Project

Movie Quality Predictor

12 completions

~ 36 hours

4.0

Get acquainted with NLP and bag-of-words — a simple yet effective NLP method. Learn how to extract valuable information from the data. Come to know the difference between regression and classification problems in a real case and understand where information surplus can be detrimental to a machine learning model.

Provided by

JetBrains Academy

About

The capabilities of DS are extensive; computers can do unimaginable things. Figures are everywhere — companies, websites, social networks. They come with statistics — sales, views, visits, likes, and reposts. Figures can tell us a lot, but what about words? Humanity has been using languages to communicate for thousands of years, and we can't ignore this rich data source. Can we teach computers to analyze words?

With this project, you'll try to make a natural language understandable for a computer, transform words into digits, and predict movie ratings using various metadata from The Movie Database.

Training project

This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.

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:

Choose between classification and regression problems and prepare your data.

Learn about bag-of-words and transform the reviews into a feature matrix.

Train your model and evaluate the results.

Upgrade your model with LASSO and compare the results.

Learn about PCA and upgrade your model with SVD.

Reviews

Aleksandr Endovitskii

3 years ago

3.7

I have learned a lot about ML and NLP, revisited basic topics about Python and some pretty advanced math and linear algebra. Don't recommend this project as a first in ML track, especially if you have never studied topics I mentioned above.The course tries to give a comprehensive overview of math b ...

4.0

Learners who completed this project within the course rated it as follows:

Usefulness

4.4

Fun

3.7

Clarity

3.9