From 0a7e7177040be77366ca4a9d45e52bc8525af2f7 Mon Sep 17 00:00:00 2001
From: 202310715001 MUHAMMAD BINTANG MUDZAFFAR
<202310715001@mhs.ubharajaya.ac.id>
Date: Wed, 19 Nov 2025 20:54:21 +0700
Subject: [PATCH] Upload files to "Regression"
---
...ession-Co2 Muhamma Bintang Mudzaffar.ipynb | 756 +++++++++++
...egression Muhammad Bintang Mudzaffar.ipynb | 1 +
...ssion-Co2 Muhammad Bintang Mudzaffar.ipynb | 874 ++++++++++++
...Reg-Simple-Linear-Regression-Co2 (1).ipynb | 1173 +++++++++++++++++
4 files changed, 2804 insertions(+)
create mode 100644 Regression/ML0101EN-Reg-Mulitple-Linear-Regression-Co2 Muhamma Bintang Mudzaffar.ipynb
create mode 100644 Regression/ML0101EN-Reg-NoneLinearRegression Muhammad Bintang Mudzaffar.ipynb
create mode 100644 Regression/ML0101EN-Reg-Polynomial-Regression-Co2 Muhammad Bintang Mudzaffar.ipynb
create mode 100644 Regression/ML0101EN-Reg-Simple-Linear-Regression-Co2 (1).ipynb
diff --git a/Regression/ML0101EN-Reg-Mulitple-Linear-Regression-Co2 Muhamma Bintang Mudzaffar.ipynb b/Regression/ML0101EN-Reg-Mulitple-Linear-Regression-Co2 Muhamma Bintang Mudzaffar.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ " \n",
+ "
\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": [
+ "Table of contents \n",
+ "\n",
+ "\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",
+ "- **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": [
+ "Reading the data in \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MODELYEAR \n",
+ " MAKE \n",
+ " MODEL \n",
+ " VEHICLECLASS \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " TRANSMISSION \n",
+ " FUELTYPE \n",
+ " FUELCONSUMPTION_CITY \n",
+ " FUELCONSUMPTION_HWY \n",
+ " FUELCONSUMPTION_COMB \n",
+ " FUELCONSUMPTION_COMB_MPG \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.0 \n",
+ " 4 \n",
+ " AS5 \n",
+ " Z \n",
+ " 9.9 \n",
+ " 6.7 \n",
+ " 8.5 \n",
+ " 33 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.4 \n",
+ " 4 \n",
+ " M6 \n",
+ " Z \n",
+ " 11.2 \n",
+ " 7.7 \n",
+ " 9.6 \n",
+ " 29 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX HYBRID \n",
+ " COMPACT \n",
+ " 1.5 \n",
+ " 4 \n",
+ " AV7 \n",
+ " Z \n",
+ " 6.0 \n",
+ " 5.8 \n",
+ " 5.9 \n",
+ " 48 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " MDX 4WD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.7 \n",
+ " 9.1 \n",
+ " 11.1 \n",
+ " 25 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " RDX AWD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.1 \n",
+ " 8.7 \n",
+ " 10.6 \n",
+ " 27 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " FUELCONSUMPTION_CITY \n",
+ " FUELCONSUMPTION_HWY \n",
+ " FUELCONSUMPTION_COMB \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2.0 \n",
+ " 4 \n",
+ " 9.9 \n",
+ " 6.7 \n",
+ " 8.5 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2.4 \n",
+ " 4 \n",
+ " 11.2 \n",
+ " 7.7 \n",
+ " 9.6 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1.5 \n",
+ " 4 \n",
+ " 6.0 \n",
+ " 5.8 \n",
+ " 5.9 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 12.7 \n",
+ " 9.1 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 12.1 \n",
+ " 8.7 \n",
+ " 10.6 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 11.9 \n",
+ " 7.7 \n",
+ " 10.0 \n",
+ " 230 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 11.8 \n",
+ " 8.1 \n",
+ " 10.1 \n",
+ " 232 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 12.8 \n",
+ " 9.0 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 13.4 \n",
+ " 9.5 \n",
+ " 11.6 \n",
+ " 267 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creating train and test dataset\n",
+ "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+ "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+ "\n",
+ "We know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+ "\n",
+ "Let's split our dataset into train and test sets. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the __np.random.rand()__ function: \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "msk = np.random.rand(len(df)) < 0.8\n",
+ "train = cdf[msk]\n",
+ "test = cdf[~msk]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Train data distribution\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Multiple Regression Model \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": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [[10.72078242 7.43076823 9.65437428]]\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": [
