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.

• Average (Mean Value) Ratings
• Popularity Ratings
• Positivity Ratings
• Association Metrics

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.