Recommendation-system

This is my self taught recommender system git repository. Recommenddation system is field of study that trying to predict item similarity in a database given:

1. The feature score of each item in the database
2. The ranking that each users given to each item in the database In this github repository, I will look at all (possibly) algorithm that help us in building a recommendation system

Getting Started

Prerequisite:

1. Recommended learning ahead about machine learning, algorithm design and mathemtic linear algebra as well as calculus
2. Andrew Ng course in Machine Learning have a good introduction to Recommendation System: [https://www.coursera.org/learn/machine-learning/home/week/9]
3. Read the [https://www.kaggle.com/rounakbanik/movie-recommender-systems/notebook], he has a good python implementation of Simple Recommender, Content Based Recommendation and Collaborative Filterting Recommendation.

Dataset:

I got my dataset from Movie Lense in the data.zip file, you can download using MovieLense Database

Jupyternotebook file:

• DataProcessing.py: This file calls the Movie Lense Api to get the movie database and do data processing so that we have a easier time using it latter
• RecommenderSystem.py: This file is the reimplementation of the Kaggle file with more explanation on how the recommendation system actually work.