diff --git a/machine_learning/Polynomial Regression/EconomiesOfScale.csv b/machine_learning/Polynomial Regression/EconomiesOfScale.csv
new file mode 100644
index 0000000..d80860f
--- /dev/null
+++ b/machine_learning/Polynomial Regression/EconomiesOfScale.csv	
@@ -0,0 +1,1001 @@
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diff --git a/machine_learning/Polynomial Regression/PolynomialRegression_Predict_Cost_per_Unit_Sold_Solutions.ipynb b/machine_learning/Polynomial Regression/PolynomialRegression_Predict_Cost_per_Unit_Sold_Solutions.ipynb
new file mode 100644
index 0000000..efa1ef4
--- /dev/null
+++ b/machine_learning/Polynomial Regression/PolynomialRegression_Predict_Cost_per_Unit_Sold_Solutions.ipynb	
@@ -0,0 +1,891 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# POLYNOMIAL REGRESSION\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PROBLEM STATEMENT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's assume that you work as a consultant to a start-up company that was looking to develp a model to estimate the cost of good sold as they vary the production volume (number of units produced). The startup gathered data and has asked you to develop a model to predict its cost vs. number of units sold. You thought a polynomial regression model might be a good candidate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#1: IMPORT LIBRARIES "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: numpy in c:\\users\\ching ye\\anaconda3\\lib\\site-packages (1.20.3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install numpy\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#2: IMPORT DATASET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cost_df = pd.read_csv(\"EconomiesOfScale.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Number of Units</th>\n",
+       "      <th>Manufacturing Cost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>95.066056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.185994</td>\n",
+       "      <td>96.531750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.191499</td>\n",
+       "      <td>73.661311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.204771</td>\n",
+       "      <td>95.566843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.298773</td>\n",
+       "      <td>98.777013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>2.761981</td>\n",
+       "      <td>48.258282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>2.762052</td>\n",
+       "      <td>50.216171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>2.763321</td>\n",
+       "      <td>63.478275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>2.766303</td>\n",
+       "      <td>58.190435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>2.768717</td>\n",
+       "      <td>52.782414</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Number of Units  Manufacturing Cost\n",
+       "0          1.000000           95.066056\n",
+       "1          1.185994           96.531750\n",
+       "2          1.191499           73.661311\n",
+       "3          1.204771           95.566843\n",
+       "4          1.298773           98.777013\n",
+       "..              ...                 ...\n",
+       "95         2.761981           48.258282\n",
+       "96         2.762052           50.216171\n",
+       "97         2.763321           63.478275\n",
+       "98         2.766303           58.190435\n",
+       "99         2.768717           52.782414\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_df.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Number of Units</th>\n",
+       "      <th>Manufacturing Cost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>8.099710</td>\n",
+       "      <td>23.855067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>8.739752</td>\n",
+       "      <td>27.536542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>8.780888</td>\n",
+       "      <td>25.973787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>8.897700</td>\n",
+       "      <td>25.138311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>21.547777</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Number of Units  Manufacturing Cost\n",
+       "995         8.099710           23.855067\n",
+       "996         8.739752           27.536542\n",
+       "997         8.780888           25.973787\n",
+       "998         8.897700           25.138311\n",
+       "999        10.000000           21.547777"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Number of Units</th>\n",
+       "      <th>Manufacturing Cost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.472799</td>\n",
+       "      <td>40.052999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.336241</td>\n",
+       "      <td>10.595322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.594214</td>\n",
