Cocalc Lab 3 Simple Linear Regression Ipynb

Leo Migdal

-Nov 17, 2025, 6:47 AM

cocalc lab 3 simple linear regression ipynb

In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, Evaluate your model using test set, and finally use model to predict unknown value FUEL CONSUMPTION in CITY(L/100 km) e.g. 9.9 FUEL CONSUMPTION in HWY (L/100 km) e.g.

8.9 FUEL CONSUMPTION COMB (L/100 km) e.g. 9.2 CO2 EMISSIONS (g/km) e.g. 182 --> low --> 0 After completing this lab you will be able to:

Use scikit-learn to implement simple Linear Regression Create a model, train it, test it and use the model To download the data, we can use pd.read_csv() We have downloaded a fuel consumption dataset, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. Dataset source After completing this lab you will be able to:

To motivate our example of Linear Regression, we will be looking at the problem of predicitng housing prices based on some given features. This notebook will help to give you a bit of a flavor of what it's like to perform predictive analytics on a given dataset. By the end of this, you should have a good enough understanding of the process behind machine learning and being able to leverage scikit-learn to perform regression analysis. Let's start off by importing some of the required libraries we will need for today... First we will have to import the data. We will use pandas again to import our CSV file.

Let's take a look at the first few records to see what we are working with... The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < In Depth: Naive Bayes Classification | Contents | In-Depth: Support Vector Machines > Just as naive Bayes (discussed earlier in In Depth: Naive Bayes Classification) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks. Such models are popular because they can be fit very quickly, and are very interpretable.

You are probably familiar with the simplest form of a linear regression model (i.e., fitting a straight line to data) but such models can be extended to model more complicated data behavior. In this section we will start with a quick intuitive walk-through of the mathematics behind this well-known problem, before seeing how before moving on to see how linear models can be generalized to account... We will start with the most familiar linear regression, a straight-line fit to data. A straight-line fit is a model of the form y=ax+b y = ax + b y=ax+b where aaa is commonly known as the slope, and bbb is commonly known as the intercept. The model can be defined as: yi=β0+β1xi+ϵiy_i = \beta_0 + \beta_1 x_i + \epsilon_iyi=β0+β1xi+ϵi The observed yiy_iyi is composed of a linear function β0+β1xi\beta_0 + \beta_1 x_iβ0+β1xi of the xix_ixi independent variable and an error term ϵi\epsilon_iϵi.

The error terms ϵ1,…,ϵn\epsilon_1, \dots, \epsilon_nϵ1,…,ϵn are generally taken from a N(0,σ2)N(0, \sigma^2)N(0,σ2) distribution for some error variance σ2\sigma^2σ2 This means that y1,…,yny_1, \dots, y_ny1,…,yn are observations from the independent random variables Yi∼N(β0+β1xi,σ2)Y_i \sim N(\beta_0 + \beta_1 x_i, \sigma^2)Yi∼N(β0+β1xi,σ2) σ2\sigma^2σ2 can be estimated from the data set

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In This Notebook, We Learn How To Use Scikit-learn To

8.9 FUEL CONSUMPTION COMB (L/100 Km) E.g. 9.2 CO2 EMISSIONS