+ "Prediction \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error (MSE) : 525.98\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": [
+ "Practice \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": 22,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [[10.75686052 7.15720891 6.05278734 3.36709249]]\n",
+ "Residual sum of squares: 525.50\n",
+ "Variance score: 0.86\n"
+ ]
+ }
+ ],
+ "source": [
+ "regr = linear_model.LinearRegression()\n",
+ "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+ "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+ "regr.fit (x, y)\n",
+ "print ('Coefficients: ', regr.coef_)\n",
+ "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+ "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+ "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+ "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
+ "print('Variance score: %.2f' % regr.score(x, y))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution \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",
+ "Joseph Santarcangelo \n",
+ "\n",
+ "## © IBM Corporation 2020. All rights reserved. \n"," \n"," \n","
\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","id":"15a65d32-f83b-441e-9b17-31ba4e2e5dc2","metadata":{},"outputs":[],"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","id":"73cb29ed-e8ec-4921-ac53-166595235341","metadata":{},"outputs":[],"source":["Importing required libraries \n"]},{"cell_type":"code","id":"ec4a49ee-e991-484b-a702-58664e2eee53","metadata":{},"outputs":[],"source":["import numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline"]},{"cell_type":"markdown","id":"e1ce1959-66c9-424d-9e69-202597a1737d","metadata":{},"outputs":[],"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","id":"1ce23c2d-f16a-4dad-99a2-9b8bf259f46c","metadata":{},"outputs":[],"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\ny = 2*(x) + 3\ny_noise = 2 * np.random.normal(size=x.size)\nydata = y + y_noise\n#plt.figure(figsize=(8,6))\nplt.plot(x, ydata, 'bo')\nplt.plot(x,y, 'r') \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"236b4327-37bb-4b7d-ae15-604a89ee5dc5","metadata":{},"outputs":[],"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","id":"4cd7c4e4-ed41-4dde-a6cc-ca6f2080564e","metadata":{},"outputs":[],"source":["Let's take a look at a cubic function's graph.\n"]},{"cell_type":"code","id":"4b87e01f-01dd-4866-b4de-ef40b6234338","metadata":{},"outputs":[],"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\ny = 1*(x**3) + 1*(x**2) + 1*x + 3\ny_noise = 20 * np.random.normal(size=x.size)\nydata = y + y_noise\nplt.plot(x, ydata, 'bo')\nplt.plot(x,y, 'r') \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"8b87c4de-8c9a-490f-b6b5-7fc477e31932","metadata":{},"outputs":[],"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","id":"b6b64321-1168-467b-8e9f-54d272e46054","metadata":{},"outputs":[],"source":["Some other types of non-linear functions are:\n"]},{"cell_type":"markdown","id":"112b0dad-85f3-4378-b0e2-bf0759b6f129","metadata":{},"outputs":[],"source":["### Quadratic\n"]},{"cell_type":"markdown","id":"31f3f05b-8a22-4b9b-8163-5ff9f10fa343","metadata":{},"outputs":[],"source":["$$ Y = X^2 $$\n"]},{"cell_type":"code","id":"fe65df68-b7a4-48cf-9070-283f080dee65","metadata":{},"outputs":[],"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\ny = np.power(x,2)\ny_noise = 2 * np.random.normal(size=x.size)\nydata = y + y_noise\nplt.plot(x, ydata, 'bo')\nplt.plot(x,y, 'r') \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"7d479697-8606-44de-8156-ded445fe2afe","metadata":{},"outputs":[],"source":["### Exponential\n"]},{"cell_type":"markdown","id":"8f77d83e-8240-41c7-b36b-175d8a5618ff","metadata":{},"outputs":[],"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","id":"7355b113-fb6e-4d50-b99f-9133fe124b61","metadata":{},"outputs":[],"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\nY= np.exp(X)\n\nplt.plot(X,Y) \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"cf97d373-4d2b-4425-8c69-c4873d4b7254","metadata":{},"outputs":[],"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","id":"26cff054-333a-4cfc-b851-ac5aaa40cca4","metadata":{},"outputs":[],"source":["X = np.arange(-5.0, 5.0, 0.1)\n\nY = np.log(X)\n\nplt.plot(X,Y) \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"07aacb86-eafb-4f32-b827-ecbfe871ea31","metadata":{},"outputs":[],"source":["### Sigmoidal/Logistic\n"]},{"cell_type":"markdown","id":"72ca83a5-d461-4a53-b64a-8a98bd6cbccb","metadata":{},"outputs":[],"source":["$$ Y = a + \\frac{b}{1+ c^{(X-d)}}$$\n"]},{"cell_type":"code","id":"2c907f44-8614-415a-88da-c3c1c88f375e","metadata":{},"outputs":[],"source":["X = np.arange(-5.0, 5.0, 0.1)\n\n\nY = 1-4/(1+np.power(3, X-2))\n\nplt.plot(X,Y) \nplt.ylabel('Dependent Variable')\nplt.xlabel('Independent Variable')\nplt.show()"]},{"cell_type":"markdown","id":"bad7ea3b-3c69-45b5-85fd-ae33e4958703","metadata":{},"outputs":[],"source":["Click here for the solution \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","Want to learn more? \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: SPSS Modeler \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 Watson Studio \n","\n"]},{"cell_type":"markdown","id":"98fa8c61-6330-4942-b756-abb84862c94c","metadata":{},"outputs":[],"source":["### Thank you for completing this lab!\n","\n","\n","## Author\n","\n","Saeed Aghabozorgi\n","\n","\n","### Other Contributors\n","\n","Joseph Santarcangelo \n","\n","\n","## © IBM Corporation 2020. All rights reserved. \n",
+ " \n",
+ " \n",
+ "
\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": [
+ "Table of contents \n",
+ "\n",
+ "\n",
+ "Downloading Data \n",
+ "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-10-19 06:14:14-- 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",
+ "2025-10-19 06:14:14 (36.4 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": [
+ "__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": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MODELYEAR \n",
+ " MAKE \n",
+ " MODEL \n",
+ " VEHICLECLASS \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " TRANSMISSION \n",
+ " FUELTYPE \n",
+ " FUELCONSUMPTION_CITY \n",
+ " FUELCONSUMPTION_HWY \n",
+ " FUELCONSUMPTION_COMB \n",
+ " FUELCONSUMPTION_COMB_MPG \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.0 \n",
+ " 4 \n",
+ " AS5 \n",
+ " Z \n",
+ " 9.9 \n",
+ " 6.7 \n",
+ " 8.5 \n",
+ " 33 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.4 \n",
+ " 4 \n",
+ " M6 \n",
+ " Z \n",
+ " 11.2 \n",
+ " 7.7 \n",
+ " 9.6 \n",
+ " 29 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX HYBRID \n",
+ " COMPACT \n",
+ " 1.5 \n",
+ " 4 \n",
+ " AV7 \n",
+ " Z \n",
+ " 6.0 \n",
+ " 5.8 \n",
+ " 5.9 \n",
+ " 48 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " MDX 4WD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.7 \n",
+ " 9.1 \n",
+ " 11.1 \n",
+ " 25 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " RDX AWD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.1 \n",
+ " 8.7 \n",
+ " 10.6 \n",
+ " 27 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " FUELCONSUMPTION_COMB \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2.0 \n",
+ " 4 \n",
+ " 8.5 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2.4 \n",
+ " 4 \n",
+ " 9.6 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1.5 \n",
+ " 4 \n",
+ " 5.9 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.6 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.0 \n",
+ " 230 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.1 \n",
+ " 232 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 11.6 \n",
+ " 267 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "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": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "msk = np.random.rand(len(df)) < 0.8\n",
+ "train = cdf[msk]\n",
+ "test = cdf[~msk]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Polynomial regression \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": 27,
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1. , 2. , 4. ],\n",
+ " [ 1. , 3.5 , 12.25],\n",
+ " [ 1. , 3.5 , 12.25],\n",
+ " ...,\n",
+ " [ 1. , 3. , 9. ],\n",
+ " [ 1. , 3.2 , 10.24],\n",
+ " [ 1. , 3.2 , 10.24]])"
+ ]
+ },
+ "execution_count": 27,
+ "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": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [[ 0. 49.59507462 -1.43096779]]\n",
