diff --git a/Regression/SyahdanFaizM-202310715258-Reg-Mulitple-Linear-Regression-Co2.ipynb b/Regression/SyahdanFaizM-202310715258-Reg-Mulitple-Linear-Regression-Co2.ipynb
new file mode 100644
index 0000000..e4ebd71
--- /dev/null
+++ b/Regression/SyahdanFaizM-202310715258-Reg-Mulitple-Linear-Regression-Co2.ipynb
@@ -0,0 +1,753 @@
+{
+ "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": 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"
+   ]
+  },
+  {
+   "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": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 11:48:16--  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 11:48:16 (35.7 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": 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_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": 4,
+     "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": 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",
+    "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": 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": [
+    "#### Train data distribution\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": [
+    "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[11.79987964  7.35962587  9.22632845]]\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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE) : 478.48\n",
+      "Variance score: 0.87\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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[11.80890723  7.33126412  5.15052854  4.05198101]]\n",
+      "Residual sum of squares: 478.03\n",
+      "Variance score: 0.87\n"
+     ]
+    }
+   ],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "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/SyahdanFaizM-202310715258-Reg-NoneLinearRegression.ipynb b/Regression/SyahdanFaizM-202310715258-Reg-NoneLinearRegression.ipynb
new file mode 100644
index 0000000..d63d733
--- /dev/null
+++ b/Regression/SyahdanFaizM-202310715258-Reg-NoneLinearRegression.ipynb
@@ -0,0 +1,858 @@
+{
+ "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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\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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\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": 6,
+   "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 11:50:18 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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\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 0x7310825dde10>]"
+      ]
+     },
+     "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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\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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 0.04\n",
+      "Residual sum of squares (MSE): 0.00\n",
+      "R2-score: 0.97\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 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"
+   ]
+  },
+  {
+   "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/SyahdanFaizM-202310715258-Reg-Polynomial-Regression-Co2.ipynb b/Regression/SyahdanFaizM-202310715258-Reg-Polynomial-Regression-Co2.ipynb
new file mode 100644
index 0000000..4b12a5e
--- /dev/null
+++ b/Regression/SyahdanFaizM-202310715258-Reg-Polynomial-Regression-Co2.ipynb
@@ -0,0 +1,871 @@
+{
+ "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 11:51:40--  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 11:51:40 (41.7 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": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdfXgU5bk/8O+ykECADRBJAiaARRSRUKtNNVooCoVq1GDw14oC2nq0SkAImCg9nlbRSzixElpUqsdWfCHaKhGUWhUwUCxYEQ4FoYdCRMNLAoolIbwkZJnfH4+z2ZfZnWdmZ3Z2N9/Pde21ZPbemdlNdO995nnu26UoigIiIiKiJNXJ6RMgIiIishOTHSIiIkpqTHaIiIgoqTHZISIioqTGZIeIiIiSGpMdIiIiSmpMdoiIiCipdXb6BOLB2bNncejQIfTs2RMul8vp0yEiIiIJiqLg+PHj6N+/Pzp1Cj9+w2QHwKFDh5Cbm+v0aRAREZEJ+/fvR05OTtjHmewA6NmzJwDxZnk8HofPhoiIiGQ0NTUhNzfX9zkeDpMdwHfpyuPxMNkhIiJKMHpTUBydoPzwww/D5XIF3IYOHep7/PTp0ygpKUFGRgZ69OiBiRMn4vDhwwH7qKurQ2FhIdLS0pCZmYmysjK0tbXF+qUQERFRnHJ8ZOfiiy/GmjVrfD937tx+SqWlpfjzn/+M119/Henp6Zg+fTqKi4vxt7/9DQDg9XpRWFiI7OxsbNy4EfX19Zg6dSq6dOmCxx9/POavhYiIiOKP48lO586dkZ2dHbK9sbERv//971FVVYVrrrkGAPDCCy/goosuwkcffYQrrrgC77//Pnbt2oU1a9YgKysLl1xyCR599FE88MADePjhh5GSkqJ5zJaWFrS0tPh+bmpqsufFERERkeMcr7OzZ88e9O/fH9/61rdw2223oa6uDgCwZcsWnDlzBmPHjvXFDh06FAMGDMCmTZsAAJs2bUJeXh6ysrJ8MePHj0dTUxN27twZ9pjz589Henq678aVWERERMnL0WTn8ssvx9KlS/Huu+9iyZIl2LdvH0aOHInjx4+joaEBKSkp6NWrV8BzsrKy0NDQAABoaGgISHTUx9XHwpk7dy4aGxt9t/3791v8yoiIiCheOHoZ69prr/X9e8SIEbj88ssxcOBA/OlPf0K3bt1sO25qaipSU1Nt2z8RERHFD8cvY/nr1asXLrjgAuzduxfZ2dlobW3FsWPHAmIOHz7sm+OTnZ0dsjpL/VlrHhARERF1PHGV7DQ3N6O2thb9+vXDZZddhi5dumDt2rW+x3fv3o26ujoUFBQAAAoKCrBjxw4cOXLEF7N69Wp4PB4MGzYs5udPRERE8cfRy1j3338/brjhBgwcOBCHDh3Cr371K7jdbkyaNAnp6em48847MXv2bPTp0wcejwczZsxAQUEBrrjiCgDAuHHjMGzYMEyZMgUVFRVoaGjAQw89hJKSEl6mIiIiIgAOJzsHDhzApEmTcPToUfTt2xff//738dFHH6Fv374AgMrKSnTq1AkTJ05ES0sLxo8fj2eeecb3fLfbjVWrVuHee+9FQUEBunfvjttvvx3z5s1z6iUREVEc8HqBDRuA+nqgXz9g5EjA7Xb6rMgpLkVRFKdPwmlNTU1IT09HY2Mj20UQESW46mpg5kzgwIH2bTk5wG9+AxQXO3deZD3Zz++4mrNDREQUjepq4OabAxMdADh4UGyvrnbmvMhZTHaIiCgpeL1iREfreoW6bdYsEUcdC5MdIiJKChs2hI7o+FMUYP9+EUcdC5MdIiJKCvX11sZR8mCyQ0RESaFfP2vjKHkw2SEioqQwcqRYdeVyaT/ucgG5uSKOOhYmO0RElBTcbrG8HAhNeNSfFy1ivZ2OiMkOEREljeJi4I03gHPPDdyekyO2s85Ox+RoBWUiIiKrFRcDRUWsoEztmOwQEVHScbuB0aOdPguKF7yMRUREREmNyQ4RERElNSY7RERElNSY7BAREVFSY7JDRERESY3JDhERESU1JjtERESU1JjsEBERUVJjskNERERJjckOERERJTUmO0RERJTUmOwQERFRUmOyQ0REREmNyQ4RERElNSY7RERElNSY7BAREVFSY7JDRERESY3JDhERESU1JjtERESU1JjsEBERUVJjskNERERJjckOERERJTUmO0RERJTU4ibZWbBgAVwuF2bNmuXbNnr0aLhcroDbPffcE/C8uro6FBYWIi0tDZmZmSgrK0NbW1usT5+IiIjiVGenTwAANm/ejGeffRYjRowIeeyuu+7CvHnzfD+npaX5/u31elFYWIjs7Gxs3LgR9fX1mDp1Krp06YLHH388JudORERE8c3xkZ3m5mbcdttt+J//+R/07t075PG0tDRkZ2f7bh6Px/fY+++/j127duGVV17BJZdcgmuvvRaPPvoonn76abS2tsbyZRAREVGccjzZKSkpQWFhIcaOHav5+LJly3DOOedg+PDhmDt3Lk6ePOl7bNOmTcjLy0NWVpZv2/jx49HU1ISdO3eGPWZLSwuampoCbkRERJScHL2M9dprr2Hr1q3YvHmz5uO33norBg4ciP79+2P79u144IEHsHv3blRXVwMAGhoaAhIdAL6fGxoawh53/vz5eOSRRyx6FURERBTPHEt29u/fj5kzZ2L16tXo2rWrZszdd9/t+3deXh769euHMWPGoLa2FoMHDzZ97Llz52L27Nm+n5uampCbm2t6f0RERBS/HLuMtWXLFhw5cgSXXnopOnfujM6dO2P9+vX47W9/i86dO8Pr9YY85/LLLwcA7N27FwCQnZ2Nw4cPB8SoP2dnZ4c9dmpqKjweT8CNiIiIkpNjyc6YMWOwY8cObNu2zXf77ne/i9tuuw3btm2D2+0Oec62bdsAAP369QMAFBQUYMeOHThy5IgvZvXq1fB4PBg2bFhsXggRERHFNccuY/Xs2RPDhw8P2Na9e3dkZGRg+PDhqK2tRVVVFa677jpkZGRg+/btKC0txahRo3xL1MeNG4dhw4ZhypQpqKioQENDAx566CGUlJQgNTXViZdFREREcSYu6uxoSUlJwZo1a7Bo0SKcOHECubm5mDhxIh566CFfjNvtxqpVq3DvvfeioKAA3bt3x+233x5Ql4eIiIg6NpeiKIrTJ+G0pqYmpKeno7GxkfN3iIiSgNcLbNgA1NcD/foBI0cCGrMjKMHJfn7H7cgOERGRGdXVwMyZwIED7dtycoDf/AYoLnbuvMg5jhcVJCKi+NPaCixaBMyYIe4TpSh9dTVw882BiQ4AHDwotn9Tpo06GF7GAi9jERH5Ky8HFi4Ul4JUbjcwezZQUeHceenxeoFBg0ITHZXLJUZ49u3jJa1kIfv5zZEdIiLyKS8HnngiMNEBxM9PPCEej1cbNoRPdABAUYD9+0UcdSxMdoiICIC4VLVwYeSYhQvj95JWfb21cZQ8mOwQEREA4JlnQkd0gnm9Ii4efVNv1rI4Sh5MdoiICADwr39ZGxdrI0eKOTkul/bjLheQmyviqGNhskNERAAS/zKQ2y2WlwOhCY/686JFnJzcETHZISIiAECE/smm4pxQXAy88QZw7rmB23NyxHbW2emYWFSQiIgAABdeaG2cU4qLgaIiVlCmdqyzA9bZISICxCqrtLTIk5TdbuDkSSAlJXbnRRQO6+wQEZEhKSmicGAks2cz0aHEw8tYRETko1ZITsQKykTh8DIWeBmLiJwVjx26W1tFPZ3aWmDwYGDatNiM6MTje0Hxi13PiYgSQLx26E5JAWbNiu0x4/W9oMTHkR1wZIeoo3Fq1CKY2qE7+P/Cak2YP/4R6Nu3Y4xy6L0XXDZOWmQ/v5nsgMkOUUcSLx299Tp0A+K8/M8zWUc52K2czOJqLCKiIPHU0VuvQzcQep4HD4rRj+pq+87LCexWTnZjskNEHUK8dfQ203JBHYefNUu/YWciSfQ2FRT/mOwQUYcQbx29zXbejtdRDq8XWLcOePVVcW8kGWO3crIbV2MRUYdQW2ttXLTUDt0HD4ZOypVh9yiHkUnc0a6i0nsv1Dk77FZOZnFkh4g6hMGDrY2LVqQO3TLsHOUoLxdtI0pLgaeeEvdpadpzmtRVVMFzbozML2K3crIbV2OBq7GIOoLmZqBnT/2448eBHj3sPx+V1qhI8Cosf3avTFIncYdTVta+as3qVVRa70Vurkh0km0FGlmDS88NYLJDlPwWLRIjFHoqK2NfTC+4avBXXwE//rF4zP//0HbXnDHaCHTdOuDqq/X3W1MDjB4tdw6soExGsIIyEZGfWM7ZMVq00O0OTQbeeEN7HoydoxxGJnHPmmXPKiqt94IoWkx2iKhDiNWcHa2ihfffb7xoYXExUFQU21GOf/3LWBxXUVGi4ARlIrJVa6sYjZgxQ9zHqo5NsGnT9BMFt1vEmRVPRQvNMDpSo66iCjfB2uUSc264ioqcxmSHiGxjZFWP3VJSxOhKJLNnm++RZXXRwupqMfn36quBW28V94MG2Vs9OTvbWBxXUVGiYLJDRLaIx1GOigqxmij4w9ftDlxlZIaVRQutWM5txoUXGo8rLhbzi849NzAmJ4fNOyl+cDUWuBqLyGpGV/XEmh1dz2fMEKNXeqZPBxYvDv+4k00xo/m9cRUVOYGNQInIMfHWmiFYSopYTbR4sbi3IuGyagK0k00x7b7UR+QUJjtEZLl4a80QC1ZNgHa6KaaZS31OzC8iMoLJDhFZLt5aM8SCVaMi8bCcu6JCXKqqrBSX3Sorxc/hEh0n5hcRGRE3yc6CBQvgcrkwy6906enTp1FSUoKMjAz06NEDEydOxOHDhwOeV1dXh8LCQqSlpSEzMxNlZWVoa2uL9ekTkZ9YLPOORxUVojaOlqIiuQnQ8bKcW+ZSn9crCh9qzfxUt82aZawDOpEd4iLZ2bx5M5599lmMGDEiYHtpaSnefvttvP7661i/fj0OHTqEYr+p/V6vF4WFhWhtbcXGjRvx4osvYunSpfjlL38Z65dARH466tyP6mrgrbdCt7tcYrtVTTGffFLM2Xn1VdGywalkwsn5RUSGKA47fvy4MmTIEGX16tXKD37wA2XmzJmKoijKsWPHlC5duiivv/66L/af//ynAkDZtGmToiiK8s477yidOnVSGhoafDFLlixRPB6P0tLSIn0OjY2NCgClsbHRoldFRIqiKGVliuJ2K4r42BM3t1tsTzZtbYqSkxP4Wv1vLpei5OaKOK3n1tQoSlWVuG9rU5Tly0P3l5sr3rvg7Tk5Ij7WqqrCv17/W1VV7M+NOgbZz2/HR3ZKSkpQWFiIsWPHBmzfsmULzpw5E7B96NChGDBgADZt2gQA2LRpE/Ly8pCVleWLGT9+PJqamrBz586wx2xpaUFTU1PAjYisZ2TuR6IzO8oRbnIvAHz+uWiiWVUl7hcuBH796/iZHxMP84uIZDjaG+u1117D1q1bsXnz5pDHGhoakJKSgl69egVsz8rKQkNDgy/GP9FRH1cfC2f+/Pl45JFHoj19IpKgzv1IdmZWUamTe4PnvKjJi39RPrX+Trj5MS6XeJ+LimJX30adX3TwoPZ5qTWB2C6CnObYyM7+/fsxc+ZMLFu2DF27do3psefOnYvGxkbfbf/+/TE9PhElH6OjHEYn98bj/Jh4aBfR3AzcdBMwYoS4b26271iUuBxLdrZs2YIjR47g0ksvRefOndG5c2esX78ev/3tb9G5c2dkZWWhtbUVx44dC3je4cOHkf1NY5bs7OyQ1Vnqz9kRmrykpqbC4/EE3IiIomF0FZXR5MXp+jvhONku4nvfA3r2BFasAHbsEPc9e4rtRP4cS3bGjBmDHTt2YNu2bb7bd7/7Xdx2222+f3fp0gVr1671PWf37t2oq6tDQUEBAKCgoAA7duzAkSNHfDGrV6+Gx+PBsGHDYv6aiKjjMjrKYTR5ief5McXFofOL9u2zP9HRmAEBQGxnwkP+HJuz07NnTwwfPjxgW/fu3ZGRkeHbfuedd2L27Nno06cPPB4PZsyYgYKCAlxxxRUAgHHjxmHYsGGYMmUKKioq0NDQgIceegglJSVITU2N+Wsioo5NHeWYOTNw1CYnRyQ6/h/+RpOXeJ8f43YDo0fH5ljNzeETHdXmzSKuR4/YnBNps6MPnRmOr8aKpLKyEtdffz0mTpyIUaNGITs7G9V+yw3cbjdWrVoFt9uNgoICTJ48GVOnTsW8efMcPGsi6shkRzmMXvaKh/kx8WLKFGvjyB7l5aKxbGmpaJJbWip+Li+P/bmw6znY9ZyInKGuxgICR2vU5EVrzkt1dejIUW5u6MhRMhsxQszR0ZOXB2zfbv/5UKjycuCJJ8I/Hq7PmlGyn99MdsBkhyjReb1iIm99vbjsM3Jk4oxwmEleEvn1WuGmm8RkZD0TJgBvvmn/+VCg1lYxghOpsrfbLWpuRXtJi8mOAUx2iBKXVrKQkyMu+STKSEdHT16Mam4Wq670HD/OOTtOWLRIXLLSU1kZfQ0u2c9vR4sKEhFFw0hRvnhm1eTejpI09egB5OdHnqScn89Exym1tdbGWSGuJygTEYXDjtuBwrWdiHULiVj5+GOR0GjJzxePkzMGD7Y2zgq8jAVexiJKROvWiQ90PTU1sVsSbVa0IzLhRrgiTXS2WnOzWP2kLjF++eXYjKw4dVwKLx7n7PAyFhElpHitKBxJXR1w8cXif/JpacDOncAnnxifc+SfHGVmRh7hMtsz69QpsWJmzx5gyBCxsqZbN+3Y4AJ/O3aIOTWxGGHp0YOTkONNSgowe3bk1VizZ8e23g5HdsCRHaJElGgjO126AG1tcrFGl57LMPI+TJgArFwZur2oKHQVVKRKxgAvKXVk5eXAwoWBIzxut0h0rFh2DnA1liFMdogSj9oFXK+i8L59zk/SNZLoqLTOP9zlKhlVVcCkSfpx4RIdlX/Cw1VRpMfuCsqyn9+coExECSlRKgrX1RlPdIDQRqCRJmTLyMzUjzl1KnKiA4jHT50S/548We7YsnGUfFJSxGXUxYvFvROtIgAmO0SUwJzsuC3r4ouje74650ivS7oVysqMxclWJ2YVY3IaJygTUUIrLhaXVuK1vszJk9E9X20EGu1E6yNH9GP27JHblxqXkSEus+nJyJDbL5FdOLJDRAlPLco3aZK4j5dEBxCrrswIbgQq2yU9HJnnDxkity817r/+Sy5eNo7ILkx2iCjheb1iddarr4r7eCokuHOn8edozTnS65IeaV/+SVMkkZYKa8Xt3SsXLxtHZBcmO0SU0OK9cvCAAUBngxMGtOYcRZqQHYmiyE/U7tZNXBKMpKiovd6OzCUsI3FEdmGyQ0RxxcgojboUO3jirtobK14SnjNnwic8nTuL1Vo1NWJ5eE2NSA60JleHm5Bt5ZyYFSvCJzzBdXbisS0AkRbW2QHr7BDFCyMdzNU6O+FWKMVTnR2VVgXlAQOM7ye4gvIdd1j/PshUUI5lWwAiLSwqaACTHSLnGe3vlGgVlCOJpvCa0+9DeXnkuT5lZdZUy7W7OB0lJhYVJKKEYaaDeSL2xtJSXi5GR0pLgaeeEvdpaWK7DKffh4oKkdAEjxq53dYlOtG+R0Sss0NEjtMrmOdfTVgdnZBdih3tkm0rBY9O7N8vegcF83rbR0v0koV4eB8qKoBHHpFvHGpEuJEjI+8RES9jgZexiJz26qtiJZUe//5OiTZfRKspoh6Z84+HHmFG5loZkWi/Y4o9XsYiooRhZnRi40b9xMHrFXFOU0cnjNb/8XpFh+hIK9Oc7hFmdkWczKq7Z56R+x0/84yZM6eOhMkOETlOr2CeVmE8p+eqyGpt1b5UJevpp/XrBznVI8zMXCtAvjZSba3cecjGUcfFOTtE5Dh1dOLmm0Vi4//hGW50Ih7mqoTjvzT8b3+zrqKzOlqilcA40SNMdq7V4sVAVpY4p6++An7849AESeu1sY4PWYVzdsA5O0TxQmvuR26uSHTC1dlxcq6KFq3XYKV4qh8kO9fKn9sdPvkLfm2cs0N6OGeHiBJOcTHw+edy1YRl5qo8+aQYfYhVz6xw81es5D9aYsXrOnUKmD4dGD9e3J86Jf9cM6Nmkc7Vf9UdIBKY2bMj72/2bCY6pI8jO+DIDlEiCzcadMstIhmweoVQOHoVne1k9nVNmACsXBm6PbgtRDinTpnv6h6J/6o7IPrzpOTFkR0i6hC0RoMWLgR+/evY9szSm79iJzOvK1wCAYjtEybo7+PZZ+WPZ4T/iFF1NfDWW6ExLpfYHi/9zyi+cWQHHNkhSiZO9cwyM3/FSi6XWI21dClw5EjkCcqyIzL//d/islK49gwzZoiKxlYJ/t0kYv8zii2O7BBRh2SkGrOs1lYxSXrGDHHf2hoa43SlZkURr3vsWP2l6mVlcvt84IHI7RmsXAWlterOjt8ldUxMdogoqVhdf0e2L5NaKyieHDgATJwYmvDs2WN8X2p7Bv/XPW2a+RGV4Odp1QRKlFpKFP+Y7BBRUrGy/k64ysdaH/xuN3DZZfLnGUt33x34GoYMMb+vhQvbR7ZSUoBLLzX2fJdL3F59VX/VXTzXUqLEwjk74JwdomQiOx/l5MnIjSqN1niRiQeAG24QE6oHDwb+3/8DJk8W2+3+P/GaNcCYMeLf0a6iqqwUlZFlX7O/cHWTtMRrLSWKH5yzQ0Qdhn+fJdn5KHoriYz2ZZKJB4BrrgG2bwfefFPMrdFq82CHdeva/92tm1i2bZbankH2NZeU6NdN0uJ03y9KHo4mO0uWLMGIESPg8Xjg8XhQUFCAv/zlL77HR48eDZfLFXC75557AvZRV1eHwsJCpKWlITMzE2VlZWhra4v1SyEihwT3WXr6abnn6c1bMdqXyWwfp+Cl85WVcvuJ1ooV5hMedWKy7NwfRRF1c0aPNp6YONX3i5KLo72xcnJysGDBAgwZMgSKouDFF19EUVER/vd//xcXX3wxAOCuu+7CvHnzfM9J8xt79Xq9KCwsRHZ2NjZu3Ij6+npMnToVXbp0weOPPx7z10NEsaVWLDZzCShc01GV0b5M0fRxcrtFIgCIkZInn4x86cZ/iXl9PTBnjv5x1f37W7FCXNIqKxOJy3nnAf/zP8DZs+H343aLicnquciQjQvHib5flGSUONO7d2/l+eefVxRFUX7wgx8oM2fODBv7zjvvKJ06dVIaGhp825YsWaJ4PB6lpaVF+piNjY0KAKWxsdH8iRORIS0tilJZqSjTp4t7A//JKoqiKG1tipKToygiJTB+W7pU//zc7sj7cLvbz9tofKT34o9/VBSXS9z8n69uW7488H3IyIh83IwMESejrCzyvsrK2mNfeknuvX7pJbljExkl+/kdN3N2vF4vXnvtNZw4cQIFBQW+7cuWLcM555yD4cOHY+7cuTh58qTvsU2bNiEvLw9ZWVm+bePHj0dTUxN27twZ9lgtLS1oamoKuBFR7Mgu544k2orF//535MeN9mUy28dJ67249VbgxhvDX7opKmqfo7RhA/C730U+7nPPyY+CVFSIkZ7geLdbbK+oaN+Wmyu3T9k4Irs4ehkLAHbs2IGCggKcPn0aPXr0wJtvvolhw4YBAG699VYMHDgQ/fv3x/bt2/HAAw9g9+7dqP6maERDQ0NAogPA93NDQ0PYY86fPx+PPPKITa+IiCJRl3MHU5dzA4EfqOFEW1ulb9/onq9FPe+FCwMn7rrdItF58EEgLw84dAjo319cWtKqQOz1ipYNc+YA118feOlm5crQqsI5OSIRqaoSl7/8t5vpmVVRATz2mJiAXFsbvoLyyJFARgZw9Gj4fWVkiDgiJzm+9Ly1tRV1dXVobGzEG2+8geeffx7r16/3JTz+PvjgA4wZMwZ79+7F4MGDcffdd+OLL77Ae++954s5efIkunfvjnfeeQfXXnut5jFbWlrQ0tLi+7mpqQm5ublcek5kM6PLuSNZu1ZUCzbLfym2ltZWsWop0vyVTp3EnJfgc21tDU0UBgwADh82do7B70W4OUrqnJg//lEkcbGa1+L1AllZ+snO4cOcX0P2SJil5ykpKTj//PNx2WWXYf78+fj2t7+N36hrDYNcfvnlAIC9e/cCALKzs3E46P8e6s/Z2dlhj5mamupbAabeiMh+RpdzO2nx4siJDiAeX7w4dHtKiqhDs3ixuDeT6ACB74XXK7q7a309VbfNmSMSHLMrn4zasCFyogOIx9nOgZzmeLIT7OzZswGjLv62bdsGAOj3TbnMgoIC7NixA0eOHPHFrF69Gh6PR3NkiIicZXZ5tha//+xN0Xv+hx/K7Ucv7uuvzSU6KvW9iMc+UWznQInC0Tk7c+fOxbXXXosBAwbg+PHjqKqqwrp16/Dee++htrYWVVVVuO6665CRkYHt27ejtLQUo0aNwogRIwAA48aNw7BhwzBlyhRUVFSgoaEBDz30EEpKSpCamurkSyMiDdEszw4WbYsAved37y63H724H/xAbj/hqO+FbMLw+98Dy5eHn2djJbZzoIQRk7VhYfzsZz9TBg4cqKSkpCh9+/ZVxowZo7z//vuKoihKXV2dMmrUKKVPnz5Kamqqcv755ytlZWUhy8s+//xz5dprr1W6deumnHPOOcqcOXOUM2fOGDoPLj0nio1olmcHU5eeBy/P1ru5XIqSm6u/FPvxx+X29/jjkffTp4/55fH+70VNjbnn+y8Vt5rM8n+Z95rILNnPb0dHdn7/+9+HfSw3Nxfr16/X3cfAgQPxzjvvWHlaRGQTdXm21mos1fTpwE9+0j659+WXgR49QuPUVgI33ywm6MostTDSYiBC9QpDcf37i0tZZvgvVVe7qocrNqjF6Ao3o9xuQG8QPSWFk5PJeXE3Z4eIkktrq0guZswQ9489Fr6OS1aWSGBWrAB27BD3PXsC3/ue9r7DtRLIzQXy80PjFUXUr5FZit3cLPf69OIkvrOF0KppE6lPlB7/TuVWam7Wn19VWyv/XhLZhckOEdkmXPFAQCyprqwUIzmVlcAll4SfyLt5c+SEx7+3VE0N8OMfi+doWblSrnihVfNR+vQRSVwkmZmB78XJk9ojMeGSOz12rXCbMsXaOCK7OF5nJx7IrtMnCok/3xQAACAASURBVOb1Jn6/HrteQ7jigSr/kYvmZjGCo+f4ce1LWv6squXz4ovAHXfon9PSpcDtt+vHZWdrJ3NZWUCEGqia/H9nf/qTGAHTM3269jL5aAwfLne57+KLgU8/tfbYREAC1dkhSlTB3bavvlr8/E2B74Rg12tobRWXTiLxv7Ri5QiBVbV8vvxS7pxk46ZONbY9ErVx6KRJ8qu9ZFfCGRGrRqBE0WKyQ2SCWsk2uO7JwYNieyIkPHa+BqMJh5X1d6za1zdlvXTJxEUa5XriCWM9wYJNm6Y/EuffqdxKV15pbRyRXZjsEBkkU8l21iz9D3sn2f0ajCYcVtbfsWpfVk1QNjrKZZTZBqRWuOgia+OI7MJkh8igeKxka5Tdr8FowvHyy3LxMnFWjXT07y93TnpxsWiRUVEhOqFrKSqyZ9k54OyoEpERTHaIDEqGEvl2vwajH4I9emgvFfeXnx9+crLXC6xbB7z6KrBxoxiVikRmpKOgIPLjsnFWXqILp7oaeOst7cfeesu+y6pOjioRGcFkh8igZCiRH81r8E8s1q3THrUw8yH48cfhE578fPG4Fq1J1n/8oxjR6BT0f7hOnULr14Rj1ciOlZfotES6JKkyc0lS5vcMiPcy0u/NrlElIkNiUs85zrFdBBmh16ZAth2Bk8y+huXLQ9sD5OSI7VrKykLbQ+i1MDh+XFEmTFCUvDxxf/x4+Njly7Vfg7otuFXDueeGP9dga9bItWRYsybyfqxskaFFto1ETY38Po38nsvKIh/XznYVRLKf30x2FCY7ZJz6IRv8Qatuk/1AdZLR1xApsYj0mltaFKWyUlGmTxf3Zj/Ug8n0ZTJ6rv6qquT2WVWlvy87EwIrz1NRjP2e7U7kiPQw2TGAyQ6ZsXy5GCmQHeWIR1rf4HNzQ1+DXmJh5WhWW5sYhaiqEvfh9mmmMaaRc7V6xMTMKJcMK8/T6O+5slLu2JWV0b1GonBkP785Z4coColeLE2r1cK+faG9o2K1As1IkUOzk6dlz1VtvBnud+xyiR5cI0fKHbeiIrRFRri2EEao5xmJ7Hka/T3HYvI1kRVMdz0/duwYPv74Yxw5cgRnz54NeGyqmZKgRAlELcinKIHb1YJ8b7wh12wyUcRiBZrR9zTaCeB65xqpq7qR7un+UlL0V4oZ5XaLSsqRWnPccovceRr9Pds9+ZrIKqZ6Y7399tu47bbb0NzcDI/HA5ffVx+Xy4Wvv/7a0pO0G3tjkRFerxhtCPcN2OUS37T37Yv/PlnV1WIlj/9ryckRH/L+icW6dWKURU9NjWhjYJSZ91R9zsGDoQmSjMpK0ZdKrx+Y7HvkFL33DhAjOzJ/j0Z/z9H0NEuGvnLkPFt7Y82ZMwc/+9nP0NzcjGPHjuHf//6375ZoiQ6RUclQVBAw1i5i5Ej9Bpw9eshf0glm5j1VR17MJDput+jALtsPLPgYZo5pF733DpD/ezR66e755+XOMTguGfrKUWIxlewcPHgQ9913H9LS0qw+H6K4lwxFBY22i/B69dsiNDebby8R6/c0+DzD9QNTE8KDB+XinWDle6cmkEBowqN16W73brlj+8clQ185Sjymkp3x48fjk08+sfpciBJCvBQVlC36psXoSIr6AahHNi5YZqbxODVhiyT4ski4yyThErxIxfoUxXixvoYGIDsb6NpV3Dc0yD83HKv/HouLxfyoc88N3J6TEzpvSvb81bhk6CtHicnUBOXCwkKUlZVh165dyMvLQ5cuXQIev/HGGy05OaJ4pA71h5sros4vMXtJR0a080iMjgasXCkXv3KlqFAcCzKXb7ze9rk5hw+LS1fh+Cd4o0cbuzwkM0+pe3ex+kp1+LBIQNLSgBMn9J8fjh1/j8XFogK13pwao4mWkSTbzNwvonBMJTt33XUXAGDevHkhj7lcLniZllMSs2OVjhFWrASLl9Ep1ZEjxuNkE7asLLFa6dVX5eLV/QZfugpHJi440fF38qR43GzCY9ffo9utn3BccIHcvtS4ZLgETInJ1GWss2fPhr0x0aGOwMhQv5WsugxgdCJquI7awWTjgvXqZTzOaMJmNP7LL+Xi9eIaGsInOqqTJ6O7pOXU36PRhq9mfs9EVmBRQSKTZAvyWUn2MsDixZHn8hidiKo3N0YlGxfs2WeNxxlN2EaOBDIyIu8/I6M9vm9fuXPSi7vkErn9yMaFU1wsivf5Fy3cu9fev0ejDV/DdWYPJhtHJMt0srN+/XrccMMNOP/883H++efjxhtvxIZ4X2tLZDF1qH/SJHFvd50Q2eF9mWXVRkcD9KpFR1NN+rPPjMcZTdiMsmoU4tgxuf3IxoVTXS2K95WWAk89Je4HD7Z/dVNFhZinpTUZPLjDvJnfM5EVTCU7r7zyCsaOHYu0tDTcd999uO+++9CtWzeMGTMGVVVVVp8jEX3DzByaSEt6ZUennnlGv7aMoog4M8xW4jWSsG3YABw9Gnn/R4+2r0B77jm5c9KLiyZpam0VCduMGeK+tVX7uU4v55ZthTFkiNz+ZOOIpJlpvDV06FBl4cKFIduffPJJZejQoWZ26Sg2AqVEoTZq1OpKbUXzy3CmT5c7zvTp5vZ//Ljc/o8fD/++6DUPNdodPC9PLj4vL/Jrq6+X2099feDzZBuHxrJJa7ROnpR7L06edPpMKVHY2gj0s88+ww033BCy/cYbb8S+ffuiTL+IKBz/SzdGqHN5Zs7UHyXQEk0PJJl6QD16APn5kfednx++irPM5USjE5St6vuUnS2Wl0eSlibiVOXlotdV8Hvl9Yrt5eXt2xKpone3bvqT2IuKRByRpcxkUoMHD1Z+97vfhWxfsmSJcv7555vZpaM4skOJpqjI2MiO1k1rlCAcs9/Ily8PHXXIyRHbteTna+83Pz+690tR9EfFgkdAjh6Ve81Hj8odPy1N+/lpaYFxLS2hIzpav7uWFhFvdMQqHoT7+y0qcvrMKNHYOrIzZ84c3Hfffbj33