diff --git a/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Multiple Linear.ipynb b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Multiple Linear.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align:center\">\n",
+    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
+    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "# Multiple Linear Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement Multiple Linear Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Table of contents</h1>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
+    "    <ol>\n",
+    "        <li><a href=\"#understanding-data\">Understanding the Data</a></li>\n",
+    "        <li><a href=\"#reading_data\">Reading the Data in</a></li>\n",
+    "        <li><a href=\"#multiple_regression_model\">Multiple Regression Model</a></li>\n",
+    "        <li><a href=\"#prediction\">Prediction</a></li>\n",
+    "        <li><a href=\"#practice\">Practice</a></li>\n",
+    "    </ol>\n",
+    "</div>\n",
+    "<br>\n",
+    "<hr>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downloading Data\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 06:21:09--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.002s  \n",
+      "\n",
+      "2025-10-20 06:21:09 (41.8 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUELTYPE** e.g. z\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"reading_data\">Reading the data in</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0         2.0          4                   9.9                  6.7   \n",
+       "1         2.4          4                  11.2                  7.7   \n",
+       "2         1.5          4                   6.0                  5.8   \n",
+       "3         3.5          6                  12.7                  9.1   \n",
+       "4         3.5          6                  12.1                  8.7   \n",
+       "5         3.5          6                  11.9                  7.7   \n",
+       "6         3.5          6                  11.8                  8.1   \n",
+       "7         3.7          6                  12.8                  9.0   \n",
+       "8         3.7          6                  13.4                  9.5   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  CO2EMISSIONS  \n",
+       "0                   8.5           196  \n",
+       "1                   9.6           221  \n",
+       "2                   5.9           136  \n",
+       "3                  11.1           255  \n",
+       "4                  10.6           244  \n",
+       "5                  10.0           230  \n",
+       "6                  10.1           232  \n",
+       "7                  11.1           255  \n",
+       "8                  11.6           267  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eN7Ip5hNPiJydl18WbRus+F+eVblFRJopFmtsbFSys7OVzZs3K0OGDFFmzJihKIqinDlzRklPT1ceffRR59iTJ08qycnJyjPPPKMoiqIcPXpUiYmJUUpLS51jqqurlXbt2imbNm2SnkN9fb0CQKmvrzfmTRGRoiiKMneuotjtiiI+9sTNbhePR5vTpxUlM9P9vbrebDZFycoS43y9futWRSkpEfenTyvK2rWtj5mVJb5/no9nZorxoVRS4vv9ut5KSkI7L2o7ZD+/LV/ZmTZtGq677jqMGDHC7fGqqirU1tZi1KhRzsfi4uIwZMgQ7Ny5EwBQWVmJU6dOuY3JyMhATk6Oc4w3zc3NaGhocLsRkfG05H5EumBWOXwl+ALAV1+JRpolJeJ+yRLgL38JjxwZq3OLiGRZ2hurtLQUH3/8MSoqKlo9V1tbCwBIS0tzezwtLQ1ff/21c0xsbCw6d+7caoz6em8WL16Mhx9+ONjpE5EENfcj2undQaUm+HrmvajBi2thPrX+jq8cGZtNfK/z8kJT40bNLaqu9j4ntR4Q20WQ1Sxb2Tl48CBmzJiBNWvWoEOHDj7H2TwuWCuK0uoxT4HGzJ8/H/X19c7bwYMHtU2eiMiDnlUOrQm+4ZYjEy7tIpqagAkTgIsvFvdNTeaejyKPZcFOZWUl6urq0L9/f7Rv3x7t27fHtm3b8P/+3/9D+/btnSs6nis0dXV1zufS09PR0tKCI0eO+BzjTVxcHDp16uR2IyIKhp4dVFqDFyvr7/hidbuIX/0KSEoC1q8H9uwR90lJ4nEilWXBzvDhw7Fnzx7s3r3bebv00ktx2223Yffu3TjvvPOQnp6OzZs3O1/T0tKCbdu2YdCgQQCA/v37IyYmxm1MTU0N9u7d6xxDRBQKelY5tAYv4Zojk5/fOreoqio0gY6XLAgA4nEGPKSyLGcnKSkJOTk5bo8lJiYiNTXV+XhhYSEWLVqE7OxsZGdnY9GiRUhISMCtt94KAEhOTsaUKVMwe/ZspKamIiUlBXPmzEFubm6rhGciIrOpqxwzZriv2GRmikDH88Nfa/ASzjkydjswdGjoztfU5DvQUVVUiHEdO4ZmTtSaGX3o9LA0QTmQefPm4cSJE5g6dSqOHDmCyy67DO+88w6SkpKcY4qLi9G+fXtMnDgRJ06cwPDhw7Fy5UrY2YGOiCyQny8ShGWaYmoNXtTVoxtuEM+5viaUOTLh4De/kR+3bp25cyHv5s0Tuwdda0DNmSNqbIV6Rya7noNdz4nIOupuLMB78OIt76WsrPXqUVaW99WjaHXxxSJHJ5DcXOCTT8yfD7mbNw94/HHfz/vqs6aV7Oc3gx0w2CGKdA6H3EpKuNITvET6ew7WhAkiGTmQ8eO5shNqLS1AQoL/qt52u6i5FewlLQY7GjDYIYpc3gKFzExxuSeSVjnaevCiVVOT2HUVSGMjc3ZC7ckngZkzA48rLg6+Bpfs53dY5+wQEfmjpSBfuDMqwbetBE0dOwIDBvhPUh4wgIGOFQ4cMHacESxvF0FEpAc7brfmq+1EKFtIhNJHH4mAxpsBA8TzFHp9+hg7zgi8jAVexiKKROXl4sM8kK1bQ7slWq9gV2R8rXL5S3Q2UlOT2PmkbjFevTp0qypWnptaC8ecHV7GIqKIFI7VhAP55hugXz/xP/mEBGDfPqBHD315R67BUbdu/le59PTMOnFC7JjZvx/IzhY7a+LjvY/1LO63Z4/IpwnV6krHjkxCDiexsWJ7ub/dWLNmhbbeDld2wJUdokgUaSs7MTHA6dOtH2/XTgQkWlZkvAVHMmS/F+PHAxs2tH48L6/1Dih/VYwBXk5qy7zV2bHbja2zw91YGjDYIYo8agfwQAX5qqqsT9D1FegE4u09+LpcJaOkBLjlFv9jfAU6KteAhzuiKBCzKyjLfn4zQZmIIlK4dNwO5Jtv9AU6QOtGoP6SsmV06+b/+RMn/Ac6gHj+xAnx79tvlzuv7DiKPrGx4hLq0qXi3opWEQCDHSKKYFZ33JbRr1/wx1DzjgJ1SQ/W3LnaxslWJmYFY7IaE5SJKKJp6UVlhePHgz+G2gg02GTrujr/z+/fL3ccdVxqqrjEFkhqqtxxiczClR0iinhqQb5bbhH34RLoAGLXlV42m2gboTYCle2S7kug12dnyx1HHfenP8mNlx1HZBYGO0QU8RwOsTvr5ZfFfTgVEty3T9/rvOUdqV3SPXOUZI7lGjT54m+rsLdxX3whN152HJFZGOwQUUQL96rBPXoA7QMkDLRrJ4IYV97yjvwlZfujKHLJ2vHx4pKgP3l5Z+vtyFzC0jKOyCwMdogo7Miu1KjbsD2TdtXeWOES8Jw65Tvgad9evL+vvhJ1cEpKxH1VlfcEa19J2Ublxaxf7zvg8ayzE45tAYi8YZ0dsM4OUTiRrSas1tnxtTspnOrsqHxVUNbDs4Ly5MnGfi9kKiiHsi0AkTcsKqgBgx2i8KClv1OkVVAOJJjia1Z+L+bN85/rM3eucdVyzS5QR5GHRQWJKKJo7WIeib2xfJk3T6yQzJwJLFsm7hMSxOMyrPxeFBWJgMZzxchuNzbQCfZ7RG0bgx0iCguBCuZ5VhOW3YYd7HZtI7W0iEThggJx39JydmXE81KQwyEel/kwt/p7UVQkWkJMmwaMGiXuGxuNDXSC/R5R28bLWOBlLKJw8PLLYjdVIGp/p0jLF/HWFFG9POfv/8Iy78HqPmF6urbLirSfM4UWL2MRUUTRujqxc2fgejoOhxhnNV8rE966nXtyOESXaH8706zsE6Z3R5zsjrunn5b7OT/9tNaZU1vCYIeIwkKggnmehfEiJWenpUWs6ATjr38NXEPIij5hWvOsVFpqIx04IDcX2XHUNrE3FhGFBXV14oYbRGDj+gHqbXXC6jwVf1y3hf/jH8ZWdFZXTLwFMKHuEyabZ7V0KZCWJubzww/AxImtAyRf74u1fMgIzNkBc3aIwom3/I+sLBHoeKuzY1Weii/e5m+0cKkhJJtn5cpu9x38eXtfzNkhf5izQ0QRKT9frpqwTJ7KE0+I1YdQ9czylb9iNNcVk2Df24kTwPTpwDXXiPsTJ+Rfq2fVzN88PXfcASKAmTXL/zFnzWKgQ/5xZQdc2SGKZL5Wgm6+WQQCZuwQ8iZQRWez6Xlv48cDGza0ftyzLYQvJ04E19XdF3XHnatg50rRiSs7RNQmeFsJWrIE+MtfQtszK1D+itm0vjdfwQMgHh8/PvAxnn1WdnbaeK4YlZUBGze2HmezicfDpQcahS+u7IArO0TRxKqeWXryV4xms4ndWCtXAnV1vhOUZVdkHntMXFby1ZqhoEBUMzaKt59NJPZAo9Dhyg4RtUlaKzHL8Fb52FM4VGpWFPHeR4zwv6V77ly54913n//WDEbugPJVD8iMnye1PQx2iCiqGF1/R7Ynk1onKNwcOgRcf717wLN/v/bjeGvNMHWq/tUUz9f5qgcUKfWUKLwx2CGiqGJk/R0tPZnsdqB/f/l5qmJifBdSNNLdd599H9nZ+o+zZMnZla3YWOCXv9T2eptN3F5+OfCOOyC86ylR5GDODpizQxRNZPNRjh8H4uN9P6+1vovMeAAYO1YkVPfpA6xeDbzzjkgsBgK3jgjWli3A8OHB76IqLhaVkWXfsytvNZP8Cdd6ShQemLNDRG2Ga58l2XyUQDuJtPZkkhkPAFdfDXzyCbBuHdCxo+82D2YoLxf38fFiy7ZeamsG2fc8bVrgFRxfrOz7RdHD0mBn+fLluPjii9GpUyd06tQJAwcOxFtvveV8fvLkybDZbG63yy+/3O0Yzc3NKCgoQJcuXZCYmIhx48bhkJX7P4kopDz7LP31r3KvC5S3orUnUzA9nDy3zxcXyx0rGOvX6w941MRk2dwfRRF1c4YO1ReUWNH3i6KLpb2xMjMz8eijj+L8888HAKxatQp5eXn417/+hX79+gEArr32WqxYscL5mliPvY+FhYV47bXXUFpaitTUVMyePRtjxoxBZWUl7Az1iaKaWrFYz+WfQHkyWnsyBdvDyW4XwQAgVkueeML/pRvXLeY1NcDs2YHPrR5ftX69uKQ1d64IXHr3Bv72N+DMGd/HsNtFYrI6DxlG5CSFuu8XRRklzHTu3Fl5/vnnFUVRlEmTJil5eXk+xx49elSJiYlRSktLnY9VV1cr7dq1UzZt2iR9zvr6egWAUl9fr3veRKRdc7OiFBcryvTp4r65Wf61p08rSmamoohwQPtt5crAc7Pb/R/Dbj87Z63jA30vXnlFUWw2cXM9hvrY2rXu34vUVP/nTk0V4wKZO9f/cebOPTv2xRflvtcvvhj4vER6yH5+h03OjsPhQGlpKY4dO4aBAwc6Hy8vL0e3bt3Qt29f/Pa3v0VdXZ3zucrKSpw6dQqjRo1yPpaRkYGcnBzs3LnT57mam5vR0NDgdiOi0JLd0u1LsBWLjxzx/7zWnkzB9HDy9r249VZg3Djfl27y8s7mKW3fDjzzjP9zP/ec3CpIUZFY6fEca7eLx4uKzj6WlRX4eFrGEZnF0stYALBnzx4MHDgQJ0+eRMeOHbFu3Tr87Gc/AwCMHj0aN954I3r27Imqqir86U9/wtVXX43KykrExcWhtrYWsbGx6Ny5s9sx09LSUFtb6/OcixcvxsMPP2zq+yIi39Qt3Z7ULd2A+4eqN8HWVenaNbjXe6POeckS98Rdu10EOn/4A5CbC3z7LZCRAWzbBjz6qO/vxYYN4vLUmDHul242bGhdVTgzUwQjJSXi8pfr41p7ZhUVAQsXigTkAwd8V1AePBhITQUOH/Z9rNRUMY7ISpZvPW9pacE333yDo0ePYu3atXj++eexbds2Z8DjqqamBj179kRpaSny8/NRUlKCO++8E83NzW7jRo4ciT59+uAZH3/qNDc3u72moaEBWVlZ3HpOFAJat3T78u67olKwXuo2bH/zjI/3n7/Srp3IefGcZ0tL60ChRw/gu++0z9Pze+ErT0nNi3nlFRHIhSKvxeEA0tICBzvffcfcGjKH7NZzy1d2YmNjnQnKl156KSoqKvDUU0/hWS/7Qrt3746ePXti/09bANLT09HS0oIjR464re7U1dVh0KBBPs8ZFxeHuLg4g98JEcnQsqW7sDAkU/Jq6VL/gQ4gnl+6tHVycGys+9zT0/UFOoD798LhEB3evf2Jqigi4Jk9O3Q1Z7Zv9x/oAOL57dtbJ0cThVLY5OyoFEVptVKjOnz4MA4ePIjuP5XK7N+/P2JiYrB582bnmJqaGuzdu9dvsENE1glmi7Yrl/Q9XQK9fscOueMEGvfjj/oDHZX6vQi3PlFs5UCRwtKVnfvvvx+jR49GVlYWGhsbUVpaivLycmzatAlNTU1YsGABrr/+enTv3h1fffUV7r//fnTp0gUTJkwAACQnJ2PKlCmYPXs2UlNTkZKSgjlz5iA3NxcjglnfJiLTBLtFWxVse4BAr09MlDtOoHFDhsgdxx/1eyEbNLzwArB2re9cG6OwlQNFjFBsDfPlrrvuUnr27KnExsYqXbt2VYYPH6688847iqIoyvHjx5VRo0YpXbt2VWJiYpQePXookyZNUr755hu3Y5w4cUKZPn26kpKSosTHxytjxoxpNSYQbj0nCp1gt2ir1K3nnluzA91sNkXJygq8DXvRIrnjLVrk/zgpKfq3x3t+L7Zu1fd61+3iRpLZ/i/zvSbSS/bz29KVnRdeeMHnc/Hx8Xj77bcDHqNDhw5YunQpli5dauTUiMgk6hZtbzuQVNOnAzfddDbBd/Vq0VrBldpG4IYbRK6KzFYLLe0F9u0LfDyZcRkZ4lKWXq7b1dXO6r6KDXqjZYebVnY7ECj9MTaWyclkvbDL2SGi6NLSIoKLggJx39Liv5ZLWpoIYtavB/bsEfdJScCvftX62L7aCGRlAQMGtB6vKKJ2jcw27KYmufcXaNy2bXLHaefxf2NvdW389YkKxLVbuVGamgLnVh04IP+9JDILgx0iMo2/woFFRWJLdXGxWMkpLgZ+8QvfybwVFb4DHte+Ulu3AhMnivHebNggV7jQqHyUlBQRwPmTlia2sLt+L44f974So7dxqGvTUqP85jfGjiMyi+V1dsKB7D59Ik8OR+T36jHrPfgqHKjyXLVoahIrOIE0Nra+pOXKqDo+q1YBkycHns/KlcCkSYHH+dp+npYG+KmB6pPrz+1//kesgAUyfbrYKm+UnBy5y339+gF79xp3XiKV7Oc3V3aIdPLstj1smPi6rMzqmckz6z20tIjLJv54XlYxapVASx0ff77/Xm4+suPuuEPb44GojUNvuUV+x5fsTjhZoWwEShQMBjtEOqhVbD1rnlRXi8cjIeAx8z3oCTiMqr9j1HF275Y7jsw4f6tcjz8u3w/Ml6lTA6/GuXYrN4psOTOWPSOrMdgh0ihQFVvgbLXbcGX2e9ATcBhVf8eo4xiVoKxnlUurYJqQBuOii4wdR2QWBjtEGoVbFVs9zH4PegKO1avlXhNonFGrHBkZcvMJNM6oy2qBFBWJTuje5OUZv+0csG5FiUgrBjtEGkVDiXyz34OeD8GOHb1vF3c1YID35GSHAygvB15+Gdi5M3BPLZlVjoED/T8vO86oy2qBlJUBGzd6f27jRnMurVq1okSkFYMdIo2ioUR+MO/BNbAoL/e+aqH3Q/Cjj3wHPAMGiOc9eUuyfuUVsZrhWbumXbvWu8B8MWplx6jLav74uyyp0npZUubnDIjvpb+fmRkrSkSahaSec5hjuwjSIlCbAtl2BFbS+x7Wrm3dHiAzUzzuzdy5rVtDyLQvaGxUlPHjFSU3V9w3Nnoft3at9/egPubZquHcc33P1dOWLXLtGLZs8X8co9pj+CPbRmLrVrnjafk5z53r/5xmtaogUhT5z28GOwqDHdJO/ZD1/KBVH5P9QLWS1vfgL7Dw956bmxWluFhRpk8X98F8qLuS6cukda6uSkrkjllSEvhYZgcERs5Vy885FIEckT8MdjRgsEN6rF0rVgpkVznCkbe/4LOyWr+HQIGF0atZp0+LVYiSEnHv7bh6mmJqmavRqyV6V7lkGDVXrT/n4mK58xYXB/8eibyR/fxmzg5RECK9WJq3VgtVVa17R4VyB5psoUO9ydOyc1Wbbvr6GdtsogfX4MFy5/XWHsNXSwit1Ln6IzNXrT/nUCVfEwVLd9fzo0eP4qOPPkJdXR3OnDnj9twdekuCEkUItSCforg/rhbke/VVuWaTkSJUO9C0fF+DTQAPNFd/XdW1dE93FRsbeKeYHna7qKTsrz3HzTcHnqvWn3Mokq+JjKCrN9Zrr72G2267DceOHUNSUhJsLn/62Gw2/Pjjj4ZO0mzsjUVaOBxipcHXX8A2m/gru6oq/PtklZWJXTyu7yUzU3zIuwZr5eVihSWQrVtFCwM9tH5f1fHV1a2DIxnFxaIvVaB+YLLfIysF+t4BYmUn0O+k1p9zMP3MoqGvHFnP1N5Ys2fPxl133YXGxkYcPXoUR44ccd4iLdAh0ioaigoC2tpFDB7sv/kmIJ6XvaTjjdbvq7ryoifQsdtFB3bZfmCe59BzTjMF+t4B5ly6e/55ufl5jouGvnIUWXQFO9XV1bj33nuRkJBg9HyIwl40FBXU2i7C4QjcFqGpKbgWGaH8vnrO01c/MDUgrK6WG28Vo753agAJtA54vF26++wzufO6jouGvnIUeXQFO9dccw127dpl9FyIIkK4FBWULfrmjdZVFPUDMBDZcd5066ZtnBqw+eN5WcTXZRJfAZ6/Qn2Kor1QX20tkJ4OdOgg7mtr5V/rj5G/k/n5Ijfq3HPdH8/MbJ2LJjt/dVw09JWjyKQrQfm6667D3Llz8emnnyI3NxcxMTFuz48bN86QyRGFI3Wp31euiJpbEswlnUCCzSPRuhKwYYPc+A0bRIXiUJC5dONwnM3N+e47cenKF9cAb+hQbZeGZPKUEhPF7ivVd9+J4CMhATh2LPDr/TH6dzI/X1SgDpRTozXI0hJk6839IvJGV7Dz29/+FgDw5z//udVzNpsNDoblFMXM2KWjhRE7wcJldcpVXZ22cbIBW1qa2Kn08sty49Xjel668kVmnGeg4+r4cfF8MAGPGb+TdnvggKNvX7ljqeOi4RIwRSZdl7HOnDnj88ZAh9oCLUv9RjLqMoDWRFRf3bQ9yY7z5pxztI3TGrBpHf/993LjA42rrfUd6KiOHw/+kpYVv5NaG75q/RkTGYVFBYl0ki3IZyTZywBLl/rP5dGaiBooN0YlO86bZ5/VNk5rwDZ4MJCa6v/Yqalnx3ftKjefQON+8Qu548iO8yc/XxTwcy1c+MUX5v1Oam346qsruyfZcUSydAc727Ztw9ixY3H++ecjOzsb48aNw/Zw32tLZDB1qf+WW8S92XVCZJf3ZbZVa10JCFQtOthq0l9+qW2c1oBNK6NWIY4elTuO7Dh/yspEAb+ZM4Fly8R9nz7m7nAqKhJ5Wt6SwT07zGv9GRMZRVews2bNGowYMQIJCQm49957MX36dMTHx2P48OEoKSkxeo5E9BM9OTT+tvTKrk49/XTg2jKKIsbppacar5aAbft24PBh/8c+fPjsDrTnnpObT6BxwQZNLS0iaCsoEPctLd7HWbmlW7YVRna23PFkxxFJ09N468ILL1SWLFnS6vEnnnhCufDCC/Uc0lJsBEqRQm3U6K0rtRHNL32ZPl3uPNOn639vjY1y52hs9P59CdQ4VGtn8NxcufG5uf7fV02N3HFqalq/VrZ5aKgbtep1/Ljc9+L4cWvnSZHD1EagX375JcaOHdvq8XHjxqGqqirI8IuIfHG9dKOFmsszY0bgFQJvgumBJFsPqGNHYMAA/8cfMMB7JWeZy4laE5SN6vuUni62l/uTkCDGuZo3T/S68vx+ORzi8Xnzzj4WKVW94+MDJ7Hn5YlxRIbSE0n16dNHeeaZZ1o9/swzzyjnn3++nkNaiis7FGny8rSt7Hi7eVsh8EXvX+Rr17ZeccjMFI/7MmCA92MPGKD726UoSuBVMc/Vj8OH5d7z4cNy509I8P76hITWY5ubW6/oePv5NTeL8VpXrazm6/c3L8/qmVGkMXVlZ/bs2bj33nvx+9//HqtXr8aaNWvwu9/9DjNmzMCcOXOMjcaIyM28efJF/vzxtkLgyz//KXdM13F6c0g++kg0jhw/HsjNFfeNjeLxYGhNaH7xRbnjyo47dkwkmKelAXFx4r6mxnt9naefDlw+wOE4myMVjnWT/Fm/XuT0TJsGjBol7o8fF48TmUJvNFVWVqZcccUVSkpKipKSkqJcccUVyvr16/UezlJc2aFIIfMXv54VHnWFwBetKweBckgA63JIvK02ZWW1Xm0KRZ6SL1Onyp176lQxnrkw1FbJfn7rqqAMABMmTMCECROMi7qIIpDDEbikvpFk/uLXSl0hKCz0PcbotgBAaNoCePv5yLZCMCpnRw/ZbfzqONkaRXPnAldcEZrfVaJwwqKCRDqVlYkaNsOGBa5pY5QDB6w57qBBcpVyBw0S/zay1YJe/n4+MgnNN98sdx7ZcVpcdpm2cbK/F3/9a+h+V4nCiXSwk5KSgh9++AEA0LlzZ6SkpPi8EUU7X/kohw6ZW9PEjFUEmePu3CmXQ7Jzp/i3Ua0W9DKi5szIkXLnkh2nRVaWtnF6fi9CUX+HKFzYFEVRZAauWrUKN998M+Li4rBy5UrY/KyzTpo0ybAJhkJDQwOSk5NRX1+PTp06WT0dCnMOh/ir2N9lmqwsUZzP6MsELS1im7KRl7LsdpEcqpb09+bll8WKQCAlJWK15KWXgNtvDzx+zRrgttvk5yoj0M9H7QAe6OeTmgr8+GPg86WkBC5WqJXMz9n156b390L2e0EUrmQ/v6VzdlwDmMmTJwc1OaJIZmU+itqL6PHHjTuma+8iX7Tm7HhWNPZFdpwWWvqHpaX5zl/p1Eku2NHy91FLi8iPOnBArMZMner9e69lJW3oUHGMMWO079JTvxdm504RWU1Xzs7HH3+MPXv2OL/esGEDxo8fj/vvvx8tGiqVLV++HBdffDE6deqETp06YeDAgXjrrbeczyuKggULFiAjIwPx8fEYOnQo9u3b53aM5uZmFBQUoEuXLkhMTMS4ceNwKNAnEVEQrM5HKSoKrru4ylvvIl+05pCoTTr9cW3SaSSj+of94Q9yx5EdN2+eWH1x7VuVkOB96//Bg3LHVMc5HEBlpdxrvJH9nhFFKl3Bzj333IPPP/8cgKimfNNNNyEhIQH/+7//i3kyRTt+kpmZiUcffRS7du3Crl27cPXVVyMvL88Z0BQVFWHJkiVYtmwZKioqkJ6ejpEjR6KxsdF5jMLCQqxbtw6lpaXYsWMHmpqaMGbMGDiM3rJC9JNwyEfR2xV62rTWvYtkKhxr7Uiu1rTx15E8mCadnlz7R/3jH9pf7y3X6sQJudfKjNNSDRnQXtdIZrXRn3Cpv0NkGj372jt16qR88cUXiqIoyqOPPqqMGjVKURRF2bFjh5KZmannkE6dO3dWnn/+eeXMmTNKenq68uijjzqfO3nypJKcnOys3nz06FElJiZGKS0tdY6prq5W2rVrp2zatMnnOU6ePKnU19c7bwcPHmSdHZK2Zo1cTZM1a4w/t0z9Gi0VdGUrHOutOSNb0yYY3vpH6b251v4x6uestRqyomivsyNbB8nzFi49s4j0MrWCsqIoOHPmDABgy5Yt+PWvfw0AyMrKcu7Y0srhcKC0tBTHjh3DwIEDUVVVhdraWowaNco5Ji4uDkOGDMHOn7Z8VFZW4tSpU25jMjIykJOT4xzjzeLFi5GcnOy8ZclufSBC8Pkosr2ivAn2L/jvvjt73ldfld+xpLfmjGxXdb18rZjo5do/yqi8I63VkAH5rt82m/h5fved3HjP1wLGrbAF83tNZDo9kdSwYcOUO+64Q3nxxReVmJgYZf/+/YqiKEp5ebnSs2dPTcf65JNPlMTERMVutyvJycnKG2+8oSiKovzjH/9QACjV1dVu43/72986V5JeeuklJTY2ttUxR44cqdx9990+z8mVHQpGMNWB9fSKcqX3L3hfqwmyf/HX1ckds67OuO9zIGZUk3ZdqTGqCrSeVTHZDvCyP09vzxu5whbs7zWRXqau7Dz55JP4+OOPMX36dDzwwAM4//zzAQCvvvoqBqlVxSRdcMEF2L17Nz788EP8/ve/x6RJk/Dpp586n/fc4q4oit9t7zJj4uLinEnR6o1Ilt0utlf7c/PNrf9aNqL2i5G5Ff7+8lYU91UO2WLpRhVV//57oHdv0eG8d2/v+U+y1aSnTROrStOmyZ8b0P9z9qRnVez55+Ve4yrQ9+KSS8xZYbOq5hSRJkZGWCdOnFBaWlqCOsbw4cOVu+++Wzlw4IACQPn444/dnh83bpxyxx13KIqiKO+++64CQPnxxx/dxlx88cXKgw8+KH1O9sYiLfT8xR/oNbK5E3r+4g/mpub4ZGXJjc/KCv77m5zs/djJye7jtK6YvPii3PgXX9T/c/bm66/lzvv119rfm9ab0b2xwrkHGrUNpq7sHDx40G1790cffYTCwkK8+OKLiImJCTb4QnNzM3r37o309HRs3rzZ+VxLSwu2bdvmXD3q378/YmJi3MbU1NRg7969mleYiGRpqbMj+xrPlRRfZLc5G0VdSerRQ2687DiV6y6qJ58UNWvq672Pra8Hzjnn7NdaV0xkC/+p4/T8nL351a/kzus6zqxK2XPnGns8o75HRGbT1Qj01ltvxd13343f/OY3qK2txciRI9GvXz+sWbMGtbW1ePDBB6WOc//992P06NHIyspCY2MjSktLUV5ejk2bNsFms6GwsBCLFi1CdnY2srOzsWjRIiQkJODWn0q5JicnY8qUKZg9ezZSU1ORkpKCOXPmIDc3FyNGjNDz1ogC+vpr7eNk65gEGvdTxQfTqZV11To4GzeKisKBaNkSP28esGSJtkTW+npxmalrV1GQb86cwFWGp04V/5aZv+s4o+opHT0qdxzXcffcI+rwGG3/fmOPZ3XNKSJZulZ29u7di1/99GfI//zP/zh3P5WUlGDlypXSx/nuu+/wm9/8BhdccAGGDx+Of/7zn9i0aRNG/tRsZt68eSgsLMTUqVNx6aWXorq6Gu+88w6SkpKcxyguLsb48eMxceJEXHHFFUhISMBrr70GO2ufh5yVuzE8Vwg01LbUbP167eO0ViD2JTFR7jjB8LZL55NP5F4rOy6YXVTqCohaTdof1+rQWld2jKqnJLvY7TpOts6OVrK7vGRZXXOKSJqea2SJiYlKVVWVoiiKMnbsWGctnK+//lrp0KGDnkNaijk7wbNyN4a3Oit2u3jcDCNHyuVHjBx59jVqboPNFlzOzsqV5ufpeNulY2RtoWB3USUmuh9P9uev9T38/e9y4//+d//v94475I7zUyqioijG7rpzvRmds2NlzSkiRTE5Z6dfv3545plnsH37dmzevBnXXnstAODbb79FquxaMUUNI3YZ6aW1Mq0R+vbVPk6tKAy0riqspd5Jz55y59aquNj/Lh0j/4KX3UXlS9eu7l8XFYlq0MXFratDu9JaN0f2klygcZdcIncc13FmVDTOywPi4409ppU90Ig00RNJbd26VTnnnHOUdu3aKXfeeafz8fnz5ysTJkzQc0hLcWVHP6N2GemhpzKtEY4f1/9XdLAVhc2qLaPuQPJF604mf4LdaaS3lo/WnUN6VvC80fN7GmglUH2N59wHDPA+Ni9P3/fM6O8pkdFMXdkZOnQofvjhB/zwww/4+9//7nz87rvvxjPPPGNQGEaRwKhdRnroqUxrhPh4oH2A1P727b3/FR1sRWGZbth6BMoR0Zrv4k8wO42Sk1uv7ABy+WJa+3XpWcHzRmtuketc1Xl5ztNmE78/rqtZX3wBfPSRWNWaNg0YNUrcHz8un2emlev31Nc8jeyBRqRbiIKvsMaVHf1kcws8ezIZQW+/pmBZWU1YNkdC603tsRTseWVyM/TWCvKss6PSmi8mu7oWzAqeN3pyy3zNde7c8KpYHIoeaETeyH5+S289/+Uvf4l3330XnTt3xiWXXOK3QvHHH39sQBhGkcCoXUZ66O3XFCwtdVOqqow9t1m7WgLt0jEyN0O2OnCHDsDp00BSErBnj/djq/liiuL+uJov9uqrrVfN8vNF/sr27WKrf/fuYou95+pDfLwYt2GD7zlqyYMpKgIWLhQrjQcOiN/LqVPdV3Q8eZvr998DN92k7T2bTfZ7SmQVm6J4/ifj3cMPP4y5c+ciISEBDz/8sN+xDz30kCGTC5WGhgYkJyejvr6erSM0amkBEhIC1zo5ftz//9T1OHFCnDuQ48eNTcxMTBTHDCQhATh2zLjzAsBLLwG3327sMQFx+SklxffzDgfQq5f/S5ZZWSK4C/QBV1AALFumbX52u7jU45p0HGhOaq0gmTn5M36894AnL8+8y0O+hOo9E0UK2c9v6ZUd1wAm0oIZMo9MDonDIcYNHWrsuWVrkfzzn8aeOy5OLtiJizPunCqzdrVMmQKsW+f7ebsd6N/ff7Dzy1/KfcDqWWlTd9cBZwMeLfliwfz8+/YVQYTrn4U2m3xOj5FC9Z6Joo2uBGVXTU1NaGhocLtR22FUZeBIOveYMcaO02LwYPGXu9ECVdZtaQFef93/mNdflyvmOHWq/lWHJUvOnkP25/ruu/oLXaqlDTzXvxVFX2mDYItfWvnfG1Ek0xXsVFVV4brrrkNiYiKSk5PRuXNndO7cGeeccw46d+5s9BwpjFmZs2PVub/80thxWrju0jFSoMttRu58k9mdJHMO2Z/rwoXArbcCw4aJOkWydZ9aWkRw5Y9r8BXIvHni0ubMmeIy3syZ4mstAZOV/70FYmUFdaJAdPXGuu222wAAf//735GWluY3WZmim7rSUF3d+q9foHWPpWg4t2z+j9EF3FQffmj8Mc87z//zBw7IHUd2nHopSmtvLNdzBPr5e1NdDVx/PbB2beAkXi0BXmGh/3HqCpG313tenvPHyv/e/CkrA2bMcL/ElpkpAvNQJ0sTeaVnq1diYqLyf//3f3peGpa49Tw4a9eK4meeBdDUx8zcfqr33KdPK8rWrWJL/Nat2oqe3XWX3Jbku+4y4h26a272X2hO761XL0XJzVWU8ePF1nBPxcVyxyku1v5+iotFeYDx47Wfw9fPP9AtNTXwz9yo0gZGF7+08r83f/PxfE9WzYfaFtnPb13BztChQ5XNmzfrmlg4YrATvFD3p3KltcZHsH28Lr9c7kPw8suNe4+qoiLjAx1vtwED3M8bimrVes/h7ecpc9uyxf98jArwzAgUw6WujZUV1IkUxeRg54svvlBGjBihrFy5Utm1a5fy73//2+0WaRjsBMfXX3bq/+xC8T9g2ZUaI/4Klf1gzcw08h0KV1wRmmDHW8Azd67/8UYEtnrP4frzv/12uff3xz/6n4tRAZ5ZxS+DWZ00ytatcu9t69bQz43aBsOLCrr6/vvvceDAAdx5553Ox2w2GxRFgc1mg4OZaW2GwyGu1SuK7zGFhaImiZl1P+z2wFtt/c1VUUS+g8xcZUsxaS3Z5HCEV1G2igqgqQno2FF87SvPxlsNHFktLe5F9hYu1HcO15//p59qn4c3aiK1t1wblWebB2/MKn4p8ztvNu4Oo4ihJ5K66KKLlPz8fOXDDz9UqqqqlK+++srtFmm4sqNfJP1lZ9RcZ82SO86sWfJzk720dvfdoVvZAUQejSfXPJviYv2Xrvxd+gzmHFu2yL23QJexZOYpw6qGtaEQSf/9U3QydWXn66+/xsaNG3H++ecbG3lRxImkv+yMmqtM1WYt47S0PLjySuC55+SOawRvu6tiYwPvPgrEqN1J3gwdCqSm+m9Kmpoqvyqip82DK6NWiMJRuO4OI/Kkq87O1VdfjX//+99Gz4UiUDjX/fDUrZsx4wYNkjuOr3Gu9Ujefdf/pTVABBbqJZ0uXeTObRSj+4oBxtev8WS3Bw4In3tO2yVCNcBbulTcR2JgYoZA3dkBdj2n8KBrZWfs2LGYOXMm9uzZg9zcXMTExLg9P27cOEMmR+FP/csuUM+kaPrLTjYn5NNPgdGj3R/zVo/EH0VxL/+/caOmqQZt9Wrjj2lk/RpfAtUi+vDD0NV/kQ3uFi6MzCAqP1+sPnqrs/Pkk6yzQ+FBV7Dzu9/9DgDw5z//udVzTFBuW4zsmWS2ujpjxn31ldxxPMf5ulwlQ720Jlu0zwgDBpxNTjbSZ58ZO85TSwvwxBP+xzzxROiCi1AEd1Zj13MKd7qCnTNnzhg9D4pQWnomWf1Xq1GX3Hr2lDuO6ziZXWv+qJfWOnTQ93qtBgwAPvrInGPX1ho7ztPSpUCg/0WdOSPGzZ4td8xgdsoZXX06XIXD7jAiXzTl7Pz6179GfX298+tHHnkER48edX59+PBh/OxnPzNschT+jOyZZDb1kpuv7iY2m3mX3AJ1q5Z10UXBH8Objh2B3Fxg/HigsdG8QAcwP89rxw5jx5WVAb16id5aao+tXr3ke2yZtfWciORpCnbefvttNDc3O79+7LHH8OOPPzq/Pn36ND7Tu/ZMESmS/mo1Kpny66/lzuc6LtjdaOqltfa61mIDy8gAPvkEWLfOnEtXrvr2NXacJ9n5y4xTLz16BqqHDonHZQIemS7vdrsYR0Tm0BTsKB5r8J5fU9vTq5ex48ymJlOee67745mZ7lu8/cnKkjuX6zjZnWC+qKscZgUioVxVMPvD/9ZbjRkX6NKjorjvlPNFpst7pG49J4oUJv2dSG1Fbq6x4wLxlTuhJaci2GTKqipjxwWSmnr20tp77xlzTE8nT8qPDbbSs9l1Z7TUv/FH5tKj6045f8yoPk1E8jQFOzabDTaP9X/Pr6lt+f57Y8f5423bdmYmcMstwEsvAd9+e/bxjAyRgOprpSaYZMovvtA+TnYnWCAuKXKGOnRI1P0JFLz4+hk89ZS2LcZmfvgblQBdXS13HNlxwRYnDHfh1u6EyJWmYEdRFEyePBlxcXEAgJMnT+J3v/sdEhMTAcAtn4fahlAFO762bR865H2F4NtvgeuvB9auNb7Ox4kT2scFU1Tx8OGzqwcDBgC7duk/li/795+9rOMreNFS6VmGWR/+Rv1OmvG7bUT16XBkVBBMZBZNOTuTJk1Ct27dkJycjOTkZNx+++3IyMhwft2tWzfccccdZs2VwlDXrsaO8yaYbduTJgXOqdAqO1v7uEGDgHa66pULaoKz2ijTTGrw4pp8G6iJKiCXv+LJjMrERv1OhuJ3Oxr4SuL29ntEZBVNKzsrVqwwax4UoTwTfYMd500w27abmkRLhlGj9J/f0//9n/Zx27cHrv3ij5rgfP/9+o8hy1sH+EA/A89Kz1Yy6ncyFL/bkS5QEOz5e0RklSD+1iQ6W7vGn2Br1wS7bdvolgd6cnbKy40599atxhwnENfgBYishq9G/U6G4nc70mkJgomsxGCHgqLWrrHZvNeusdmCbwQYbBPRpqbgXu9J9lKNkZfP1Maheptj6qUGL5HU8NWo38lQ/G5HukgKgqltY7BDQTOido0/l1wS3OuvvLL1Y66dx8vLtQUmerqeB3tpZ+FCkUAs25fLKOrlMyurT+th1O+k2b/bkS6SgmBq22wKKwOioaEBycnJqK+vR6dOnayeTsQya+vphAnA+vX6XtuundgV5Zr4GuzOkcceA/7wh8DjHn0UuO8+8W+HAzjnHONXmcy2ZQswfLj4t5qICrjnaKgBUDh++Bv1O8lt1d45HKJgaHW197wdm038t1VVxe8XmUP285tFBckwZjUClM2R8Wb27NaBTrDbp/V2PY+Li7xgx7V2kbrK4S1QfPLJ8At0AON+J9nk0jv1Ut8NN4jAxlsQ3NYv9VF4sPQy1uLFizFgwAAkJSWhW7duGD9+fKveWpMnT3YWM1Rvl19+uduY5uZmFBQUoEuXLkhMTMS4ceNwyIiui6RJMJeG/NGz2Ga3A3PnuhenM2r79O7dcnNwHbd9u6iXE2k++MD96/x8EcRt3QqUlIj7qqrwDHQoNHipjyKBpSs727Ztw7Rp0zBgwACcPn0aDzzwAEaNGoVPP/3UWagQAK699lq3be+xHsU4CgsL8dprr6G0tBSpqamYPXs2xowZg8rKStj5J0VImFlU7IorgJ07A48bNgzo1893cTqjtk/LBi2u4yI1QdN1ZUfFVQ7yFGwLFiKzWRrsbNq0ye3rFStWoFu3bqisrMRVV13lfDwuLg7p6elej1FfX48XXngBq1evxogRIwAAa9asQVZWFrZs2YJrrrmm1Wuam5vdqj03NDQY8XbaLH/VjfVU1vX0U8HugK64Avjv//b9vFE7R2Tn4zouUhM0mcJmvUjJF2IQTOEsrHZj1dfXAwBSUlLcHi8vL0e3bt3Qt29f/Pa3v0WdS6OhyspKnDp1CqNcqsZlZGQgJycHO30sByxevNhZ9Tk5ORlZsm2sqRWjOkP7I/s/0EDjjNo5cvvtcsdxHTdoUHh+QAVyyy1Wz6BtKysTCcDDhondeMOGia9ZlZhIm7AJdhRFwaxZs3DllVciJyfH+fjo0aPx0ksv4b333sMTTzyBiooKXH311c6VmdraWsTGxqJz585ux0tLS0Otj05/8+fPR319vfN28OBB895YlNPSGVqvoUOBDh38j+nQIXCwY9T26Zkz/T/vbdzOnca3rQiF//zH6hm0XWzDQGScsNmNNX36dHzyySfYsWOH2+M33XST8985OTm49NJL0bNnT7zxxhvI93NtRFEUnx3Z4+LinM1MKThff23sOG8cjsDF9FpaxDh/qydG7RzRU1QwUnN2DhywegZtE9swEBkrLFZ2CgoKsHHjRmzduhWZAeqzd+/eHT179sT+/fsBAOnp6WhpacGRI0fcxtXV1SEtLc20OZMgW/9Gb50cQHTFDtRX6swZMS4QI3aOzJ0beIznuEjN2fG1CkbmYhsGImNZGuwoioLp06ejrKwM7733Hnr37h3wNYcPH8bBgwfR/adPj/79+yMmJgabN292jqmpqcHevXsxSLbULel27Jix47z5/HNjxwW7fVrPfAYPBlJT/Y9PSRFF/EpKgEsvlTuH2QYMsHoGbRPbMBAZy9LLWNOmTUNJSQk2bNiApKQkZ45NcnIy4uPj0dTUhAULFuD6669H9+7d8dVXX+H+++9Hly5dMGHCBOfYKVOmYPbs2UhNTUVKSgrmzJmD3Nxc5+4sMk+vXsaO80b2f+jbtwMFBb63nrsKZueIS1UEQ8apbDYxJ7XL+K5dmqdmOI8FUwoRtmEgMpalKzvLly9HfX09hg4diu7duztvr7zyCgDAbrdjz549yMvLQ9++fTFp0iT07dsXH3zwAZKSkpzHKS4uxvjx4zFx4kRcccUVSEhIwGuvvcYaOyEg22wkmKYkPqoOtLJ3L7BsmUgMTkgA5s3Tf05/xo/XPk6mqODhw2cvS4wbp2dmxuva1eoZtE2R1ouMKNxZurITqC1XfHw83n777YDH6dChA5YuXYqlS5caNTWS9M03xo7z5oILtL/G4QAef1z827WKsuvzemuX9OypfZzWyxLhsqLimdtEocE2DETGCosEZYpc551n7Dhvpk7V/z/1JUta7+QKtnaJ+le3P55/dWu9LBEovycUuHJgLbZhIDIOgx0KiuzllmAuy8TGAr/8pb7XOhzuu7R81S5Rqz3LBDx2e+DgJT3dPUDTelliz57A8zCTzcaVg3DAXmRExmCwQ0H54Qdjx3nT0gJ8/LH+16u1Yoyq9nziBFBR4X9MRYUYp1IvS/g7t2tw8eWX/o9vlD59Wq9SZWVx5SCcqMn0t9xyNoGdiLRhsNOGmNGV/J//NHacN08/Hdxc+/QR90ZVe9ZTZ0erUNW3OXAAuOkmrhwQUXRjsNNGmNVj5/hxY8d589ln+l9rt4ucH0AEMjICjdNTZ0ddVfJFrYirBnWXXSZ3DiM8+aTo3cWVAyKKVgx22gAze+xs3So37s039a8oBZO/8stfnq23Y9QqlJ46O1or4oayN61nXhMRUbRhsBPlAvXYAYLrSh6oZ5Xqu+/0rygF08bs44/PztGomkC//rXccVzHad16HsqVHYA9sIgoujHYiXJm99jREyRpXVH6qcG9Lq6rFtnZcq8JNO7NN+WO4zpO69bzZ5+VG28UNa+JiCgaMdiJcmb32JENIFxpXVEKpkYPcHbV4p575MYHGtfQIHcc13Fat56HcqXFNa+JiCgaMdiJcmb32Pmp+bxmWlaUXn9d3zlU6qqFUTk7dXVyx3Edp249B1oHPN4q4oZypWXWLP99xIiIIh2DnSinp9pvKMmsKJ08Gdw5/uu/xL1Ru7G6dJE7juc4LRVxg6kaLctuF9vjvbXTICKKJgx2opzdLrYU+3Pzzfo/WI8d0/c6lcyKUocOwZ3j+efFvVErO7I5RN7GyVbEDaZqtD/Z2cD06UBxsSgHwECHiNoCSxuBkvkcDrHl25/SUmDxYn0BT2Ii0NSk/XU2m1jRkFlRuu46YM0a7edQ7dsn7o3ajSXbt8rXOLUirj/BVo325amngNGjjT8uEVE448pOlDOqarAvetpAaO3avGOH9nO4+vBDcW/UbqxOneSOIzvOm2CrRnvToQMwapSxxyQiigQMdqJcdbWx4zy10/EbpLVr86lT2s/h6vRpcS+TByOzMykpSe68suO8MWM31jXXsDoyEbVNDHai3PffGzvOU8eO8uP09l7q1UvX1JzUla3YWLHzyB+ZnUmyAUMwgYUZu7Fef12+CCQRUTRhsBPlgs0vCWT+fLlxDz6ov/fSxo2ap+WmvUtmWlGR2IHkOQctO5N695Y7r+w4b8zYjcW2EETUVjHYiXKHD+sf19Ii8moKCsS9t1UB2Q/PYD5kP/lE/2uB1u0miorETqTi4vDdmSSzCqUH20IQUVvE3VhRrmtXfePmzQOWLHFPkp0zR3wAuwYFeqoJa6W3urPquutaPxYbKyo46/H118aO80X9Pnv+HILBthBE1BZxZSfKpadrHzdvHvD4460/YB0O8fi8eWcfM7tCMwCcc47+1wLAhRcG93pPsgGDEYGF5ypUTo7+Y7EtBBG1VQx2opzsioA6rqVFrCT4s2TJ2Uta48fLHV92nDf/+7/6XwsEv5vLk1G7umSpq1BLlwIffKD/OGwLQURtFYOdCOZwAOXlomhgebn3wEa2fo46Tqa+i2ui60svyR1fdpw3a9fqfy3gvaiiTD6SL7GxwJgx/seMGWNOYNGxo/YVI7aFIKK2jjk7EaqsDJgxw71gYGamqJDruq37zBm546njZBNY1XHHj8uNlx3nTbD5Kp6BjGw+kr/5BCp0uGOHGGfGjqpA7SoyM4GZM8UW/z59xAoTV3SIqC3jyk4EKisDbrihdWXk6mrxeFnZ2cdSUuSOqY7Tmo8SimAnN1f/awHg6qvP/ltLPpIv5eWBd7kdPizGGU2mIvahQ6Kv1tKl4vIXAx0iausY7EQYh0Os6Hjr36Q+Vlh49sNca4JyqPNRZARbZ0fNQdKaj+TLe+/JnVd2nBayO9OC3cFGRBRNGOxEmEB/2SuKe6+rc8+VO646TmuVYbUVQyCy47wJJt8nLw+Ijxf/1pqP5