diff --git a/Machine Learning with Python - Regression/ML0101EN-Reg-Mulitple-Linear-Regression-Co2-Adhwa.ipynb b/Machine Learning with Python - Regression/ML0101EN-Reg-Mulitple-Linear-Regression-Co2-Adhwa.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align:center\">\n",
+    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
+    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "# Multiple Linear Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement Multiple Linear Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Table of contents</h1>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
+    "    <ol>\n",
+    "        <li><a href=\"#understanding-data\">Understanding the Data</a></li>\n",
+    "        <li><a href=\"#reading_data\">Reading the Data in</a></li>\n",
+    "        <li><a href=\"#multiple_regression_model\">Multiple Regression Model</a></li>\n",
+    "        <li><a href=\"#prediction\">Prediction</a></li>\n",
+    "        <li><a href=\"#practice\">Practice</a></li>\n",
+    "    </ol>\n",
+    "</div>\n",
+    "<br>\n",
+    "<hr>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "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": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 13:46:20--  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 13:46:20 (35.1 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": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RLX</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>230</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",
+       "5       2014  ACURA         RLX     MID-SIZE         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",
+       "5          AS6        Z                  11.9                  7.7   \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  \n",
+       "5                  10.0                        28           230  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </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",
+       "\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  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\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.ENGINESIZE, cdf.CO2EMISSIONS,  color='green')\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": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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(train.ENGINESIZE, train.CO2EMISSIONS,  color='green')\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": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[10.99646535  7.61620514  9.4874738 ]]\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": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE) : 573.97\n",
+      "Variance score: 0.86\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Mean Squared Error (MSE) : %.2f\"\n",
+    "      % np.mean((y_hat - y) ** 2))\n",
+    "\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % regr.score(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Explained variance regression score:__  \n",
+    "Let $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n",
+    "\n",
+    "$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var\\{ y - \\hat{y}\\}}{Var\\{y\\}}$  \n",
+    "The best possible score is 1.0, the lower values are worse.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[11.07046945  7.23764833  6.17307568  3.02316987]]\n",
+      "Residual sum of squares: 516.35\n",
+      "Variance score: 0.87\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\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"
+   ]
+  },
+  {
+   "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/Machine Learning with Python - Regression/ML0101EN-Reg-NoneLinearRegression-Adhwa.ipynb b/Machine Learning with Python - Regression/ML0101EN-Reg-NoneLinearRegression-Adhwa.ipynb
new file mode 100644
index 0000000..c8c35f1
--- /dev/null
+++ b/Machine Learning with Python - Regression/ML0101EN-Reg-NoneLinearRegression-Adhwa.ipynb	
@@ -0,0 +1,868 @@
+{
+ "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": 2,
+   "metadata": {
+    "tags": []
+   },
+   "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": 3,
+   "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": [
+    "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": 4,
+   "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": [
+    "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": 5,
+   "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": [
+    "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": {
+    "tags": []
+   },
+   "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": 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": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "Y= np.exp(X)\n",
+    "\n",
+    "plt.plot(X, Y, label='e^x') \n",
+    "plt.plot(X, np.exp(-X), label='e^(-x)')\n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.title('Perbandingan e^x dan e^(-x)')\n",
+    "plt.legend()\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": 11,
+   "metadata": {
+    "tags": []
+   },
+   "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": 12,
+   "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": [
+    "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": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-10-20 14:01:10 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",
+       "  </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"
+      ]
+     },
+     "execution_count": 14,
+     "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(6)"
+   ]
+  },
+  {
+   "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": 15,
+   "metadata": {
+    "tags": []
+   },
+   "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": 16,
+   "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": [
+    "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": 17,
+   "metadata": {
+    "tags": []
+   },
+   "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": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x70bb4d47d750>]"
