH7AKMLi6aNATuKSVJkiRJUlV4HTgXmAw8RJ4hdTl5lpRzTlR9LKUkSZIkqYpM7eymvaOLGT29jGlqpLWlmQnjxhYdSyPqVeAs4AjgMeCD5A3MtwGiwFzS4rGUkiRJkqQqMbWzm7Yp0+md2QdAd08vbVOmA1hM1aSXgdOAduBJ4CPAGcCWWEapFji/T5IkSZKqRHtH1xuFVL/emX20d3QVlEgj40XyEr21gf2A9YGbgFuAFiykVCucKSVJkiRJVWJGT++QxlVtngdOIC/Nex74f8AhwGZFhpJGjDOlJEmSJKlKjGlqHNK4qsUzwEHAWsDPgE8Afwd+i4WUapmllCRJkiRVidaWZhpHN8wx1ji6gdaW5oISafE8AexPXqZ3OHlm1D+AXwPji4sllYnL9yRJkiSpSvRvZu7pe9XuMeBI8qbls4CvAW3Ae4oMJZWdpZQkSZIkVZEJ48ZaQlWtB8kzon4FJGBnchn1zgIzzWlqZ7elp8rGUkqSJEmSpBHVRT5N7wKgAfgOcACwZpGh3mRqZzdtU6a/ccJjd08vbVOmA1hMaUS4p5QkSZIkSSPibuCrwPrAZcBewEPASVRaIQV5WWh/IdWvd2Yf7R1dBSVSrXOmlCRJkiRJw+pOYBJ5w/K3AK3AvsCqRYZaqBk9vUMalxaXM6UkSZIkSRoWfwO2ATYGbgR+AjxM3keqsgspgDFNjUMalxaXpZQkSZIkSYvlT8BngA+Ti6lJwCPAz4GVCsw1NK0tzTSObphjrHF0A60tzQUlUq1z+Z4kSZIkSUOWgD8AE4FbgNWAdmB38pK96tO/mbmn76lcLKUkSZIkSRq0BPyWXEbdBowFTgC+DVT/MrcJ48ZaQqlsXL4nSZIkSdJCzQamkPeL2gZ4CjgV+A+wJ7VQSEnlZiklSZIkSdJ89QGXAO8HtgP+B5wD/Bv4LrBUcdGkKmcpJUmSJEnSm8wEzgXWB75Knil1IXAf8E1gdGHJpFrhnlKSJEmSJL3hNXIZdTjwELARcAXwRZzXIQ0vv6IkSZIkSaIXOBFYl7wsbxXgGuBO8rI9f3yWhpszpSRJkiRJdexl8oblRwFPAh8FzgI+A0SBuaTaZyklSZIkSapDLwInAccAzwJbkDc0/0SRoaS6YiklSZIkSaojzwPHl/70AJ8FDgE+XGAmqT5ZSkmSJEmS6sAz5FlRJwEvARPIZdTGBWaS6pullCRJkiSphj1B3i/qVPJm5l8BDgbeW2QoSVhKSZIkSZIqwNTObto7upjR08uYpkZaW5qZMG7sYjzjY8ARwJnALODrQBvw7mFIK2k4WEpJkiRJkgo1tbObtinT6Z3ZB0B3Ty9tU6YDLEIx9SAwGTi39PnOwIHAO4cnrKRhM6roAJIkSZKk+tbe0fVGIdWvd2Yf7R1dQ3iWLnIB9S7gfGA34AHgDCykpMrkTClJkiRJUqFm9PQOaXxOdwOTgMuApYG9gP2BMcMVT9IIcaaUJEmSJKlQY5oahzSe3QlsS96w/DfAAcDD5BP2LKSkamApJUmSJEkqVGtLM42jG+YYaxzdQGtL8zwe/Tdga2Bj4EbgJ8Aj5H2kVh3hpJKGk8v3JEmSJEmF6t/MfMGn791MXqb3B2Al4FDg+8Bbyx1X0jCxlJIkSZIkFW7CuLHzOGkvkUuoicAtwGrAUcB3gbeUN6CkYWcpJUmSJEmqMIm8T9Qk4DZgLHAC8G1gQftMSaom7iklSZIkSaoQs4Ep5P2iPgc8BZwG/AfYEwspqbZYSkmSJEmSCtYHXAy8D9gO+B9wDvBvYDdgqeKiSRoxllKSJEmSpILMBH4FvAf4GnnZ3oXAfcA3gdFFBZNUBu4pJUmSJEkqs9eAc4HDgYeAjYArgC/i3AmpfvjVLkmSJEkqk17gl8C65BP0VgGuAe4kL9vzR1SpnjhTSpIkSZI0wv4HnAocRd68/GPA2cCngSgwl6QiWUpJkiRJkkbIi8CJwDHAf4EtgEuBTxQZSlKFsJSSJEmSJA2z54DjgROAHuCzwCHAhwvMJKnSWEpJkiRJkobJ08CxwEnAS+SNyw8BPlBkKEkVylJKkiRJkrSYngDayftGvQp8BTgYeG+RoSRVOEspSZIkSdIiehQ4AjgLmAV8HTgIaC4ylKQqYSklSZIkSRqiB4HJwLmlz78JHAi8o6hAkqqQpZQkSZIkaZD+BRwGXET+cXI34EfAmkWGGjZTO7tp7+hiRk8vY5oaaW1pZsK4sUXHkmqWpZQkSZIkaSGmA4cClwGNwN7A/sDqRYYaVlM7u2mbMp3emX0AdPf00jZlOoDFlDRCRhUdQJIkSZJUqe4gn6D3PuC35CV6DwNHU0uFFEB7R9cbhVS/3pl9tHd0FZRIqn3OlJIkSZIkzeVWYBK5iGoCfgrsBaxYYKaRNaOnd0jjkhafM6UkSZIkSUAC/gh8GtgMuJ28f9QjwM+o5UIKYExT45DGJS0+SylJkiRJqmsJuB74OPBJ4G7gKPIyvTZg+cKSlVNrSzONoxvmGGsc3UBrS3NBiaTa5/I9SZIkSapLCbiWvEzvdmAN4JfAruTNzOtL/2bmnr4nlY+llCRJkiTVldnAr8ll1F3A2sBpwM7AUoWlqgQTxo21hJLKyOV7kiRJklQX+oCLgPcCXwJeAX4F/BvYjXovpCSVn6WUJEmSJNW0mcA5wHuArwMBXAzcS54dNbq4aJLqmsv3JEmSJKkmvUaeCXU4edPyccCVwAScnyCpEvhfIkmSJEmqKb3ACcA7gd2B1cgbmt8BbIs/BkqqFM6UkiRJkqSa8D/gVOAo4Cng4+SZUluQl+xJUmWxlJIkSZKkqvYCcCJwLPBf4DPAIeRSSpIql6WUJEmSJFWl54DjyEv1XgC2JpdRmxaYSZIGz1JKkiRJkqrK08AxwEnkJXvbksuocUWGkqQhs5SSJEmSpKowA2gHTgNeBbYHDgY2LDKUJC0ySylJkiRJqmiPAEcAZwF9wDeANqC5yFCStNgspSRJkiSpIj0AHA6cSz4975vAgcA7CswkScPHUkqSJEmSKsp9wGHARcBoYHfgR8DbiwwlScPOUkqSJEmSKsI/gUnAFUAj8ENgP2D1IkNJ0oixlJIkSZKkQk0jl1FXAcuRl+j9EFilyFCSNOIspSRJkiSpEH8FJgK/A5qAnwF7ASsUF0mSyshSSpIkSZLKJgF/JJdRNwErA5OB7wHLFxdLkgpgKSVJkiRJQzC1s5v2ji5m9PQypqmR1pZmJowbu5CrEtBBXqb3F+BtwNHAd4FlRzawJFUoSylJkiRJGqSpnd20TZlO78w+ALp7emmbMh1gPsVUAq4hl1F/J5+gdyLwLfJm5pJUv0YVHUCSJEmSqkV7R9cbhVS/3pl9tHd0zfXI2cDlwEbAF4BngdOBB4DvYyElSZZSkiRJkjRoM3p6FzI+C7gA2BD4CvAqcC7QBXwHWHLkQ0pSlbCUkiRJkqRBGtM07xlOa664BHA28B5gR/KPWhcD9wI7AaPLlFCSqoellCRJkiQNUmtLM42jG974fKklXmeXza7jun12BXYFlgOmAP8EdgAa5vk8kiQ3OpckSZKkQevfzPyEG/7B5s1T2WPzKayy3H+BDwGnAZ8FosCEklQ9RmymVEScHRFPR8TdA8ZWjIjfR8T9pY8rDLivLSIeiIiuiGgZML5xREwv3XdCRERpfKmIuLQ0fltErD3gmp1Lr3F/ROw8Uu9RkiRJUr15iQnjLuTG/XfhJ587g1WW2wD4PXArsDUWUpI0eCO5fO9XwFZzjR0I3JBSWg+4ofQ5EbE+eW7rBqVrTo6I/nmupwC7AeuV/vQ/567A8ymldYFjgSNKz7Ui8FPyryo2AX46sPySJEmSpKHrASYCawMHAO8Hbi79+TSWUZI0dCNWSqWU/gQ8N9fwF8hHT1D6OGHA+CUppddSSg+Rz0ndJCJWB5ZPKd2aUkrAeXNd0/9cVwBblGZRtQC/Tyk9l1J6nvxri7nLMUmSJEkahP8ChwBrAT8BNiPPiroe+HiBuSSp+pV7T6nVUkpPAKSUnoiIVUvjY4G/DXjc46WxmaXbc4/3X/NY6blmRcQLwEoDx+dxzRwiYjfyLCzWXHPNRX9XkiRJkmrMU8DRwMnAy8C25HJqXJGhJKmmVMrpe/Oa65oWML6o18w5mNLpKaXxKaXxq6yyyqCCSpIkSapl3cDe5GV6RwOfB+4GrsRCSpKGV7lLqadKS/IofXy6NP448PYBj1sDmFEaX2Me43NcExFLAG8lLxec33NJkiRJ0nw8DOwBvAM4ibzl7X3AReStbyVJw63cpdTVQP9peDsDVw0Y36F0ot465A3Nby8t9XspIjYt7Re101zX9D/Xl4AbS/tOdQBbRsQKpQ3OtyyNSZIkSdJcHgC+Rf4R5Czgm8D9wDnAu4qLJUl1YMT2lIqIi4HNgZUj4nHyiXiHA5dFxK7Ao8CXAVJK90TEZcC9wCzg+ymlvtJT7UE+ya8RuK70B/K/GOdHxAPkGVI7lJ7ruYiYCPy99LhfpJTm3nBdkiRJUl27FzgMuBhYkvxjRytzLrqQJI2kyJOLNH78+DRt2rSiY0iSJEkaUXcBh5L3iGoEvgfsB7ytwEySVLsi4o6U0vh53Vfu0/ckSZIkqQB/ByYC1wDLAW3AD4GViwwlSXXNUkqSJEmqUVM7u2nv6GJGTy9jmhppbWlmwrixRccqsz8Dk8jbzK4A/BzYs3RbklQkSylJkiSpBk3t7KZtynR6Z+atWrt7emmbMh2gDoqpBNxEnhn1R/JsqMnkpXrLFxdLkjSHcp++J0mSJKkM2ju63iik+vXO7KO9o6ugROWQyOcifRTYAugCjgEeBg7EQkqSKoszpSRJkqQaNKOnd0jj1W02ea+oScA08gl6JwHfApYuMJckaUGcKSVJkiTVoDFNjUMar059wGXAOGAC8BxwBvAAeamehZQkVTJLKUmSJKkGtbY00zi6YY6xxtENtLY0F5RoOM0Czgc2BLYHXgPOIy/X+zawZHHRJEmD5vI9SZIkqQb1b2ZeW6fvvU4uoyYD/yGXUpcAXwIaFnCdJKkSWUpJkiRJNWrCuLFVXkL1exU4GzgCeBT4APBr4PO4+EOSqpellCRJkqQK9QpwGtAOPAF8GDgV2AqIAnNJkoaDpZQkSZKkCvMScDJwNPAM8Anysr1PYRklSbXDUkqSJElShegBTgCOA54HtgQOAT5WXCRJ0oixlJIkSZJUsGfJRdQvgReBz5HLqE0KzCRJGmmWUpIkSZIK8iR5id4pwMvAduQyaqMCM0mSysVSSpIkSVKZPU7evPx04HVgB+AgYIMiQ0mSysxSSpIkSVKZPAwcDpwDzAZ2BNqA9QrMJEkqiqWUJEmSpBF2PzCZfILeKGAX4EBg7QIzSZKKZiklSZIkaYTcCxwKXAIsCXwPaAXWKDKUJKlCWEpJkiRJGmZ3AZOAKcAywL7AfsDbCswkSao0llKSJEmShsnt5DLqGmB58ubl+wArF5hJklSpLKUkSZIkLaY/AxOB64EVgF8AewJNBWaSJFU6SylJkiRJiyABN5LLqJuBVcgn630PWK7AXJKkamEpJUmSJGkIEnAdeZnercDqwLHAbuT9oyRJGhxLKUmSJEmDMBu4mlxG3QGsCZwM7AIsXWAuSVK1GlV0AEmSJEmVrA+4FNgI+CLQA5wJ3A/sgYWUJGlROVNKkiSpzk3t7Ka9o4sZPb2MaWqktaWZCePGFh1LhZsFXAwcBvwLeDdwPrAD/hghSRoO/msiSZJUx6Z2dtM2ZTq9M/sA6O7ppW3KdACLqbr1OnAeMBl4EHgfcBmwLdBQYC5JUq1x+Z4kSVIda+/oeqOQ6tc7s4/2jq6CEqk4r5L3iFoP+A6wAjAV6AS+jIWUJGm4OVNKkiSpjs3o6R3SuGrRy8DpQDvwBLAZcBrQAkSBuSRJtc6ZUpIkSXVsTFPjkMZVS14CDgfWAfYl7xl1A/BnYCsspCRJI81SSpIkqY61tjTTOHrOZVmNoxtobWkuKJFG3vPAL4C1gDbgA+Qi6kbgU1hGSZLKxeV7kiRJdax/M3NP36sHzwLHAicCLwKfBw4BPlhkKElSHbOUkiRJqnMTxo21hKppTwJHA6cArwDbkcuo9xcZSpIkSylJkiSpNj0OHAmcAbwOfBU4CFi/yFCSJL3BUkqSJEmqKQ+RNzA/B0jATsCBwHpFhpIk6U0spSRJkqSa8G9gMnA+0ADsChwArF1gJkmS5s9SSpIkSapq9wCHApcCSwI/AFoB9wmTJFU2SylJkiRpBEzt7B7hUw07gUnAFGBZYH9gX2C1YXwNSZJGjqWUJEmSNMymdnbTNmU6vTP7AOju6aVtynSAYSimbgcmAtcCy5NP0tsHWGkxn1eSpPIaVXQASZIkqda0d3S9UUj1653ZR3tH12I86y3AlsCHgL+Si6lHSh8tpCRJ1ceZUpIkSdIwm9HTO6Tx+UvAjeTi6WZgVeAIYA9gucVIKElS8ZwpJUmSJA2zMU2NQxp/swT8FtgM+DRwP3Ac8BDwIyykJEm1wFJKkiRJGmatLc00jm6YY6xxdAOtLc0LuXI28GtgPLA18ARwCvAgsDewzPCHlSSpIC7fkyRJkoZZ/2bmgz99rw+4gnya3t3AO4GzgB2B0WVILElS+VlKSZIkSSNgwrixgzhpbxZwEXAY0AW8B7gA2B6/VZck1Tr/pZMkSZLK7nXgPGAyeWne+4HLgW1xhw1JUr3wXzxJkiSpbF4FTgLWBb4DrAhcBXQCX8JvzyVJ9cSZUpIkSdKIexk4DWgHngQ+ApwBbAlEgbkkSSqOpZQkSZI0Yl4ETgaOAZ4BPkXeQ2pzLKMkSfXOUkqSJEkads8DJwDHl25vBfwY2KzIUJIkVRRLKUmSJGnYPAscC5xIniX1BeBg4INFhpIkqSJZSkmSJEmL7UngKOAUoJe8afkhwPuKDCVJUkWzlJIkSZIW2WPkzcvPAF4HvgYcBLynyFCSJFUFSylJkiRpyB4CDgfOARKwM3AgsG6RoSRJqiqWUpIkSdKg/Rs4DLgAaAC+DRwArFVkKEmSqpKllCRJkrRQd5PLqEuBpYA9gVZgTJGhJEmqapZSkiRJ0nx1ApOAKcBbgP2BfYHVigwlSVJNsJSSJEmS3uQ2chl1LfBW4MfA3sBKRYaSJKmmWEpJkiRJb7gFmAj8HliRXEz9gFxMSZKk4WQpJUmSpDqXgBvIZdSfgFWBI4E9yEv2JEnSSLCUkiRJUp1KwG/Js6H+BowFjge+AzQWmEuSpPpgKSVJkqQ6Mxu4ilxG3QmsBZwKfJN8sp4kSSqHUUUHkCRJksqjD7gEeD+wLfAicDZwP/BdLKQkSSovSylJkiTVuFnAucD6wFfJM6UuBO4DdgFGFxdNkqQ65vI9SZIk1ajXyWXUZOAh8gypy8mzpPzdrCRJRfNfY0mSJNWYV4ETgXcCuwErA1cDncCX8FtgSZIqgzOlJEmSVCNeBk4D2oEngY8CZwGfAaLAXJIkaV4spSRJklTlXgROAo4BngU+BVwMfALLKEmSKpellCRJkqrU88AJwPGl2/8POATYrMhQkiRpkCylJEmSVGWeAY4l7xv1EjABOBgYX2AmSZI0VJZSkiRJqhJPAEcDpwC9wJfJZdT7igwlSZIWkaWUJEmSKtxjwJHAGcAs4GvAQcC7iwwlSZIWk6WUJEmSKtSDwOHAr4AE7Ay0Ae8sMJMkSRoullKSJEmqMF3AZOACoAH4DnAAsGaRoSRJ0jCzlJIkSVKFuBs4FLgUWBrYC9gfGFNkKEmSNEIspSRJklSwO4FJwK+BtwA/AvYFVi0ylCRJGmGWUpIkSSrI38hl1G+AtwI/Ic+OWqnIUJIkqUwspSRJklRmfwImAn8gF1CHAt8nF1OSJKleWEpJkiSpDBK5hJoI3AKsBrQDu5OX7EmSpHpjKSVJkqQRlIDfksuo24CxwAnAt4HGAnNJkqSijSo6gCRJkmrRbGAKsDGwDfAUcCrwH2BPLKQkSZKllCRJkoZRH3Ax8D5gO+B/wDnAv4HvAksVF02SJFUUSylJkiQNg5nAucD6wNfIy/YuBO4DvgmMLiyZJEmqTO4pJUmSpMXwGrmMOhx4CNgIuAL4Iv7+U5IkLYjfKUiSJGkR9AInAuuSl+WtAlwD3Eletue3mZIkacGcKSVJkmrO1M5u2ju6mNHTy5imRlpbmpkwbmzRsWrEy+QNy48CngQ+CpwFfAaIAnNJkqRqYyklSZJqytTObtqmTKd3Zh8A3T29tE2ZDmAxtVheBE4CjgGeBbYALgE+UWQoSZJUxZxXLUmSakp7R9cbhVS/3pl9tHd0FZSo2j0P/AxYCzgI2AT4K/AHLKQkSdLicKaUJEmqKTN6eoc0rvl5hjwr6iTgJWACcAiwcYGZJElSLRnUTKmIaBjpIJIkScNhTFPjkMY1tyeA/YC1gSOAzwL/BH6NhZQkSRpOg12+90BEtEfE+iOaRpIkaTG1tjTTOHrO36c1jm6gtaW5oETV4jHgB8A6wPHAl4B7yftGvbfAXJIkqVYNdvne+4AdgDMjYhRwNnBJSunFEUsmSZK0CPo3M/f0vcF6EJgMnFv6fGfgQOCdhSWSJEn1IVJKQ7sg4uPAxUATcAUwMaX0wPBHK6/x48enadOmFR1DkiSpTLqAw4ALyb+n/DbwI2DNIkNJkqQaExF3pJTGz+u+Qc2UKu0ptTWwC3mDgaPJ38F8DPgt8K5hSSpJkqQRNh04FLgMWBrYC9gfGFNkKEmSVIcGu3zvfuAmoD2l9NcB41eUZk5JkiSpot0JTASmAm8BDgB+CKxaYCZJklTPFlpKlWZJ/Sql9It53Z9S2mvYU0mSJGmxTO3spr2ji1WX66S15XI2W/c24K3AT4C9gRWLDShJkureQkuplFJfRHwSmGcpJUmSpMoytbObKXdeyOHbXczH1ruL515enmN/vzPrrXoA27z/PUXHkyRJAga/fO+vEXEicCnwcv9gSunOEUklSZKkRZCAP7D2yvtx3q7TeealJg79zbe48Lb/xyuvNzK26QlLKUmSVDEGW0ptVvo4cLZUAj41vHEkSZI0dAn4DTAJuI3Vll+Jn179XS65fUtem7XUG4+a0dNbVEBJkqQ3GVQplVL65EgHkSRJ0lDNBn5NLqPuIh+SfBpfPX1NHv5v35sePaapsZzhJEmSFmiwM6WIiK2BDchnBwMwv83PJUmSNJL6gMuAQ4F7gPWAXwFfA0azz6e7aZsynd6Z/1dMNY5uoLWluYCskiRJ8zaoUioiTgWWAT4JnAl8Cbh9BHNJkiTpTWYCFwKHAfeTf194EfAVoOGNR00YNxaA9o4uZvT0MqapkdaW5jfGJUmSKsGg95RKKb0vIv6ZUvp5RBwNTFnUF42IHwLfJm+AMB3YhVx6XUqed/4w8JWU0vOlx7cBu5J/LbhXSqmjNL4x+deCjcBvgb1TSikilgLOAzYG/gtsn1J6eFHzSpIkFes18rc8h5O/TdoIuBKYAIya5xUTxo21hJIkSRVt3t/FvFn/rpivRMQY8q/p1lmUF4yIscBewPiU0obkX+vtABwI3JBSWg+4ofQ5EbF+6f4NgK2AkyOi/1eBpwC7keesr1e6H3KB9XxKaV3gWOCIRckqSZJUrF7gl8C6wO7AqsA1wJ3Atgz+WzlJkqTKM9jvZK6NiCagnfxd0MPAJYvxuksAjRGxBHmG1AzgC8C5pfvPJf/qj9L4JSml11JKDwEPAJtExOrA8imlW1NKiTwzauA1/c91BbBFRMRi5