Ms338idW3acFqHY/UZEFG0Y7EQYrX/ZDx4cuDpyaqp79/GiIhEkeJOX557XovUymR56C+ElJwPr12s/TqBxPXrIHUd2nBZ6fp5ERG0dg50Io+cv+yNH/I/1fL6szPulI5tNPO6aE6S3aKEWGRn6XldfD/zqV2e/Nqo+jmsOkBHjiIjIXDZF8Zb90bY0NDQgOTkZ9fX16NSpk9XT8aulBUhI8H85xm4XCcGxscCGDXI1btavF6s2DodovOnvUllWltjpY7eLAEiW3t+0ceOA117T91oAaGwUW7a1fu98cTiAtDT/ScqpqcB33xm/G6u8HBg2LPC4rVtFHzIiomgm+/nNlZ0Is3OnXN7Jzp3i33Pnyh1XHSez28c1JygUvvoquNf/5jfi3siu588953/Mc88ZH+gATFAmItKDwU6E0fphd/So3Hh1XHW13HjZcUYItu3C/v1n/21E13NA1DJau1bUtHGVmSked611ZCQmKBMRaceighFG64dd167A998HHq/m1MiMdR3Xp49c4m8wAcvq1UBSkv7XHzvm/nVREbBwodh1deCA/sJ7+fni0t/27SK47N5dJAabsaKjGjxYBFSBLjMyQZmI6CwGOxFG/bCrrvaeA2OziefVD7tZs4D/+q/Ax1Uv72hNOJ4/X+748+fLHdebjh1Fjsx33+l7fe/erR8Lpuu5K7s9tLkxdjtwyy2iAKIvN99sbsBFRBRpLL2MtXjxYgwYMABJSUno1q0bxo8fj88++8xtjKIoWLBgATIyMhAfH4+hQ4di3759bmOam5tRUFCALl26IDExEePGjcOhQIknEcpuFy0hfCX7Koro9aR+2PXqJXdcdZzWIoTdusmNlx3nTUsL8MMP+l9vdNdzKzkc3nt9uSotDb7FBhFRNLE02Nm2bRumTZuGDz/8EJs3b8bp06cxatQoHHO57lBUVIQlS5Zg2bJlqKioQHp6OkaOHInGxkbnmMLCQqxbtw6lpaXYsWMHmpqaMGbMGDj4f3zTPfqoseO8kSkG6M+4cfpfG27CMYGciCjcWXoZa9OmTW5fr1ixAt26dUNlZSWuuuoqKIqCJ598Eg888ADyf8r4XLVqFdLS0lBSUoJ77rkH9fX1eOGFF7B69WqMGDECALBmzRpkZWVhy5YtuOaaa0L+vsyktovwxWYTzycnA3V1wO7dcsdVP0C//VZuvDpOdqdUMDuq/vMf/a8FAtcZiiTcjUVEpF1Y7caqr68HAKT8VG63qqoKtbW1GDVqlHNMXFwchgwZgp0/7a2urKzEqVOn3MZkZGQgJyfHOcZTc3MzGhoa3G6RQqZdxKFDwIgRwK23yu8uUgsF/uMfcuPVcVp3e+nxySf6XwsEl9wcbrgbi4hIu7AJdhRFwaxZs3DllVciJycHAFBbWwsASEtLcxublpbmfK62thaxsbHo3LmzzzGeFi9ejOTkZOctKyvL6LdjGrP+Yle3ku/dKzdeHXfypNz448dFrkl5ufZLUoE6jAcSqCZOJFET1H0Vc7TZuBuLiMhT2AQ706dPxyeffIKXvWRf2jz+z64oSqvHPPkbM3/+fNTX1ztvBw8e1D/xEAsm0defjh3FvWyVY3XcmTPy57j1VlH9t1cv95YTgWjdEu6pqiq414cTNUEdaB3wqF+7JqgTEVGYBDsFBQXYuHEjtm7dikyXKm3pP2358Vyhqaurc672pKeno6WlBUc8EjNcx3iKi4tDp06d3G5tXV2duA/UZFIlO86b6mrghhvkA55Bg/SfCxD5S9EkPx949dXWHe0zM8XjZhU0JCKKVJYGO4qiYPr06SgrK8N7772H3h4FUXr37o309HRs3rzZ+VhLSwu2bduGQT99Avbv3x8xMTFuY2pqarB3717nmGiiBiVGUze3eX6A+iI7zht1VaiwUO6SVrCXZKZMCe71/jgc4tKc3kt0euXni4KIxcXA9Oni/osvGOgQEXlj6W6sadOmoaSkBBs2bEBSUpJzBSc5ORnx8fGw2WwoLCzEokWLkJ2djezsbCxatAgJCQm49dZbnWOnTJmC2bNnIzU1FSkpKZgzZw5yc3Odu7OiSTArKv707CnuL7hAbrw6Li4OaG7Wfj5FObtFOlBRvmBTqrwVFTRCWZnY+eaaMJ6ZKS4zmR10lJUBBQXuu+cefxxYupQBDxFRK4qFAHi9rVixwjnmzJkzykMPPaSkp6crcXFxylVXXaXs2bPH7TgnTpxQpk+frqSkpCjx8fHKmDFjlG+++UZ6HvX19QoApb6+3qi3Zpq//EVRRKhg7O3oUXH85ma58c3NYnxMTHDnLSkJ/J5Pn1aUzEx9x09NFa832tq1imKztT6fzSZua9caf07Xc/t7z2aem4gonMh+ftsURTYlNXrJtogPB1OnAsuXG3/cmhpRFfnECSAhIfD448eB+HigXTv5pGZvtm6Va7cwfjywYYP243fsKLa9G5mw63CIJGtfJQDUlh1VVcYnCjsc4j352wXXoQPQ1MQkZSKKfrKf32GRoEzyzNp6/otfiPvp0+XGq+Pa67wQqmWLdEsL8Prr+s7T1CRyaYwkU+vIrCrGmzcH3u5/8qQYR0REAoOdCCPbu0ortejfxo1y49Vxsjk+rrRukQ62XYTRwY6VVYyXLDF2HBFRW8BgJ8LoCS5knHOOuJetm6OOO+887efSukX6wAHt5zCTlVWMZVtfRFOLDCKiYDHYiTD33GPOcT/6SNwPHCg3Xh33q1/JjZ88GSgpETk6VVXadgwFu5vqqquCe70nK6sYDxhg7DgioraAwU6E+ec/zTmuWuCvtFRuvDpOdj6HDwO33CKSkbUmzubmahvvqZ3Bv+VWVjF+4gljxxERtQUMdiKMWZ0t1O7osq0Z1HGyl5iCuRT1ww/6XwsAPlqkBcWqKsbx8UBenv8xeXliHBERCQx2Isz775tz3JIScf/kk3Lj1XGyTTqDaeYZbD+w778P7vW+5OcDX30lLs3pvUSnx/r1vgOevDzxPBERnWVpBWXSbssWc457+rS4X71abvzq1cC8eUBKCvDdd4HHp6Ton1uwunY179h2u1ydIKOtXy9qIs2dC+zfD2RniwrKXNEhImqNwU6E0dOaQUZiorhvaZEbr46TzUsJJn8l2H5gwfTxCmfx8cCyZVbPgogo/PEyVoQJNlnXl3ffFfeyqxTqOLUYYSCy47wJZgu3WbuiiIgocjDYiTBmJSiPGyfuJ0yQG6+OUxObA5Ed502grd7+3Hxz9LZNaGkRuVMFBeJedlWOiKitYbATYWTyY/RQi9BpTTjWWoRQD39bvQMpLQ2u+nK4mjdP9DCbOVNcypo5U3w9b57VMyMiCj8MdiKMWX1K1YBAdueSOk62hk2wtW58bfUOxKweVVaaN08kI3sGcQ6HeJwBDxGROwY7Eeb++805rrpbKjVVbrw6btAgufGy4/zx3Or9hz/Ivc6sS39WaGkJ3PdqyRJe0iIicsVgJ8Lo6UUl48QJca/1MtZFF8mNlx0XiLrV+5ZbgMZGudeYVXXaCjJNUR0OMY6IiAQGOxHGrPwT9fKYbE0addzUqYETgO12Mc5oimLsuEiwf7+x44iI2gIGOxHGrPwTtdmmbE6MOi42Fhgzxv/YMWPk21BokZ1t7LhIIJugrWfnGhFRtGKwQwCAN94Q94MGya3UqDk4DgdQWel//Mcfm7MiJdsB3qxO8Va47DJjxxERtQUMdiKMGa0J0tKA5GTx75075XJCdu4U/96+HTh0yP94s3ZEyebiRFPOTlaWseOIiNoCBjsRxohdTZ5iY88GONXVcq9Rx9XUyI2XHafF118bOy4SqAUW/WHVaCIidwx2Isyzzxp/TNeVF611dmRbOQTT8sEX2e7e0dQFXC2w6Csnx2YT1ZSjtWo0EZEeDHYijFm7bNSVF611di69VG687Dgtjh0zdlykUAsseq7wZGWJx/PzrZkXEVG4YrATYczaZaOuvGitszN3rtx42XFa9O1r7LhI4llgcetWoKqKgQ4RkTftrZ4AaTNggPHHdM3x0Lqy8957cuNlx2nx+OPAX/8qNy4aqQUWiYjIP67shBGZLtZqw04juXYG17qyI9uWwIz2BfHxQF6e/zF5eWIcERG1XQx2woRsF2vZCsdauHYG79xZ7jXquJ495cbLjtNq/XrfAU9eXnQlJxMRkT4MdsKAli7WWrt+y3DdjVVRIfcadVx7yQuhsuP0WL8eOH4cmDYNGDVK3B8/zkCHiIgE5uxYTLaL9cKFoh6OWmclUCE/rdTdWLKVjtVxiYly42XH6RUfL1bEiIiIPHFlx2Jau1jb7aLjt9HU3Viy9VnUcRkZcuNlxxERERmNwY7FDhzQNs7hAF5+2bjz22zuu7Fkd3up42QrOptR+ZmIiEgGL2OZxOEQeTA1NWLVZPBg76smffrIHU8dJ9OLSivXiruyu73UcezVRERE4Y4rOyYoKwN69QKGDQNuvVXc9+olHvc0dapcl/GpU8W/jewxZbcDc+a4F6KT3e2ljmOvJiIiCncMdgxWVgbccEPr1ZfqavG4Z8ATGwvMmuX/mLNmiXGAsT2mzpwB/vIX9znJ7vZSx7FXExERhTtLg533338fY8eORUZGBmw2G9Z77BWePHkybDab2+3yyy93G9Pc3IyCggJ06dIFiYmJGDduHA4ZfZ1HksMBzJgBKErr59THCgtbJyQXFYl2Cp4Bgd0uHi8qOvuYupLir22Et+N4421OelZq2KuJiIjCmaXBzrFjx/Dzn/8cy/zsGb722mtRU1PjvL355ptuzxcWFmLdunUoLS3Fjh070NTUhDFjxsAhu4faQIHyaRTFvaaNq6IioLHRvVZMY6N7oAOcXUkBWgc8Npu4vfzy2Z5JxcX+d3t5zknvSg17NRERUdhSwgQAZd26dW6PTZo0ScnLy/P5mqNHjyoxMTFKaWmp87Hq6mqlXbt2yqZNm6TPXV9frwBQ6uvrtU7bTUmJoojwwf+tpKT1a9euVZTMTPdxmZnicW+8jc/Kaj1e75xkj09ERGQV2c/vsN+NVV5ejm7duuGcc87BkCFD8Mgjj6Bbt24AgMrKSpw6dQqjRo1yjs/IyEBOTg527tyJa665xusxm5ub0dzc7Py6oaHBkLnK5tN4jlPzfDwvf6l5Pt4uBeXni3YI3nZ8ue4E++47fXPyd3xfZHegBSMU5yAiougS1sHO6NGjceONN6Jnz56oqqrCn/70J1x99dWorKxEXFwcamtrERsbi84eDZ3S0tJQW1vr87iLFy/Gww8/bPh8L7tM+7hAeT42m8ipycvznovj2fW6rEwcz/VymhoAeWOziVwbb7ultHTV9nbezExxScyoS1mhOAcREUWfsN6NddNNN+G6665DTk4Oxo4di7feeguff/453njjDb+vUxQFNj8ZvPPnz0d9fb3zdvDgQUPm++yz2sfJ5vksXSpyccrLfQcuvnaC+Qt0gOB3S2ndgRau5yAiougU1sGOp+7du6Nnz57Yv38/ACA9PR0tLS044lEJr66uDmlpaT6PExcXh06dOrndjKC1GjIgXzdn5kz/NXv8rRCpPAOazMzgd0vp3YFm5DkUJfhzEBFR9IqoYOfw4cM4ePAguv+UYNK/f3/ExMRg8+bNzjE1NTXYu3cvBlnQn0BrNWRAX90cb6sZMpWVHQ6xO8vI3VLB7EAz6hxA8OcgIqLoZWmw09TUhN27d2P37t0AgKqqKuzevRvffPMNmpqaMGfOHHzwwQf46quvUF5ejrFjx6JLly6YMGECACA5ORlTpkzB7Nmz8e677+Jf//oXbr/9duTm5mLEiBEhfz9aqyEDcnVzPHlbMZFdIUpLE41Ehw41JrFX9rzBVH6urjZ2HBERtS2WBju7du3CJZdcgksuuQQAMGvWLFxyySV48MEHYbfbsWfPHuTl5aFv376YNGkS+vbtiw8++ABJSUnOYxQXF2P8+PGYOHEirrjiCiQkJOC1116D3YItOlqrIQP+6+b447lioncnWLBCcd7vv5cb9+qrQEGByEFqadF/PiIiii42RfGX5dE2NDQ0IDk5GfX19Ybk78ybByxZ4p5DYreLQMezSKC/18goKRErNQ6HyOWprvae26LuuqqqMnartnpef5eZsrKCO+9LLwG3367tNYG+30REFPlkP78jKmcnUhQVAcePi/yY6dPF/fHjvj94y8pEjyo9CbbqikmgysqAOT2q7HYRbPlz883BnVe2X5crhwN4/HERRBIRUdvGlR0Yv7KjhczKiDe+Vmr0rCoFIxQrO3q/R4A45/Hj7pcOiYgoOnBlJ0LI7DTy5GulxtcKkcPRuru5UUKxUypQvy5/HA7g6af1n5uIiCIfgx2L6dml5K0+jkydHTNq0YRiNxbgu7O6DNn6R0REFJ3Cul1EWyC7S6m4WGwb99UPSku9G9kWEDJCuQvMs1/XP/4B/PWvgV8nW/+IiIiiE3N2EB45O8Huonr5ZVFhORB195ZRrNoFBojt5QkJ/lermLNDRBS9mLMTIYzaRWXGCovDIXpx+evJZdUuMEBfXSMiImp7GOyEATUfxXOLtZbeVYEqMdtsYleUt+7m3pSViRWbYcP89+Qyav56XX55cM8TEVH042UsWHsZy5XDcTYfxVdujj9qZ3DA/ZKSGgDJBh7qcTx/MwIdJ9j5axVoS7qZl9CIiMh6vIwVgex2kTyst3eVESssoehibpRQNCElIqLIx91YUcZzx5LWFRa9u7rKykSQ5PrazEyRz2PWZaxQbXsnIqLIxmAngvm6bKSuEOmhJ4Dwddmrulo8blbejlXNT4mIKLLwMlaE0pJArEW3btrGWXnZy+ikbCIiik4MdiKQupLieblJXUkxoy2EL1bmzVi57Z2IiCIHg50IY/ZKSl2dtnFW581Yue2diIgiA3N2IozZbSG05sGEQ95MsEnZREQU3RjsRBizV1LUPJhA7R/UPBit480STFI2ERFFN17GijBmr6RozYNh3gwREYU7BjsRJhQ7kLTmwTBvhoiIwhnbRSB82kXIMqotRCBa2z+Eul0EERG1bbKf3wx2EHnBDuC9YnFWlrhkxJUUIiJqC2Q/v5mgHKG4A4mIiEgOg50Ixh1IREREgTHYIZ+Ys0NERNGAwQ55pbWLuRVdz4mIiGRw63kUcjiA8nLg5ZfFvdbWEVp7b4VTry4iIiJP3I2FyNyN5UuwKywOh+ie7qslhVoRuapKXKLSOp6IiMgosp/fXNmJIkassGjtYm5l13MiIiIZDHaihFHd0LX23rK66zkREVEgDHaihFErLJHY9ZyIiMgfBjtRwqgVFq29t0LRq4uIiCgYDHaihFErLOx6TkRE0YbBTpQwcoWFXc+JiCiaWBrsvP/++xg7diwyMjJgs9mwfv16t+cVRcGCBQuQkZGB+Ph4DB06FPv27XMb09zcjIKCAnTp0gWJiYkYN24cDvlLXolSRq+w5OcDX30FbN0KlJSI+6oq34GL1vFEREShYmmwc+zYMfz85z/HsmXLvD5fVFSEJUuWYNmyZaioqEB6ejpGjhyJxsZG55jCwkKsW7cOpaWl2LFjB5qamjBmzBg4tFbSiwJGr7CovbduuUXcBwqUtI4nIiIKhbApKmiz2bBu3TqMHz8egFjVycjIQGFhIe677z4AYhUnLS0Njz32GO655x7U19eja9euWL16NW666SYAwLfffousrCy8+eabuOaaa6TOHU1FBQH2qCIiorYh4osKVlVVoba2FqNGjXI+FhcXhyFDhmDnzp0AgMrKSpw6dcptTEZGBnJycpxjvGlubkZDQ4PbLZpwhYWIiOissA12amtrAQBpaWluj6elpTmfq62tRWxsLDp37uxzjDeLFy9GcnKy85aVlWXw7ImIiChchG2wo7J5ZNsqitLqMU+BxsyfPx/19fXO28GDBw2ZKxEREYWfsA120tPTAaDVCk1dXZ1ztSc9PR0tLS04cuSIzzHexMXFoVOnTm43IiIiik5hG+z07t0b6enp2Lx5s/OxlpYWbNu2DYMGDQIA9O/fHzExMW5jampqsHfvXucYIiIiatvaW3nypqYmfPHFF86vq6qqsHv3bqSkpKBHjx4oLCzEokWLkJ2djezsbCxatAgJCQm49dZbAQDJycmYMmUKZs+ejdTUVKSkpGDOnDnIzc3FiBEjrHpbREREFEYsDXZ27dqFYcOGOb+eNWsWAGDSpElYuXIl5s2bhxMnTmDq1Kk4cuQILrvsMrzzzjtISkpyvqa4uBjt27fHxIkTceLECQwfPhwrV66EnVuQiIiICGFUZ8dK0VZnh4iIqC2I+Do7REREREZgsENERERRzdKcnXChXsmLtkrKRERE0Uz93A6UkcNgB3A2FmUlZSIiosjT2NiI5ORkn88zQRnAmTNn8O233yIpKSlgdWYtGhoakJWVhYMHD7aZxGe+Z77naMX3zPccrSL5PSuKgsbGRmRkZKBdO9+ZOVzZAdCuXTtkZmaadvy2WKWZ77lt4HtuG/ie24ZIfc/+VnRUTFAmIiKiqMZgh4iIiKIagx0TxcXF4aGHHkJcXJzVUwkZvue2ge+5beB7bhvawntmgjIRERFFNa7sEBERUVRjsENERERRjcEOERERRTUGO0RERBTVGOyY5P3338fYsWORkZEBm82G9evXWz0lUy1evBgDBgxAUlISunXrhvHjx+Ozzz6zelqmWr58OS6++GJnIa6BAwfirbfesnpaIbN48WLYbDYUFhZaPRVTLViwADabze2Wnp5u9bRMV11djdtvvx2pqalISEjAL37xC1RWVlo9LdP06tWr1c/ZZrNh2rRpVk/NNKdPn8Yf//hH9O7dG/Hx8TjvvPPw5z//GWfOnLF6aoZjBWWTHDt2DD//+c9x55134vrrr7d6Oqbbtm0bpk2bhgEDBuD06dN44IEHMGrUKHz66adITEy0enqmyMzMxKOPPorzzz8fALBq1Srk5eXhX//6F/r162fx7MxVUVGB5557DhdffLHVUwmJfv36YcuWLc6v7Xa7hbMx35EjR3DFFVdg2LBheOutt9CtWzccOHAA55xzjtVTM01FRQUcDofz671792LkyJG48cYbLZyVuR577DE888wzWLVqFfr164ddu3bhzjvvRHJyMmbMmGH19AzFYMcko0ePxujRo62eRshs2rTJ7esVK1agW7duqKysxFVXXWXRrMw1duxYt68feeQRLF++HB9++GFUBztNTU247bbb8Le//Q0LFy60ejoh0b59+zaxmqN67LHHkJWVhRUrVjgf69Wrl3UTCoGuXbu6ff3oo4+iT58+GDJkiEUzMt8HH3yAvLw8XHfddQDEz/jll1/Grl27LJ6Z8XgZi0xRX18PAEhJSbF4JqHhcDhQWlqKY8eOYeDAgVZPx1TTpk3DddddhxEjRlg9lZDZv38/MjIy0Lt3b9x888348ssvrZ6SqTZu3IhLL70UN954I7p164ZLLrkEf/vb36yeVsi0tLRgzZo1uOuuuwxtDh1urrzySrz77rv4/PPPAQD//ve/sWPHDvz617+2eGbG48oOGU5RFMyaNQtXXnklcnJyrJ6Oqfbs2YOBAwfi5MmT6NixI9atW4ef/exnVk/LNKWlpfj4449RUVFh9VRC5rLLLsOLL76Ivn374rvvvsPChQsxaNAg7Nu3D6mpqVZPzxRffvklli9fjlmzZuH+++/HRx99hHvvvRdxcXG44447rJ6e6davX4+jR49i8uTJVk/FVPfddx/q6+tx4YUXwm63w+Fw4JFHHsEtt9xi9dQMx2CHDDd9+nR88skn2LFjh9VTMd0FF1yA3bt34+jRo1i7di0mTZqEbdu2RWXAc/DgQcyYMQPvvPMOOnToYPV0Qsb1cnRubi4GDhyIPn36YNWqVZg1a5aFMzPPmTNncOmll2LRokUAgEsuuQT79u3D8uXL20Sw88ILL2D06NHIyMiweiqmeuWVV7BmzRqUlJSgX79+2L17NwoLC5GRkYFJkyZZPT1DMdghQxUUFGDjxo14//33kZmZafV0TBcbG+tMUL700ktRUVGBp556Cs8++6zFMzNeZWUl6urq0L9/f+djDocD77//PpYtW4bm5uaoT9wFgMTEROTm5mL//v1WT8U03bt3bxWwX3TRRVi7dq1FMwqdr7/+Glu2bEFZWZnVUzHd3Llz8Yc//AE333wzABHMf/3111i8eDGDHSJvFEVBQUEB1q1bh/LycvTu3dvqKVlCURQ0NzdbPQ1TDB8+HHv27HF77M4778SFF16I++67r00EOgDQ3NyM//znPxg8eLDVUzHNFVdc0ap0xOeff46ePXtaNKPQUTdXqEm70ez48eNo1849dddut3PrOclramrCF1984fy6qqoKu3fvRkpKCnr06GHhzMwxbdo0lJSUYMOGDUhKSkJtbS0AIDk5GfHx8RbPzhz3338/Ro8ejaysLDQ2NqK0tBTl5eWtdqZFi6SkpFY5WImJiUhNTY3q3Kw5c+Zg7Nix6NGjB+rq6rBw4UI0NDRE3V++rmbOnIlBgwZh0aJFmDhxIj766CM899xzeO6556yemqnOnDmDFStWYNKkSWjfPvo/HseOHYtHHnkEPXr0QL9+/fCvf/0LS5YswV133WX11IynkCm2bt2qAGh1mzRpktVTM4W39wpAWbFihdVTM81dd92l9OzZU4mNjVW6du2qDB8+XHnnnXesnlZIDRkyRJkxY4bV0zDVTTfdpHTv3l2JiYlRMjIylPz8fGXfvn1WT8t0r732mpKTk6PExcUpF154ofLcc89ZPSXTvf322woA5bPPPrN6KiHR0NCgzJgxQ+nRo4fSoUMH5bzzzlMeeOABpbm52eqpGc6mKIpiTZhFREREZD7W2SEiIqKoxmCHiIiIohqDHSIiIopqDHaIiIgoqjHYISIioqjGYIeIiIiiGoMdIiIiimoMdoiIiCiqMdghooizcuVKnHPOOZbOYejQoSgsLLR0DkQkhxWUicgwkydPxqpVq1o9fs011xjaM+zEiRNobGxEt27dDDumVj/++CNiYmKQlJRk2RyISE70dzojopC69tprsWLFCrfH4uLiDD1HfHy85Q1mU1JSLD0/EcnjZSwiMlRcXBzS09Pdbp07d3Y+b7PZ8Pzzz2PChAlISEhAdnY2Nm7c6HaMjRs3Ijs7G/Hx8Rg2bBhWrVoFm82Go0ePAmh9GWvBggX4xS9+gdWrV6NXr15ITk7GzTffjMbGRucYRVFQVFSE8847D/Hx8fj5z3+OV1991e97efrpp5GdnY0OHTogLS0NN9xwg/M518tY5eXlsNlsrW6TJ092jn/ttdfQv39/dOjQAeeddx4efvhhnD59WuN3l4j0YLBDRCH38MMPY+LEifjkk0/w61//Grfddht+/PFHAMBXX32FG264AePHj8fu3btxzz334IEHHgh4zAMHDmD9+vV4/fXX8frrr2Pbtm149NFHnc//8Y9/xIoVK7B8+XLs27cPM2fOxO23345t27Z5Pd6uXbtw77334s9//jM+++wzbNq0CVdddZXXsYMGDUJNTY3z9t5776FDhw7O8W+//TZuv/123Hvvvfj000/x7LPPYuXKlXjkkUe0fuuISA9Le64TUVSZNGmSYrfblcTERLfbn//8Z+cYAMof//hH59dNTU2KzWZT3nrrLUVRFOW+++5TcnJy3I77wAMPKACUI0eOKIqiKCtWrFCSk5Odzz/00ENKQkKC0tDQ4Hxs7ty5ymWXXeY8R4cOHZSdO3e6HXfKlCnKLbfc4vW9rF27VunUqZPbMV0NGTJEmTFjRqvHf/jhB6VPnz7K1KlTnY8NHjxYWbRokdu41atXK927d/d6bCIyFnN2iMhQw4YNw/Lly90e88xvufjii53/TkxMRFJSEurq6gAAn332GQYMGOA2/le/+lXA8/bq1cstWbh79+7OY3766ac4efIkRo4c6faalpYWXHLJJV6PN3LkSPTs2RPnnXcerr32Wlx77bXOS2++nDp1Ctdffz169OiBp556yvl4ZWUlKioq3FZyHA4HTp48iePHj/s9JhEFj8EOERkqMTER559/vt8xMTExbl/bbDacOXMGgMitsdlsbs8rEptG/R1TvX/jjTdw7rnnuo3zlTydlJSEjz/+GOXl5XjnnXfw4IMPYsGCBaioqPC57f33v/89vvnmG1RUVKB9+7P/ez1z5gwefvhh5Ofnt3pNhw4dAr43IgoOgx0iCisXXngh3nzzTbfHdu3aFdQxf/aznyEuLg7ffPMNhgwZIv269u3bY8SIERgxYgQeeughnHPOOXjvvfe8Bi1LlizBK6+8gg8++ACpqaluz/3yl7/EZ599FjAIJCJzMNghIkM1NzejtrbW7bH27dujS5cuUq+/5557sGTJEtx3332YMmUKdu/ejZUrVwJAqxUfWUlJSZgzZw5mzpyJM2fO4Morr0RDQwN27tyJjh07YtKkSa1e8/rrr+PLL7/EVVddhc6dO+PNN9/EmTNncMEFF7Qau2XLFsybNw9//etf0aVLF+f7j4+PR3JyMh588EGMGTMGWVlZuPHGG9GuXTt88skn2LNnDxYuXKjrPRGRPO7GIiJDbdq0Cd27d3e7XXnlldKv7927N1599VWUlZXh4osvxvLly527sYKp1/Pf//3fePDBB7F48WJcdNFFuOaaa/Daa6+hd+/eXsefc845KCsrw9VXX42LLroIzzzzDF5++WX069ev1dgdO3bA4XDgd7/7ndv7njFjBgBRVPH111/H5s2bMWDAAFx++eVYsmQJevbsqfv9EJE8VlAmorD3yCOP4JlnnsHBgwetngoRRSBexiKisPP0009jwIABSE1NxT/+8Q88/vjjmD59utXTIqIIxWCHiMLO/v37sXDhQvz444/o0aMHZs+ejfnz51s9LSKKULyMRURERFGNCcpEREQU1RjsEBERUVRjsENERERRjcEOERERRTUGO0RERBTVGOwQERFRVGOwQ0RERFGNwQ4RERFFtf8PvJlBIb+scX0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+    "\n",
+    "We know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+    "\n",
+    "Let's split our dataset into train and test sets. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the  __np.random.rand()__ function: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"multiple_regression_model\">Multiple Regression Model</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In reality, there are multiple variables that impact the co2emission. When more than one independent variable is present, the process is called multiple linear regression. An example of multiple linear regression is predicting co2emission using the features FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that multiple linear regression model is the extension of the simple linear regression model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[8.93139025 8.8082197  9.49265722]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit (x, y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__  are the parameters of the fitted line. \n",
+    "Given that it is a multiple linear regression model with 3 parameters and that the parameters are the intercept and coefficients of the hyperplane, sklearn can estimate them from our data. Scikit-learn uses plain Ordinary Least Squares method to solve this problem.\n",
+    "\n",
+    "#### Ordinary Least Squares (OLS)\n",
+    "OLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ($\\hat{y}$) over all samples in the dataset.\n",
+    "\n",
+    "OLS can find the best parameters using of the following methods:\n",
+    "* Solving the model parameters analytically using closed-form equations\n",
+    "* Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"prediction\">Prediction</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE) : 595.99\n",
+      "Variance score: 0.86\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Mean Squared Error (MSE) : %.2f\"\n",
+    "      % np.mean((y_hat - y) ** 2))\n",
+    "\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % regr.score(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Explained variance regression score:__  \n",
+    "Let $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n",
+    "\n",
+    "$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var\\{ y - \\hat{y}\\}}{Var\\{y\\}}$  \n",
+    "The best possible score is 1.0, the lower values are worse.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[9.04049848 8.31238169 6.40689532 2.72679392]]\n",
+      "Residual sum of squares (MSE): 598.51\n",
+      "Variance score (R^2): 0.86\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "import numpy as np\n",
+    "\n",
+    "# Membuat model regresi linear\n",
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "# Menentukan fitur dan target\n",
+    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "\n",
+    "# Melatih model\n",
+    "regr.fit(x, y)\n",
+    "\n",
+    "# Menampilkan koefisien\n",
+    "print('Coefficients: ', regr.coef_)\n",
+    "\n",
+    "# Memprediksi nilai CO2EMISSIONS pada data test\n",
+    "y_ = regr.predict(np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']]))\n",
+    "\n",
+    "# Menghitung residual sum of squares dan variance score\n",
+    "x_test = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y_test = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_ - y_test) ** 2))\n",
+    "print('Variance score (R^2): %.2f' % regr.score(x_test, y_test))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python\n",
+    "regr = linear_model.LinearRegression()\n",
+    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit (x, y)\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
+    "print('Variance score: %.2f' % regr.score(x, y))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "    \n",
+    "<!--\n",
+    "\n",
+    "## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  | Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   |\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "--!>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "c1170d4cb1c9bbce7dbbef74b645fc6b265a5aaf4ce89c4ac861feed8769ed99"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Non Linear.ipynb b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Non Linear.ipynb
new file mode 100644
index 0000000..0bad65f
--- /dev/null
+++ b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Non Linear.ipynb	
@@ -0,0 +1,890 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align:center\">\n",
+    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
+    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "# Non Linear Regression Analysis\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **20** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Differentiate between linear and non-linear regression\n",
+    "* Use non-linear regression model in Python\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the data shows a curvy trend, then linear regression will not produce very accurate results when compared to a non-linear regression since linear regression presumes that the data is linear. \n",
+    "Let's learn about non linear regressions and apply an example in python. In this notebook, we fit a non-linear model to the datapoints corrensponding to China's GDP from 1960 to 2014. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"importing_libraries\">Importing required libraries</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although linear regression can do a great job at modeling some datasets, it cannot be used for all datasets. First recall how linear regression, models a dataset. It models the linear relationship between a dependent variable y and the independent variables x. It has a simple equation, of degree 1, for example y = $2x$ + 3.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ESEiI+OCDD6xe5969eyI9Pd30SE1NFQBEenq6PYdPRKSL7GwhwsOFkIsy+R+KIkREhDzP1a1bJ8dr6XtQFPm6oyQmWv+Z5n4kJmq4qMEgxJo1QtSp8+ACFSsK8d57Qty7Z6fvxPGMv8e8v0t7/x7T09NV3b/dasanIMnJybh48SI6duxoOubv74927drhl19+sfq+uXPnIiQkxPSIiIhwxHCJiHThDsmkarjabiBdZy6EADZtApo1A3r2BI4fB8qWlQnMp07JWR9/f13H70z2Wn7Ui8cEPhcvXgQAhIWFmR0PCwszvWbJpEmTkJ6ebnqkpqbadZxERHpyh2RSNVwtgNMtcTopCXjsMeDpp4EDB4CgIJnAnJws83oCAnQdt6uwx/KjXjyucrOS52+oECLfsdz8/f3h70GRNhF5F3dIJlXDFQM448yFpQrECxYUchP/7Tdg8mTghx/k85IlZT+tCROAcuXsOWyXobZytKN5TOBT8X89Si5evIhKuf4Lv3TpUr5ZICIiT2Fckimoi3d4uJOTSVVw1QAuJgaIjpYzTRcuyM9v0yb/TI/BIM+5++shtPhqKsr/8pV8oXhx4IUXZBDk6tGnl/CYpa7IyEhUrFgRW7duNR3LysrCtm3b0KpVKyeOjIjIfuxZy8aRXHk3UGE7nRISgPZVTuB8hz7o9EpjlP/lKxjgg7MdBgJ//QW89x6DHhfiVoHPrVu3cODAARw4cACATGg+cOAAUlJSoCgKxowZgzlz5mD9+vX4888/MXDgQAQEBKBPnz7OHTgRkR25ejKpGu4awH37YQquPzMUiX/XQx+shg8E1uA5NMBhRCYtR8L+6s4eIuWhCFFQXUrXkpSUhA4WKlgOGDAAK1asgBACcXFx+PDDD3H9+nVERUXh/fffR4MGDVR/RkZGBkJCQpCeno7g4GA9h09EZFfG5ZaClmRcnaWu3hERKnJqHO3vv5Ezew7uv/sB/JEFAPgGT2EqZuIAmgB4sMyYnGz/34Mn/O6LSu39260CH0dg4ENElvDG4jgu/bO+dg2YPx945x3gzh0AQCLa41XMwW60tPiWxET7JvlaChbDw+UMmksFi3am9v7tMcnNRET2whuLY7nkbqCbN+Uv/PXXgfR0AMCVmv9Ar1Oz8SP+D4D13cP23IlmrNqddwrDWLXbXZY6HcmtcnyIiBzNk9pBkA3u3gXefFM2EJ06VQY9jzwCbNyIP5fsxo94HAUFPYD98ppdreiju2DgQ0RkBW8sXiwrC/jgA6B2beC//wWuXJF/Xr1aFiLs2hVt2ipO3YnmakUf3QUDHyIiK3hjcV1aOrhrvvAnnwD16gEvviin9iIigKVLgSNHgF69TA1Enb0TzRWLProDBj5ERFbwxuKabOngXighgHXrgIYNgf79gdOngbAwmcR84gQwZAhQLH9arDNLCbhq0UdXx+RmIiIreGNxPbon8woBfP+9rKy8f788VqYMMHEiMHIkUKpUoZdQW91Zb55StdvRuJ09D25nJyIjg0HOJBR2Y3FEnRZ68Puwtvyo+fexfbsMeH7+WT4PDATGjZOPkBC9hm1XxkAQMP87alxq86ZdXWrv31zqIiKywtk5HGROt5yrvXuBTp2Adu1k0OPvL4Od06eBuDi3CXoAz6ja7Whc6iIiKkCROnRTPkUpTljknKvDh+WW9PXr5fNixYChQ4EpU/JHDm7EWUtt7oqBDxFRIXhj0UdRC0HanHN18iQQGwusWiWnhRQF6NcPmD5d1ufxAC5Z9NFFMccnD+b4EBHpz1pSspZcFM05V+fOATNnAsuWAdnZ8qRnngFmzADq1y/qt0Quhjk+RETkMAXV1dGrEKTqnKurl4CxY4FatYDFi2XQ07kzsG+fjLAY9Hg1Bj5ERFQkhdXV0bMQZEHJvBtXXEfMvsly+WrBAiAzE2jbVl5482agaVMbv0PyJMzxISIim6mpq5OZqe5aapOX8+ZchZe+hcf2vQOf0fOBGzfkSc2bA7NnA088Yb2nBHklBj5ERGSTwpawFEUuYS1fru56WgpB+voC7R+9J/tpjZkLXLokX3j4YZnX060bAx6yiEtdRERkE7VLWAD0beZ5/z6wZIlsGjp2rAx6atYEPv0UOHgQ6N6dQQ9ZZXPgk5WVhePHjyPbmClPREReRe3S1KVLOhWCNBiAzz6TDURfeEFGXeHhMoH56FGgb1/WGKBCaQ587ty5gyFDhiAgIAAPP/wwUlJSAACjRo1CfHy87gMkIiLXpKWuTpEqDAsBbNgANG4MPP88cOoUEBoqo6UTJ4Bhw4DixW37JsjraA58Jk2ahIMHDyIpKQklSpQwHX/88cexZs0aXQdHRESuyWCQj7JlrZ+TdwkrJgY4cwZITJS1BBMTZc0dq0GPEMCWLUBUlFy++vNPoHRpmbR8+rRMMMp1HyJSQ3Ny84YNG7BmzRo8+uijUHLNWdavXx+nTp3SdXBERGS7orSHKIilCsx5WVvCUl1heOdO2UB02zb5vFQp+aHjx8vgh8hGmgOfy5cvo0KFCvmO37592ywQIiIi5ylqe4iCrmtp+3peNvcy279f9s769lv53N8fePFFYNIkwMK9h0grzUtdLVq0wKZNm0zPjcHOkiVL0LJlS/1GRkRENjEGJ3lnZIy1dYyFBbUqaPu6UdmywA8/FLKEZcmRI3JwzZrJoMfXV+bunDgBvPUWgx7SjeYZn7lz5+Jf//oXjhw5guzsbLz99ts4fPgwdu3ahW3GKUkiInIKtbV1oqO1L3sVtn0dAK5dk9dVfe3Tp4G4OLkVPSdHDrBPH9lUtFYtbQN0cfZaeiRtNM/4tGrVCjt37sSdO3dQs2ZNbNmyBWFhYdi1axeaNWtmjzESEXm1gvpg5aVne4i81G5fN55X4LjT0uQSVp06wMcfy6Cne3fg0CEZBHlY0FNYWw9yHJsqNz/yyCNYuXKl3mMhIqI8tObqaA1OtNCyfd3auBfNuIyn/4wHFi4E7t2TL3TsCMyaBbRooX1QbkBNW4+i5F2RNqoCn4yMDNUXLKgVPBERqWfLDVNLcKJVmzYyeElLs7yUpijy9StXgOeeMz8nGOl44dwbaDf4LQC35MHWreXW9LZttQ/GTdhz6ZFsowhRWG4+4OPjU+iOLSEEFEWBoaA5WDeQkZGBkJAQpKenM4gjIqcxGORSiLVlK2OQkZxsfsM0vq+w4CTv+9QyBmOA+fWNt4g1a4Bx4x6MOwC38RLexQTMQ1lcBwAcKt4MD6+fBd8nO8GQozg178XeeTdJSXJZqzCJiSq3+ZNVau/fqmZ8EhMTdRsYEREVTkuuTu4bpq+vXAbr0UMGI5aCE1XtIawwVmC2tIy1YIHc1XXuHOCHTLyAxZiM2aiIvwEAR1APUzAL6+93R2IpBdfW22fLvVr22vKfmz2XHsk2qgKfdu3a2XscRESUS1FumIUFJ0W9qcfEyKUZSzMln3+ajcFYiWmYgWqQLY1OIxLTEYdV6IMcyIhr40YZYDgr78VReTf2XHok26ha6srr+vXr+Oijj3D06FEoioJ69eph0KBBKFtQ7XI3waUuInIFeiyROHT7dE4O8MUXuDNhOgJS/wIApKEyZmAalmEwsmHeSys0FLh82fKlirocVxhblxGL8ln2WnqkB9TevzVvZ9+2bRuqV6+Od955B9evX8e1a9fwzjvvIDIyknV8iIh0YkwktpZembcPliXG9hC9e8uvdrmxCgF8/TXQpAnQuzcCUv/CVZ/y+C/eQC2cxGL82yzoUZSCgx7jJW3dcq+GPbf852VcegSK2JmedKM58BkxYgR69uyJ5ORkJCQkICEhAadPn0avXr0wYsQIe4yRiMhjqK3J46gbppYaQfn8+CPQsiXQtausvxMcDMyYgZ0fn8ZbyjhkKiUtjrtvX3WXt1fei6PzborUmZ70JzQqUaKEOHbsWL7jx44dEyVKlNB6OZeTnp4uAIj09HRnD4WIPMy6dUKEhwsh5xTkIzxcHtfynoiIgt9jz/EIIYT45RchOnR48KaAACFeeUWIq1dVjTsx0fy4tUdiYtG/R0uc9fnZ2fKaq1bJr9nZ+l7f26m9f2vO8Xnssccwfvx4dOvWzez4hg0b8Nprr2HXrl36RWVOwBwfIrIHa8m0xlmQgv7lb49cHZvGc+CAbCBq7Nfo5wf8+9/Aq68CFSuqHrez816c/flkH2rv36oCn0OHDpn+fPToUUyYMAEvvfQSHn30UQDA7t278f777yM+Ph49e/bUYfjOw8CHiPTmyGRau4zn2DFg+nTgiy/kCb6+wMCBwLRpQNWqNo2hsHpAjtrV5azPJ/3pGvgYCxgWdioLGBIR5edqRezUjmfX6jN49Pu4B720AKBXL9lU9KGHijwOS3V0IiL02XLvDp9P+tK1gGFycrJuAyMi8jauVsSusM+piAuYjNlo8fxiwHBfHuzaFZg5E2jYULdxFFQPKDd7bctX+/nkWVQFPtWqVbP3OIiIPJY9itgVJRiw9jllcRUT8RpG4j0E4C5gAK41exwh78yCb6so9YPTwLjl3hp7V1cu7PPJ89hUwBAAjhw5gpSUFGRlZZkd79q1qy4DcxYudRG5JocW49OZ3sm0RQ0G8o4nCBkYi7fwX7yBYNwEAOxEK0zBLCShg0PbSORWlIRwwL3/zpB2qu/fWreLnTp1SjRs2FAoiiJ8fHyEoiimP/v4+Gi9nMvhdnYi12PztmsXsm6dEIoiH7m/D+Mxtd+L8Tp5t17bcp0A3BbjMU9cRjnThfajseiMTQLIsfnaesjOzv87z/v9RkRY3xLuCX9nSBu192/NBQxHjx6NyMhI/P333wgICMDhw4exfft2NG/eHElJSTZHakRElhj/1Z93B5Kxp1JCgnPGpZUeRewMBjnTY2nWyHhszBgVRQizshBz4X1cKVML8zAB5XEVx1AHz+ILNMM+fIsnATyomqjp2jopSnVlT/k7Q/ahOfDZtWsXZsyYgdDQUPj4+MDHxwetW7fG3LlzMWrUKHuMkYi8lG43ehcREwOcOSN3b61aJb8mJ6tfQipyq4XsbGDFCqBOHWDkSJS8fgGienUcm7gcq1/9E2vxLISV24K920jkZWtCuKf9nSH9qUpuzs1gMCAwMBAAUL58eZw/fx516tRBtWrVcPz4cd0HSETuq6g5Flpu9O6SoFqUZFqbd4fl5ADr1sm6O8eOyWOVKgFTpkAZOhR1/fxQd7W+YygqWxPCPfHvDOlLc+DToEEDHDp0CDVq1EBUVBTmzZsHPz8/LF68GDVq1LDHGInIDemxG8fVtoE7m+ZgQAhg82ZZbfnAAXmsbFnglVeAESOAgADbr21nxiathSWE523Syr8zVBjNS11TpkxBzv8KWc2aNQtnz55FmzZtsHnzZrzzzju6D5CI3I9eORaudjN2Nk0d2xMTgdatgaeflkFPUBAQGyvX1saPNwt6NF/bAWxt0sq/M1QYm7ez53bt2jWUKVMGirX/YtwIt7MTFY2e7RnYUym/wlot/DjnV3T4YbLsnA4AJUsCI0cCEycC5coVuPzoim0ctFZX9pa/M9yqn5/dtrN7Om5nJyoavTtf67UN3JNY2qr9RNhBkdai64MDxYsLMWKEEOfPF/i+vFu87dkN3lZau5p7+t8ZbtW3TNfu7DExMVixYgWCg4MRU0i4n+Dm+wQ540NUNKtXA336FH7eqlVA797qrsmeSvkZ/8V/a/9faLFpOiokroEiBODjAwwYIBOZq1c3na+lGKAnzCZ46t+ZohZ19GS69uoKCQkxLWOFhIToM0I7iI2NRVxcnNmxsLAwXLx40UkjIvI+9sixYE+l/HzTUtD+0xlye7pxb3bPnrKBaJ06ZucWtsVbUeQW7+ho+TP1hDYOnvh3RuvvkSzTlOMjhEBKSgpCQ0MRkCcxzhXExsZi7dq1+OGHH0zHfH19ERoaqvoanPEhKhpvybFwmosXgTlzgA8/BIwtg55+WjYQbdzY4ltcrTs82Ya/x4LpOuNjJIRA7dq1cfjwYdSuXbvIg7SHYsWKoWLFis4eBpHXMu7G6dFDBjmWkmQt7cZxFrdZ1rl2DZg/H3jnHeDOHXmsQwdg9mygZcsC38ot3p6Bv0d9aNrO7uPjg9q1a+Pq1av2Gk+RnThxApUrV0ZkZCR69eqF06dPF3h+ZmYmMjIyzB5EVDR6tGdwhIQEOTvVoYPMS+rQQT53qVTFmzflbE5kJBAfL4OeqCjghx+An34qNOgBuMXbU/D3qA/N29k3bdqE+Ph4LFq0CA0aNLDXuGzy7bff4s6dO3jooYfw999/Y9asWTh27BgOHz6McuXKWXyPpbwgAFzqItKBK8+muHyS6N27wKJFwNy5wJUr8tgjj8gZnqeftl5wxwIuP3oG/h4LpnapS3PgU6ZMGdy5cwfZ2dnw8/NDyZIlzV6/du2abSO2g9u3b6NmzZqYMGECxo0bZ/GczMxMZGZmmp5nZGQgIiKCgQ+RB9Oz1pAeYzELDqOyoCxfhvvTZ8H/ShoAQNSuDWXGDOC55+SuLRu4Yo0e0o6/R+vskuMDAAsWLCjKuByqVKlSeOSRR3DixAmr5/j7+8Pf39+BoyIiZ3OVfk65t1z7wIC++AzVfOIQmXMa/gBSEIFYxOKnO/3xpl8xxNgW8wB4sPxoqY1IYVu8XXnmztsU5fdIkubAZ8CAAfYYh11kZmbi6NGjaOOoGutE5BZcIUn0wVKbQAwSMAPT8DCOADnARYRhNiZjMV5AFvyhnJfnFvVf87Zs8daj5xrpyxO36jtSkVpW3L17F/fv3zc75szloZdffhldunRB1apVcenSJcyaNQvbtm3DH3/8gWrVqqm6BrezE3k+Z28LNhiA6tUEGqR9h1mYgmbYDwC4hjJ4DRPxHkbiDkqZvccZ+RsunwdFlIva+7fmidPbt29j5MiRqFChAgIDA1GmTBmzhzOdO3cOvXv3Rp06dRATEwM/Pz/s3r1bddBDRN7B2Q05D723HavS2uJbPIlm2I+bCMQMTEUkkjEPE/MFPYD58psjFFYsD5DF8oy1E4nchealrgkTJiAxMRELFy5E//798f777yMtLQ0ffvgh4uPj7TFG1T7//HOnfj4RuQen1RrasweYMgVNtmwBANyDP97HCMTjFVyBukKrjqrR4ip5UER60zzj8/XXX2PhwoXo0aMHihUrhjZt2mDKlCmYM2cOPvvsM3uMkYhIdw6tNfTnn0D37sA//gFs2YIc32JYhOGoiVN4GW+oDnoAx9VocYU8KCJ70Bz4XLt2DZGRkQBkPo9x+3rr1q2xfft2fUdHRGRHMTHAmTMyl2fVKvk1OVnHoOfkSeD554GGDYENG+RW9P79IY4ex5zwRbigVCn0Ekb2Xn7Li8XyyFNpDnxq1KiBM2fOAADq16+PL774AoCcCSpdurSeYyMisjtjQ87eveVXXZa3UlOBF14A6tYFPvtMrgv16CFnflauhG/tGnj7bXmqmjqEzmj14ew8KCJ70Rz4DBo0CAcPHgQATJo0CQsXLoS/vz/Gjh2L8ePH6z5AIiK3cekSMHYsULs2sGSJzPzt3BnYtw/48kugXj3TqdaW2sqVk4/cnNHqw5gHBeQPflyx5xqRWqq3s48ZMwZDhw7N16YiJSUFe/fuRc2aNdGoUSO7DNKRuJ2diDS7fh14/XUZKdy+LY+1bSu7qD/2WIFvtVQcEHCdGi2W6vhERLBYHrke3VtW1K1bFydOnECzZs0wdOhQ9OrVyyMDAwY+RKTarVuyW/r8+cCNG/JY8+ayn9YTT8CQo7hMAFMUrNxM7sAuvbp27tyJZcuW4csvv0ROTg5iYmIwdOhQtG3bVpdBuwIGPkRUqHv3gA8+kA1EL12Sxxo0kF3Uo6MBRWHFYyIHs0sBw8ceewwfffQRLl68iHfffRdnzpxB+/btUbt2bcTHx+P8+fNFHjgRkcu6fx9YvFjm8IwdK4OemjVlAvOBA0C3bqagp0eP/HVw0tLk8YQEZwyeiIAitqwAgFOnTmHZsmVYtGgRbt26haysLL3G5hSc8SGifAwG4PPPgenTgVOn5LHwcGDaNGDgQKB4cbNTXaXzO5E3sVvLitxu376Nbdu2Ydu2bbhx4wZq1qxZlMsREbkWIYD164FGjWQ9nlOngAoVgAULYDh2Akm1h2H12uJISnrQukFLxWMicjzNLSsAYPv27Vi+fDnWrl0LAHj22Wfx2muv4bFCdi8QEbkFIYCtW4HJk4G9e+Wx0qWBCROAl15CwpZAjK5rOX8nM1PdR7DiseMxSZsADYHPuXPnsHLlSqxYsQKnTp1CVFQU3nrrLfTq1QuBgYH2HCMRkeP8/LMMeIyV6EuVkt04X34ZKF3aasdyY/5ObKy6j2HFY8disjkZqc7xKVasGMqVK4d+/fphyJAhqJerEJcnYY4PkZfavx+YMgX49lv53N8fePFFYNIkubyFwvN3AKB8eSAnB/hfN598mOPjeNaCVWMhRkcXhyT7UHv/Vj3j88UXX6Br164oVsym1TEiItd05IhMUl63Tj4vVgwYPBiYOlVGKLkUlr8DAFeuWH+NFY8dz2CQMz2W/okvhPydjBkjqxDwd+IdVCc3x8TEMOghIs9x+jQwYADwyCMy6FEUmcB87Bjw4Yf5gh6g6Hk5zmg94e2YbE55MZIhIu+SlgbMmgUsXQpkZ8tj3bsDM2bIIoQFsDUvp2xZ4IsvdGyCSqqpDVaZbO49GPgQkccocNfOlStAfDzw/vuy8jIAdOokg6DmzVVd39ixPC3N8tKJNdeuyXEw6HE8tcEqk829BwMfIvII1nbtvD8nHV1PvAG89ZbsrQUArVvLfloa2+0YO5b36CFXxrQEP5xRcI7CglVjsrmxOSx5Ps0FDAcPHoybN2/mO3779m0MHjxYl0ERkX4MBiApCVi9GmaF9jyJpRYRAbiN58/Fo3X/SNlD69YtoGlTuWtr+3bNQY9RTIzM06lSRdv7OKPgHMZgFXiQXG7EZHPvpLllha+vLy5cuIAK/9veaXTlyhVUrFgR2cY1czfF7ezkSbyhdkneLeZ+yMQLWIzJmI2K+BsAcKJYPdRYNRO+PWLy3/2K8Lk7dsiZhLFj5UpaQTMK3L7uXJb+W4iIkEGPp/y34O10386ekZEBIQSEELh58yZKlChhes1gMGDz5s35giEicp7CCu15yu4i464dX2RjAFZiGmagGlIAAKdQA7GIxarsPvgx1Bft9Yl5AMggpn17+eeSJS0vf3FGwXXExMgt66zcTKoDn9KlS0NRFCiKgoceeijf64qiIC4uTtfBEZFtvKl2yYW0HPTCGsRhOh7CCQDAOVTBTEzFMgxGNmQDUXvm2BiXvyzNrnFGwXXkDlbJe6kOfBITEyGEwD//+U+sW7cOZcuWNb3m5+eHatWqoXLlynYZJBFpo6V2idveCIQAvv4aXaZNRW8cAgBcRnnMxSQswou4h5Jmp9s7x4YzCkTuQXXg065dOwBAcnIyIiIi4ONTpMbuRGRHHl+75McfIV6dDOW3XxEIIF0JweviZSzAaNxCkNmpjty1wxkFIteneTt7tWrVcOPGDfz222+4dOkScnJyzF7v37+/boMjItt4bO2SXbtkA9HERCgAbiMA72AU5ovxuI6y+U5njg0R5aV5V9fXX3+Nvn374vbt2wgKCoKSa4eEoii4Zq0zn5vgri7yBMadToXVLnGbnUYHDsgGops2AQAy4YcPMBxzMQl/o6LVt3HXDpH3UHv/1rxe9d///tdUy+fGjRu4fv266eHuQQ+Rp/CY2iXHjwM9ewJNmgCbNkH4+mJ1wBDUxgmMwdsWg57QUODTT4HERBnYMeghotw0Bz5paWkYNWoUAgIC7DEeItKJtUJ7btEo88wZYNAgoH592eRKUYDevfHbiqPoc2cpUlHV6lsvX5bfM/tiEZElmnN8OnXqhL1796JGjRr2GA8RFUHeXlXR0W620+jCBdk7a8kS4P59eaxrV1l5uWFDnF6t/jJERJZoDnyeeuopjB8/HkeOHMEjjzyC4sWLm73etWtX3QZHROq5dZXmq1eB114D3nsPuHtXHnv8cRkERUWZTvPYpG0ichjNyc0FbWNXFAUGN28ExORmckfWqjQb83lcdmkrIwN48035MPYAbNlSNhDt0CHf6R6XtE1EurFbcnNOTo7Vh7sHPUTuqLAqzYCs0uxS/3neuQPMnw9ERgJxcTLoadwY+OYbYOdOi0EP4EFJ20TkNEWqQnjv3j29xkFENtJSpdnpsrKA998HatUCJkwArl0D6taVCcz79gFPPVVoE1G3TtomIqfTHPgYDAbMnDkTVapUQWBgIE6fPg0AmDp1Kj766CPdB0hEBXOLKs3Z2cDy5cBDDwEjR8rBVK8OrFgB/PEH8OyzgIZq8DExcuNXYiKwahW3rhORepoDn9mzZ2PFihWYN28e/Pz8TMcfeeQRLF26VNfBEVHhXDrhNydHzuY0aAAMHgycPSsH8v77skbPgAFAMc17LAA8aA/Ruze3rhORepoDn48//hiLFy9G37594Zvr/zQNGzbEsWPHdB0cERWuTRu5zGNthUhRZAVjR/SqMhFCVllu1kwWIDx+HChXTub1nDwJ/Oc/QK5/OOVlMABJScDq1fKrS+UnEZFbs6mAYa1atfIdz8nJwX1j3Q0ichiXS/hNSgIeewx4+mnZaiIoCIiNBU6fBl5+GSik+GlCglwF69AB6NNHfq1eXR4nIioqzYHPww8/jB0WsiS//PJLNGnSRJdBEZE2LpHw++uvsvZOhw6ymWjJkjKBOTkZmD4dUFEewrgtP2+ydlqaPM7gh4iKSvPi+vTp09GvXz+kpaUhJycHCQkJOH78OD7++GN888039hgjEakQE+OkKs2HDgFTpwJffSWfFy8OvPCC7KKuIbGosG35iiK35UdHM5+HiGynuYAhAHz//feYM2cO9u3bh5ycHDRt2hTTpk1Dx44d7TFGh2IBQyKV/vpLLmF9/rmMTHx8ZLLytGlybUqjpCSr5XvMJCbKZGYiotzU3r9t2k7RqVMndOrUyebBEXmzvP20XLp3liUpKcCMGXIrujHr+LnnZCHCunVtvqxbbMsnIrdn2z5SIrKJW/fTungRmDMH+PBDWYgQkAnMM2fKqstF5Kht+W4feBJRkaha6ipTpgyUQqqpGl27dq3Ig3ImLnWRvbhtP61r14B584B33nnQQPSf/5QNRFu21Hw5a4GHI/pwqQ08GRwRuR9dl7oWLFhg+vPVq1cxa9YsdOrUCS3/9z+9Xbt24fvvv8fUqVOLNmoiD+WWibs3b8p98K+/LpuJArJT+uzZwP/9n02XLCzwePttGRwqivnPSo9t+dYCT+OOMWPg6dazckRUOKFRTEyMePfdd/Mdf/fdd0V0dLTWy7mc9PR0AUCkp6c7eyjkQRIThZC33IIfiYnOHqkQ4s4dIV5/XYjy5R8MrGFDIb7+WoicHJsvu26dEIqS/3tWFPlYt+7BeeHh5udERDx43RbZ2fmvmXcMERFCfPmlujESketRe//WvKsrMDAQBw4cyFfE8MSJE2jSpAlu3bqlY1jmeFzqIntYvVoW4yvMqlWyBYNTZGUBy5bJnJ3z5+Wx2rXlcwu9tLQsBxmXsaw1U827jKX3UpPaHWOhocDly+rGSESuxW67usqVK4f169dj/PjxZsc3bNiAcuXKaR8pkRdw6X5aBgPw2Wdya3pysjxWtaosOti/v8VeWtaWg958UwYPeQMWLR3kjX239NyyrnYnmLWgB8g/RiJyT5oDn7i4OAwZMgRJSUmmHJ/du3fju+++Y5NSIiuM/bQKS9x1eD+thARZd+fIEXksLAyYMgUYNgzw9zc73TgLs3GjzLXJ69w5uas9N2NuTGamuiHpvVXdOGbjt6cHbqcncm+aW1YMHDgQv/zyC0qXLo2EhASsW7cOISEh2LlzJwYOHGiHIWq3cOFCREZGokSJEmjWrJnFFhtE9mCtuaZL9dMSAvj2W6B5c5nVe+QIUKYMEB8PnDoFjByZL+jJ3T/LUtBjjTFx+MQJdefrOeOVe8yzZhV8rqLImSo1nDIrR0T6cUjGkQN9/vnnonjx4mLJkiXiyJEjYvTo0aJUqVLi7Nmzqt7P5GaylaWk3PBw84RYeyTuapKUJETr1g8+PDBQiGnThLhxw+pbrCUlq30oivyew8OtX8eYXJydrc+3qWXMxsTlL75w7BiJSF92S24GZCf2kydP4tKlS8jJyTF7rW3btjqFZLaJiopC06ZNsWjRItOxevXqoVu3bpg7d26h72dyM9lCS40ep9SI2bNH9s7aulWOwa8E/npiBK4NnYhHu4TanJSsRVycTCMCLG9V16uOkdYxR0TIWSzjVvYePew/RiLSn+r7t9aIateuXSIyMlL4+PgIRVHMHj4+PjbGafrIzMwUvr6+IiEhwez4qFGjRNu2bS2+5969eyI9Pd30SE1N5YwPaaJ2q7RTZgr++EOIbt1MgzH4FhMrS70oKuOc1Vkp4/eUmCjElCm2z/Tkfaxa5ZgZL7WlA6ZMkefm/b04fVaOiGyidsZHc3Lz8OHD0bx5c2zatAmVKlVSXdHZEa5cuQKDwYCwsDCz42FhYbh48aLF98ydOxdxcXGOGB55KK07lhzi5Em5K2v1ajkAHx+cbfs8/pk0Hadv1zA7VU0BPz1UqiS/f3t3kFebfFy/vuXfh9O63BORQ2gOfE6cOIG1a9fmq+PjSvIGY0IIqwHapEmTMG7cONPzjIwMRERE2HV85Flcqrlmaqqsu7Ns2YPM6h49YJgWh9ZP1oelWCZ35eicHLkzS/sCuHV5d6zpvVU9Lz1KB9h7jETkPJp3dUVFReHkyZP2GEuRlS9fHr6+vvlmdy5dupRvFsjI398fwcHBZg8iLVyiRs+lSzJyqVULWLJEBj1PPgns2wd8+SV2XK2valbqP//RP+gBHLhjDQ9KB1ibjFYUmdfj0NIBROQyNM/4vPTSS/jvf/+Lixcv4pFHHkHx4sXNXm/YsKFug9PKz88PzZo1w9atW9G9e3fT8a1btyI6Otpp4yLP5tQaPdevy15ab78N3L4tj7VrJ/tpPfaY6TQ9CvhZY+wxduUKMHZs/qKGxsRhRzGWDrBXzy8icnNak4fyJjQbk5pdIblZiAfb2T/66CNx5MgRMWbMGFGqVClx5swZVe/ndnayhXH7dN6t0Hbr8XTzphCzZglRuvSDD2vRQogtWyz201Kb8KvlYSnh15gUvWqV5cRhR2KSMpF3sdt29rNnzxb4erVq1YoQhulj4cKFmDdvHi5cuIAGDRrgrbfeUr3NntvZyVaWkoJzb5XWxb17wAcfAHPmPJieadBA5vVER1td3zFu8S5oVqp8eXUzPlOmyObs7pDw65TSAUTkFGrv3zbV8fFkDHyoKOx2o71/H1ixApgx40FkVauWLI7Ts6eqDymsRs2aNcC4cYUv2bFJJxG5IrX3b83JzQDwySef4LHHHkPlypVNM0ALFizAxo0bbRstkYcw7gbq3ftBs80iMTYQrVcPeOEFGfSEh8sE5iNHZMt3lR8SEyO3rFepYn48PFwef/ZZF2qrQURkJ5oDn0WLFmHcuHF48skncePGDRj+t2W2dOnSWKCliQ8RWScEsGED0Lgx8PzzsodWhQoy8jhxAhg6FMizsUCNmBjgzBkgMRFYtUp+TU5+sBRXWHDEqsVE5O40L3XVr18fc+bMQbdu3RAUFISDBw+iRo0a+PPPP9G+fXtcuXLFXmN1CC51kVMJAWzZIhNp9u6Vx0qXBsaPB0aNAgIDHTIM5sYQkbtRe//WvJ09OTkZTZo0yXfc398ft43baYlIu59/lv20tm+Xz0uVknvFX35ZBj8OxAJ+ROSpNC91RUZG4sCBA/mOf/vtt6hfv74eYyLyLvv2AZ07y2mV7dsBf39ZEOf0aWDWLIcHPUREnkzzjM/48eMxYsQI3Lt3D0II/Pbbb1i9ejXmzp2LpUuX2mOMRJ7pyBFg6lS53QoAihUDBg+Wx8LDHToULm0RkbfQHPgMGjQI2dnZmDBhAu7cuYM+ffqgSpUqePvtt9GrVy97jJHIs5w+DcTGAp9++qBRVt++8ljNmlbfZq/gxFL9ofBwucPLUcnMDLyIyFGKVMfnypUryMnJQYUKFfQck1MxuZnsJi1NLl0tXQpkZ8tjMTGyNs/DD5udmjcQsNYOoqjBibG2T97/Cxi3rztiJ5crBF5E5P7sXsDw0qVLOH78OBRFQZ06dRAaGmrzYF0JAx/S3eXLQHw8sHChrLwMAJ06ySCoefN8p1sKBCwpanBirOZs7XMcUbDQFQIvIvIMditgmJGRgX79+qFy5cpo164d2rZti8qVK+P5559Henp6kQZN5FHS04Fp04AaNYA335RBT+vWMoH5u++sBj09ehQe9AAPgoUxY2QQo9WOHQV/jrFj+44d2q+thsEgAzxL//Qq6vdGRGSN5sBn6NCh+PXXX7Fp0ybcuHED6enp+Oabb7B3714MGzbMHmMkci+3b8sZnshI2UPr1i2gWTPg229l0GOlTXtBgYA1RQlO1HZsV3ueVs4OvIjIO2lObt60aRO+//57tG7d2nSsU6dOWLJkCf71r3/pOjgit5KZCXz4oWwg+vff8li9enJJq3t3qw1EjQoLBApiS3BSqZK+52nl7MCLiLyT5hmfcuXKISQkJN/xkJAQlClTRpdBEbmV7GyZsFy7tpyy+ftvOdvz8cfAH3/IJJVCgh6gaDd4W4KTNm1kDo+1oSmK7C5vZYKqyJwdeBGRd9Ic+EyZMgXjxo3DhVz/l7548SLGjx+PqVOn6jo4Ij0YDEBSErB6tfyqW85ITo68aP36wLBhcl2mcmVg0SLg2DGgXz9NWcG23OCLEpz4+jq3KamzAy8i8k6ad3U1adIEJ0+eRGZmJqpWrQoASElJgb+/P2rXrm127v79+/UbqYNwV5dnsctWaSGAr76ShQb/+EMeK18emDQJePFFoGRJmy5r3GWVlqYuz0evnU+WfkYRETLoccRW9h495J9zf8/c1UVEWtmtV1e3bt2KMi4ih7G2VTotTR7XfFMVAvjxR9lP67ff5LHgYNlLa8wYICjI5rEa6/b06CEDDkUpPPgJD9cnOImJAaKjnVNA0NgN3lJw6ojAi4i8T5EKGHoizvh4Bt1r1OzaJQOexET5PCBAdksfPx4oW7ZIY7U04+Lra74kFxEBvPEGEBpqHpwAnlHxmJWbiaio7DbjAwA3btzA2rVrcerUKYwfPx5ly5bF/v37ERYWhipVqtg8aPJMam5qet/4tGyVLrAL+YEDwJQpwKZN8rmfHzB8uFzWqljR9gH+j7VZKWPQM2aMnI2x9PPwpIrH7AZPRA4jNDp48KAIDQ0VtWrVEsWKFROnTp0SQggxZcoU0a9fP62Xcznp6ekCgEhPT3f2UDzCunVChIcLIW/t8hEeLo9rOUerVavMr2ftsWqVlQscPSrEc889ONHXV4ihQ4U4e9b2QeWRnZ3/+879UBQhIiLkeXmtWydft/QeRSnaz46IyB2pvX9r3tU1btw4DBw4ECdOnECJEiVMxzt37ozt27frGJKRu7NWhdiYY5OQUPA5zzwj21jZshvL5q3SZ84AgwbJ3llffCHXxHr3Bo4eBZYsAf6X0K8HWwv4seIxEZHtNC917dmzBx9++GG+41WqVMHFixd1GRS5v8JuzooiXzc+t3QOAEyf/uCYlmUc41ZpazukjDk+pq3SFy7IQoNLlgD378tjXbvKyssNGxb+gTawtYCfbst4REReSPOMT4kSJZCRkZHv+PHjxz2mUSkVnZqb87lz2ioV554pKozqGjU3rgITJgA1a8omovfvA48/DuzeDWzcaLegB7B9VooVj4mIbKc58ImOjsaMGTNw/3//KlYUBSkpKXjllVfwzDPP6D5Ack/2uOlqXcYxbpXOm28fHg5s+DgDMX/EyQrL8+cDd+8CLVsCP/0EbN0KREXpPv68bC3gx4rHRES207ydPSMjA08++SQOHz6MmzdvonLlyrh48SJatmyJzZs3o1SpUvYaq0NwO7s+kpKADh3sd/3ERPXLOLl3jFUpcwetfn8fIj4exTOuAQBE48ZQZs0CnnxSVWsJPdlSwK+wQoeat+oTEXkAu21nDw4Oxs8//4yffvoJ+/fvR05ODpo2bYrHH3+8SAMmz6Imx8Y4E6O2UnFuWmaUfH2B9i0zgaVLce8/s1DshsxFO4Y6mIqZ+PXyM1iQ6YMYx8Y8AGwr4GdcxuvRI3+hQ0e0miAicmcsYJgHZ3z0o2Y2A7B8TmFUz/hkZwOffALExQFnzwIAklEdcZiOT/E8DCjmEu0RbKlj5MxWE0RErkbt/VtT4JOTk4MVK1YgISEBZ86cgaIoiIyMRI8ePdCvXz8oDl4msAcGPvpSc3O2dI41qpdxcnKAL7+U28KOHwcA/O1TCXE5U7AUQ3Effpqva2uRRXtWJWbFYyIiSffARwiBLl26YPPmzWjUqBHq1q0LIQSOHj2KP/74A127dsWGDRv0Gr/TMPDRn9bKzSdOALGx8rjmxpVCyCrLU6YABw/KY+XK4dSzr+CRD/6DuwgocKzWZpJsrZLsSdWViYhcmer7t9qKiMuWLRNBQUHip59+yvfajz/+KIKCgsTKlSvVXs5lsXKza7BUzTkiopCKxD/9JETLlg/eEBQkRFycEOnpRarkbGuVZFZXJiJyHLX3b9UzPh07dsQ///lPvPLKKxZfnzNnDrZt24bvv//ehjjNdXDGx3WoXsb59VfZQPTHH+XzkiWBl16S9XnKlQOgfpdZ3hmfwpqdArJx6FtvyWRt4xh1b5JKREQF0n2pq2LFivjuu+/QuHFji6///vvv6Ny5s9tXb2bg40YOHQKmTgW++ko+L14c+Pe/gVdfzVfExtYt4Fq35RuXscqWtS3QIiIi26i9f6suYHjt2jWEhYVZfT0sLAzXr1/XNkryOAaDDBZs6a+l2l9/yf5ZjRvLoMfHR/bX+usv4N13LVbuU13JOc/si9ZCjMbq0hs3qjuf1ZWJiBxLdeBjMBhQrJj1sj++vr7Izs7WZVDknhIS5KxKhw5Anz7ya/Xq6lpMqJKSAgwdCtSvD3z+uZy66dkTOHIEWLZMflgBCqrkbC1hWmv1Y+Ns0mefqTuf1ZWJiBxL9VKXj48POnfuDH9/f4uvZ2Zm4rvvvoPBzVtCc6nLNsaaPXn/NulSI+fiRWDOHODDD4GsLHns6adlA1ErS68F0bIFvLAlsoKEhgJXrrC6MhGRI+ie4zNo0CBVH7x8+XJ1I3RRDHy0s1si77Vrso/WO+8Ad+7IYx06yC7qrVoVddiqWSvEWJgxYx4sr2nelk9ERJrYpYChN2Dgo52tO6asunlTJty8/jqQkSGPRUXBMGM2dvj9n1MKAWopsmiUmChjN1ZXJiKyP7v16iLKS22CbqHn3b0LLFwIxMfLNSIAaNgQmDkTCfe7YPQQxS6FANUUGYyJAaKjZXCUlgaMHVv4MpYxeDK+j9WViYicj4EPFZnaBF2r52VlyeTkmTOB8+flsdq1gRkzgOeeQ8IGH/R4Nn+QYdxBVZQlI2u5SZau7ev7YMaqZEn1TUJzv4+IiJyLS115cKlLO1tr5MBgkNufYmPliwBQtarsr9W/P1CsmM0FBLWM29Zrs0koEZHrYI6Pjdwt8HGVJpVqOrGbgoGcHPmGadOAo0flsbAw2V9r2DAg185BWwsIqgk89Li2q/z8iYi8ne4FDMn12L1ujgaqauQIAXz7LdCiBfDsszLoKVNG5vScOgWMHGkW9AC2FxBU8zPQ49rGZazeveVXBj1ERK6NMz55uMuMj13r5hSB1RmQ7dtlK4mdO+WJgYHAuHHyERJi9XpaZ2UA9dvn7XltIiJyLC512cgdAh+3aoC5Z49sILp1q3xeooSc2Zk4EShfvtC3F6WAYGHb5+15bSIiciwudXmwHTsKTsgVAkhNledZY/eeWn/+CXTvDvzjHzLoKVYMGD4cOHlSFiVUEfQABffYKkxhS1n2vDYREbkmBj5uqKh1c+yaG3TyJPD887L+zoYNsoFo//7A8ePAokX5k4BUsJY/VBg12+zteW0iInI9DHzcUFHq5hhzg/LOGJ07BzzzjCzMZ9MMUGoq8MILQN26cou6EPKD/vwTWLkSqFFD4wXNxcQAZ87IJaZPP5XbzK3N0iiK3Fbepo3zr01ERK6FOT55uFOOj9a6OWrq1hip3hb+99/A3LlyNsfYQLRzZ9lPq2lTld+Rdpq2z7vQtYmIyD6Y4+MBrOXhFJSbYqlysFFhuUG5Fbot/Pp14NVXIWrUkIPJysKNhm1hSNoBbN5s16AHULl93gWvTUREzsUZnzxcZcZHTf8orZWDV6+WOT1qWZw5unVLDmL+fCA9HQCwB80xGbOxFU8gPFzRpX+WWvYsIMjihERE7sMrt7NXr14dZ8+eNTs2ceJExMfHq76GKwQ+Wmr0aLk521K3Bvjf1u1H7wEffADMmQNcvgwA+AMNMBUzsRHRABSrYyQiIrI3rw18hgwZgmHDhpmOBQYGIjAwUPU1nB342LNGjy11a4rhPnYOWY5/fD/TNChRqxZGXYvDwms9kYP8g3CpOkJEROQVvDbHJygoCBUrVjQ9Cgt6MjMzkZGRYfZwJj1q9FijpW6NDwzog89wFPXwj4/+DZw7BxEejuMvL0Hss0fw3rU+FoOeoo6RiIjInjwu8HnttddQrlw5NG7cGLNnz0aWcaeRFXPnzkVISIjpERER4aCRWlbUGj2FKbxujUA3rMdBNMJneB61cAqiQgUcHLQAtcUJ1H19KGbMLW7XMRIREdlLMWcPQE+jR49G06ZNUaZMGfz222+YNGkSkpOTsXTpUqvvmTRpEsaNG2d6npGR4dTgpyg1etSKiQGio+WMzMaNMhlagcAT2IJZmIIW2AsAuI7SON9nPE4+OQrd+wVqbuvgjCJ/TEgmIqICCRc3ffp0AaDAx549eyy+d+3atQKAuHLliurPS09PFwBEenq6Xt+CJtnZQoSHC6EoQshFI/OHoggRESHP00vSzO1it18b04fcRCnxdvBk8dXH103jsTQWaw97jFGNdevyjzU8XB4nIiLPpvb+7fLJzVeuXMGVK1cKPKd69eooUaJEvuNpaWkIDw/H7t27ERUVperznJ3cDDiwgN6+fcCUKcB33wEAcor746/H/4Orw17Bo10rwNdX+04wZ+3qctVu9URE5Bhq798uv9RVvnx5lFfZ0DKv33//HQBQyQUbKxW0JGPMw7FUx8dajR611wYAHDkCTJsGrFsnnxcrBgweDJ+pU1E3PNzsWlrzdNSOUU8Gg/xZWQrhhZDBz5gxcnmPy15ERN7N5QMftXbt2oXdu3ejQ4cOCAkJwZ49ezB27Fh07doVVatWdfbwzKgpTpg7D8cYwLRqBfzyiyxEaC1/pcBrNz4NxMbKhlTGiKBvX2D6dKBWLYtjVRszTpkC/N//OSenRstOuPbtHTYsIiJyQR4T+Pj7+2PNmjWIi4tDZmYmqlWrhmHDhmHChAnOHpoZa0syxhYRuZdkfH0f3KgTEoCaNQuv5Gzp2uJcGi49Mws5vkvhY8iWB7t3B2bMABo0KHC8bdrIzymsL1hsrPNmU+y9E46IiDyHy+f4OJo9c3xsLU6oJn8lOjr/tcvjMl5BPEbgfZRAJgBAdOwEZfYsoHlz1eN29aadavOQEhM540NE5Km8toChK7OlOGFh+SuAzF9JSnpw7RDcwAxMxWnUwH/xJkogEzvQGm2xDdsmfacp6AFcv2mncVbKWlFGRZE9zNq0cey4iIjI9XjMUpc7sGVJRm2wlJQEBOA2XsK7mIB5KIvrAIB9aIrJmI3v0QmAUqTCh3lzjlylRo6xInWPHjLIsTQrZalbPREReR8GPg5kS3FCNYGKHzLx6G8f4hTmoCL+BgAcQT1MxUwkIAbGBqJaxmBJ7pwjV1PUnXBEROQdmOOThyNyfApLFM6d41NQ/oovsjEAKzEdcaiKVADAaURiOuKwCua9tLylcSgrNxMReSePqePjSWxZkrG0q0pBDnpiDeIwHQ/hBABAVK6MA12mIerDwchWinvtco8rz0oREZHzMbnZwbQmCpt1VIdAF3yFA2iM1eiDh3ACl1Eehwa8AeXkSTT54N/4fF1xl01CJiIicjYudeXhqJYVmpZkhMCO2B8ROHcymtz/DQBwAyFYEvwy6iwcja59g2y/NhERkQdQe/9m4JOHK/TqMvPLL8DkyTLZB4ChRACOdxyFa0PGo+VTZVUFNAyEiIjI0zHHx90dOCD7QGzaJJ/7+QHDh8N30iTUr1hR9WXUtMcgIiLyFgx8XM2xY7J31hdfyOe+vsDAgbKpqMaeY1raYzgaZ6GIiMgZmNzsKs6cAQYNAh5+WAY9igL07g0cPQosXao56FFb8dlgKPLINUtIkNv6O3QA+vSRX6tXl8eJiIjsiYGPs124AIwYATz0ELBiBZCTA3TtKpe6Vq0Cate26bK2tMdwBOMsVN6xGWehGPwQEZE9MfBxlqtXgQkTZMv1hQuB+/eBxx8Hdu8GNm4EGjYs0uVdsWO5K89CERGRd2Dg42gZGUBsLBAZCcyfD9y9C7RqJVuHb90KREXp8jG2tMewN1edhSIiIu/B5GYHMBiAnVvvIOjj99FgUzyKZ1yTLzRuDMyaBTz5pPXW4jayVPE5N2MLC0d2LHfFWSgiIvIunPGxs4QEoEHVDNTsXBtNVk9A8YxrOFGsLn797xfAvn3AU0/pHvQAeSo+57m8s1pYuOIsFBEReRcGPnZkTOQ9dj4YO9AGyaiOAViB+tl/oOWbzyJhg31//FrbY9ibcRbKWpynKEBEhGNnoYiIyLuwcnMeelVuNnZiN+a0lMMVZCAY9+EHwLHd0l2pZo4xGAQsN2llTzEiIrKF2vs3Z3zsJG8i71WUNwU9gGMTeY0dy3v3ll+dWSjQ1WahiIjIuzC52U6YyGtdTAwQHe06s1BEROQ9GPjYCRN5C2achSIiInIkLnXZCRN5iYiIXA8DHztxxe3kRERE3o6Bjx0xkZeIiMi1MMfHzpjIS0RE5DoY+DgAE3mJiIhcA5e6iIiIyGtwxoescqWKz0RERHpg4EMWJSQAo0ebV58OD5c71ZiUTURE7opLXZSPsZ9W7qAHANLS5PGEBOeMi4iIqKgY+JAZg0HO9FhqXWs8NmaMPI+IiMjdMPAhM3mbq+blyOaqREREemPgQ2bYXJWIiDwZAx8yw+aqRETkyRj4kBk2VyUiIk/GwIfMsLkqERF5MgY+lA+bqxIRkadiAUOyiM1ViYjIEzHwIavYXJWIiDwNl7qIiIjIazDwISIiIq/BwIeIiIi8BgMfIiIi8hoMfIiIiMhrMPAhIiIir8HAh4iIiLwGAx8iIiLyGgx8iIiIyGu4TeAze/ZstGrVCgEBAShdurTFc1JSUtClSxeUKlUK5cuXx6hRo5CVleXYgRIREZHLcpuWFVlZWXj22WfRsmVLfPTRR/leNxgMeOqppxAaGoqff/4ZV69exYABAyCEwLvvvuuEERMREZGrcZvAJy4uDgCwYsUKi69v2bIFR44cQWpqKipXrgwAeOONNzBw4EDMnj0bwcHBFt+XmZmJzMxM0/OMjAx9B05EREQuw22Wugqza9cuNGjQwBT0AECnTp2QmZmJffv2WX3f3LlzERISYnpEREQ4YrhERETkBB4T+Fy8eBFhYWFmx8qUKQM/Pz9cvHjR6vsmTZqE9PR00yM1NdXeQyUiIiIncWrgExsbC0VRCnzs3btX9fUURcl3TAhh8biRv78/goODzR5ERETkmZya4zNy5Ej06tWrwHOqV6+u6loVK1bEr7/+anbs+vXruH//fr6ZICIiIvJOTg18ypcvj/Lly+tyrZYtW2L27Nm4cOECKlWqBEAmPPv7+6NZs2a6fAYRERG5N7fZ1ZWSkoJr164hJSUFBoMBBw4cAADUqlULgYGB6NixI+rXr49+/fph/vz5uHbtGl5++WUMGzaMy1dEREQEwI0Cn2nTpmHlypWm502aNAEAJCYmon379vD19cWmTZvwn//8B4899hhKliyJPn364PXXX3fWkImIiMjFKEII4exBuJKMjAyEhIQgPT2dM0VERERuQu3922O2sxMREREVhoEPEREReQ0GPkREROQ1GPgQERGR12DgQ0RERF6DgQ8RERF5DQY+RERE5DUY+BAREZHXYOBDREREXsNtWlZ4OoMB2LEDuHABqFQJaNMG8PV19qiIiIg8CwMfF5CQAIweDZw79+BYeDjw9ttATIzzxkVERORpuNTlZAkJQI8e5kEPAKSlyeMJCc4ZFxERkSdi4ONEBoOc6bHUJtZ4bMwYeR4REREVHQMfJ9qxI/9MT25CAKmp8jwiIiIqOgY+TnThgr7nERERUcEY+DhRpUr6nkdEREQFY+DjRG3ayN1bimL5dUUBIiLkeURERFR0DHycyNdXblkH8gc/xucLFrCeDxERkV4Y+DhZTAywdi1QpYr58fBweZx1fIiIiPTDAoYuICYGiI5m5WYiIiJ7Y+DjInx9gfbtnT0KIiIiz8alLiIiIvIaDHyIiIjIazDwISIiIq/BwIeIiIi8BgMfIiIi8hoMfIiIiMhrMPAhIiIir8HAh4iIiLwGAx8iIiLyGqzcnIcQAgCQkZHh5JEQERGRWsb7tvE+bg0Dnzxu3rwJAIiIiHDySIiIiEirmzdvIiQkxOrriigsNPIyOTk5OH/+PIKCgqAoirOH43QZGRmIiIhAamoqgoODnT0cj8aftePwZ+04/Fk7jrf/rIUQuHnzJipXrgwfH+uZPJzxycPHxwfh4eHOHobLCQ4O9sr/kJyBP2vH4c/acfizdhxv/lkXNNNjxORmIiIi8hoMfIiIiMhrMPChAvn7+2P69Onw9/d39lA8Hn/WjsOftePwZ+04/Fmrw+RmIiIi8hqc8SEiIiKvwcCHiIiIvAYDHyIiIvIaDHyIiIjIazDwIc0yMzPRuHFjKIqCAwcOOHs4HufMmTMYMmQIIiMjUbJkSdSsWRPTp09HVlaWs4fmERYuXIjIyEiUKFECzZo1w44dO5w9JI80d+5ctGjRAkFBQahQoQK6deuG48ePO3tYHm/u3LlQFAVjxoxx9lBcFgMf0mzChAmoXLmys4fhsY4dO4acnBx8+OGHOHz4MN566y188MEHePXVV509NLe3Zs0ajBkzBpMnT8bvv/+ONm3aoHPnzkhJSXH20DzOtm3bMGLECOzevRtbt25FdnY2OnbsiNu3bzt7aB5rz549WLx4MRo2bOjsobg0bmcnTb799luMGzcO69atw8MPP4zff/8djRs3dvawPN78+fOxaNEinD592tlDcWtRUVFo2rQpFi1aZDpWr149dOvWDXPnznXiyDzf5cuXUaFCBWzbtg1t27Z19nA8zq1bt9C0aVMsXLgQs2bNQuPGjbFgwQJnD8slccaHVPv7778xbNgwfPLJJwgICHD2cLxKeno6ypYt6+xhuLWsrCzs27cPHTt2NDvesWNH/PLLL04alfdIT08HAP49tpMRI0bgqaeewuOPP+7sobg8NiklVYQQGDhwIIYPH47mzZvjzJkzzh6S1zh16hTeffddvPHGG84eilu7cuUKDAYDwsLCzI6HhYXh4sWLThqVdxBCYNy4cWjdujUaNGjg7OF4nM8//xz79+/Hnj17nD0Ut8AZHy8XGxsLRVEKfOzduxfvvvsuMjIyMGnSJGcP2W2p/Vnndv78efzrX//Cs88+i6FDhzpp5J5FURSz50KIfMdIXyNHjsShQ4ewevVqZw/F46SmpmL06NH49NNPUaJECWcPxy0wx8fLXblyBVeuXCnwnOrVq6NXr174+uuvzW4QBoMBvr6+6Nu3L1auXGnvobo9tT9r4/+8zp8/jw4dOiAqKgorVqyAjw//nVIUWVlZCAgIwJdffonu3bubjo8ePRoHDhzAtm3bnDg6z/XSSy9hw4YN2L59OyIjI509HI+zYcMGdO/eHb6+vqZjBoMBiqLAx8cHmZmZZq8RAx9SKSUlBRkZGabn58+fR6dOnbB27VpERUUhPDzciaPzPGlpaejQoQOaNWuGTz/9lP/j0klUVBSaNWuGhQsXmo7Vr18f0dHRTG7WmRACL730EtavX4+kpCTUrl3b2UPySDdv3sTZs2fNjg0aNAh169bFxIkTubRoAXN8SJWqVauaPQ8MDAQA1KxZk0GPzs6fP4/27dujatWqeP3113H58mXTaxUrVnTiyNzfuHHj0K9fPzRv3hwtW7bE4sWLkZKSguHDhzt7aB5nxIgRWLVqFTZu3IigoCBTHlVISAhKlizp5NF5jqCgoHzBTalSpVCuXDkGPVYw8CFyMVu2bMHJkydx8uTJfEElJ2iLpmfPnrh69SpmzJiBCxcuoEGDBti8eTOqVavm7KF5HGPJgPbt25sdX758OQYOHOj4ARH9D5e6iIiIyGswW5KIiIi8BgMfIiIi8hoMfIiIiMhrMPAhIiIir8HAh4iIiLwGAx8iIiLyGgx8iIiIyGsw8CEiIiKvwcCHiMwoioINGzY4exiqxMbGonHjxs4ehu7at2+PMWPGqD4/KSkJiqLgxo0bVs9ZsWIFSpcuXeSxEbk7Bj5EHmLgwIHo1q2bs4fh9tQECG+88QZCQkJw586dfK/du3cPpUuXxptvvmnzGBISEjBz5kyb309E1jHwISLSqH///rh79y7WrVuX77V169bhzp076Nevn+br3r9/HwBQtmxZBAUFFXmcRJQfAx8iD9W+fXuMGjUKEyZMQNmyZVGxYkXExsaanXPixAm0bdsWJUqUQP369bF169Z810lLS0PPnj1RpkwZlCtXDtHR0Thz5ozpdeNMU1xcHCpUqIDg4GD8+9//RlZWlukcIQTmzZuHGjVqoGTJkmjUqBHWrl1ret24VPPjjz+iefPmCAgIQKtWrXD8+HGzscTHxyMsLAxBQUEYMmQI7t27l2+8y5cvR7169VCiRAnUrVsXCxcuNL125swZKIqChIQEdOjQAQEBAWjUqBF27dplGsegQYOQnp4ORVGgKEq+nxkAhIaGokuXLli2bFm+15YtW4auXbsiNDQUEydOxEMPPYSAgADUqFEDU6dONQU3wIOlumXLlqFGjRrw9/eHECLfUtenn36K5s2bIygoCBUrVkSfPn1w6dKlfJ+9c+dONGrUCCVKlEBUVBT++OOPfOfk9vXXX6NZs2YoUaIEatSogbi4OGRnZxf4HiK3J4jIIwwYMEBER0ebnrdr104EBweL2NhY8ddff4mVK1cKRVHEli1bhBBCGAwG0aBBA9G+fXvx+++/i23btokmTZoIAGL9+vVCCCFu374tateuLQYPHiwOHTokjhw5Ivr06SPq1KkjMjMzTZ8bGBgoevbsKf7880/xzTffiNDQUPHqq6+axvLqq6+KunXriu+++06cOnVKLF++XPj7+4ukpCQhhBCJiYkCgIiKihJJSUni8OHDok2bNqJVq1ama6xZs0b4+fmJJUuWiGPHjonJkyeLoKAg0ahRI9M5ixcvFpUqVRLr1q0Tp0+fFuvWrRNly5YVK1asEEIIkZycLACIunXrim+++UYcP35c9OjRQ1SrVk3cv39fZGZmigULFojg4GBx4cIFceHCBXHz5k2LP+9NmzYJRVHE6dOnTceSk5OFoihi8+bNQgghZs6cKXbu3CmSk5PFV199JcLCwsRrr71mOn/69OmiVKlSolOnTmL//v3i4MGDIicnR7Rr106MHj3adN5HH30kNm/eLE6dOiV27dolHn30UdG5c2fT68afX7169cSWLVvEoUOHxNNPPy2qV68usrKyhBBCLF++XISEhJje891334ng4GCxYsUKcerUKbFlyxZRvXp1ERsba/kvGJGHYOBD5CEsBT6tW7c2O6dFixZi4sSJQgghvv/+e+Hr6ytSU1NNr3/77bdmgc9HH30k6tSpI3JyckznZGZmipIlS4rvv//e9Llly5YVt2/fNp2zaNEiERgYKAwGg7h165YoUaKE+OWXX8zGMmTIENG7d28hxIMb9w8//GB6fdOmTQKAuHv3rhBCiJYtW4rhw4ebXSMqKsos8ImIiBCrVq0yO2fmzJmiZcuWQogHgc/SpUtNrx8+fFgAEEePHhVC5A8QrMnOzhZVqlQR06ZNMx2bNm2aqFKlisjOzrb4nnnz5olmzZqZnk+fPl0UL15cXLp0yey8vIFPXr/99psAYArKjD+/zz//3HTO1atXRcmSJcWaNWssfl9t2rQRc+bMMbvuJ598IipVqlTwN07k5oo5aaKJiBygYcOGZs8rVapkWiI5evQoqlativDwcNPrLVu2NDt/3759OHnyZL58k3v37uHUqVOm540aNUJAQIDZdW7duoXU1FRcunQJ9+7dwxNPPGF2jaysLDRp0sTqeCtVqgQAuHTpEqpWrYqjR49i+PDhZue3bNkSiYmJAIDLly8jNTUVQ4YMwbBhw0znZGdnIyQkRNXn1K1bF2r5+vpiwIABWLFiBaZPnw5FUbBy5UoMHDgQvr6+AIC1a9diwYIFOHnyJG7duoXs7GwEBwebXadatWoIDQ0t8LN+//13xMbG4sCBA7h27RpycnIAACkpKahfv77Zz8OobNmyqFOnDo4ePWrxmvv27cOePXswe/Zs0zGDwYB79+7hzp07Zr9PIk/CwIfIgxUvXtzsuaIoppumECLf+YqimD3PyclBs2bN8Nlnn+U7t7Cbdd7P27RpE6pUqWL2ur+/v9XxGsdifH9hjOctWbIEUVFRZq8ZAxE9Pie3wYMHY+7cufjpp58AyEBk0KBBAIDdu3ejV69eiIuLQ6dOnRASEoLPP/8cb7zxhtk1SpUqVeBn3L59Gx07dkTHjh3x6aefIjQ0FCkpKejUqZNZHpU1eX+nRjk5OYiLi0NMTEy+10qUKFHodYncFQMfIi9Vv359pKSk4Pz586hcuTIAmJJ8jZo2bYo1a9aYkpatOXjwIO7evYuSJUsCkDf9wMBAhIeHo0yZMvD390dKSgratWtn83jr1auH3bt3o3///qZju3fvNv05LCwMVapUwenTp9G3b1+bP8fPzw8Gg0HVuTVr1kS7du2wfPlyU1JyzZo1AchE42rVqmHy5Mmm88+ePat5PMeOHcOVK1cQHx+PiIgIAMDevXstnrt7925UrVoVAHD9+nX89ddfVmexmjZtiuPHj6NWrVqax0Tkzhj4EHmpxx9/HHXq1EH//v3xxhtvICMjw+wmDQB9+/bF/PnzER0djRkzZiA8PBwpKSlISEjA+PHjTctkWVlZGDJkCKZMmYKzZ89i+vTpGDlyJHx8fBAUFISXX34ZY8eORU5ODlq3bo2MjAz88ssvCAwMxIABA1SNd/To0RgwYACaN2+O1q1b47PPPsPhw4dRo0YN0zmxsbEYNWoUgoOD0blzZ2RmZmLv3r24fv06xo0bp+pzqlevjlu3buHHH380LeEVtOyTe2lt6dKlpuO1atVCSkoKPv/8c7Ro0QKbNm3C+vXrVY0ht6pVq8LPzw/vvvsuhg8fjj///NNqjZ8ZM2agXLlyCAsLw+TJk1G+fHmrtZ2mTZuGp59+GhEREXj22Wfh4+ODQ4cO4Y8//sCsWbM0j5PIXXA7O5GX8vHxwfr165GZmYl//OMfGDp0qFm+BwAEBARg+/btqFq1KmJiYlCvXj0MHjwYd+/eNZsB+r//+z/Url0bbdu2xXPPPYcuXbqYbQOfOXMmpk2bhrlz56JevXro1KkTvv76a0RGRqoeb8+ePTFt2jRMnDgRzZo1w9mzZ/Hiiy+anTN06FAsXboUK1aswCOPPIJ27dphxYoVmj6nVatWGD58OHr27InQ0FDMmzevwPOfeeYZ+Pv7w9/f32zZKDo6GmPHjsXIkSPRuHFj/PLLL5g6darqcRiFhoZixYoV+PLLL1G/fn3Ex8fj9ddft3hufHw8Ro8ejWbNmuHChQv46quv4OfnZ/HcTp064ZtvvsHWrVvRokULPProo3jzzTdRrVo1zWMkcieKsLTQT0Sk0sCBA3Hjxg23aXNBRN6NMz5ERETkNRj4EBERkdfgUhcRERF5Dc74EBERkddg4ENEREReg4EPEREReQ0GPkREROQ1GPgQERGR12DgQ0RERF6DgQ8RERF5DQY+RERE5DX+H/xRHcKJ6AdiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "y = 2*(x) + 3\n",
