The notebooks presented in this project follow the labs from the course on "Introduction to Recommender Systems" taught by the University of Minnesota at Coursera: https://www.coursera.org/learn/recommender-systems-introduction
I'm not exactly following the course labs, however I'm inspired by the ideas behind them and the course in general, and I hope that by the end of the course I will gain enough skills and knowledge to convert this into a Big Data project on Recommender Systems.
Before creating the workbooks, we are going to tidy up our data and prepare it for the analysis. We are going to create the following datasets:
Movies: movie_id, movie_name, movie_year Users: user_id, user_gender Ratings: movie_id, user_id, rating
We will create a SQLite database to store our analysis data. It might seem a redundant procedure, however, I think, that it is quite good and useful exercise, and it is always a good idea to keep analysis data well structured and conveniently organized.
The code for this part of the exercise will be stored in a directory called data_preparation.