Data Science: A triple-model approach to recommending movies using neural networks, random forest, and linear regression. As many of us can assume, the availability of movies is endless to the point that a person could watch a new movie every waking hour.
As many of us can assume, the availability of movies is endless to the point that a person could watch a new movie every waking hour. However, we often end up finding ourselves searching through the movie selection that our streaming services have available (Netflix, Amazon Prime Video, Hulu, etc.), hoping to find the right movie to fit our interests, if not just to fit our mood. Unlike physically browsing through Blockbuster in the past, streaming services attempt to lessen the time we browse through their selection by providing us movie recommendations immediately after logging into the service. And to produce those recommendations, they use data science—specifically machine learning. In this article, I will explain step-by-step on how I made my version of a recommendation system.
Click Here to Access the GitHub Repository: To run, follow the instructions in the README or in the script. Quick model stats: the accuracy of this system was ~65% for predicting near the actual rating and ~73% for predicting whether a user would like or dislike a movie.
The first thing to cover in all data science projects is the data source. There are many different databases available to use for movie recommendation systems. I’ve decided to design my system using the MovieLens 25M Dataset that is provided for free by grouplens, a research lab at the University of Minnesota. This dataset contains 25,000,095 movie ratings from 162541 users, with the rating scale ranging between 0.5 to 5.0.
All the files in the MovieLens 25M Dataset file; extracted/unzipped on July 2020.
Though there are many files in the downloaded zip file, I will only be using movies.csv, ratings.csv, and tags.csv.
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