+    "y_noise = 2 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Non-linear regression is a method to model the non-linear relationship between the independent variables $x$ and the dependent variable $y$. Essentially any relationship that is not linear can be termed as non-linear, and is usually represented by the polynomial of $k$ degrees (maximum power of $x$).  For example:\n",
+    "\n",
+    "$$ \\ y = a x^3 + b x^2 + c x + d \\ $$\n",
+    "\n",
+    "Non-linear functions can have elements like exponentials, logarithms, fractions, and so on. For example: $$ y = \\log(x)$$\n",
+    "    \n",
+    "We can have a function that's even more complicated such as :\n",
+    "$$ y = \\log(a x^3 + b x^2 + c x + d)$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at a cubic function's graph.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "y = 1*(x**3) + 1*(x**2) + 1*x + 3\n",
+    "y_noise = 20 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, this function has $x^3$ and $x^2$ as independent variables. Also, the graphic of this function is not a straight line over the 2D plane. So this is a non-linear function.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some other types of non-linear functions are:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$ Y = X^2 $$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "\n",
+    "y = np.power(x,2)\n",
+    "y_noise = 2 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exponential\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An exponential function with base c is defined by $$ Y = a + b c^X$$ where b ≠0, c > 0 , c ≠1, and x is any real number. The base, c, is constant and the exponent, x, is a variable. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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T7OuOQG+lxGksJxNCCKlDSE2j0UClUiE/P5/zd4iIiO6Q8Hsa5m85iSea18fqMV2kjmNS3e9vu52zQ0RERPbh1nwdxxwQYNkhIiKiezpuLDsNHG9yMsCyQ0RERPegLdfj7M0LCnJkh4iIiJzO2cuFKDcIqDxc0cDPQ+o4NcKyQ0RERFW6/WKC97uDgb1i2SEiIqIqHb/o2JOTAZYdIiIiugfjyE5rB52cDLDsEBERURX0BoFTlxx7cjLwAGWnrKwMKSkpKC8vt2YeIiIishNpV4tQotPDw1WByEBvqePUmMVlp7i4GOPHj4enpydatWqFzMxMAMC0adPw7rvvWj0gERERScN4CKt5iA8UcsecnAzUoOzMmTMHR44cwe7du+Hu7m5a3qdPH2zYsMGq4YiIiEg6xisnt3bAO53fzuIbgW7evBkbNmzAI488YnYKWsuWLXHu3DmrhiMiIiLp3H7auSOzeGTnypUrqF+/fqXlRUVFDnv+PREREZkTQtx2TyzHHtmxuOx06dIFW7duNb02FpyPP/4YXbt2tV4yIiIikkx2filuFOvgIpfhoWDHnZwM1OAwVnx8PJ566imcPHkS5eXl+Oc//4kTJ07gjz/+QFJSki0yEhERUS07frHiEFbT+t5QuigkTvNgLB7Z6datG37//XcUFxejSZMm2LFjB9RqNf744w906tTJFhmJiIiolpkmJzvwxQSNLB7ZAYA2bdpg7dq11s5CREREduKkk0xOBqpZdjQaTbV36Ovr+B8KERFRXXfrnlh1ZGTHz8/vvmdaCSEgk8mg1+utEoyIiIikcVlTihxNKeSyOjSys2vXLlvnICIiIjtxOOsGAOAhtQ+8lDWa8WJXqvUb9OzZ09Y5iIiIyE4cuVl22jX0kzSHtdSoruXl5WH16tU4deoUZDIZWrRogbFjx8Lf39/a+YiIiKiWGUd22of7SZrDWiw+9TwpKQkRERH417/+hby8PFy/fh3/+te/EBkZyevsEBEROTiDQeDohYozsersyM6UKVMwbNgwrFy5EgpFxUWG9Ho9Jk+ejClTpuD48eNWD0lERES149yVQhRqy+HhqsBDase+crKRxSM7586dw4wZM0xFBwAUCgXi4uJ4I1AiIiIHZzyE1aaBCi4Ki2uCXbL4t+jYsSNOnTpVafmpU6fQvn17a2QiIiIiiRy5cAMA0C7M8a+vY1Stw1hHjx41PZ82bRqmT5+O1NRUPPLIIwCAvXv34t///jfeffdd26QkIiKiWmGanBxWT9ogViQTQoj7bSSXyyGTyXC/TR31ooIajQYqlQr5+fm8AjQREdVZpTo9Ws/bjnKDwG+ze6NhPU+pI91Tdb+/qzWyk5aWZrVgREREZJ9OZOej3CAQ6O2GBn4eUsexmmqVnUaNGtk6BxEREUnscFbFKeftw+5/myhHUuNrQJ88eRKZmZkoKyszWz5w4MAHDkVERES1z9munGxkcdk5f/48nn32WRw7dsxsHo+xATrinB0iIiJyvisnG1l86vn06dMRGRmJy5cvw9PTEydOnEBycjI6d+6M3bt32yAiERER2dr1ojJkXi8GALRt4CdtGCuzeGTnjz/+wC+//IKgoCDI5XLI5XJ0794d8fHxmDZtGg4dOmSLnERERGRDxkNYjQO9oPJ0lTaMlVk8sqPX6+HtXXH56MDAQGRnZwOomMSckpJi3XRERERUK25dX8dP0hy2YPHITuvWrXH06FE0btwY0dHRWLJkCdzc3LBq1So0btzYFhmJiIjIxm5dOdlP0hy2YHHZeeONN1BUVAQAePvtt9G/f3/06NEDAQEB2LBhg9UDEhERkW0JIUyHsTiyAyAmJsb0vHHjxjh58iSuX7+OevXqOdU5+URERHVF5vVi5BXr4KaQo3mIj9RxrK7G19m5nb+/vzV2Q0RERBIwztdpEeoLpYtC2jA2UK2yM2TIECQkJMDX1xdDhgy557YbN260SjAAKC8vx/z58/H5558jJycHISEhGDNmDN544w3I5RVzq4UQWLBgAVatWoW8vDxER0fj3//+N1q1amW1HERERM7sYEYeAKCDEx7CAqpZdlQqlekQlUpVe7d8X7x4Mf7zn/9g7dq1aNWqFfbv34+xY8dCpVJh+vTpAIAlS5Zg6dKlSEhIwEMPPYS3334bTz75JFJSUuDj43xDcURERNa2L72i7HSJcM4jNdW667mREAKZmZkICgqCp6ft74Tav39/qNVqrF692rRs6NCh8PT0xGeffQYhBEJDQxEbG4vZs2cDALRaLdRqNRYvXoyJEydW6+fwrudERFRXaUp1aL9gBwwC+PO1J6D2dZc6UrVV9/vbouvsCCEQFRWFixcvPnDA6ujevTt+/vlnnDlzBgBw5MgR/Pbbb3j66acBVNyNPScnB3379jW9R6lUomfPntizZ0+V+9VqtdBoNGYPIiKiuuhQ5g0YBBDu7+lQRccSFk1QlsvliIqKwrVr1xAVFWWrTCazZ89Gfn4+mjdvDoVCAb1ej3feeQd///vfAQA5OTkAALVabfY+tVqNjIyMKvcbHx+PBQsW2C44ERGRg9iXdh0A0DminsRJbMfiKygvWbIEM2fOxPHjx22Rx8yGDRuwbt06fPHFFzh48CDWrl2L999/H2vXrjXb7s5T3oUQ9zwNfs6cOcjPzzc9srKybJKfiIjI3u1Lryg7zjpfB6jBqef/+Mc/UFxcjHbt2sHNzQ0eHh5m669fv261cDNnzsSrr76KF154AQDQpk0bZGRkID4+HqNHj0ZwcDAAmM7UMsrNza002nM7pVIJpVJptZxERESOqKzcYDrtnGXnNsuWLbNBjLsrLi42nWJupFAoYDAYAACRkZEIDg5GYmIiOnToAAAoKytDUlISFi9eXGs5iYiIHNHx7Hxoyw2o5+mKJkFeUsexGYvLzujRo22R464GDBiAd955B+Hh4WjVqhUOHTqEpUuXYty4cQAqDl/FxsZi0aJFiIqKQlRUFBYtWgRPT08MHz681nISERE5ov3pxvk6/k59F4QHuoJySUkJdDqd2TJrnrr90Ucf4c0338TkyZORm5uL0NBQTJw4EXPnzjVtM2vWLJSUlGDy5Mmmiwru2LGD19ghIiK6j1vX13HeycmAhdfZAYCioiLMnj0bX331Fa5du1ZpvV6vt1q42sLr7BARUV0jhEDHtxKRV6zDxsnd0DHc8QqPTa6zA1SMpPzyyy9YsWIFlEolPvnkEyxYsAChoaH49NNPHyg0ERER1Y5zV4qQV6yDu6scrUNr7+4IUrD4MNaWLVvw6aefolevXhg3bhx69OiBpk2bolGjRvj8888xYsQIW+QkIiIiKzLO12kf5gc3F4vHPhyKxb/d9evXERkZCaBifo7xVPPu3bsjOTnZuumIiIjIJpz9fli3s7jsNG7cGOnp6QCAli1b4quvvgJQMeLj5+dnzWxERERkI/szbp2J5ewsLjtjx47FkSNHAFRcidg4d+fll1/GzJkzrR6QiIiIrCtXU4qMa8WQy4CO4X5Sx7G5as/ZiY2NxYsvvoiXX37ZtKx37944ffo09u/fjyZNmqBdu3Y2CUlERETWYzyE1TzYFz7urhKnsb1qj+z89NNPaNeuHR5++GGsWrXKdKfw8PBwDBkyhEWHiIjIQdy6H5bjnW5eE9UuO6dPn0ZycjLatGmDV155BaGhoRg1ahQnJRMRETkY43ydLpHOP18HsHDOzqOPPorVq1cjJycHH330EdLT09GrVy9ERUXh3XffRXZ2tq1yEhERkRVoSnU4mV1xdKZzI5adKnl6emLs2LFITk7G2bNn8fzzz2PJkiWIiIiwcjwiIiKypr/OX4dBAJGBXghWuUsdp1Y80FWEioqKkJSUhKSkJNy4cQNNmjSxVi4iIiKygT3nKm711K1JgMRJak+Nyk5ycjLGjh2L4OBgTJ8+HQ899BB+/fVXnDp1ytr5iIiIyIr2nLsKAOjWJFDiJLWn2qeeX7hwAWvXrkVCQgLOnTuH6OhofPjhh3jhhRfg7e1ty4xERERkBVcLtTidUwAAeKRx3ZivA1hQdiIiIhAQEICRI0di/PjxaNGihS1zERERkZX9cfMQVosQXwR4KyVOU3uqXXa++uorDBw4EC4uFt87lIiIiOxAXZyvA1hQdoYMGWLLHERERGRjxvk6jzatW2XHue/pTkRERACAC3nFyLhWDIVcVifudH47lh0iIqI6wHgIq11DVZ24H9btWHaIiIjqgD9M83XqzinnRhaXnXHjxqGgoKDS8qKiIowbN84qoYiIiMh6hBD4PdV4fZ26NV8HqEHZWbt2LUpKSiotLykpwaeffmqVUERERGQ9564UIbdACzcXOTo2qht3Or9dtc/G0mg0EEJACIGCggK4u9+6n4Zer8e2bdtQv359m4QkIiKimjOehdW5UT24uyokTlP7ql12/Pz8IJPJIJPJ8NBDD1VaL5PJsGDBAquGIyIioge3J7Vivs6jTevefB3AgrKza9cuCCHw+OOP49tvv4W//63T1tzc3NCoUSOEhobaJCQRERHVjN4g8Mf5irLTtQ7O1wEsKDs9e/YEAKSlpSEsLAxyOU/kIiIisnenLmmQX6KDt9IFbRuopI4jCYvv/dCoUSPcuHEDf/31F3Jzc2EwGMzWjxo1ymrhiIiI6MEYz8KKjvSHi6JuDlRYXHa2bNmCESNGoKioCD4+PpDJZKZ1MpmMZYeIiMiOJJ+9AgDoVkfn6wA1OPV8xowZpmvt3LhxA3l5eabH9evXbZGRiIiIaqBIW46/0iq+m3s3C5I4jXQsLjsXL17EtGnT4OnpaYs8REREZCW/p16FTi8Q7u+JyEAvqeNIxuKyExMTg/3799siCxEREVnR7jMVh7B6NQsym3ZS11g8Z+eZZ57BzJkzcfLkSbRp0wauruY3Exs4cKDVwhEREVHNCCGw+3QuAKB3s7p90V+Ly86ECRMAAAsXLqy0TiaTQa/XP3gqIiIieiBncwuRnV8KNxc5HmlcN6+vY2Rx2bnzVHMiIiKyP7tTKkZ1ujYOgIdb3btFxO0e6IT70tJSa+UgIiIiK9p1+tZ8nbrO4rKj1+vx1ltvoUGDBvD29sb58+cBAG+++SZWr15t9YBERERkmYJSHfZnGE85r9vzdYAalJ133nkHCQkJWLJkCdzc3EzL27Rpg08++cSq4YiIiMhyv6deg04vEBHgiYg6fMq5kcVl59NPP8WqVaswYsQIKBS3jgG2bdsWp0+ftmo4IiIislzSmYr5Or04qgOghhcVbNq0aaXlBoMBOp3OKqGIiIioZoQQnK9zB4vLTqtWrfDrr79WWv7111+jQ4cOVglFRERENZNyuQA5mlIoecq5icVlZ968eZg6dSoWL14Mg8GAjRs3YsKECVi0aBHmzp1r9YAXL17EP/7xDwQEBMDT0xPt27fHgQMHTOuFEJg/fz5CQ0Ph4eGBXr164cSJE1bPQURE5Ah2p9y88WeTALi71u1Tzo0sLjsDBgzAhg0bsG3bNshkMsydOxenTp3Cli1b8OSTT1o1XF5eHh599FG4urrixx9/xMmTJ/HBBx/Az8/PtM2SJUuwdOlSLF++HPv27UNwcDCefPJJFBQUWDULERGRI9h1mvN17iQTQgipQ1Tl1Vdfxe+//37Xw2ZAxahOaGgoYmNjMXv2bACAVquFWq3G4sWLMXHixGr9HI1GA5VKhfz8fPj6+lotPxERUW3SlOrQcWEiyg0CyTN7IzzAuW/aXd3v7we6qKCtff/99+jcuTOee+451K9fHx06dMDHH39sWp+WloacnBz07dvXtEypVKJnz57Ys2dPlfvVarXQaDRmDyIiIke363Quyg0CTYK8nL7oWKJat4uoV69ete+Wev369QcKdLvz589j5cqViIuLw2uvvYa//voL06ZNg1KpxKhRo5CTkwMAUKvVZu9Tq9XIyMiocr/x8fFYsGCB1XISERHZgx0nLgMAYloFS5zEvlSr7Cxbtsz0/Nq1a3j77bcRExODrl27AgD++OMPbN++HW+++aZVwxkMBnTu3BmLFi0CAHTo0AEnTpzAypUrMWrUKNN2dxYxIcQ9y9mcOXMQFxdneq3RaBAWFmbV7ERERLWpVKfHrpv3w2LZMVetsjN69GjT86FDh2LhwoWYOnWqadm0adOwfPly7Ny5Ey+//LLVwoWEhKBly5Zmy1q0aIFvv/0WABAcXPEPMycnByEhIaZtcnNzK4323E6pVEKpVFotJxERkdR+O3sVxWV6hKjc0bahSuo4dsXiOTvbt2/HU089VWl5TEwMdu7caZVQRo8++ihSUlLMlp05cwaNGjUCAERGRiI4OBiJiYmm9WVlZUhKSkK3bt2smoWIiMiebT9RMbWjb0t1taee1BUWl52AgABs2rSp0vLNmzcjIMC6Fy96+eWXsXfvXixatAipqan44osvsGrVKkyZMgVAxeGr2NhYLFq0CJs2bcLx48cxZswYeHp6Yvjw4VbNQkREZK/K9QbsPMX5OlWp1mGs2y1YsADjx4/H7t27TXN29u7di59++snqNwLt0qULNm3ahDlz5mDhwoWIjIzEsmXLMGLECNM2s2bNQklJCSZPnoy8vDxER0djx44d8PHxsWoWIiIie7UvPQ95xTr4ebri4Uh/qePYnRpdZ+fPP//Ev/71L5w6dQpCCLRs2RLTpk1DdHS0LTLaHK+zQ0REjmz+9yeQsCcdf+vUEO8/107qOLWmut/fFo/sAEB0dDQ+//zzGocjIiIi6xBCIPEkD2HdS43KjsFgQGpqKnJzc2EwGMzWPfbYY1YJRkRERPd3/KIGF2+UwNNNgR5RgVLHsUsWl529e/di+PDhyMjIwJ1HwGQyGfR6vdXCERER0b0Zz8Lq+VAQb/xZBYvLzqRJk9C5c2ds3boVISEhPL2NiIhIQj/dLDs8hFU1i8vO2bNn8c0336Bp06a2yENERETVdO5KIVJzC+Eil6F3c97lvCoWX2cnOjoaqamptshCREREFjAewuraJAAqD1eJ09gvi0d2XnrpJcyYMQM5OTlo06YNXF3NP9y2bdtaLRwRERFVbduxSwB4COt+LC47Q4cOBQCMGzfOtEwmk5luvskJykRERLZ37kohjl/UwEUuw9NtQu7/hjrM4rKTlpZmixxERERkge8PZwMAekQFwt/LTeI09s3ismO8CScRERFJQwiB749UlJ2B7UMlTmP/LJ6gDACfffYZHn30UYSGhiIjIwMAsGzZMnz33XdWDUdERESVHbuYj7SrRXB3lePJlpyvcz8Wl52VK1ciLi4OTz/9NG7cuGGao+Pn54dly5ZZOx8RERHd4bubh7D6tFDDW1mjmyHUKRaXnY8++ggff/wxXn/9dSgUt67U2LlzZxw7dsyq4YiIiMic3iDww9GKsjOofQOJ0zgGi8tOWloaOnToUGm5UqlEUVGRVUIRERHR3f2Zdg2XNVr4urvgsYd4L6zqsLjsREZG4vDhw5WW//jjj2jZsqU1MhEREVEVjGdhPd0mBEoX3gurOiw+0Ddz5kxMmTIFpaWlEELgr7/+wpdffon4+Hh88skntshIREREALTletOFBHkWVvVZXHbGjh2L8vJyzJo1C8XFxRg+fDgaNGiAf/7zn3jhhRdskZGIiIgAJJ+5Ck1pOer7KBEdGSB1HIdRoyncEyZMwIQJE3D16lUYDAbUr8+bjxEREdnad4cvAgAGtAuFQi6TOI3jqPH5arm5uUhJSYFMJoNMJkNQUJA1cxEREdFtirTl2HnqMgBgEA9hWcTiCcoajQYjR45EaGgoevbsicceewyhoaH4xz/+gfz8fFtkJCIiqvO2HruEUp0BkYFeaNNAJXUch2Jx2XnxxRfx559/YuvWrbhx4wby8/Pxww8/YP/+/ZgwYYItMhIREdV5X+3LAgD8rVNDyGQ8hGUJiw9jbd26Fdu3b0f37t1Ny2JiYvDxxx/jqaeesmo4IiIiAlJzC7E/Iw9yWUXZIctYPLITEBAAlary8JlKpUK9evWsEoqIiIhu+Xp/xahO72b1ofZ1lziN47G47LzxxhuIi4vDpUuXTMtycnIwc+ZMvPnmm1YNR0REVNfp9AZ8e/ACAOD5LmESp3FMFh/GWrlyJVJTU9GoUSOEh4cDADIzM6FUKnHlyhX897//NW178OBB6yUlIiKqg345nYurhWUI9Fbi8ea81EtNWFx2Bg8ebIMYREREdDfGiclDOzaAq8LiAzKEGpSdefPm2SIHERER3eGyphS7UnIBAM915iGsmqpRRbxx4wY++eQTzJkzB9evXwdQccjq4sWLVg1HRERUl31z4AIMAujcqB6a1veWOo7Dsnhk5+jRo+jTpw9UKhXS09MxYcIE+Pv7Y9OmTcjIyMCnn35qi5xERER1isEg8NXNs7CGcWLyA7F4ZCcuLg5jxozB2bNn4e5+6/S3fv36ITk52arhiIiI6qo/064j41oxvJUueKZtiNRxHJrFZWffvn2YOHFipeUNGjRATk6OVUIRERHVdcZRnQHtQuDpVuNbWRJqUHbc3d2h0WgqLU9JSeHNQImIiKzgaqEWW49WXM9uWJdwidM4PovLzqBBg7Bw4ULodDoAgEwmQ2ZmJl599VUMHTrU6gGJiIjqmi//zESZ3oB2YX5oH+YndRyHZ3HZef/993HlyhXUr18fJSUl6NmzJ5o2bQofHx+88847tshIRERUZ+j0Bqz7MwMAMLZbhLRhnITFBwF9fX3x22+/4ZdffsHBgwdhMBjQsWNH9OnTxxb5iIiI6pSfjufgskaLQG8lnm7DicnWUOMZT48//jgef/xxa2YhIiKq89buSQcAjIgOh5sLr5hsDRaVHYPBgISEBGzcuBHp6emQyWSIjIzE3/72N4wcORIymcxWOYmIiJzesQv52J+RBxe5DCOiOTHZWqpdGYUQGDhwIF588UVcvHgRbdq0QatWrZCRkYExY8bg2WeftWVOIiIip5dwc1TnmbYhqO/rfu+NqdqqPbKTkJCA5ORk/Pzzz+jdu7fZul9++QWDBw/Gp59+ilGjRlk9JBERkbO7WqjFliPZAIDRnJhsVdUe2fnyyy/x2muvVSo6QMX8nVdffRWff/65VcPdKT4+HjKZDLGxsaZlQgjMnz8foaGh8PDwQK9evXDixAmb5iAiIrK29X/dPN28oQodeLq5VVW77Bw9ehRPPfVUlev79euHI0eOWCXU3ezbtw+rVq1C27ZtzZYvWbIES5cuxfLly7Fv3z4EBwfjySefREFBgc2yEBERWZNOb8C6vZkAKkZ1OAfWuqpddq5fvw61Wl3lerVajby8PKuEulNhYSFGjBiBjz/+GPXq1TMtF0Jg2bJleP311zFkyBC0bt0aa9euRXFxMb744gubZCEiIrK2bccuIUdTikBvN94HywaqXXb0ej1cXKqe4qNQKFBeXm6VUHeaMmUKnnnmmUrX8klLS0NOTg769u1rWqZUKtGzZ0/s2bOnyv1ptVpoNBqzBxERkRSEEFi5+xwAYOQjEVC6KCRO5HyqPUFZCIExY8ZAqVTedb1Wq7VaqNutX78eBw8exL59+yqtM9549M4RJ7VajYyMjCr3GR8fjwULFlg3KBERUQ3sSsnF6ZwCeLkpMLpbI6njOKVql53Ro0ffdxtrn4mVlZWF6dOnY8eOHXB3r/oUvDuPbQoh7nm8c86cOYiLizO91mg0CAsLe/DAREREFlqxq2JUZ3h0OPw83SRO45yqXXbWrFljyxx3deDAAeTm5qJTp06mZXq9HsnJyVi+fDlSUlIAVIzwhITcOsaZm5t7z/lFSqWyyhEqIiKi2vJX2nXsz8iDm0KOF3s0ljqO07Lr61A/8cQTOHbsGA4fPmx6dO7cGSNGjMDhw4fRuHFjBAcHIzEx0fSesrIyJCUloVu3bhImJyIiur8Vu1MBAEM7NYSaFxG0mRrfG6s2+Pj4oHXr1mbLvLy8EBAQYFoeGxuLRYsWISoqClFRUVi0aBE8PT0xfPhwKSITERFVy4nsfOxOuQK5DJjUk6M6tmTXZac6Zs2ahZKSEkyePBl5eXmIjo7Gjh074OPjI3U0IiKiKhnPwHqmbSgaBXhJnMa5yYQQQuoQUtNoNFCpVMjPz4evr6/UcYiIyMmlXy3C4x/shkEA26b1QMtQfvfURHW/v+16zg4REZEz+m/yORgE0LtZEItOLWDZISIiqkVZ14vxzYELAIDJvZtKnKZuYNkhIiKqRct2noVOL9AjKhBdIvyljlMnsOwQERHVktTcAmw6VDGq80rfZhKnqTtYdoiIiGrJ0sQzMAggppUa7cL8pI5TZ7DsEBER1YJjF/Kx7VgOZDJgBkd1ahXLDhERUS14f0fFLY4Gt2+Ah9S8FlxtYtkhIiKysb/SriPpzBW4yGWI7RMldZw6h2WHiIjIhoQQeG/7aQDAsC5hvFqyBFh2iIiIbGh3yhXsS8+D0kWOlx7nqI4UWHaIiIhsRKc34O2tJwEAo7tFIFjFO5tLgWWHiIjIRj77IwPnrhQhwMsNU3i1ZMmw7BAREdnAtUItPtx5BgDwSkwzqDxcJU5Ud7HsEBER2cDSxDMoKC1HyxBfPN85TOo4dRrLDhERkZWduqTBl39lAgDmDWgJhVwmcaK6jWWHiIjIioQQWLjlJAwCeKZNCKIbB0gdqc5j2SEiIrKi7Sdy8Mf5a1C6yPFqv+ZSxyGw7BAREVlNSZkeb289BQCY+FhjhPl7SpyIAJYdIiIiq/lw5xlcyCtBqModk3o1kToO3cSyQ0REZAXHL+bjk1/PAwDefrY1PN1cJE5ERiw7RERED6hcb8Dsb4/CIIAB7ULxeHO11JHoNiw7RERED2j1b2k4ka2BysMVc/u3lDoO3YFlh4iI6AFkXCvC0sSKKyW//kwLBPkoJU5Ed2LZISIiqiEhBF7bdAzacgO6NQnAc50aSh2J7oJlh4iIqIa+3n8Bv6dWXFNn0bNtIJPxSsn2iGWHiIioBjKvFWPBlhMAgJeffAgRgV4SJ6KqsOwQERFZqFxvQOyGQygq0+PhCH9M6NFY6kh0Dyw7REREFlqx+xwOZt6Aj9IFHzzfjjf6tHMsO0RERBY4lJmHf/58FgCwcHAr3hLCAbDsEBERVVORthwvbzgMvUFgQLtQDG7fQOpIVA0sO0RERNW0cMtJpF8rRqjKHW8Pas2zrxwEyw4REVE1bD50ERv2Z0EmAz54vj1Unq5SR6JqYtkhIiK6j1OXNHh141EAwNTeTdG1SYDEicgSLDtERET3kF+iw/+tO4BSnQE9ogIR2+chqSORhVh2iIiIqmAwCMz46gjSrxWjgZ8H/vVCB55m7oBYdoiIiKqwMukcdp66DDeFHCv/0RH1vNykjkQ1wLJDRER0F7+evYIPdqQAABYOaoW2Df2kDUQ1xrJDRER0h7OXCzD584MwCGBY5zC88HC41JHoAbDsEBER3eZKgRZjE/ahoLQcnRvVw4JBraSORA/IrstOfHw8unTpAh8fH9SvXx+DBw9GSkqK2TZCCMyfPx+hoaHw8PBAr169cOLECYkSExGRIysp0+PFT/fjQl4JGgV4YtWoznB3VUgdix6QXZedpKQkTJkyBXv37kViYiLKy8vRt29fFBUVmbZZsmQJli5diuXLl2Pfvn0IDg7Gk08+iYKCAgmTExGRozEYBF7ecBhHsm7Az9MVa8Z0gT8nJDsFmRBCSB2iuq5cuYL69esjKSkJjz32GIQQCA0NRWxsLGbPng0A0Gq1UKvVWLx4MSZOnFit/Wo0GqhUKuTn58PX19eWvwIREdmpRdtOYVXyebgp5Fj3YjQejvSXOhLdR3W/v+16ZOdO+fn5AAB//4q/gGlpacjJyUHfvn1N2yiVSvTs2RN79uypcj9arRYajcbsQUREddeq5HNYlXweAPDec21ZdJyMw5QdIQTi4uLQvXt3tG7dGgCQk5MDAFCr1WbbqtVq07q7iY+Ph0qlMj3CwsJsF5yIiOzaZ3szsGjbaQDAzJhmGMQ7mTsdhyk7U6dOxdGjR/Hll19WWnfnXWeFEPe8E+2cOXOQn59vemRlZVk9LxER2b9vD1zAm5uPAwAm92qCKb2bSpyIbMFF6gDV8dJLL+H7779HcnIyGjZsaFoeHBwMoGKEJyQkxLQ8Nze30mjP7ZRKJZRKpe0CExGR3fvx2CXM/OYIAGBMtwjMjGkmcSKyFbse2RFCYOrUqdi4cSN++eUXREZGmq2PjIxEcHAwEhMTTcvKysqQlJSEbt261XZcIiJyELtO52La+kMwCOC5Tg0xt3/Lex4RIMdm1yM7U6ZMwRdffIHvvvsOPj4+pnk4KpUKHh4ekMlkiI2NxaJFixAVFYWoqCgsWrQInp6eGD58uMTpiYjIHv10PAcvfXkQOr1A/7YheHdoW8h5c0+nZtdlZ+XKlQCAXr16mS1fs2YNxowZAwCYNWsWSkpKMHnyZOTl5SE6Oho7duyAj49PLaclIiJ7t+nQBbzy9VHoDQJPtwnGh8Pa8y7mdYBDXWfHVnidHSIi5/f5nxl4Y/NxCAEM7dgQi4e2gYvCrmdz0H1U9/vbrkd2iIiIrOHj5PN4Z9spAMCoro0wf0ArHrqqQ1h2iIjIaRkMAu/+dNp0wcD/69UEs2KacTJyHcOyQ0RETqmkTI+XNxzGTycqTm6ZGdOM19Gpo1h2iIjI6eQWlGLC2v04ciEfbgo53nuuLa+MXIex7BARkVM5c7kAY9fsw8UbJfDzdMWqkZ15r6s6jmWHiIicxrZjlzDz6yMoKtMjIsATa8Y+jMhAL6ljkcRYdoiIyOHp9AYs/vE0PvktDQDwSGN/rBzRCfW83CRORvaAZYeIiBxabkEppn5+CH+lXwcATHysMWbGNOM1dMiEZYeIiBzWntSriN1wGLkFWngrXfD+c23xVOuQ+7+R6hSWHSIicjjacj3e356Cj3+tOGz1kNob//lHJzQO8pY4Gdkjlh0iInIop3M0iF1/GKdzCgAAw6PD8cYzLeDpxq80ujv+zSAiIoegNwis+T0NS7anoKzcgAAvNywe2hZ9WqqljkZ2jmWHiIjs3slsDeZsPIojF/IBAI83r4/FQ9siyEcpcTJyBCw7RERkt0p1evzz57P4OPk8yg0CPu4ueO3pFnihSxjvb0XVxrJDRER2KenMFcz77jjSrxUDAJ5qFYwFg1pB7esucTJyNCw7RERkV85fKcQ7W0/h59O5AAC1rxILB7VGTKtgiZORo2LZISIiu6Ap1eGjn88iYU86dHoBF7kMo7tFYHqfKPi6u0odjxwYyw4REUmqVKfHur0ZWLH7HK4XlQEAejcLwhv9W6IJr5tDVsCyQ0REkigrN+Cr/Vn46JezuKzRAgCaBHnhzf4t0atZfYnTkTNh2SEiolpVVm7A5kMXsXxXKjKvV0w+buDngelPRGFIxwa8pxVZHcsOERHViiJtOb78KxOf/JqGHE0pACDQW4mpvZvg79HhULooJE5Izoplh4iIbOpKgRbr9mZg7R/puFGsAwDU91FiQo/GGPFIOG/zQDbHv2FERGQTh7NuYO2edPxwNBs6vQAARAR4YmLPJhjSsQFHcqjWsOwQEZHVFJeVY+vRS1j3ZyaOZN0wLe8Q7odxj0bi6TYhUMh55WOqXSw7RET0QIQQOJx1A1/tz8KWI5dQqC0HALgp5OjfLgRjukWgbUM/aUNSncayQ0RENZJ1vRjfH8nGd4cv4szlQtPyRgGeeL5zGIZ1CUOgN2/USdJj2SEiomrLLSjFj8dy8N3hiziYecO0XOkix9NtQvB85zBER/pDzkNVZEdYdoiI6J4yrhVh+4kcbD9xGQcz8yAq5hpDJgO6Ng7AoPaheKp1CFQevKUD2SeWHSIiMqPTG3AwIw+7z1zBrtO5OJ1TYLa+fZgfBrQLRf+2IbwDOTkElh0iIkLmtWLsOXcVyWev4NezV1FQWm5ap5DL8Ehjf8S0CsaTLdUIUXlImJTIciw7RER1UPaNEuxLv47fU69iz7lruJBXYra+nqcrej4UhF7N6qNXsyD4ebpJlJTowbHsEBE5uXK9AWdzC3EgIw/70q9jf3oeLt4wLzcuchnah/nh0aaB6NUsCG0b+vF6OOQ0WHaIiJyIEAKZ14tx/KIGRy7cwOGsGzh2IR8lOr3Zdgq5DC1DfNG1SQC6NQlAlwh/eCn5lUDOiX+ziYgcVKlOj7OXC3E6R4OTlzQ4ka3BqWwNCrTllbb1VrqgXZgKXSL80SXCH+3D/FhuqM7g33QiIjtXpC3H+StFOHelEOeuFOLs5UKkXC5AxrUiGETl7d0UcjQL9kG7MBXah9VD+zAVGgd689o3VGex7BAR2YGCUh0u5JUg41oxMq4VIf1aEdKuFiH9ajFyNKVVvq+epyuaBfugebAvWjdQoVWoL5rW94arQl6L6YnsG8sOEZGNGQwCV4u0yMkvRfaNEmTfuPlnfgmyrpcgK68YN4p199xHoLcbGgd5o0mQN5oEeaF5sC8eCvZGkLcSMhlHbIjuhWWHiKiGtOV6XC8qw7XCMlwp1OKKRosrhVrkakqRW6BFjqYUl/Mrnpff7XjTHep5uiLc3xONArwQEeiFiICK502CvHjqN9EDYNkhojpPCIGiMj00JTrkl+hwo1iH/JIy3CjW4UaJDnnFZcgrKsP1oorn14vKcLVQa3bhvfuRyYBAbyUa+HmggZ8HQlTuCPHzQFg9D4T5eyLM3xPenDBMZBNO82/WihUr8N577+HSpUto1aoVli1bhh49ekgdi4hsxGAQKNHpKx5lehSVlaNIq0fxzT+LtOUoKitHobYchaXlKNKWo0BbjoLSchSU6lB487mmRAdNaTn01Rh5uRsXuQz+Xm4I9Faivq8SQd5KBPlUPIJ93aFWuSPY1x1BPkrOoyGSiFOUnQ0bNiA2NhYrVqzAo48+iv/+97/o168fTp48ifDwcKnjETkVIQR0egGd3oByvUCZ3oBygwG6cgGdwQCdvuJ5md6AsnIDyvQG6G7+WVZe8dDqDdDq9CjTG6DVGaAtN0Bbroe23IBSnR5a3c0/b74uLdejuEyP0jK9qeCU6gxW/91cFTL4urvCz9MVfp5u8PNwhcrDFfW83ODv5YZ6nm7w96pYF+itRKC3G3zdXXmWE5Gdkwkhava/M3YkOjoaHTt2xMqVK03LWrRogcGDByM+Pv6+79doNFCpVMjPz4evr6/VcuUVlaGorPIwt7U+8Tv3I1B5x5W3udt+RDW2qby2qn3fvtyYybjsXuvutt/bt7m1f3GXnyVM2whx63cSt20jULHBnctuvc/4/Oafty83vadinUFU/V6DoWKZQQjTcoO47TUqRiWMy3Bz21vbAHqDMC0TouK53lDxXH/be/UGAb0QMBjuvdz4Wm8wf5Sb/Wm49VpvXF6xrNxYbm6utzcergp4uingpXQx+9Nb6QJvpQu8bv7p7e4CH/eK577urvBxd4Gvhyt83StKjburnJN9iRxIdb+/HX5kp6ysDAcOHMCrr75qtrxv377Ys2fPXd+j1Wqh1WpNrzUajU2yvbcjBV/8mWmTfRPZExe5DC4KGdwUcri5yOGmkMPltueuLnIoFXK4usigdFHATSGH0lVu+tPdRQGlq7xinYsc7i5yuLsqbj7kULoq4OmqgIebAh43l3u4VRQcdxcFR1aI6J4cvuxcvXoVer0earXabLlarUZOTs5d3xMfH48FCxbYPJurXAZ31+odo5fh/v+xvtv/cN656G7/V1ppSU33c4/3Gbe/229x632ySvuR3baN7Lb1t+/n9izGp3dub9yXWY47lstue59Zhpvr5PJb62Q3d3zrPbdtd3OBcZ1cJjPbr0wmg/zm+op1spvbVWwjlxu3uW07+e2vZTdfV1zSX34zrEIug0IuM/1Mhey27W4+V8hvLVcY3y+XweXmfoz7cJHLbz1XVLzH5bbXxvXG58Yy46owPjcWmor3cTSEiOyZw5cdozv/YyuEqPI/wHPmzEFcXJzptUajQVhYmNUzLRjUGgsGtbb6fomIiKj6HL7sBAYGQqFQVBrFyc3NrTTaY6RUKqFUKmsjHhEREUnM4c+DdHNzQ6dOnZCYmGi2PDExEd26dZMoFREREdkLhx/ZAYC4uDiMHDkSnTt3RteuXbFq1SpkZmZi0qRJUkcjIiIiiTlF2Rk2bBiuXbuGhQsX4tKlS2jdujW2bduGRo0aSR2NiIiIJOYU19l5ULa6zg4RERHZTnW/vx1+zg4RERHRvbDsEBERkVNj2SEiIiKnxrJDRERETo1lh4iIiJwayw4RERE5NZYdIiIicmosO0REROTUWHaIiIjIqTnF7SIelPEi0hqNRuIkREREVF3G7+373QyCZQdAQUEBACAsLEziJERERGSpgoICqFSqKtfz3lgADAYDsrOz4ePjA5lMJnUcyWk0GoSFhSErK4v3CrMxfta1h5917eFnXXvq+mcthEBBQQFCQ0Mhl1c9M4cjOwDkcjkaNmwodQy74+vrWyf/5ZECP+vaw8+69vCzrj11+bO+14iOEScoExERkVNj2SEiIiKnxrJDlSiVSsybNw9KpVLqKE6Pn3Xt4Wdde/hZ1x5+1tXDCcpERETk1DiyQ0RERE6NZYeIiIicGssOEREROTWWHSIiInJqLDtULVqtFu3bt4dMJsPhw4eljuN00tPTMX78eERGRsLDwwNNmjTBvHnzUFZWJnU0p7FixQpERkbC3d0dnTp1wq+//ip1JKcTHx+PLl26wMfHB/Xr18fgwYORkpIidaw6IT4+HjKZDLGxsVJHsUssO1Qts2bNQmhoqNQxnNbp06dhMBjw3//+FydOnMCHH36I//znP3jttdekjuYUNmzYgNjYWLz++us4dOgQevTogX79+iEzM1PqaE4lKSkJU6ZMwd69e5GYmIjy8nL07dsXRUVFUkdzavv27cOqVavQtm1bqaPYLZ56Tvf1448/Ii4uDt9++y1atWqFQ4cOoX379lLHcnrvvfceVq5cifPnz0sdxeFFR0ejY8eOWLlypWlZixYtMHjwYMTHx0uYzLlduXIF9evXR1JSEh577DGp4zilwsJCdOzYEStWrMDbb7+N9u3bY9myZVLHsjsc2aF7unz5MiZMmIDPPvsMnp6eUsepU/Lz8+Hv7y91DIdXVlaGAwcOoG/fvmbL+/btiz179kiUqm7Iz88HAP49tqEpU6bgmWeeQZ8+faSOYtd4I1CqkhACY8aMwaRJk9C5c2ekp6dLHanOOHfuHD766CN88MEHUkdxeFevXoVer4darTZbrlarkZOTI1Eq5yeEQFxcHLp3747WrVtLHccprV+/HgcPHsS+ffukjmL3OLJTB82fPx8ymeyej/379+Ojjz6CRqPBnDlzpI7ssKr7Wd8uOzsbTz31FJ577jm8+OKLEiV3PjKZzOy1EKLSMrKeqVOn4ujRo/jyyy+ljuKUsrKyMH36dKxbtw7u7u5Sx7F7nLNTB129ehVXr1695zYRERF44YUXsGXLFrMvBL1eD4VCgREjRmDt2rW2jurwqvtZG/9jlZ2djd69eyM6OhoJCQmQy/n/Iw+qrKwMnp6e+Prrr/Hss8+alk+fPh2HDx9GUlKShOmc00svvYTNmzcjOTkZkZGRUsdxSps3b8azzz4LhUJhWqbX6yGTySCXy6HVas3W1XUsO1SlzMxMaDQa0+vs7GzExMTgm2++QXR0NBo2bChhOudz8eJF9O7dG506dcK6dev4Hyorio6ORqdOnbBixQrTspYtW2LQoEGcoGxFQgi89NJL2LRpE3bv3o2oqCipIzmtgoICZGRkmC0bO3YsmjdvjtmzZ/PQ4R04Z4eqFB4ebvba29sbANCkSRMWHSvLzs5Gr169EB4ejvfffx9XrlwxrQsODpYwmXOIi4vDyJEj0blzZ3Tt2hWrVq1CZmYmJk2aJHU0pzJlyhR88cUX+O677+Dj42OaE6VSqeDh4SFxOufi4+NTqdB4eXkhICCARecuWHaI7MCOHTuQmpqK1NTUSkWSg68PbtiwYbh27RoWLlyIS5cuoXXr1ti2bRsaNWokdTSnYjy1v1evXmbL16xZgzFjxtR+IKKbeBiLiIiInBpnPxIREZFTY9khIiIip8ayQ0RERE6NZYeIiIicGssOEREROTWWHSIiInJqLDtERETk1Fh2iIiIyKmx7BARgIq7gm/evFnqGNUyf/58tG/fXuoYVterVy/ExsZWe/vdu3dDJpPhxo0bVW6TkJAAPz+/B85G5MhYdogc3JgxYzB48GCpYzi86pSCDz74ACqVCsXFxZXWlZaWws/PD0uXLq1xho0bN+Ktt96q8fuJ6O5YdoiIqmnUqFEoKSnBt99+W2ndt99+i+LiYowcOdLi/ep0OgCAv78/fHx8HjgnEZlj2SFyMr169cK0adMwa9Ys+Pv7Izg4GPPnzzfb5uzZs3jsscfg7u6Oli1bIjExsdJ+Ll68iGHDhqFevXoICAjAoEGDkJ6eblpvHFFasGAB6tevD19fX0ycOBFlZWWmbYQQWLJkCRo3bgwPDw+0a9cO33zzjWm98TDMzz//jM6dO8PT0xPdunVDSkqKWZZ3330XarUaPj4+GD9+PEpLSyvlXbNmDVq0aAF3d3c0b94cK1asMK1LT0+HTCbDxo0b0bt3b3h6eqJdu3b4448/TDnGjh2L/Px8yGQyyGSySp8ZAAQFBWHAgAH43//+V2nd//73PwwcOBBBQUGYPXs2HnroIXh6eqJx48Z48803TYUGuHUY7n//+x8aN24MpVIJIUSlw1jr1q1D586d4ePjg+DgYAwfPhy5ubmVfvbvv/+Odu3awd3dHdHR0Th27FilbW63ZcsWdOrUCe7u7mjcuDEWLFiA8vLye76HyKEJInJoo0ePFoMGDTK97tmzp/D19RXz588XZ86cEWvXrhUymUzs2LFDCCGEXq8XrVu3Fr169RKHDh0SSUlJokOHDgKA2LRpkxBCiKKiIhEVFSXGjRsnjh49Kk6ePCmGDx8umjVrJrRarennent7i2HDhonjx4+LH374QQQFBYnXXnvNlOW1114TzZs3Fz/99JM4d+6cWLNmjVAqlWL37t1CCCF27dolAIjo6Gixe/duceLECdGjRw/RrVs30z42bNgg3NzcxMcffyxOnz4tXn/9deHj4yPatWtn2mbVqlUiJCREfPvtt+L8+fPi22+/Ff7+/iIhIUEIIURaWpoAIJo3by5++OEHkZKSIv72t7+JRo0aCZ1OJ7RarVi2bJnw9fUVly5dEpcuXRIFBQV3/by3bt0qZDKZOH/+vGlZWlqakMlkYtu2bUIIId566y3x+++/i7S0NPH9998LtVotFi9ebNp+3rx5wsvLS8TExIiDBw+KI0eOCIPBIHr27CmmT59u2m716tVi27Zt4ty5c+KPP/4QjzzyiOjXr59pvfHza9GihdixY4c4evSo6N+/v4iIiBBlZWVCCCHWrFkjVCqV6T0//fST8PX1FQkJCeLcuXNix44dIiIiQsyfP//uf8GInADLDpGDu1vZ6d69u9k2Xbp0EbNnzxZCCLF9+3ahUChEVlaWaf2PP/5oVnZWr14tmjVrJgwGg2kbrVYrPDw8xPbt200/19/fXxQVFZm2WblypfD29hZ6vV4UFhYKd3d3sWfPHrMs48ePF3//+9+FELe+rHfu3Glav3XrVgFAlJSUCCGE6Nq1q5g0aZLZPqKjo83KTlhYmPjiiy/MtnnrrbdE165dhRC3ys4nn3xiWn/ixAkBQJw6dUoIUbkUVKW8vFw0aNBAzJ0717Rs7ty5okGDBqK8vPyu71myZIno1KmT6fW8efOEq6uryM3NNdvuzrJzp7/++ksAMBUx4+e3fv160zbXrl0THh4eYsOGDXf9vXr06CEWLVpktt/PPvtMhISE3PsXJ3JgLhINKBGRDbVt29bsdUhIiOnwx6lTpxAeHo6GDRua1nft2tVs+wMHDiA1NbXS/JHS0lKcO3fO9Lpdu3bw9PQ0209hYSGysrKQm5uL0tJSPPnkk2b7KCsrQ4cOHarMGxISAgDIzc1FeHg4Tp06hUmTJplt37VrV+zatQsAcOXKFWRlZWH8+PGYMGGCaZvy8nKoVKpq/ZzmzZujuhQKBUaPHo2EhATMmzcPMpkMa9euxZgxY6BQKAAA33zzDZYtW4bU1FQUFhaivLwcvr6+Zvtp1KgRgoKC7vmzDh06hPnz5+Pw4cO4fv06DAYDACAzMxMtW7Y0+zyM/P390axZM5w6dequ+zxw4AD27duHd955x7RMr9ejtLQUxcXFZv88iZwFyw6RE3J1dTV7LZPJTF+UQohK28tkMrPXBoMBnTp1wueff15p2/t9Qd/587Zu3YoGDRqYrVcqlVXmNWYxvv9+jNt9/PHHiI6ONltnLB/W+Dm3GzduHOLj4/HLL78AqCgfY8eOBQDs3bsXL7zwAhYsWICYmBioVCqsX78eH3zwgdk+vLy87vkzioqK0LdvX/Tt2xfr1q1DUFAQMjMzERMTYzYvqip3/jM1MhgMWLBgAYYMGVJpnbu7+333S+SIWHaI6piWLVsiMzMT2dnZCA0NBQDTRF2jjh07YsOGDaaJx1U5cuQISkpK4OHhAaDii97b2xsNGzZEvXr1oFQqkZmZiZ49e9Y4b4sWLbB3716MGjXKtGzv3r2m52q1Gg0aNMD58+cxYsSIGv8cNzc36PX6am3bpEkT9OzZE2vWrDFNLG7SpAmAisnCjRo1wuuvv27aPiMjw+I8p0+fxtWrV/Huu+8iLCwMALB///67brt3716Eh4cDAPLy8nDmzJkqR6s6duyIlJQUNG3a1OJMRI6KZYeojunTpw+aNWuGUaNG4YMPPoBGozH7YgaAESNG4L333sOgQYOwcOFCNGzYEJmZmdi4cSNmzpxpOgRWVlaG8ePH44033kBGRgbmzZuHqVOnQi6Xw8fHB6+88gpefvllGAwGdO/eHRqNBnv27IG3tzdGjx5drbzTp0/H6NGj0blzZ3Tv3h2ff/45Tpw4gcaNG5u2mT9/PqZNmwZfX1/069cPWq0W+/fvR15eHuLi4qr1cyIiIlBYWIiff/7ZdHjuXod0bj9s9sknn5iWN23aFJmZmVi/fj26dOmCrVu3YtOmTdXKcLvw8HC4ubnho48+wqRJk3D8+PEqr8GzcOFCBAQEQK1W4/XXX0dgYGCV116aO3cu+vfvj7CwMDz33HOQy+U4evQojh07hrffftvinESOgKeeE9UxcrkcmzZtglarxcMPP4wXX3zRbP4GAHh6eiI5ORnh4eEYMmQIWrRogXHjxqGkpMRspOeJJ55AVFQUHnvsMTz//PMYMGCA2Snbb731FubOnYv4+Hi0aNECMTEx2LJlCyIjI6udd9iwYZg7dy5mz56NTp06ISMjA//3f/9nts2LL76ITz75BAkJCWjTpg169uyJhIQEi35Ot27dMGnSJAwbNgxBQUFYsmTJPbcfOnQolEollEql2SGhQYMG4eWXX8bUqVPRvn177NmzB2+++Wa1cxgFBQUhISEBX3/9NVq2bIl3330X77///l23fffddzF9+nR06tQJly5dwvfffw83N7e7bhsTE4MffvgBiYmJ6NKlCx555BEsXboUjRo1sjgjkaOQibsdwCciuo8xY8bgxo0bDnOLCSKquziyQ0RERE6NZYeIiIicGg9jERERkVPjyA4RERE5NZYdIiIicmosO0REROTUWHaIiIjIqbHsEBERkVNj2SEiIiKnxrJDRERETo1lh4iIiJza/wNdhxie1akKtAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "\n",