+       "      <td>32.912036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.435958</td>\n",
+       "      <td>38.345781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.324780</td>\n",
+       "      <td>44.531822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Number of Units  Manufacturing Cost\n",
+       "count      1000.000000         1000.000000\n",
+       "mean          4.472799           40.052999\n",
+       "std           1.336241           10.595322\n",
+       "min           1.000000           20.000000\n",
+       "25%           3.594214           32.912036\n",
+       "50%           4.435958           38.345781\n",
+       "75%           5.324780           44.531822\n",
+       "max          10.000000          100.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   Number of Units     1000 non-null   float64\n",
+      " 1   Manufacturing Cost  1000 non-null   float64\n",
+      "dtypes: float64(2)\n",
+      "memory usage: 15.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cost_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#3: VISUALIZE DATASET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x15de0752880>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(x='Number of Units', y='Manufacturing Cost', data = cost_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x15de107cdc0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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m3MRbaNauKMriqZsBi0gf0GeM+THwZ8EHgRKiAxhakRUuMY7rMVVw6Uw1X0VpNkOsZZx+erJA1LJmZcWVmWnl8M1jY1m2NZmFhq83nCkEI5B8LPHryKcnCrxyR3fDY9TL2pvJnBVFWRyNShB/CXymxu1bgf8K/OKyrGgFGJkqMFko0dceJx6xGz52PtKzeiWKbV0zSwm1/B0aNX7UOwGEr1dwPGwLHM8PwhERjDE4prkseq6NRUVRlodGJYiXGWO+XX2jMebfgJZ7AS+WouNxfCzHSKZAI0/k+UjPqksUUUuIRyyOjOZ46sQ4Pz45wcBQhuFM8/KyRiWC8PVSMRsDxG0hEbFAwLKEPX3pprLolTAeUhRlNo0y4EbX6GtGBTGeK5EtuvSmYzNKByFzZYi1stM7r7+8HDhjtpAFjPH/l3dc8hmPX3x1T93nVwbNRiWCyvsjlkVPW5Tetji5kst4roSIcOVt95WPGz6+8rXm2lhUFGX5qDsRQ0T+Ffi0MearVbe/GbjRGPPmFVhfQxYyEeORw6O0JyKzXMcA0okIvW1xbGvavP66Ox6YVdcNFQph8Co6btm/wbaED+w7l/sHRhmczHNqPE/B8fCMwTN+fXZjR5ydveny88PJG8OZAmPZEu2JCHv627nhqt187J4nZ03NMMZwajxHKh6t+dwNbTFGpop0JKPloDqeKyGAbcmstV68rWve9WdFUeZFzYkYjQLw+cC/AN9nWnZ2GXAF8NZAD9xS5huAj5/J8Yb/sZ+NHQk++PrzuPSc2RtUtiX0tMXKnryNxhPdfmCAF4YzjEwVsRBEwDUGS4T2RIRNHQmeOT2JLYKIYDAUHY9k1CbveLTFbNriNhvSCSZyJU6M5wCIWsLmriQl19AWsym63qwTwNCkP7ao1okBKJ80JnIlhjMFskXXrw9bEqxneq23v/tSDbiKsrzMbyRREGBfBnwb2Bl8fBu4eLHBV0S6RORuETkoIk+LyBUi0iMi/y4ih4LPjbfvF8D3Dg1TcDxeHMnyn+/+ER+/5ylOBkEvxPUMQ5MFTo7nKLleQ+nZ0bEsk3kHC8Gy/CDrtxJ7FB2PXMklZls4rkfBccmXPDwD+ZJL3BayRZfhySKTeT9IWvjPL3mmXGs2xtSUqMUiVt2pHZXytBPjORzXlBUSjmcouB5F10MAx/N04oaitIg5h3Iuy4uKfAH4jjHmsyISA1L4yopRY8ytInIz0G2MuanRcRZSgnj86Bgf++cneeL4BABRW/iFV23nulfvmBXQRITuVJTOqhJAyHV3PMBDh0eJBMEXoOS6eMav+cYiFhELporerOdubI+TKTgUXY+YbVF0PWyRcpa6uy+NMYbxXIk/eNtFs0oEtx8YqFsaAcrlD8c1WJaQL7lU/qZD2VrcFvo7Enznpqvn9XNUFGVe1MyAm+mEW9pViHQAVwG/CmCMKQJFEXkbsC942BeA/UDDALwQLtzSyZ//wiu47+AQtx94nuFMkS89cIR/e/I0N7xuN69/SV85mBpjGJ0qkik4bEjHSVQF6Buu2s2jR8ZwjcEWX2PseGBbkIhYdCSjnJ70NboifoOEABFbysc8MZ4j77gkInaQlQp97XFgWo1QT6LWaPOsUp5Wcj3qnWYNqOJBUVrEimfAIvIK4A7gx8DL8evLHwKOG2O6Kh43ZoyZVYYQkeuB6wF27Nhx6Ysvvjiv13dcjyOjvoIhV3K588Ej/MNDRym5/s/hZVs7+e2rz+O8/vSs53Yko/SkYlgVm3Sf/MazfHr/8ziehzF+oLXFYksw2+3pkxPEbOHc/nYGhjI4rkEsv9Sxd1MHQ5N5skWXmC1MFtxZSoa+dLyuuU6ooKi1ebb/4CA33vUYk3kHoGYAjlj+7Z/7lVdpDVhRlpf5bcIt2ypELgMeAF5rjPmBiPwFMAH8djMBuJKFlCAqA3DIyfEcn9k/wHefGwZ8pcJbLt7Mr79m16yOuYhl0ZuO0RafvngIA+GDh0fLl/ThJt7AUIa84/LSzZ11N9rCmnJlQG2L2bOUDJWPbYZPfuNZ/uc3D1H9Kw7/EuJRi509Kb7+kdc1++NTFGVhLKwEISJPMDuBGgceBv7QGDMyz4UcA44ZY34QfH83cDNwWkQ2G2NOishmYFnMCKygmSJbdMq3be5McsvbLuSRF8f41Lee48WRLP/38ZN86+AQv/qanbztFVvK0jTH8zg9kefxI2f4h4ePcuxMbobOtrou256I4GT9zbP2RIReJ8ZYtkQqHilL2SpbksOvr7vjgfJmHNRvEa6nIw5HHIW/9fAXaItfAnE8Q397gpvffMFy/JgVRWmCOTNgEfkTwGV6EvK7gs8TwJXGmP9Y84mNj/kd4H3GmGdE5L8BbcFdIxWbcD3GmP/S6DgLyYBD8iWXkakihaouMMf1uOfxE/zt9w8zVfDv29mb4oNXn8clga/CgwOj/MV9h4jaQlssQtH1KLmGay/Zyt2PHp+lC/6ZizZyaqJYLhVcsbuH+wdGazZfNMqmw025cMOsWiJXqQUuOh5tcZuJnIPjGjxjKHnTv+tU1OavfumSWa+rjmiKsiwsrAQhIt8zxry21m0i8oQx5mXzXolfB/4sEAMG8OfOWcBXgB3AEeCdxpjRRsdZTAAOmSo4jE4VKbkzlQpnskU+/73D/OuPTpazx6v2bOA3X3cuf/L1ZxiZKpRVEyJC0XWJ2xaZfIlj4wXAVxj0pGNEbXtGmaGerhgo33dqPF/elAvryaHK4c7rLwemm0Qc12/MKAR17LgtuMGGX3cqyvBUkYrYS8Ty1R2fuPblc65Jg7CiLAkLVkGkReQnwpKBiLwaCHeonPpPq48x5of4TR3VvGEhx1sMbfEIqZjNZMHhzFQJx/MDcVcqxu/81Pm89eLN/OV9z/HUiQkOHBrmgRdGiVrCps54+RjGGAollxdHskRtIWYHjRcG4hEb25r25Z2rtTi8L1RIGAyDE3lfH1zVInx0LIstcCKQm4UUXOM7ogFTRZeobVEM7CotgW3dyabXpAFYUZaPZgLw+4DPi0ga/9/0BPA+EWkD/ng5F7dSiAgdiSjt8QjjuRJnsiW84Mrg/I3tfPJdr+C+4BJ9OFOkCBweztHfHiMd9016hqeKRCzB9Qy2CJYIHn5Tx64NbWXviEbeEgbK94XG7MOZAnnHm1UvBt9I57GjY1j4XXaVhBlvvuRiWULUFqjIpo0xTa1pKdDyhqLUppmpyA8BLxORTvySxZmKu7+yXAtrBSJCV8pvQx7LFpnM+91nIsIbLtjIa87dwJcfPMJdDx3F9QwnJwokoyU6ElEc17CpI85YtuRLzXz3HYqumeEuVukZHLYJFxyPVMxma1eSXMktZ6IdySgRW2aUHSq54ardvPeLD2NXXdyE30ogQLZEEGBT53Q9ud6aQpbKEW05J0krytlOM1OR4yLyi8AHgBtF5OMi8vHlX1rrsC1hQzrOtu4k6Qq5WTJm894rd/G3v/YqLtjUDkCu5HF6shA8TuhOxTAYXM/geQYLZpQOwukXQ5N5Toznyi3BbXGboUyBiVyp6ckY+/b2c35/GquqS8+EHwZSUYvb330p/R0J8iWXgaEMT5+c4NhYjit298xY03wncjTDck2SVpS1QDMjie4B3oZf752q+FjzRG2L/o4EW7qSJGPTXXBbu5J8+pcu4bafexk7evwscSLvcHLCb6roS8f8ZgsDW7tTfPgNe3jNeRuAac/gbNHFC4Zvbu1OsiGdoDMZpbctVnfkUS1uetNe+jsSJKLWrF+mZUExqA1fe8lWxrIl8o7f9JGKWXx6//Nc9of/zu0HBrj2kq3zet1mqTWmSQ3fFcWnmRrwNmPMm5Z9JauYRNRmc2eSbNFXTBQdf6PuVTt7+OyvdPHPPzzBF75/mKmiy5lciUxBOKcnxW/85G5eHWSZx8/kyr4S+/b205GMsqMnNcNjIhm1Gc+V+PpHZpcboHYtFfws13E9Qh1H5SZgT1uU2w8McCZbRPB10K5nmMy7iEC24DA4mefuR4/PGXQXUstdzvKGopztNJMBf19E5i01W4ukYhG2dafoa48TsfwfXcS2uPbSbXzxva/mZy7aFDiMGZ4fnuJrT53i9EQemPaVOH4mR77kznsSxSe/8Sw3fOkRHjw8wmimwOGRDB+9+3H+892PUwramsNYXnQNJdejO+W3NR86PcGzgxk8Y7AtoegZHM8gMMN5rVFZYKHDO5ezvKEoZzvN6IB/DJwHvAAUCIy0jDEtH0u0FDrghWKMYSLnMJYtlhUTAM+cmuSPv3aw3O4sAm/Y289/+qnzefzoOHc9dJSTEzna4zZnsiU6U7E5tbf7Dw5yw5ceKQdQE7itge/nu2djOxO5EkdGs2WpWdS2MAZ601GmCi5F18N4zHBGEyAVs2c4r9VzRatlTH90dIrJoFmlLWbzvit3ceMbz5/13EaeFYqyTliwDrjlky9WIyJCZypKOhHhTLbIRKCYGM+WKDouPako43kH1zN84+lBHhgYIWJbpGM2HYkI+ZKHa/wBmuO5UsPAdPuBAVzP+LaXgfF7yXPxKyGGgaFMIH8Dx0y7rnkYRqdKxCMWnutR9EAqkm6Dbwp08NQEtiXs7KlfFqiWqp0az3Em58vA4xEhV3L5i/ueAyhP2Kge06QoykwajaXvMMZMAJMruJ6zDtsSetNxOpK+dO2uh44StS06Er5z2mi2yGi2RKbgAi4TwWig0FAtU3C4eGsX77tyF+BnmtU11qNjWeIRy5e3VdhehkwV/ahqi+9w5hkoBp19JWMoup6fFVt+gK686PGMX4pwXMPIVLE87LOa6lruyFQRgvdhiYUlvk/GZ779PH3tCZWdKUoTNBpJ9C/GmLeKyAtQvmINMcaYlhfxWlmCqMdrb/0m6XhkRpArOC4vjuZqPt4CtnYnyRQcLPE78KpLErd9/SDPDU5S8phlrlNNxBJ6UlHGciXAz3DBD8oRS/xmEWNwXONP3ABitkVbzGai4GAMXLKju6b1ZeX8uzDoRywhGszX84xHwTGc29dW0yhes2BlHTPvkURvFX+L/nXGmN3GmF0VHy0PvquVHT1tOJ4haltlhUM4jDNsD67Ewzdvnyo6TOYdIpaFgfLG2K1fe5qhTIHQqiLU+IbYMp1Ng/91GByF0Fjev8/xfEOemG0h4nf57d3UwYZ0nLFciZLjj1J66PAoN3zpET75jenJU/v29s+QsoW4npkR5AVUdqYoTdJQBWH89Pj/rNBa1gThrn/ecYlYfinA8QzJqI2FPy2jmiNjOVzXD2T+DDgPx/NIRCxeGMn6tV+r5gkUNygpRG0hZvm16bzjEbWErqSfiVdevtgidCQjpOORsgpjOFPAeIbQTiJiCZ4xfHr/8zNUDvcPjLKtO8lLN3fSl44B/rHD9XrG95mYj7pDUdYzzcjQHhCRVy37StYA4W7/VKHE0GSB05MFNncm+aO3X8QFmzvobouVL9dhOigWHH9DLgxmGHBdw2TB39ibzDtERGpfwxA+z+Ah9LVFaYvZFFyP4anijJKFPwfO35h735W7yvKwouvhmOnH1RvYWdlUsakzSV86hiX+c5JRmw9dfR5/8LaLVHamKE3SjAri9cANIvIifgfcqpGhrSYqPQ82dyZnzGjbt7cfS4Tfu+dJUvEIJcfl9IRvWdmdijKRdyi6Bs/A4ZEsPW3+FAzX+PPaXhyZKg/RbITjGU5MFOhMRrBEZvj/gi+Ji9kWyajFjW88v6xWODIyszzgb+L5m3OHTk+UTyxDkwWGJwtlT4lNnUk6ktFZ9d1bQGVnitIEzeiAz6l1uzFmfsPYloHVtAlXrZOdyJU4PZmfsakFfmA6OjqF63qM5R2Kjl9quPScbn54dJzJgi/tilhCb1uMfMlhPO/Wfd1q/IGgNqmYzVCmWL49avsStt50lJ296RkB803/89s8czpTd25cdypGRzKK43ocP+M3lmztShCxLfUNVpTmmN8mXAWmzodSQeXleTj7zfMMrueVpVgAd15/OX/49pcRjUbY3JngvP40vek4zw1N8Z9+6nx+4bJt2OKPDDo9WaDoGlLRZn5NPp7n11xDmViIP/LeLz9UlwOGp4oN/hCEybxDKhahIxlja1eSiC2cmigsqWeEoqxHmilB/CvTJcQEsAt4BrhwGdd1VlDpjTCRK+F6HhvSCYYzBSx8yUPctmYZnFcboEcsi0yhxBfvP0xHMoaIQYKacK7kNV5EFaELWjUlz5CI+OWHyjFEt37taYYzxdlPgHJbdeVVUkcySnsiwqlguOjH7nmS7QeaKzOoL7CizGTO1MoY8zJjzMXB5z3Aq4HvLv/SVjfV3gipmM3gZJHhjD9KyGAwBvra/ckZlVKsaocw2xIwhsOjWUazfm04YvkSM7uO+mE+RG0hEbHY1Jlgz8aOGesfGK5vbBe1Lb+jriqgD2cKTBbceflCLNRLQlHWMs1f2wYYYx4F1r0qotrntq89QV86xlTBxQpM0MPpEzDbAL1aqjWYKRILOuhitoUg/iQLCza0xeoqIJrB83xZXLgpuP/gIDfe9RhHR7Nlu8paBH7uvmtahaphLFuipy06L4/f2w8MUHRcjo1meerEBANDU5waz3Hr155exDtTlLObZgzZf6fi46Mi8mVgaAXWtqqp5XO7IR2nMxktG6D7xjmzpVj1HMI2dsTLwdzDF/iWXL/kXks/3Cyu8WvDbTGbHx07w8fvfYps0a2zLTCNP40jzvn96Rlewe2JCL1t8RmPnavZ4tnTEwxOFii4pryJUHQNzw5mNAtW1i3N1IDbK7528GvC/3t5lnP20Mjndt/e/oZSrFr3x2yr7N/gz4NLcXoyj+d4TOQdulNRorbFiWDi8nyJRSwODWY4+M1DdCejxCNWuWOuFnFb2NSZoOQabn7zBTNqtbWc0Ro1W+w/OMiZnFMuZUj4v6CJJMyctT6srDeaCcA/Nsb8Y+UNIvJO4B/rPH5dcMNVu4NM0pnh3RBmufv29jcMINX3hzXS8HjhLLi2mE3R9UhEbVzPMJQpBlnxPAm8IBzHMJot0Z+OkS26NeUsgm/aU2sQ6P6Dg4xNFTg8MkXUstjYES/L0Wo1W4Tvq3IjzwASfGuJrzXWuXHKeqSZC9vfbfK2dUU4WmipxvjUO95kwQ/Ilkjg8WuI2f4mXSJSvzuumqLrUaiwUJsqumwI2olDQq8KS/xyRa3g+9G7H+fwaBYTOK4dHcsRs6267z2slSej9kw3J/wSR9S2KAbGQGFN2XENg5N5bvjSI1x3xwNaolDWLI3sKN8M/AywVUQ+WXFXB34pYt0zV5a7FMfbfmBmqSMescmXXDzjS8TAV0t4prE4u/q+qaKL43rEbQg81cslAs/AVMHhhi89wgf2nVs2Wb/1a09zJlvCFiEWGL67xpep1fs5hD7CG9Jxjo5lZ0jkjIH2ZKTcygzTGmpffWE0G1bWNI1KECeAh4FrgEcqbp8EPrKci1pvNNLHVpc6orZQ1WOBa6AtapEPPCVC5mpfLgQPtoBKtXE4UcM1hk/ed4ivPXmKyYLD8TM5330tGMckAsYzvDBSf/MtrJV3JKNsJ8Wp8VxZebGnP81Nb9rL7QcGyieZuTTUirKWaKYVuQOYMsa4wfc2EDfGtNxfcDW1Ii+USg+JeqOJKkf6jGSK5Eq1a7chzfhGLIaYbWFbEhjD+6qGK3b31ixZ3Pq1p3l2MIMx04G9PRHhE9e+fMb7C38GR0azQaliWsZnjOHUeI49Gzt0k045W6lZLWwmAD8AvNEYkwm+TwP/zxjzmiVf4jw52wJwrUy3MvsLaWRg/pKPfQ3HnZnptgJbKK8hbgvbelIzThyVBu6Dk4UZtpgi8JKN7dz0pr2zTjKPHhlDoGz4M5ErceJMlpLn+yZXbvppG7RyFrFgL4hEGHwBgq/V3HWe1OsEOzQ4OS8Dc8+YlgdfYGapIxh1X9mMEW6+TeYdopZFpKKjL2IJLwxPzeiE27e3nzuvv3yGhnoi50+Rdjx/09EAJ8cLs15LUc5WmpGhTYnIJUEHHCJyKVB7vo5Sl2r/h7C2WXQ8RqYKTOR8X96YbdGRjLCzNz3rGPsPDjLXFQv4p9pYxMIWaItHGJ0qYszMOu9S4hrDiTN5NnfGyyeOQ4OTZAsO2ZI369RvWTMDdpgxh1cH6ZiNiHBsLEfE8scmRSxBRPAwDE0W2LWhrfxa6jGhnK00kwF/GPhHEfmOiHwH+Afgg8u6qjVIrc65ZNTGGI/ByWJ5cGbR9b+/YnfPrGPcfmCADen4rNurMUDR8WhPRNnYkaArGeEV27tINXO6nSeCP5ZIBE5PFNjWnWL/wUEm8w4lz5QN28PThvi2F4EvsZ/pV18dlDzDVNGlPRHhvP40EUsoOB75kkspkNOFjR/qMaGczTRjxvMQsBd4P/BbwAXGmEcaP0upppb/Q67kImLRl44Rsy28IDD1pWPcPzA66xhHx7JsSMfpiNuz7qsmYgtj2RIjUwXO39TJvpf0UVyGFNjgy9YKjkfe8bhidw+3HxigO+V7YFT/gfnt2b7O+LmhDIOTBW686zFKrjvLW6LoeAxnCpRcrxzEQ/ndRK5UrqFXaoib8aVQlNVCsznRS4CX4ttRvlJEMMZ8cfmWtfao1zkXi1hsSMfpa0+UH2uMmVEDDi+xT43nOT42d/VH8P1/XeP7/16+K8affePQcrytGcRs4YsPvMh4toTBYIsgtoXnemWNcehtMRhYYPanY4xmS2TyDmNTJVzjDw1NxSyyRYeJvC85t8UvofjZs29Wv29vPx+750m6ktEZ69AhoMrZwpwBWER+H9iHH4C/CrwZ345SA/A8qOcPUUsFUemrUKkm8IypKS+r9JGIWuB4lAOZJYZ7Hj/ZcG3NyNYqZ8vVI2IJZ7IlPGP8TTcRPOO7uY1mS3iemVGOsC3hTM5BMHj4WXQ8YlEouUwVXWK2YFvB5GVD2VIzHY8wnisB/pXF4ZFMUzV0RVltNJMBXwu8HHjMGPNrIrIR+OzyLmttEna6hRntx+55knTMZiIIJrU8JcJL7JGMrybwjJk16822BPEA4zdJpCLC7r402aLD0GQBzzSuPXQnI4zm6jc3Vgbo6qaNSrIV5vEl13dwM2Y627WEstE8BLVjC4Jzh3+7QFio8duv/dZkAm/k9kSUbNEpn6Cu2N3Dg4dHsYQZNfTLd8W47o4HdGNOWdU0E4BzxhhPRJygKWMQ0BG3C6Sy6aArGS03VcRsi/FcaZZzWtjKW3Q9bBEilgW4lLxpLW7BccstvgXHoyRw8OQ4BPKwuWgUfGFm1ttsGdkw3Spdfm6NpVS2U4ebh+F7yTseUQtKXmAQFPgSV56g7h8YpS8dYzI/nQFHbeGrT55mW3dSzX2UVU0zAfhhEekC/ga/JTkDPLici1rL1JKjAXSlYnztw1fNenzYyhuzLRzXVxtYlkVcDGIJpuQHrMos1TP+hy2GqG3NMOFZzWxoizGWK2Hc6VJLqSI7doNNyt97y94ZJ6jqGvrAUAbH82ZJ/rSdWVltNDLjea0x5nvAR4wxBeCvReTrQIcx5kcrtsI1RqiPLXl+jbavPU46Hqm7aXTF7h4+vf95ikEQtQxYCBvaY0Rtu2xXeWo8j+MaLEvIO65vP+nXAFby7S0cgYmC43fKUbvW3JOK0pWKzQiitXyZC45HvMrBXjfmlNVIIxla6IB2f3iDMeawBt+FU6mPtS1/8vGJM3lGpgo1zcz3Hxzk7keP053yZ86F89mSMZudvekZdpVF10OCnbJy22+gHOirsp1cjQj+ur0wna8iYgmZfIlHj4xx5W33lW0qa00XMcaQK3k8cXycp06MMziRb2gYryitolEJoiQi/4vZdpQAGGNuXL5lrU1CfezIVBHjhQHSl4r98c/OLquH5YrOZIK+YC5JtU9EaFdZWaKQimkTFnAmVyKY+7msJj2LwfFgT38bRdfjyGh2RhkC/M28ooGExcy67jUXcss1F5bVJcab6ZPhGTg9WSDvuPzeW1664u9LURrRKAN+K/BvQB6/9lv9ocyTsF65pTNJxJayVKw9btesTdbrnqu8lA4zwPZEBA+D43lI4DxWcjyKnj9XzhhIRpsfLBexZNZl/HJzfCzLRG5arlZJGFQ3dSaYzDucGs9z4kyOG+96DIA7r7+c79x0dd0NxVzR1fqvsuqomwEbY4aBu0TkaWPM4yu4pjVLpTduR9A8EGa0jR7faPZapb7YcScouoZY0iJhC6eD8UWhA1mlTKwR6ZjFVNHDk5XNl4uuoSMhdc2GIkFJ+8R4DgtfIzxVdMoKB8AfNhoQzp7zp3es1txfWc80o4L4sMjsf4nGmF9fhvWsaeaaI7fQx9eapHHdHQ8Qj+XLm3Nho0YjQsOybClo/V3hmFVyPU5N1h46Gm7MhYbtluU3eSQivuzs1q89PesEUzl7TmrUlWuhxj7KStJMAP6Xiq8TwM/iT8tQ5slc05IX+/hKjo5lsQUc1ytPvmhE2L4cNnmEpjnhfSsRi10zHTCr8R3RfEmd5xm8INEtCUzkikzkXXraojWfB7C1Y24To2qNtuqHleVmzgBsjJkxgl5E7gS+sWwrWuPMNUeuVgZWy5h9LtrjEQ4NZmg2dBooB19LZjZNSNDBthJq4oYjlGromT0DQ5kSgu8VXAsBfv5VO+Z87XqWoaofVpaLheyy7AHm/mtW5s1SWiuGvsFNVB5mUd2x5q1Q8F0MjQL3ps54TXe5aprZ9FSUpWTOACwikyIyEX4G/i9w0/Ivbf2xlNaKmaLL1q7am3vN0Oy4+9XAXGs9PVHg0SNjc57I6lmGqn5YWS6aKUG0r8RClGnfh0oWmoFNKyhsporu3E+o4mzQDFQ7tNWrVXvGvyKorudWl3uu2N3D3Y8en7XpecXunmUx9tENP6WpEoSIbBWR14jIVeHHci9sPbKUGVilPnipsFdZWlxpbZmOWQ1PGiXXlHXD+w8O1iz33P3oca69ZCv97QlOjecYmiwwNlXg0/uf5/BIZkknbugkDwWam4p8G/ALwI+Zdgo0xphrlnltc3K2TUWei/0HB/no3Y+TKTi4QbtyOh7hTytGuM/3eB/7Pz/iWJ3NqbVEM0qNSOCVbFtCPGLRmYywIe2XaSZyJU5P5jEGdvWmGJkq0pGMcmo8T77k+uZGwfNCv+GFbI6GXHfHA/Oahq2c9dRMX5pJj94OvCQw5Fm61YjYwMPAcWPMW0WkB3/e3E7gMPDzxpixpXzNswGBoI3YgJFF12KHpkoN72/k7zufx7SaZkomjhfK7fyGjZLjEY/Y5eYO32vDcHg0i+Ma2uIR8o7f2hze53iG4ckiJXdyUetdynKTcvbSTAAeAKLAUqdRHwKeBjqC728GvmmMuVVEbg6+X1ebfbcfGKAjGWVTZ7J8WyiDCu+fT73w9gMDc/oByxypYyJi4RqDt0Y6yQzTXXElz3B4JOtP7zAGJ3iA6/kDUocmC9NTqANdtCWCJ6bsTrdQmulyVNY+zdSAs8APReR2Eflk+LGYFxWRbcBbmDlZ423AF4Kvv4Cfea8r6smgDp2eaFgv3H9wkOvueGCGS1h4vHjEaphFN4qrFtCesIM5bmsXxzM4gXlRJGgHdM1M3XEYh73ghBZbZEG8lotbo65IZW3STAZ8b/CxlPw58F+ASoXFRmPMSQBjzEkRqZneicj1wPUAO3asLTlyvayo6Bo66zQIAHW7t7Z3p3Bcf4R7dalfgFhk2qw9FbVqtvIOZRqXMNYarjFYwaQR1zOkYnb552dZQsT2xyLt2rC4mXOL6XJU1g5zbsIt+QuKvBX4GWPMb4nIPuCjQQ34jDGmq+JxY8aY7kbHWoubcGEwrZRBZYsOmzoSSIWhgTGmPMJocNL3exjOFHzPB2OI2jbJmEWm4PoThgt+IDcEdVBLgtbj6YnFiYg/3DP8PmbLujKxqZS1CRCxhc5EhMmC3+bc2xYv/05uueZCDZbKfFjYJpyI7AH+mOmx9AAYYxZ6rfRa4BoR+ZngeB0i8iXgtIhsDrLfzfiz59YVC5mcHHo+nBjPYwWFypIHJc+lrz1G1LYYy5ZoT0bZ09/OocFJckUXxzV4xszoess7XtVfyUq5QKwOKv0vwK/37tnYwRW7e7h/YJRDpwO3uYhVvvqoDsJLoe1VffD6oRkZ2neB3wf+J/AfgV8Lnvf7i37xmRnwJ4CRik24HmPMf2n0/LWWAdejXmYcGpE/dnQM4/mXyAXHl0wJkIrZ5enIobzpujse4AcvjNQckFlNtSfEWidiCYIpz6HrS0eZLPg/665EhImCg2cMNvgDTw2c35/mpjftLU+7rvd7ajaALsUxlFVJzQy4mU24pDHmm/hB90VjzH8Drl7KlQXcCvyUiBwCfir4XiHIjK+5kP72BOO5Ev3tifI/yHAzJ/wvDJhRW8oWlJXypit29zQdVNdT8AV/My4MvlELRqZKFEoermcYyZb8zUgDRc9XUhjP8MzpSd77xYd5858f4LavH5zRSu64hsHJPDd86ZEZm6ONWMp2dGX108wmXF5ELOCQiHwQOA4syanYGLMf2B98PQK8YSmOuxap56K2b28/5/eneWF4CtfzN5AsEUSEaLCjXylvasaURvE34WqdgGaMOwIwYGN4+tS0LjgRsehIRBjLlcr64WatLVUfvL5oJgP+MJACbgQuBX4ZeM8yrkmZJze9aS/9HQl29KTY3u1riF3PsCEdI1t0GM+VOJMtcuVt9/HokeZ6W6yKC6bQ9Hy9MN/SS/U+Zd7xGMoUMQZEhHjEbjqTVUOg9cWcAdgY85AxJmOMOWaM+TVjzDuMMQ+sxOKU5qgsUXgGzutrY09/Gs9A1PK76YquR1cy2tRkiJgtnN+f5ordvZzb18bGjsSa0AIL0J2KzDi5VN4X3rwUpReDX9IwBvrafTP4ZjLZSn3wRK7IocFJDo9kOZMtqk/EGqTuJpyINNT+qhfE2UG158BErsSLo7WDQMQStnUnSccjjOdK/MHbLuKjdz/OaKa4aluRF6rTsGV25iqEtfOFReBwLZUZ9M7eFO2J6fl/UUvobovPcGC7f2B0huIB4NavPc2hoQxRy2JjR5yIbelm3NnNvGVoVwBHgTuBH9Q7gDKT1SYhqq4pdiSj9KWjsxosLIFt3UmMgeeGMhjjbwglo/aqVqMtdFm1YmzEkvLxFqIAMRWfbYGIbWFbwkSuyOnJAoWSR8QWNuRL9LbFeWE4w4OHR+lvj9HbFp+uE19zId1tcXZ6hlQswkSuxKnxPAXH48a7HuOT73plWXVRbadZHcw1WK9uGmXANr4a4TrgYuBfgTuNMU+t3PIas9oy4NUoIarlunVocBIM7NnYzkSuxInxHABiDKESeGtXgohtcXjEz5bn8pRYK1gCUduqOf6okmrNcC2SUb/CV3A8YrZvl+kZgyBs6UowNOk3zsRsi919fmddKBkMT5yTeac8BRrxzYC2dqW49pKt3P3o8fLf2nCmwFCmWA7mq+FvT5nB/GRoxhjXGPN1Y8x7gMuB54D9IvLby7TAs57VKCGq5zmwMRhS2ZGMsqUzScy2KHrTZYiOZKy8/pXulmwlnpk9e06YWSOGmQNLKx8XYktlSULY1JnwNcQiSGD0U3R905/KidVhnTjcjKucAg1CImITtYXPfveFGX9rY9kSrmc4NV4oK2Ja/benzE3DTTgRiYvIO4AvAR8APgn800os7GxkpWeK1TPhqaRygy40GbfEH9MzmffLEL4DW4KoJVgCx8/kGBjKMJkvsbE93tR4+ois3RqVqfiodR9A3BYu2tpJW8wmbltEIxYlz5SlgUOTBT8LNn72HGa+noGYPf3PMFQ8hCfOguOB+F2L4YZeMupPOQn/1iZypfJJI9z8O3Emj+N6Kl9b5dQNwCLyBeD7wCXAfzfGvMoY8wfGmOMrtrqzjJWUEM1nokLYsJGKR+lrj7OlM4HjGY6N5ZjIFctSNcS3aLQtKf8jLroeL9mYJlV1YonZFvGIRdwW2mI2joF4xGJnb4pzelZWMrUaAn/BNTxxfJypokvB9SiUfE8O1zMUXUO26NIWs/Hwb4taQnsigmegIxmZ5YgWnjhTMRvHM0Qsv2zRnoiSK/nHCv/WhjOF8s9gWgfun2RVvra6abQJ98vAFHA+cGOFEYzgT8ToqPfE9coNV+3m4/c+NWum2HJYDM41Qr16g+ZMtlh+/ESuhC1+N9eR0Rx7N7XTl44TsYSRqSLG87M0D8PoVIk//tmLAcoB3/NMuQa6pSuJbQlHRrLl7DlmWyQiFvlFeuY2S7jptZqUcgYoedMZjgHGsiXSCZts0cO2hGzRJRW1mCq4lNw8e/rby8E3/P1FbSFi+VM40vFIOUi/78pd5fl1YSnDDfyKjTEYY3CM2luuduoGYGPMQkbWr2tW0mKwUcdU5WZgmB0fHsmyrStR3nSzEGIR/3J1quiWHdfiEbvsqhazLZJRq7z+a4+d4TPffp6iaxAMfekYtiU1s2fHXTnhmm35uuWB4ak5N89WmvDkELUtHM/gevCBfefO2ECrPFFXe0ps7kwynCkwOuW3QlcG6Yu3dQV/azlsW+hNRJgquhRdP8Dv7mnTDbhVztJNbFSA+i3DS02jiQq1suOoLZyeKGBbUt7U8cx0l1vR8X2DO5JROpLTutX+dt8Ab//BQb74wItlnSvA8FSR3nS8ZvZsWYLbZEq6GNOf7lSEbNHjmVOTq1KrbIDtPb4WOLQQvX9gtOHVS/Xvr689QVs8MmteXPi3Vhmw+zsS5YB+85svaMVbVuaBZrlnKY0mKtTaDNzYHqfkeXU3dWK2NJzQcOvXnuZMtoTx/Ppv1LLKl7uTBYcN6ThbOpNEbPFNzefxXhYafFNR3++44HirMviGhJuaI1OFsoVo5QbawFCGI6NZHj0yxv6Dgw0no9TadK210RoGdO2eW92suCH7UrLadMArTVgnrC531Ju4G7WEE+N5pooOiYhNX3uc9kS0nOmG3sPh8SqF/cfP5AJplR9gwz8b24JX7eytqTUuOh7G1G+WCGVaAnQmI0wVHEqrOZIGzLcvpdLofXvQaVh0PRzXlMtBBoMlQjoRIVt0KThu+XdkDJw4k6Xk+Vcs9TrjVqMOXSlTc69YA/AapNE/RKCpf6TVx3jyxETd1/udN+6ZVdN8YXgKC3Aa/Hklozae8Xf4d21oYzxXIldyyRYcsiVv1TbgLXRdwnQDR8SenngtIhgDXckIo9kSnmdqZvQRC2zLCjY/E9iWlMsS+w8OcuNdj9U9ueqo+5az4LH0ylnGXJuBzWwUVtch4xXz48JJygbfuOcrDx1hJFsiFwTN3rYodlAolmAMUi0cz0MQ+trjM+R6g5N5To37Y5YKK7iZ1ywLPSkY/OArUDY3EiBm++qFoUyx4bEF8acyYxiaLLBrQ9uMTdds0SVSISHc0gXpeES1wKsYzYCVmlx5232Be5ofSGuZ+FRmgrbld9F5xg8u6ZhFITAtr8yCKzfcBNjRk5xxOQ1+hl50XEamimvCha0ec2XS1fcLkIjamEBL3NsWC8oV06OkwuGh4ZXFps7EvDPg1eZnskZY8EQMZR1S3VTSkYwSCy6bLZke7BnieWCMlCNGpuhh4RvSVJKM2vSnY8Rt3/jm1ITfIRaWQMINpa5kdM23QM/17qrvN+CXJzwDxjCUKdIWt4PZfoaS6+Ga4H4MeWf+OvT5NPgoi0cDsFKTWiqLjmSUDekYsUC6VhmAwW+vLVVIGjyYIXVri9lsSMcZnipSCDLbouOVzYDADwChFWPEsjinJ0kqqn+mIa4xuAaiEZv+9hgb0glitoUgvvuaABiKjsHzoC1m86NjZ+ZsWQ9ZjX4maxn9y1ZqEmaiUUs4NJjh2FiOvnScK8/rpVDyKLp+xlW5w19NyTUUXY+8489V60hGODGem1GCABjPOfzePU+Ws6/Do1ls8TPkk+MF+jsSdCUjZUOcWobq6wXHM3SnorieKXtIbEjH8fC730quH6BtS9jRk2QsW+Qv7nuOwyOZuhltpafIo0fGZjXR6Eik5UMDsNKQbMljW3eSPf1pxrJF7v3RKaK2ELOFqN38n086ZtOVjM3YfGK6YsHRsRw33vUYJdcNDGwEzzOUPI8XR7J+XTlucfnuXrZ2JUlF7VXhAbHU1HpPAlROhNrUkUAEjp/JM5kvlR3tLEswMtPRbjLvYAlM5JyaGW1lycEWKLkeL47mOHR6smzWpCORlg9VQaxzGm24VCshwn/Mgl9eEONPkHDcmbKpsr43kFy1xWz6OhJ0pWLlx5jy/6bJFBwm8g4Apao7fb8DKa/vytvuwxY4MppblVK1WjQjXzNAfzrG8FSx7JRmW0LB8evxlviytY3tCY6fyXFqPE86HiFi+5K0sKU83DxtZHkJ07/jiVyJ4QoVRt7xOD6WY0O7S9S21VNimdAMeB0z14ZLdUdW+I/Zg3LXmwGiEYsLNrWzqSPu+weHTwgkV20xm1PjeR483Hgic6OOOMc1pGJWOXPb3p0iYlvs6En5hjWrPB0WIBWzGz7G94wQ/+fbEccSMBg845V/Nn3paR/nrV0JDDCeK9HfnuCWay5kT387w5kCA0MZDp6awPUMjmtqWl6C/zt2XK+mBM4zhqmCq40cy4gG4HXMXBsu1UqISv/ajmSU3X1pdvSkuGRHNze9aS+xiE1vW2xGjTYcz150PeK2EFlgAdd3E3P4wQsjXHnbfYxNFZjIlYjYwpbOxKpyQqtFXzrGhnS8btkklJjt6Emxp7+d7/7uG/nwG/aQjNo4nl/T7UlF6e9IlJ9TcDziEWtG4Lxidw9DmSJF18MYg2d8l7S845atRyuVEdu7U5w4k6MWEUvoTEY1+C4jWoJYxzRyVINpe82hyTyTeYdc0cUD2uMWxphZLl5hg4fjekwWXHraokzk/JKCIEQtIbvIXmPPQLHkUgoCT8y2/JbnRR11+RnNFokVnLr3+8NAvRmOdvcPjNKRjHLhlk6u2N3D3Y8en/W76ElFZ1y9tMV8dcRopkjBm57kYYxfrknGbFIxu3ySveGq3dw/MFJzTQXXaO13mdEMeB0zl4H8vr39XHvJVsayJfKOSyJq0Z3yLQ9PTeTLl71hhrRvbz93Xn85D//eT3P7uy9lZ2+avOMRtYRkRBgvuLPWsBCGp4qkYhE6k1G6UjH+4G0XLclxlxPH8zc06xUhwpNJaLZeXRq6+9HjbO2Mc3qiwFQQfAFGsyWeG8yURxANDE/R2+Z7RcRti0TUJhbI+GzL39jc1JEoB2yAaIOrEq39Li/aCbeOaca8pZ6xT7PdVeHzXxieWrDrWS3aYnZ5Y273hjaePjW5dAdfZuptxm3ujBO17fJ7q/yZD03mOT1ZIBLYfFZvekZti82dcU5NFNjWneRIIOUTkXKTRtimnIzaZc/gnT0pRHypIfglCy+o3W/tSvDdm9+wrD+LdYR2wikzqbQxrNzIqaz5zWfOXa0ZdWFDx1IPVXZcU768fnYwQ9Sun8VZzK0dXu49vBkDPWvcbwns7E1zyzUXMllwZv3MJ/OO70BnZhv1GPwN0qNjOfraopRc3xg/7JDz59AJrud/7bj+cFDPMxwayvDmizbRnYoSsSws8RtnNqRj/OHbX7a0PwRlFloDXufMZSDfyPi9klpTOD5+71Pccs2F3HLNhfz6Fx6qG4SjtmA8v4HAsqS8eVQPu1zY9L0OTk3kcYOMzq3xRA8a6r8iltCdijKUKdZ/0CIQ/BH13W0xTo3nZ20YRm04pzvFF9/7aqK2xfYDs3/mBcdXoDQa+OEZODFRYEtngg1tMU5O5ImKsKkzzunJAoWSH5it0CgJiIrw1SdO0tsWYyLvYFsWuza0cdOb9urm2wqgGbDSkEbG75XMpaiol6BGbWHvpg7O2dDG5bt7ef7/+xk+/55XYVtS9zkSNBuEQyo3tsfx8JUGbTG7/Efdk4o2zHwjVvhZ2Bh024VrjVm+05ttCdu6EvUP0gS2JUE3X5SORIT2+MzstuTCsfE8t/zfp7j2M9/nmVPjHBvLMZzJl3/mtiUko9acm40RSxjOFLEsiw9dvYdX7