+ "Intercept: [109.19803969]\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": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Emission')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": [
+ "Evaluation \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean absolute error: 22.64\n",
+ "Residual sum of squares (MSE): 853.69\n",
+ "R2-score: 0.79\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": [
+ "Practice \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": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [[ 0. 28.60682627 4.34859112 -0.47824097]]\n",
+ "Intercept: [131.43552557]\n",
+ "Mean absolute error: 22.56\n",
+ "Residual sum of squares (MSE): 851.53\n",
+ "R2-score: 0.79\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "poly3 = PolynomialFeatures(degree=3)\n",
+ "train_x_poly3 = poly3.fit_transform(train_x)\n",
+ "clf3 = linear_model.LinearRegression()\n",
+ "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+ "\n",
+ "# The coefficients\n",
+ "print ('Coefficients: ', clf3.coef_)\n",
+ "print ('Intercept: ',clf3.intercept_)\n",
+ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+ "XX = np.arange(0.0, 10.0, 0.1)\n",
+ "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
+ "plt.plot(XX, yy, '-r' )\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "test_x_poly3 = poly3.transform(test_x)\n",
+ "test_y3_ = clf3.predict(test_x_poly3)\n",
+ "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+ "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+ "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution \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",
+ "Want to learn more? \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: SPSS Modeler \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 Watson Studio \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",
+ "Joseph Santarcangelo \n",
+ "\n",
+ "\n",
+ "## © IBM Corporation 2020. All rights reserved. \n",
+ " \n",
+ " \n",
+ "
\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": 29,
+ "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": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-10-19 05:56:26-- 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",
+ "2025-10-19 05:56:26 (33.6 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": 31,
+ "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": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MODELYEAR \n",
+ " MAKE \n",
+ " MODEL \n",
+ " VEHICLECLASS \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " TRANSMISSION \n",
+ " FUELTYPE \n",
+ " FUELCONSUMPTION_CITY \n",
+ " FUELCONSUMPTION_HWY \n",
+ " FUELCONSUMPTION_COMB \n",
+ " FUELCONSUMPTION_COMB_MPG \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.0 \n",
+ " 4 \n",
+ " AS5 \n",
+ " Z \n",
+ " 9.9 \n",
+ " 6.7 \n",
+ " 8.5 \n",
+ " 33 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX \n",
+ " COMPACT \n",
+ " 2.4 \n",
+ " 4 \n",
+ " M6 \n",
+ " Z \n",
+ " 11.2 \n",
+ " 7.7 \n",
+ " 9.6 \n",
+ " 29 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " ILX HYBRID \n",
+ " COMPACT \n",
+ " 1.5 \n",
+ " 4 \n",
+ " AV7 \n",
+ " Z \n",
+ " 6.0 \n",
+ " 5.8 \n",
+ " 5.9 \n",
+ " 48 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " MDX 4WD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.7 \n",
+ " 9.1 \n",
+ " 11.1 \n",
+ " 25 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2014 \n",
+ " ACURA \n",
+ " RDX AWD \n",
+ " SUV - SMALL \n",
+ " 3.5 \n",
+ " 6 \n",
+ " AS6 \n",
+ " Z \n",
+ " 12.1 \n",
+ " 8.7 \n",
+ " 10.6 \n",
+ " 27 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " MODELYEAR \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " FUELCONSUMPTION_CITY \n",
+ " FUELCONSUMPTION_HWY \n",
+ " FUELCONSUMPTION_COMB \n",
+ " FUELCONSUMPTION_COMB_MPG \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 1067.0 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " 1067.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 2014.0 \n",
+ " 3.346298 \n",
+ " 5.794752 \n",
+ " 13.296532 \n",
+ " 9.474602 \n",
+ " 11.580881 \n",
+ " 26.441425 \n",
+ " 256.228679 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 0.0 \n",
+ " 1.415895 \n",
+ " 1.797447 \n",
+ " 4.101253 \n",
