Project

Salary Prediction

Hard

155 completions

~ 24 hours

4.5

Practice fitting linear models with scikit-learn to predict values on the unknown data. Apply polynomial feature engineering, test your data for multicollinearity, and evaluate models with the MAPE score.

Provided by

JetBrains Academy

About

Linear regression is one of the simplest yet powerful tools for finding regularities in data and using them for prediction. It is widely applied both in science and practice. In this project, you will learn how to apply scikit-learn library to fit linear models, use them for prediction, compare the models, and select the best one. You will also learn how to carry out testing for certain issues with data.

Graduate project

This project covers the core topics of the Introduction to Data Science course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Fit a simple model with one predictor and evaluate it.

Use the linear model to handle the polynomial relationship between independent and dependent variables.

Fit a linear model with many independent variables and compare it with the previous models.

Check whether your variables have a high correlation and try to improve the model's performance by removing them.

Get rid of negative predictions and see whether the model performance improves.

Reviews

Krzysztof Kopel

4 months ago

3.7

I have learned about math connected with linear regression, its implementation in SciPy and how to use it in real-life scenarios.

Mamadou Traore

9 months ago

5.0

In this project, several key skills and concepts have been demonstrated: #Data Handling and Preprocessing:Checking for the existence of directories and files. ...

Saber Ghaderi

1 year ago

5.0

During the Salary Prediction project, I gained deeper insights into how to approach real-world data analysis and machine learning tasks involving linear regression. I learned how to handle different challenges, such as transforming predictor variables to improve model performance and dealing with pr ...

4.5

Learners who completed this project within the Introduction to Data Science course rated it as follows:

Usefulness

4.7

Fun

4.4

Clarity

4.4