ujGMwSlBiqkbX423J6wOTSUoeQZ9vSn2dadZKpYu1bfzBTuxbISr7Ga0ACsNKSZMgXUL1UcOj3Bx+99CoPM+mOzhZpBfd/efs7vTxO1pz0kdvamOLevjY5EhHN629jdl6Y94Ss4IrbFnr40uzak6WmL8RO7e/mdN+7hJZsaz421xDcXCr2IL9jcye+8cQ+v3tXLxs4kl57Tw+d+5TK+e/Mb2N6drHkMwQ96EQs2tM0O+L49Z4z2RJS847F3cydbOpNErZllkXzJ4+8eOMILw1P0tMXpTEQYyRTLm50f2Hduw+yXYB2W5RvmR23h/oFR7rz+cr5z09Xc/OYLSERsiq6hENiGbulKMFlwiVrWnN4PK2HSsx6NgLQEocxJM3Pu6pUqiq6h0xbiEQvHNWWrRIxBLN+IvL89wRW7e7j9wAAfu+dJtnenePNFm2oOrqycBlx5+9tevoX7B0bLGeLF27q48Y3nc90dD3B4JMPpicIsDwrLEl6yoY2vffiqGe/lxuBz2CX4sXueJB2z6UxGKJS8wMcYEF8nGw7KvP3AQPlnEA4/BZjMl0gnIuWTzMfueZLzN7b7kjrHYzRbZCzrt/2OTBXJFV362uMkYjYb2xN8+Td+AhHxZ/JlixTrBGI/q5VgmOp0nT4MbB3JCCXPf7LrGQqBW1p1hl+rxj/XFO6lYCVeY7WhAVhZEkLNcHVgjEX8YLAhHfcDUtDD7BjD1vbkrCkdlbKray/Zyv0Do7OM46enAU+PTgqDdfj8j979OH3pOEfHppgqBEbygGWBhbChPUbUtusOrqyuaedKLlHbYktnkkzBqWlk/7F7nizrqsPBpsOZAnnHK4982re3n/avR3h+aMrPVC2hpy3GeNafLO0ZyJZcXhzN0pWMkC+5HBvL0ZXyA3044drzZhvdBy6Vswzuw8DWmZyeel1wPKYKLuf3p2e0KUPtGn89zXg4p24pvIPn0qWvRTQAK0tCvSkcYVZYGZAKjqEtFimXMq6744GamU94CV3rtSr/kVc/33ENZ7IlxrNFEEEqTOBdD7DAFr+08bF7nmT7gdmB47avH2RwIo9r/DbevvZ4WXdcnTGHVF8FdCSjZY+G8H3sPzjIUKaA45lgU81wcjyPZUF3KkrUthnKFCi5hjM5v/b7fx47xpsv2szPXbKVP//mIb9dOfCEcIL28FLQdLG5M45tyYySTmVgC6dehxOab3rT3ponzuoaf60rnOFMgcmCO6tkcEvwO5ov4Ws4rmE4U5ghlVuraA1YWTLCRozv3HQ1d15/Ofv29s/YxGtPRNjUmWBLV5JPvuuV5X+k85G61aL6+cOZQlkxYCHYMr3rH7MFC8PJiQJncsWatcb9Bwd5djCDZ3zVQDjix3G9hmtqZsPy9gMDdCajbO1KErWtspfGpmD45kimgOua8gak6xn+7N8P8f6/f5RYxOKD+85ja2cCx/NleNu6k+zobWNDOsbeTX5Zo7pO36jhptkaf633NpYt0dMWXTLv4Buu2s14rsTxM7my1anjGkamimu2DqwZsLKsI2jmmk8HzUvd6lH9/KI7PdCz6E63KRv8um+hZIjYvkXjhnRiVq0xvGQ3nt9CLQIehtMTBV65o7vuz6veewXKl+lDkwU2dcTpSMbKVwXGGF4cyZAreeWNtqhAOhHh3A1pHj16hucGM9x41w95w95+/r93XMwLQ1P84yPHOD2RY0tXgt97y0tnTEeurKeHJZp6WW4zNf5a7208V6K3LT7jcYspGezb209fOk4m78y48rAtWbN1YO2EW+eshlHmi11D9fOfG8pQLPmBt/qvO7TPjEX8+XV7A6VEeEn+B2+7iBu+9AilwF/CtiSYdWdwjeFDV8+eAN1orbXW5riGbd3JsoqjssvNDjYmPc/Q3RZlS2eK9/3kLv7yvud45rTf7ZeIWvzST+zgnZduJx61eeLoGb784BGOncmRjtmMTBXpSEZnrK9WPR1Y1Il3sV2StaieRQgwkStyaqJAX3v8bJ5Rp2Ppldksxz+ihRBmbY0mNTf7/LaYzfPDU7je7IYOwZ/wHGZYu/vSgP+eQ7Ogwcm8P3vNGFzPz5qjtl+L7G6Lz/h5TeRKnJ7MYwxcsqO7vO5wPY8eGUPwXeGmii65kotnfAleImpTcPw5bn55xCoHHs/4ZYiethj/8JuvIVd0+My3nueex0/gBG+qpy3GWy7azL8/fYpYxCIVtXlhZArXY0aAr/X73H9wkP989+NM5h0czyNiWbQnInzi2pc3/LlXZv/1gv1iTt7Vf48TQUkiYgnn9adbkiAsETqWXpnNatl5buYyeD7Pv+wP/51swSHveDPkZ5ZAV9D11pGMzHB1iwW6443tCU6M57DFwrL96Rz97QlufvMFM5QOodRM8ANmWEu+9tiZcpbsBUMyBzNFf8MlrO0ayBZdklGLguOfKJzAUAf8ZpOC67G9p42tXUn+9fETfH9ghI0dcaYKDmdyDqNTRf7uBy8Sj1hs6kj41pNBbXhwIl8OwLV+n7d9/SBj2ZKf4dsWxsBYtsRtXz9Y9/dQSxkSmgiN50oLOnFWU62mOT2ZB2BTZ6JcZ15L0jTdhFvnzOWIdrayp7+dzV1JLtzSycu2dnJOT4p41CJiW+zakOZDV5/Hzt70jI2n0IMhHPETGs4bKGdclT+v4UwBC9/wJh6xy5tQn/3uC2VVRuihDNNDSivp70iQiNpIoIjIl/wsOV+angAC8KUfHCEZtWhPROlvT7CzJ0UiaOUrOB4vjmYZDMoY4Ne+w7bsWr/PgeEpLAmbUQRLBEv82+sR1sYd1/DC8BRHRrNkCg6TuSLbulMcHcty+4GBRW2YVW8KGuObAoUnE1hb0jTNgNc59fS7Z7sNYfX7CuVglZeu1Q0XQ5MFhicLbOpMlOVa4eV7+JzK44abfRihr93fjEpGbaaKLt2ux8BQppwlhlQ6oRlgaLLAhnSco1UBRYBkZDo/Cq9URARXDJmCQ3WF+0yu5LcaG7/NuuS6ZAr+FcBS/D6PjmWxBU6M58vqkpLjcWy8gAv0BuWZxUjRYObVTFiSqGQtJAghmgGvc5qVIc2H1dDP3+z7qmx/3dQRx/EMx8ZyNadHVB/XzxqnPSnADw5x2+L4mTyO5zeiVBb/RCo68YJZbR3JKBHLb4uOBK3X5/Sm6OtI1JxOMlVwfL2sZ4iK/4849CfyTDCPz7IYyRTpScX54L7zeMnm9vJcOYBdvSnfetLzpyl7Qb18V2/9wLa9O8XpST/rt6zA6jK4r97Qz8XSrBfJ2YpuwilLympQVcyH6+54gBeGM4xNFSm6fndZ6O9w2c6ehjXNeu/V8zxOTRQCP15wXA8n+GeWiFr+9x7YFsRti81dSQ6PZIOWYCk3IfgtxRYP/95Pz3itU+P5IPv2nc5c1zA0VcDzDO2JaLmtGeCNF/Rz/VW72RDMkkvHI3S3xfjeoWE+evfjZAoOrufrndPxCH/aYBNu/8FB3vvFh7EldK3zyx9RCxCZpSj5zk1XL8nvaLEbtKsE3YRTGrMUeuCzrZ//2dMTjGVLM2qzBnCNmfP919P9fuyeJ9nalWA4mM0Wj9qkxDBR8Ci5HnHboj1hkS16pOIR+tsTxGyLsWyRkali+fK+6Ho4nmH/wcEZr3V4JEvc9h3W2hN+V1t7MsqZbJEv/8blPHl8nE996zmePZ3hG08P8t3nhnn3T5zDtZduI4PDVNHlom2d/Mk7LuZvvvtC04EtNEl6IVCYxGwLqVCUhCx1iWCxG7SrGc2AFWDpMtdaOs6lzoiWkov/27/5Zuf45QGgXFp9ycY03W3xeZ+Q6kn7opbQ3RavGfD2HxzkN/7uYUqB0Tzit0v3tcfY2ZueISGrd/wN6Th//q5XkCu6uJ7h60+e4nPffYEzOT8j3tKV4Lf2ncsVu3uRoHzSmYzSmYyWPYLnIvw7KTrurNl0W7qSq/6Kp4XoRAylPnP5+TbL2aaqiFVNFa785tBQZkHWiPXqlje/+YJZrdqVhKoFU7GOmG3N2vGvd/z3v+5cNncm6WuPE4tYvOXizXzx11/Nz12yFdsSTpzJ87F/forf/acnODKaxTOGsWyRo2PZQHEwdzI23zmBSmM0ACvA4v0YQs62TZM9/e3EbAnUDIHZu+3Xbpvxya3FQjY2bz8wQCziD9JMRm0SURvbEk5PFGadvOY6fnsiyrbuFO2JKOlEhA+8/jz+5lcu5dJzugF48PAY7/3Cw3xm//NMBTXgkUyBY2O5QF3RmPsHRtnWneSlmzs5t7+dbd1tbOtOsqe/veaJZS2wXBvLWgNWgMX7MYQ04/2wmrjhqt189O5JzmRLZYP0sB68sWPhPgfzrVseHcuysT3OifE8eP6JwBjftrPWyWuu49uWL41rT0QYmiyws7eNP/m5l/H950f4q/3Pc3I8zz8+coxvPH2a9/3kbv7DhRspuR6DE3nGozY9qRjJWO0ZzquleWelqDtui4VL7UI0ACvA0uqBV3LTZLEbh/v29vOn176cW7/2NC+M+AFkT18bQFM+uUtFeALc0pmcYcW4u6dtUT/LRNRmW3eS8VyJsWyJ1563gVft7OEfHznK3z9whLFsiU/82zPc+/gJbrz6PC7Y3EGh5HJyPEcyZtPTFiMemRmIl+pkfbawnBvLugmnlDnb5D7LKXlbaTndSrxeyfUYyfj6ZvCbQG4/MMB9FZfTP/3SjfzGT+6iNz2d/YfStWigdDjbpIaLZYk2lleHGY+IbAe+CGzC7868wxjzFyLSA/wDsBM4DPy8MWas0bE0AK9vlttIaKVPSCv1epmC4/sOB7WWJ46N85ffeo7nBjOAX0745ct38I5LthELuvFEhPZEhO5UDNuSs+5kvRiW6O9s1QTgzcBmY8yjItIOPAK8HfhVYNQYc6uI3Ax0G2NuanQsDcDrm7NN8raa8Dzf6Hwy70vUXM/wtUC2Nh7I1rZ1J/mtfediIdz10FFOTuTY3JnkfVfu4i0Xb57xc1/LLFHGvzoC8KwFiNwDfCr42GeMORkE6f3GmJc0eq4G4PXNarHSPJvJl1yGJguUgnr3ZL7EF77/Iv/8w+Plzch4xKI7FaU9ESFf8ptDPvLGPbzpZZvpSEQbHH3tsAQZ/+oLwCKyEzgAXAQcMcZ0Vdw3ZozpbvR8DcDrm/VWi1wuwquGsey0FviF4Sk+/a3nePTImfLjulNRelIxiq5Hb1ucP/uFlxO1LXraYrTFdT9/DlZXI4aIpIH/DXzYGDMxj+ddLyIPi8jDQ0NDy7dAZdWzHEZC6xERoSsVY2tXsiw927WhjU9cezGdyUjZ4nIsW+LwaJai43Jy3FeMlFyP0xN5TpzJka9qwFHmpiWnLRGJ4gffvzfG/FNw82kR2VxRgqipdDbG3AHcAX4GvCILVlYta9knYKWJRSw2dyaZzJcYnSrierCrN81QJk++5AW3GU5PFklFbZ4+OcEFm30DnnzJ5cSZHG1xf6MuFtEer2ZY8Z+S+JX7zwFPG2P+rOKue4H3BF+/B7hnpdemKMp0J106EeFdr9qOZyAVszmnN0kq6JbMllw+8OXH+JOvP8PoVLH83KmCw7Fg+KhTpaNWZtMKFcSVwHeAJ6BsJ/pfgR8AXwF2AEeAdxpjRhsdS2vAirK85Iou//L4Cf7+B0c4NZFjU0eSV+/q4ZsHT/P8kD89IxWz+eXLz+Edl2wta4XBL210JCJ0BdK1dc7q24RbLBqAFWX5McYwli3NMOxxPcNXnzjJ5777AhN5v7FjW3eSD77+PF69q2fG8y0RulK+69p6ka7VQAOwoigLp+C4DGeKFCo22yZyJf72+4e59/ETZdna5bt7+MC+89janZzx/Ihl0dUWXTfStSo0ACuKsnjGcyXGpop4FbHjheEpPvWt53gskK1FbeHnLtnGuy/fMUOn7d+3LqVrGoAVRVkaHNdjZKrIVIV9pTGGA4eG+cz+5xmcLADQ2xbjN67azRsv6MeqKj/Eoza9bTES0dqua2sMDcCKoiwtmYLDaKaI400rHgoll394+Ch3PniUguPf/tLNHfz21efxkk3ts46RikXobovOcl1bY2gAVhRl6an2lQg5PZHn9m8PsP9Zv2FKgDdftIn3/uQuulOxWcdJB2Y/lUqKNYQGYEVRlo9qX4mQx4+e4S+/9RwDgWytLWbzK1ecw8++ciuRqmC7hqVrGoAVRVleavlKgC9b+5cfneDz3zvMZCBb29GT4gOvP5dX7eyZdZw1KF3TAKwoyspQcj2GMwVyxZn+EOO5En/7vcP83x9Ny9Zec24v7993Llu7krOOs4akaxqAFUVZWarN30OeH8rwqfue4/Fj44AvW3vnpdv4pZ84p+YsujUgXdMArCjKyuN6htEam3TGGL797BB//e2BadlaOsYNV+3mDXv7a5YeElF/Tt1ZKF3TAKwoSuuot0mXL7nc9dBR7nroKMVAtnbRlg4+ePV5nL9xtmwNOBtd1zQAK4rSWowxnMmWOJObuUkHcGo8z18feJ4Dzw4DgWztZZt475W1ZWvgO7d1p6Kz1BSrEA3AiqKsDoqOv0lXy8T9sSNjfOpbz/PCcCBbi9v86mt28raXb6kZaEWEzmSUrmQUa/VK1zQAK4qyupjI+74S1Zt0rme49/ET/O33p2Vr5/Sm+MC+c7mshmwNwLaErmSMjmRkNUrXNAArirL6cD3DSKZApsJXImQ8W+J/ff8w/1IhW3vteb28/3XnsqWGbA186Vp3W5T21SVd0wCsKMrqJVt0GMkUZ23SATw3mOEv73uOJ45Py9Z+/rLt/OJP7CBZRxERi/jStWo3thahAVhRlNWNMb5kbTxXqnnf/md82dpQxpetbUjHuOGqc7l6b1/dssMqka5pAFYU5eyglvl7SK7kcueDR/iHh45Scv349bKtHXzw9eexp45sDVouXdMArCjK2cV4tsRYdqb5e8jJ8Ryf2T/Ad5+blq299eLN/Pprd9GZql//bZF0TQOwoihnH7XM3yt55MUxPvWt53hxJAtAOh7xZWuv2FLXUa0F0jUNwIqinL1MFfxNukrz9xDH9QLZ2otlNcXO3hQffP15XHJOd91jrqB0TQOwoihnN55nGM0WmaixSQdwJlvk8987zL/+6CRhZPvJPRt4/+vOZVNnou5xo7ZFV2pZpWsagBVFWRvkSy7DmULZO6KaZ09P8qn7nuPJExOAL0n7hcu2cd2rdzRUQyyjdE0DsKIoa4d65u+V9993cIjbDzzPcKYIQH97nBuu2s2+l9SXrQEkYzbdqSWVrmkAVhRl7VHP/D0kV3T58oNH+MrD07K1i7d18tuvP49z+9MNj72E0jUNwIqirF0m8yVGa/hKhBw/k+Ov9z/P954fAcASeOvFW/i11+6kM1m/9isipOORxUrXNAArirK2cT3DyFSBTL62ZA3gocOj/NW3nufFUV+21p6I8Guv2cl/fHl92Rr4c+rO6U0tVC2hAVhRlPVBruhv0tXylQBftvbPPzzBF+4/zFTBL13s3tDGB15/Lq/cUV+2trO3baG6YQ3AiqKsH4wxjGVLjNcwfw8Zyxb5/HcP89UnpmVrV52/gd983bls6pgtW9MAXIEGYEVR5qKR+XvIs6cn+eQ3n+PHJ6dla+961Xbe9artM5QQGoAr0ACsKEqzTORLjGZq+0qAnzF/8+Agt397gJGpadnab77uXF53/gZERANwJRqAFUWZD47rMTpVrGn+HpIruvz9D17kHx85VpatvWJ7Jx98/XlcvXejBuAQDcCKoiyEbNFheLK2r0TI8bEcf7X/ee4fmJat/dJPnMN/+unz6aozJLQBNQPwqh8lqiiKstSkYhG2dScb6n+3dif5o5+9iFvf8TK2dyfxDPzdAy/yQBCQl4JVMatDURRlpbEsoTcdpy0eaegr8epdPbxyx2X8n8eOMzA0xX+4cNOSrUEDsKIo65pE1GZbd6qh+XvUtvj5y7azs7dtSW0rtQShKIoCdKaibO1OrugQTw3AiqIoAVHbYlNngv6OBBFr+cOjBmBFUZQq0nF/k24ZDdoBDcCKoig1sSyhrz3Olq4k0WUa4KmbcIqiKA3wN+mSjOdKLPXYOA3AiqIocyAiC2m+mBMtQSiKorQIDcCKoigtQgOwoihKi9AArCiK0iI0ACuKorQIDcCKoigtQgOwoihKi9AArCiK0iJWXQAWkTeJyDMi8pyI3Nzq9SiKoiwXqyoAi4gNfBp4M/BS4DoReWlrV6UoirI8rKoADLwaeM4YM2CMKQJ3AW9r8ZoURVGWhdUWgLcCRyu+PxbcVkZErheRh0Xk4aGhoRVdnKIoylKy2gJwLa+hGfNBjDF3GGMuM8Zc1tfXt0LLUhRFWXpWmxvaMWB7xffbgBP1HvzII48Mi8iLy76qpWMDMNzqRawg6+39wvp7z/p+m+Prxpg3Vd8opsYAulYhIhHgWeANwHHgIeAXjTFPtXRhS4SIPGyMuazV61gp1tv7hfX3nvX9Lo5VlQEbYxwR+SDwb4ANfH6tBF9FUZRqVlUABjDGfBX4aqvXoSiKstystk24tc4drV7ACrPe3i+sv/es73cRrKoasKIoynpCM2BFUZQWoQFYURSlRWgAXgFEZLuIfEtEnhaRp0TkQ61e00ogIraIPCYi/9LqtSw3ItIlIneLyMHg93xFq9e0nIjIR4K/5SdF5E4RSbR6TUuNiHxeRAZF5MmK23pE5N9F5FDwuXsxr6EBeGVwgP9kjLkAuBz4wDoxGfoQ8HSrF7FC/AW+2H4v8HLW8PsWka3AjcBlxpiL8CWj72rtqpaFvwWqmyduBr5pjNkDfDP4fsFoAF4BjDEnjTGPBl9P4v/j3Nr4WWc3IrINeAvw2VavZbkRkQ7gKuBzAMaYojHmTEsXtfxEgGTQPJWiQcfq2Yox5gAwWnXz24AvBF9/AXj7Yl5DA/AKIyI7gVcCP2jxUpabPwf+C+C1eB0rwW5gCPhfQcnlsyLS1upFLRfGmOPAnwJHgJPAuDHm/7V2VSvGRmPMSfATK6B/MQfTALyCiEga+N/Ah40xE61ez3IhIm8FBo0xj7R6LStEBLgE+Iwx5pXAFIu8NF3NBHXPtwG7gC1Am4i8u7WrOjvRALxCiEgUP/j+vTHmn1q9nmXmtcA1InIY39P5ahH5UmuXtKwcA44ZY8KrmrvxA/Ja5Y3AC8aYIWNMCfgn4DUtXtNKcVpENgMEnwcXczANwCuAiAh+ffBpY8yftXo9y40x5neNMduMMTvxN2fuM8as2QzJGHMKOCoiLwluegPw4xYuabk5AlwuIqngb/sNrOFNxyruBd4TfP0e4J7FHGzVeUGsUV4L/DLwhIj8MLjtvwa+F8ra4LeBvxeRGDAA/FqL17NsGGN+ICJ3A4/iK3weYw22JIvIncA+YIOIHAN+H7gV+IqIvBf/RPTORb2GtiIriqK0Bi1BKIqitAgNwIqiKC1CA7CiKEqL0ACsKIrSIjQAK4qitAgNwMqKIyJGRP5HxfcfFZH/tkTH/lsRuXYpjjXH67wzcD37VtXt+6rd35pZk4hcIyI3B1+/fZ2YNa17NAArraAAvENENrR6IZWIiD2Ph78X+C1jzOuX4rWNMfcaY24Nvn07oAF4HaABWGkFDr5w/yPVd1RniyKSCT7vE5Fvi8hXRORZEblVRH5JRB4UkSdE5NyKw7xRRL4TPO6twfNtEfmEiDwkIj8SkRsqjvstEfky8ESN9VwXHP9JEbktuO3jwJXAX4vIJ+bzxkXksIj8dxF5NDju