+    "Y= np.exp(X)\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logarithmic\n",
+    "\n",
+    "The response $y$ is a results of applying the logarithmic map from the input $x$ to the output $y$. It is one of the simplest form of __log()__: i.e. $$ y = \\log(x)$$\n",
+    "\n",
+    "Please consider that instead of $x$, we can use $X$, which can be a polynomial representation of the $x$ values. In general form it would be written as  \n",
+    "\\begin{equation}\n",
+    "y = \\log(X)\n",
+    "\\end{equation}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "Y = np.log(X)\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sigmoidal/Logistic\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$ Y = a + \\frac{b}{1+ c^{(X-d)}}$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "\n",
+    "Y = 1-4/(1+np.power(3, X-2))\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"ref2\"></a>\n",
+    "# Non-Linear Regression example\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For an example, we're going to try and fit a non-linear model to the datapoints corresponding to China's GDP from 1960 to 2014. We download a dataset with two columns, the first, a year between 1960 and 2014, the second, China's corresponding annual gross domestic income in US dollars for that year. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-10-20 06:32:43 URL:https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/china_gdp.csv [1218/1218] -> \"china_gdp.csv\" [1]\n"
+     ]
+    },
+    {
+     "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>Year</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>5.918412e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1961</td>\n",
+       "      <td>4.955705e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>4.668518e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1963</td>\n",
+       "      <td>5.009730e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1964</td>\n",
+       "      <td>5.906225e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1965</td>\n",
+       "      <td>6.970915e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1966</td>\n",
+       "      <td>7.587943e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>7.205703e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1968</td>\n",
+       "      <td>6.999350e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>7.871882e+10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year         Value\n",
+       "0  1960  5.918412e+10\n",
+       "1  1961  4.955705e+10\n",
+       "2  1962  4.668518e+10\n",
+       "3  1963  5.009730e+10\n",
+       "4  1964  5.906225e+10\n",
+       "5  1965  6.970915e+10\n",
+       "6  1966  7.587943e+10\n",
+       "7  1967  7.205703e+10\n",
+       "8  1968  6.999350e+10\n",
+       "9  1969  7.871882e+10"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "#downloading dataset\n",
+    "!wget -nv -O china_gdp.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/china_gdp.csv\n",
+    "    \n",
+    "df = pd.read_csv(\"china_gdp.csv\")\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the Dataset ###\n",
+    "This is what the datapoints look like. It kind of looks like an either logistic or exponential function. The growth starts off slow, then from 2005 on forward, the growth is very significant. And finally, it decelerates slightly in the 2010s.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHCvV7g4AAAAkJb9fqq6WfD4pJ0cqLpZcLrt75TiEWQAAgN7m9UqzZ0u7dn3Z5vFICxZIZWX29cuBbF9msGjRIhUWFio9PV2jRo1SdXX1UetbWlp05513Kj8/X2lpaTr55JP11FNP9VJvAQAAesjrla68MjTISlJDQ6Dd67WnXw5l68zssmXLVF5erkWLFun888/X448/rgkTJmjz5s066aSTwr7nqquu0u7du/Xkk0/qlFNO0Z49e9TW1tbLPQcAAOgGvz8wI2tM59eMkSxLKi+XJk1iyUGELGPC/TZ7x5gxYzRy5Eg9+uijwbYRI0Zo8uTJqqio6FS/cuVKff/739f27duVlZXVrc9sbm6W2+1WU1OTMjMzu913AACAqK1eLY0ff+y6VaukkpJ49yZhRZPXbFtm0Nraqk2bNqm0tDSkvbS0VOvXrw/7nr/+9a8aPXq0fvWrX2nYsGEaPny4brvtNn322Wddfk5LS4uam5tDHgAAALbw+WJbB/uWGezdu1d+v1/Z2dkh7dnZ2WpsbAz7nu3bt2vt2rVKT0/XCy+8oL179+rf//3f9fHHH3e5braiokL33ntvzPsPAAAQtZyc2NbB/hvALMsKeW6M6dTWob29XZZl6dlnn9W5556ryy67TA899JD+8Ic/dDk7O3fuXDU1NQUf9fX1Mf8OAAAAESkuDuxa0EXWkWVJeXmBOkTEtjB74oknyuVydZqF3bNnT6fZ2g45OTkaNmyY3G53sG3EiBEyxmjXkXcE/n9paWnKzMwMeQAAANjC5QpsvyV1DrQdz+fP5+avKNgWZvv3769Ro0apqqoqpL2qqkrjxo0L+57zzz9fH374oT799NNg2/vvv6+UlBR5PJ649hcAACAmysqkykpp2LDQdo8n0M4+s1GxdTeDZcuWaerUqXrsscc0duxYPfHEE1q8eLHeeecd5efna+7cuWpoaNAzzzwjSfr00081YsQInXfeebr33nu1d+9e3Xjjjbrwwgu1ePHiiD6T3QwAAEBC4ASwLkWT12zdZ3bKlCnat2+f7rvvPvl8PhUVFWnFihXKz8+XJPl8Pu3cuTNYf/zxx6uqqkq33HKLRo8erUGDBumqq67S/fffb9dXAAAA6B6XK6m334oVW2dm7cDMLAAAQGJzxD6zAAAAQE8RZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjhX1CWAffPCBXnrpJX3xxRe68MILdfrpp8ejXwAAAMAxRRVm16xZo8suu0yHDh0KvDk1VX/84x919dVXx6VzAAAAwNFEtczgP//zPzV+/Hjt2rVL+/bt0/XXX6+f/vSn8eobAAAAcFSWMcZEWpyVlaU1a9aoqKhIknTw4EFlZmZq7969+spXvhK3TsZSNGf9AgAAoPdFk9eimpndv3+/hgwZEnyekZGhAQMGaP/+/d3qKAAAANATUd8AtnnzZjU2NgafG2P07rvv6sCBA8G2M888Mza9AwAAAI4iqmUGKSkpsixL4d7S0W5Zlvx+f0w7GUssMwAAAEhs0eS1qGZm6+rqetQxAAAAIJaiCrP5+fnx6gcAAAAQtajXzErSli1b9Je//EU7duyQZVkqLCzU5MmT9dWvfjXW/QMAAAC6FHWYraio0F133aX29nYNGTJExhh99NFHuuOOO/TAAw/otttui0c/AQAAgE6i2ppr1apVmjdvnu68807t3btXPp9PjY2NwTB7xx13aM2aNfHqKwAAABAiqt0MpkyZooEDB+rxxx8P+/qPfvQjHThwQEuXLo1ZB2ON3QwAAEBc+f1SdbXk80k5OVJxseRy2d0rR4nboQlvvPGGpk6d2uXrU6dO1WuvvRbNJQEAAPoOr1cqKJDGj5euuSbws6Ag0I64iCrM7t69WwUFBV2+XlhYGHKgAgAAQNLweqUrr5R27Qptb2gItBNo4yKqMPv555+rf//+Xb7er18/tba29rhTAAAAjuL3S7NnS+FWb3a0lZcH6hBTUe9m8Pvf/17HH3982NcOP9IWAAAgaVRXd56RPZwxUn19oK6kpNe6lQyiCrMnnXSSFi9efMwaAACApOLzxbYOEYsqzO7YsSNO3QAAAHCwnJzY1iFiUYXZzz//XC+//LIuv/xySdLcuXPV0tLy5cVSU3XfffcpPT09tr0EAABIZMXFkscTuNkr3LpZywq8Xlzc+33r46IKs3/84x/1P//zP8Ewu3DhQp1++uk67rjjJEn/+te/NHToUM2ZMyf2PQUAAEhULpe0YEFg1wLLCg20lhX4OX8++83GQVS7GTz77LO6/vrrQ9qWLFmiVatWadWqVfr1r3+t559/PqYdBAAAcISyMqmyUho2LLTd4wm0l5XZ068+LqqZ2ffff1/Dhw8PPk9PT1dKypd5+Nxzz9XNN98cu94BAAA4SVmZNGkSJ4D1oqjCbFNTk1JTv3zLRx99FPJ6e3t7yBpaAACApONysf1WL4pqmYHH49Hbb7/d5ev//Oc/5fF4etwpAAAAIBJRhdnLLrtMd911lz7//PNOr3322We699579e1vfztmnQMAAACOxjIm3P4R4e3evVtnn322+vfvr5kzZ2r48OGyLEv/+te/tHDhQrW1tammpkbZ2dnx7HOPNDc3y+12q6mpSZmZmXZ3BwAAAEeIJq9FtWY2Oztb69ev149//GPdcccd6sjBlmXpkksu0aJFixI6yAIAAKBviSrMSlJhYaFWrlypjz/+WFu3bpUknXLKKcrKyop55wAAAICjiTrMdsjKytK5554by74AAAAkLr+fLbcSULfDLAAAQNLweqXZs6Vdu75s83gCp35xGIKtotrNAAAAIOl4vYFjag8PspLU0BBo93rt6RckEWYBAAC65vcHZmTDbf7U0VZeHqiDLQizAAAAXamu7jwjezhjpPr6QB1sQZgFAADois8X2zrEHGEWAACgKzk5sa1DzBFmAQAAulJcHNi1wLLCv25ZUl5eoA62IMwCAAB0xeUKbL8ldQ60Hc/nz2e/WRsRZgEAAI6mrEyqrJSGDQtt93gC7ewzaysOTQAAADiWsjJp0iROAEtAhFkAAIBIuFxSSYndvcARWGYAAAAAxyLMAgAAwLEIswAAAHAswiwAAAAcizALAAAAxyLMAgAAwLEIswAAAHAswiwAAAAcy/Ywu2jRIhUWFio9PV2jRo1SdXV1RO9bt26dUlNTdfbZZ8e3gwAAAEhYtobZZcuWqby8XHfeeadqampUXFysCRMmaOfOnUd9X1NTk6ZNm6ZvfvObvdRTAAAAJCLLGGPs+vAxY8Zo5MiRevTRR4NtI0aM0OTJk1VRUdHl+77//e/r1FNPlcvl0osvvqja2tqIP7O5uVlut1tNTU3KzMzsSfcBAAAQB9HkNdtmZltbW7Vp0yaVlpaGtJeWlmr9+vVdvu/pp5/Wtm3bdPfdd0f0OS0tLWpubg55AAAAoG+wLczu3btXfr9f2dnZIe3Z2dlqbGwM+54tW7bojjvu0LPPPqvU1NSIPqeiokJutzv4yMvL63HfAQAAkBhsvwHMsqyQ58aYTm2S5Pf7dc011+jee+/V8OHDI77+3Llz1dTUFHzU19f3uM8AAABIDJFNb8bBiSeeKJfL1WkWds+ePZ1mayXpwIED2rhxo2pqajRz5kxJUnt7u4wxSk1N1UsvvaSLLrqo0/vS0tKUlpYWny8BAAAAW9k2M9u/f3+NGjVKVVVVIe1VVVUaN25cp/rMzEy99dZbqq2tDT5mzJihr33ta6qtrdWYMWN6q+sAAABIELbNzErSnDlzNHXqVI0ePVpjx47VE088oZ07d2rGjBmSAksEGhoa9MwzzyglJUVFRUUh7x8yZIjS09M7tQMAACA52Bpmp0yZon379um+++6Tz+dTUVGRVqxYofz8fEmSz+c75p6zAAAASF627jNrB/aZBQAASGyO2GcWAAAA6CnCLAAAAByLMAsAAADHIswCAADAsWzdzQAAAMBWfr9UXS35fFJOjlRcLLlcdvcKUSDMAgCA5OT1SrNnS7t2fdnm8UgLFkhlZfb1C1FhmQEAAEg+Xq905ZWhQVaSGhoC7V6vPf1C1AizAAAgufj9gRnZcFvtd7SVlwfqkPAIswAAILlUV3eekT2cMVJ9faAOCY8wCwAAkovPF9s62IowCwAAkktOTmzrYCvCLAAASC7FxYFdCywr/OuWJeXlBeqQ8AizAAAgubhcge23pM6BtuP5/PnsN+sQhFkAAJB8ysqkykpp2LDQdo8n0M4+s47BoQkAACA5lZVJkyZxApjDEWYBAEDycrmkkhK7e4EeYJkBAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxUu3uAAAAQMz5/VJ1teTzSTk5UnGx5HLZ3SvEAWEWAAD0LV6vNHu2tGvXl20ej7RggVRWZl+/EBcsMwAAAH2H1ytdeWVokJWkhoZAu9drT78QN4RZAADQN/j9gRlZYzq/1tFWXh6oQ59BmAUAAH1DdXXnGdnDGSPV1wfq0GcQZgEAQN/g88W2Do5AmAUAAH1DTk5s6+AIhFkAANA3FBcHdi2wrPCvW5aUlxeoQ59BmAUAAH2DyxXYfkvqHGg7ns+fz36zfQxhFgAA9B1lZVJlpTRsWGi7xxNoZ5/ZPodDEwAAQN9SViZNmsQJYEmCMAsAAPoel0sqKbG7F+gFLDMAAACAYxFmAQAA4FiEWQAAADgWYRYAAACOxQ1gAADAGfx+dihAJ4RZAACQ+LxeafZsadeuL9s8nsAhCewdm9RYZgAAABKb1ytdeWVokJWkhoZAu9drT7+QEAizAAAgcfn9gRlZYzq/1tFWXh6oQ1IizAIAgMRVXd15RvZwxkj19YE6JCXCLAAASFw+X2zr0OcQZgEAQOLKyYltHfocwiwAAEhcxcWBXQssK/zrliXl5QXqkJQIswAAIHG5XIHtt6TOgbbj+fz57DebxAizAAAgsZWVSZWV0rBhoe0eT6CdfWaTmu1hdtGiRSosLFR6erpGjRql6qPcjej1enXJJZdo8ODByszM1NixY/X3v/+9F3sLAABsUVYm7dghrVolLVkS+FlXR5CFvWF22bJlKi8v15133qmamhoVFxdrwoQJ2rlzZ9j6NWvW6JJLLtGKFSu0adMmjR8/XhMnTlRNTU0v9xwAAMSM3y+tXi0tXRr42dWesS6XVFIiXX114CdLCyDJMibcLsS9Y8yYMRo5cqQeffTRYNuIESM0efJkVVRURHSN008/XVOmTNFdd90VUX1zc7PcbreampqUmZnZrX4DAIAY4ZhahBFNXrNtZra1tVWbNm1SaWlpSHtpaanWr18f0TXa29t14MABZWVldVnT0tKi5ubmkAcAAEgAHFOLGLAtzO7du1d+v1/Z2dkh7dnZ2WpsbIzoGg8++KAOHjyoq666qsuaiooKud3u4CMvL69H/QYAADHAMbWIEdtvALOO2GbDGNOpLZylS5fqnnvu0bJlyzRkyJAu6+bOnaumpqbgo76+vsd9BgAAPcQxtYiRVLs++MQTT5TL5eo0C7tnz55Os7VHWrZsmW644QY9//zzuvjii49am5aWprS0tB73FwAAxBDH1CJGbJuZ7d+/v0aNGqWqqqqQ9qqqKo0bN67L9y1dulTXXnutlixZom9/+9vx7iYAAIgHjqlFjNg2MytJc+bM0dSpUzV69GiNHTtWTzzxhHbu3KkZM2ZICiwRaGho0DPPPCMpEGSnTZumBQsW6LzzzgvO6h533HFyu922fQ8AABCljmNqGxrCr5u1rMDrHFOLY7B1zeyUKVM0f/583XfffTr77LO1Zs0arVixQvn5+ZIkn88Xsufs448/rra2Nt18883KyckJPmbPnm3XVwAAAN3BMbWIEVv3mbUD+8wCAJBAwu0zm5cXCLLsM5u0oslrti4zAAAAfZTfH9iJwOcLrHstLg4/y1pWJk2aFFktEAZhFgAAxFa0p3p1HFMLdIPt+8wCAIA+hFO90MsIswAAIDY41Qs2IMwCAIDY4FQv2IAwCwAAYoNTvWADbgADAACRO9ouBZzqBRswMwsAACLj9UoFBdL48dI11wR+FhR8eVNXx6leRx6C0MGyAnvIcqoXYogwCwAAji2SXQo41Qs2IMwCAICji2aXgrIyqbJSGjYstM7jCbRzqhdijDWzAADg6KLZpaCkhFO90KsIswAA4Og3dnVnlwJO9UIvIcwCAJDsjnX8LLsUIIGxZhYAgGQWyY1d7FKABEaYBQAgWUV6Y5fELgVIWIRZAAD6Kr9fWr1aWro08NPvD309mhu72KUACYo1swAA9EXHWgcrRX9jF7sUIAERZgEA6Gs61sEeuXygYx1sx0xqd27sYpcCJBiWGQAA0JdEc8ABN3ahDyDMAgDgNEdbCxvNOliOn0UfQJgFAMBJvF6poEAaP1665prAz4KCQLvUvXWw3NgFB2PNLAAAieBoJ3B1iGQtbHfWwXJjFxzMMibcopq+q7m5WW63W01NTcrMzLS7OwAARLbzgN8fmIHtagmBZQXes3WrdPLJgYAb7j/xHXV1dYRVJKxo8hrLDAAAiKdj7fUayQlcUuRrYdevZx0skgphFgCAeDnW+tZodh6IZi0s62CRRFgzCwBAPESyvjUrK/KdB6JdC8s6WCQJwiwAALF2rBlXywrMuFZURHY9n0+66qrAzOqx1sIevicsBxwgCbDMAACAaB1rHWyk61s/+iiyz8vJYU9YoAuEWQAAonGsdbBS5OtbBw+O7gQu1sICnRBmAQCIVKQ7D0S6vnXYsOhnW8vKpB07pFWrpCVLAj/r6giySFrsMwsAQCQi3ee1ri7wvKAg8r1ew+0zm5cXCLKEVCShaPIaN4ABANDhaKdwRboOtro6cNPVggWB2VrLCg204WZc2XkA6DbCLAAA0rFP4Ypmn1fpy/Wt4a4ZbsaVnQeAbiHMAgAQyZ6w0e7zKjHjCvQC1swCAJJbpGtht26VTj458nWwALotmrzGbgYAgOQW6VrY9evZ5xVIQIRZAEByi2YtLPu8AgmHNbMAgL7taDsUSNGvhWUdLJBQCLMAgL7rWDsUSIEg6vEcey1sxylcEjsPAAmEZQYAgL4p0tO6XC7WwgIORpgFAPQ9fn9gRjbcTGtHW3l5oE5iLSzgYCwzAAAklmOtcY2kLtrTuiTWwgIORZgFACSOSNa4RlIX7WldHVgLCzgOYRYAEH+RzLZGcgpXWVn8TusC4EicAAYACBXpX/NHWhvJbGu0p3BxWhfQp3ECGAAkKr9fWr1aWro08LPjBqRE+WyvNxAqx4+Xrrkm8LOg4Ms7/6OtjXRHgUjXuC5axGldAEIQZgGgt0QTFGMdemMZPCOtjWZHgUjXuG7bFlkdp3UBSYNlBgDQUz1ZD9oxS3h4uIr0JqhYfvakSZH9NX9dXeB5JLVPPy1dfHH4msOtWhX4OX78sWsffli69dbIrtlxI1c0yyYAJIRo8hphFgC60tvrQevqpL/8JbahN9LPjkfwnDdPuv/+Y9ctWSJddVWgn8da48paWCApsGYWQN8QzV+1R1obaZ0d60FXr478r+Xj8dmR8PkiXxIQqZycyE/h6t+ftbAAQhBmgb4kHjcX2XXNWN+IFG2dHetBV6+OfeiNR/CMdDurkpLALOmRobODZUl5eYEZbynyNa6shQVwOJNkmpqajCTT1NRkd1eQ7NrajFm1ypglSwI/29p6Vrt8uTEejzGBOBN4eDyB9t64ZqTfJ5JrLl9ujGWF1kiBNsvqXm2kdW1tnft3ZH1enjEvv9x1zeGPVasCj0hq582LbV00n/3yy4HvHe53dPj3bmv78ncUSW3H7/3I2nBjGe0/S9H8GQLgKNHkNcIsEAk7g2esw188rhnp94nkmpGGycND1bFqW1oiv2asg+eSJZGHv0gDcjw+O9rgGW3tkb//vLyu/0cLQNIjzB4FYbaH4jFjkujXtDN4xjr8xeOaTpjxfPjhyK+5ZElsA+WqVaG/96OFv1iH3mg++2j/DHcVPKOpZRYVQBQIs0fR62E21kHNzpAYzexbLMOfnde0M3jGI/zF+podfzUdyfeJx4xnpMFz5szIrxmPv5Y/2j9zR4a/WIbeaD+7Qzz+PQMAUSDMHkWvhtlYBzU7Q2Ks1yU64Zp2B894hD87127GY8YzHjOzibAeNFahtzufDQAJgDB7FL0WZmMd1JxwQ0w06xKdcE27g2c8wl+srxmPIB2PG5E6xjzSmcxEWA8aq9ALAA7kqDD7yCOPmIKCApOWlmZGjhxp1qxZc9T61atXm5EjR5q0tDRTWFhoHn300ag+r1fCbKxDld0hMR6zX064pt3BMx7hL9bX7M4SB7tuRIp2JtMp60GZbQXQBzkmzD733HOmX79+ZvHixWbz5s1m9uzZJiMjw3zwwQdh67dv324GDBhgZs+ebTZv3mwWL15s+vXrZyorKyP+zF4Js7EOVXaHxHisS3TCNe0OnvEIf7G+ZrRrN+2+ESnamUyCIgDYwjFh9txzzzUzZswIaTvttNPMHXfcEbb+pz/9qTnttNNC2qZPn27OO++8iD+zV8JsrEOV3SHRCbOo8bim3cHTmPiEv1hfM1FmPNmbFAD6DEeE2ZaWFuNyuYzX6w1pnzVrlvnGN74R9j3FxcVm1qxZIW1er9ekpqaa1tbWsO/5/PPPTVNTU/BRX18f8S+n2/razGw81iU65Zp2B89oa3uyM0VPrsmMJwAghhwRZhsaGowks27dupD2n//852b48OFh33Pqqaean//85yFt69atM5LMhx9+GPY9d999t5HU6dEra2ZjFarsDnTGxGddopOuafe+m/EIf7G+JgEVABAjjgqz69evD2m///77zde+9rWw7zn11FPNAw88ENK2du1aI8n4fL6w77FlZtaY2IcquwNdR32s1yU65Zp2B08AAJKII8Jsby0zOJLt+8z2JFTZHeiMccbhDvG6JgAA6BXR5DXLGGNkkzFjxmjUqFFatGhRsO3rX/+6Jk2apIqKik71t99+u/72t79p8+bNwbYf//jHqq2t1YYNGyL6zObmZrndbjU1NSkzM7PnX+JY/H6pulry+aScHKm4WHK54l8Xr2sCAADEWTR5zdYwu2zZMk2dOlWPPfaYxo4dqyeeeEKLFy/WO++8o/z8fM2dO1cNDQ165plnJEl1dXUqKirS9OnTddNNN2nDhg2aMWOGli5dqiuuuCKiz+z1MAsAAICoRJPXUnupT2FNmTJF+/bt03333Sefz6eioiKtWLFC+fn5kiSfz6edO3cG6wsLC7VixQrdeuuteuSRR5Sbm6vf/va3EQdZAAAA9C22zszagZlZAACAxBZNXkvppT4BAAAAMUeYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4lq0ngNmh44yI5uZmm3sCAACAcDpyWiRneyVdmD1w4IAkKS8vz+aeAAAA4GgOHDggt9t91JqkO862vb1dH374oU444QRZltUrn9nc3Ky8vDzV19dzhG4CYnwSG+OT+BijxMb4JDbGJzxjjA4cOKDc3FylpBx9VWzSzcympKTI4/HY8tmZmZn8g5rAGJ/ExvgkPsYosTE+iY3x6exYM7IduAEMAAAAjkWYBQAAgGMRZntBWlqa7r77bqWlpdndFYTB+CQ2xifxMUaJjfFJbIxPzyXdDWAAAADoO5iZBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYjcCaNWs0ceJE5ebmyrIsvfjiiyGv7969W9dee61yc3M1YMAAXXrppdqyZUun62zYsEEXXXSRMjIyNHDgQJWUlOizzz4Lvv7JJ59o6tSpcrvdcrvdmjp1qvbv3x/nb9c39HSMduzYIcuywj6ef/75YB1j1D2x+DPU2NioqVOnaujQocrIyNDIkSNVWVkZUsP4dF8sxmjbtm367ne/q8GDByszM1NXXXWVdu/eHVLDGEWvoqJC//Zv/6YTTjhBQ4YM0eTJk/Xee++F1BhjdM899yg3N1fHHXecSkpK9M4774TUtLS06JZbbtGJJ56ojIwMfec739GuXbtCahif7onVGD3xxBMqKSlRZmamLMsK+7tnjDojzEbg4MGDOuuss7Rw4cJOrxljNHnyZG3fvl1/+ctfVFNTo/z8fF188cU6ePBgsG7Dhg269NJLVVpaqjfeeENvvvmmZs6cGXJE2zXXXKPa2lqtXLlSK1euVG1traZOndor39HpejpGeXl58vl8IY97771XGRkZmjBhQvBajFH3xOLP0NSpU/Xee+/pr3/9q9566y2VlZVpypQpqqmpCdYwPt3X0zE6ePCgSktLZVmWXnnlFa1bt06tra2aOHGi2tvbg9dijKL36quv6uabb9Zrr72mqqoqtbW1qbS0NOTPx69+9Ss99NBDWrhwod58800NHTpUl1xyiQ4cOBCsKS8v1wsvvKDnnntOa9eu1aeffqrLL79cfr8/WMP4dE+sxujQoUO69NJL9bOf/azLz2KMwjCIiiTzwgsvBJ+/9957RpJ5++23g21tbW0mKyvLLF68ONg2ZswYM2/evC6vu3nzZiPJvPbaa8G2DRs2GEnmX//6V2y/RB/X3TE60tlnn22uv/764HPGKDa6Oz4ZGRnmmWeeCblWVlaW+f3vf2+MYXxiqTtj9Pe//92kpKSYpqamYM3HH39sJJmqqipjDGMUK3v27DGSzKuvvmqMMaa9vd0MHTrU/OIXvwjWfP7558btdpvHHnvMGGPM/v37Tb9+/cxzzz0XrGloaDApKSlm5cqVxhjGJ5a6M0aHW7VqlZFkPvnkk5B2xig8ZmZ7qKWlRZKUnp4ebHO5XOrfv7/Wrl0rSdqzZ49ef/11DRkyROPGjVN2drYuvPDC4OtSYObW7XZrzJgxwbbzzjtPbrdb69ev76Vv0zdFMkZH2rRpk2pra3XDDTcE2xij+Ih0fC644AItW7ZMH3/8sdrb2/Xcc8+ppaVFJSUlkhifeIpkjFpaWmRZVsjG7+np6UpJSQnWMEax0dTUJEnKysqSJNXV1amxsVGlpaXBmrS0NF144YXB3+umTZv0xRdfhNTk5uaqqKgoWMP4xE53xigSjFF4hNkeOu2005Sfn6+5c+fqk08+UWtrq37xi1+osbFRPp9PkrR9+3ZJ0j333KObbrpJK1eu1MiRI/XNb34zuOassbFRQ4YM6XT9IUOGqLGxsfe+UB8UyRgd6cknn9SIESM0bty4YBtjFB+Rjs+yZcvU1tamQYMGKS0tTdOnT9cLL7ygk08+WRLjE0+RjNF5552njIwM3X777Tp06JAOHjyon/zkJ2pvbw/WMEY9Z4zRnDlzdMEFF6ioqEiSgr+77OzskNrs7Ozga42Njerfv7++8pWvHLWG8em57o5RJBij8AizPdSvXz8tX75c77//vrKysjRgwACtXr1aEyZMkMvlkqTgerHp06fruuuu0znnnKOHH35YX/va1/TUU08Fr2VZVqfrG2PCtiNykYzR4T777DMtWbIkZFa2A2MUe5GOz7x58/TJJ5/o5Zdf1saNGzVnzhx973vf01tvvRWsYXziI5IxGjx4sJ5//nn97W9/0/HHHy+3262mpiaNHDkyZBwZo56ZOXOm/vnPf2rp0qWdXjvydxjJ7/XIGsan52I9Rse6Rnev05ek2t2BvmDUqFGqra1VU1OTWltbNXjwYI0ZM0ajR4+WJOXk5EiSvv71r4e8b8SIEdq5c6ckaejQoZ3u+pWkjz76qNP/ySF6xxqjw1VWVurQoUOaNm1aSDtjFD/HGp9t27Zp4cKFevvtt3X66adLks466yxVV1frkUce0WOPPcb4xFkkf4ZKS0u1bds27d27V6mpqRo4cKCGDh2qwsJCSfwZ6qlbbrlFf/3rX7VmzRp5PJ5g+9ChQyUFZu06/nsjBZa4dfxehw4dqtbWVn3yySchs7N79uwJ/g0U49NzPRmjSDBG4TEzG0Nut1uDBw/Wli1btHHjRk2aNEmSVFBQoNzc3E7bdLz//vvKz8+XJI0dO1ZNTU164403gq+//vrrampqCvmrbvRMV2N0uCeffFLf+c53NHjw4JB2xij+uhqfQ4cOSVLI7h9SYN1mx998MD69I5I/QyeeeKIGDhyoV155RXv27NF3vvMdSYxRdxljNHPmTHm9Xr3yyivB/znoUFhYqKFDh6qqqirY1traqldffTX4ex01apT69esXUuPz+fT2228Haxif7ovFGEWCMeqCLbedOcyBAwdMTU2NqampMZLMQw89ZGpqaswHH3xgjDHmz3/+s1m1apXZtm2befHFF01+fr4pKysLucbDDz9sMjMzzfPPP2+2bNli5s2bZ9LT083WrVuDNZdeeqk588wzzYYNG8yGDRvMGWecYS6//PJe/a5OFYsxMsaYLVu2GMuyzP/93/+F/RzGqHt6Oj6tra3mlFNOMcXFxeb11183W7duNb/5zW+MZVnmf//3f4N1jE/3xeLP0FNPPWU2bNhgtm7dav70pz+ZrKwsM2fOnJAaxih6P/7xj43b7TarV682Pp8v+Dh06FCw5he/+IVxu93G6/Wat956y1x99dUmJyfHNDc3B2tmzJhhPB6Pefnll80//vEPc9FFF5mzzjrLtLW1BWsYn+6J1Rj5fD5TU1NjFi9ebCSZNWvWmJqaGrNv375gDWPUGWE2Ah1bZBz5+OEPf2iMMWbBggXG4/GYfv36mZNOOsnMmzfPtLS0dLpORUWF8Xg8ZsCAAWbs2LGmuro65PV9+/aZH/zgB+aEE04wJ5xwgvnBD37QaVsOhBerMZo7d67xeDzG7/eH/RzGqHtiMT7vv/++KSsrM0OGDDEDBgwwZ555Zqetuhif7ovFGN1+++0mOzvb9OvXz5x66qnmwQcfNO3t7SE1jFH0wo2LJPP0008Ha9rb283dd99thg4datLS0sw3vvEN89Zbb4Vc57PPPjMzZ840WVlZ5rjjjjOXX3652blzZ0gN49M9sRqju++++5jXYYw6s4wxJl6zvgAAAEA8sWYWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWABKIMUYXX3yxvvWtb3V6bdGiRXK73dq5c6cNPQOAxESYBYAEYlmWnn76ab3++ut6/PHHg+11dXW6/fbbtWDBAp100kkx/cwvvvgiptcDgN5EmAWABJOXl6cFCxbotttuU11dnYwxuuGGG/TNb35T5557ri677DIdf/zxys7O1tSpU7V3797ge1euXKkLLrhAAwcO1KBBg3T55Zdr27Ztwdd37Nghy7L05z//WSUlJUpPT9d///d/2/E1ASAmLGOMsbsTAIDOJk+erP379+uKK67Qf/3Xf+nNN9/U6NGjddNNN2natGn67LPPdPvtt6utrU2vvPKKJGn58uWyLEtnnHGGDh48qLvuuks7duxQbW2tUlJStGPHDhUWFqqgoEAPPvigzjnnHKWlpSk3N9fmbwsA3UOYBYAEtWfPHhUVFWnfvn2qrKxUTU2NXn/9df39738P1uzatUt5eXl67733NHz48E7X+OijjzRkyBC99dZbKioqCobZ+fPna/bs2b35dQAgLlhmAAAJasiQIfrRj36kESNG6Lvf/a42bdqkVatW6fjjjw8+TjvtNEkKLiXYtm2brrnmGn31q19VZmamCgsLJanTTWOjR4/u3S8DAHGSancHAABdS01NVWpq4F/V7e3tmjhxon75y192qsvJyZEkTZw4UXl5eVq8eLFyc3PV3t6uoqIitba2htRnZGTEv/MA0AsIswDgECNHjtTy5ctVUFAQDLiH27dvn9599109/vjjKi4uliStXbu2t7sJAL2KZQYA4BA333yzPv74Y1199dV64403tH37dr300ku6/vrr5ff79ZWvfEWDBg3SE088oa1bt+qVV17RnDlz7O42AMQVYRYAHCI3N1fr1q2T3+/Xt771LRUVFWn27Nlyu91KSUlRSkqKnnvuOW3atElFRUW69dZb9etf/9rubgNAXLGbAQAAAByLmVkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBY/w99KbzwXu7KKQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "x_data, y_data = (df[\"Year\"].values, df[\"Value\"].values)\n",
+    "plt.plot(x_data, y_data, 'ro')\n",
+    "plt.ylabel('GDP')\n",
+    "plt.xlabel('Year')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choosing a model ###\n",
+    "\n",
+    "From an initial look at the plot, we determine that the logistic function could be a good approximation,\n",
+    "since it has the property of starting with a slow growth, increasing growth in the middle, and then decreasing again at the end; as illustrated below:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "Y = 1.0 / (1.0 + np.exp(-X))\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "The formula for the logistic function is the following:\n",
+    "\n",
+    "$$ \\hat{Y} = \\frac1{1+e^{-\\beta_1(X-\\beta_2)}}$$\n",
+    "\n",
+    "$\\beta_1$: Controls the curve's steepness,\n",
+    "\n",
+    "$\\beta_2$: Slides the curve on the x-axis.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building The Model ###\n",
+    "Now, let's build our regression model and initialize its parameters. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(x, Beta_1, Beta_2):\n",
+    "     y = 1 / (1 + np.exp(-Beta_1*(x-Beta_2)))\n",
+    "     return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets look at a sample sigmoid line that might fit with the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7749e1c55550>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "beta_1 = 0.10\n",
+    "beta_2 = 1990.0\n",
+    "\n",
+    "#logistic function\n",
+    "Y_pred = sigmoid(x_data, beta_1 , beta_2)\n",
+    "\n",
+    "#plot initial prediction against datapoints\n",
+    "plt.plot(x_data, Y_pred*15000000000000.)\n",
+    "plt.plot(x_data, y_data, 'ro')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our task here is to find the best parameters for our model. Lets first normalize our x and y:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets normalize our data\n",
+    "xdata =x_data/max(x_data)\n",
+    "ydata =y_data/max(y_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### How we find the best parameters for our fit line?\n",
+    "we can use __curve_fit__ which uses non-linear least squares to fit our sigmoid function, to data. Optimize values for the parameters so that the sum of the squared residuals of sigmoid(xdata, *popt) - ydata is minimized.\n",
+    "\n",
+    "popt are our optimized parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " beta_1 = 690.451712, beta_2 = 0.997207\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import curve_fit\n",
+    "popt, pcov = curve_fit(sigmoid, xdata, ydata)\n",
+    "#print the final parameters\n",
