Build a Movie Recommendation System in Python

  • Guided Project
  • 2 hours
  • Intermediate
  • Python

Practice using Python, pandas, and scikit-learn to build an interactive movie search and recommendation system.

Overview

In this project, you'll assume the role of a data scientist at a movie streaming company, tasked with developing a system that enables users to search for movies and get personalized recommendations to enhance user engagement. Leveraging your Python skills and libraries like pandas and scikit-learn, you'll work with movie data, build a search engine using TF-IDF and cosine similarity, and create a recommendation algorithm based on user ratings. The project will culminate in an interactive Jupyter widget showcasing the system's capabilities, allowing users to input a movie and receive instant recommendations. This hands-on project provides an opportunity to apply your data science skills to a real-world problem. You'll gain practical experience in data manipulation, natural language processing, machine learning, and interactive widget development. The final product will be a valuable addition to your portfolio, demonstrating your ability to create intelligent systems that deliver user value. Objective: Apply data science techniques using Python to build a movie search and recommendation system that enhances user experience and engagement on a streaming platform.

What You'll Learn

  • Prepare data for machine learning
  • Create a search widget in Jupyter
  • Build a recommendation system

Before You Start

  • Working with Python data types, variables, functions, and control flow
  • Exploring and preparing data using pandas DataFrames
  • Using regular expressions to clean text data in Python
  • Working in Jupyter Notebooks to write and run Python code

Project Steps

13 steps

  1. 1 Introduction
  2. 2 Reading in Our Movie Data in Pandas
  3. 3 Cleaning Movie Titles Using Regex
  4. 4 Creating a TFIDF Matrix
  5. 5 Creating a Search Function
  6. 6 Building an Interactive Search Box in Jupyter
  7. 7 Reading in Movie Ratings Data
  8. 8 Finding Users Who Liked the Same Movie
  9. 9 Determining How Much Users Like Movies
  10. 10 Creating a Recommendation Score
  11. 11 Building a Recommendation Function
  12. 12 Create an Interactive Recommendation Widget
  13. 13 Next Steps

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