3uD2XxWRT4nIa4BrgE+IyA9F5FwRuVFEfhys+675vJ6yutFOOKVVfBr4kYj8yTye83LgAnyLwAHgs8aYVwcG978NfDh43E7gdcC5wLdE5DzgV/Bdu14lInHgeyISOni9GrjIGPNC5YuJyBbgNuBSYAz4fyLydmPMLSJyNfBRY8zD833jwLAx5hIR+S3go8D7wjuMMd8XkXuBfzHG3B2s42ZglzGmICJdC3g9ZZWiGbDSEgI3uC/iG3s3y0OBt3IBeB4IA+gT+EE35CvGGM8Ycwg/UO8Ffhr4laAV/AdAL7AnePyD1cE34FXA/sB0xgH+Ht/3t+Fba+L20Izpkap11+NH+G3O78a/elDWCBqAlVby5/i11ErvXIfg7zIweolV3Feo+Nqr+N5j5tVcdRA0gAC/bYx5RfCxq8LDdqrO+qTJ91HJCFA9pqYHGK74Ply3S3NXoW/Bv2K4FHgkMEFX1gAagJWWYYwZBb6CH4RDDuMHGvA9Z6MLOPQ7RcQK6sK7gWeAfwPeH9iCIiLnN2Ga/gPgdSKyIdiguw749hzPOQRsEZELgtc5B7908sN5rH8SaA+ebwHbjTHfwje47wLS8ziWsorRM6nSav4H8MGK7/8GuEdEHsSfuVUvO23EM/iBciPwm8aYvIh8Fv9y/9Egsx5ijnEyxpiTIvK7wLfws+GvGmMa2g8Gddp340/HSAAl4H3GmPF5rP8u4G9E5EZ8O8/PiUhnsIb/uQ7GHa0b1A1NURSlRWgJQlEUpUVoAFYURWkRGoAVRVFahAZgRVGUFqEBWFEUpUVoAFYURWkRGoAVRVFaxP8PeIBVFtcEJHcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='Number of Units', y='Manufacturing Cost', data=cost_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#4: CREATE TRAINING DATASET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = cost_df[['Number of Units']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = cost_df['Manufacturing Cost']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that we used the entire dataset for training only \n",
+    "X_train = X\n",
+    "y_train = y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# (SOLUTION #1: LINEAR ASSUMPTION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#5 MODEL TRAINING "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000,)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regressor = LinearRegression(fit_intercept =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Coefficient:  [-6.0333683]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Model Coefficient: ', regressor.coef_)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#6: VISUALIZE THE RESULTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Unit Cost vs. Number of Units [in Millions](Training dataset)')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_train, y_train, color = 'red')\n",
+    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
+    "plt.ylabel('Cost Per Unit Sold [dollars]')\n",
+    "plt.xlabel('Number of Units [in Millions]')\n",
+    "plt.title('Unit Cost vs. Number of Units [in Millions](Training dataset)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# (SOLUTION #2: POLYNOMIAL ASSUMPTION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#5 MODEL TRAINING "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "poly_regressor = PolynomialFeatures(degree=4)\n",
+    "# import a class and instantiate an object from that class \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform the matrix of features X into a multi array of features X_Columns \n",
+    "# which contains the original features and their associated polynomial terms \n",
+    "X_columns = poly_regressor.fit_transform(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
+      "  1.00000000e+00]\n",
+      " [1.00000000e+00 1.18599365e+00 1.40658094e+00 1.66819606e+00\n",
+      "  1.97846993e+00]\n",
+      " [1.00000000e+00 1.19149864e+00 1.41966901e+00 1.69153369e+00\n",
+      "  2.01546010e+00]\n",
+      " ...\n",
+      " [1.00000000e+00 8.78088812e+00 7.71039962e+01 6.77041565e+02\n",
+      "  5.94502623e+03]\n",
+      " [1.00000000e+00 8.89769971e+00 7.91690601e+01 7.04422522e+02\n",
+      "  6.26774007e+03]\n",
+      " [1.00000000e+00 1.00000000e+01 1.00000000e+02 1.00000000e+03\n",
+      "  1.00000000e+04]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor = LinearRegression()\n",
+    "regressor.fit(X_columns, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Model Coefficient (m):  [ 0.00000000e+00 -5.43308190e+01  1.22452385e+01 -1.29910949e+00\n",
+      "  5.12914120e-02]\n",
+      "Linear Model Coefficient (b):  131.7171595360391\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Linear Model Coefficient (m): ', regressor.coef_)\n",
+    "print('Linear Model Coefficient (b): ', regressor.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP#6: VISUALIZE THE RESULTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predict = regressor.predict(poly_regressor.fit_transform(X_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000,)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predict.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Unit Cost vs. Number of Units [in Millions](Training dataset)')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_train, y_train, color = 'red')\n",
+    "plt.plot(X_train, y_predict, color = 'blue')\n",
+    "plt.ylabel('Cost Per Unit Sold [dollars]')\n",
+    "plt.xlabel('Number of Units [in Millions]')\n",
+    "plt.title('Unit Cost vs. Number of Units [in Millions](Training dataset)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}