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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",
+    "# 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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"download_data\">Downloading Data</h2>\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n",
+    "\n",
+    "In essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n",
+    "\n",
+    "\n",
+    "$$y = b + \\theta_1  x + \\theta_2 x^2$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "Now, the question is: how we can fit our data on this equation while we have only x values, such as __Engine Size__? \n",
+    "Well, we can create a few additional features: 1, $x$, and $x^2$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "__PolynomialFeatures()__ function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, _ENGINESIZE_. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         29.92154206  3.7724906  -0.41363227]]\n",
+      "Intercept:  [131.29858492]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 23.82\n",
+      "Residual sum of squares (MSE): 950.52\n",
+      "R2-score: 0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "df = pd.read_csv(\"FuelConsumptionCo2.csv\")\n",
+    "cdf = df[['ENGINESIZE', 'CO2EMISSIONS']]\n",
+    "\n",
+    "train, test = train_test_split(cdf, test_size=0.2, random_state=42)\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "\n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "print(\"Coefficients: \", clf3.coef_)\n",
+    "print(\"Intercept: \", clf3.intercept_)\n",
+    "\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "YY = (clf3.intercept_[0]\n",
+    "      + clf3.coef_[0][1]*XX\n",
+    "      + clf3.coef_[0][2]*np.power(XX, 2)\n",
+    "      + clf3.coef_[0][3]*np.power(XX, 3))\n",
+    "plt.plot(XX, YY, '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.title(\"Polynomial Regression (Degree 3)\")\n",
+    "plt.show()\n",
+    "\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y, test_y3_))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf3.coef_)\n",
+    "print ('Intercept: ',clf3.intercept_)\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Want to learn more?</h2>\n",
+    "\n",
+    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
+    "\n",
+    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
+    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "4dc110debac287dfd374a575573c16e62a80a935b3bbe2b2f6d5a0598e6e33f6"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Innoval Bagus Wianda L-Reg-Simple-Linear-Regression-Co2.ipynb b/Innoval Bagus Wianda L-Reg-Simple-Linear-Regression-Co2.ipynb
new file mode 100644
index 0000000..eefca88
--- /dev/null
+++ b/Innoval Bagus Wianda L-Reg-Simple-Linear-Regression-Co2.ipynb	
@@ -0,0 +1,1105 @@
+{
+ "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downloading Data\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Exploration\n",
+    "Let's first have a descriptive exploration on our data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1067.0</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.346298</td>\n",
+       "      <td>5.794752</td>\n",
+       "      <td>13.296532</td>\n",
+       "      <td>9.474602</td>\n",
+       "      <td>11.580881</td>\n",
+       "      <td>26.441425</td>\n",
+       "      <td>256.228679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.415895</td>\n",
+       "      <td>1.797447</td>\n",
+       "      <td>4.101253</td>\n",
+       "      <td>2.794510</td>\n",
+       "      <td>3.485595</td>\n",
+       "      <td>7.468702</td>\n",
+       "      <td>63.372304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>4.700000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>108.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>10.250000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>207.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>10.900000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>251.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>15.550000</td>\n",
+       "      <td>10.850000</td>\n",
+       "      <td>13.350000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>294.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>30.200000</td>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>25.800000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>488.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
+       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
+       "mean      2014.0     3.346298     5.794752             13.296532   \n",
+       "std          0.0     1.415895     1.797447              4.101253   \n",
+       "min       2014.0     1.000000     3.000000              4.600000   \n",
+       "25%       2014.0     2.000000     4.000000             10.250000   \n",
+       "50%       2014.0     3.400000     6.000000             12.600000   \n",
+       "75%       2014.0     4.300000     8.000000             15.550000   \n",
+       "max       2014.0     8.400000    12.000000             30.200000   \n",
+       "\n",
+       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
+       "count          1067.000000           1067.000000               1067.000000   \n",
+       "mean              9.474602             11.580881                 26.441425   \n",
+       "std               2.794510              3.485595                  7.468702   \n",
+       "min               4.900000              4.700000                 11.000000   \n",
+       "25%               7.500000              9.000000                 21.000000   \n",
+       "50%               8.800000             10.900000                 26.000000   \n",
+       "75%              10.850000             13.350000                 31.000000   \n",
+       "max              20.500000             25.800000                 60.000000   \n",
+       "\n",
+       "       CO2EMISSIONS  \n",
+       "count   1067.000000  \n",
+       "mean     256.228679  \n",
+       "std       63.372304  \n",
+       "min      108.000000  \n",
+       "25%      207.000000  \n",
+       "50%      251.000000  \n",
+       "75%      294.000000  \n",
+       "max      488.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarize the data\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features to explore more.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "\n",
+    "df = pd.read_csv(\"FuelConsumptionCo2.csv\")\n",
+    "\n",
+    "df.head()\n",
+    "\n",
+    "df.describe()\n",
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)\n",
+    "\n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Regression Model\n",
+    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modeling\n",
+    "Using sklearn package to model data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Evaluation\n",
+    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
+    "\n",
+    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
+    "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
+    "\n",
+    "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
+    "\n",
+    "* Root Mean Squared Error (RMSE). \n",
+    "\n",
+    "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "test_y_ = regr.predict(test_x)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
+    "\n",
+    "Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[16.0539031]]\n",
+      "Intercept:  [69.68888946]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]\n",
+    "\n",
+    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show() \n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)\n",
+    "\n",
+    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "\n",
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\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": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[16.0539031]]\n",
+      "Intercept:  [69.68888946]\n"
+     ]
+    }
+   ],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      " [244.67643322]\n",
+      " [275.17884911]\n",
+      " [307.2866553 ]\n",
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+      " [251.09799446]\n",
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+      " [223.8063592 ]\n",
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+      " [206.14706579]\n",
+      " [270.36267818]\n",
+      " [279.99502004]\n",
+      " [276.78423942]\n",
+      " [223.8063592 ]\n",
+      " [186.88238207]\n",
+      " [146.74762433]\n",
+      " [212.56862703]\n",
+      " [251.09799446]\n",
+      " [318.52438747]\n",
+      " [329.76211964]\n",
+      " [246.28182353]\n",
+      " [215.77940765]\n",
+      " [223.8063592 ]\n",
+      " [225.41174951]\n",
+      " [180.46082083]\n",
+      " [201.33089486]\n",
+      " [201.33089486]\n",
+      " [207.7524561 ]\n",
+      " [183.67160145]\n",
+      " [241.4656526 ]\n",
+      " [254.30877508]\n",
+      " [259.12494601]\n",
+      " [254.30877508]\n",
+      " [259.12494601]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = regr.predict(test_x)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "predictions = regr.predict(test_x)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 19.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "Azim Hirjani\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
+    "\n",
+    "<!--\n",
+    "## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03 | 2.1  | Lakshmi Holla  |  Changed URL of the csv |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   |\n",
+    "\n",
+    "--!>\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "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
+}