JUkSSqj/wFHkX8HuFfp4/XA34BtAL+tkSRJ1W+wp+9NLN28MiKuBZZOKb2wKC+YUuqOiKOAR8m//rs+pXR9RKyWUnqi9JgnImLV0iVjyd+B9Xu8NDazdHvu8f5rHis916yIeAFYCXh2YJaI2I0804o111xzUd6OJEnSMHoBOAk4hrwDwafJuxt8oshQkiRJI2KBpVREbLuA+0gpDXlfqYhYgTyTaR2gB7g8Ir6xoEvmMZYWML6ga+YcSOl04HSA8ePHv+l+SZKk8ngOOB44gfzt0dbAwcCHC8wkSZI0shY2U+pzC7gvsWibnX8aeCil9AxAREwBNgOeiojVS7OkVgeeLj3+ceDtA65fg7zc7/HS7bnHB17zeGmJ4FvJ3+1JkiRVkKfJ21+eSF6y90XgEOADRYaSJEkqiwWWUimlXUbgNR8FNo2IZcjL97YApgEvAzuTj5XZGbiq9PirgYsi4hhgDHlD89tTSn0R8VJEbArcBuxE3gm0/5qdgVuBLwE3lvadkiRJqgBPkLfqPBV4FdgeOAh4b5GhJEmSympQe0oBRMTW5BPwlu4fSyn9YqgvmFK6LSKuIG+YPgvoJC+hewtwWUTsSi6uvlx6/D0RcRlwb+nx308p9ZWebg/y+ciNwHWlPwBnAedHxAPkGVI7DDWnJEnS8HuUfCjwWeRva75OLqOaiwwlSZJUiBjMBKKIOJV8St4ngTPJs49uTyntOrLxymf8+PFp2rRpRceQJEk16T/kyeD9hwN/EzgQeEdRgSRJksoiIu5IKY2f132jBvkcm6WUdgKeTyn9nLzr5tsXco0kSVKd+xd5h4Fm4Hzyob8PkCeJW0hJkqT6Ntjle72lj69ExBjykrh1RiaSJElStZsOTAIuJ+8ysDewP7B6kaEkSZIqymBLqWsjogk4ErijNHbmiCSSJEmqWneQy6ipwHLkJXo/BFYpMJMkSVJlWmApFREfBB5LKU0sff4W8q/+/kU+v1iSJEncCkwkn7nSBPwU2AtYscBMkiRJlW1he0qdBrwOEBEfJ+/QeRrwAnkzBEmSpDqVgD8CnwY2A/4OHAY8AvwMCylJkqQFW9jyvYaU0nOl29sDp6eUrgSujIi7RjSZJElSRUrA78kzo/4MrAYcBewOLFtgLkmSpOqysJlSDRHRX1xtAdw44L7B7kclSZJUAxJwDbAp0AI8DPwSeAjYDwspSZKkoVlYsXQxcHNEPEs+ge8WgIhYl7yET5IkqcbNBqaQNzD/B/kA4tOBnYClCswlSZJU3RZYSqWUDo2IG8jnF1+fUkqlu0YBe450OEmSpOL0AZcChwL3Au8CfgV8DRhdXCxJkqQasdAleCmlv81j7N8jE0eSJKloM4ELgMnA/cAG5MnjXwYaCswlSZJUW9wXSpIkCYDXyDOhDifvFzUOuBKYwMK34ZQkSdJQ+R2WJEmqc73ACcA7ySforQZcC9wBbIvfLkmSJI0MZ0pJkqQ69T/gVOAo4Cng4+SZUlsAUVwsSZKkOmEpJUmS6swLwInAscB/gc8Ah5BLKUmSJJWLpZQkSaoTzwHHkZfqvQBsTS6jNi0wkyRJUv2ylJIkSTXuaeAY4CTykr1tyWXUuCJDSZIk1T1LKUmSVKNmAO3AacCrwPbAwcCGRYaSJElSiaWUJEmqMY8ARwBnAX3AN4A2oLnIUJIkSZqLpZQkSaoR/wEmA+eST8/bBTgAeEeRoSRJkjQfllKSJKnK3QccBlwEjAZ2B34EvL3IUJIkSVoISylJklSl/glMAq4AGoEfAvsBqxcZSpIkSYNkKSVJkqrMNHIZdRWwHHAguZBapchQkiRJGiJLKUmSVCX+CkwEfgc0AT8D9gJWKC6SJEmSFpmllCRJqmAJ+CO5jLoJWJm8mfn3gOWLiyVJkqTFZiklSZIqUAI6yMv0/gK8DTga+C6wbIG5JEmSNFwspSRJUgVJwDXkMurv5BP0TgR2BZYuMJckSZKG26iiA0iSJMFs4HJgI+ALwLPA6cADwPexkJIkSao9llKSJKlAs4ALgQ2BrwCvAucC/wa+AyxZXDRJkiSNKEspSZJUgNeBs4H3AN8AGoCLgXuBnXCHAUmSpNrnd3ySJKmMXiOXUYcDjwLjgCnkJXv+rkySJKmeWEpJkqQyeAU4AzgSmAFsCpwC/D8gCswlSZKkolhKSZKkEfQSuXw6Gnga+ARwHvApLKMkSZLqm6WUJEkaAT3AicCxwHPAZ4AfAx8rMJMkSZIqiaWUJEkaRv8FjgNOAF4EtgEOAT5UYCZJkiRVIkspSZI0DJ4iL9E7GXgZ2A44mLyRuSRJkvRmllKSJGkxdAPtwOnkk/V2AA4CNigylCRJkqqApZQkSVoEjwCHA2cDfcCOQBvwriJDSZIkqYpYSkmSpCF4AJhMPkEvgG8BBwDrFBlKkiRJVchSSpIkDcJ9wKHAxcCSwB7Aj4A1igwlSZKkKmYpJUmSFuAfwCTgSmAZYF9gP+BtRYaSJElSDbCUkiRJ8/B3chl1NbA8efPyfYCVC8wkSZKkWmIpJUmSBvgLMBHoAFYAfg7sBTQVmEmSJEm1yFJKkqS6l4CbyDOjbgJWIZ+s9z1guQJzSZIkqZZZSkmSVLcSeUbUROCvwOrAMcBuwLIF5pIkSVI9sJSSJKnuJPJeUZOAacDbgZOAbwFLF5hLkiRJ9WRU0QEkSVK5zAYuBzYCJgDPAWcCD5CX6llISZIkqXwspSRJqnmzgAuADYGvAK8B5wFdwK7AksVFkyRJUt2ylJIkqWa9DpwNvBvYkbxq/1LgngGfS5IkScXwu1FJkmrOq8A55BP0HgU2Bn4NfB5/HyVJkqRKYSklSVLNeAU4HWgHZgAfBk4FtgKiwFySJEnSm1lKSZJU9V4CTgGOBp4GNgfOBz6JZZQkSZIqlaWUJElVqwf4JXAc+SS9LYEfAx8tLpIkSZI0SJZSkiRVnf+Si6gTgBeBzwGHAJsUmEmSJEkaGkspSZKqxlPkJXonAy8D25HLqI0KzCRJkiQtGkspSZIqXjd58/LTgdeAHYCDgA2KDCVJkiQtFkspSZIq1iPA4cDZwGxgR6ANWK/IUJIkSdKwsJSSJKniPAAcRj5BbxSwC3AgsHaBmSRJkqThZSklSVLFuA84FLgYWBL4HtAKrFFkKEmSJGlEWEpJklS4fwCTgCuBZYB9gf2AtxUZ6k2mdnbT3tHFjJ5exjQ10trSzIRxY4uOJUmSpCplKSVJUmH+Ti6jrgaWJ29evg+wcoGZ5m1qZzdtU6bTO7MPgO6eXtqmTAewmJIkSdIiGVV0AEmS6s9fgK2ATYBbgF8AD5MLqsorpADaO7reKKT69c7so72jq6BEkiRJqnbOlJIkqSwScBMwEfgjsAr5ZL3vAcsVF2uQZvT0DmlckiRJWhhnSkmSNKIScB3wUWALoAs4FngIOIBqKKQAxjQ1DmlckiRJWhhLKUmSRsRs4CryEr3PAo8DJwMPkveNWrawZIuitaWZxtENc4w1jm6gtaW5oESSJEmqdi7fkyRpWPWRT9E7FPgn8A7gTGBHYMkCcy2e/s3MPX1PkiRJw8VSSpKkYTELuBg4DPgX8G7gfGAHauWf2wnjxlpCSZIkadi4fE+SpMXyOnAWuYTaCRgNXArcDXyDWimkJEmSpOHmd8qSJC2SV4GzgSOAR4GNganA5/B3PpIkSdLCWUpJkjQkrwCnA+3ADGAz4DSgBYgCc0mSJEnVxVJKkqRBeYl8et7RwDPA5uQ9oz6JZdTimdrZ7QbqkiRJdchSSpKkBeoBTgCOA54nz4g6BPhocZFqyNTObtqmTKd3Zh8A3T29tE2ZDmAxJUmSVOPc9EKSpHl6llw+rQX8FPgYcBvwOyykhk97R9cbhVS/3pl9tHd0FZRIkiRJ5eJMKUmS5vAkeYneKeT9o7YDDgY2KjBT7ZrR0zukcUmSJNUOZ0pJkgTA48DewDrAMcAE4G7gciykRs6YpsYhjUuSJKl2WEpJkurcQ8DuwDvJG5l/DfgXcAGwfoG56kNrSzONoxvmGGsc3UBrS3NBiSRJklQuLt+TJNWp+4HDyCfoNQDfAg4A1i4wU/3p38zc0/ckSZLqj6WUJKnO3EMuoy4BlgR+ALQCliBFmTBurCWUJElSHbKUkiTVibuAScCVwLLAfqU/qxWYSZIkSapfllKSNMymdna7FKmi3E4uo64BlgcOAfYBViowkyRJkiRLKUkaRlM7u2mbMp3emX0AdPf00jZlOoDFVNn9GZgIXA+sWLr9A6CpwEySJEmS+nn6niQNo/aOrjcKqX69M/to7+gqKFG9ScANwObAx8hL9o4AHibPkGoqJpYkSZKkN3GmlCQNoxk9vUMa13BJwHXkZXq3AmOA44DvAMsUF0uSJEnSfDlTSpKG0ZimxiGNa3HNBqYCHwS2BmYApwD/AfbGQkqSJEmqXJZSkjSMWluaaRzdMMdY4+gGWluaC0pUq/qAS4GNgC8CPcBZwP3A7sDSRQWTJEmSNEgu35OkYdS/mbmn742UWcDFwKFAF/Ae4AJge/wnTZIkSaoufgcvScNswrixllCDNLWze5AF3uvAecBk4EHgfcBlwHY46VeSJEmqTpZSkqRCTO3spm3K9DdOK+zu6aVtynSAAcXUq+RleUcAjwHjgWOAz2EZJUmSJFU3v6OXJBWivaPrjUKqX+/MPto7uoCXgWOBdwA/AN5OPl3vduAL+M+XJEmSVP2cKSVJKsSMnt43jS275Ct8/v2XAzsAzwCfBC4ENgeinPEkSZIkjTBLKUlSIcY0NdJdKqaWb/wf39zsGr71katoWuZ/wFbAIcBHiowoSZIkaQRZSkmSCtHa0swRv/szX//QFHba7FqWX/oV/nDfh1li1MFs3rx10fEkSZIkjTBLKUlSAZ5kwrhj2eZ9JzNq1KtcN/0jXDZtJ7447rOeXChJkiTVCUspSVIZPQa0A2cAr7NEw9eANrZ+3/ps/b5ik0mSJEkqL0spSVIZPAQcDpwDJGAnoA1Yt8hQkiRJkgpkKSVJGkH/BiYD5wMNwLeBHwFrF5hJkiRJUiWwlJIkjYC7gcOAS4ElgR8ArYD7RUmSJEnKLKUkScOoE5gETAGWBfYH9gVWKzJURZva2U17RxczenoZ09RIa0uzm71LkiSpLlhKSZKGwW3kMupaYHngEGAfYKUCM1W+qZ3dtE2ZTu/MPgC6e3ppmzIdwGJKkiRJNW9U0QEkSdXsT8CWwKbAX4GJwCOljxZSC9Pe0fVGIdWvd2Yf7R1dBSWSJEmSyseZUpKkIUrADeTi6U/AqsCRwO7AcgXmqj4zenqHNC5JkiTVEmdKSZIGKQG/AT4MfAZ4ADgOeIi8ibmF1FCNaWoc0rgkSZJUSyylJEkLMZu8cfnGwDbAk8ApwIPA3sAyxUWrcq0tzTSObphjrHF0A60tzQUlkiRJksrH5XuS6panni1MH3A5cChwN/BO4CzgG8CSBeaqHf3/f/P/h5IkSapHhZRSEdEEnAlsSF4P8i2gC7gUWBt4GPhKSun50uPbgF3JPyHtlVLqKI1vDPwKaAR+C+ydUkoRsRRwHvnX+v8Ftk8pPVyWNyepKnjq2YLMBC4CDgP+DbwHuADYHn+XMfwmjBvr/+ckSZJUl4pavnc88LuU0ruB9wP3AQcCN6SU1iPvoHsgQESsD+wAbABsBZwcEf1rHU4BdgPWK/3ZqjS+K/B8Smld4FjgiHK8KUnVw1PP5uV14AygGfgmue+/nDxL6utYSEmSJEkaTmUvpSJieeDj5DUgpJReTyn1AF8Azi097FxgQun2F4BLUkqvpZQeIu+su0lErA4sn1K6NaWUyDOjBl7T/1xXAFtERIzk+5JUXTz1bKBe4ETy8rzdgJWAq4BO4Eu4/aAkSZKkkVDETxrvAJ4BzomIzog4MyKWBVZLKT0BUPq4aunxY4HHBlz/eGlsbOn23ONzXJNSmgW8QP4paw4RsVtETIuIac8888xwvT9JVcBTzwBeBo4m/2d5T2At4HfA7cDnAbt8SZIkSSOniFJqCeADwCkppXHkn4oOXMDj5/VTUVrA+IKumXMgpdNTSuNTSuNXWWWVBaeWVFPq+9SzF4HJ5C389gfWB24EbgFasIySJEmSVA5FlFKPA4+nlG4rfX4FuaR6qrQkj9LHpwc8/u0Drl8DmFEaX2Me43NcExFLAG8Fnhv2dyKpak0YN5bJ276XsU2NBDC2qZHJ2763xjecfh74GXlG1EHAB4G/kLfx+ySWUZIkSZLKqey71qaUnoyIxyKiOaXUBWwB3Fv6szNweOnjVaVLrgYuiohjgDHkDc1vTyn1RcRLEbEpcBuwE/DLAdfsDNxK3hDlxtK+U5L0hvo59ewZ8pkPJwIvkbfdOwQYX2QoSZIkSXWuqKOU9gQujIglgQeBXcizti6LiF2BR4EvA6SU7omIy8il1Szg+yml/iOz9gB+RT4i6rrSH8ibqJ8fEQ+QZ0jtUI43JUmV5QngKOBU8mbmXwYOBt5XZChJkiRJAiCcQJSNHz8+TZs2regYkjQMHgOOBM4AZgJfIy/Xe0+RoSRJkiTVoYi4I6U0z2UaRc2UkiQNuwfJK6B/RT7bYWfyORLrFphJkiRJkubNUkqSql4X+TS9C4AG4NvAAeQNzSVJkiSpMllKSVLVuhs4FLgUWJq8XV8r+UwISZIkSapsllKSVHXuBCYBvwbeAvwI2BdYtchQkiRJkjQkllKSVDX+Ri6jfgO8FfgxsDewUpGhJEmSJGmRWEpJUsX7EzAR+AO5gJoE/IBcTEmSJElSdbKUkqSKlMgl1ETgFmA1oB3Ynbxkr/ymdnbT3tHFjJ5exjQ10trSzIRxYwvJIkmSJKn6WUpJUkVJwG/JZdRtwFjgeOA7QGNhqaZ2dtM2ZTq9M/sA6O7ppW3KdICKLKYs0CRJkqTKZyklSRVhNjCVvDSvE1gLOBX4JrBUYan6tXd0vVFI9eud2Ud7R1fZyp7BFk3VVqBJkiRJ9cpSSlJNqb4ZMn3A5cChwN3AusDZwDeA0QXmmtOMnt4hjQ+3oRRNlVCgSZIkSVq4UUUHkKTh0l9cdPf0kvi/4mJqZ3fR0eZhJnAusD7wVfJMqQuB+4BdqKRCCmBM07yXDs5vfLgtqGiaW9EFmiRJkqTBsZSSVDOGUlwU5zXgdKCZvDSvEbgCmA58jUqdwNra0kzj6IY5xhpHN9Da0lyW1x9K0VR0gSZJkiRpcCylJNWMyp4h0wucSF6e911gZeBq8v5R21Hp/zmeMG4sk7d9L2ObGglgbFMjk7d9b9mWww2laCq6QJMkSZI0OJX5K3lJWgRjmhrpHsLMmfJ4mbxh+VHAk8BHgbOAzwBRYK6hmzBubGF7MrW2NM+xpxTMv2jqz1hde4tJkiRJ9cdSSlLNGEpxMfJeBE4CjgGeBbYALgY+QbWVUZVgwrixTHvkOS6+7TH6UqIhgu02nn9JVmSBJkmSJGlwKnu9iCQNQdFLzLLngZ8BawEHAZsAfwX+AGyOhdSimdrZzZV3dNOXEgB9KXHlHd0Vuom9JEmSpMFwppSkmlLcDJlnyLOiTgJeAiYAhwAbF5Cl9ixoE3tnREmSJEnVyVJKkhbLE+T9ok4lb2b+FfIMqfcVGarmVPYm9pIkSZIWhcv3JGmRPAb8AFgHOJ58gt69wCVYSA2/oZy+J0mSJKk6WEpJ0pA8CHwHeCdwOrAj0AWcB7y7wFy1rbWlmcbRDXOMFbeJvSRJkqTh4PI9SRqULuAw4ELyfzq/AxwArFlkqLrRv29Ue0cXM3p6GdPUSGtLs/tJSZIkSVXMUkqSFmg6cChwGbA0sBewPzCmyFB1qbhN7CVJkiSNBEspSZqnO4BJwFTgLeRZUT8EVl3olVM7u53RI0mSJEkLYSklSXO4lVxG/RZ4K/ATYG9gxUFdPbWzm7Yp0+md2QdAd08vbVOmA1hMSZIkSdIAbnQuSQDcDHwa2Ay4jbxk7xHg5wy2kIK851F/IdWvd2Yf7R1dw5ZUkiRJkmqBM6Uk1bEE/J48M+oWYDWgHdidvGRv6Gb09A5pXJIkSZLqlTOlJNWhBFwLbAq0AA8CJwAPkTcxX7RCCmBMU+OQxiVJkiSpXllKSaojs4ErgQ8AnwOeBk4D/gPsCSx+cdTa0kzj6IY5xhpHN9Da0rzYzy1JkiRJtcTle5LqQB9wKXmfqHuB9YBzgK8Do4f1lfo3M/f0PUmSJElaMEspSTVsJnAhcBhwP7ABcBHwFaBhAdctngnjxlpCSZIkSdJCWEpJqkGvAb8CDgceBsaRl+1NwFXLkiRJklQZ/OlMUg3pBX4JrEs+QW818obmdwDb4n/yJEmSJKlyOFNKUg34H3AqcBTwFPAx4Gzg00AUmEuSJEmSND+WUpKq2AvAicCxwH/JJdRlwMeLDCVJkiRJGgRLKUlV6DngeOAEoAfYGjgE2LTATJIkSZKkobCUklRFngaOAU4iL9n7IrmM+kCRoSRJkiRJi8BSSlIVmEHeL+pU4FVge+BgYMMiQ0mSJEmSFoOllKQK9ghwJHAWMAv4BtAGNBcZSpIkSZI0DCylJFWg/wCTgXPJp+d9EzgQeEeBmSRJkiRJw8lSSlIF+RdwGHAR+T9PuwM/At5eZChJkiRJ0giwlJJUAaYDk4DLgUZgH2A/YPUCM0mSJEmSRpKllKQC3QFMBK4CliMv0fshsEqRoSRJkiRJZWApJakAt5LLqOuAJuBnwJ7AisVFkiRJkiSVlaWUpDJJwM3kMupGYGXy/lHfB5YvMJckSZIkqQiWUpJGWAKuJ+8Z9WfgbcDRwHeBZQvMJUmSJEkqkqWUpBGSgGvJM6P+Tj5B70TgW+TNzCVJkiRJ9WxU0QEk1ZrZwBXAOODzwLPA6cAD5KV6FlKSJEmSJEspScNmFnAhsCHwZaAXOBfoAr4DLFlcNEmSJElSxbGUkrSYZgLnAO8BvkH+z8rFwL3ATsDo4qJJkiRJkiqWe0pJWkSvkcuow4FHyMv1pgBfwL5bkiRJkrQwllKShugV4AzgSGAG8CHgJOCzQBSYS5IkSZJUTSylJA3S/4BTgKOAp4GPk/eM2gLLKEmSJEnSUFlKSVqIF4BfAscCzwGfAQ4hl1KSJEmSJC0aSylJ8/Ff4HjgBHIxtQ1wMLBpkaEkSZIkSTXCUkrSXJ4GjgZOJi/Z25Y8M2pckaEkSZIkSTXGUkpSSTd5v6jTyCfrbU+eGbVBkaEkSZIkSTXKUkqqe48ARwBnAX3AjkAb8K4iQ0mSJEmSapyllFS3HgAmA+eRT8/bBTgQWKfIUJIkSZKkOmEpJdWd+4DDgIuAJYE9gFbg7UWGkiRJkiTVGUspqW78E5gEXAE0Aj8E9gfeVmQoSZIkSVKdspSSat40chl1FbAceb+ofYBVCswkSZIkSap3llJSzfoLuYz6HdAE/AzYC1ihuEiSJEmSJJVYSkk1JQF/BCYCNwErkzcz/x6wfHGxJEmSJEmai6WUVBMS0EGeGfUXYHXgGGA3YNkCc0mSJEmSNG+WUlJVS8A15JlR08gn6J0I7AosXWAuSZIkSZIWbFTRASQtitnA5cBGwBeA54AzgAeA72MhJUmSJEmqdJZSUlWZBVwAbAh8BXgNOBfoAr4NLFlcNEmSJEmShsDle1qoqZ3dtHd0MaOnlzFNjbS2NDNh3NiiY9WZ18ll1GHAf8il1CXAl4CGAnNJkiRJkrRoLKW0QFM7u2mbMp3emX0AdPf00jZlOoDFVFm8CpwDHA48CnwA+DXweZzoKEmSJEmqZv5UqwVq7+h6