+    "print(\" beta_1 = %f, beta_2 = %f\" % (popt[0], popt[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we plot our resulting regression model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NHDhQK1asaLL/G2+8of79+ysmJkbJycm6+eabVVxcHKBqAQAAglBWlnvVgkaC6b74Tnp50DWmtlH9ktUvrV0AivMvS8PsvHnzNHnyZD3wwANas2aNsrKyNHLkSO3atavB/p9//rkmTJigW2+9VRs3btQ777yjb7/9VrfddluAKwcAAAgidrt7+S2pfqC12fSPi8er0h5R2xQeZtO9w3sFsED/sTTMPvHEE7r11lt12223qU+fPpo5c6bS09P1zDPPNNj/q6++Uvfu3XX33XcrIyNDw4YN0x133KFVq1YFuHIAAIAgk5srzZ8vpZpXJtjSd7AW9LvC1Db+wq7q3rFtIKvzG8vCbGVlpVavXq3hw4eb2ocPH66VK1c2uM/QoUO1Z88eLVy4UIZhaP/+/Zo/f76uvvrqRo9TUVGh0tJS0wMAAKBFys2VduyQli6V3nxTWrpU/zPlSbl0crS2baRd/33Z2dbV6GOWhdmioiI5nU4lJSWZ2pOSklRQUNDgPkOHDtUbb7yhcePGKTIyUl26dFG7du30z3/+s9HjzJgxQwkJCbWP9PR0n74PAACAoGK3S9nZ0q9+pa+7nqdPtxwwPX37z3uoU1yUNbX5geUXgNW9gs4wjEavqtu0aZPuvvtuPfjgg1q9erUWLVqk/Px8TZw4sdHXnzZtmkpKSmofu3fv9mn9AAAAwcgwDP21gdvW3pbVw6KK/MOymyZ07NhRdru93ihsYWFhvdHaGjNmzNDFF1+s++67T5LUr18/tW3bVllZWfrLX/6i5AaWpYiKilJUVMv5vw8AAABPfLJxv9bsOmxqu/vysxUb1bLumWXZyGxkZKQGDhyoJUuWmNqXLFmioUOHNrhPeXm5wsLMJdvtdknu//sAAACAVO106X8+MY/KdusQoxt+1tWiivzH0mkGU6ZM0YsvvqiXXnpJmzdv1j333KNdu3bVThuYNm2aJkyYUNt/9OjRysvL0zPPPKPt27friy++0N13363BgwcrJSXFqrcBAAAQVN5ZvUfbDxw1td03opciwy2fYepzlo4zjxs3TsXFxZo+fbocDocyMzO1cOFCdevWTZLkcDhMa87edNNNKisr01NPPaXf/e53ateunS677DL97W9/s+otAAAABJXyymr9Y8mPprZ+aQm6KrP5u4SFIpvRyr6fLy0tVUJCgkpKShQfH291OQAAAD719NKteuyTLaa2N2+7UEPP6mhRRd7zJq+1vLFmAACAVurg0Uo9u2ybqe3n53QKqSDrLcIsAABAC/HsZ9tUVlFdu22zSVN/0dvCivyPMAsAANAClB2v0ptf7zK1jRmQqnNTWva0SsIsAABACzDv2906csqobJhNmnxFy7ltbWMIswAAACGu2unSy1/sMLWN6NtF3Tq0taagACLMAgAAhLhFGwu09/AxU1tLu21tYwizAAAAIcwwDL2wIt/Udn7XdhrYrb1FFQUWYRYAACCErd55SOt2Hza13TasdYzKSoRZAACAkPZinVHZ1HZtNKJvkkXVBB5hFgAAIETtLD6qTzYVmNpuGZahcHvriXit550CAAC0MC9/sUOGcXI7Lipc1w9Ks64gCxBmAQAAQlBJeZXeXrXb1HbD4HTFRUdYVJE1CLMAAAAh6M1vdqm80lm7bQ+z6aaLMyysyBqEWQAAgBBTWe3SKyvNF35ddV6yUtu1sagi6xBmAQAAQszC7x3aX1pharttWOsblZWkcKsLAAAAQDOcTmnFCsnhkNGli15Yb45wg7snqn96O2tqsxhhFgAAIJjl5UmTJkl79kiSvko/TxvHzzB1uTWrdY7KSoRZAACA4JWXJ40dq1PX35rzszGmLt06xOiKPq3nJgl1MWcWAAAgGDmd7hHZU4LstsRU/evsC03dbhnSTfYwW6CrCxqEWQAAgGC0YkXt1IIaLw3KMW0nHCvTL4/vDGRVQYcwCwAAEIwcDtPmwTbxWpB5malt/NpFijlgvp1ta0OYBQAACEbJyabNNwaM1PGI6NrtcGe1fv3dR/X6tTaEWQAAgGCUlSWlpUk2myrs4Xr1glGmp6/ZvFxd2se4+7VihFkAAIBgZLdLs2ZJkj44N1tFse1NT9+y6n1p5kx3v1aMpbkAAACCVW6ujHfma87iA6bmIQVblDn7f6TcXIsKCx6EWQAAgCD27QXZ+uHbL01tt9/3K6lv654rW4NpBgAAAEFs3re7TdsZHdsqu08Xi6oJPoRZAACAIFV2vEoLvzcv0TXuZ+kKa8U3SaiLMAsAABCkPlzn0LEqZ+22Pcym3AtSLawo+BBmAQAAgtS8VeYpBpf17qzOcdGN9G6dCLMAAABBaEtBmdbtPmxqGzco3ZpighhhFgAAIAjVvfCrc1yUsnt1sqia4EWYBQAACDIV1U69u2aPqe26gWkKtxPd6uIvAgAAEGT+talQh8qrTG3XM8WgQYRZAACAIFP3wq/BGYnK6NjWomqCG2EWAAAgiOw9fEwrfjLfvvaGnzEq2xjCLAAAQBCZv2qPDOPkdlxUuEZmcuvaxhBmAQAAgoTLZejtOlMMrhmQojaRdosqCn6EWQAAgCDxxbYi7T18zNQ2jikGTSLMAgAABIm6a8v27hKn81ITLKomNBBmAQAAgsCho5VavHG/qW3cz9Jls9ksqig0EGYBAACCwHtr96rS6ardjrSHacyAVAsrCg2EWQAAAIsZhlFvisHwvklq3zbSoopCB2EWAADAYt/vLdEPBWWmNi788gxhFgAAwGJ1R2VT27XRxT07WlRNaCHMAgAAWOhYpVMfrN1navvloDSFhXHhlycIswAAABb6vw0OlVVU127bbNIvBzHFwFOEWQAAAAvVnWIw7KyOSm3XxqJqQg9hFgAAwCI7io7q6/yDpjYu/PIOYRYAAMAib68yj8q2j4nQlecmWVRNaCLMAgAAWKDa6dL81XtMbWPOT1VUuN2iikITYRYAAMACy7YcUGFZhamNKQbeI8wCAABYIG+NeVS2f1qCeneJt6ia0EWYBQAACLDS41X61+ZCU9tYluM6LYRZAACAAPtkQ4Eqq1212+FhNo06L9nCikIXYRYAACDA3q9zx6/sXp3Uvm2kRdWENsIsAABAABWWHtfKbUWmtpwBqRZVE/oIswAAAAH04XqHXMbJ7baRdl3Rh7VlTxdhFgAAIIA+WLvXtD2ibxe1iWRt2dNleZidPXu2MjIyFB0drYEDB2rFihVN9q+oqNADDzygbt26KSoqSj179tRLL70UoGoBAABOX37RUa3bU2Jqu2ZAikXVtAzhVh583rx5mjx5smbPnq2LL75Yzz33nEaOHKlNmzapa9euDe5z/fXXa//+/ZozZ47OOussFRYWqrq6OsCVAwAAeO/9OqOyHdpGathZHS2qpmWwGYZhNN/NPy688EJdcMEFeuaZZ2rb+vTpozFjxmjGjBn1+i9atEg33HCDtm/frsTExNM6ZmlpqRISElRSUqL4eBYmBgAAgWEYhi57/DPlFx2tbfv1kG56OCfTwqqCkzd5zbJpBpWVlVq9erWGDx9uah8+fLhWrlzZ4D4ffPCBBg0apP/5n/9RamqqzjnnHN177706duxYo8epqKhQaWmp6QEAABBo3+8tMQVZSco5n1UMzpRl0wyKiorkdDqVlGS+ei8pKUkFBQUN7rN9+3Z9/vnnio6O1rvvvquioiLdeeedOnjwYKPzZmfMmKGHH37Y5/UDAAB447015rVluybG6Pz0dtYU04JYfgGYzWYzbRuGUa+thsvlks1m0xtvvKHBgwfrqquu0hNPPKFXXnml0dHZadOmqaSkpPaxe/dun78HAACApjhdhj5cbw6zOQNSZHO5pGXLpLlz3T+dTkvqC2WWjcx27NhRdru93ihsYWFhvdHaGsnJyUpNTVVCQkJtW58+fWQYhvbs2aOzzz673j5RUVGKiorybfEAAABe+HJbsQ6UVZjacg5skrpfIe3Zc7IxLU2aNUvKzQ1whaHLspHZyMhIDRw4UEuWLDG1L1myREOHDm1wn4svvlj79u3TkSNHatt+/PFHhYWFKS0tza/1AgAAnK66qxj0jXHprBuvMwdZSdq7Vxo7VsrLC2B1oc3SaQZTpkzRiy++qJdeekmbN2/WPffco127dmnixImS3FMEJkyYUNt//Pjx6tChg26++WZt2rRJy5cv13333adbbrlFbdq0septAAAANOp4lVOLNpi/iR6zIk9qaEGpmrbJk5ly4CFL15kdN26ciouLNX36dDkcDmVmZmrhwoXq1q2bJMnhcGjXrl21/WNjY7VkyRL993//twYNGqQOHTro+uuv11/+8her3gIAAECTlv5QqLKKk2vi2ySN/urDxncwDGn3bmnFCik72+/1hTpL15m1AuvMAgCAQLrjf1fpk437a7eHxFZr7p/GNL/jm29Kv/qV/woLYiGxziwAAEBLV3KsSkt/OGBqy+kW49nOycl+qKjlIcwCAAD4yaINDlU6XbXbkfYwjbw2y71qQSNLkcpmk9LTpaysAFUZ2gizAAAAflL3RgnZvTopITbavfyWVD/Q1mzPnCnZ7f4vsAUgzAIAAPhBQclxfZVfbGobU3P72txcaf58KbXO7WzT0tztrDPrMUtXMwAAAGipPly3z7T6VlxUuC7r3flkQ26ulJPjXrXA4XDPkc3KYkTWS4RZAAAAP3h/nflGCSMyuyg6ok5QtdtZfusMMc0AAADAx7YWHtGGvaWmtjEDUhvpjTNBmAUAAPCxD+rcvrZTXJSG9OxgUTUtG2EWAADAhwzD0HtrzasYjO6XIntYI0tx4YwQZgEAAHxo7e7D2nWw3NSWMyDFompaPsIsAACAD71fZ1Q2o2Nb9UtLsKialo8wCwAA4CPVTpc+Wm8OszkDUmRr7G5fOGOEWQAAAB/5avtBFR2pNLVd058pBv5EmAUAAPCRD9eZR2XPS01Qj06xFlXTOhBmAQAAfKCy2qVFGwtMbaP7J1tUTetBmAUAAPCBz7ceUMmxKlPb1f2YYuBvXt/OdufOnVq8eLGqqqp0ySWXqG/fvv6oCwAAIKR8uM5h2h7Yrb1S27WxqJrWw6swu3z5cl111VUqL3evnRYeHq5XX31Vv/rVr/xSHAAAQCg4XuXU4rpTDPoxxSAQvJpm8Kc//UmXXnqp9uzZo+LiYt1yyy36/e9/76/aAAAAQsKyLYU6Wums3Q6zSVcRZgPCqzD7/fffa8aMGUpJSVH79u31+OOPa9++fTp06JC/6gMAAAh6dacYXJjRQZ3joi2qpnXxKswePnxYnTt3rt1u27atYmJidPjwYV/XBQAAEBKOVFTr0x/2m9pGs7ZswHh9AdimTZtUUHByTohhGNq8ebPKyspq2/r16+eb6gAAAILcp5v363iVq3Y7PMymX2R2sbCi1sXrMHv55ZfLMAxT26hRo2Sz2WQYhmw2m5xOZyN7AwAAtCx1pxgMO7ujEttGWlRN6+NVmM3Pz/dXHQAAACGnpLxKn/1YaGobxdqyAeVVmO3WrZu/6gAAAAg5n2wqUJXz5DfWkfYwDe+bZGFFrY/X0wwk6aefftL777+vHTt2yGazKSMjQ2PGjFGPHj18XR8AAEDQ+nDdPtN2dq9Oio+OsKia1snrMDtjxgw9+OCDcrlc6ty5swzD0IEDBzR16lQ9+uijuvfee/1RJwAAQFApPlKhlduKTW2sYhB4Xi3NtXTpUv3xj3/UAw88oKKiIjkcDhUUFNSG2alTp2r58uX+qhUAACBo/N+GAjldJ6cYtImw6/I+J5YwdTqlZcukuXPdP7k43m+8Gpl99tlnddttt+nPf/6zqT0xMVHTp09XQUGBnnnmGf385z/3ZY0AAABBp+4Ug8v7dFZMZLiUlydNmiTt2XPyybQ0adYsKTc3wFW2fF6NzH7zzTe68cYbG33+xhtv1FdffXXGRQEAAASz/aXH9c2Og6a20f1T3EF27FhzkJWkvXvd7Xl5AayydfAqzO7fv1/du3dv9PmMjAzTDRUAAABaoo/XO3TqsvtxUeG6pGeie0S2znr8kk62TZ7MlAMf8yrMHj9+XJGRjS8CHBERocrKyjMuCgAAIJh9uN48xeDKvkmK/mpl/RHZUxmGtHu3tGKFn6trXbxezeDFF19UbGxsg8+dektbAACAlmj3wXKt2XXY1Da6f4r03WbPXsDhaL4PPOZVmO3atateeOGFZvsAAAC0VB+tN4fRdjERGnZWR8mR7NkLJHvYDx7xKszu2LHDT2UAAACEho/qTDEYmdlFEfYwKSvLvWrB3r0Nz5u12dzPZ2UFqNLWwaswe/z4cf3rX//SqFGjJEnTpk1TRUXFyRcLD9f06dMVHR3t2yoBAACCwLYDR7RxX6mpbXS/EzdKsNvdy2+NHesOrqcGWpvN/XPmTHc/+IxXF4C9+uqreu6552q3n3rqKa1cuVJr1qzRmjVr9L//+7+aPXu2z4sEAAAIBh+tM08x6BgbpQt7dDjZkJsrzZ8vpaaad0xLc7ezzqzPeTUy+8Ybb+iee+4xtb355pvq0aOHJOn111/X008/rSlTpviuQgAAgCBgGEa9VQxG9UuWPcxm7pibK+XkuFctcDjcc2SzshiR9ROvwuyPP/6oc845p3Y7OjpaYWEnB3cHDx6su+66y3fVAQAABIkt+8u0tfCIqW1Uv0Yu5rLbpexs/xcF78JsSUmJwsNP7nLgwAHT8y6XyzSHFgAAoKWoe/valIRoXdC1vUXVoIZXc2bT0tK0YcOGRp9fv3690tLSzrgoAACAYGIYhj6sM192VP8UhdWdYoCA8yrMXnXVVXrwwQd1/Pjxes8dO3ZMDz/8sK6++mqfFQcAABAM1u8p0a6D5aa22lUMYCmvphn84Q9/0Ntvv61evXrpt7/9rc455xzZbDb98MMPeuqpp1RdXa0//OEP/qoVAADAEh/UmWLQrUOMMlPjLaoGp/IqzCYlJWnlypX6r//6L02dOlXGifXTbDabrrzySs2ePVtJSUl+KRQAAMAKTpdRb77s6H4pstmYYhAMvAqzkpSRkaFFixbp4MGD2rp1qyTprLPOUmJios+LAwAAsNrX24tVWGa+wH3MvrXSMgdLbgUBr8NsjcTERA0ePNiXtQAAAASd99eaR2XP3b9NZ/1tknsjLc191y9uhmAZry4AAwAAaE2OVzm1cIN5FYOcTZ+d3Ni713372ry8AFeGGoRZAACARizbckBlx6trt22GS6M3Lz/Z4cT1Q5o8WXI6A1scJBFmAQAAGvXBur2m7cG7NyqlrMjcyTCk3bvdt69FwBFmAQAAGlB2vEr/2lxoasvZtKzxHRyOxp+D3xBmAQAAGrBoQ4Eqq1212xHOKo3csrLxHZKTA1AV6jrt1QwAAABasro3Srgk/zu1P15Wv6PN5l7VICsrQJXhVIzMAgAA1FFYdlxfbDXPjc3ZtMwdXE9Vsz1zJuvNWoQwCwAAUMfH6x1yGSe3YyLtuuLhyVJqqrljWpo0fz7rzFqIaQYAAAB1vFfnRgkj+nZRm18OkHJz3KsWOBzuObLcAcxyhFkAAIBT7Cg6qnW7D5varhmQ4v7FbpeyswNeExrHNAMAAIBT1L3wq0PbSA07q6NF1aA5hFkAAIATDMPQe2vNN0q4ul+yIuxEpmDFmQEAADhh475SbT9w1NSWUzPFAEHJ8jA7e/ZsZWRkKDo6WgMHDtQKD28F98UXXyg8PFwDBgzwb4EAAKDVeL/OqGxa+za6oGt7i6qBJywNs/PmzdPkyZP1wAMPaM2aNcrKytLIkSO1a9euJvcrKSnRhAkTdPnllweoUgAA0NI5XUa9+bI5A1Jkq7u2LIKKpWH2iSee0K233qrbbrtNffr00cyZM5Wenq5nnnmmyf3uuOMOjR8/XkOGDAlQpQAAoKX7Jv+g9pdWmNpyBqQ20hvBwrIwW1lZqdWrV2v48OGm9uHDh2vlysbve/zyyy9r27Zteuihhzw6TkVFhUpLS00PAACAuj5YZ55i0LtLnM5JirOoGnjKsjBbVFQkp9OppKQkU3tSUpIKCgoa3Oenn37S1KlT9cYbbyg83LMlcmfMmKGEhITaR3p6+hnXDgAAWpaKaqc+Xu8wtTEqGxosvwCs7jwUwzAanJvidDo1fvx4PfzwwzrnnHM8fv1p06appKSk9rF79+4zrhkAALQsn205oNLj1aa2a1jFICRYdgewjh07ym631xuFLSwsrDdaK0llZWVatWqV1qxZo9/+9reSJJfLJcMwFB4ersWLF+uyyy6rt19UVJSioqL88yYAAECL8H6dC78Gd09Uars2FlUDb1g2MhsZGamBAwdqyZIlpvYlS5Zo6NCh9frHx8fr+++/19q1a2sfEydOVK9evbR27VpdeOGFgSodAAC0IEcqqvWvTftNbYzKhg7LRmYlacqUKbrxxhs1aNAgDRkyRM8//7x27dqliRMnSnJPEdi7d69ee+01hYWFKTMz07R/586dFR0dXa8dAADAU59sKFBFtat2OzzMpqvOS7awInjD0jA7btw4FRcXa/r06XI4HMrMzNTChQvVrVs3SZLD4Wh2zVkAAIAzUXeKwSXndFJi20iLqoG3bIZhGFYXEUilpaVKSEhQSUmJ4uPjrS4HAABY6EBZhS6a8amcrpNxaNYNA1jJwGLe5DXLVzMAAACwysLvHaYg2ybCrivPrX8hOoIXYRYAALRa76013yhheN8kxURaOgsTXiLMAgCAVmlrYZnW7DpsahvD9IKQQ5gFAACt0tur9pi2O8ZGadjZHS2qBqeLMAsAAFqdKqdLed+Zw+x1F6Qqwk40CjWcMQAA0Or8+4dCFR2pNLX9clC6RdXgTBBmAQBAq/P2t7tN2wO7tddZnWMtqgZngjALAABalf2lx7V0S6GpbRyjsiGLMAsAAFqVBd/t0SlLyyom0q6r+nH72lDFQmoAAKDVMAxD79RZxWDUeV0U++XnksMhJSdLWVmS3W5RhfAWYRYAALQa3+44pPyio6a262dMltZ8drIhLU2aNUvKzQ1scTgtTDMAAACtxturzBd+9Sjeo4GnBllJ2rtXGjtWyssLYGU4XYRZAADQKpQdr9LH6x2mtuvXL5GtbkfjxITayZMlpzMQpeEMEGYBAECr8PF6h45VnQyndpdTuRs/bbizYUi7d0srVgSoOpwuwiwAAGgV5tWZYnDptm/V+ejhpndyOJp+HpYjzAIAgBbvp/1lWrPrsKlt3PrFze+YzJJdwY4wCwAAWry6F351jI1UdkWBZKs3Y9bNZpPS093LdCGoEWYBAECLVuV0Ke+7vaa26wamKWLmP9wbdQNtzfbMmaw3GwIIswAAoEX7dHOhio9Wmtp+OTDdvY7s/PlSaqp5h7Q0dzvrzIYEbpoAAABatHfqTDEY1K29zuoc697IzZVyctyrFnAHsJBEmAUAAC3W/tLjWrql0NR2/aB0cye7XcrODlxR8CmmGQAAgBZrwXd75DJObsdE2nV1P1YoaEkIswAAoEUyDEPvrNpjahvVL1lto/hiuiUhzAIAgBbp2x2HlF901NQ27mfpjfRGqCLMAgCAFqnu2rI9OrXVBV3bW1QN/IUwCwAAWpyy41X6eL35VrTjBqXL1thNEhCyCLMAAKDF+Xi9Q8eqnLXb9jCbrr0gtYk9EKoIswAAoMWZV2eKwaW9OqtzXLRF1cCfCLMAAKBF+XF/mdbsOmxq48KvloswCwAAWpSXv9hh2u4YG6XsXp2sKQZ+R5gFAAAtxsGjlcr7zry27LifpSnCTuRpqTizAACgxXjjq52qqHbVbkfYbZowpLt1BcHvCLMAAKBFqKh26rWvdpraRvdLUVI8F361ZIRZAADQIny4zqEDZRWmtluGZVhUDQKFMAsAAEKeYRia83m+qe3CjERlpiZYVBEChTALAABC3pfbi7XZUWpquy2rh0XVIJDCrS4AAADgTM1ZYR6V7d4hRpef3UFatkxyOKTkZCkrS7LbrSkQfkOYBQAAIW37gSP69IdCU9stsSUK65Eh7Tllma60NGnWLCk3N8AVwp+YZgAAAEJa3ZskxNsNXTd5vDnIStLevdLYsVJeXuCKg98RZgEAQMg6XF6p+avNofVX6xerbeWx+p0Nw/1z8mTJ6fR/cQgIwiwAAAhZb36zS8eqTgbTcJt002dvNr6DYUi7d0srVgSgOgQCYRYAAISkymqXXl25w9R2VUKVksuKm9/Z4fBPUQg4wiwAAAhJC793aH+p+SYJt/aJ82zn5GQ/VAQrEGYBAEDIMQxDL36+3dT2s+7t1X9UtnvVAput4R1tNik93b1MF1oEwiwAAAg53+Qf1Ia95psk3Dosw72O7KxZ7oa6gbZme+ZM1pttQQizAAAg5NS9dW16YhtdeW4X90ZurjR/vpSaat4pLc3dzjqzLQo3TQAAACFlZ/FRLdm839R289AM2cNOGYnNzZVyctyrFnAHsBaNMAsAAELKy1/sqF0yVpLiosJ1/c/S63e026Xs7IDVBWswzQAAAISMkmNVenvVblPbuJ+lKzaK8bnWijALAABCxlvf7FJ55cmbJITZpJsu7m5dQbAcYRYAAISEamf9mySMzExWWvsYawpCUCDMAgCAkPB/Gwq0r+S4qe2WYRkWVYNgQZgFAABBz+Uy9PTSraa282OqNTB/neR0NrIXWgPCLAAACHofrNunHwrKTG23zv27dOmlUvfuUl6eNYXBcoRZAAAQ1CqrXXp8yRZT2zkHdmrklpXujb17pbFjCbStFGEWAAAEtTe/3qndB4+Z2u5b/qrshsu9UbPo7OTJTDlohQizAAAgaB2pqNY//22eKztoz0ZdsfUbc0fDkHbvdt/xC60KYRYAAAStOSvyVXy00tR2/7JXZWukvxwOv9eE4EKYBQAAQan4SIWeX77N1Hb51m/0s72bGt8pOdnPVSHYWB5mZ8+erYyMDEVHR2vgwIFa0cTXA3l5ebryyivVqVMnxcfHa8iQIfrkk08CWC0AAAiUp5Zu1dFT7vZlM1y6b/lrDXe22aT0dCkrK0DVIVhYGmbnzZunyZMn64EHHtCaNWuUlZWlkSNHateuXQ32X758ua688kotXLhQq1ev1qWXXqrRo0drzZo1Aa4cAAD40+6D5XrjK3MeuLajod5FO93B9VQ12zNnSnZ7YApE0LAZRs0lgIF34YUX6oILLtAzzzxT29anTx+NGTNGM2bM8Og1+vbtq3HjxunBBx/0qH9paakSEhJUUlKi+Pj406obAAD415S31yrvu72125H2MH36u0uUvmyRNGmStGfPyc7p6e4gm5sb+ELhF97ktfAA1VRPZWWlVq9eralTp5rahw8frpUrV3r0Gi6XS2VlZUpMTGy0T0VFhSoqKmq3S0tLT69gAAAQED8UlOrdNXtNbf9xUVelJ8a4A2tOjnvVAofDPUc2K4sR2VbMsjBbVFQkp9OppKQkU3tSUpIKCgo8eo3HH39cR48e1fXXX99onxkzZujhhx8+o1oBAEAAOJ3SihV6bOVhGUZEbXPbSLt+e+lZJ/vZ7VJ2duDrQ1Cy/AIwW515L4Zh1GtryNy5c/XnP/9Z8+bNU+fOnRvtN23aNJWUlNQ+du/efcY1AwAAH8vLk7p317f/eZc+LY0wPXX7z3uoQ2yURYUh2Fk2MtuxY0fZ7fZ6o7CFhYX1Rmvrmjdvnm699Va98847uuKKK5rsGxUVpagoPgAAAAStvDxp7FgZhqG//cffTE91OHpYtx3aIOkca2pD0LNsZDYyMlIDBw7UkiVLTO1LlizR0KFDG91v7ty5uummm/Tmm2/q6quv9neZAADAn5xO9wVdhqFPew7WqrS+pqf/+8t5iv3dZG5Ti0ZZNjIrSVOmTNGNN96oQYMGaciQIXr++ee1a9cuTZw4UZJ7isDevXv12mvuNeXmzp2rCRMmaNasWbroootqR3XbtGmjhIQEy94HAAA4TStWSHv2yGkL0/9c8mvTU+mHCzR+zf9Jrmp3P+bJogGWhtlx48apuLhY06dPl8PhUGZmphYuXKhu3bpJkhwOh2nN2eeee07V1dW66667dNddd9W2//rXv9Yrr7wS6PIBAMCZOnH72Xf7ZuvHTt1MT/1uxeuKdFWb+gF1WbrOrBVYZxYAgCCybJkqrrhSl93+vPYmnLygu3dhvha+fLfCdCKmLF3KyGwrEhLrzAIAACgrS69njzcFWUm6/7NX3UHWZpPS0rhNLRpl+dJcAACg9Tp03KmnL/qlqW3wru+VvX0Vt6mFRxiZBQAA/nPiRgiN3a3roQ826mC1eX35+z97VTbJPSLLbWrRDMIsAADwj7w897Jbe/acbEtLk2bNknJztWiDQx+s22fa5RdJdg38+4PcphYeI8wCAADfO3EjBNW9znzvXmnsWB2cO19//CnO9FR8dLgevvUSKT46gIUi1DFnFgAA+NYpN0Ko50Tbg+99r6IjlaanHs7pqySCLLxEmAUAAL514kYIjVl4zlB91G2Qqe2KPkkaMyDV35WhBSLMAgAA32riBgdFMQn64/A7TW3tYiL0aG6mbDZbI3sBjSPMAgAA30pObrDZkPSn4XfqYIz5FvQPX9NXneOYXoDTwwVgAADAe00tuZWV5V61YO9e07zZj3pn6f96XWx6mRF9k3RN/5RAVo4WhpFZAADgnbw8qXt36dJLpfHj3T+7d3e3S+5QO2uW+/cTUwcOxLTTg1dONL1M+5gI/WXMeUwvwBkhzAIAAM/VLLlV9wKvE0tu1Qba3Fxp/nwpNVWGpD8Ov1OH6kwvmJ6TqU5xUYGpGy0WYRYAAHjGgyW3NHmyu5/kDrQ7duiDN5bok15DTd2vOq+LRvVreG4t4A3CLAAAOMnplJYtk+bOdf+sCaZSs0tuyTCk3bvd/U4oLK/SQ1tdpm6JbSM1PYfVC+AbXAAGAADcmrn9bFNLbpmc6GcYhh54d4MOl1eZnn4kJ1MdY5leAN8gzAIAgGZvP6v58xtdcqueE/3eW7tXSzbtNz11db9kXc30AvgQ0wwAAGjtPJ0LO3Soe6S2sekBNpuUni5lZWl/6XH9+YNNpqc7xkbqkZxM39aOVo8wCwBAa+CLubArV9ZbcqtWzfbMmSp3Grr9tVUqOWaeXvCXMZlKbBt5xm8FOBVhFgCAlq65dWG9mQt7ypJbJmlp0vz5co65VpPeWqv1e0pMT1/TP0W/yGR6AXyPObMAALRkfpgLq9xcKSenwTuA/eXDjfXmyaYnttHD1/T1wZsB6rMZRkMTZFqu0tJSJSQkqKSkRPHx8VaXAwCA/zid7hHYxqYQ2GzuEdWtW6WePevdfrZev/z8k7esbcBLn+dr+kfmebLx0eHKu/NindU59gzeCFobb/Ia0wwAAAhVTc2DlXw+F7apILt4Y4Ee+dgcZCPsNj0/YRBBFn5FmAUAIBQ1Nw9W8ulcWOXmNrr7ut2Hdfdba+oN6j42tr8u6tHBsxqA08ScWQAAQo0n82Bzc306F7Yxuw+W69ZXv9XxKvNdvn535Tkac35qI3sBvsOcWQAAgo3T2Xig9HQebH6+e7t79zOeC9uYkvIqXffsSm0tPGJqv35Qmv52XT9uV4vTxpxZAABCVXPTBzydB7tihTugnuFc2MZUVrt0x+ur6gXZYWd11P+79jyCLAKGMAsAQKA0d8FWzfSBumG1ZvpAXp5382ClM5oL2xjDMDR1wXp9tf2gqb1XUpxm/+cFirATLxA4zJkFACAQ8vLct4w9NaimpblHTnNzm7+lrM3mvqXsyy97drxT58uexlzYpsz810/KW7PX1NY5Lkov3fwzxUdHnNZrAqeLMAsAgL95csFWYqJn0wckdwhubh5sVpa53W6XsrPP6G1I0rxvd2nWpz+Z2mIi7Xrppp8ptV2bM359wFt8DwAAwJlqavpAcyOuknvEde/e+s83pLDQb/Ngm2IYhp5eulX3L/je1B5mk54af74yUxN8ejzAU4RZAADOhK8u2DpwwLPjJSf7ZR5sU6qcLk3L+16PfbKl3nMPX9NXl/VO8unxAG8wzQAAgNPlyfSBigrPXqtTJ++mD/h4Hmxjyo5X6c43vtOKn4rqPXdndk/dOKS7T48HeIswCwDA6fD1BVupqe7pA2PHuvc99XUbmz7go3mwjXGUHNPNL3+rHwrKTO02m/Snq8/VLcMy/HZswFNMMwAAoCHNLaPl6fQByT2i2ti6qzablJ7uHlUN8PSBpmzaV6prn15ZL8hGhYfpmf8YSJBF0GBkFgCAuppbRkvyfL3Xmgu2PB1xDdD0gaZ89uMB3fn6ah2tNAf4Dm0j9eKvB+n8ru0DVgvQHMIsAACn8mQebG6ueR3XpiQnu6cCzJ/fcECeObP+iKufpw805a1vdumB9zbI6TK//x6d2uqVmwara4cYS+oCGmMzjIYm+7Rc3tzrFwDQyjid7pUIGps+UHMRVn6+e7t79+Yv2MrPPzmq6nRaOuLaFJfL0ONLtujppdvqPTe4e6KenzBQ7WIiLagMrZE3eY2RWQAAang6D3bFCvfIaZBdsHW6So5V6YF3v9dH6+tPnRjdP0WPje2n6IjgCN1AXVwABgBADU/nwdb0C6ILtk7XJxsLdOUTnzUYZO/M7qlZ4wYQZBHUGJkFALQuTX3V78082BpBcMHW6SgsO64/f7BRC78vqPecPcymR3IyNf7CrhZUBniHMAsAaD2aW6UgK8u7GxfUCNLpAw0xDEPvrNqjv3y8SaXHq+s9HxsVrn+OP1+X9upsQXWA95hmAABoHWpWKag7J7ZmlYK8PHconTXL3V53XdjG5sGGkF3F5frPOV/r9wvWNxhkLzmnkxZNziLIIqSwmgEAoOXzZpUCu73hEdz09IaX0QoB1U6XXv5ihx5fskXHq1z1nm8fE6EHR5+rMQNSZWvs5g5AALGaAQCg9WlqLqy3qxSE6DzYhmx2lOr+Beu1fk9Jg89f0z9FD40+Vx1iowJcGeAbhFkAQHDzZG3W5ubCertKgRRS82AbsrXwiJ77bJveXbNX1a76X8KmJETrL9dm6rLeSRZUB/gOYRYAEHie3jzAk9vKenLHrtNZpSBErdt9WM8s26ZPNhU0eA2bJE0Y0k2//0VvxUYRAxD6mDMLAPAdX4yintqvoZBaM6dz/nz3VABP5sJu3Sr17Ond3bpCiGEYWrmtWLOXbdUXW4sb7dezU1v97bp+GtQ9MYDVAd7zJq8RZgEAzfNVSPUkoObmen7B1ssvS1dc0Xz9S5dKBw+6jy01fLeuELnJwalcLkOLNxXomWXbtK6RObGSFBUept/8vIfuuvQsboCAkMAFYAAQSjz9yt3Tfr4+tq++6s/Jcb9OQ2MohuEOlZMnn7zwypMLtpYt8+x9OhzSr37lrqOh9xJiqxRUVDv1/tp9evazbdp+4Gij/eKiwzVhSDfdfHGGOnKBF1oowiwA+Iuvv3L3pJ+nx/X0NX0ZUhMSPF9RwNMLtjxVMxc2hFcpqHK6tHJbsT5at0+fbCxocJ3YGp3ionTrsAz9x4VdFRcdEcAqgcAjzAKANwIdFHNzPe/n6XE9PbavQ6o3o6ieXoiVnS298op3d+wKoVUKnC5DX28v1ofrHVq0waFD5VVN9u+aGKM7Lumh6y5IYzoBWg3CLABIgblw6XSC4qhRnn81//77noVep9OakOqpmr+/J7eVzc52//3HjnW3NTQXNsTu2OVyGVq185A+Wr9PC78vUNGRimb36ZMcr//K7qmrMrso3M7NPdG6EGYBnD5fz+H05vV8Oc/Ul6Oovg6Ks2d7Hiitmo/qKW9GUWtuK+tJSM3NDfm5sMVHKvRN/kF9tb1Yn2zcr4LS4x7td1GPRN1xSU9ln9OJO3eh1SLMAqHOHxcP+XKU0h+v58t5psF+4dK2bZ71W7bMuvmonoZUb0dRvQmpITYXdn/pcX21vVjf5B/U1/kHtbXwiMf79k9vp9H9knXVeclKadfGj1UCoYEwCwRaoEcUvennaV9fz+H09vV8Nc80FC5c6tnTt6/nj/mo3oRUb0dRvQmpQToX1jAM7Tl0TF/nH9Q3+cX6Ov+gdhaXe/Ua5ybHa3T/FI3ql6z0xBg/VQqEJtaZBZri61HPQIwo1l0z09N+nr6mp4vU5+c3PofzdF9P8m6BfF+tUfrHP0p/+Uvz/d58033+L720+b7/+pd0003NB0VPF/v3Zr3VrCz339GTGwjUnEOp+bVZG/rnNj294ZDqj2XGgkBJeZV+KCjVj/vL9ENBmbYUlGnL/jKVNbHyQGPOSYrVqH7uANujU6wfqgWCFzdNaELQhtlQ+Be7r4OdVV+Pe9rP16Oevgyf3t71yJdB0dPQVBPWfLnoveRZUPzHP6R77mm+n6ch1dN+/gqKNf9MNNWv5p8JT+9w5clrElIbZBiGDhyp0J5Dx5R/4Ki27D8RWgvKPJ7r2pCYSLsGdmuvCzMSNbxvF52TFOfDqoHQwk0TQo0/1o8M5hFFf/Tz9Wv6eskkTy8K8vTKdV9fPLRihXvbl3M9PZ3D6c1yTZ7ydJ6pp6y+cMnTfsEwHzVIv+r3hst1MqzuOVR+4ucx7T3s3t576Jgqql1nfJy46HD9rHuiLsxI1IU9OqhvSrwiWIkA8Bojs1bz9itgqwKlL7/OturrcStHPVessGZE8be/lZ56qvl+b77p/jl+vO+O7Y9RT8m3f0dPv+r39ut2yT+jmaf7jUJjx/Xm2CHueJVTJceqdKCsQsVHK1VUVqHioxUqOlKpoiMnfp5oKz5SqWqX7//T2D4mQoMzEnVhRgcNzkhUn+R42cNYgQBoSEhNM5g9e7Yee+wxORwO9e3bVzNnzlTWqYtb1/HZZ59pypQp2rhxo1JSUvT73/9eEydO9Ph4QRVmPb33uKdzD/0RFD2t0dNg5+t+Vs6j9DQwLV3qDgqeBEVPw6enAdCbGiXfzvX0djqCJ4FS8uyrdE/nmfpzTqhkXVBsIQG12ulSeZVT5RVOlVdWq7zSeeJRrWOVTh2tdOpYZbXKKqpVeqxapcerVHqsSqXHq0/8rKptr/TBSKqnIuw29ewUq15d4tSrS5x6d4lTry7xSkmIZvkswEMhM81g3rx5mjx5smbPnq2LL75Yzz33nEaOHKlNmzapa9eu9frn5+frqquu0u23367XX39dX3zxhe6880516tRJ1113nQXv4Ax5s4SPL7+i9mYRdk9r9PTrbF/38+brcW/W6/SEp19le3P1uKdXrnv6tfedd0qPP+753ZF8uUh9drZ/Fr33pG9kpH+urvd2+acz/MrdMAwZhmSc+N1lSIZOtJ3yu8sw3H1c7t9dhiHXwCG1+7jKKt19TvR1uk70Mdx3mKq7XdvHZaj6xPPun66T205ze5XTUJXTdeLR8O/VTkMVTpcqqlyqqHaqotrlflQ5VVnze/WJ56pcqnQGLoCejgi7Tant2uiszjWB1f3I6NiW6QJAAFk6MnvhhRfqggsu0DPPPFPb1qdPH40ZM0YzZsyo1//+++/XBx98oM2bN9e2TZw4UevWrdOXX37p0TEDOTL78hf5emXljsY7lB2R9hc0/0LtE6VDB5vv17GjVFTku36pqVK107MaExKkkpLT7mfI5lG/epKS3D/37/ddje3bS4cONd+vYwepqLj5fikpUptoaecuqbqJK5rDw6WuXaVdHvTr1lU6Wi4VNHxuDJvN/bdpG+Pu19Tfp6af1GRfQzapc2dz34PF5lrDw6XEDuY+hYW1r3CSTYZNUqfOUsyJvuXl0sGD7lHF2tezS+0TZbSps5bmsWMyDh0y97XbZbRrL53a99gxGSUl9fopIUFGdLS5KkMyKisll1MKs0sREdIpObimX82/Mk9un/I3OhEqa1+vgfZTQ6hR288cWo06rwtrRNrDlNq+jdJqHzFKbXfy985xUQpjmgDgFyExMltZWanVq1dr6tSppvbhw4dr5cqVDe7z5Zdfavjw4aa2ESNGaM6cOaqqqlJERES9fSoqKlRRcfJWgKWlpT6o3jOHy6uaWUswTGqf4tmLedLP6eN+5Ya8q7Gtj/t5sJZiZU1fD0c+PXlNT1/P6WG/Y4Z07JgU16n5voc87HfwmCRb08evkFRxol+7Lh70U/N9KyVVntI3tmPzfdolefF6HRrpU/cKcZvUNrF+3ypJVcfN/WLaNdKvsVuEhkkypMrKRp5HKAuzSYlto9QxNlIdY6PUoc7PTid+T4qPVqdYwioQCiwLs0VFRXI6nUpKMv+HLikpSQWNjDYVFBQ02L+6ulpFRUVKbuCr3BkzZujhhx/2XeEAAEuEh9kUE2lXTGS4+2eUXTER4YqJsiuhTYTioyMU3yb8xM+GtsPVLiaSi66AFsbypbnqToY3DKPJCfIN9W+ovca0adM0ZcqU2u3S0lKlp6efbrkA0KKE2aQwm01hYTbZbTbZw2wKs0n2MFvtIzws7MTPU9rsNtnDwmrbIu1hCrfbFGEPU0TtT/Pv4XZ3v+gIu6LCw0487IqKOOX38DBFRYQp0m5Xm0i72p4IrG0i7YoMZx4qgPosC7MdO3aU3W6vNwpbWFhYb/S1RpcuXRrsHx4erg4dGvh6UlJUVJSioqJ8U7SXrhmQoszUhOY7fvml9MILUvEp81g7dpJuu00aMuRkn7/91f17Qxez3D/V3dfX/byp0cp+/npNl0vatFE6eEhKbC+d21cKa+A/qJ72O0NWjyd5eiG2x/28eUcNdG1o74b+x7axo9R0PbWOU3e31fmlpt/J/eof12Zzt9tO2flkm612P3eb7eRrnbJt+r3mOZv76GE2d3vYKcerabOf2CHMZjvxcO8XdkpbTf+a0MrV9QBCneUXgA0cOFCzZ8+ubTv33HOVk5PT6AVgH374oTZt2lTb9l//9V9au3ZtUF4A5hVfrh/p637e1GhlP3+9JgAACKiQWWd23rx5uvHGG/Xss89qyJAhev755/XCCy9o48aN6tatm6ZNm6a9e/fqtddek+RemiszM1N33HGHbr/9dn355ZeaOHGi5s6d6/HSXEEbZj1lZVAEAAAIgJBYzUCSxo0bp+LiYk2fPl0Oh0OZmZlauHChunXrJklyOBzatWtXbf+MjAwtXLhQ99xzj55++mmlpKToySefDM01Zk+Xp+tW+rofAABAELL8DmCBFvIjswAAAC2cN3mNS0MBAAAQsgizAAAACFmEWQAAAIQswiwAAABCFmEWAAAAIYswCwAAgJBFmAUAAEDIIswCAAAgZBFmAQAAELIIswAAAAhZ4VYXEGg1d+8tLS21uBIAAAA0pCan1eS2prS6MFtWViZJSk9Pt7gSAAAANKWsrEwJCQlN9rEZnkTeFsTlcmnfvn2Ki4uTzWazupygV1paqvT0dO3evVvx8fFWl4NTcG6CF+cmuHF+ghfnJngF+twYhqGysjKlpKQoLKzpWbGtbmQ2LCxMaWlpVpcRcuLj4/kXS5Di3AQvzk1w4/wEL85N8ArkuWluRLYGF4ABAAAgZBFmAQAAELIIs2hSVFSUHnroIUVFRVldCurg3AQvzk1w4/wEL85N8Armc9PqLgADAABAy8HILAAAAEIWYRYAAAAhizALAACAkEWYBQAAQMgizLZws2fPVkZGhqKjozVw4ECtWLGiyf5PP/20+vTpozZt2qhXr1567bXXTM9nZ2fLZrPVe1x99dW1ff785z/Xe75Lly5+eX+hzNfnRpJmzpypXr16qU2bNkpPT9c999yj48ePn9FxWyMrzg2fG8/5+vxUVVVp+vTp6tmzp6Kjo9W/f38tWrTojI/bGllxbvjsNG/58uUaPXq0UlJSZLPZ9N577zW7z2effaaBAwcqOjpaPXr00LPPPluvz4IFC3TuuecqKipK5557rt599916fQLyuTHQYr311ltGRESE8cILLxibNm0yJk2aZLRt29bYuXNng/1nz55txMXFGW+99Zaxbds2Y+7cuUZsbKzxwQcf1PYpLi42HA5H7WPDhg2G3W43Xn755do+Dz30kNG3b19Tv8LCQn+/3ZDij3Pz+uuvG1FRUcYbb7xh5OfnG5988omRnJxsTJ48+bSP2xpZdW743HjGH+fn97//vZGSkmJ8/PHHxrZt24zZs2cb0dHRxnfffXfax22NrDo3fHaat3DhQuOBBx4wFixYYEgy3n333Sb7b9++3YiJiTEmTZpkbNq0yXjhhReMiIgIY/78+bV9Vq5cadjtduPRRx81Nm/ebDz66KNGeHi48dVXX9X2CdTnhjDbgg0ePNiYOHGiqa13797G1KlTG+w/ZMgQ49577zW1TZo0ybj44osbPcY//vEPIy4uzjhy5Eht20MPPWT079//9AtvBfxxbu666y7jsssuM/WZMmWKMWzYsNM+bmtk1bnhc+MZf5yf5ORk46mnnjL1ycnJMf7jP/7jtI/bGll1bvjseMeTMPv73//e6N27t6ntjjvuMC666KLa7euvv974xS9+YeozYsQI44YbbqjdDtTnhmkGLVRlZaVWr16t4cOHm9qHDx+ulStXNrhPRUWFoqOjTW1t2rTRN998o6qqqgb3mTNnjm644Qa1bdvW1P7TTz8pJSVFGRkZuuGGG7R9+/YzeDcti7/OzbBhw7R69Wp98803kqTt27dr4cKFtVNATue4rY1V56YGn5um+ev8NNbn888/P+3jtjZWnZsafHZ868svv6x3LkeMGKFVq1bVnpvG+tSc70B+bgizLVRRUZGcTqeSkpJM7UlJSSooKGhwnxEjRujFF1/U6tWrZRiGVq1apZdeeklVVVUqKiqq1/+bb77Rhg0bdNttt5naL7zwQr322mv65JNP9MILL6igoEBDhw5VcXGx795gCPPXubnhhhv0yCOPaNiwYYqIiFDPnj116aWXaurUqad93NbGqnMj8bnxhL/Oz4gRI/TEE0/op59+ksvl0pIlS/T+++/L4XCc9nFbG6vOjcRnxx8KCgoaPJfV1dW156axPjXnO5CfG8JsC2ez2UzbhmHUa6vxpz/9SSNHjtRFF12kiIgI5eTk6KabbpIk2e32ev3nzJmjzMxMDR482NQ+cuRIXXfddTrvvPN0xRVX6OOPP5Ykvfrqqz54Ry2Hr8/NsmXL9P/+3//T7Nmz9d133ykvL08fffSRHnnkkdM+bmtlxbnhc+M5X5+fWbNm6eyzz1bv3r0VGRmp3/72t7r55pvr/XuPz07zrDg3fHb8o6FzWbfdk/MdiM8NYbaF6tixo+x2e73/+yksLKz3f0k12rRpo5deeknl5eXasWOHdu3ape7duysuLk4dO3Y09S0vL9dbb71Vb1S2IW3bttV5552nn3766fTfUAvir3Pzpz/9STfeeKNuu+02nXfeebr22mv16KOPasaMGXK5XKd13NbGqnPTED439fnr/HTq1Envvfeejh49qp07d+qHH35QbGysMjIyTvu4rY1V56YhfHbOXJcuXRo8l+Hh4erQoUOTfWrOdyA/N4TZFioyMlIDBw7UkiVLTO1LlizR0KFDm9w3IiJCaWlpstvteuuttzRq1CiFhZn/UXn77bdVUVGh//zP/2y2loqKCm3evFnJycnev5EWyF/npry8vN55stvtMtwXep7RcVsLq85NQ/jc1Ofvf69FR0crNTVV1dXVWrBggXJycs74uK2FVeemIXx2ztyQIUPqncvFixdr0KBBioiIaLJPzfkO6OfGp5eTIajULIkxZ84cY9OmTcbkyZONtm3bGjt27DAMwzCmTp1q3HjjjbX9t2zZYvzv//6v8eOPPxpff/21MW7cOCMxMdHIz8+v99rDhg0zxo0b1+Bxf/e73xnLli0ztm/fbnz11VfGqFGjjLi4uNrjwj/n5qGHHjLi4uKMuXPnGtu3bzcWL15s9OzZ07j++us9Pi6sOzd8bjzjj/Pz1VdfGQsWLDC2bdtmLF++3LjsssuMjIwM49ChQx4fF9adGz47zSsrKzPWrFljrFmzxpBkPPHEE8aaNWtql8iqe25qlua65557jE2bNhlz5syptzTXF198YdjtduOvf/2rsXnzZuOvf/1ro0tz+ftzQ5ht4Z5++mmjW7duRmRkpHHBBRcYn332We1zv/71r41LLrmkdnvTpk3GgAEDjDZt2hjx8fFGTk6O8cMPP9R7zS1bthiSjMWLFzd4zHHjxhnJyclGRESEkZKSYuTm5hobN270+XsLdb4+N1VVVcaf//xno2fPnkZ0dLSRnp5u3HnnnaZ/6Td3XLhZcW743HjO1+dn2bJlRp8+fYyoqCijQ4cOxo033mjs3bvXq+PCzYpzw2eneUuXLjUk1Xv8+te/Ngyj/rkxDPff/vzzzzciIyON7t27G88880y9133nnXeMXr16GREREUbv3r2NBQsW1OsTiM+NzTAa+Y4LAAAACHLMmQUAAEDIIswCAAAgZBFmAQAAELIIswAAAAhZhFkAAACELMIsAAAAQhZhFgAAACGLMAsAAICQRZgFgCBiGIauuOIKjRgxot5zs2fPVkJCgnbt2mVBZQAQnAizABBEbDabXn75ZX399dd67rnnatvz8/N1//33a9asWeratatPj1lVVeXT1wOAQCLMAkCQSU9P16xZs3TvvfcqPz9fhmHo1ltv1eWXX67BgwfrqquuUmxsrJKSknTjjTeqqKiodt9FixZp2LBhateunTp06KBRo0Zp27Zttc/v2LFDNptNb7/9trKzsxUdHa3XX3/dircJAD5hMwzDsLoIAEB9Y8aM0eHDh3XdddfpkUce0bfffqtBgwbp9ttv14QJE3Ts2DHdf//9qq6u1r///W9J0oIFC2Sz2XTeeefp6NGjevDBB7Vjxw6tXbtWYWFh2rFjhzIyMtS9e3c9/vjjOv/88xUVFaWUlBSL3y0AnB7CLAAEqcLCQmVmZqq4uFjz58/XmjVr9PXXX+uTTz6p7bNnzx6lp6dry5YtOuecc+q9xoEDB9S5c2d9//33yszMrA2zM2fO1KRJkwL5dgDAL5hmAABBqnPnzvrNb36jPn366Nprr9Xq1au1dOlSxcbG1j569+4tSbVTCbZt26bx48erR48eio+PV0ZGhiTVu2hs0KBBgX0zAOAn4VYXAABoXHh4uMLD3f+qdrlcGj16tP72t7/V65ecnCxJGj16tNLT0/XCCy8oJSVFLpdLmZmZqqysNPVv27at/4sHgAAgzAJAiLjgggu0YMECde/evTbgnqq4uFibN2/Wc889p6ysLEnS559/HugyASCgmGYAACHirrvu0sGDB/WrX/1K33zzjbZv367FixfrlltukdPpVPv27dWhQwc9//zz2rp1q/79739rypQpVpcNAH5FmAWAEJGSkqIvvvhCTqdTI0aMUGZmpiZNmqSEhASFhYUpLCxMb731llavXq3MzEzdc889euyxx6wuGwD8itUMAAAAELIYmQUAAEDIIswCAAAgZBFmAQAAELIIswAAAAhZhFkAAACELMIsAAAAQhZhFgAAACGLMAsAAICQRZgFAABAyCLMAgAAIGQRZgEAABCyCLMAAAAIWf8fXBoC3zSbYjgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(1960, 2015, 55)\n",
+    "x = x/max(x)\n",
+    "plt.figure(figsize=(8,5))\n",
+    "y = sigmoid(x, *popt)\n",
+    "plt.plot(xdata, ydata, 'ro', label='data')\n",
+    "plt.plot(x,y, linewidth=3.0, label='fit')\n",
+    "plt.legend(loc='best')\n",
+    "plt.ylabel('GDP')\n",
+    "plt.xlabel('Year')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "Can you calculate what is the accuracy of our model?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error (MAE): 0.03\n",
+      "Mean Squared Error (MSE): 0.00\n",
+      "Root Mean Squared Error (RMSE): 0.04\n",