+      ]
+     },
+     "execution_count": 18,
+     "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": 19,
+   "metadata": {
+    "tags": []
+   },
+   "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": 20,
+   "metadata": {
+    "tags": []
+   },
+   "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": 21,
+   "metadata": {
+    "tags": []
+   },
+   "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": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 0.03\n",
+      "Residual sum of squares (MSE): 0.00\n",
+      "R2-score: 0.98\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "\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"
+   ]
+  },
+  {
+   "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/Machine Learning with Python - Regression/ML0101EN-Reg-Polynomial-Regression-Co2-Adhwa.ipynb b/Machine Learning with Python - Regression/ML0101EN-Reg-Polynomial-Regression-Co2-Adhwa.ipynb
new file mode 100644
index 0000000..31279a3
--- /dev/null
+++ b/Machine Learning with Python - Regression/ML0101EN-Reg-Polynomial-Regression-Co2-Adhwa.ipynb	
@@ -0,0 +1,952 @@
+{
+ "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": {
+    "tags": []
+   },
+   "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": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 13:50:13--  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 13:50:13 (37.9 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": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RLX</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>TL</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>TL AWD</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</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",
+       "5       2014  ACURA         RLX     MID-SIZE         3.5          6   \n",
+       "6       2014  ACURA          TL     MID-SIZE         3.5          6   \n",
+       "7       2014  ACURA      TL AWD     MID-SIZE         3.7          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",
+       "5          AS6        Z                  11.9                  7.7   \n",
+       "6          AS6        Z                  11.8                  8.1   \n",
+       "7          AS6        Z                  12.8                  9.0   \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  \n",
+       "5                  10.0                        28           230  \n",
+       "6                  10.1                        28           232  \n",
+       "7                  11.1                        25           255  "
+      ]
+     },
+     "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(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </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"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": {},
+   "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": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "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": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.  ,  2.  ,  4.  ],\n",
+       "       [ 1.  ,  2.4 ,  5.76],\n",
+       "       [ 1.  ,  1.5 ,  2.25],\n",
+       "       ...,\n",
+       "       [ 1.  ,  3.  ,  9.  ],\n",
+       "       [ 1.  ,  3.2 , 10.24],\n",
+       "       [ 1.  ,  3.2 , 10.24]])"
+      ]
+     },
+     "execution_count": 9,
+     "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": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         52.03884901 -1.6971995 ]]\n",
+      "Intercept:  [104.27341021]\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": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 11,
+     "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": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 23.62\n",
+      "Residual sum of squares (MSE): 918.98\n",
+      "R2-score: 0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x_poly = poly.transform(test_x)\n",
+    "test_y_ = clf.predict(test_x_poly)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         38.49813327  2.09672931 -0.32050742]]\n",
+      "Intercept:  [118.48379347]\n",
+      "Mean absolute error: 23.41\n",
+      "Residual sum of squares (MSE): 907.86\n",
+      "R2-score: 0.77\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# write your code here\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_ ) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf3.coef_)\n",
+    "print ('Intercept: ',clf3.intercept_)\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "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": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
+    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "4dc110debac287dfd374a575573c16e62a80a935b3bbe2b2f6d5a0598e6e33f6"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Machine Learning with Python - Regression/ML0101EN-Reg-Simple-Linear-Regression-Co2-Adhwa.ipynb b/Machine Learning with Python - Regression/ML0101EN-Reg-Simple-Linear-Regression-Co2-Adhwa.ipynb
new file mode 100644
index 0000000..1f0c8d6
--- /dev/null
+++ b/Machine Learning with Python - Regression/ML0101EN-Reg-Simple-Linear-Regression-Co2-Adhwa.ipynb	
@@ -0,0 +1,1252 @@
+{
+ "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": 7,
+   "metadata": {
+    "tags": []
+   },
+   "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": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-20 13:34:04--  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 13:34:04 (42.4 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": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RLX</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>TL</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>TL AWD</td>\n",
+       "      <td>MID-SIZE</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</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",
+       "5       2014  ACURA         RLX     MID-SIZE         3.5          6   \n",