o5Dq1zuzj/aOroIS1YtXgOOAdwLfA8YAvyFvZj4Bv3QlSZIkSdXOmVJaoBk9vUMa1+J6CTgFOBp4GvgEcB7wKSAKzCVJkiRJ0vByuoUWaExT45DGtah6gInA2sAB5FP1/gT8EdgCCylJkiRJUq2xlNICtbY00zh6zo20G0c30NrSXFCiWvNf4MfAWsBPgM2AvwEdwMcKzCVJkiRJ0shy+Z4WqH8zc0/fG25PkZfonQy8DGwHHAyMKzKUJEmSJEllYymlhZowbqwl1LDpBtqB04HXgO3JZdQGRYaSJEmSJKnsLKWksngEOBw4G+gDdgTagHcVGUqSJEmSpMJYSkkj6gHgMOB88mbluwAHAusUGUqSJEmSpMJZSkkj4j7gUOBiYElgD+BHwBpFhpIkSZIkqWJYSknD6h/kMuoKoBHYF9gPeFuRoSRJkiRJqjiWUtKw+DswCbgaWI68X9QPgZWLDCVJkiRJUsWylJIWy1+AiUAHsALwc2DP0m1JkiRJkjQ/llLSkCXgJnIZ9UdgFfLJensAyxcXS5IkSZKkKmIpJQ1aIs+Imgj8FVgdOAbYDVgWgKmd3bR3dDGjp5cxTY20tjQzYdzYogJLkiRJklSxLKWkhZoNXEPeM2oa8HbgJOBbwNJvPGpqZzdtU6bTO7MPgO6eXtqmTAewmJIkSZIkaS6jig4gVa4+4DJgHDABeA44A3gA+B4DCymA9o6uNwqpfr0z+2jv6CpDVkmSJEmSqoullPQms4ALgA2B7YHXgPOALuDbwJLzvGpGT++QxiVJkiRJqmeWUtIbXgfOAt4N7AiMBi4F7il9vuDVrmOaGoc0LkmSJElSPbOUkngVOAVYjzwTqgn4NXAX8BWgYVDP0trSTOPoOR/bOLqB1pbm4YsqSZIkSVKNcKNz1bFXgNOBdmAG8GHgVGArIIb8bP2bmXv6niRJkiRJC2cppTr0EnAycDTwDLA5cD7wSRaljBpowrixllCSJEmSJA2CpZTqSA/wS+A48kl6WwI/Bj5aXCRJkiRJkuqUpZTqwLPkIuqXwIvA54BDgE0KzCRJkiRJUn2zlFINe5K8RO8U8v5R2wEHAxsVmEmSJEmSJIGllGpSN3AkeRPz14EdyGXU+kWGkiRJkiRJA1hKqYY8DBwOnAPMBnYE2oD1CswkSZIkSZLmxVJKNeB+YDL5BL1RwC7AgcDaBWaSJEmSJEkLYimlKnYvcChwCbAk8D2gFVijyFCSJEmSJGkQLKVUhe4CJgFTgGWA/YB9gbcVmEmSJEmSJA2FpZSqyO3kMuoaYHngIGAfYOUCM0mSJEmSpEVhKaUq8GdgInA9sCLwC2BPoKnATJIkSZIkaXFYSqlCJeBGchl1M7AKcASwB7BcgbkkSZIkSdJwsJRShUnAdeRlercCqwPHAruR94+SJEmSJEm1YFRRLxwRDRHRGRHXlj5fMSJ+HxH3lz6uMOCxbRHxQER0RUTLgPGNI2J66b4TIiJK40tFxKWl8dsiYu2yv0EN0WxgKvBBYGugGzgZeJC8b1R9F1JTO7v5yOE3ss6Bv+Ejh9/I1M7uoiNJkiRJkrRYCiulgL2B+wZ8fiBwQ0ppPeCG0udExPrADsAGwFbAyRHRULrmFPIUmvVKf7Yqje8KPJ9SWpc8zeaIkX0rWnR9wKXARsAXgR7gTOB+8lK9pYsKVjGmdnbTNmU63T29JKC7p5e2KdMtpiRJkiRJVa2QUioi1iBPhzlzwPAXgHNLt88FJgwYvySl9FpK6SHgAWCTiFgdWD6ldGtKKQHnzXVN/3NdAWzRP4tKlWIWcD6wIblznFn6/F/kTnHJ4qJVmPaOLnpn9s0x1juzj/aOroISSZIkSZK0+IqaKXUc8CPymq1+q6WUngAofVy1ND4WeGzA4x4vjY0t3Z57fI5rUkqzgBeAleYOERG7RcS0iJj2zDPPLOZb0uC8Tu4im4GdgNHAZcDdwDdwm7M3m9HTO6RxSZIkSZKqQdlLqYjYBng6pXTHYC+Zx1hawPiCrplzIKXTU0rjU0rjV1lllUHGqVyVve/Qq+Q9otYDvgOsSN5D6i7gy0DD/C6se2OaGoc0LkmSJElSNShiptRHgM9HxMPAJcCnIuIC4KnSkjxKH58uPf5x4O0Drl8DmFEaX2Me43NcExFLAG8FnhuJN1MpKnffoVfI23q9A/g++X+m64Dbyassi9zWrDq0tjTTOHrO0q5xdAOtLc0FJZIkSZIkafGVvRFIKbWllNZIKa1N3kzoxpTSN4CrgZ1LD9sZuKp0+2pgh9KJeuuQp9rcXlri91JEbFraL2qnua7pf64vlV7jTTOlaknl7Tv0EnA4sDawL/Bu8v71fybvR+8WX4M1YdxYJm/7XsY2NRLA2KZGJm/7XiaMG7vQayVJkiRJqlSVtIHP4cBlEbEr8Ch5TRcppXsi4jLgXvLu2N9PKfW3L3sAvwIaydNvriuNnwWcHxEPkGdI7VCuN1GUytl3qAc4gbxt2PPkAuoQ8gQ5LaoJ48ZaQkmSJEmSakqhpVRK6Y/AH0u3/wtsMZ/HHQocOo/xaeTj2+Yef5VSqVUvxjQ10j2PAqp8+w49S16mdyLwIvB5chn1wTK9viRJkiRJqiZu6FMjitt36EmglbxMbzLQQt68/CospCRJkiRJ0vxU0vI9LYb+pV3tHV3M6OllTFMjrS3NI7jk63HgSOAM4HXgq8BBwPoj9HqSJEmSJKmWWErVkPLsO/QQefuvc4BE3l++DVh3hF9XkiRJkiTVEkspDdL9wGHA+UAD8G3gR+Rle5IkSZIkSUNjKaWFuIe8x/ylwJLAD8h7SC3ejKypnd1lXGooSZIkSZIqjaWU5qOTXEZdCSwL7A/sC6y22M88tbObtinT6Z3ZB0B3Ty9tU6YDWExJkiRJklQnPH1Pc7kd+BzwAeD3wCHAI8ARDEchBXkz9v5Cql/vzD7aO7qG5fklSZIkSVLlc6aUSm4BJgHXAysCE8lL9ZqG/ZVm9PQOaVySJEmSJNUeZ0rVtQTcAGwOfBy4CzgSeJg8Q6ppRF51TFPjkMYlSZIkSVLtsZSqSwn4LfAR4NPkk/WOAx4ib2K+3Ii+emtLM42jG+YYaxzdQGtL84i+riRJkiRJqhwu36srs4Grycv07gDWAk4BvgksXbYU/ZuZe/qeJEmSJEn1y1KqLvQBV5BP05sOvBM4C9gRGF1IognjxlpCSZIkSZJUx1y+V9NmAecBGwA7lD6/APgX8C2KKqQkSZIkSZIspWrS68CZQDOwM7AUcBlwN/B1nCAnSZIkSZKKZilVc84H1gO+A6wIXAV0Al/G/7klSZIkSVKlcMpMzXkCWAM4DWgBotg4kiRJkiRJ82ApVXP2A1qxjJIkSZIkSZXMUqrmNBQdQJIkSZIkaaHcZEiSJEmSJEllZyklSZIkSZKksrOUkiRJkiRJUtlZSkmSJEmSJKnsLKUkSZIkSZJUdpZSkiRJkiRJKjtLKUmSJEmSJJWdpZQkSZIkSZLKzlJKkiRJkiRJZWcpJUmSJEmSpLKzlJIkSZIkSVLZWUpJkiRJkiSp7CylJEmSJEmSVHaWUpIkSZIkSSo7SylJkiRJkiSVnaWUJEmSJEmSys5SSpIkSZIkSWVnKSVJkiRJkqSys5SSJEmSJElS2VlKSZIkSZIkqewspSRJkiRJklR2llKSJEmSJEkqu0gpFZ2hIkTEM8AjRedQzVgZeLboEFKN8utLGjl+fUkjx68vaeT49VXZ1koprTKvOyylpBEQEdNSSuOLziHVIr++pJHj15c0cvz6kkaOX1/Vy+V7kiRJkiRJKjtLKUmSJEmSJJWdpZQ0Mk4vOoBUw/z6kkaOX1/SyPHrSxo5fn1VKfeUkiRJkiRJUtk5U0qSJEmSJEllZyklSZIkSZKksrOUkoZRRLw9Im6KiPsi4p6I2LvoTFItiYiGiOiMiGuLziLVmohoiogrIuJfpX/HPlx0JqkWRMQPS98X3h0RF0fE0kVnkqpZRJwdEU9HxN0DxlaMiN9HxP2ljysUmVGDZyklDa9ZwH4ppfcAmwLfj4j1C84k1ZK9gfuKDiHVqOOB36WU3g28H7/WpMUWEWOBvYDxKaUNgQZgh2JTSVXvV8BWc40dCNyQUloPuKH0uaqApZQ0jFJKT6SU7izdfon8Df3YYlNJtSEi1gC2Bs4sOotUayJieeDjwFkAKaXXU0o9hYaSascSQGNELAEsA8woOI9U1VJKfwKem2v4C8C5pdvnAhPKmUmLzlJKGiERsTYwDrit4ChSrTgO+BEwu+AcUi16B/AMcE5pieyZEbFs0aGkapdS6gaOAh4FngBeSCldX2wqqSatllJ6AvJEAWDVgvNokCylpBEQEW8BrgT2SSm9WHQeqdpFxDbA0ymlO4rOItWoJYAPAKeklMYBL+PSB2mxlfa1+QKwDjAGWDYivlFsKkmqHJZS0jCLiNHkQurClNKUovNINeIjwOcj4mHgEuBTEXFBsZGkmvI48HhKqX927xXkkkrS4vk08FBK6ZmU0kxgCrBZwZmkWvRURKwOUPr4dMF5NEiWUtIwiogg78dxX0rpmKLzSLUipdSWUlojpbQ2eYPYG1NK/qZZGiYppSeBxyKiuTS0BXBvgZGkWvEosGlELFP6PnELPERAGglXAzuXbu8MXFVgFg3BEkUHkGrMR4AdgekRcVdp7KCU0m+LiyRJ0qDsCVwYEUsCDwK7FJxHqnoppdsi4grgTvIpzZ3A6cWmkqpbRFwMbA6sHBGPAz8FDgcui4hdyWXwl4tLqKGIlFLRGSRJkiRJklRnXL4nSZIkSZKksrOUkiRJkiRJUtlZSkmSJEmSJKnsLKUkSZIkSZJUdpZSkiRJkiRJKjtLKUmSVFci+3NE/L8BY1+JiN8VlOfdEXFXRHRGxDvnuu/hiJheuv+uiDhhhLOMH+nXkCRJ6hcppaIzSJIklVVEbAhcDowDGoC7gK1SSv9ZhOdqSCn1LUaWA4HGlNJP53Hfw8D4lNKzi/r8Q8ixREpp1ki/jiRJUj9nSkmSpLqTUrobuAY4APgpcAFwcET8vTRj6QsAEbF2RNwSEXeW/mxWGt88Im6KiIuA6RGxbET8JiL+ERF3R8T2c79mRGwUEX+LiH9GxK8jYoWI+CywD/DtiLhpMNkjYolSzs1Ln0+OiENLtx+OiCMi4vbSn3VL46tExJWl6/4eER8pjf8sIk6PiOuB80rv69rSfctGxNnz+Dv5ZkRMiYjfRcT9EXHkgGxblf6e/hERNyzoeSRJkpYoOoAkSVJBfg7cCbwOXAvcmFL6VkQ0AbdHxB+Ap4HPpJRejYj1gIuB8aXrNwE2TCk9FBHbATNSSlsDRMRb5/F65wF7ppRujohfAD9NKe0TEacC/0spHTWfnDdFRP9MrHNTSsdGxDeBKyJiL2Ar4EMDHv9iSmmTiNgJOA7YBjgeODal9OeIWBPoAN5TevzGwEdTSr39RVfJwfP5OwHYiDzL7DWgKyJ+CbwKnAF8vPR3suKCniel9PJ83q8kSaoTllKSJKkupZRejohLgf8BXwE+FxH7l+5eGlgTmAGcGBEbAX3AuwY8xe0ppYdKt6cDR0XEEcC1KaVbBr5WqaRqSindXBo6l7x8cDA+OffyvZTSPRFxPnm214dTSq8PuPviAR+PLd3+NLB+RPQ/ZvmIWK50++qUUu88XndL4PPz+DsBuCGl9ELpvd0LrAWsAPyp/+8kpfTcQp7nvkG9e0mSVLMspSRJUj2bXfoTwHYppa6Bd0bEz4CngPeTtz14dcDdb8z0SSn9OyI2Bj4LTI6I61NKvxjh7O8FeoDV5hpP87g9ilxezVE+lUqq+c1Ymt/fyYfIM6T69ZG/p4y5XnuBzyNJkuSeUpIkSXk5255RamkiYlxp/K3AEyml2cCO5E3R3yQixgCvpJQuAI4CPjDw/tKsoucj4mOloR2Bm1lEEbEtsBLwceCE0rK4ftsP+Hhr6fb1wA8GXL/RIF5mfn8n83Mr8ImIWKf0+P7le0N9HkmSVCecKSVJkgQTyfsv/bNUnjxM3ovpZODKiPgycBPzn1X0XqA9ImYDM4E95vGYnYFTI2IZ4EFgl0FmG7in1D+BfYHDgS1SSo9FxInkPaN2Lj1mqYi4jfzLx6+WxvYCToqIf5K///sTsPtCXnd+fyfzlFJ6JiJ2A6ZExChK+3EN9XkkSVL9iJTmNctakiRJ1SYiHgbGz70HlSRJUiVy+Z4kSZIkSZLKzplSkiRJkiRJKjtnSkmSJEmSJKnsLKUkSZIkSZJUdpZSkiRJkiRJKjtLKUmSJEmSJJWdpZQkSZIkSZLK7v8D5zXtQmQqfy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x, y_predicted, color='yellow')\n",
    "plt.title('Graph with best fit line')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}