nvx8ssv4+WXX8Y999yDWbNm4f7777c2GyOiAOXl8kX+ItEaJQjn73+X26d/nJl5JB9/LJpGTpgA5OWJ++PHxfZoGZ3Q/NJLcvuVjTtxQkwwz8oCUlPFfX19aH2dZ57RLx3g9bbPj4q3mkkyVqwQc3pKSoBx48T9yZNiO5EtzGZT1dXVylVXXaX06dNH6dOnj3LVVVcpK1asMLs7R3FkhxKFzLd+MyM86ihBOEZHD/TmkQDOzSPRGm3KzQ0dbbJ7nlI406bJHXfaNBHPeTDUkcl+fpuqoAwAN910E2666Sbrsi6iBOT16pfUt5LMt36j1FGCWbPCx1jdFgCITVsArd+PbCsEq+bsGCW7hF+Nk61RtGQJcOmlsftbJYonppMdoo4u2v5UZtTWOrPfK68UH4yREi23W8QB1rZaMEvv96OXZN1yS+ReWv5xVrr8cuDpp+XiAPm/iV/9KrBzvd1/q0TxRHrOTp8+ffDVV18BAHr37o0+ffqEvRElu3DzUQ4csLeuidWjCLL73bhRbh7Jxo3i31a1WjDLirozP/yh3LFk42Tl5hqLk/2b8E90gNjV4CGKBy5FURSZwBdffBG33HILUlNTsXTpUrgijLXefvvtlp1gLDQ1NSE9PR2NjY3weDxOnw7FOa9XVCWOdJkmN1cU57P6MkFrq1imbOWlLLdbTA5VS/prefVVUZFZT1WVWP69bBkwebJ+/CuvALfdJn+uMvR+P2oXcL3fT0YG8PXX+sfr00e/WKERMr9j/99ZNH8Tsu8FUbyS/fyWvozln8DccccdUZ0cUSJzcj6K2ovoiSes26d/76JwjM7ZCa5oHI5snBFG+odlZYWfv+LxyCU7Rr4ftbaK+VG1tWJEZtq00PfeyCja6NHi+ddfb26Fnvpe2D13ishpppaeb926FTt27PD9vHLlSkyYMAG/+MUv0GqgUtmSJUswYsQIeDweeDweFBQU4C9/+Yvv8dOnT6OkpAQZGRno0aMHJk6ciMOHDwfso66uDoWFhUhLS0NmZibKysrQ1tZm5mURSXF6PkpFhfnu4v60eheFo84PkY1Tm3RG4t+k00pW9Q978EG5/cjGlZeLERj/3lVpaaFL//fvl9ufGuf1Alu2yD0nHNn3jChRmUp2fv7zn+Nf//oXAFFN+Sc/+QnS0tLw+uuvo1ymaMc3cnJysGDBAmzZsgWffPIJrrnmGhQVFWHnzp0AgNLSUrz99tt4/fXXsX79ehw6dAjFfrPpvF4vCgsL0draio0bN+LFF1/E0qVL8ctf/tLMyyKSEg/zUcx2hS4pCe1dJFPh2GhXcrWmTaSO5NE06Qzm30Pqb38z/nytuVanTsk9VybOSEVkozWNZEYa9cRTDR4iW5hZ1+7xeJS9e/cqiqIoCxYsUMaNG6coiqJ8+OGHSk5Ojpld+vTu3Vt5/vnnlWPHjildunRRXn/9dd9j//znPxUAyqZNmxRFUZR33nlH6dSpk9LQ0OCLWbJkieLxeJSWCIVDTp8+rTQ2Nvpu+/fvZ50dkvbKK3J1TV55xfpjy9SvMVJFV7bCsdmaM7I1baKh1UPK7M2/9o9Vv2ejFZGN1tmRrYGkdYunvllEZthaQVlRFJw9exYAsGbNGlx33XUAgNzcXN+KLaO8Xi9ee+01nDhxAgUFBdiyZQvOnDmDsWPH+mKGDh2KAQMGYNOmTQCATZs2IS8vD1lZWb6Y8ePHo6mpyTc6pGX+/PlIT0/33XJllz8QIbr5KDKjKJFE+y3+8OH2Y7/xhvyKpWi6kst0VTcr3IiJWf49pKyad2S0IrJsx2+XS/wug67sS9OqGh2NaP+2iWxlJpO6+uqrlalTpyovvfSS0qVLF2XPnj2KoijKunXrlIEDBxra1/bt25Xu3bsrbrdbSU9PV/785z8riqIoy5YtU1JSUkLi8/PzlfLyckVRFOWuu+7yjSqpTpw4oQBQ3nnnnbDH5MgORcNsdWCjfaK0RPMtXms0QfYb/5Ejcvs8csS691mPHdWk/UdqrKoCbXRUTLYDvOzvElCUjAx7R9is+NsmMsPWkZ1FixZh69atmD59Ov7zP/8T559/PgDgjTfewJVqVTFJF154IbZt24a///3vuPfee3H77bdj165dZk5LWmpqqm9StHojkuV2i+XVkdxyS+C3ZSvqvgDWzq2I9M1bUQJHOWSLpVtRVP3LL4HzzhMdzs87L/zcJ9lq0iUlYlSppET++IC537MWo6Nizz8vF+9P73341rfsG2FzquYUkSFWZlinTp1SWltbo9rHmDFjlLvvvltZu3atAkD597//HfD4gAEDlIULFyqKoij/9V//pXz7298OePyzzz5TAChbt26VPiZ7Y5ERRr/x68UbmTdh5lt/NDd1jk9urlx8bm507216uvZ+09NDY42OmLz0klz8Sy+Z+z2H88UXcsf94gtjr8vozY7eWPHcA406BltHdvbv348Dfmn8xx9/jFmzZuGll15Cly5dokq+zp49i5aWFlx22WXo0qUL1q5d63ts9+7dqKurQ0FBAQCgoKAAO3bswJEjR3wxq1evhsfjwbBhw6I6D6JwjNTZkYkPHkWJRHaZs1XUkaQBA+TiZeOAwBVUixaJejWNjdqxjY1Ar16B24yOmMgW/lPjjP6ew/ne9+SOq8bZVSW7rMz6fVr1HhHZzkwm9f3vf1956ZuvP/X19YrH41EKCgqUc845R3nkkUek9/Pggw8q69evV/bt26ds375defDBBxWXy6W8//77iqIoyj333KMMGDBA+eCDD5RPPvlEKSgoUAoKCnzPb2trU4YPH66MGzdO2bZtm/Luu+8qffv2VebOnWvo9XBkh4xYulTum/TSpSLeaMfwSH74w9iM6ASPNh09Kve8o0fl3kOzK6j85wQZXeVkdGTHqtVYqaly+0lNFfGyXcyN3oKmN1rCyZWJRIpi88jOp59+iu998zXkT3/6E4YPH46NGzdi2bJlWLp0qfR+jhw5gqlTp+LCCy/EmDFjsHnzZrz33nv44TfNZiorK3H99ddj4sSJGDVqFLKzs1HtdwHY7XZj1apVcLvdKCgowOTJkzF16lTMmzfPzMuiKDm5GiN4lMBAbUvDVqwwFme0+nAk3bvL7SsaWqt0tm+Xe65MXDQrqPxHSdRq0pH4V4c2OrJjVT0l2cFuNU62zo5Rsqu8jHC65hSRNDOZVPfu3ZV9+/YpiqIoN9xwg7JgwQJFURTliy++ULp27Wpml47iyE70nFyNoTVK4HaL7XaQHV354Q9FvDqvweWSG0WJRHZUKZqb1iqdWNaciXTr3j10n7K/f6Ov4Q9/kIv/wx8iv+apU+X2M3WqiLdyxZ3/zY45OxzZIafZOrJz8cUX43e/+x02bNiA1atX40c/+hEA4NChQ8jIyLAwFaNEYNVKIzOMVKa1ygUXGItTqwkDoRWFjdY6GThQ7thGVVZGXqVj1Td42RVU4fTtG7qtokJUg66sDK0O7c9o3RzZKtV6cd/5jtx+1Dg7qhkXFQHdulm/Xyd7oBEZYiaTqqmpUXr16qV06tRJ+elPf+rbPnfuXOWmm24ys0tHcWTHPCtXGhlldM6GVWTnVAR/k7aimrBdtWXUeSrhGJ3vEk60K42iqeNjdOWQ0RG8cIz+neqNBKrxweedn68dW1Rk/j2z+j0lspqtIzujR4/GV199ha+++gp/+MMffNvvvvtu/O53v7MoDaNEYOVKI6OMVqa1SrduQOfOkWM6dw79Jm1FNWGZjthm6M0TMTrfJZxoVhqlp2uP7ABy88WM9usyOoIXjtG5RXojgS6X+PvxH8nauxf4+GMxolVSAowbJ+5PnpSfY2aG/3sa7lyt7IFGZFqMkq+4xpEd86xcaWSU2X5N0XKymrDsHAmjN7XPUrTH1ZubYbZOkFadHZXR+WKyI2xmR/DCMTq3LNx5lpXFX7XiWPRAI9Ii+/mt8/203aWXXoq1a9eid+/e+M53vgNXuK9HALZu3WpBGkaJwMqVRkaZ7dcULSN1U/bts/bYdq1q0VupY9XcDNnqwF27Am1tQM+ewI4d4ferzhdTlMDt6nyxN94IHTkrLhZzWDZsAOrrxd/myJGhow/duom4lSvDn6eRuTAVFcBjj4mRxtpa8Xc5bVr7iE4wrfP88kvgJz8x9npjQfY9JXKKS1GC/7PR9sgjj6CsrAxpaWl45JFHIsb+6le/suTkYqWpqQnp6elobGxk6wiDWluBtLTIl1bcbjGcHu5/6madOiWOrefkSWsnZ3bvLvapJy0NOHHCuuMCwLJlwOTJ1u4TEJef+vQJ/7jXCwwaFPmSZW6uSO4ifcDNmAE89ZSxc3O7xWWe4AnHeufkcgE5OfrnpGfCBO2Ep6jI3ktEwWL1eokSieznt/TIjn8Ck2jJDNlHZg6J1yviRo+29tiy9Uj+/ndrj52aKpfspKZad0yVXata7rwTePPN8I+73cBll0VOdi691Lo+Uf7UlXVAYMJjZL5YNL//Cy4QiYT/10KXS35Oj1Vi9XqJkpGpCcr+mpub0dTUFHCjjqO+3tq4RDj29ddbG2fEyJHi27vV9uyJ/HhrK7BqVeSYVav0izlOm2Z+1GHhwsD9y/5e1641X+hSLW0QPP6tKOZKG0RT/NLJ/9aIEp2pZGffvn0oLCxE9+7dkZ6ejt69e6N3797o1asXevfubfU5Uhxzcs6OU8f+7DNr44zwX6ljJb3LbVatfJNZmSS7f9nf62OPAbfeClx9tahTJFv3qbVVJFiRBCdgkZSXi0ubpaXiUl5pqfhZNmFy8r81GU5WUCfSI30Zy9/kyZOhKAr+8Ic/ICsrK+JkZUpu6kjDwYOh336B9nkEI0cmz7Fl5//YUcQNAD76yPp9futbkR+vrZXbj0yceilq4ULjH4j++9f7/Ws5eBCYOBFYvlx/Iq+RBG/WrMhx6giR1vO1LtFpcfK/NT3V1cDMmYGX2XJyRGLuxIRpohBmlnp1795d+b//+z8zT41LXHoeneXLRQG04CJo6jY7l5+aPXZbm6LU1Igl8TU1xoqe/exnckuSf/YzK15hoJaWyMXmzN4GDVKUvDxFmTBBLA8PVlkpt5/KSmOvpbJSlAaYMMHc/sP9/vVuGRn6v3OrShtYWfzSyf/W9M4p+DU5eU7Ucch+fptKdkaPHq2sXr3a1InFIyY70Yt1fyp/Rmt8RNvH64or5D4Er7jCuteoqqiwPtHRuuXnBx7X7mrV0exf6/cpc1uzJvI5WZXgWZ0oxlNNGycrqBMpis3Jzt69e5WxY8cqS5cuVT755BPlH//4R8At0TDZiU64b3bq/+xi8T9h2ZEaK76Fyn6w5uRY+QqFq66KTbKjlfCUlUWOjzaxjWb//r//yZPlXt9DD0U+H6sSPDuKX0YzMmmlmhq511ZT48z5UfKzvKigvy+//BK1tbX46U9/6tvmcrmgKApcLhe8nJnWYXi94lq9ooSPmTVL1CSxs/aH262/3DbSuSqKmPMgc66ypZiMlGzyeuOvINvmzUBzM9Cjh/g53FybcHVw9LS2BhbYe+wx8/v3//3v2mXsPMJRJ1NrzbVR+bd5CMeO4pcyf++xwBVilDDMZFIXXXSRUlxcrHz00UfKvn37lM8//zzglmg4smNeIn2zs+pcZ8+W28/s2XLnZeSy2t13x25kBxBzaYL5z7WprDR36SrSZc9o979mjdxr07uMJXOuMpxqWBsLifTfPyUnW0d2vvjiC7z11ls4//zzrc28KOEk0jc7q85VpmqzbJzRdgff/z7w3HNyx7eC1uqqlBT91UeRWLEyKZLRo4GMjMhNSTMy5EdGjLZ5CGbVCFE8iucVYkT+TNXZueaaa/CPf/zD6nOhBBTvtT/8ZWZaE3fllXL70Yrzr0Wydm3ky2qASCr8L+mcc47csa1idV8xq2vXaHG79RPC554zdplQTfAWLxb3iZiY2EGvQzvArucUH0yN7Nxwww0oLS3Fjh07kJeXhy5dugQ8fuONN1pychT/1G92ej2TkumbneyckF27gGuvbf9ZqxZJJIoSWv7/rbcMnWrUXn7Z2v1ZWbsmEr1aRB99FLv6L7IJ3mOPJWYSVVwsRiC16uwsWsQ6OxQfTCU799xzDwBg3rx5IY9xgnLHYlXPpFg4csSauM8/l9uPf1y4y1Uy/C+ryRb3s0J+fvvkZKvs3m1tnJbWVuDJJyPHPPlk7JKLWCV4TmLXc4p3ppKds2fPWn0elKCM9Exy+lurVZfcBg6U248aJ7NiLRL/y2pdu5rbh1H5+cDHH1u/34YGa+O0LF4M6P0v6uxZETdnjtw+o1ktZ2X16XgWLyvEiLQYmrNz3XXXobGx0ffzggULcOzYMd/PR48exbBhw6w7O4p7VvVMigX1klu47iYulz2X3PS6VRtx0UXW7CdYjx5AXh4wYQJw/Lg9iQ4QmzleH35obVx1NTBokOitpfbYGjRIvseWHUvPicgYQ8nOe++9h5aWFt/Pjz/+OL7++mvfz21tbdgdzfgzJZxE+tZq1WTKL76QO54aF+1KNP/Lap1NjcXq698f2L4dePNN6y9d+bvgAmvjtMiev0ycevkxOFk9cEBsl0l4ZDq9u90ijojsYSjZUYLG4YN/po5n0CBr4+ymTqY899zA7Tk5ocu8w8nNlTuWGie7Ciwc/1EOuxKRWI0qxOKD/9ZbrYnTu/yoKKGr5bTIdHpP1KXnRInCpu+J1FHk5Vkbpyfc3AkjcyqinUy5b5+1cZFkZAReVvvgg+j3qeX0afnYaOavxKLmjJH6N5HIXH4MXi0XjtXVp4nIGEPJjsvlgito/D/4Z+pYvvzS2rhItJZu5+QAkyYBy5YBhw61b+/fX0xADTdSE81kyr17jcXJrgKT4TdFzlIHDojaP3rJS7jfwW9+I7/E2O4PfqsmQR88KLcf2bhoixPGu3hseUKkMpTsKIqCO+64A6mpqQCA06dP45577kH37t0BIGA+D3UMsUp2wi3dPnBAe5Tg0CFg4kRg+XLr63ycOmUsLprJtkePBo4c5OcDn3xifn/h7NnTflknXPJitNpzJHZ+8Fv1N2nH33a01afjlRVJMJGdDM3Zuf3225GZmYn09HSkp6dj8uTJ6N+/v+/nzMxMTJ061a5zpTjUt6+1cVqiWbp9++36cyqMGjLEWNyVVwKdTNUqF/wnOKvNMu2kJi/+k2/1mqgCcvNX/NlVldiqv8lY/G0ng3CTuLX+joicYmhk54UXXrDrPChBBU/0jTZOSzRLt5ubRVuGcePMHz/Y//2fsbgNG/TrvkTiP8H5F78wvx9ZWh3g9X4HWtWenWLV32Qs/rYTnV4SHPx3ROSUKL5vErXXrokk2to10S7dtrrlgdE5O+vWWXfsmhrr9hWJf/ICJFbDV6v+JmPxt53ojCTBRE5iskNRUWvXuFzatWtcrugbAUbbRLS5ObrnB5O9VGPV5bOGhvbmodE0yDRDTV4SqeGrVX+TsfjbTnSJlARTx8Zkh6JmRe2aSL7zneie//3vh27z7z6+bp2xxMRo1/NoL+uUlrZX75Xty2UV9RKaU9WnzbLqb9Luv+1El0hJMHVsLoWVAdHU1IT09HQ0NjbC4/E4fToJy66lpzfdBKxYYe65nTqJVVH+k1+jXTny3/8NPPigftyCBcADD4j3pVcv60eYYmHNGmDMGPFvdSIqEDhHQ02A4vHD36q/SS6r1ub1ioKhBw9qz9txucR/W/v28f0ie8h+frOoIFnGrkaAsnNktMyZE5roRLt82kzX89TUxEx2/GsXqaMcWoniokXxl+gA1v1NssmlNvVS3803i8RGKwnu6Jf6KD44ehlr/vz5yM/PR8+ePZGZmYkJEyaE9NYaPXq0r5ihervnnnsCYurq6lBYWIi0tDRkZmairKwMbW1tsXwphOguDUViZrDN7QbKygIL1Fm1fHrbNrlzUOM2bBD1chLRpk2BPxcXiySupgaomJSevgAAIABJREFUqhL3+/bFZ6JDscFLfZQIHB3ZWb9+PUpKSpCfn4+2tjb84he/wLhx47Br1y5foUIAuOuuuzBv3jzfz2lpab5/e71eFBYWIjs7Gxs3bkR9fT2mTp2KLl264PHHH4/p6+nI7CwqdtVVwMaN+nFXXw1cfHH4AnVWLZ+WTVzUuESenOk/sqPiKAcFi7YFC5HdHE123n333YCfly5diszMTGzZsgWjRo3ybU9LS0N2drbmPt5//33s2rULa9asQVZWFi655BI8+uijeOCBB/Dwww8jRaNSWUtLS0C156amJoteUccUqbqx0cq6Wr4p2K3rqquARx8N/7hVK0dkz0eNS+TJmZzC5qxEmivEJJjiWVytxmpsbAQA9OnTJ2D7smXLcM4552D48OGYO3cuTp486Xts06ZNyMvLQ1ZWlm/b+PHj0dTUhJ07d2oeZ/78+b6qz+np6ciVbWNNIazqDB2J7P9A9eKsWjkyebLcftS4K6+M3w8oPZMmOX0GHVd1tZj8q67Eu/pq8TMrEhMZFzfJztmzZzFr1ixcddVVGD58uG/7rbfeildeeQU1NTWYO3cuXn75ZUz2+7RpaGgISHQA+H5uCNPpb+7cuWhsbPTd9u/fb8Mr6hiMdIY2a/RooGvXyDFdu+onO1Ytny4tjfx4cNzGjda3rIiVf/7T6TPomNiCgchacbMaq6SkBJ9++ik+/PDDgO1333237995eXno168fxowZg9raWgwePNjUsVJTU33NTCk6X3xhbZwWr1e/mF5rq4iLNIJi1coRo0UFE3nOTm2t02fQ8bAFA5H14mJkZ/r06Vi1ahVqamqQo1Of/fLLLwcA7P1mPXJ2djYOHz4cEKP+HG6eD1lHtv6N2To5gOiMrddb6uxZEafHipUjZWX6Mf5xZubs+M3Pd1S4UTCyD1swEFnP0WRHURRMnz4db775Jj744AOcd955us/Z9s163n7ffIIUFBRgx44dOHLkiC9m9erV8Hg8GDZsmD0nTj4nTlgbp+Vf/7I2Ltrl00bPZ+RIICMjcmyfPqKAn3o+slWa7Zaf7/QZdDxswUBkPUcvY5WUlKCqqgorV65Ez549fXNs0tPT0a1bN9TW1qKqqgrXXXcdMjIysH37dpSWlmLUqFEYMWIEAGDcuHEYNmwYpkyZgoqKCjQ0NOChhx5CSUkJL1XFwKBB1sZpkf2f+oYNwIwZ4Zee+4tm5YjsqIuR0RmXS5yPelniT38CVq82fGqW+/e/nT6DjoctGIis5+jIzpIlS9DY2IjRo0ejX79+vtsf//hHAEBKSgrWrFmDcePGYejQoZgzZw4mTpyIt99+27cPt9uNVatWwe12o6CgAJMnT8bUqVMD6vKQfWSbjUTTlET2auSnnwJPPSUmBqelAeXl5o8ZyYQJxuJkigoePRp4WeLGG82dm9X69nX6DDqeROtDRpQIHB3Z0WvLlZubi/Xr1+vuZ+DAgXjnnXesOi0yoK7O2jgtF15o/DleL/DEE+Lf/lWU/R83W79k4EBjcWYuS8TLiErw3CayH1swEFkvLiYoU+L61resjdMybZr5/7EvXBi6kiva+iXqN+9I/L95m7ksoTfHJxY4euActmAgshaTHYqK7OWWaC7LpKQAl15q7rleb+AqrXD1S9RqzzIJj9utn8BkZ7cnaGYuS+zYoX8ednK5OHrgNPYhI7IOkx2KyldfWRunpbUV2LrV/PPVWjFWVXs+dQrYvDlyzObNIg5ovywR6bjBicVnn0Xev1UGDw4dpcrN5ehBvFAn0k+aFDiBnYiMYbLTgdjRlfzvf7c2Tsszz0R3rmrtSauqPRuts2NGrOrb1NYCP/kJRw+IKLkx2ekg7Oqz49emzJI4Lbt3m3+u2y3m/AAikZGhF2e0zo46ohSOWhHXP6H7pnZmTCxaJOr6cPSAiJIVk50OwM4+OzU1cnHvvGN+RCma+SuXXtpeb8eqUSijdXbMVMSNZW/a4HlNRETJhslOktPrswNE15Vcr2eV6vBh8yNK0dSG3Lq1/Rytqgl03XVy+1HjzCw9j+XIDsAeWESU3JjsJDm7++yYSZKMjii1tBg/hsp/1GLIELnn6MXJlnRS48wsPX/2WbnnWMVkT10iooTAZCfJ2d1nRzaB8Gd0RCmaGj1A+6jFz38uF68X19Qktx81zszS81iOtPjPayIiSkZMdpKc3X129uwx9zwjI0qrVpk7hkodtbBqzo5fz1mpOHXpORCa8ISriBvLkZbZsyP3ESMiSnRMdpKc0Wq/sSYzonT6dHTH+I//EPdWrcY65xy5/fjHGa2IG03VaFlut1ger9VOg4gomTDZSXJut1hSHMktt5j/YD1xwtzzVDIjSl27RneM558X91aN7MjOIQqOM1IRN5qq0ZEMGQJMnw5UVopyAEx0iKgjcLQRKNnP6xVLviN57TVg/nxzCU/37kBzs/HnuVxiVENmRKmwEHjlFePHUO3cKe6tWo0l27dKK06tiKsn2qrR4fzmN8C111q/XyKieMaRnSRnVdXgcMy0gTDaufnDD40fw99HH4l7q1ZjeTxy+5GN0xJt1WgtXbsC48ZZu08iokTAZCfJHTxobVywTib+gox2bj5zxvgx/LW1iXuZeTAyK5N69pQ7rmycFjtWY40fz+rIRNQxMdlJcl9+aW1csB495OPM9l4aNMjUqfmoI1spKWLlUSQyK5NkE4ZoEgs7VmOtWiVfBJKIKJkw2Uly0cwvkTF3rlzcL39pvvfSW28ZPq0Anf1mplVUiBVIwedgZGXSeefJHVc2Tosdq7HYFoKIOiomO0nu6FHzca2tYl7NjBniXmtUQPbDM5oP2e3bzT8XCG03UVEhViJVVsbvyiSZUSgz2BaCiDoirsZKcn37mosrLwcWLgycJHv//eID2D8pMFpN2Ayz1Z1VhYWh21JSRAVnM774wtq4cNT3Ofj3EA22hSCijogjO0kuO9t4XHk58MQToR+wXq/YXl7evs3uCs0A0KuX+ecCwNCh0T0/mGzCYEViETwKNXy4+X2xLQQRdVRMdpKc7IiAGtfaKkYSIlm4sP2S1oQJcvuXjdPy+uvmnwtEv5ormFWrumSpo1CLFwObNpnfD9tCEFFHxWQngXm9wLp1omjgunXaiY1s/Rw1Tqa+i/9E12XL5PYvG6dl+XLzzwW0iyrKzEcKJyUFuP76yDHXX29PYtGjh/ERI7aFIKKOjnN2ElR1NTBzZmDBwJwcUSHXf1n32bNy+1PjZCewqnEnT8rFy8ZpiXa+SnAiIzsfKdL56BU6/PBDEWfHiiq9dhU5OUBpqVjiP3iwGGHiiA4RdWQc2UlA1dXAzTeHVkY+eFBsr65u39anj9w+1Tij81Fikezk5Zl/LgBcc037v43MRwpn3Tr9VW5Hj4o4q8lUxD5wQPTVWrxYXP5iokNEHR2TnQTj9YoRHa3+Teq2WbPaP8yNTlCO9XwUGdHW2VHnIBmdjxTOBx/IHVc2zgjZlWnRrmAjIkomTHYSjN43e0UJ7HV17rly+1XjjFYZVlsx6JGN0xLNfJ+iIqBbN/Fvo/ORwqmrkzu2bJwRsVj9RkSUbJjsJBij3+xHjtSvjpyREdh9vKJCJAlaiooC57UYvUxmhtlCeOnpwIoVxvejFzdggNx+ZOOMMPP7JCLq6JjsJBgz3+z//e/IscGPV1drXzpyucR2/zlBZosWGtG/v7nnNTYC3/te+89W1cfxnwNkRRwREdnLpShasz86lqamJqSnp6OxsREej8fp04motRVIS4t8OcbtFhOCU1KAlSvlatysWCFGbbxe0Xgz0qWy3Fyx0sftFgmQLLN/aTfeCLz9trnnAsDx42LJttH3LhyvF8jKijxJOSMDOHzY+tVY69YBV1+tH1dTI/qQERElM9nPb47sJJiNG+XmnWzcKP5dVia3XzVOZrWP/5ygWPj88+ieP2WKuLey6/lzz0WOee456xMdgBOUiYjMYLKTYIx+2B07Jhevxh08KBcvG2eFaNsu7NnT/m8rup4DopbR8uWipo2/nByx3b/WkZU4QZmIyDgWFUwwRj/s+vYFvvxSP16dUyMT6x83eLDcxN9oEpaXXwZ69jT//BMnAn+uqAAee0ysuqqtNV94r7hYXPrbsEEkl/36iYnBdozoqEaOFAmV3mVGTlAmImrHZCfBqB92Bw9qz4FxucTj6ofd7NnAf/yH/n7VyztGJxzPnSu3/7lz5farpUcPMUfm8GFzzz/vvNBt0XQ99+d2x3ZujNsNTJokCiCGc8st9iZcRESJxtHLWPPnz0d+fj569uyJzMxMTJgwAbt37w6IOX36NEpKSpCRkYEePXpg4sSJOBz0qVdXV4fCwkKkpaUhMzMTZWVlaIumsEscc7tFS4hwk30VRfR6Uj/sBg2S268aZ7QIYWamXLxsnJbWVuCrr8w/3+qu507yerV7ffl77bXoW2wQESUTR5Od9evXo6SkBB999BFWr16NM2fOYNy4cTjhd92htLQUb7/9Nl5//XWsX78ehw4dQrHfhAiv14vCwkK0trZi48aNePHFF7F06VL88pe/dOIldTgLFlgbp0WmGGAkN95o/rnxJh4nkBMRxTtHL2O9++67AT8vXboUmZmZ2LJlC0aNGoXGxkb8/ve/R1VVFa75pmjJCy+8gIsuuggfffQRrrjiCrz//vvYtWsX1qxZg6ysLFxyySV49NFH8cADD+Dhhx9GSpI1BlLbRYTjconH09OBI0eAbdvk9qt+gB46JBevxsmulIpmRdU//2n+uYB+naFEwtVYRETGxdVqrMbGRgBAn2/K7W7ZsgVnzpzB2LFjfTFDhw7FgAEDsGnTJgDApk2bkJeXh6ysLF/M+PHj0dTUhJ07d2oep6WlBU1NTQG3RCHTLuLAAWDsWODWW+VXF6mFAv/2N7l4Nc7oai8ztm83/1wgusnN8YarsYiIjIubZOfs2bOYNWsWrrrqKgwfPhwA0NDQgJSUFPTq1SsgNisrCw0NDb4Y/0RHfVx9TMv8+fORnp7uu+Xm5lr9cmxj1zd2dSn5p5/Kxatxp0/LxZ88KeaarFtn/JKUXodxPXo1cRKJOkE9XDFHl4ursYiIgsVNslNSUoJPP/0Ur732mu3Hmjt3LhobG323/fv3235Mq0Qz0TeSHj3EvWyVYzXu7Fn5Y9x6q6j+O2hQYMsJPdFeidy3L7rnxxN1gjoQmvCoP/tPUCciojhJdqZPn45Vq1ahpqYGOX5V2rKzs9Ha2opjQddADh8+jOxvlgNlZ2eHrM5Sf84Os7QoNTUVHo8n4NbRHTki7vWaTKpk47QcPAjcfLN8wnPlleaPBYj5S8mkuBh4443QjvY5OWK7XQUNiYgSlaPJjqIomD59Ot5880188MEHOC+oIMpll12GLl26YO3atb5tu3fvRl1dHQoKCgAABQUF2LFjB46on9YAVq9eDY/Hg2HDhsXmhcSQ38u01PHj4j74AzQc2Tgt6qjQrFlyl7SivSRz553RPT8Sr1dcmjN7ic6s4mJRELGyEpg+Xdzv3ctEh4hIi6OrsUpKSlBVVYWVK1eiZ8+evjk26enp6NatG9LT03HnnXdi9uzZ6NOnDzweD2bMmIGCggJcccUVAIBx48Zh2LBhmDJlCioqKtDQ0ICHHnoIJSUlSE1NdfLl2SKaEZVIBg4U9xdeKBevxqWmAi0txo+nKO1LpPWK8kU7pUqrqKAVqqvFyjf/CeM5OeIyk91JR3U1MGNG4Oq5J54AFi9mwkNEFEJxEADN2wsvvOCLOXXqlDJt2jSld+/eSlpamnLTTTcp9fX1Afv5/PPPlWuvvVbp1q2bcs455yhz5sxRzpw5I30ejY2NCgClsbHRqpdmm1//WlFEqmDt7dgxsf+WFrn4lhYR36VLdMetqtJ/zW1tipKTY27/GRni+VZbvlxRXK7Q47lc4rZ8ufXH9D92pNds57GJiOKJ7Oe3S1Fkp6QmL9kW8fFg2jRgyRLr91tfL6oinzoFpKXpx588CXTrBnTqJD+pWUtNjVy7hQkTgJUrje+/Rw+x7N3KCbter5hkHa4EgNqyY98+6ycKe73iNUVaBde1K9DczEnKRJT8ZD+/42KCMsmza+n5JZeI++nT5eLVuM4mL4QaWSLd2gqsWmXuOM3NYi6NlWRqHdlVxXj1av3l/qdPizgiIhKY7CQY2d5VRqkL3t56Sy5ejZOd4+PP6BLpaNtFWJ3sOFnFeOFCa+OIiDoCJjsJxkxyIUOt2yhbN0eN+9a3jB/L6BLp2lrjx7CTk1WMZVtfJFOLDCKiaDHZSTA//7k9+/34Y3H/zYp+XWrc974nF3/HHUBVlZijs2+fsRVD0a6mGjUquucHc7KKcX6+tXFERB0Bk50E8/e/27NftcCfbAFrNU72fI4eBSZNEpORjU6czcszFh+sk8V/5U5WMX7ySWvjiIg6AiY7CcauzhZqd3TZ1gxqnOwlpmguRX31lfnnAkCYFmlRcaqKcbduQFFR5JiiIhFHREQCk50E89e/2rPfqipxv2iRXLwaJ9ukM5pmntH2A/vyy+ieH05xMfD55+LSnNlLdGasWBE+4SkqEo8TEVE7Rysok3Fr1tiz37Y2cf/yy3LxL78MlJcDffoAQa3JNPXpY/7cotW3r337drvl6gRZbcUKUROprAzYswcYMkRUUOaIDhFRKCY7CcZMawYZ3buL+9ZWuXg1TnZeSjTzV6LtBxZNH6941q0b8NRTTp8FEVH842WsBBPtZN1w1F6rsqMUapxajFCPbJyWaJZw27UqioiIEgeTnQRj1wTlG28U9zfdJBevxqkTm/XIxmnRW+odyS23JG/bhNZWMXdqxgxxLzsqR0TU0TDZSTAy82PMUIvQGZ1wbLQIoRmRlnrree216Kovx6vyctHDrLRUXMoqLRU/l5c7fWZERPGHyU6CsatPqZoQyK5cUuNka9hEW+sm3FJvPXb1qHJSebmYjBycxHm9YjsTHiKiQEx2EswvfmHPftXVUhkZcvFq3JVXysXLxkUSvNT7wQflnmfXpT8ntLbq971auJCXtIiI/DHZSTBmelHJOHVK3Bu9jHXRRXLxsnF61KXekyYBx4/LPceuqtNOkGmK6vWKOCIiEpjsJBi75p+ol8dka9KocdOm6U8AdrtFnNUUxdq4RLBnj7VxREQdAZOdBGPX/BO12absnBg1LiUFuP76yLHXXy/fhsKIIUOsjUsEshO0zaxcIyJKVkx2CADw5z+L+yuvlBupUefgeL3Ali2R47dutWdESrYDvF2d4p1w+eXWxhERdQRMdhKMHa0JsrKA9HTx740b5eaEbNwo/r1hA3DgQOR4u1ZEyc7FSaY5O7m51sYREXUETHYSjBWrmoKlpLQnOAcPyj1Hjauvl4uXjTPiiy+sjUsEaoHFSFg1mogoEJOdBPPss9bv03/kxWidHdlWDtG0fAhHtrt3MnUBVwsshpuT43KJasrJWjWaiMgMJjsJxq5VNurIi9E6O9/9rly8bJwRJ05YG5co1AKLwSM8ublie3GxM+dFRBSvmOwkGLtW2agjL0br7JSVycXLxhlxwQXWxiWS4AKLNTXAvn1MdIiItHR2+gTImPx86/fpP8fD6MjOBx/IxcvGGfHEE8DTT8vFJSO1wCIREUXGkZ04ItPFWm3YaSX/zuBGR3Zk2xLY0b6gWzegqChyTFGRiCMioo6LyU6ckO1iLVvh2Aj/zuC9e8s9R40bOFAuXjbOqBUrwic8RUXJNTmZiIjMYbITB4x0sTba9VuG/2qszZvlnqPGdZa8ECobZ8aKFcDJk0BJCTBunLg/eZKJDhERCZyz4zDZLtaPPSbq4ah1VvQK+RmlrsaSrXSsxnXvLhcvG2dWt25iRIyIiCgYR3YcZrSLtdstOn5bTV2NJVufRY3r318uXjaOiIjIakx2HFZbayzO6wVefdW647tcgauxZFd7qXGyFZ3tqPxMREQkg5exbOT1irkw9fVi5GTkyNCRk8GD5falxsn0ojLKv+Ku7GovNY69moiIKN5xZMcm1dXAoEHA1VcDt94q7gcNEtv9TZsm12V82jTxbyt7TLndwP33Bxaik13tpcaxVxMREcU7Jjs2qK4Gbr45dATm4EGx3T/hSUkBZs+OvL/Zs0UcYG2PqbNngV//OvB8ZFd7qXHs1URERPHO0WTnr3/9K2644Qb0798fLpcLK4LWCt9xxx1wuVwBtx/96EcBMV9//TVuu+02eDwe9OrVC3feeSeam5tj+TICeL3AzJmAooQ+pm6bNStwUnJFhWinEJwQuN1ie0VF+zZ1JCVS2wit/WjROh8zIzXs1URERPHM0WTnxIkT+Pa3v42nI9T8/9GPfoT6+nrf7dWg2bm33XYbdu7cidWrV2PVqlX461//irvvvtvuUw9Lb06NogTWtVFVVADHjwfWijl+PDDRAdpHUoDQhMflErdXX23vmVRZGXm1V/D5mB2pYa8mIiKKW0qcAKC8+eabAdtuv/12paio6P+3d/dRUZV5HMC/w5CALC+i8jLxIqQJoriYLKGuSuAKtqSZFR7cxeRUuwsJmi+p6wsFYnWOndpV0HYPuK3Y8SiaS0mmhiSaDhYWuYfEKMlCd5d4kxVz5u4fs0wODDADc+fOXL6fc+6hufPMvb+L1fx8nt/zPL1+5tKlSwIAQa1W688dPXpUUCgUwrVr10y+d0tLiwBAaGlpMT/wboqLBUGXQvR9FBcbfu7gQUHw9zds4++vO2+MsfYBAT3bWzIeY9cnIiKSiqnf3zY/G6u8vBze3t4YMWIEHnroIeTk5GDk/3ehPHv2LDw9PTF16lR9+/j4eDg4OODcuXN49NFHjV6zs7MTnZ2d+tetra0Wi9fUmpq723XV+HQf+uqq8TE2FLRwoW47BGOzve6eBXb9+sDi7uv6vTFl9tlgWeMeREQkLzad7CQkJGDhwoUIDg7GlStXsH79eiQmJuLs2bNQKpVobGyEt7e3wWccHR3h5eWFxsbGXq+bl5eH7OxsUWKOjjavXX81PgqFrqZm/nzjtTjdd70uKdFd7+6htK4EyBiFQldrY2y2lDm7ahu7r7+/bkjMUkNZ1rgHERHJj03PxkpOTsYjjzyCSZMmYcGCBSgtLYVarUZ5efmgrrtu3Tq0tLToj4aGBssEDGDXLvPamVrj86c/6Wpxyst7T1x6mwXWV6IDDH62lDmzz2z5HkREJE82nex0FxISglGjRqGurg4A4Ovrixs3bhi0uXPnDpqamuDr69vrdZycnODu7m5wWIq5KyKbum7OihV9r9fTVw9Rl+4Jjb//4GdLDWT2maXvIQiDvwcREcmXXSU73377Lf7zn//A7/8FJjExMWhubsaFCxf0bU6ePAmtVotoU8eTLMzcFZEHsm6Osd4MU1ZW1mh0s7MsOVtqoLPPLHkPYPD3ICIi+ZI02Wlvb0d1dTWqq6sBAPX19aiursbVq1fR3t6O1atX4+OPP8bXX3+NEydOYP78+Rg7dizmzp0LAAgLC0NCQgKefvppnD9/HpWVlcjIyEBycjJUEu08ae6KyKasm9OdsR4TU3uIfHx0G4nOnm2Zwl5T7zuYlZ+vXbNsOyIiGlokTXaqqqoQGRmJyMhIAMDKlSsRGRmJTZs2QalU4rPPPsMjjzyC+++/H2lpaXjggQfw0UcfwcnJSX+NvXv3IjQ0FHFxcZg3bx5mzJiB3bt3S/VIZq+I3Ne6OX3p3mMykFlglmCN+/7rX6a1O3AAeO45XQ3S7dsDvx8REcmLQhD6qvIYGlpbW+Hh4YGWlhaL1e+sWQNs325YR6JU6hKd7gsF9tbeFMXFup4ajUZXy3PtmvHalq5ZV/X1lp2q3XXfvoaZAgIGd9+9e4ElS8z7TF+/ayIikgdTv7/tqmbHnrzyCtDRoauRycjQ/ezoMP7lW1Ki26NqIAW2XT0m/a2sDIizR5VSqUu2+pKcPLj7mrpf1900GuDVV3VJJBERDW3s2YE4PTumMqVnxJjeemrM7VEaLGv07Az0dwTo7tnR8dOwIRERyQd7duyEKTONuuutp6a3HiKNpufu5pZijZlS/e3X1ReNBti5c+D3JiIi+8dkR2IDmaVkbH0cU9bZEWMtGmvMxgJ631ndFKaufURERPJk09tFDAWmzlJ67TXdtPHe9oMyZ70bU7eAMIU1Z4F136+rshLYsaP/z5m69hEREckTa3ZgGzU7g51FtW+fboXl/nTN3rIUqWaBAbrp5cOH991bxZodIiL5Ys2OnbDULCoxelg0Gt1eXH3tySXVLDDA/DWNiIhoaGKyYwO66lG6T7E2Z++q/lZiVih0s6KM7W5uTEmJrscmNrbvPbksFf9APfjg4N4nIiL54zAWpB3GuptG81M9Sm+1OX3p2hkcMBxS6kqATE08uq7T/d+M/q4z2PjN1d+UdDGH0IiISHocxrJDSqWueHige1dZoofFGruYW4o1NiElIiL7x9lYMtN9xpK5PSwDndVVUqJLku7+rL+/rp5HrGEsa017JyIi+8Zkx471NmzU1UM0EANJIHob9rp2TXderLodqTY/JSIi+8JhLDtlTgGxOby9zWsn5bCXpYuyiYhInpjs2KGunpTuw01dPSlibAvRGynrZqSc9k5ERPaDyY6dEbsn5cYN89pJXTcj5bR3IiKyD6zZsTNibwthbh2MLdTNDLYom4iI5I3Jjp0Ruyelqw6mv+0fuupgzG0vlsEUZRMRkbxxGMvOiN2TYm4dDOtmiIjI1jHZsTPWmIFkbh0M62aIiMiWcbsI2M52Eaay1LYQ/TF3+wdrbxdBRERDm6nf30x2YH/JDmB8xeKAAN2QEXtSiIhoKDD1+5sFynaKM5CIiIhMw2THjnEGEhERUf+Y7FCvWLNDRERywGSHjDJ3F3Mpdj0nIiIyBaeey5BGA5SXA/v26X6au3WEuXtv2dJeXURERN1xNhbsczZWbwbbw6Isz+lqAAAQk0lEQVTR6HZP721Liq4VkevrdUNU5rYnIiKyFFO/v9mzIyOW6GExdxdzKXc9JyIiMgWTHZmw1G7o5u69JfWu50RERP1hsiMTluphscddz4mIiPrCZEcmLNXDYu7eW9bYq4uIiGgwmOzIhKV6WLjrORERyQ2THZmwZA8Ldz0nIiI5kTTZqaioQFJSElQqFRQKBQ4fPmzwviAI2LRpE/z8/ODi4oL4+HhcvnzZoE1TUxNSUlLg7u4OT09PpKWlob293ZqPYRMs3cOycCHw9dfAhx8CxcW6n/X1vScu5rYnIiKyFkmTnZs3b2Ly5MnYsWOH0fdfeeUVvPHGGygoKMC5c+fg6uqKuXPn4tatW/o2KSkp+OKLL/DBBx+gtLQUFRUVeOaZZ6z1CDbF0j0sXXtvLV6s+9lfomRueyIiImuwmUUFFQoFDh06hAULFgDQ9eqoVCo8//zzWLVqFQCgpaUFPj4+KCoqQnJyMv75z39iwoQJUKvVmDp1KgCgrKwM8+bNw7fffguVSmXSveW0qCDAPaqIiGhosPtFBevr69HY2Ij4+Hj9OQ8PD0RHR+Ps2bMAgLNnz8LT01Of6ABAfHw8HBwccO7cuV6v3dnZidbWVoNDTtjDQkRE9BObTXYaGxsBAD4+PgbnfXx89O81NjbC29vb4H1HR0d4eXnp2xiTl5cHDw8P/REQEGDh6ImIiMhW2GyyI6Z169ahpaVFfzQ0NEgdEhEREYnEZpMdX19fAMD169cNzl+/fl3/nq+vL27cuGHw/p07d9DU1KRvY4yTkxPc3d0NDiIiIpInm012goOD4evrixMnTujPtba24ty5c4iJiQEAxMTEoLm5GRcuXNC3OXnyJLRaLaKjo60eMxEREdkeRylv3t7ejrq6Ov3r+vp6VFdXw8vLC4GBgcjKykJOTg7GjRuH4OBgbNy4ESqVSj9jKywsDAkJCXj66adRUFCAH3/8ERkZGUhOTjZ5JhYRERHJm6TJTlVVFWJjY/WvV65cCQBITU1FUVER1qxZg5s3b+KZZ55Bc3MzZsyYgbKyMjg7O+s/s3fvXmRkZCAuLg4ODg547LHH8MYbb1j9WYiIiMg22cw6O1KS2zo7REREQ4Hdr7NDREREZAlMdoiIiEjWJK3ZsRVdI3lyW0mZiIhIzrq+t/uryGGyA6CtrQ0AuJIyERGRHWpra4OHh0ev77NAGYBWq8V3330HNzc3KBQKi167tbUVAQEBaGhoGBLFz0PteQE+M59ZvvjM8n9me39eQRDQ1tYGlUoFB4feK3PYswPAwcEB/v7+ot5jqK3UPNSeF+AzDxV85qFhqD2zPT9vXz06XVigTERERLLGZIeIiIhkTblly5YtUgchd0qlErNnz4aj49AYNRxqzwvwmYcKPvPQMNSeeSg8LwuUiYiISNY4jEVERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7IqmoqEBSUhJUKhUUCgUOHz4sdUiiysvLQ1RUFNzc3ODt7Y0FCxagtrZW6rBElZ+fj4iICP1iXDExMTh69KjUYVnNtm3boFAokJWVJXUootqyZQsUCoXBERoaKnVYorp27RqWLFmCkSNHwsXFBZMmTUJVVZXUYYlmzJgxPf6MFQoF0tPTpQ5NNBqNBhs3bkRwcDBcXFxw33334aWXXup3jyl7Jd95ZhK7efMmJk+ejGXLlmHhwoVShyO6U6dOIT09HVFRUbhz5w7Wr1+PX/3qV7h06RJcXV2lDk8U/v7+2LZtG8aNGwdBELBnzx7Mnz8fn376KcLDw6UOT1RqtRq7du1CRESE1KFYRXh4OI4fP65/Lecpuj/88AOmT5+O2NhYHD16FKNHj8bly5cxYsQIqUMTjVqthkaj0b+uqanBnDlz8Pjjj0sYlbhefvll5OfnY8+ePQgPD0dVVRWeeuopeHh4YPny5VKHZ3Hy/S9WYomJiUhMTJQ6DKspKyszeF1UVARvb29cuHABM2fOlCgqcSUlJRm8zs3NRX5+Pj7++GNZJzvt7e1ISUnBm2++iZycHKnDsQpHR0f4+vpKHYZVvPzyywgICEBhYaH+XHBwsIQRiW/06NEGr7dt24b77rsPs2bNkigi8Z05cwbz58/Hww8/DEDXu7Vv3z6cP39e4sjEwWEsEkVLSwsAwMvLS+JIrEOj0eDtt9/GzZs3ERMTI3U4okpPT8fDDz+M+Ph4qUOxmsuXL0OlUiEkJAQpKSm4evWq1CGJ5siRI5g6dSoef/xxeHt7IzIyEm+++abUYVnN7du38fe//x3Lli2z+MbQtmTatGk4ceIEvvzySwDAxYsXcfr0adn+JZ09O2RxWq0WWVlZmD59OiZOnCh1OKL6/PPPERMTg1u3buFnP/sZDh06hAkTJkgdlmjefvttfPLJJ1Cr1VKHYjXR0dEoKirC+PHj8f333yM7Oxu//OUvUVNTAzc3N6nDs7ivvvoK+fn5WLlyJdavXw+1Wo3ly5dj2LBhSE1NlTo80R0+fBjNzc1YunSp1KGI6oUXXkBraytCQ0OhVCqh0WiQm5uLlJQUqUMTBZMdsrj09HTU1NTg9OnTUociuvHjx6O6uhotLS04cOAAUlNTcerUKVkmPA0NDcjMzMQHH3wAZ2dnqcOxmrv/phsREYHo6GgEBQVh//79SEtLkzAycWi1WkydOhVbt24FAERGRqKmpgYFBQVDItn561//isTERKhUKqlDEdX+/fuxd+9eFBcXIzw8HNXV1cjKyoJKpZLlnzOTHbKojIwMlJaWoqKiAv7+/lKHI7phw4Zh7NixAIAHHngAarUar7/+Onbt2iVxZJZ34cIF3LhxA1OmTNGf02g0qKiowJ///Gd0dnZCqVRKGKF1eHp64v7770ddXZ3UoYjCz8+vR7IeFhaGgwcPShSR9XzzzTc4fvw4SkpKpA5FdKtXr8YLL7yA5ORkAMCkSZPwzTffIC8vj8kOUW8EQcBzzz2HQ4cOoby8XPYFjb3RarXo7OyUOgxRxMXF4fPPPzc499RTTyE0NBRr164dEokOoCvQvnLlCn7zm99IHYoopk+f3mPZiC+//BJBQUESRWQ9hYWF8Pb21hftyllHRwccHAzLdpVKJbRarUQRiYvJjkja29sN/uZXX1+P6upqeHl5ITAwUMLIxJGeno7i4mK88847cHNzQ2NjIwDAw8MDLi4uEkcnjnXr1iExMRGBgYFoa2tDcXExysvL8f7770sdmijc3Nx61GC5urpi5MiRsq7NWrVqFZKSkhAUFITvvvsOmzdvhlKpxOLFi6UOTRQrVqzAtGnTsHXrVjzxxBM4f/48du/ejd27d0sdmqi0Wi0KCwuRmpoq66UFuiQlJSE3NxeBgYEIDw/Hp59+iu3bt2PZsmVShyYOgUTx4YcfCgB6HKmpqVKHJgpjzwpAKCwslDo00SxbtkwICgoShg0bJowePVqIi4sTjh07JnVYVjVr1iwhMzNT6jBE9eSTTwp+fn7CsGHDhHvvvVd48sknhbq6OqnDEtU//vEPYeLEiYKTk5MQGhoq7N69W+qQRPf+++8LAITa2lqpQ7GK1tZWITMzUwgMDBScnZ2FkJAQYcOGDUJnZ6fUoYlCIQgyXS6RiIiICFxnh4iIiGSOyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4jsSlFRETw9PSWNYfbs2cjKypI0BiIyHVdQJiKLWLp0Kfbs2dPj/Ny5c1FWVmax+/z3v/9FW1sbvL29LXZNczU1NeGee+6Bm5ubZDEQkenkv9sZEVlNQkICCgsLDc45OTlZ9B4uLi6Sby7r5eUl6f2JyDwcxiIii3FycoKvr6/BMWLECP37CoUCf/nLX/Doo49i+PDhGDduHI4cOWJwjSNHjmDcuHFwdnZGbGws9uzZA4VCgebmZgA9h7G2bNmCn//853jrrbcwZswYeHh4IDk5GW1tbfo2Wq0WeXl5CA4OhouLCyZPnowDBw70+Sw7d+7Ux+Hj44NFixbp37t7GKu8vBwKhaLHsXTpUn37d955B1OmTIGzszNCQkKQnZ2NO3fumP8LJqIBYbJDRFaVnZ2NJ554Ap999hnmzZuHlJQUNDU1AQDq6+uxaNEiLFiwABcvXsSzzz6LDRs29HvNK1eu4PDhwygtLUVpaSlOnTqFbdu26d/Py8vD3/72NxQUFOCLL77AihUrsGTJEpw6dcro9aqqqrB8+XK8+OKLqK2tRVlZGWbOnGm07bRp0/D999/rj5MnT8LZ2Vnf/qOPPsJvf/tbZGZm4tKlS9i1axeKioqQm5tr7q+OiAZK2k3XiUguUlNTBaVSKbi6uhocubm5+jYAhD/+8Y/61+3t7QIA4ejRo4IgCMLatWuFiRMnGlx3w4YNAgDhhx9+EARBEAoLCwUPDw/9+5s3bxaGDx8utLa26s+tXr1aiI6OFgRBEG7duiUMHz5cOHPmjMF109LShMWLFxt9loMHDwru7u4G17zbrFmzhMzMzB7n//3vfwshISHCH/7wB/25uLg4YevWrQbt3nrrLcHPz8/otYnI8lizQ0QWExsbi/z8fINz3etbIiIi9P/s6uoKd3d33LhxAwBQW1uLqKgog/a/+MUv+r3vmDFjDIqF/fz89Nesq6tDR0cH5syZY/CZ27dvIzIy0uj15syZg6CgIISEhCAhIQEJCQn6obfe/Pjjj3jssccQFBSE119/XX/+4sWLqKysNOjJ0Wg0uHXrFjo6Ovq8JhFZBpMdIrIYV1dXjB07ts8299xzj8FrhUIBrVY7qPv2dc329nYAwLvvvot7773XoF1vxdNubm745JNPUF5ejmPHjmHTpk3YsmUL1Gp1r9Pef//736OhoQHnz5+Ho+NP/2ttb29HdnY2Fi5c2OMzzs7Opj8kEQ0Ykx0ishnjx4/He++9Z3BOrVYP6poTJkyAk5MTrl69ilmzZpn8OUdHR8THxyM+Ph6bN2+Gp6cnTp48aTRp2b59O/bv348zZ85g5MiRBu9NmTIFtbW1/SaBRCQeJjtEZDGdnZ1obGw0OOfo6IhRo0aZ9Plnn30W27dvx9q1a5GWlobq6moUFRUB0PXWDISbmxtWrVqFFStWQKvVYsaMGWhpaUFlZSXc3d2Rmpra4zOlpaX46quvMHPmTIwYMQLvvfcetFotxo8f36Pt8ePHsWbNGuzYsQOjRo3SP7+Liws8PDywadMm/PrXv0ZgYCAWLVoEBwcHXLx4ETU1NcjJyRnQMxGReTgbi4gspqysDH5+fgbHjBkzTP58cHAwDhw4gJKSEkRERCA/P18/G2sw6/W89NJL2LhxI/Ly8hAWFoaEhAS8++67CA4ONtre09MTJSUleOihhxAWFoaCggLs27cP4eHhPdqePn0aGo0Gv/vd7wyeOzMzE4BuUcXS0lIcO3YMUVFRePDBB/Haa68hKChowM9DRObhCspEZNNyc3NRUFCAhoYGqUMhIjvFYSwisik7d+5EVFQURo4cicrKSrz66qvIyMiQOiwismNMdojIply+fBk5OTloampCYGAgnn/+eaxbt07qsIjIjnEYi4iIiGSNBcpEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpK1/wGbPyDGnMHL5AAAAABJRU5ErkJggg==\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": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.  ,  2.  ,  4.  ],\n",
+       "       [ 1.  ,  2.4 ,  5.76],\n",
+       "       [ 1.  ,  1.5 ,  2.25],\n",
+       "       ...,\n",
+       "       [ 1.  ,  3.2 , 10.24],\n",
+       "       [ 1.  ,  3.  ,  9.  ],\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.         47.33765502 -1.04483635]]\n",
+      "Intercept:  [112.41683927]\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": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 23.79\n",
+      "Residual sum of squares (MSE): 971.85\n",
+      "R2-score: 0.74\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": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         33.77909491  2.89825903 -0.34550049]]\n",
+      "Intercept:  [126.13710244]\n",
+      "Mean absolute error: 23.52\n",
+      "Residual sum of squares (MSE): 953.26\n",
+      "R2-score: 0.74\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "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": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "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"
+   ]
+  }
+ ],
+ "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/SyahdanFaizM-202310715258-Reg-Simple-Linear-Regression-Co2.ipynb b/Regression/SyahdanFaizM-202310715258-Reg-Simple-Linear-Regression-Co2.ipynb
new file mode 100644
index 0000000..bf60b2e
--- /dev/null
+++ b/Regression/SyahdanFaizM-202310715258-Reg-Simple-Linear-Regression-Co2.ipynb
@@ -0,0 +1,1152 @@
+{
+ "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": 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"
+   ]
+  },
+  {
+   "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": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 11:52:27--  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 11:52:27 (16.8 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": [
+    "In case you're working **locally** uncomment the below line. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "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": 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>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": 4,
+     "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": {
+    "tags": []
+   },
+   "source": [
+    "### Data Exploration\n",
+    "Let's first have a descriptive exploration on our data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 5,
+     "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 6,
+     "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": 7,
+   "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": 8,
+   "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": [
+    "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": 9,
+   "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": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Practice\n",
+    "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\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": [
+    "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": {
+    "tags": []
+   },
+   "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": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "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": 12,
+   "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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[38.66591065]]\n",
+      "Intercept:  [126.72971482]\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": {
+    "tags": []
+   },
+   "source": [
+    "#### Plot outputs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the fit line over the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 14,
+     "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": {},
+   "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 25.41\n",
+      "Residual sum of squares (MSE): 1073.69\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": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x = train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "regr.fit(train_x, train_y)\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": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = regr.predict(test_x)"
+   ]
+  },
+  {
+   "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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 21.49\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n"
+   ]
+  },
+  {
+   "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": {
+    "tags": []
+   },
+   "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"
+   ]
+  }
+ ],
+ "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
+}