+       "6       2014  ACURA          TL     MID-SIZE         3.5          6   \n",
+       "7       2014  ACURA      TL AWD     MID-SIZE         3.7          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",
+       "5          AS6        Z                  11.9                  7.7   \n",
+       "6          AS6        Z                  11.8                  8.1   \n",
+       "7          AS6        Z                  12.8                  9.0   \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  \n",
+       "5                  10.0                        28           230  \n",
+       "6                  10.1                        28           232  \n",
+       "7                  11.1                        25           255  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head(8)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Exploration\n",
+    "Let's first have a descriptive exploration on our data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MODELYEAR</th>\n",
+       "      <th>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": 12,
+     "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": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 13,
+     "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": 14,
+   "metadata": {
+    "tags": []
+   },
+   "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": 16,
+   "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='green')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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='navy')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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": [
+    "# write your code here\n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='purple')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+    "\n",
+    "This means that we know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+    "\n",
+    "Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "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": {
+    "tags": []
+   },
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AKPX19cacFBEJ8+crit2uKGJKrbjZ7eLxWHP2rKJkZ3ueq/vNZlOUnBwRJ2vt2tbbzMkR75/349nZIp6oDZH9/rZ8ZGf69Om44YYbMHLkSI/Hq6qqUFtbi9GjR7sei4+Px9ChQ7Fz504AQGVlJc6cOeMRk5WVhdzcXFeML83NzWhoaPC4EZEJ2tLcD6Pr7ADistehQ6KRZkmJuF+6VIwShbP+DlGUs3TpeWlpKXbt2oWKiopWz9XW1gIA0tPTPR5PT0/HV1995YqJi4tDly5dWsWor/dlyZIleOSRR0I9fCKSYcYy70gUjqaYav0df1WabTbxXufns8YNkRvLRnaqq6sxa9YsrFmzBh07dvQbZ/O6Xq0oSqvHvAWLefDBB1FfX++6VVdXazt4IiJv4VhBZcboUSxoagJuvBHo31/cNzVZfUQUYSxLdiorK1FXV4cBAwagffv2aN++PbZt24b/+q//Qvv27V0jOt4jNHV1da7nMjIy4HA4cPz4cb8xvsTHx6Nz584eNyKikIRjBZWV9Xci1WWXAcnJwPr1wN694j45WTxO9CPLkp0RI0Zg79692L17t+t26aWXYtKkSdi9ezcuuOACZGRkYPPmza7XOBwObNu2DYMHDwYADBgwAB06dPCIqampwb59+1wxRERhEY6mmFbX34k0l10G+JgGAUA8zoSHfmTZnJ3k5GTk5uZ6PJaUlIS0tDTX44WFhVi8eDH69OmDPn36YPHixUhMTMTtP/afSUlJwdSpUzF37lykpaUhNTUV8+bNQ15eXqsJz0REplObYs6a5Xm5KTtbJDqhNsVUR48OH/Y9b0ftmWVm/Z1I0dTkP9FRVVSIuE6dwnNM1JoZfeh0iOjeWAsWLMDp06cxbdo0HD9+HJdffjk2bdqE5ORkV0xxcTHat2+PiRMn4vTp0xgxYgRWrVoFOyfnEZEVCgrEBGEzmmKqo0c33ywSG/eEx6jRo2hx553ycevWmXss5NuCBWL1oHsNqHnzRI2tMK/IZNdzsOs5EUWZsrLWo0c5OcaMHkWL/v3FHJ1g8vKAPXvMPx7ytGBB4IayBvVZk/3+ZrIDJjtEFIWcTnNGj6LFjTeKycjBTJjAkZ1wcziAxMTAVb3tdlFzK8RLWrLf3xF9GYuIiPxwr7/TFq1eLVZdycRReD37bPD2JU6niAtTDS7LKygTEZGBnE7RKyvWe2Z16gQMHBg4ZuBATk62wsGDxsYZgMkOEVGsKCsTFZaHDwduv13c9+wZuy0kPvnEf8IzcKB4nsKvd29j4wzAOTvgnB0iigChzsEpKxOrtLz/l66u0nr9dXMnL58+LSadHjgA9OkjJqcmJJi3P3dNTWLVlbq8efVqjuhYKQLn7DDZAZMdIgqTw4fF6qDGRjHfZO9e4Cc/8b26KjtbLDP3l6C4J0fdugFTpvhvJaHW36mqkk+gtNRHmTABeOON1o/n58tNIqbYw9VYkYfJDhGZLj5eJBDe2rcXiYuWERlfyZGMrVvlJjX7qo9it/uuj+Iv0VEx4Wm7tPwe6cRkRwMmO0RkKn+JTjC+RmT8Xa6SUVIC3HZb4Bgtf5GfPi0uVwRz6lT4LmlRZDG5gjKTHQ2Y7BCRaQ4fFglLKNQRGadTTDjWOqKj2rIFGDHC//Na51pMmwasWBF8v7/9rfjCIzKY7Pc3V2MREZkpLy/0bahdzLdv15/oyNBSHwUI3ptKJRtHZBImO0REZmpsDH0bahdzNenRq64u8PNa66N06SIXLxtHZBImO0REZpKp8uuPzSZ6XqldzNWkR69gr9daH2X2bLl42TgikzDZISIyk0yzSl98dTEfMkTM/1Gf07It96TJn2nTgi9Nt9tFHCA/0dTACalEejDZISIy009+EvzLvn371pOYs7NbLzu320XtHUA+4fGVNPkTFyeWBQcyZ8658wl2WUwlG0dkEjYCJaLIE2sdvZub/S8/j4sTz8uec0GBSIK86+zk5AC33ip6YnkXJ3z6afnqyeqycpn6KLKX1UK9/EYUIi49B5eeE0UUPdWEo4W/Csp6+EuOjEoUZeqjqEvhDx/2XfdHT+VmIg1YZ0cDJjtEEUJvf6dYGAkyufiaadTPDPD83MzoyRWt7xGZhsmOBkx2iCJAsIJ5/kYJYmEkKAxl9U3l6zPIydF2+SyYaH+PyBRMdjRgskMUAcrLgeHDg8e593eyutO3Vr5GJn73u7A0TDSdmaMuYWoqSdGHyY4GTHaIIsCrrwK33x48Tu3vpHckyCq+RibUpCzQ/4bd2zNEKjNH17S2sKA2he0iiCi6aF3ZE6x1gqIA1dUizmrqyIT3F7aiBG/o6XSKSzWvvipGv4K1cwg3dXTN+7M4fFg8Xlbm+3VOpzifYOeltYUFkQ9MdogoMgQrmOddGE+2dUKoLRZC5XCIEZ1Q/PnPYtRr+HAxmuUvgQg3p1OM6PhK2NTHCgtbJytlZeI8hg8Pfl5aW1gQ+cBkh4giQ6CCeb4K40VLjReZkQktgo2YhJOe0TWtI0FaW1gQ+cBkh4gih1owz7v2jK9qwlpHgqxi9IhDoBGTcNM6uqZnJEhrCwsiH5jsEFFkKSgADh0Sq65KSsR9VVXria5aR4KskpVl/DbVEZNly0Kfy+NwiPdp5kxx76vKsz/hmGeltYUFkS8KKfX19QoApb6+3upDISKt1q5VlOxsdaqvuOXkiMcjwbhxnsdm1i07W/s5z5+vKHa753bsdvG4jLNnxX5tNt/HZLOJz+LsWRFfUiJ3LiUlrfeVn+87Nj9f2zlTTJH9/ubIDhFFN9mRIKscOhSe/Widy+NvhZjTKR5fsCD4NsI1z6qsDNiwoXWczSYej4T5SxTRWGcHrLNDRCa68UZg/frw7MtmE/OdVq0Sncb9tc6QqV3Trh2wcSPw3XfBW3DIVlDW00sr2uopUVjJfn+z6zkRxa5wNsX0F3/55eFLdhRFJAUjR557zFdxP5kVYi0twOjRgbejKigAxowRlYwPHAD69BGjQwkJnnHqSNDNN4skxVcvLe95Vlrm+aiVtYm8heWiWoTjnB2iGORrLk845rX4io+Em/t5z5ih/fU2m7j5ev+0vkda5lmFMs+HYh7n7BBR26W3qq83rfNa/MUH0qGD/+XzRvr1r88dl56aNP6WhuuZ+6NlnlW01FOiiMY5O+CcHaKYYtQcD609mWTiAWDcOPFF37s3sHo1sGmTSMAA3/NYjLRlCzBihPyx+qM2Yw1H3yo983yozWBvLCJqO9z7LC1bZkzPLK09mWQrJV9zDbBnD7BuHdCpk/9CimYoLxf3MrVrAlGLBIajb1W01FOiiGZpsrNixQr0798fnTt3RufOnTFo0CC8++67ruenTJkCm83mcbviiis8ttHc3IyZM2eia9euSEpKwvjx4/FNoP/REVFs8e6zNHu23OuCVf/V2pMplB5O3pd1iovlthWKoiIxoVhPkqBeMjpwQC5eNs4fLZW1iXywdDVWdnY2Hn/8cVx44YUAgJdffhn5+fn4xz/+gX79+gEArrvuOqxcudL1mjivodDCwkK8+eabKC0tRVpaGubOnYuxY8eisrISdmb6RLFNnZuj5/JPsDkeWnsyhdrDyW4/t5rI6QSeeirwpRv3JeY1NcDcucH37b1aqagIePTRcyvHevUSTUuPHAl8yUhtwSE718iIOUkFBUB+vjGr66jNibg5O6mpqXjiiScwdepUTJkyBSdOnMB6P8s26+vrcf7552P16tW45ZZbAABHjhxBTk4O3nnnHVx77bVS++ScHaIoFGxuTiAy80jMmLMTaL/ey9uzsoBbbxXP+Vqi7T6i4XQC6enAsWP+952WBhw9Gjw5UBNImf2uXg3cdVfg7QHAK68Ad94ZPI5Io6ibs+N0OlFaWoqTJ09i0KBBrsfLy8vRrVs39O3bF/feey/q6upcz1VWVuLMmTMY7VYLIisrC7m5udi5c6fffTU3N6OhocHjRkRRJlj9lUCcTiDA/yMAaO/JFEoPpwULRKI0ezawfLm4v/12YPx4/5du8vPPzVPavh147rnA+37hBblREC2XjHJygm9PSxyRSSwvKrh3714MGjQIP/zwAzp16oR169bhZz/7GQBgzJgx+OUvf4kePXqgqqoKv//973HNNdegsrIS8fHxqK2tRVxcHLp06eKxzfT0dNTW1vrd55IlS/DII4+Yel5EZDLZjttmvd6XoiJxv3Sp5wiP3S4SnYceAq66Cvj6a6B7d+Dtt4HHHhNLtL05ncAbb4jLU2PHel66eeON1qNa2dliDk5Jibj85f64v2KA/sheMhoyRIwYBRtRsrrzPLV5ll/Gcjgc+Prrr3HixAmsXbsWL774IrZt2+ZKeNzV1NSgR48eKC0tRUFBAUpKSnD33XejubnZI27UqFHo3bs3nvPzl05zc7PHaxoaGpCTk8PLWETR5P33PSsFa6Uuw/bH4RAVgFta/Me0awecPt16tMZXxeWf/Ux+ErM770tf/uYpqZeZXnsNOP/88MxrMfLyGZEOUdMuIi4uzjVB+dJLL0VFRQWeeeYZPP/8861iMzMz0aNHDxz4cWZ/RkYGHA4Hjh8/7jG6U1dXh8GDB/vdZ3x8POLj4w0+EyKKKcuWBU50APH8smWtJwfHxYnie6oLL9SX6ADnlm6rxfxmzfI9eVhRRMIzd274as5s3x440QHE82zlQBaLmDk7KkVRWo3UqI4dO4bq6mpk/riKYsCAAejQoQM2b97siqmpqcG+ffsCJjtEFAPc5u+Z8vodO+S2Eyyuvl5/oqNSX6+lT1Q4yF4KNOOSIZEGlo7sLFy4EGPGjEFOTg4aGxtRWlqK8vJybNy4EU1NTVi0aBFuuukmZGZm4tChQ1i4cCG6du2KG2+8EQCQkpKCqVOnYu7cuUhLS0NqairmzZuHvLw8jAxleJuIIl+o7QGCvT4pSW47weJuuEFuO4Goy9Vlk4aXXgLWrpVrWhoKtnKgKGFpsnP06FHceeedqKmpQUpKCvr374+NGzdi1KhROH36NPbu3YtXXnkFJ06cQGZmJoYPH47XXnsNycnJrm0UFxejffv2mDhxIk6fPo0RI0Zg1apVrLFDFOuGDBGTb/3VovHHu1aMP3l5ctsLFvf113Lb8cduFwkLIJ80rFlz7t/z5onJ0erkaSOpn0Gg0aacHE5QJstZmuy89NJLfp9LSEjAe++9F3QbHTt2xLJly7Bs2TIjD42IrNTUJOqyqBN8V68WrRXcqW0Ebr5ZJDAyCY+W9gKyJSmCxXXvLi4t6eW+XF1Pgqc25ASMT3jsdiDY/Me4OE5OJstF3JwdIooxDodILmbOFPcOR+D4yy4DkpOB9euBvXvFfXKyeNybv5owOTnAwIGt4xVF1K4JZ3uBt9+Wi2vn9b9ju10sJXdPUAL1iQpm6dLg771WTU3B5yMdPCjiiCzEZIeIzOOrWF5ionjcl8suAyoqfD9XUeE/4XHvK7V1KzBxov/tvPGG//27S00NHiMTl5ISvJVE795iCXtxMTBjhrg/dcr3SIzexqGhNuT0RbYqMqsnk8UsX3pOFNWczujv1WPWOSxY4L9Ynq/LKk1N/hMUVUWFiPN1SUtd2uxwBK+/s3Sp6AkVaOJut26Bt6El7osv/C8/791bPA94LlcPxLvo3//8jxgBCybUVWHewtUIlChEHNkh0su72/bw4eLnsjKrj0yeWefgcIiEIhDvyypGjRI8+2zg/lSA3CjH0aNyxyMb5+/Smd5LamqCd9ttwNChcq+RbVYqK5yNQIlCwGSHSA+1iq33KpTDh8Xj0ZDwmHkOehIO2VEHmTkiRmxn92657cjE+RvlAsTjMpfVApk2LfhonPuqLqPI1jNj3TOyGJMdIq2CVbEFzlW7jVRmn4OehEN21EFm/osR25GdVBssTs8ol1ahNCENxU9/amwckUmY7BBpFWlVbPUw+xz0JByrV8u9JlicUaMcWVlyxxMszqjLasEUFYl5PL7k55tTZ8eqESUijZjsEGkVCyXyzT4HPV+CnTr5Xi7ubuDA1pOTvRk1yjFoUODnZeOMuqwWTFkZsGGD7+c2bDDn0qpVI0pEGjHZIdIqFkrkh3IOTidQXg68+qq49zVqofdL8JNP/Cc8AweK52UUFYkaNd4Jl6/aNf7k5MjtK1icUZfVAgl0WVJl1qVVI95rIpPZFEVLnfXYJNsingiA+MLo2dN/FVu1HUG4Ok/rofccysrEl6r7JbDsbFHozteqogULxHwU9y9Zuz14+wKZCsoyHA5xeUjdjpY+Uep7FKwVQrDP2eEQtYUCJRp2u6iro3cEpLxcrKQLZutW87qPh/JeE+kk/f2tkFJfX68AUOrr660+FIoWa9cqis0mbiJdEDf1sbVrrT7C4LSegxrvHitzzs3NilJcrCgzZoj75mazz8w4Rn3O8+e3ft/cb/Pnh3acJSWBt6/eSkpC2w9RhJH9/uZlLCI91Cq23pNTf/IT8Xg42xHo5a8Sb3Z263MIZfVWXJx4btkycS/z177MpbJw0PIeBWL2pZ5IuLQaKZ8ZkQ+8jAVexiKdtF7SiVQyFZTDeZkkEt9Xo6pMm3Wpx+pLq5H4mVGbIPv9rTvZOXHiBD755BPU1dWhpaXF47m77rpLzyYtw2SHNFML8nn/56NWio2W0R1Zr74qKiwHU1IiKvrq1dbeVyOp7x3g+f6Z/d75+8zUffMzIxOZmuy8+eabmDRpEk6ePInk5GTY3EqB22w2fP/99/qO2iJMdkiTYBNXo2GCslbhGNkJ5X3VOvJidrxVfI2w5OSIbvNmJByhTOKOlveUIpqpE5T79OmjzJo1Szl58qSel0ccTlAmTbZulZsMunWr1UdqnLNnFaVTp8Dn26mTiNNL7/u6dq2iZGd7xmRn+588bHa81c6eFe9RSYm4D+UzCSZcnxmRH6ZOUD58+DDuv/9+JCYm6kvFiKJZLBQV1MrpBE6eDBxz8mRok1L1vK9a+3sZFf/NN5HbA829QeiwYeaOllRXa4+Lhb5yFHV0JTvXXnstPv30U6OPhSg6RMLKFyC8q1+WLQtcsA4Qzy9bpn8f3bppi9O6QszIePU1Wgv1ffst0KuXqBnUq5f4OZr9/e/a4mKhrxxFpfZ6XnTDDTdg/vz5+Oyzz5CXl4cOHTp4PD9+/HhDDo4oIg0ZIuaOBFv5MmSIeccQ7tUvO3bIx82da/z+fdHS32vYMOPjAc/4YM47D6ivP/fzyZMicUtJAU6cCP76SCQ75VON0/oZEBlEV7Jz7733AgD++Mc/tnrOZrPByaycYpndLpKKm28WiY2vlS9PP23e5QN/q1/UywBmrH6RrV6sp8qxqq5OW5zWy15a4w8flouXifNOdNzV14vnozHh6dNHW1xbvARMEUHXZayWlha/NyY61CYYVWxOK6suA9x5p7FxvqSmaovTejlRa7zsJaZgcd9+6z/RUdXXR+clLa0NX887T267snFEklhBmUivggLg0CGx3LqkRNxXVZlbU0T2MsCyZcbO5RkxAujYMXBMx44iTq833tAWp15OdCt94cFmE8ue1cuJQ4YAaWmBt52Wdi7+/PPljidY3GWXyW1HNi6YcM7l0trw1V9Xdm+ycUSSdCc727Ztw7hx43DhhReiT58+GD9+PLZv327ksRFFvnCufAHkh/dnzxZFAIcPF3VQjFjhkpQU2vPBHDyoLU69nAi0TniMuJxoVLJj1AiRjLIy8XkPH2785++PllYYX34pt03ZOCJJupKdNWvWYOTIkUhMTMT999+PGTNmICEhASNGjEBJSYnRx0hEKj0rvIxY0rt9O3DsWOCYY8dEnF5a538A2i4naj2HvXvljidYXKhJk+xIjZVLuouKRNf24mJgxgxxf+pU655fej5jIiPoKeJz8cUXK0uXLm31+FNPPaVcfPHFejZpKRYVpKhx9qwovuar+3igm82mKDk5+gvMhaOr9qlTcvs4dcr3+xKskJ7Wc5gxQy5+xozA51VXJ7edurrWr5Utvqf+Xpj1+RsllM+YyAdTiwp++eWXGDduXKvHx48fj6qqqhDTLyLyy/3SjRbqXJ5Zs4CZM8XlHYdD/vXhqC2UkADk5weOyc8Xcd5kLidqPYfeveXig8Wdf75YXh5ISkrrkR0tIzValnRbKZTPmCgEupKdnJwcvP/++60ef//995GTkxPyQRFRAAUFgN5aVn/+M7B8uZjTk5gILFgg97rBg+VW3QwerO+4VOvX+/8yzM8Xz+uldULzr34lt12ZuBMn/Cc8vursBFt1513QMJqWdJv5GRP5oavOzty5c3H//fdj9+7dGDx4MGw2G3bs2IFVq1bhGT1/dRKRvAUL5FcuBeJ0Ak88If7tPbfC286dwVf1OJ0izrsYnNaGj+vXA6dPi8mtBw6I+RtPPBH6X/ta6yO9+KLcdl98USQewZw4ISYhX3aZuD//fOCTT3zP1dFa0DBSqnrLMuszJvJH73WysrIy5corr1RSU1OV1NRU5corr1TWr1+vd3OW4pwdihrNzYpit2ubrxPsZreL7Qaid85OJDZ89HVMOTmtj8moOTt6rFkjt+81a0S8zO+FzOdMFGVkv791jewAwI033ogbb7zRuKyLKBppHbUI1bPPGl83xekU2w00OqFn5MCKSs/efH0+BQXickmwz82oOTt6aF2uLjvy9uyzQHp6eH5XiSKI7mSHqM0Ld38qQL4WjdHbVefsBPpCdZ+zE2zOic0mkqv8fHPbagT6fIL1Xrr1VjG3KZhbbw3pMH3SulxdS/0lldm/q0QRRHqCcmpqKr777jsAQJcuXZCamur3RhTzrKppYsYogsx2tczZAaxfHWTE5zNqlNy+ZOO08K4bFCzOqvpLRFFCemSnuLgYycnJrn/b/K1oIIp1Vo5aTJsGzJtn7KUs995F/pjddNNIRn0+R47I7U82TgutI2nqSrNgk5rdhWuEjSgCSCc7kydPdv17ypQpZhwLUXTQMmoR7FKJVmovInUVlRHcexf5Y3bTTSNp6R8WaP5K587A998H31/nzvLH5nCIeTMHD4rRtGnTfL/3Wle/2e2ixpDW3wszf1eJIoiuOju7du3CXrcS6W+88QYmTJiAhQsXwqGhUNmKFSvQv39/dO7cGZ07d8agQYPw7rvvup5XFAWLFi1CVlYWEhISMGzYMOzfv99jG83NzZg5cya6du2KpKQkjB8/Ht9o+euGSCura5oUFQUvzCbDV+8ify6/XG6bapzWmjZGMqp/2P/7f3LbkY1bsEDUNpo9O3ito+pquW2qcU6naCehVyTU3yEyka5k57777sO///1vAKKa8i233ILExET87//+LxbIFikDkJ2djccffxyffvopPv30U1xzzTXIz893JTRFRUVYunQpli9fjoqKCmRkZGDUqFFobGx0baOwsBDr1q1DaWkpduzYgaamJowdOxZOMzv9UttmdU2TsjL9XaGnT2/du0im99Lzz8ttX40zu0mnN/dzOHpU++u/+ab1/JXTp+VeKxO3YIEYdfF+b9VaR97/3/z73+X2rcbJ1OUJJFLq7xCZRc+69s6dOytffPGFoiiK8vjjjyujR49WFEVRduzYoWRnZ+vZpEuXLl2UF198UWlpaVEyMjKUxx9/3PXcDz/8oKSkpCjPPfecoiiKcuLECaVDhw5KaWmpK+bw4cNKu3btlI0bN/rdxw8//KDU19e7btXV1ayzQ/KC9acysw9RsB5IZtXB0VtzRramTSh87UNvLSL3z01rrRt/9NTAmTZNbt/Tpol42TpI4fxdJQoDU3tjKYqClpYWAMCWLVtw/fXXAxBtJNQVW1o5nU6Ulpbi5MmTGDRoEKqqqlBbW4vRo0e7YuLj4zF06FDs/HHFR2VlJc6cOeMRk5WVhdzcXFeML0uWLEFKSorrxhYXpIlMf6pAoxayXax9CfUv+KNHz+339dflVyzprTlTUAAcOgRs3QqUlIj7qirjljv7W3Wld2TXfYWY1hVR/sjURlJr4Khku37bbPpHs4weYQvl95rIbHoyqeHDhyt33XWX8sorrygdOnRQDhw4oCiKopSXlys9evTQtK09e/YoSUlJit1uV1JSUpS3335bURRF+dvf/qYAUA4fPuwRf++997pGkv76178qcXFxrbY5atQo5de//rXffXJkhwwxf37rv9jtdvG4P6FWFNb7F7y/0QTZv/iPHZPb5rFjob+vsmRGufSM8KgjNTLblxkV0TMqpqdStszokfexGzXCFomVsqlNMHVk5+mnn8auXbswY8YMPPTQQ7jwwgsBAK+//joGa2wEeNFFF2H37t34+OOP8dvf/haTJ0/GZ5995nree4m7oihBl70Hi4mPj3dNilZvRJqUlQFPPul7DsaTT/quXWJE7Rcj51YE+stbUTxHOaZOldumbFww338P5OUBaWni3teqKJlRLqdTzE8qKRHzlWSoVYnVETybzfe8I5tNblREz6iYuupOi2AjKTfcYM4Im1U1p4i0MDLDOn36tOJwOELaxogRI5Rf//rXysGDBxUAyq5duzyeHz9+vHLXXXcpiqIo77//vgJA+f777z1i+vfvr/zhD3+Q3id7Y5Emev7iD/Ya2bkTjY3GjezI3NQ5Pnl5cvF5eaG/v+npvrednu4Zp7Vf1yuvyMW/8ornfkKdd1RTI7ffmprWr83PN+6zNKM3llG/10Q6mTqyU11d7bG8+5NPPkFhYSFeeeUVdOjQIdTkC83NzejVqxcyMjKwefNm13MOhwPbtm1zjR4NGDAAHTp08IipqanBvn37NI8wEUnT0pFa9jXeIyn+yC5zNoo6kmRWnyiHQ4yOzJwp7rt18z//5OhRICOj9bEFo8YdOyYX7x1XUCDq4hQXn1vJ9sUX8qMieisxh7LqzhfveUFGsLpSNpEsPZnUVVddpbzy418/NTU1SufOnZVBgwYpaWlpyiOPPCK9nQcffFD58MMPlaqqKmXPnj3KwoULlXbt2imbNm1SFEWs9EpJSVHKysqUvXv3KrfddpuSmZmpNDQ0uLbxm9/8RsnOzla2bNmi7Nq1S7nmmmuUn//858pZDX9JcGSHNNEzQqC3a7i3UaPCM6Lj/Rf5iRNyrztxQv599DXnSeamzgvSuipO7+qqUOejpKbK7Tc19dxrQl115+9mdId2o36viXQydWRn3759uOyyywAA//M//+Na/VRSUoJVq1ZJb+fo0aO48847cdFFF2HEiBH4+9//jo0bN2LUj3/hLFiwAIWFhZg2bRouvfRSHD58GJs2bXK1rQBE64oJEyZg4sSJuPLKK5GYmIg333wTdpY+Dz8rV2N4jxBoKG6pmdYaKIBxtXmSkuS2Ewpfq3T+8Q+518rG+as7I2PoUHGvtZaPntVVRsxHkR3tdo8LddWdP0b3VrO65hSRLD2ZVFJSklJVVaUoiqKMGzfOVQvnq6++Ujp27Khnk5biyI4BrFyNoWdVVCi01kBRFONq86xaZf6ojq/5KEbVnFEUfSuN/I2AKIr8nBqtc62Mmo9yzz1y53XPPedeY+SqO/f/Jsyas2NFzSkixeSRnX79+uG5557D9u3bsXnzZlx33XUAgCNHjiAtLc3AVIyigpWrMbRWpjWCbA0U9zijKgr36CG3b63UFUv+VumoK5SCkYmTqTsTSFaW58+ytXzcV1f54r26yqj5KHl5gZ/3FWfGSIhMDzStwl0pm0gvPZnU1q1blfPOO09p166dcvfdd7sef/DBB5Ubb7xRzyYtxZGdEFi5GkNPZVqr9xvqyp5QR0X0jsjoXcnki2zdGX+3UGv5yH4GRs1H0fP7EmzERH2N9znk54d3lFMVjkrZRD7Ifn9Ldz13N2zYMHz33XdoaGhAly5dXI//+te/RmJiokFpGEUFKzuAa6lMW1ho3H7j4sRfqoH2bbf7/iu6oEA08dy+XTRf9Ndx2x+Zbth6BBuR0buSyZdQ5o2kpwOpqa0fdzrl31PZz8Co+Sgyneq9R13UEZObbxYjJIpy7jl1xKSkBDhypHUHddnO6kYK9feayGxhSr4iGkd2QmDlagy9/ZpCVVcnt9+6OmP3qyjyc2eMHtkxcs6O3lpB3nV2VGbNFzN6PopRFbdzcsRrWLGYyPiRnV/84hd4//330aVLF1xyySUBKxTv2rXLgDSMooKVqzHMqv0SzI8rEaXiqqqM3bfs3Bmtgq1UMqpPFAC8+KLctjp1As6cAc47D9i927PGjkqdL+Y+8gGcmy/2+uv6qwTLjK5omY9SVAQ8+qi2URdfIybffgvccos550wUo2yK4v1fjG+PPPII5s+fj8TERDzyyCMBYx9++GFDDi5cGhoakJKSgvr6eraO0MrhABITg1/SOXXK+KH006fFvoM5dQpISDBuv0lJYpvBJCYCJ08at18A+OtfgTvuMHabAHDiBJCS4v95pxPo2TPwJcucHJHcBfvynzkTWL5c2/HZ7eJST1GR/DHZbEB2ttwxBVJWBtx/v0gmVNnZIhEKd1IRrnMmihKy39/SIzvuCUy0JTNkIpk5JE6niDN6zo6WejdG7js+Xi7ZiY83bp8q2REWraZMAdat8/+83Q4MGBA42fnFL+S+YPWMtKmr64BzCU+45ot9/LEYVXF35Ih4PNzJjpVz5IiimK6l5+6amprQ0NDgcaM2xPtLINS4aNj3+PHGxmkxZIj4y91oX3wR+HmHA3jrrcAxb70lV8xx2jT9ow5Ll57bh+zn+v77+gtdqqUNWlo8H29p0VfaINTil1b+90YUxXQlO1VVVbjhhhuQlJSElJQUdOnSBV26dMF5553nsTqL2gAr5+xYte/6emPjtHCvaxJOWla+BaOno7evfch+ro8+Ctx+OzB8uKhTJFv3yeEQyVUg7slXMAsWiEubs2eLy3izZ4uftSRMkVyx2MoK6kRB6Fp6PmnSJADAf//3fyM9PT3gZGWKcepIw+HDrSdMAufmEAwZEjv7juQvHL2CNc49eFBuO7Jx6qWopUu1fymq+wj2+fty+DBw003A2rXBL0EZWdpAHSHy9Xrvy3OBWPnfWyBlZcCsWZ6X2Kya10Tki56lXklJScr//d//6XlpROLS8xCtXSuW4Xov0VUfM3M5rN59nz2rKFu3iiXxW7dqK3r4+ONyS6V/bKNiKLMaRObmKkpenqJMmCCWhnsrLpbbTnGxtvNpbhavmTFD7FvrPvx9/sFuaWnBP3OjShsYXfzSyv/eAh2Pr6X5VhwPtSmy39+6kp1hw4Ypmzdv1nVgkYjJjgGsrKCqdd+h1mX59a/lvgR//WvjzlG1davxiY6v28CBnvsNR7Vqvfvw9XnK3LZsCXw8RiV4ZiSKkVKx2MoK6kSKycnOF198oYwcOVJZtWqV8umnnyr//Oc/PW7RhsmOQUIZLQnXvo34K3TUKLkvr1GjjDxDwYwGkbIJz/z5geONaEmgdx/un/8dd8id3+9+F/hYjErwzCp+aeV/byrZ5Hvr1vAfG7UJpraL+Pbbb3Hw4EHcfffdrsdsNhsURYHNZoOTE9PaJrvduuWuMvt2OsW8AkVp/ZyiiPkOhYWiiFug1UIdO8odk2yc+/EFK7ffrZu2bYaiogJoahLF/QD/82x81cCR5X3OS5bo24f75//ZZ9qPwxc9bR58Mav4pZX/vam4OoyihK7VWPfccw8uueQSfPTRR/jyyy9RVVXlcU8UkYzqYv3Tn8rtTzYOEBM8e/YUK4bUlUM9exrfMb5fP2DGDCA3Vy7+zjs9fy4qEjWGiovFdoqLxc96Eh1/53zFFaHtQzYBkIkrKgLmz2+ddNrt4nGZY5JZam+3i7hoE4uT9Skm6RrZ+eqrr7BhwwZceOGFRh8PkXmM+itUthK0bJyWlgd1dXLb9GfSJFEQT7axp6/VVXFxoTdW9XfO33xz7pz17mPYMCAtLfA5pqXJJ0V62jy4M2qEKBJF6uowIi+6kp1rrrkG//znP5nskCcrui1rIXsJKFhcsGXaWuK0XloL9TLWwoXa4o3uKwYEPmdAPC5zOdEfux144QWxxNyfF17Qtm0jErxYZHT/MCKT6LqMNW7cOMyePRuLFi3C2rVrsWHDBo8btUFGFEyLFrJzQmTijLq0ZpbVq43fZrBzBkI/548/Du15IxldnDDSFBSIkTjvVibZ2WxKShFD18jOb37zGwDAH//4x1bPcYJyG2RUwTSzyV4CChZ36JDcdmTitF5aC/UylhYDB56bnGyk6mpj47w5HMBTTwWOeeopcWkqHCOPRhYnjFS+urP7mmBPZBFdIzstLS1+b0x02pho+qvVqMmUPXvKbcdfnHtZ/aNHtR1TuFZjDRwIfPKJOdvW0sBVj2XLWvey8tbSIuJkhdIKwejq05FKXR12223inokORRBNyc7111+Perd+P4899hhOnDjh+vnYsWP42c9+ZtjBURQwsmeS2dTJlP7am9hsQE5O8MmUeXly+/MV570CafbswF8KsscUqvR0cbwTJgCNjeYlOoD/uTp647zt2GFsXKgr5cxaek5E0jQlO++99x6am5tdP//pT3/C999/7/r57Nmz+Pzzz407Oop80fRXq3sTTe+ER8tkStmVTN5x6gok7/kq/pJFX8dUWyu3b61GjgT27AHWrTPn0pW7Pn2MjfMme/wycf4+M3XVmEzCE8tLz4mihKZkR/H6S8v7Z2qDou2vViMmU553nty+3OOCrUDypV07YN48z2M6ckT+9Vr062fOdn0x+8v/9tuNiZNdNRZsZFOmy3u0Lj0nihK65uwQuUybJr6UA2nXzri/WkOZO6EqKBCTh7duBUpKxH1VlfyqEdkVh+5xMiuQvDmdwJNPeo4e/POf2rYha/9++dhQPwOzv/yNqoNk5KoxI4oTEpFumlZj2Ww22LyG/71/pjbGbhdLzJua/MckJhozWbGsTPyl7f4FlJ0tLk2NHautxk8opfa/+EJ7XCjl8t1rzpw8qX87gTQ2ysUF+gy0LDE2o/WESvZSX7C4w4fltiMbF2pxQiLSTVOyoygKpkyZgvj4eADADz/8gN/85jdISkoCAI/5PNRGbN8eONEBxPPbt4fWxydQleGbbhKjR+4rcObNC/1L05/Tp7XH6S2X715nZ9gwMVF5/Xp92wq2n2C0VHqWYdaX/7ffGhNn1HbcsTghkSU0JTuTJ0/2+PmOO+5oFXPXXXeFdkQUXcLRCDBYlWGg9VJjM2v89OkD/O1vcnGqwYNbJ2RaqO/fb38LzJ2rbxuBZGcHft6oJqrezPjyP/98Y+KM2g4RWU5TsrNy5UqzjoOiVTgaAeqZ76JautT44nH/93/a47Zv15/oAOfq68gul9aqb9/Az2up9Gx1J27vyed644zaDhFZjhOUKTRG1a4JJNRRIaNr/MgmXu5x5eXG7NuM9g0yK5/CMYJnFPV3MhCZ30mjtkNElmOyQ6ExqnZNIKGMCgGRUeMnVOrqpy+/NH7b118vEsKZM8Vn5avadThG8Iyi/k7abL5/J202ud9Jo7ZDRNZTSKmvr1cAKPX19VYfSvRau1ZRsrMVRVzQELecHPF4qE6d8tyu1ltxcettnj2rKFu3KkpJibg/e1b+eO6+W26/d9997jVbtoR2DuG8tWunKPPnt36/srMVxWbz/RqbTXzeWt5Hsxn1O2nm7zYRhUT2+1tXI1CiVsxsBPj88/pf6+sSTajLpy++WG7f7nHDhomKvcFWrkWClpbWk7vVUY6bbxYjGu4TlY0awTOaUb+TbHJJFPWY7JBxQqldE8iBA/pf612czojl03q7nsfHR0eyo/LuDK5Wn/aVKD79tLZl5+Fi1O+kWb/bRBQWls7ZWbJkCQYOHIjk5GR069YNEyZMaNVba8qUKa5ihurtiiuu8Ihpbm7GzJkz0bVrVyQlJWH8+PH4Ru/qHdLPiOrGvuhZxeSrMq3MEnaZ8v/V1XLH4B63fbt8T61I0dICLF/u+Vio1aeJiCxgabKzbds2TJ8+HR9//DE2b96Ms2fPYvTo0TjpVSX2uuuuQ01Njev2zjvveDxfWFiIdevWobS0FDt27EBTUxPGjh0Lp1FfthRcqJ2hA5HtRXXNNcCMGUBxMXDqVOv6OlqWTwfi1vxWOi4SVinp4eu9UEc5brtN3PNyDhFFOEsvY23cuNHj55UrV6Jbt26orKzE1Vdf7Xo8Pj4eGRkZPrdRX1+Pl156CatXr8bIkSMBAGvWrEFOTg62bNmCa6+9ttVrmpubPao9NzQ0GHE6bZe/S0NqZ2itlXW9tZf8NR08GPjP//T/vFHLp2VbpLjHRcIqJT2Sk60+AnI6OV+IKEQRtfS8vr4eAJCamurxeHl5Obp164a+ffvi3nvvRV1dneu5yspKnDlzBqNHj3Y9lpWVhdzcXOzcudPnfpYsWYKUlBTXLScnx4SzaSOM6gwdiOxciWBxRi2fzs+X24573ODB0fkFNWmS1UfQtpk5YkrUhtgURaYpjvkURUF+fj6OHz+O7W5D56+99ho6deqEHj16oKqqCr///e9x9uxZVFZWIj4+HiUlJbj77rtb9eUaPXo0evXqhed9rOTxNbKTk5OD+vp6dO7c2byTjEXl5eJ/wMFs3ap/gqfTKVYy/fCD/5iOHcXk30AJhdMpvigOH/adnNlsYrJtVVXg7TgcYrJxMM3N5yb3yr5PkWbLFmDECKuPom3yN2KqjhiGOmJKFAMaGhqQkpIS9Ps7YlZjzZgxA3v27MEOr3L4t9xyi+vfubm5uPTSS9GjRw+8/fbbKAjwH7qiKH47ssfHx7uamVKI9EzW1crp9F3ozp3DIeICJSlGLZ+224MvI+/UyXM70TpnR7aDOBnLrF5kRG1URFzGmjlzJjZs2ICtW7ciO0h59szMTPTo0QMHflyOnJGRAYfDgePHj3vE1dXVIT093bRjph/9/e/Gxvny7LPBV2S1tMi1hVCXT3v3M8rOlv9LWUund1W0ztnR0tGbjGPUZHoiAmBxsqMoCmbMmIGysjJ88MEH6NWrV9DXHDt2DNXV1cj88ctjwIAB6NChAzZv3uyKqampwb59+zB48GDTjp1+JHsVNJSrpbJ1dmTjQl0+ffiw9rghQ4C0tMDxqanislFJCfC738ntw2zBjpnMEU29yIiigKWXsaZPn46SkhK88cYbSE5ORu2PQ+YpKSlISEhAU1MTFi1ahJtuugmZmZk4dOgQFi5ciK5du+LGG290xU6dOhVz585FWloaUlNTMW/ePOTl5blWZ5GJLrjA2DhfZFc//fOfor9T796ianKgTuehFImTHe3QOipis51byv3++6Kgn9WirTZQrIimXmREUcDSkZ0VK1agvr4ew4YNQ2Zmpuv22muvAQDsdjv27t2L/Px89O3bF5MnT0bfvn3x0UcfIdltSWxxcTEmTJiAiRMn4sorr0RiYiLefPNN2Hkt23x5ecbG+XL55XJxf/ubKII3ezaQmAgsWKB/n4Gcf772OJmigseORd5lCdlzJWOpHdf9Jfo2GzuuE2lg6chOsIVgCQkJeO+994Jup2PHjli2bBmWLVtm1KGRLNm//EMZIdBTGsDpbN3fyft5vbVLvOf7yMRpvSzhVl7BUrLnSsaKxl5kRBEsIiYoUxTr1s3YOF/Uv3L1WLq09UquUGuXyByP91/dWi9LRMJcGY4cWMuIyfREBIDJDkUDu120JtDD6fRcpaXWLvFe6aJWe5ZJeOx2YMCAwDG/+IXnX91aL0vs3Rv8OMxks3HkIBKwFxmRIZjsUGiOHDE2zhenUzQX1evgwXPbMaLas8MBvPVW4Ji33vIcUVIvSwTat3tyIdtZPVS9e7cepcrJ4chBJGEvMqKQMdlpS8zoSh6OOjvBao4E07u3/HZkapc8+2zw9857REkr9ZjNdvAgcMstHDkgopjGZKetMKvHjlebjpDjfPnqK/2vtdvFMnRAX30cX/TU/VFHlfxRK+KqSdS0aeH7C/7pp0XvLo4cEFGMYrLTFvibp3L4sPw8FX/+9S+5uI8/1j+itH691qM65xe/OFdvx6j6OHq6nmutiBsXJ449HEIdhSIiinBMdmJdsB47QGhdyWV7jO3bp39EqbFR16EBAHbtOjd3Rk99HF+CTU72Fad16bnDIY49XNR5TUREMYjJTqwzu8eOnstTWkeUAnU7D8Z91EJPfRxfKivltuMep3Xpucy8ICOFa44QEZEFmOzEOrN77Fx8sfbXaB1RCqXVBHBu1GLwYLmO5sF6qp09K7df9zitS8/DOdLiPq+JiCgGMdmJdWb32Pm//9P3Oi0jSsGWeQejjlrs3Cm3imrnzsAxR4/K7dc9Tl16DrROeHxVxA3nSMucOYH7iBERRTkmO7FOT7VfLTp00Pc6lcyIUigruQDgV78S90atxtJbNVpLRdxwrMay24H583230yAiiiFMdmKdTPXhW2/V/8UaavE7mRElt6avurz4org3ajVWO8n/bHzFyVbENWs11oQJwIwZQHExcOoUEx0iahMsbQRKYSBTfbi0FFiyRF/Co3dkx2YTIxoyI0oPPCAutei1f7+4N2o11nnnyW3HX5xaETcQM1Zj2e3Aa6/xkhURtTkc2Yl1RlUN9kfPyI7Wrs3//d/a9+Hu44/FvVGrsfTU2dHKjNVYnJtDRG0Uk51YZ9Q8FX9kL+m409q1+cQJ7ftwp66KMmr+kuzxhHLcrHtDRGQYJjuxzqh5Kv7IXtLp0kV/76UePXQdmos6sqWuiAq0/FtmtCkcIztmrMZautSzOSkRURvBZCfWpaUZG+dt2za5uI8+0t97acMGzYflob3b1DR1RVQonb579ZLbr2ycL2asxmJbCCJqo5jsxLpjx/THORxipGPmTHHva1Rg6lS57cvG+bJnj/7XAq1bWsiuiLJSXFxok7L94eUxImqDuBor1uldgbRggbjs4T5Jdt488QXsvlz566/lti8b54ve6s6qG25o/ZjMiih/ZLuwh9KtHTj3Pnt/DqFgWwgiaoM4shPrMjK0xy1YADzxROsvWKdTPL5gwbnHcnLkti8b54vsvCB/9LS0CEQ2YTAisSgqEvVwiotFfZwJE/Rvi20hiKiNYrIT62RHBNQ4h0OMJATiPtH1/vvlti8b58v//q/+1wK+O76HQmY+jZGJRVyc6CO2bJmok6N3Lg+XnhNRG8VkJ5o5nUB5uSgaWF7uO7GRrZ+jxsnUd3Gf6CpbgTeUSr1r1+p/LSAmR3uTmY/kT1wcMHZs4JixY81JLGTm8nivAmNbCCJq4zhnJ1qVlQGzZnkWDMzOFkur3SfatrTIbU+Nk53AqsaFMgFaVqjzVTp18vxZdj5SoOPZsSNwzI4dIs6M/lb+5vLY7eIcHn1UJKMHD4pLadOmcUSHiNo0JjvRqKwMuPnm1pdnDh8Wj7svoU5NldumGqd1PsoPP8jFy8b5kpd3rgqyHnfeee7f6nwkb+p8JCB4wlNeHjx5O3ZMxI0YoeVI5RUVBU5qCgvN2S8RURTiZaxo43SKER1f81DUxwoLz/3Fr3WCstb5KKH2iZIRSp2dTp3OJRxa5yP588EHcvuWjdPLfS5PYSFHb4iI/GCyE22C9bpSFM9eV1r7QcnMCXGf6BqO1gl//av+17788rnkTet8JH/CsdyeiIgMw2Qn2sjWnFHjhgwJXh05Lc2zH1RREZCf7zs2P9/zMo/ZFZoBfYXwbDYxKdd9/pLW+Uj+dO8utx3ZOCIiMhWTnWiTmak9rqEhcKz382Vlvi8d2Wzi8bKyc49lZckdj2ycL3p6YykK8OSTnsdqVH2ca66R245sHBERmcqmKEYXIYk+DQ0NSElJQX19PTp37mz14QTmcACJiYEvx9jtohBdXBzw7rvA9dcH3+477wBjxojt9uwZ+FJZTo5or2C3izkxJ08G335SEtDUFDzOlyefFKM0ergfq9b3zh+nE0hPDzxJOS0NOHrUnNVYREQEQP77myM70WbnTrl5Jzt3in8Hm5CrUuOCzQkCPOcEnTolt33ZOF9Cabvgfqxa5yP5Y7cDL7wQOOaFF5joEBFFCCY70UbrnJ3jx+Xi1bjDh+Xi1bh2kr9CsnG+hNp2wT1ZKioSo0TeiYjWwnsFBaLYoXf39Oxs8Xg4morKFJUkIiImO1FH65wdrXNqvv1WLl6NC8f8FZnl8IGsX+/5s3e/qeJi8bPWCsO+uqcfOhSeRKesTFxuHD4cuP12cd+zp+ccJSIiAsCigtFnyBAxenD4sO9aOzabeF5dXXXTTcCbbwbf7k03iXutXdInTQI2bw4eP2mS3HZ9iYsDfvELoKJC3+t9zSlSa9SEKpTu6XppKSpJRETWjuwsWbIEAwcORHJyMrp164YJEybg888/94hRFAWLFi1CVlYWEhISMGzYMOzfv98jprm5GTNnzkTXrl2RlJSE8ePH45tg806ild0uWkL4m1euKKLXkzoS4n2ZxR81TmsRwq5d5eJl43xxOIBdu/S//sIL9b820mgtKklERNYmO9u2bcP06dPx8ccfY/PmzTh79ixGjx6Nk25/iRcVFWHp0qVYvnw5KioqkJGRgVGjRqGxsdEVU1hYiHXr1qG0tBQ7duxAU1MTxo4dCyf/h2++YBN1tcb5IlMMMJDx4/W/NtJoLSpJRETWXsbauHGjx88rV65Et27dUFlZiauvvhqKouDpp5/GQw89hIIfh+VffvllpKeno6SkBPfddx/q6+vx0ksvYfXq1Rg5ciQAYM2aNcjJycGWLVtw7bXXhv28TKX+Ze+PzSb+ss/PF6M7siNcatyRI3LxatwXX8jFy8b54jXap5nsJO1ooHWCOhERRdYE5fr6egBA6o9NKauqqlBbW4vRo0e7YuLj4zF06FDs/HFpdWVlJc6cOeMRk5WVhdzcXFeMt+bmZjQ0NHjcoobWv+y9J+f6o8b97W9y8Wrcd9/JxcvG+VJbq/+1QGh9uSKNnqKSRERtXMQkO4qiYM6cObjqqquQm5sLAKj98UsuPT3dIzY9Pd31XG1tLeLi4tClSxe/Md6WLFmClJQU1y0nJ8fo0zGP1r/sZQr+ucft2ycXr8adPi0X39iof4l0qF/coTQSjTTqBHWbzffzNpsopOje/oOIqI2LmGRnxowZ2LNnD1599dVWz9m8/seuKEqrx7wFinnwwQdRX1/vulVXV+s/8HDr1k1bnGyrBTVOtqC2GnfmjFz86dP6l0iHWmdHT2+tSKVOUAdaJzzqz+4T1ImIKDKSnZkzZ2LDhg3YunUrst1WD2X8uOLHe4Smrq7ONdqTkZEBh8OB417zMtxjvMXHx6Nz584et5glewlIjdPa2PPsWe3HpC6Rlk148vK078NdUlJor480BQViebl3R/vsbC47JyLywdJkR1EUzJgxA2VlZfjggw/Qq1cvj+d79eqFjIwMbHar4+JwOLBt2zYMHjwYADBgwAB06NDBI6ampgb79u1zxcSUujptcbKtFtQ47y9Qf9S4lha5eHdal0gH6kElY8KE0F4fiFVVjAsKxIiVe2HEL75gokNE5IOlq7GmT5+OkpISvPHGG0hOTnaN4KSkpCAhIQE2mw2FhYVYvHgx+vTpgz59+mDx4sVITEzE7bff7oqdOnUq5s6di7S0NKSmpmLevHnIy8tzrc6KKVpHXnr3BvbuDR6vXiq66CK57atxiYn6Gny6T6QOVpQv1Dk7erqmyygrEyvj3CeMZ2eLy0xmJx1lZcD993u293jySeC//osJDxGRN8VCAHzeVq5c6YppaWlRHn74YSUjI0OJj49Xrr76amXv3r0e2zl9+rQyY8YMJTU1VUlISFDGjh2rfP3119LHUV9frwBQ6uvrjTo18zz5pKKIVCHw7cknRXxjo1x8Y6OIb25WFJstcKzNJuIURVF69pTbvr9bSUnwcz57VlGys/VtPy1NvN5oa9f6fp9sNnFbu9b4fbrvO9A5m7lvIqIIIvv9bVMU2RmpsUu2RXxEmDYNWLEieNxvfyuK8X37rdyk5ro60QLC4QDi44PHNzeLlgupqaHVsdm6Va7dwoIFwBNPaN9+p07AiRPGTth1OsUka38lANSWHVVVxk8UdjrFUvpAo2nJyeIz4SRlIopxst/fETFBmTSQXXq+Z4+YRyI7ufeyy8S9utInGDVOb3KoZYm00ynORY+mJjGXxkhWVjH+4IPglw0bG0UcEREBYLITfWR7V/3tb2Kp99GjcvFqF/M33pCLV+P69ZOLd6d1iXSw5CIYo5MdK6sYr15tbBwRURvAZCfayE4g1kq227m37t21v0brEulIa31gZRVjt55whsQREbUBTHaizX33mbPdHTvEfX6+XLwa17OnXPyvfgWUlIg5OlVV2lYMyRZS9Ofqq0N7vTcrqxjLbpMVlImIXJjsRJu//92c7f7v/4r7QE1G3alxVVVy8XY7cNttYjJyuCfOtjP419zKKsYzZgQ/n3btRBwREQFgshN93OuqGGnPHnFvtwf/km7f/lxMOLqeyxZS9CfURqK+WFXFOC4OmDs3cMzcuSKOiIgAMNmJPl9/bc521ZGdzZuDVwE+e1bEAfIToGXjfAn1MpY6+dpoBQXAoUPi0pzeS3R6FBUB8+e3HuFp1048XlRk7v6JiKKMpRWUSYfXXzdnuw6HuF+6VC5+6VLguuvkJzbrnQBtBDP3bbfL1QkyWlER8OijopbSwYOiAva0aRzRISLygclOtAmlgF8gXbpo274apyZJwcjG+RLqZSzZfl/RJi5O9BcjIqKAeBkr2vTta8521SJ0AwfKxatxffrIxcvG+RLKEm6zVkUREVHUYLITbWTn7HTvLuaRyC4Nv+UWcf/UU3LxatyBA3LxsnG+BFvqHcitt8Zu2wSHQ6z6mjlT3IcyekZEFMOY7EQb2Ym+TU1iqXd9vVy8usorLg7o2DFwbELCubkhwWJVsnG+BFrqHUxpafAJ19FowQLRcX72bGD5cnGfmCgeJyIiD0x2oo1sL6oOHUQ/qdOn5eLPnBH327cDP/wQOPb06XN9n5KT5bYvG+ePv6XewZjVo8pKalNU7yTO6RSPM+EhIvLAZCfaLFwoF3f0qOiNFSxxUWVliXvZOj5q3IQJcvGycYF4L/WWfS/Mqk1kBYcj+Iq5pUt5SYuIyA2TnWhzwQXmbFetRSNbk0aN69FDLl42Lhh1qfdtt8kvKTerzo4Vnn02+GU5p1PEERERACY70ces+ScJCeJea90cdfJwIGatiIqGGj9GC8eEcCKiGMNkJ9qYNf+kVy9xLzsnRo1Te14FYtaKKK3HGgtkJ2jrWblGRBSjmOyQ8Pbb4l7rSI3TKSZCB2LWiigrR5WscvnlxsYREbUBTHaijRmtCdLTgZQU8W91mbe/kQGbzbOj9/btwDffBN6+WSui7PbgBQczMmKrzk5OjrFxRERtAJOdaDN4sPHbjIvzHHkpKADmzWudJNjt4nH3Rpc1NXL7kI3T4vRpoKIicExFhfzy+2jQFkeziIhCxGQn2jz/vPHb9B55KSsDnnzSdx2XJ58Uz6tkWzmE0vLBn/nzjY2LBlpH3oiIiMlO1DFrlY068uJ0ArNmAYriO05RRPNJNRG69FK57cvGadFWVyapBRa9R3hycsTj7iNvRETEZCfqmLXKRh150ToHx8rRlXA0IY1U3gUWt24FqqqY6BAR+dDe6gMgjWS7ksuy2cQIgTrHQ2sFZbVbejCycVo88QTw5z/LxcUitcAiEREFxJGdSCLTxfr4ceP2p44Suc/x0FpBWbYtgRntCxISgPz8wDH5+ecKJhIRUZvEZCdSyHaxNrIacHZ26zkeXbrIvVaNC3e7CG/r1/tPePLzxfNERNSm8TJWJFC7WHtTu1gDQFGRuA+1GnBxsairk5kpLl15r9oJtpTbPW7yZKC95K+QbJwe69eL5eXz54vJyH36iPeNIzpERAQmO9aT7WL96KOiHo5aZyXYJGJf7HZg2jSxHX9kKx2rcZ06ycXLxumVkCBGxIiIiLzwMpbVtHaxDlZnJdh2du4MHCNbn0WNky1exyJ3RERkESY7Vjt4UHucvzorMoJVMpZd7aXGzZwJtAvya9SunYgjIiKyAJMdszidQHm5aJJZXu5/9KZ3b7ntecd511kpLpbbztGjgY9JdrWXGhcXB8ydGzh27tzAl86IiIhMZFMUf6Vy246GhgakpKSgvr4enTt3Dn2DZWWiCrH7vJrsbHH5ybvom8MhVl0FupRltwOnTgWfa9Ozp6h/4+8jtds99+PrmP76V+COO/zvR7VmDTBp0rmfFywQc4vct2+3A3PmnJtcTUREZCDZ72+O7BitrAy4+ebWE4gPHxaPu/eVAkQCM2dO4G3OmRN8ZESdywP4n8/jnVD5OibZ1V7ecUVFIiErLgZmzBD3p04x0SEiIstZmux8+OGHGDduHLKysmCz2bDeqybKlClTYLPZPG5XXHGFR0xzczNmzpyJrl27IikpCePHj8c3elYqGSFQXyn1Mfe+UqqiIrFs2leX8fnz5RMGdS6PdyLib9Kxr2MKpat2XJzY1rJl4p6XroiIKAJYmuycPHkSP//5z7E8wJLh6667DjU1Na7bO++84/F8YWEh1q1bh9LSUuzYsQNNTU0YO3YsnLJLqI0UrK+UorTuMK4qKgIaG4Hp04HRo8V9Y6P2kRFfc3kCvRfex8Su2kREFGMsrbMzZswYjBkzJmBMfHw8MjIyfD5XX1+Pl156CatXr8bIkSMBAGvWrEFOTg62bNmCa6+91vBjDijYSqdAcd7zfDZtAt54w/c8n2Dceya9+qr2Y1JHiLznHeXkiESHzSaJiCiKRPycnfLycnTr1g19+/bFvffei7q6OtdzlZWVOHPmDEaPHu16LCsrC7m5udgZoJ5Mc3MzGhoaPG6GUDuHa43TOs8nHMekp6u27Aq0UIRjH0REFFMiOtkZM2YM/vrXv+KDDz7AU089hYqKClxzzTVobm4GANTW1iIuLg5dvPo5paeno7a21u92lyxZgpSUFNctJyfHmAMePDj45R27XcSp9M7zkaXOwQl0WcrfHBx1hOi228R9oHMrKxOrwYYPB26/Xdz37BlaombFPoiIKOZEdLJzyy234IYbbkBubi7GjRuHd999F//+97/x9ttvB3ydoiiwBagw/OCDD6K+vt51q66uNuaAd+6Uq4bsPuokO89n2TJ9oxmBVmn56nquh5kjU+HcBxERxaSITna8ZWZmokePHjhw4AAAICMjAw6HA8e9CuHV1dUhPT3d73bi4+PRuXNnj5sh9MzZkX3N7Nn6RzP8rdLy1fVcK7NHpmT2oSih74OIiGJWVCU7x44dQ3V1NTJ/nF8yYMAAdOjQAZs3b3bF1NTUYN++fRjsfqkoXPTMj5F9jTs9oxl65uDICGUFmlH7AELfBxERxSxLV2M1NTXhiy++cP1cVVWF3bt3IzU1FampqVi0aBFuuukmZGZm4tChQ1i4cCG6du2KG2+8EQCQkpKCqVOnYu7cuUhLS0NqairmzZuHvLw81+qssFLnx/irYmyziefd58cEe40viiK2VVgI5Odra96prtIySigr0GQdPmxsHBERtSmWjux8+umnuOSSS3DJJZcAAObMmYNLLrkEf/jDH2C327F3717k5+ejb9++mDx5Mvr27YuPPvoIycnJrm0UFxdjwoQJmDhxIq688kokJibizTffhN2KOjB65sfIVD72xYgREyPoXe2lxbffysV99BFXaRERUSvsjYUw9cYKVqPGV28pGSUlYrWUVYL15FJHs6qq9E+Clu3X5c5fLzIiIooZ7I1lJa3zY8rKgCef1Dca4WvEJJy1aMKx2ku2X5c7rtIiIqIfcWQHJozsaKGOjGjt5+VvxERLx3Uj6RnNkmX0e0RERDGBIzvRQmalkTd/IyZW1qIxa7UXELxflz+RMq+JiIgsxWTHanpWKfmqjxOOejfBaKm4rJVaKyhYR3ZfQlkJRkREUc/SpecE+VVKxcVAerqIHzKkdSKhpd6N0cvPw6WgQCy1375dJDBHj4pii8GEshKMiIiiHpMdq8nW5pk5M/BISTjq3UQC91pBTifw1FPa6hoREVGbw8tYVjNqNZMZ9W4ivcO4+t75m2OvKKGvBCMioqjHZCcSGNG7KpTu5r6wwzgREcUILj2HxUvP3Tmd5+aj+JubE4i6GgvwHO1QEyDZxEndjvevhtbtmC3YknQuPSciimmy399MdhBByY4RQq13E00JRHm5GHEKZuvW6J2UTUREfsl+f3OCcqzxXrGkdYQomlZ1tZVJ2UREFBImO9HM32WvULqbR1MCEY4mpEREFPU4QTlamTWBuFs3Y+PMZPSkbCIiiklMdqKRlW0hIkk4mpASEVHUY7ITbcxuC1FXZ2yc2YxYtk9ERDGNc3aijdkTiKNxHkyok7KJiCimMdmJNmZPIJZtXxFp82BCmZRNREQxjZexoo3ZIy+cB0NERDGGyU60CccKJM6DISKiGMLLWNFGHXm5+WaR2PhqC2HEyAvnwRARUYxgshON1JEX77YQ2dnybSFkcB4MERHFACY70YojL0RERFKY7EQzjrwQEREFxWSH/PPXe8uoeCIiojBgskO+lZX5nhP0zDO+5wRpjSciIgoTLj2PRU4nUF4OvPqquNfaOkJr7y326iIioghmUxRfZXLbloaGBqSkpKC+vh6dO3e2+nBCE+oIi9Mpuqf7a0mhVlCuqhKXqLTGExERGUT2+5sjO7HEiBEWLb239MQTERGFGZOdWGFUN3StvbfM7tVFREQUIiY7scKoERatvbeisUs6ERG1KUx2YoVRIyxae2+Fo1cXERFRCJjsxAqjRli0dj1nl3QiIopwTHZihZEjLFq7nrNLOhERRTBLk50PP/wQ48aNQ1ZWFmw2G9avX+/xvKIoWLRoEbKyspCQkIBhw4Zh//79HjHNzc2YOXMmunbtiqSkJIwfPx7fBJq7EquMHmEpKAAOHQK2bgVKSsR9VZX/xEVrPBERUZhYmuycPHkSP//5z7F8+XKfzxcVFWHp0qVYvnw5KioqkJGRgVGjRqGxsdEVU1hYiHXr1qG0tBQ7duxAU1MTxo4dC6fWQnqxwOgRFrX31m23iftgiZLWeCIiojCImKKCNpsN69atw4QJEwCIUZ2srCwUFhbigQceACBGcdLT0/GnP/0J9913H+rr63H++edj9erVuOWWWwAAR44cQU5ODt555x1ce+21UvuOqaKCAHtUERFRmxD1RQWrqqpQW1uL0aNHux6Lj4/H0KFDsXPnTgBAZWUlzpw54xGTlZWF3NxcV4wvzc3NaGho8LjFFI6wEBERuURsslNbWwsASE9P93g8PT3d9VxtbS3i4uLQpUsXvzG+LFmyBCkpKa5bTk6OwUdPREREkSJikx2VzWuyraIorR7zFizmwQcfRH19vetWXV1tyLESERFR5InYZCcjIwMAWo3Q1NXVuUZ7MjIy4HA4cPz4cb8xvsTHx6Nz584eNyIiIopNEZvs9OrVCxkZGdi8ebPrMYfDgW3btmHw4MEAgAEDBqBDhw4eMTU1Ndi3b58rhoiIiNq29lbuvKmpCV988YXr56qqKuzevRupqano3r07CgsLsXjxYvTp0wd9+vTB4sWLkZiYiNtvvx0AkJKSgqlTp2Lu3LlIS0tDamoq5s2bh7y8PIwcOdKq0yIiIqIIYmmy8+mnn2L48OGun+fMmQMAmDx5MlatWoUFCxbg9OnTmDZtGo4fP47LL78cmzZtQnJysus1xcXFaN++PSZOnIjTp09jxIgRWLVqFexcgURERESIoDo7Voq5OjtERERtQNTX2SEiIiIyApMdIiIiimmWztmJFOqVvJirpExERBTD1O/tYDNymOwArsairKRMREQUfRobG5GSkuL3eU5QBtDS0oIjR44gOTk5aHVmLRoaGpCTk4Pq6uo2M/GZ58xzjlU8Z55zrIrmc1YUBY2NjcjKykK7dv5n5nBkB0C7du2QnZ1t2vbbYpVmnnPbwHNuG3jObUO0nnOgER0VJygTERFRTGOyQ0RERDGNyY6J4uPj8fDDDyM+Pt7qQwkbnnPbwHNuG3jObUNbOGdOUCYiIqKYxpEdIiIiimlMdoiIiCimMdkhIiKimMZkh4iIiGIakx2TfPjhhxg3bhyysrJgs9mwfv16qw/JVEuWLMHAgQORnJyMbt26YcKECfj888+tPixTrVixAv3793cV4ho0aBDeffddqw8rbJYsWQKbzYbCwkKrD8VUixYtgs1m87hlZGRYfVimO3z4MO644w6kpaUhMTER//Ef/4HKykqrD8s0PXv2bPU522w2TJ8+3epDM83Zs2fxu9/9Dr169UJCQgIuuOAC/PGPf0RLS4vVh2Y4VlA2ycmTJ/Hzn/8cd999N2666SarD8d027Ztw/Tp0zFw4ECcPXsWDz30EEaPHo3PPvsMSUlJVh+eKbKzs/H444/jwgsvBAC8/PLLyM/Pxz/+8Q/069fP4qMzV0VFBV544QX079/f6kMJi379+mHLli2un+12u4VHY77jx4/jyiuvxPDhw/Huu++iW7duOHjwIM477zyrD800FRUVcDqdrp/37duHUaNG4Ze//KWFR2WuP/3pT3juuefw8ssvo1+/fvj0009x9913IyUlBbNmzbL68AzFZMckY8aMwZgxY6w+jLDZuHGjx88rV65Et27dUFlZiauvvtqiozLXuHHjPH5+7LHHsGLFCnz88ccxnew0NTVh0qRJ+Mtf/oJHH33U6sMJi/bt27eJ0RzVn/70J+Tk5GDlypWux3r27GndAYXB+eef7/Hz448/jt69e2Po0KEWHZH5PvroI+Tn5+OGG24AID7jV199FZ9++qnFR2Y8XsYiU9TX1wMAUlNTLT6S8HA6nSgtLcXJkycxaNAgqw/HVNOnT8cNN9yAkSNHWn0oYXPgwAFkZWWhV69euPXWW/Hll19afUim2rBhAy699FL88pe/RLdu3XDJJZfgL3/5i9WHFTYOhwNr1qzBPffcY2hz6Ehz1VVX4f3338e///1vAMA///lP7NixA9dff73FR2Y8juyQ4RRFwZw5c3DVVVchNzfX6sMx1d69ezFo0CD88MMP6NSpE9atW4ef/exnVh+WaUpLS7Fr1y5UVFRYfShhc/nll+OVV15B3759cfToUTz66KMYPHgw9u/fj7S0NKsPzxRffvklVqxYgTlz5mDhwoX45JNPcP/99yM+Ph533XWX1YdnuvXr1+PEiROYMmWK1YdiqgceeAD19fW4+OKLYbfb4XQ68dhjj+G2226z+tAMx2SHDDdjxgzs2bMHO3bssPpQTHfRRRdh9+7dOHHiBNauXYvJkydj27ZtMZnwVFdXY9asWdi0aRM6duxo9eGEjfvl6Ly8PAwaNAi9e/fGyy+/jDlz5lh4ZOZpaWnBpZdeisWLFwMALrnkEuzfvx8rVqxoE8nOSy+9hDFjxiArK8vqQzHVa6+9hjVr1qCkpAT9+vXD7t27UVhYiKysLEyePNnqwzMUkx0y1MyZM7FhwwZ8+OGHyM7OtvpwTBcXF+eaoHzppZeioqICzzzzDJ5//nmLj8x4lZWVqKurw4ABA1yPOZ1OfPjhh1i+fDmam5tjfuIuACQlJSEvLw8HDhyw+lBMk5mZ2Sph/+lPf4q1a9dadETh89VXX2HLli0oKyuz+lBMN3/+fPy///f/cOuttwIQyfxXX32FJUuWMNkh8kVRFMycORPr1q1DeXk5evXqZfUhWUJRFDQ3N1t9GKYYMWIE9u7d6/HY3XffjYsvvhgPPPBAm0h0AKC5uRn/+te/MGTIEKsPxTRXXnllq9IR//73v9GjRw+Ljih81MUV6qTdWHbq1Cm0a+c5dddut3PpOclramrCF1984fq5qqoKu3fvRmpqKrp3727hkZlj+vTpKCkpwRtvvIHk5GTU1tYCAFJSUpCQkGDx0Zlj4cKFGDNmDHJyctDY2IjS0lKUl5e3WpkWK5KTk1vNwUpKSkJaWlpMz82aN28exo0bh+7du6Ourg6PPvooGhoaYu4vX3ezZ8/G4MGDsXjxYkycOBGffPIJXnjhBbzwwgtWH5qpWlpasHLlSkyePBnt28f+1+O4cePw2GOPoXv37ujXrx/+8Y9/YOnSpbjnnnusPjTjKWSKrVu3KgBa3SZPnmz1oZnC17kCUFauXGn1oZnmnnvuUXr06KHExcUp559/vjJixAhl06ZNVh9WWA0dOlSZNWuW1YdhqltuuUXJzMxUOnTooGRlZSkFBQXK/v37rT4s07355ptKbm6uEh8fr1x88cXKCy+8YPUhme69995TACiff/651YcSFg0NDcqsWbOU7t27Kx07dlQuuOAC5aGHHlKam5utPjTD2RRFUaxJs4iIiIjMxzo7REREFNOY7BAREVFMY7JDREREMY3JDhEREcU0JjtEREQU05jsEBERUUxjskNEREQxjckOERERxTQmO0QUdVatWoXzzjvP0mMYNmwYCgsLLT0GIpLDCspEZJgpU6bg5ZdfbvX4tddea2jPsNOnT6OxsRHdunUzbJtaff/99+jQoQOSk5MtOwYikhP7nc6IKKyuu+46rFy50uOx+Ph4Q/eRkJBgeYPZ1NRUS/dPRPJ4GYuIDBUfH4+MjAyPW5cuXVzP22w2vPjii7jxxhuRmJiIPn36YMOGDR7b2LBhA/r06YOEhAQMHz4cL7/8Mmw2G06cOAGg9WWsRYsW4T/+4z+wevVq9OzZEykpKbj11lvR2NjoilEUBUVFRbjggguQkJCAn//853j99dcDnsuzzz6LPn36oGPHjkhPT8fNN9/ses79MlZ5eTlsNlur25QpU1zxb775JgYMGICOHTviggsuwCOPPIKzZ89qfHeJSA8mO0QUdo888ggmTpyIPXv24Prrr8ekSZPw/fffAwAOHTqEm2++GRMmTMDu3btx33334aGHHgq6zYMHD2L9+vV466238NZbb2Hbtm14/PHHXc//7ne/w8qVK7FixQrs378fs2fPxh133IFt27b53N6nn36K+++/H3/84x/x+eefY+PGjbj66qt9xg4ePBg1NTWu2wcffICOHTu64t977z3ccccduP/++/HZZ5/h+eefx6pVq/DYY49pfeuISA9Le64TUUyZPHmyYrfblaSkJI/bH//4R1cMAOV3v/ud6+empibFZrMp7777rqIoivLAAw8oubm5Htt96KGHFADK8ePHFUVRlJUrVyopKSmu5x9++GElMTFRaWhocD02f/585fLLL3fto2PHjsrOnTs9tjt16lTltttu83kua9euVTp37uyxTXdDhw5VZs2a1erx7777Tundu7cybdo012NDhgxRFi9e7BG3evVqJTMz0+e2ichYnLNDRIYaPnw4VqxY4fGY9/yW/v37u/6dlJSE5ORk1NXVAQA+//xzDBw40CP+sssuC7rfnj17ekwWzszMdG3zs88+ww8//IBRo0Z5vMbhcOCSSy7xub1Ro0ahR48euOCCC3Ddddfhuuuuc1168+fMmTO46aab0L17dzzzzDOuxysrK1FRUeExkuN0OvHDDz/g1KlTAbdJRKFjskNEhkpKSsKFF14YMKZDhw4eP9tsNrS0tAAQc2tsNpvH84rEotFA21Tv3377bfzkJz/xiPM3eTo5ORm7du1CeXk5Nm3ahD/84Q9YtGgRKioq/C57/+1vf4uvv/4aFRUVaN/+3P9eW1pa8Mgjj6CgoKDVazp27Bj03IgoNEx2iCiiXHzxxXjnnXc8Hvv0009D2ubPfvYzxMfH4+uvv8bQoUOlX9e+fXuMHDkSI0eOxMMPP4zzzjsPH3zwgc+kZenSpXjttdfw0UcfIS0tzeO5X/ziF/j888+DJoFEZA4mO0RkqObmZtTW1no81r59e3Tt2lXq9ffddx+WLl2KBx54AFOnTsXu3buxatUqAGg14iMrOTkZ8+bNw+zZs9HS0oKrrroKDQ0N2LlzJzp16oTJkye3es1bb72FL7/8EldffTW6dOmCd955By0tLbjoootaxW7ZsgULFizAn//8Z3Tt2tV1/gkJCUhJScEf/vAHjB07Fjk5OfjlL3+Jdu3aYc+ePdi7dy8effRRXedERPK4GouIDLVx40ZkZmZ63K666irp1/fq1Quvv/46ysrK0L9/f6xYscK1GiuUej3/+Z//iT/84Q9YsmQJfvrTn+Laa6/Fm2++iV69evmMP++881BWVoZrrrkGP/3pT/Hcc8/h1VdfRb9+/VrF7tixA06nE7/5zW88znvWrFkARFHFt956C5s3b8bAgQNxxRVXYOnSpejRo4fu8yEieaygTEQR77HHHsNzzz2H6upqqw+FiKIQL2MRUcR59tlnMXDgQKSlpeFvf/sbnnjiCcyYMcPqwyKiKMVkh4gizoEDB/Doo4/i+++/R/fu3TF37lw8+OCDVh8WEUUpXsYiIiKimMYJykRERBTTmOwQERFRTGOyQ0RERDGNyQ4RERHFNCY7REREFNOY7BAREVFMY7JDREREMY3JDhEREcW0/w9jVL9PI1SL0gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='red')\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": 30,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[39.22675497]]\n",
+      "Intercept:  [125.2607308]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
+    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
+    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot outputs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the fit line over the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 31,
+     "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='green')\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": 32,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 23.07\n",
+      "Residual sum of squares (MSE): 905.55\n",
+      "R2-score: 0.75\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": {
+    "tags": []
+   },
+   "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": 33,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "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": 34,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 34,
+     "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": 35,
+   "metadata": {
+    "tags": []
+   },
+   "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": 39,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 19.526630\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean Absolute Error: %.6f\" % 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": {},
+   "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
+}