+ " 2.794510 \n",
+ " 3.485595 \n",
+ " 7.468702 \n",
+ " 63.372304 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 2014.0 \n",
+ " 1.000000 \n",
+ " 3.000000 \n",
+ " 4.600000 \n",
+ " 4.900000 \n",
+ " 4.700000 \n",
+ " 11.000000 \n",
+ " 108.000000 \n",
+ " \n",
+ " \n",
+ " 25% \n",
+ " 2014.0 \n",
+ " 2.000000 \n",
+ " 4.000000 \n",
+ " 10.250000 \n",
+ " 7.500000 \n",
+ " 9.000000 \n",
+ " 21.000000 \n",
+ " 207.000000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 2014.0 \n",
+ " 3.400000 \n",
+ " 6.000000 \n",
+ " 12.600000 \n",
+ " 8.800000 \n",
+ " 10.900000 \n",
+ " 26.000000 \n",
+ " 251.000000 \n",
+ " \n",
+ " \n",
+ " 75% \n",
+ " 2014.0 \n",
+ " 4.300000 \n",
+ " 8.000000 \n",
+ " 15.550000 \n",
+ " 10.850000 \n",
+ " 13.350000 \n",
+ " 31.000000 \n",
+ " 294.000000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 2014.0 \n",
+ " 8.400000 \n",
+ " 12.000000 \n",
+ " 30.200000 \n",
+ " 20.500000 \n",
+ " 25.800000 \n",
+ " 60.000000 \n",
+ " 488.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ENGINESIZE \n",
+ " CYLINDERS \n",
+ " FUELCONSUMPTION_COMB \n",
+ " CO2EMISSIONS \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2.0 \n",
+ " 4 \n",
+ " 8.5 \n",
+ " 196 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2.4 \n",
+ " 4 \n",
+ " 9.6 \n",
+ " 221 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1.5 \n",
+ " 4 \n",
+ " 5.9 \n",
+ " 136 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.6 \n",
+ " 244 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.0 \n",
+ " 230 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 3.5 \n",
+ " 6 \n",
+ " 10.1 \n",
+ " 232 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 11.1 \n",
+ " 255 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 3.7 \n",
+ " 6 \n",
+ " 11.6 \n",
+ " 267 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "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": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Practice\n",
+ "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"Cylinders\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution \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",
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Modeling\n",
+ "Using sklearn package to model data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [[39.60619393]]\n",
+ "Intercept: [123.82649696]\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": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Emission')"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+ "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+ "plt.xlabel(\"Engine size\")\n",
+ "plt.ylabel(\"Emission\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Evaluation\n",
+ "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
+ "\n",
+ "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
+ "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
+ "\n",
+ "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
+ "\n",
+ "* Root Mean Squared Error (RMSE). \n",
+ "\n",
+ "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean absolute error: 23.44\n",
+ "Residual sum of squares (MSE): 1029.97\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": {},
+ "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": 44,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+ "\n",
+ "test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click here for the solution \n",
+ "\n",
+ "```python \n",
+ "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+ "\n",
+ "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
+ "\n",
+ "```\n",
+ "\n",
+ "Click here for the solution \n",
+ "\n",
+ "```python \n",
+ "regr = linear_model.LinearRegression()\n",
+ "\n",
+ "regr.fit(train_x, train_y)\n",
+ "\n",
+ "```\n",
+ "\n",
+ "Click here for the solution \n",
+ "\n",
+ "```python \n",
+ "predictions = regr.predict(test_x)\n",
+ "\n",
+ "```\n",
+ "\n",
+ "Click here for the solution \n",
+ "\n",
+ "```python \n",
+ "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
+ "\n",
+ "```\n",
+ "\n",
+ "Joseph Santarcangelo \n",
+ "\n",
+ "Azim Hirjani\n",
+ "\n",
+ "## © IBM Corporation. All rights reserved. ![\"Skills](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\")
\n",
+ " \n",
+ "