This repository contains the files for a Data Science project about recommender systems and machine learning. The project revolves around building a hybrid recommender system with collaborative and content-based filtering.

All data was sourced from the MovieLens 1M Dataset, which contains 1 million movie ratings given by 6000 users on 4000 movies in the years 2000-2003.

In order to learn as much as possible about recommender systems and data science in general, I decided early on to follow the Data Science Process for this project:

This project has largely been motivated by wanting to learn how to implement a recommender system in Python. I started with wondering whether I would be able to improve the root mean squared error (RMSE) of a superior recommender system by combining it with a different recommender system approach. I had read about the "cold start problem", and though it intuitively made sense to combine content-based and collaborative filtering models to improve recommendations, I was curious about how one would practically apply this to create a hybrid recommender system. After taking a look at the dataset for the first time, I also wrote down a string of questions which were only answerable through data wrangling and visualization.

Cleaning the data was more difficult than I originally thought, as a lot of the data was missing or incorrect. I quickly came to learn the power of value imputations, especially using KNN. I also learned a surprisingly lot about the US postal code system...

Before I began modelling, I had to answer the questions I had about possible patterns in the data. After figuring out that I could potentially improve the training dataset through engineering three new features, I also wanted to know how much RMSE would improve from adding the new features to the training dataset. Therefore, I first created different content-based and collaborative filtering models using a variety of machine learning algorithms. I then re-built and re-trained the most promising models using the new dataset with the engineered features.

Finally, I ran the improved hybrid recommender a last time and predicted ratings from all users to all movies. Going forward, I'm considering whether I should implement the hybrid recommender in a simple website. I am hoping that I'll soon find the time to do so.

• raw data - The raw data files.
• Part 1: cleanup.ipynb - Cleaning the raw data files.
• cleaned data - The resulting files after performing the data cleaning.
• Part 2: analysis-and-modelling.ipynb - Data Analysis, Exploration and Modelling.
• Part 3: predict.ipynb (Coming soon... Maybe!) - Predicting ratings for all movies from all users.