+      "R2-score: 0.51\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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z5ubmWLJkCVavXo2ff/650jh7e3ucOnVKp7w2Jkja29sjOztbp/zmzZsAAAcHB73btWjRAjKZTG+bqtLOJk2aaCZ5A8ALL7wAHx8fLFu2DBMmTICbm5umDXv37oW7u/tj+wLoThZ/XJsqJiXldS1atAjBwcF6t+nQoQMAwMrKCsuWLcOyZctw69YtzWjTiBEj8OuvvwIA3N3dNZOnr1y5gj179mDp0qUoLi7Gxo0bK+3HyZMnIYTQal9ubi5KS0srPSY1JS4uDkII7N27F3v37tV5/bPPPsN7770HuVyOli1b4saNG9WuS6lUakZ4HlVxBHbXrl0wNzfHV199pfXHwZOsDdSpUyc899xz2Lp1K2bMmIGtW7fCxcVFk5iVCwoKQlBQEIqKinDixAlERUVh/Pjx8PDwgJ+f32PrsLW11Xp/V+ZJ3of29vbVPg9btmyJ77//HmVlZdVKmhwcHNClSxetEeFHlf/hVZe/06jmcYSJapW+JAQALl26BOC/v0j06du3L+7evYtDhw5plZf/JVyTBgwYgO+++06TIJXbtm0bmjZtWulSAlZWVnjuuecQHx+vNWJ19+5dfPnll9Vuj0KhwPr16/Hw4UPNZYtBgwbBzMwMV69eha+vr94HoP4AcXJy0rmLKCMjQ3PHj5QOHTrg6aefxvnz5yut69HLX+UcHR0xdepUjBs3DpcvX9Z7N1n79u2xePFidO7cGT/++GOlbRgwYADu3bunkwyUL3g4YMAAg/pSHSqVCp999hnatWuHo0eP6jzeeOMNZGdna96bQ4YMwdGjRzWXh/Qpvz1e3yiQh4cHrly5ojUpPz8/X+d4yWQymJmZQS6Xa8r++usvbN++/Yn6+/LLL+PkyZP4/vvv8eWXX2LKlCladVTsR9++ffH+++8DgN47NGtKVd6HAQEBOHLkiNYfCiqVCrt375asZ8iQIXj48KHkwpGVjVgOHz4cP//8M9q1a6e3jeW/5wICAnD37l0cOHBAa3tDJqaT8XGEiWrVoEGD4OrqihEjRqBjx44oKytDamoqPvroIzRr1gxz586tdNspU6Zg9erVmDhxIt577z089dRTOHToEL755hsAussSPIklS5Zo5iG8++67sLOzw+eff46DBw9i1apVsLW1rXTbf/7znxg8eDBeeOEFvPHGG1CpVHj//fdhZWWFP/74o9pt6tu3L4YOHYqtW7di4cKF8PT0RGRkJCIiInDt2jUMHjwYLVq0wK1bt3Dq1CnNSE+TJk2wbNkyzJgxA6NHj8a0adNw584dLFu2DM7Ozgb/3DZt2oQhQ4Zg0KBBmDp1Klq3bo0//vgDly5dwo8//ogvvvgCgPr28+HDh6NLly5o0aIFLl26hO3bt8PPzw9NmzbFTz/9hH/84x/4+9//jqeffhoWFhb47rvv8NNPP2HhwoWV1j958mSsX78eU6ZMQXp6Ojp37ozvv/8eK1aswNChQ7Xu6qoKMzMz9O3b97HzmA4dOoSbN2/i/fff13u7v7e3N9atW4fY2FgMHz4ckZGROHToEJ5//nm8/fbb6Ny5M+7cuYOvv/4a4eHh6NixI9q1awdLS0t8/vnn8PLyQrNmzeDi4gIXFxdMmjQJmzZtwsSJE/Hqq68iPz8fq1at0tyFVW7YsGH4+OOPMX78eLz22mvIz8/Hhx9++MRrFY0bNw7h4eEYN24cioqKNPPEyr377ru4ceMGBgwYAFdXV9y5cwdr1qyBubk5+vbt+0R1SzH0fbh48WIcOHAA/fv3x7vvvoumTZti/fr1uH//vmQd48aNw9atWxEaGorLly8jICAAZWVlOHnyJLy8vDRz2Dp37oxjx47hyy+/hLOzM6ytrdGhQwdERkYiMTERvXr1wpw5c9ChQwc8fPgQ6enpSEhIwMaNG+Hq6orJkydj9erVmDx5MpYvX46nn34aCQkJmt9pVM8ZedI5mbjdu3eL8ePHi6efflo0a9ZMmJubizZt2ohJkybp3FGk766ejIwMERwcLJo1ayasra3FqFGjREJCgs6dJuV3mlXUt29f8cwzz+iUu7u7i2HDhmmVXbhwQYwYMULY2toKCwsL0bVrV527VvTdJSeEEAcOHBBdunQRFhYWok2bNmLlypViyZIlVbpLTp8LFy6IJk2aiJdffllTtn//fhEQECBsbGyEQqEQ7u7uYvTo0eLbb7/V2nbz5s3iqaeeEhYWFqJ9+/YiLi5OBAUFie7du+v054MPPtBb//nz58WYMWNEq1athLm5uXBychL9+/fX3DUmhBALFy4Uvr6+okWLFkKhUIi2bduKefPmae6wunXrlpg6daro2LGjsLKyEs2aNRNdunQRq1evFqWlpZr96Dv++fn5IjQ0VDg7OwszMzPh7u4uFi1aJB4+fKgVB0DMnj1bp/3u7u46dzUBkLx7bOTIkcLCwkLk5uZWGjN27FhhZmamuSsrMzNTTJs2TTg5OQlzc3Ph4uIixowZI27duqXZZufOnaJjx47C3NxcABBLlizRvPbZZ58JLy8voVQqRadOncTu3bv13iUXFxcnOnTooPlZR0VFidjYWMk7waSMHz9eABC9e/fWee2rr74SQ4YMEa1btxYWFhaiVatWYujQoSIpKUlyv/rOtYpq4n0ohBA//PCD+Nvf/iYUCoVwcnISb775pti8ebNBP5u//vpLvPvuu+Lpp58WFhYWwt7eXvTv318kJydrYlJTU0Xv3r1F06ZNdd5Ht2/fFnPmzBGenp7C3Nxc2NnZCR8fHxERESHu3bunibtx44YYNWqU1u+05ORk3iXXAMiEqMZqZ0RGVL4WUUZGRp1MtDUVd+7cQfv27TFy5Ehs3rzZ2M0hImpQeEmO6rV169YBADp27IiSkhJ89913WLt2LSZOnMhk6TFycnKwfPlyBAQEwN7eHtevX8fq1atx9+7dx14GJSIi/ZgwUb3WtGlTrF69Gunp6SgqKkKbNm2wYMECLF682NhNq9cUCgXS09Mxa9Ys/PHHH5qJ6xs3btTc6kxERIbjJTkiIiIiCVxWgIiIiEgCEyYiIiIiCUyYiIiIiCRw0rceZWVluHnzJqytrXWW6iciIqL6SQiBu3fvPtF3A1aGCZMeN2/e1PnWeiIiImoYMjMza3zpGSZMepR/N1FmZqbOVxMQERFR/VRYWAg3Nze933X5pJgw6VF+Gc7GxoYJExERUQNTG9NpOOmbiIiISAITJiIiIiIJTJiIiIiIJHAO0xNQqVQoKSkxdjOomszNzSGXy43dDCIiagCYMFWDEAI5OTm4c+eOsZtCT6h58+ZwcnLieltERPRYTJiqoTxZatWqFZo2bcoP2wZICIEHDx4gNzcXAODs7GzkFhERUX3GhKmKVCqVJlmyt7c3dnPoCVhaWgIAcnNz0apVK16eIyKiSnHSdxWVz1lq2rSpkVtCNaH8OHIuGhERPY7RE6aYmBh4enpCqVTCx8cHSUlJlcZmZ2dj/Pjx6NChA5o0aYKwsDC9cXfu3MHs2bPh7OwMpVIJLy8vJCQk1Gi7eRnONPA4EhGRIYyaMO3evRthYWGIiIjAuXPn4O/vjyFDhiAjI0NvfFFREVq2bImIiAh07dpVb0xxcTFeeOEFpKenY+/evbh8+TK2bNmC1q1b12ZXiIiIyAAqFXDsGLBzp/pflcrYLTKMUROmjz/+GK+88gqmT58OLy8vREdHw83NDRs2bNAb7+HhgTVr1mDy5MmwtbXVGxMXF4c//vgD+/fvR+/eveHu7o4+ffpUmmBR/SCTybB//35jN4OIiGpRfDzg4QEEBADjx6v/9fBQl9d3RkuYiouLcfbsWQQGBmqVBwYGIjk5udr7PXDgAPz8/DB79mw4OjrC29sbK1asgKqhpLB1IDk5GXK5HIMHD67Sdh4eHoiOjq6dRhERkUmLjwdGjwZu3NAuz8pSl9f3pMloCVNeXh5UKhUcHR21yh0dHZGTk1Pt/V67dg179+6FSqVCQkICFi9ejI8++gjLly+vdJuioiIUFhZqPeqCsYYl4+Li8Prrr+P777+v9PInERFRTVGpgLlzASF0XysvCwur35fnjD7pu+KkWyHEE03ELSsrQ6tWrbB582b4+Phg7NixiIiIqPQyHwBERUXB1tZW83Bzc6t2/YYy1rDk/fv3sWfPHsycORPDhw/Hp59+qvX6gQMH4OvrC6VSCQcHBwQHBwMA+vXrh+vXr2PevHmQyWSaY7R06VJ069ZNax/R0dHw8PDQPD99+jReeOEFODg4wNbWFn379sWPP/5Ym90kIqJ6JClJd2TpUUIAmZnquPrKaAmTg4MD5HK5zmhSbm6uzqhTVTg7O6N9+/Zaa+p4eXkhJycHxcXFerdZtGgRCgoKNI/MzMxq128IYw5L7t69Gx06dECHDh0wceJEbN26FeI/6f3BgwcRHByMYcOG4dy5czhy5Ah8fX3/0+Z4uLq6IjIyEtnZ2cjOzja4zrt372LKlClISkrCiRMn8PTTT2Po0KG4e/durfSRiIjqF0M/Mqrw0VLnjLZwpYWFBXx8fJCYmIiXXnpJU56YmIigoKBq77d3797YsWMHysrK0KSJOh+8cuUKnJ2dYWFhoXcbhUIBhUJR7TqrQmpYUiZTD0sGBQG1sY5ibGwsJk6cCAAYPHgw7t27hyNHjmDgwIFYvnw5xo4di2XLlmniyyfL29nZQS6Xw9raGk5OTlWqs3///lrPN23ahBYtWuD48eMYPnz4E/aIiIjqO0O/TKE+f+mCUS/JhYeH45NPPkFcXBwuXbqEefPmISMjA6GhoQDUIz+TJ0/W2iY1NRWpqam4d+8ebt++jdTUVFy8eFHz+syZM5Gfn4+5c+fiypUrOHjwIFasWIHZs2fXad8qY8xhycuXL+PUqVMYO3YsAMDMzAwhISGIi4sDoP7ZDhgwoMbrzc3NRWhoKNq3b6+57Hnv3j3OnyIiaiT8/QFXV/WggD4yGeDmpo6rr4z61SghISHIz8/XXObx9vZGQkIC3N3dAagXqqz4odq9e3fN/8+ePYsdO3bA3d0d6enpAAA3NzccPnwY8+bNQ5cuXdC6dWvMnTsXCxYsqLN+PY4xhyVjY2NRWlqqtSaVEALm5ub4888/NV8VUhVNmjTRXNIrV3HV7KlTp+L27duIjo6Gu7s7FAoF/Pz8Kr1ESkREpkUuB9asUU87kcm0r7KUJ1HR0bVzZaWmGP275GbNmoVZs2bpfa3ihGQAOh/O+vj5+eHEiRNP2rRaYaxhydLSUmzbtg0fffSRzlIOo0aNwueff44uXbrgyJEjePnll/Xuw8LCQmd5hpYtWyInJ0drsn5qaqpWTFJSEmJiYjB06FAAQGZmJvLy8mqoZ0RE1BAEBwN796qnpTx6pcXVVZ0s/eceo3rL6AlTY1M+LJmVpX8ek0ymfr2mhyW/+uor/Pnnn3jllVd0Fv0cPXo0YmNjsXr1agwYMADt2rXD2LFjUVpaikOHDuGtt94CoF6H6d///jfGjh0LhUIBBwcH9OvXD7dv38aqVaswevRofP311zh06BBsbGw0+3/qqaewfft2+Pr6orCwEG+++Wa1RrOIiKhhCw5Wz9FNSlJfSXF2Vn/e1eeRpXJGX1agsSkflgR0r+XW5rBkbGwsBg4cqHeF9FGjRiE1NRU2Njb44osvcODAAXTr1g39+/fHyZMnNXGRkZFIT09Hu3bt0LJlSwDqOxBjYmKwfv16dO3aFadOncL8+fO19h8XF4c///wT3bt3x6RJkzBnzhy0atWqZjtIREQNglwO9OsHjBun/rchJEsAIBOGXONqZAoLC2Fra4uCggKtkRIAePjwIdLS0jRfGFxd8fG6w5Jubg1jWNKU1NTxJCIi43vc5/eT4iU5I2nIw5JERESNDRMmIyofliQiIqL6jXOYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmqnFLly5Ft27dNM+nTp2KkSNH1nk70tPTIZPJdL4MmIiIqKqYMDUiU6dOhUwmg0wmg7m5Odq2bYv58+fj/v37tVrvmjVr8OmnnxoUyySHiIjqI670bUwqVZ1/N8rgwYOxdetWlJSUICkpCdOnT8f9+/exYcMGrbiSkhKYm5vXSJ36vvCXiIioIeEIk7HExwMeHkBAADB+vPpfDw91eS1SKBRwcnKCm5sbxo8fjwkTJmD//v2ay2hxcXFo27YtFAoFhBAoKCjAa6+9hlatWsHGxgb9+/fH+fPntfa5cuVKODo6wtraGq+88goePnyo9XrFS3JlZWV4//338dRTT0GhUKBNmzZYvnw5AMDT0xMA0L17d8hkMvR75Ltjtm7dCi8vLyiVSnTs2BExMTFa9Zw6dQrdu3eHUqmEr68vzp07V4M/OSIiasw4wmQM8fHA6NGAENrlWVnq8r171d/OWwcsLS1RUlICAPj999+xZ88e7Nu3D/L/jHQNGzYMdnZ2SEhIgK2tLTZt2oQBAwbgypUrsLOzw549e7BkyRKsX78e/v7+2L59O9auXYu2bdtWWueiRYuwZcsWrF69Gn369EF2djZ+/fVXAOqk57nnnsO3336LZ555BhYWFgCALVu2YMmSJVi3bh26d++Oc+fO4dVXX4WVlRWmTJmC+/fvY/jw4ejfvz/+9a9/IS0tDXPnzq3lnx4RETUagnQUFBQIAKKgoEDntb/++ktcvHhR/PXXX9XbeWmpEK6uQqjTJd2HTCaEm5s6roZNmTJFBAUFaZ6fPHlS2NvbizFjxoglS5YIc3NzkZubq3n9yJEjwsbGRjx8+FBrP+3atRObNm0SQgjh5+cnQkNDtV7v2bOn6Nq1q956CwsLhUKhEFu2bNHbxrS0NAFAnDt3Tqvczc1N7NixQ6vsn//8p/Dz8xNCCLFp0yZhZ2cn7t+/r3l9w4YNevf1qCc+nkREVG887vP7SfGSXF1LSgJu3Kj8dSGAzEx1XC346quv0KxZMyiVSvj5+eH555/H//zP/wAA3N3d0bJlS03s2bNnce/ePdjb26NZs2aaR1paGq5evQoAuHTpEvz8/LTqqPj8UZcuXUJRUREGDBhgcJtv376NzMxMvPLKK1rteO+997Ta0bVrVzRt2tSgdhAREVUFL8nVtezsmo2rooCAAGzYsAHm5uZwcXHRmthtZWWlFVtWVgZnZ2ccO3ZMZz/NmzevVv2WlpZV3qasrAyA+rJcz549tV4rv3QoKl7eJCIiqkFMmOqas3PNxlWRlZUVnnrqKYNie/TogZycHJiZmcHDw0NvjJeXF06cOIHJkydryk6cOFHpPp9++mlYWlriyJEjmD59us7r5XOWVCqVpszR0RGtW7fGtWvXMGHCBL377dSpE7Zv346//vpLk5Q9rh1ERERVwUtydc3fH3B1BWQy/a/LZICbmzrOyAYOHAg/Pz+MHDkS33zzDdLT05GcnIzFixfjzJkzAIC5c+ciLi4OcXFxuHLlCpYsWYJffvml0n0qlUosWLAAb731FrZt24arV6/ixIkTiI2NBQC0atUKlpaW+Prrr3Hr1i0UFBQAUC+GGRUVhTVr1uDKlSu4cOECtm7dio8//hgAMH78eDRp0gSvvPIKLl68iISEBHz44Ye1/BMiIqLGgglTXZPLgTVr1P+vmDSVP4+OrvX1mAwhk8mQkJCA559/HtOmTUP79u0xduxYpKenw9HREQAQEhKCd999FwsWLICPjw+uX7+OmTNnPna/77zzDt544w28++678PLyQkhICHJzcwEAZmZmWLt2LTZt2gQXFxcEBQUBAKZPn45PPvkEn376KTp37oy+ffvi008/1SxD0KxZM3z55Ze4ePEiunfvjoiICLz//vu1+NMhIqLGRCY4+UNHYWEhbG1tUVBQABsbG63XHj58iLS0NHh6ekKpVFa/kvh4YO5c7Qngbm7qZKmOlhSgGjyeRERkdI/7/H5SnMNkLMHBQFBQna/0TURERFXHhMmY5HLgkZWsiYiIqH7iHCYiIiIiCUyYiIiIiCTwklw1ca68aeBxJCJ6MipV45iOyxGmKipfGfvBgwdGbgnVhPLj+OiK50REZJj4eMDDAwgIAMaPV//r4aEuNzUcYaoiuVyO5s2ba9YNatq0KWSVLUJJ9ZYQAg8ePEBubi6aN2+u+YoVIiIyTHw8MHq0+itQH5WVpS7fu9e0Vskx+jpMMTEx+OCDD5CdnY1nnnkG0dHR8K9klevs7Gy88cYbOHv2LH777TfMmTMH0dHRle57165dGDduHIKCgrB//36D2yS1joMQAjk5Obhz547B+6T6qXnz5nBycmLSS0RUBSqVeiSpsu+Sl8nUX2qRlla3l+dMdh2m3bt3IywsDDExMejduzc2bdqEIUOG4OLFi2jTpo1OfFFREVq2bImIiAisXr36sfu+fv065s+fX2ny9SRkMhmcnZ3RqlUrlJSU1Pj+qW6Ym5tzZImIqBqSkipPlgD1qFNmpjrOVFbPMWrC9PHHH+OVV17RfAlrdHQ0vvnmG2zYsAFRUVE68R4eHljzn68ViYuLq3S/KpUKEyZMwLJly5CUlFRrI0FyuZwfuERE1OhkZ9dsXENgtEnfxcXFOHv2LAIDA7XKAwMDkZyc/ET7joyMRMuWLfHKK6880X6IiIhIl7NzzcY1BEYbYcrLy4NKpdJ8iWs5R0dH5OTkVHu/P/zwA2JjY5GammrwNkVFRSgqKtI8LywsrHb9REREps7fXz1HKStLd9I38N85TLUwK8ZojL6sQMXJtkKIak/AvXv3LiZOnIgtW7bAwcHB4O2ioqJga2urebi5uVWrfiIiosZALgf+M0MGFT+yy59HR5vWekxGS5gcHBwgl8t1RpNyc3N1Rp0MdfXqVaSnp2PEiBEwMzODmZkZtm3bhgMHDsDMzAxXr17Vu92iRYtQUFCgeWRmZlarfiIiosYiOFi9dEDr1trlrq6mt6QAYMRLchYWFvDx8UFiYiJeeuklTXliYiKCgoKqtc+OHTviwoULWmWLFy/G3bt3sWbNmkpHjhQKBRQKRbXqJCIiaqyCg4GgoMax0rdR75ILDw/HpEmT4OvrCz8/P2zevBkZGRkIDQ0FoB75ycrKwrZt2zTblM9NunfvHm7fvo3U1FRYWFigU6dOUCqV8Pb21qqjefPmAKBTTkRERE9OLjedpQMex6gJU0hICPLz8xEZGYns7Gx4e3sjISEB7u7uANQLVWZkZGht0717d83/z549ix07dsDd3R3p6el12XQiIiJqRIy+0nd9VJsrhRIREVHtqM3Pb6PfJUdERERU3zFhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEiCmbEbQERERI2ISgUkJQHZ2YCzM+DvD8jlxm6VJKOPMMXExMDT0xNKpRI+Pj5ISkqqNDY7Oxvjx49Hhw4d0KRJE4SFhenEbNmyBf7+/mjRogVatGiBgQMH4tSpU7XYAyIiIjJIfDzg4QEEBADjx6v/9fBQl9dzRk2Ydu/ejbCwMERERODcuXPw9/fHkCFDkJGRoTe+qKgILVu2REREBLp27ao35tixYxg3bhyOHj2KlJQUtGnTBoGBgcjKyqrNrhAREdHjxMcDo0cDN25ol2dlqcvredIkE0IIY1Xes2dP9OjRAxs2bNCUeXl5YeTIkYiKinrstv369UO3bt0QHR392DiVSoUWLVpg3bp1mDx5skHtKiwshK2tLQoKCmBjY2PQNkRERFQJlUo9klQxWSonkwGurkBa2hNdnqvNz2+jjTAVFxfj7NmzCAwM1CoPDAxEcnJyjdXz4MEDlJSUwM7OrtKYoqIiFBYWaj2IiIiohiQlVZ4sAYAQQGamOq6eMlrClJeXB5VKBUdHR61yR0dH5OTk1Fg9CxcuROvWrTFw4MBKY6KiomBra6t5uLm51Vj9REREjV52ds3GGYHRJ33LZDKt50IInbLqWrVqFXbu3In4+HgolcpK4xYtWoSCggLNIzMzs0bqJyIiIqjvhqvJOCMw2rICDg4OkMvlOqNJubm5OqNO1fHhhx9ixYoV+Pbbb9GlS5fHxioUCigUiieuk4iIiPTw91fPUcrKUl9+q6h8DpO/f923zUBGG2GysLCAj48PEhMTtcoTExPRq1evJ9r3Bx98gH/+85/4+uuv4evr+0T7IiIioicklwNr1qj/X/EqUvnz6Oh6vR6TUS/JhYeH45NPPkFcXBwuXbqEefPmISMjA6GhoQDUl8oq3tmWmpqK1NRU3Lt3D7dv30ZqaiouXryoeX3VqlVYvHgx4uLi4OHhgZycHOTk5ODevXt12jciIiJ6RHAwsHcv0Lq1drmrq7o8ONg47TKQUZcVANQLV65atQrZ2dnw9vbG6tWr8fzzzwMApk6divT0dBw7dkwTr29+k7u7O9LT0wEAHh4euH79uk7MkiVLsHTpUoPaxGUFiIiIakktrvRdm5/fRk+Y6iMmTERERA2PSa7DRERERNRQMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISILRE6aYmBh4enpCqVTCx8cHSUlJlcZmZ2dj/Pjx6NChA5o0aYKwsDC9cfv27UOnTp2gUCjQqVMn/O///m8ttZ6IiIgaA6MmTLt370ZYWBgiIiJw7tw5+Pv7Y8iQIcjIyNAbX1RUhJYtWyIiIgJdu3bVG5OSkoKQkBBMmjQJ58+fx6RJkzBmzBicPHmyNrtCREREJkwmhBDGqrxnz57o0aMHNmzYoCnz8vLCyJEjERUV9dht+/Xrh27duiE6OlqrPCQkBIWFhTh06JCmbPDgwWjRogV27txpULsKCwtha2uLgoIC2NjYGN4hIiIiMpra/Pw22ghTcXExzp49i8DAQK3ywMBAJCcnV3u/KSkpOvscNGjQY/dZVFSEwsJCrQcRERFROaMlTHl5eVCpVHB0dNQqd3R0RE5OTrX3m5OTU+V9RkVFwdbWVvNwc3Ordv1ERERkeow+6Vsmk2k9F0LolNX2PhctWoSCggLNIzMz84nqJyIiItNiZqyKHRwcIJfLdUZ+cnNzdUaIqsLJyanK+1QoFFAoFNWuk4iIiEyb0UaYLCws4OPjg8TERK3yxMRE9OrVq9r79fPz09nn4cOHn2ifRERE1LgZbYQJAMLDwzFp0iT4+vrCz88PmzdvRkZGBkJDQwGoL5VlZWVh27Ztmm1SU1MBAPfu3cPt27eRmpoKCwsLdOrUCQAwd+5cPP/883j//fcRFBSE//u//8O3336L77//vs77R0RERKbBqAlTSEgI8vPzERkZiezsbHh7eyMhIQHu7u4A1AtVVlyTqXv37pr/nz17Fjt27IC7uzvS09MBAL169cKuXbuwePFivPPOO2jXrh12796Nnj171lm/iIiIyLQYdR2m+orrMBERETU8JrkOExEREVFDwYSJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJJgZuwFERETUgKlUQFISkJ0NODsD/v6AXG7sVtU4JkxERERUPfHxwNy5wI0b/y1zdQXWrAGCg43XrlrAS3JERERUdfHxwOjR2skSAGRlqcvj443TrlrChImIiIiqRqVSjywJoftaeVlYmDrORDBhIiIioqpJStIdWXqUEEBmpjrORDBhIiIioqrJzq7ZuAaACRMRERFVjbNzzcY1AEyYiIiIqGr8/dV3w8lk+l+XyQA3N3WciWDCREREVEUqFXDsGLBzp/pfE5rbbBi5XL10AKCbNJU/j442qfWYmDARERFVQXw84OEBBAQA48er//XwMLm76KUFBwN79wKtW2uXu7qqy01sHSaZEPruCWzcCgsLYWtri4KCAtjY2Bi7OUREVE+ULz1U8ZOzfFDFBPMEafVope/a/PxmwqQHEyYiIqpIpVKPJFV2N71Mph5cSUszqStRDUptfn7zkhwREZEBGuHSQ/QIJkxEREQGaIRLD9EjmDAREREZoBEuPUSPMDhhuvG4ccgnEBMTA09PTyiVSvj4+CBJYizz+PHj8PHxgVKpRNu2bbFx40admOjoaHTo0AGWlpZwc3PDvHnz8PDhw1ppPxERNQ6NcOkheoTBCZO3tze2b99eo5Xv3r0bYWFhiIiIwLlz5+Dv748hQ4YgIyNDb3xaWhqGDh0Kf39/nDt3Dm+//TbmzJmDffv2aWI+//xzLFy4EEuWLMGlS5cQGxuL3bt3Y9GiRTXadiIialwa4dJD9ChhoPXr1wtra2sRHBws8vLyDN3ssZ577jkRGhqqVdaxY0excOFCvfFvvfWW6Nixo1bZjBkzxN/+9jfN89mzZ4v+/ftrxYSHh4s+ffoY3K6CggIBQBQUFBi8DRERNQ779gnh6iqEepq3+uHmpi4n46rNz2+DR5hmzZqF8+fP488//8QzzzyDAwcOPFGiVlxcjLNnzyIwMFCrPDAwEMnJyXq3SUlJ0YkfNGgQzpw5g5KSEgBAnz59cPbsWZw6dQoAcO3aNSQkJGDYsGGVtqWoqAiFhYVaDyIiIn2Cg4H0dODoUWDHDvW/aWmNcP2lRsasKsGenp747rvvsG7dOowaNQpeXl4wM9PexY8//mjQvvLy8qBSqeDo6KhV7ujoiJycHL3b5OTk6I0vLS1FXl4enJ2dMXbsWNy+fRt9+vSBEAKlpaWYOXMmFi5cWGlboqKisGzZMoPaTUREJJcD/foZuxVUl6qUMAHA9evXsW/fPtjZ2SEoKEgnYaoqWYULwUIInTKp+EfLjx07huXLlyMmJgY9e/bE77//jrlz58LZ2RnvvPOO3n0uWrQI4eHhmueFhYVwc3OrVn+IiIjI9FQp29myZQveeOMNDBw4ED///DNatmxZ7YodHBwgl8t1RpNyc3N1RpHKOTk56Y03MzODvb09AOCdd97BpEmTMH36dABA586dcf/+fbz22muIiIhAkya6VyEVCgUUCkW1+0JERESmzeA5TIMHD8aCBQuwbt06xMfHP1GyBAAWFhbw8fFBYmKiVnliYiJ69eqldxs/Pz+d+MOHD8PX1xfm5uYAgAcPHugkRXK5HEIIzWgUERERUVUYPMKkUqnw008/wdXVtcYqDw8Px6RJk+Dr6ws/Pz9s3rwZGRkZCA0NBaC+VJaVlYVt27YBAEJDQ7Fu3TqEh4fj1VdfRUpKCmJjY7Fz507NPkeMGIGPP/4Y3bt311ySe+edd/Diiy9Czns9iYiIqBoMTpgqjuzUhJCQEOTn5yMyMhLZ2dnw9vZGQkIC3N3dAQDZ2dlaazJ5enoiISEB8+bNw/r16+Hi4oK1a9di1KhRmpjFixdDJpNh8eLFyMrKQsuWLTFixAgsX768xttPREREjYNM8DqVjtr8tmMiIiKqHbX5+c3vkiMiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglGT5hiYmLg6ekJpVIJHx8fJCUlPTb++PHj8PHxgVKpRNu2bbFx40admDt37mD27NlwdnaGUqmEl5cXEhISaqsLREREZOKMmjDt3r0bYWFhiIiIwLlz5+Dv748hQ4YgIyNDb3xaWhqGDh0Kf39/nDt3Dm+//TbmzJmDffv2aWKKi4vxwgsvID09HXv37sXly5exZcsWtG7duq66RURERCZGJoQQxqq8Z8+e6NGjBzZs2KAp8/LywsiRIxEVFaUTv2DBAhw4cACXLl3SlIWGhuL8+fNISUkBAGzcuBEffPABfv31V5ibm1erXYWFhbC1tUVBQQFsbGyqtQ8iIiKqW7X5+W20Eabi4mKcPXsWgYGBWuWBgYFITk7Wu01KSopO/KBBg3DmzBmUlJQAAA4cOAA/Pz/Mnj0bjo6O8Pb2xooVK6BSqSptS1FREQoLC7UeREREROWMljDl5eVBpVLB0dFRq9zR0RE5OTl6t8nJydEbX1pairy8PADAtWvXsHfvXqhUKiQkJGDx4sX46KOPsHz58krbEhUVBVtbW83Dzc3tCXtHREREpsTok75lMpnWcyGETplU/KPlZWVlaNWqFTZv3gwfHx+MHTsWERERWpf9Klq0aBEKCgo0j8zMzOp2h4iIiEyQmbEqdnBwgFwu1xlNys3N1RlFKufk5KQ33szMDPb29gAAZ2dnmJubQy6Xa2K8vLyQk5OD4uJiWFhY6OxXoVBAoVA8aZeIiIjIRBlthMnCwgI+Pj5ITEzUKk9MTESvXr30buPn56cTf/jwYfj6+momePfu3Ru///47ysrKNDFXrlyBs7Oz3mSJiIiISIpRL8mFh4fjk08+QVxcHC5duoR58+YhIyMDoaGhANSXyiZPnqyJDw0NxfXr1xEeHo5Lly4hLi4OsbGxmD9/viZm5syZyM/Px9y5c3HlyhUcPHgQK1aswOzZs+u8f0RERGQajHZJDgBCQkKQn5+PyMhIZGdnw9vbGwkJCXB3dwcAZGdna63J5OnpiYSEBMybNw/r16+Hi4sL1q5di1GjRmli3NzccPjwYcybNw9dunRB69atMXfuXCxYsKDO+0dERESmwajrMNVXXIeJiIio4THJdZiIiIiIGgomTEREREQSmDARERERSWDCRERERCSBCRMRERGRBCZMRERERBKYMBERERFJYMJEREREJIEJExEREZEEJkxEREREEpgwEREREUlgwkREREQkgQkTERERkQQmTEREREQSmDARERERSWDCRERERCSBCRMRERGRBCZMRERERBKYMBERERFJYMJEREREJIEJExEREZEEJkxEREREEpgwEREREUlgwkREREQkgQkTERERkQQmTEREREQSmDARERERSWDCRERERCTBzNgNICKixk2lApKSgOxswNkZ8PcH5HJjt4pIGxMmIiIymvh4YO5c4MaN/5a5ugJr1gDBwcZrF1FFvCRHRERGER8PjB6tnSwBQFaWujw+3jjtItLH6AlTTEwMPD09oVQq4ePjg6SkpMfGHz9+HD4+PlAqlWjbti02btxYaeyuXbsgk8kwcuTIGm41ERE9CZVKPbIkhO5r5WVhYeo4ovrAqAnT7t27ERYWhoiICJw7dw7+/v4YMmQIMjIy9ManpaVh6NCh8Pf3x7lz5/D2229jzpw52Ldvn07s9evXMX/+fPj7+9d2N4iIqIqSknRHlh4lBJCZqY4jqg+MmjB9/PHHeOWVVzB9+nR4eXkhOjoabm5u2LBhg974jRs3ok2bNoiOjoaXlxemT5+OadOm4cMPP9SKU6lUmDBhApYtW4a2bdvWRVeIiKgKsrNrNo6othktYSouLsbZs2cRGBioVR4YGIjk5GS926SkpOjEDxo0CGfOnEFJSYmmLDIyEi1btsQrr7xS8w0nIqIn5uxcs3FEtc1od8nl5eVBpVLB0dFRq9zR0RE5OTl6t8nJydEbX1pairy8PDg7O+OHH35AbGwsUlNTDW5LUVERioqKNM8LCwsN7wgREVWZv7/6brisLP3zmGQy9eucVUH1hdEnfctkMq3nQgidMqn48vK7d+9i4sSJ2LJlCxwcHAxuQ1RUFGxtbTUPNze3KvSAiIiqSi5XLx0AqJOjR5U/j47mekxUfxgtYXJwcIBcLtcZTcrNzdUZRSrn5OSkN97MzAz29va4evUq0tPTMWLECJiZmcHMzAzbtm3DgQMHYGZmhqtXr+rd76JFi1BQUKB5ZGZm1kwniYioUsHBwN69QOvW2uWurupyrsNE9YnRLslZWFjAx8cHiYmJeOmllzTliYmJCAoK0ruNn58fvvzyS62yw4cPw9fXF+bm5ujYsSMuXLig9frixYtx9+5drFmzptKRI4VCAYVC8YQ9IiKiqgoOBoKCuNI31X9GXek7PDwckyZNgq+vL/z8/LB582ZkZGQgNDQUgHrkJysrC9u2bQMAhIaGYt26dQgPD8err76KlJQUxMbGYufOnQAApVIJb29vrTqaN28OADrlRERUP8jlQL9+xm4F0eMZNWEKCQlBfn4+IiMjkZ2dDW9vbyQkJMDd3R0AkJ2drbUmk6enJxISEjBv3jysX78eLi4uWLt2LUaNGmWsLhAREVEjIBNC3/0JjVthYSFsbW1RUFAAGxsbYzeHiIiIDFCbn99Gv0uOiIiIqL5jwkREREQkwahzmIiIyHhUKt6dRmQoJkxERI1QfDwwd672F+C6uqoXk+T6R0S6eEmOiKiRiY8HRo/WTpYA9deUjB6tfp2ItDFhIiJqRFQq9ciSvvujy8vCwtRxRPRfTJiIiBqRpCTdkaVHCQFkZqrjiOi/mDARETUi2dk1G0fUWDBhIiJqRJydazaOqLFgwkRE1Ij4+6vvhpPJ9L8ukwFubuo4IvovJkxERI2IXK5eOgDQTZrKn0dHcz0mooqYMBERNTLBwcDevUDr1trlrq7qcq7DRKSLC1cSETVCwcFAUBBX+iYyFBMmIqJGSi4H+vUzdiuIGgZekiMiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpLAhImIiIhIAhMmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiIiIpJg9IQpJiYGnp6eUCqV8PHxQVJS0mPjjx8/Dh8fHyiVSrRt2xYbN27Uen3Lli3w9/dHixYt0KJFCwwcOBCnTp2qzS4QERGRiTNqwrR7926EhYUhIiIC586dg7+/P4YMGYKMjAy98WlpaRg6dCj8/f1x7tw5vP3225gzZw727duniTl27BjGjRuHo0ePIiUlBW3atEFgYCCysrLqqltERERkYmRCCGGsynv27IkePXpgw4YNmjIvLy+MHDkSUVFROvELFizAgQMHcOnSJU1ZaGgozp8/j5SUFL11qFQqtGjRAuvWrcPkyZMNaldhYSFsbW1RUFAAGxubKvaKiIiIjKE2P7+NNsJUXFyMs2fPIjAwUKs8MDAQycnJerdJSUnRiR80aBDOnDmDkpISvds8ePAAJSUlsLOzq5mGExERUaNjZqyK8/LyoFKp4OjoqFXu6OiInJwcvdvk5OTojS8tLUVeXh6cnZ11tlm4cCFat26NgQMHVtqWoqIiFBUVaZ4XFhZWpStERERk4ow+6Vsmk2k9F0LolEnF6ysHgFWrVmHnzp2Ij4+HUqmsdJ9RUVGwtbXVPNzc3KrSBSIiIjJxRkuYHBwcIJfLdUaTcnNzdUaRyjk5OemNNzMzg729vVb5hx9+iBUrVuDw4cPo0qXLY9uyaNEiFBQUaB6ZmZnV6BERERGZKqMlTBYWFvDx8UFiYqJWeWJiInr16qV3Gz8/P534w4cPw9fXF+bm5pqyDz74AP/85z/x9ddfw9fXV7ItCoUCNjY2Wg8iU6FSAceOATt3qv9VqYzdIiKihseol+TCw8PxySefIC4uDpcuXcK8efOQkZGB0NBQAOqRn0fvbAsNDcX169cRHh6OS5cuIS4uDrGxsZg/f74mZtWqVVi8eDHi4uLg4eGBnJwc5OTk4N69e3XePyJji48HPDyAgABg/Hj1vx4e6nIiIjKc0SZ9A0BISAjy8/MRGRmJ7OxseHt7IyEhAe7u7gCA7OxsrTWZPD09kZCQgHnz5mH9+vVwcXHB2rVrMWrUKE1MTEwMiouLMXr0aK26lixZgqVLl9ZJv4jqg/h4YPRooOLCIVlZ6vK9e4HgYOO0jYiooTHqOkz1FddhooZOpVKPJN24of91mQxwdQXS0gC5vE6bRkRUa0xyHSYiqj1JSZUnS4B61CkzUx1HRETSmDARmaDs7JqNIyJq7JgwEZkgPWu4PlEcEVFjx4SJyAT5+6vnKFW2BqxMBri5qeOIiEgaEyYiEySXA2vWqP9fMWkqfx4dzQnfRESGYsJEZKKCg9VLB7RurV3u6solBYiIqsqo6zARUe0KDgaCgtR3w2Vnq+cs+ftzZImIqKqYMBGZOLkc6NfP2K0gImrYeEmOiIiISAJHmIiIiKpKpeK17kaGCRMREVFVxMcDc+dqL6fv6qq+NZV3U5gsXpIjIiIyVPm3Wlf87qHyb7WOjzdOu6jWMWEiIiIyhEqlHlnS95315WVhYeo4MjlMmIiIiAzBb7Vu1JgwERERGYLfat2oMWEiIiIyBL/VulFjwkRERGQIfqt1o8aEiWqMSgUcOwbs3Kn+l/Meicik8FutGzUmTFQj4uMBDw8gIAAYP179r4cH77AlIhPDb7VutGRC6Ls/snErLCyEra0tCgoKYGNjY+zm1Hvly5JUfCeV/8HF3yFEZHK40ne9VJuf30yY9GDCZDiVSj2SVNmdtjKZ+g+vtDT+LiEiotpVm5/fvCRXh0xxjg+XJSEiosaA3yVXR0z1q4e4LAkRETUGHGGqA6b81UNcloSIiBoDJky1zNS/eojLkhARUWPAhKmWmfocHy5LQkREjQETplrWGOb4cFkSInoipnhHDJkcTvquZY1ljk9wMBAUxGVJiKiKTPWOGDI5XIdJj5pcx6F8naKsLP3zmLhOERE1Wlz1lmoY12FqwDjHh4jqLWNeCjP1O2LI5DBhqgOc40NE9Y6xvwDS1O+IIZPDOUx1JDgYCBquwoWYJDy4mo2m7ZzReZY/5BYmNLTE71aqn3hcqKLKLoWVLw5XF3/JNYY7YsikGH2EKSYmBp6enlAqlfDx8UGSxF8Tx48fh4+PD5RKJdq2bYuNGzfqxOzbtw+dOnWCQqFAp06d8L//+7+11XzDxcdD3s4D3eYFoNe68eg2LwDydh4Ne9XKRxn7r1XSj8eFKqovl8Iayx0xZDqEEe3atUuYm5uLLVu2iIsXL4q5c+cKKysrcf36db3x165dE02bNhVz584VFy9eFFu2bBHm5uZi7969mpjk5GQhl8vFihUrxKVLl8SKFSuEmZmZOHHihMHtKigoEABEQUHBE/dRCCHEvn1CyGRCqH8d/fchk6kf+/bVTD3GYur9a6h4XEifo0d13xP6HkeP1m47SkuFcHXV/x4tf5+6uanjiAxU45/fjzDqXXI9e/ZEjx49sGHDBk2Zl5cXRo4ciaioKJ34BQsW4MCBA7h06ZKmLDQ0FOfPn0dKSgoAICQkBIWFhTh06JAmZvDgwWjRogV27txpULtqdJZ9+W1ylV2rb+i3yZl6/xoqHheqzM6d6tFGKTt2AOPG1W5byi8NAtojXrxLjqrJJO+SKy4uxtmzZxEYGKhVHhgYiOTkZL3bpKSk6MQPGjQIZ86cQUlJyWNjKtsnABQVFaGwsFDrUWNMfWKjqfevoeJxocrUp0thvCOGGhCjJUx5eXlQqVRwdHTUKnd0dEROTo7ebXJycvTGl5aWIi8v77Exle0TAKKiomBra6t5uLm5VadL+pn6xEZT719DxeNClalvXwAZHAykpwNHj6pHtY4eVY98Mlmiesbok75lFU5aIYROmVR8xfKq7nPRokUoKCjQPDIzMw1uv6T69NdcbTD1/jVUPC5Umfq4OJxcDvTrp74E2K8fLxNTvWS0hMnBwQFyuVxn5Cc3N1dnhKick5OT3ngzMzPY29s/NqayfQKAQqGAjY2N1qPG1Le/5mqaqfevoeJxocfhpTCiKjNawmRhYQEfHx8kJiZqlScmJqJXr156t/Hz89OJP3z4MHx9fWFubv7YmMr2Wevq419zNcnU+9dQ8biQFF4KI6qaGr/vrgrKlxWIjY0VFy9eFGFhYcLKykqkp6cLIYRYuHChmDRpkia+fFmBefPmiYsXL4rY2FidZQV++OEHIZfLxcqVK8WlS5fEypUrjb+sgBDqW7hdXbVvm3VzM51bu029fw0VjwsRNSImu6wAoF64ctWqVcjOzoa3tzdWr16N559/HgAwdepUpKen49ixY5r448ePY968efjll1/g4uKCBQsWIDQ0VGufe/fuxeLFi3Ht2jW0a9cOy5cvR3AV/mqqtdsSTX3FZVPvX0PF40JEjURtLitg9ISpPqrNHzgRERHVDpNch4mIiIiooWDCRERERCSBCRMRERGRBCZMRERERBKYMBERERFJYMJEREREJIEJExEREZEEJkxEREREEpgwEREREUkwM3YD6qPyxc8LCwuN3BIiIiIyVPnndm18iQkTJj3u3r0LAHBzczNyS4iIiKiq7t69C1tb2xrdJ79LTo+ysjLcvHkT1tbWkMlkdVZvYWEh3NzckJmZabLfYcc+Nnym3j+AfTQVpt5HU+8fUPU+CiFw9+5duLi4oEmTmp11xBEmPZo0aQJXV1ej1W9jY2Oyb/5y7GPDZ+r9A9hHU2HqfTT1/gFV62NNjyyV46RvIiIiIglMmIiIiIgkMGGqRxQKBZYsWQKFQmHsptQa9rHhM/X+AeyjqTD1Ppp6/4D61UdO+iYiIiKSwBEmIiIiIglMmIiIiIgkMGEiIiIiksCEiYiIiEgCE6YnEBMTA09PTyiVSvj4+CApKemx8evXr4eXlxcsLS3RoUMHbNu2Tev1fv36QSaT6TyGDRumiVm6dKnO605OTlr7EUJg6dKlcHFxgaWlJfr164dffvmlQfTPw8NDb8zs2bM1MVOnTtV5/W9/+1uV+1cbfQSA6OhodOjQAZaWlnBzc8O8efPw8OHDKtVbU8fQWH2MiorCs88+C2tra7Rq1QojR47E5cuXtfbR0I9jQz4XDelfQz8XS0pKEBkZiXbt2kGpVKJr1674+uuvq1xvfT4XDeljQz8XDeljnZ2Lgqpl165dwtzcXGzZskVcvHhRzJ07V1hZWYnr16/rjY+JiRHW1tZi165d4urVq2Lnzp2iWbNm4sCBA5qY/Px8kZ2drXn8/PPPQi6Xi61bt2pilixZIp555hmtuNzcXK26Vq5cKaytrcW+ffvEhQsXREhIiHB2dhaFhYX1vn+5ublaMYmJiQKAOHr0qCZmypQpYvDgwVpx+fn5BvetNvv4r3/9SygUCvH555+LtLQ08c033whnZ2cRFhZWpXpr4hgas4+DBg0SW7duFT///LNITU0Vw4YNE23atBH37t3TxDT049iQz0VD+tfQz8W33npLuLi4iIMHD4qrV6+KmJgYoVQqxY8//lileuvzuWhIHxv6uWhIH+vqXGTCVE3PPfecCA0N1Srr2LGjWLhwod54Pz8/MX/+fK2yuXPnit69e1dax+rVq4W1tbXWG3vJkiWia9eulW5TVlYmnJycxMqVKzVlDx8+FLa2tmLjxo2P65IWY/Wvorlz54p27dqJsrIyTdmUKVNEUFCQAb14vNro4+zZs0X//v21YsLDw0WfPn0MrremjqEhdVVUU32sKDc3VwAQx48f15Q19OPYkM/F6hzDhnYuOjs7i3Xr1mnFBAUFiQkTJhhcb30/Fw3pY0UN7Vw0pI91dS7yklw1FBcX4+zZswgMDNQqDwwMRHJyst5tioqKoFQqtcosLS1x6tQplJSU6N0mNjYWY8eOhZWVlVb5b7/9BhcXF3h6emLs2LG4du2a5rW0tDTk5ORotU2hUKBv376Vtq2+9e/RdvzrX//CtGnTdL4E+dixY2jVqhXat2+PV199Fbm5uQb17dF910Yf+/Tpg7Nnz+LUqVMAgGvXriEhIUFz2dGQemviGBqzj/oUFBQAAOzs7LTKG+pxLNdQz8WqHsOGeC5WFvP9998bXG99Pxel+qhPQzsXDe1jbZ+LAHhJrjqysrIEAPHDDz9olS9fvly0b99e7zaLFi0STk5O4syZM6KsrEycPn1atGrVSgAQN2/e1Ik/efKkACBOnjypVZ6QkCD27t0rfvrpJ5GYmCj69u0rHB0dRV5enhBCiB9++EEAEFlZWVrbvfrqqyIwMLDe9+9Ru3fvFnK5XKcvu3btEl999ZW4cOGCOHDggOjatat45plnxMOHDw3qX233ce3atcLc3FyYmZkJAGLmzJlVqrcmjqEx+1hRWVmZGDFihM7oRUM+jkI0/HOxKsewIZ6L48aNE506dRJXrlwRKpVKHD58WFhaWgoLCwuD663v56JUHytqiOeiIX2si3NRCF6Sq5byN0ZycrJW+XvvvSc6dOigd5sHDx6Il19+WZiZmQm5XC5cXFzEW2+9JQCIW7du6cS/9tprwtvbW7It9+7dE46OjuKjjz4SQvz3jVExSZk+fboYNGhQg+pfYGCgGD58uGR7b968KczNzcW+ffskY8vVVh+PHj0qHB0dxZYtW8RPP/0k4uPjhZubm4iMjDS43po4hsbsY0WzZs0S7u7uIjMz87HtbUjHUZ+GdC5WtX8N8VzMzc0VQUFBokmTJkIul4v27duLWbNmCUtLS4Prre/nolQfK2qI52JV+yhE7ZyLQvCSXLU4ODhALpcjJydHqzw3NxeOjo56t7G0tERcXBwePHiA9PR0ZGRkwMPDA9bW1nBwcNCKffDgAXbt2oXp06dLtsXKygqdO3fGb7/9BgCaOwOq0rb62L/r16/j22+/Nehn4OzsDHd3d83PwBC11cd33nkHkyZNwvTp09G5c2e89NJLWLFiBaKiolBWVmZQvTVxDI3Zx0e9/vrrOHDgAI4ePQpXV9fHtrchHUd9GtK5WJX+NdRzsWXLlti/fz/u37+P69ev49dff0WzZs3g6elpcL31/VyU6uOjGuq5WJU+lquNcxHgsgLVYmFhAR8fHyQmJmqVJyYmolevXo/d1tzcHK6urpDL5di1axeGDx+OJk20D8OePXtQVFSEiRMnSralqKgIly5dgrOzMwDA09MTTk5OWm0rLi7G8ePHJdtWn/q3detWtGrV6rHzYsrl5+cjMzNT8zMwRG318cGDBzr9lcvlEOrRXIPqrYljaMw+AupbeP/xj38gPj4e33333WN/uZVrSMdRn4Z0Llalfw31XCynVCrRunVrlJaWYt++fQgKCjK43vp+Lkr1EWj456IhfayoNs5FAJzDVF3lt0/GxsaKixcvirCwMGFlZSXS09OFEEIsXLhQTJo0SRN/+fJlsX37dnHlyhVx8uRJERISIuzs7ERaWprOvvv06SNCQkL01vvGG2+IY8eOiWvXrokTJ06I4cOHC2tra029Qqhvn7S1tRXx8fHiwoULYty4cdW+lbmu+yeEECqVSrRp00YsWLBA57W7d++KN954QyQnJ4u0tDRx9OhR4efnJ1q3bl3t23xrso9LliwR1tbWYufOneLatWvi8OHDol27dmLMmDEG1ytEzRxDY/Zx5syZwtbWVhw7dkzrVt8HDx4IIUzjODbkc9GQ/gnRsM/FEydOiH379omrV6+Kf//736J///7C09NT/PnnnwbXK0T9PhcN6WNDPxcN6WNdnYtMmJ7A+vXrhbu7u7CwsBA9evTQuU2zb9++mucXL14U3bp1E5aWlsLGxkYEBQWJX3/9VWefly9fFgDE4cOH9dZZvnaEubm5cHFxEcHBweKXX37RiikrKxNLliwRTk5OQqFQiOeff15cuHChQfRPCCG++eYbAUBcvnxZ57UHDx6IwMBA0bJlS2Fubi7atGkjpkyZIjIyMqrcv9roY0lJiVi6dKlo166dUCqVws3NTcyaNUvr5JaqV4iaO4bG6iMAvY/yNbdM4Tg25HPR0PdpQz4Xjx07Jry8vIRCoRD29vZi0qRJOpN+peoVon6fi4b0saGfi4b0sa7ORZkQlYwvExEREREAzmEiIiIiksSEiYiIiEgCEyYiIiIiCUyYiIiIiCQwYSIiIiKSwISJiIiISAITJiIiIiIJTJiIiIiIJDBhIiKTp1Kp0KtXL4waNUqrvKCgAG5ubli8eLGRWkZEDQVX+iaiRuG3335Dt27dsHnzZkyYMAEAMHnyZJw/fx6nT5+GhYWFkVtIRPUZEyYiajTWrl2LpUuX4ueff8bp06fx97//HadOnUK3bt2M3TQiqueYMBFRoyGEQP/+/SGXy3HhwgW8/vrrvBxHRAZhwkREjcqvv/4KLy8vdO7cGT/++CPMzMyM3SQiagA46ZuIGpW4uDg0bdoUaWlpuHHjhrGbQ0QNBEeYiKjRSElJwfPPP49Dhw5h1apVUKlU+PbbbyGTyYzdNCKq5zjCRESNwl9//YUpU6ZgxowZGDhwID755BOcPn0amzZtMnbTiKgBYMJERI3CwoULUVZWhvfffx8A0KZNG3z00Ud48803kZ6ebtzGEVG9x0tyRGTyjh8/jgEDBuDYsWPo06eP1muDBg1CaWkpL80R0WMxYSIiIiKSwEtyRERERBKYMBERERFJYMJEREREJIEJExEREZEEJkxEREREEpgwEREREUlgwkREREQkgQkTERERkQQmTEREREQSmDARERERSWDCRERERCSBCRMRERGRhP8HT8+hIqcaoMIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "# Misal fungsi sigmoid\n",
+    "def sigmoid(x, beta1, beta2):\n",
+    "    return 1 / (1 + np.exp(-beta1*(x-beta2)))\n",
+    "\n",
+    "# split data into train/test\n",
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train_x = xdata[msk]\n",
+    "test_x = xdata[~msk]\n",
+    "train_y = ydata[msk]\n",
+    "test_y = ydata[~msk]\n",
+    "\n",
+    "# build the model using train set\n",
+    "popt, pcov = curve_fit(sigmoid, train_x, train_y)\n",
+    "\n",
+    "# predict using test set\n",
+    "y_hat = sigmoid(test_x, *popt)\n",
+    "\n",
+    "# evaluation metrics\n",
+    "mae = mean_absolute_error(test_y, y_hat)\n",
+    "mse = mean_squared_error(test_y, y_hat)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(test_y, y_hat)\n",
+    "\n",
+    "print(\"Mean Absolute Error (MAE): %.2f\" % mae)\n",
+    "print(\"Mean Squared Error (MSE): %.2f\" % mse)\n",
+    "print(\"Root Mean Squared Error (RMSE): %.2f\" % rmse)\n",
+    "print(\"R2-score: %.2f\" % r2)\n",
