Python is a popular programming language used in data science and analytics, as it provides a wide range of libraries and tools for working with data. You can use Python to create recommendation systems, which are a collection of algorithms used to recommend items based on information from a user.

In this guided project, you’ll learn how to use Python and the pandas library to create a content-based recommendation system.

You’ll begin by downloading a dataset from GroupLens, then importing the necessary libraries, storing the data in DataFrames, and preprocessing it to prepare the data for analysis. Finally, you’ll use this data to create a content-based recommendation system for movies that will attempt to determine a user’s preferences then make suggestions.

Completing this guided project will prepare you to successfully create content-based recommendations on your own with different sets of data and criteria.

A Look at the Project Ahead

After completing this project, you'll be able to:

Create a recommendation system using content-based filtering

What You'll Need

For this project, you will need:

Familiarity with Python and pandas
Familiarity with machine learning algorithms
A web browser

Everything else is provided to you via the IBM Skills Network Labs environment, where you’ll have access to the Python environment that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of Chrome, Edge, Firefox, or Safari.

Creating a Content-Based Recommendation System

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