+    "\n",
+    "# Optional: plot actual vs predicted\n",
+    "plt.scatter(test_x, test_y, label='Actual', color='blue')\n",
+    "plt.scatter(test_x, y_hat, label='Predicted', color='red')\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Sigmoid Regression: Actual vs Predicted')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "# split data into train/test\n",
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train_x = xdata[msk]\n",
+    "test_x = xdata[~msk]\n",
+    "train_y = ydata[msk]\n",
+    "test_y = ydata[~msk]\n",
+    "\n",
+    "# build the model using train set\n",
+    "popt, pcov = curve_fit(sigmoid, train_x, train_y)\n",
+    "\n",
+    "# predict using test set\n",
+    "y_hat = sigmoid(test_x, *popt)\n",
+    "\n",
+    "# evaluation\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(y_hat - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_hat - test_y) ** 2))\n",
+    "from sklearn.metrics import r2_score\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,y_hat) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Want to learn more?</h2>\n",
+    "\n",
+    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
+    "\n",
+    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03  | 2.1 | Lakshmi |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "f873d3177bf529d2d648c46bab1627042a257e5ec6ce42ca68028520459f817e"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Polynomial.ipynb b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Polynomial.ipynb
new file mode 100644
index 0000000..40ef188
--- /dev/null
+++ b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Polynomial.ipynb	
@@ -0,0 +1,930 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align:center\">\n",
+    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
+    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "# Polynomial Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement Polynomial Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Table of contents</h1>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
+    "    <ol>\n",
+    "        <li><a href=\"#download_data\">Downloading Data</a></li>\n",
+    "        <li><a href=\"#polynomial_regression\">Polynomial regression</a></li>\n",
+    "        <li><a href=\"#evaluation\">Evaluation</a></li>\n",
+    "        <li><a href=\"#practice\">Practice</a></li>\n",
+    "    </ol>\n",
+    "</div>\n",
+    "<br>\n",
+    "<hr>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"download_data\">Downloading Data</h2>\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 06:29:48--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.002s  \n",
+      "\n",
+      "2025-10-20 06:29:48 (38.0 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://www.ibm.com/us-en/cloud/object-storage?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\n"
+   ]
+  },
+  {
+   "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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\n"
+   ]
+  },
+  {
+   "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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
+       "0         2.0          4                   8.5           196\n",
+       "1         2.4          4                   9.6           221\n",
+       "2         1.5          4                   5.9           136\n",
+       "3         3.5          6                  11.1           255\n",
+       "4         3.5          6                  10.6           244\n",
+       "5         3.5          6                  10.0           230\n",
+       "6         3.5          6                  10.1           232\n",
+       "7         3.7          6                  11.1           255\n",
+       "8         3.7          6                  11.6           267"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n",
+    "\n",
+    "In essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n",
+    "\n",
+    "\n",
+    "$$y = b + \\theta_1  x + \\theta_2 x^2$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "Now, the question is: how we can fit our data on this equation while we have only x values, such as __Engine Size__? \n",
+    "Well, we can create a few additional features: 1, $x$, and $x^2$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "__PolynomialFeatures()__ function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, _ENGINESIZE_. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  precompute=False, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, random_state=None,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.  ,  2.  ,  4.  ],\n",
+       "       [ 1.  ,  2.4 ,  5.76],\n",
+       "       [ 1.  ,  1.5 ,  2.25],\n",
+       "       ...,\n",
+       "       [ 1.  ,  3.  ,  9.  ],\n",
+       "       [ 1.  ,  3.2 , 10.24],\n",
+       "       [ 1.  ,  3.2 , 10.24]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn import linear_model\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "\n",
+    "\n",
+    "poly = PolynomialFeatures(degree=2)\n",
+    "train_x_poly = poly.fit_transform(train_x)\n",
+    "train_x_poly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**fit_transform** takes our x values, and output a list of our data raised from power of 0 to power of 2 (since we set the degree of our polynomial to 2).   \n",
+    "\n",
+    "The equation and the sample example is displayed below.   \n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "    v_1\\\\\\\\\\\\\n",
+    "    v_2\\\\\\\\\n",
+    "    \\vdots\\\\\\\\\n",
+    "    v_n\n",
+    "\\end{bmatrix}\\longrightarrow \\begin{bmatrix}\n",
+    "    [ 1 & v_1 & v_1^2]\\\\\\\\\n",
+    "    [ 1 & v_2 & v_2^2]\\\\\\\\\n",
+    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
+    "    [ 1 & v_n & v_n^2]\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "    2.\\\\\\\\\n",
+    "    2.4\\\\\\\\\n",
+    "    1.5\\\\\\\\\n",
+    "    \\vdots\n",
+    "\\end{bmatrix} \\longrightarrow \\begin{bmatrix}\n",
+    "    [ 1 & 2. & 4.]\\\\\\\\\n",
+    "    [ 1 & 2.4 & 5.76]\\\\\\\\\n",
+    "    [ 1 & 1.5 & 2.25]\\\\\\\\\n",
+    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like feature sets for multiple linear regression analysis, right? Yes. It Does. \n",
+    "Indeed, Polynomial regression is a special case of linear regression, with the main idea of how do you select your features. Just consider replacing the  $x$ with $x_1$, $x_1^2$ with $x_2$, and so on. Then the 2nd degree equation would be turn into:\n",
+    "\n",
+    "$$y = b + \\theta_1  x_1 + \\theta_2 x_2$$\n",
+    "\n",
+    "Now, we can deal with it as a 'linear regression' problem. Therefore, this polynomial regression is considered to be a special case of traditional multiple linear regression. So, you can use the same mechanism as linear regression to solve such problems. \n",
+    "\n",
+    "\n",
+    "\n",
+    "so we can use __LinearRegression()__ function to solve it:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         50.06359412 -1.48589036]]\n",
+      "Intercept:  [107.65985247]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = linear_model.LinearRegression()\n",
+    "train_y_ = clf.fit(train_x_poly, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf.coef_)\n",
+    "print ('Intercept: ',clf.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__ , are the parameters of the fit curvy line. \n",
+    "Given that it is a typical multiple linear regression, with 3 parameters, and knowing that the parameters are the intercept and coefficients of hyperplane, sklearn has estimated them from our new set of feature sets. Lets plot it:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"evaluation\">Evaluation</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 25.17\n",
+      "Residual sum of squares (MSE): 1059.07\n",
+      "R2-score: 0.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x_poly = poly.transform(test_x)\n",
+    "test_y_ = clf.predict(test_x_poly)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         33.08928706  3.25815397 -0.40004482]]\n",
+      "Intercept:  [125.51640013]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 25.06\n",
+      "Residual sum of squares (MSE): 1050.14\n",
+      "R2-score: 0.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import r2_score\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "print('Coefficients: ', clf3.coef_)\n",
+    "print('Intercept: ', clf3.intercept_)\n",
+    "\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+    "XX = np.arange(min(train.ENGINESIZE), max(train.ENGINESIZE), 0.1)\n",
+    "yy = clf3.intercept_[0] + clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX,2) + clf3.coef_[0][3]*np.power(XX,3)\n",
+    "plt.plot(XX, yy, '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"CO2 Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y, test_y3_))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf3.coef_)\n",
+    "print ('Intercept: ',clf3.intercept_)\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Want to learn more?</h2>\n",
+    "\n",
+    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
+    "\n",
+    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
+    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "4dc110debac287dfd374a575573c16e62a80a935b3bbe2b2f6d5a0598e6e33f6"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Simple Linear.ipynb b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Simple Linear.ipynb
new file mode 100644
index 0000000..2cf48f3
--- /dev/null
+++ b/Regression/Regiska Sari Putri Prasetyo_202310715132_Regresi Simple Linear.ipynb	
@@ -0,0 +1,1456 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align:center\">\n",
+    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
+    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "# Simple Linear Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement simple Linear Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downloading Data\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 06:27:34--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.004s  \n",
+      "\n",
+      "2025-10-20 06:27:35 (17.3 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you're working **locally** uncomment the below line. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "tags": []
+   },
+   "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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Exploration\n",
+    "Let's first have a descriptive exploration on our data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "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>MODELYEAR</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1067.0</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.346298</td>\n",
+       "      <td>5.794752</td>\n",
+       "      <td>13.296532</td>\n",
+       "      <td>9.474602</td>\n",
+       "      <td>11.580881</td>\n",
+       "      <td>26.441425</td>\n",
+       "      <td>256.228679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.415895</td>\n",
+       "      <td>1.797447</td>\n",
+       "      <td>4.101253</td>\n",
+       "      <td>2.794510</td>\n",
+       "      <td>3.485595</td>\n",
+       "      <td>7.468702</td>\n",
+       "      <td>63.372304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>4.700000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>108.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>10.250000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>207.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>10.900000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>251.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>15.550000</td>\n",
+       "      <td>10.850000</td>\n",
+       "      <td>13.350000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>294.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>30.200000</td>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>25.800000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>488.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
+       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
+       "mean      2014.0     3.346298     5.794752             13.296532   \n",
+       "std          0.0     1.415895     1.797447              4.101253   \n",
+       "min       2014.0     1.000000     3.000000              4.600000   \n",
+       "25%       2014.0     2.000000     4.000000             10.250000   \n",
+       "50%       2014.0     3.400000     6.000000             12.600000   \n",
+       "75%       2014.0     4.300000     8.000000             15.550000   \n",
+       "max       2014.0     8.400000    12.000000             30.200000   \n",
+       "\n",
+       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
+       "count          1067.000000           1067.000000               1067.000000   \n",
+       "mean              9.474602             11.580881                 26.441425   \n",
+       "std               2.794510              3.485595                  7.468702   \n",
+       "min               4.900000              4.700000                 11.000000   \n",
+       "25%               7.500000              9.000000                 21.000000   \n",
+       "50%               8.800000             10.900000                 26.000000   \n",
+       "75%              10.850000             13.350000                 31.000000   \n",
+       "max              20.500000             25.800000                 60.000000   \n",
+       "\n",
+       "       CO2EMISSIONS  \n",
+       "count   1067.000000  \n",
+       "mean     256.228679  \n",
+       "std       63.372304  \n",
+       "min      108.000000  \n",
+       "25%      207.000000  \n",
+       "50%      251.000000  \n",
+       "75%      294.000000  \n",
+       "max      488.000000  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarize the data\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features to explore more.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
+       "0         2.0          4                   8.5           196\n",
+       "1         2.4          4                   9.6           221\n",
+       "2         1.5          4                   5.9           136\n",
+       "3         3.5          6                  11.1           255\n",
+       "4         3.5          6                  10.6           244\n",
+       "5         3.5          6                  10.0           230\n",
+       "6         3.5          6                  10.1           232\n",
+       "7         3.7          6                  11.1           255\n",
+       "8         3.7          6                  11.6           267"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot each of these features:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
+    "viz.hist()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.describe()\n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+    "\n",
+    "This means that we know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+    "\n",
+    "Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Regression Model\n",
+    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modeling\n",
+    "Using sklearn package to model data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[38.74434612]]\n",
+      "Intercept:  [125.60720169]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
+    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
+    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot outputs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the fit line over the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Evaluation\n",
+    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
+    "\n",
+    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
+    "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
+    "\n",
+    "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
+    "\n",
+    "* Root Mean Squared Error (RMSE). \n",
+    "\n",
+    "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 23.55\n",
+      "Residual sum of squares (MSE): 1018.06\n",
+      "R2-score: 0.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "test_y_ = regr.predict(test_x)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
+    "\n",
+    "Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[16.40024771]]\n",
+      "Intercept:  [67.43562042]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]\n",
+    "\n",
+    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show() \n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)\n",
+    "\n",
+    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "\n",
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[16.40024771]]\n",
+      "Intercept:  [67.43562042]\n"
+     ]
+    }
+   ],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "regr.fit(train_x, train_y)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the predictions using the model's `predict` function and the `test_x` data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[127.21873633]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [165.33604443]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [135.15984218]\n",
+      " [171.68892912]\n",
+      " [138.33628452]\n",
+      " [138.33628452]\n",
+      " [119.27763047]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [166.9242656 ]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [119.27763047]\n",
+      " [119.27763047]\n",
+      " [119.27763047]\n",
+      " [119.27763047]\n",
+      " [141.51272687]\n",
+      " [141.51272687]\n",
+      " [141.51272687]\n",
+      " [141.51272687]\n",
+      " [141.51272687]\n",
+      " [141.51272687]\n",
+      " [119.27763047]\n",
+      " [141.51272687]\n",
+      " [119.27763047]\n",
+      " [128.8069575 ]\n",
+      " [103.39541876]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [170.10070794]\n",
+      " [170.10070794]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [109.74830345]\n",
+      " [109.74830345]\n",
+      " [139.9245057 ]\n",
+      " [155.80671741]\n",
+      " [155.80671741]\n",
+      " [147.86561155]\n",
+      " [128.8069575 ]\n",
+      " [139.9245057 ]\n",
+      " [139.9245057 ]\n",
+      " [155.80671741]\n",
+      " [170.10070794]\n",
+      " [100.21897642]\n",
+      " [155.80671741]\n",
+      " [109.74830345]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [162.15960209]\n",
+      " [173.27715029]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [173.27715029]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [109.74830345]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [ 93.86609174]\n",
+      " [ 93.86609174]\n",
+      " [ 93.86609174]\n",
+      " [ 93.86609174]\n",
+      " [103.39541876]\n",
+      " [157.39493858]\n",
+      " [144.68916921]\n",
+      " [179.63003497]\n",
+      " [130.39517867]\n",
+      " [ 97.04253408]\n",
+      " [103.39541876]\n",
+      " [ 97.04253408]\n",
+      " [103.39541876]\n",
+      " [157.39493858]\n",
+      " [103.39541876]\n",
+      " [127.21873633]\n",
+      " [130.39517867]\n",
+      " [130.39517867]\n",
+      " [130.39517867]\n",
+      " [130.39517867]\n",
+      " [151.04205389]\n",
+      " [ 97.04253408]\n",
+      " [103.39541876]\n",
+      " [ 95.45431291]\n",
+      " [ 97.04253408]\n",
+      " [130.39517867]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [155.80671741]\n",
+      " [155.80671741]\n",
+      " [155.80671741]\n",
+      " [166.9242656 ]\n",
+      " [166.9242656 ]\n",
+      " [147.86561155]\n",
+      " [155.80671741]\n",
+      " [109.74830345]\n",
+      " [128.8069575 ]\n",
+      " [155.80671741]\n",
+      " [109.74830345]\n",
+      " [100.21897642]\n",
+      " [109.74830345]\n",
+      " [ 95.45431291]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [109.74830345]\n",
+      " [103.39541876]\n",
+      " [130.39517867]\n",
+      " [130.39517867]\n",
+      " [160.57138092]\n",
+      " [127.21873633]\n",
+      " [151.04205389]\n",
+      " [151.04205389]\n",
+      " [151.04205389]\n",
+      " [151.04205389]\n",
+      " [109.74830345]\n",
+      " [122.45407281]\n",
+      " [122.45407281]\n",
+      " [109.74830345]\n",
+      " [122.45407281]\n",
+      " [103.39541876]\n",
+      " [109.74830345]\n",
+      " [109.74830345]\n",
+      " [162.15960209]\n",
+      " [128.8069575 ]\n",
+      " [103.39541876]\n",
+      " [109.74830345]\n",
+      " [109.74830345]\n",
+      " [109.74830345]\n",
+      " [103.39541876]\n",
+      " [ 97.04253408]\n",
+      " [103.39541876]\n",
+      " [ 97.04253408]\n",
+      " [109.74830345]\n",
+      " [ 97.04253408]\n",
+      " [109.74830345]\n",
+      " [109.74830345]\n",
+      " [103.39541876]\n",
+      " [119.27763047]\n",
+      " [119.27763047]\n",
+      " [151.04205389]\n",
+      " [111.33652462]\n",
+      " [144.68916921]\n",
+      " [127.21873633]\n",
+      " [162.15960209]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [103.39541876]\n",
+      " [130.39517867]\n",
+      " [130.39517867]\n",
+      " [ 95.45431291]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [111.33652462]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [103.39541876]\n",
+      " [104.98363993]\n",
+      " [158.98315975]\n",
+      " [158.98315975]\n",
+      " [158.98315975]\n",
+      " [104.98363993]\n",
+      " [127.21873633]\n",
+      " [158.98315975]\n",
+      " [170.10070794]\n",
+      " [170.10070794]\n",
+      " [ 97.04253408]\n",
+      " [ 97.04253408]\n",
+      " [ 97.04253408]\n",
+      " [ 97.04253408]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [103.39541876]\n",
+      " [ 90.6896494 ]\n",
+      " [ 90.6896494 ]\n",
+      " [103.39541876]\n",
+      " [ 97.04253408]\n",
+      " [135.15984218]\n",
+      " [125.63051516]\n",
+      " [125.63051516]\n",
+      " [131.98339984]\n",
+      " [131.98339984]\n",
+      " [125.63051516]\n",
+      " [114.51296696]\n",
+      " [119.27763047]\n",
+      " [178.0418138 ]\n",
+      " [178.0418138 ]\n",
+      " [ 92.27787057]\n",
+      " [111.33652462]\n",
+      " [103.39541876]\n",
+      " [111.33652462]\n",
+      " [128.8069575 ]\n",
+      " [111.33652462]\n",
+      " [128.8069575 ]\n",
+      " [135.15984218]\n",
+      " [100.21897642]\n",
+      " [100.21897642]\n",
+      " [135.15984218]\n",
+      " [127.21873633]\n",
+      " [127.21873633]\n",
+      " [135.15984218]\n",
+      " [144.68916921]\n",
+      " [127.21873633]\n",
+      " [111.33652462]\n",
+      " [111.33652462]\n",
+      " [100.21897642]\n",
+      " [128.8069575 ]\n",
+      " [128.8069575 ]\n",
+      " [122.45407281]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = regr.predict(test_x)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "predictions = regr.predict(test_x)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 113.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "Azim Hirjani\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
+    "\n",
+    "<!--\n",
+    "## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03 | 2.1  | Lakshmi Holla  |  Changed URL of the csv |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   |\n",
+    "\n",
+    "--!>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "20d6dc1d9e74df451be22381c972d7921c93657bea402a00c749dca52bb85996"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}