A project to explore different recommendation algorithms using Netflix Competition data.
This project was built to explore different recommendation algorithms and understand each of their shortcomings/merits. The project is designed in a way where each of the algorithms will be developed using jupyter notebooks, followed by a Python script implementation. Eventually, the goal is to build a library of recommendation algorithms where users can understand implementation details, compare performance and pick algorithms that best fit their needs.
Before you can run any code, you'll need to download the raw data associated with this project, which can be found here. Download the data, put it at the base of your project directory in a folder called data and add that folder to the .gitignore file.
This project is primarily built in Jupyter notebooks/python 3.7.1 and involves packages which need to be installed. To make getting started with this project easier, we've created a requirements.txt file which contains all the different python packages needed. You can utilize this file (after cloning this repository) by using the following command:
pip install -r requirements.txt
We suggest starting with a specific algorithm, exploring it's corresponding jupyter notebook and go from there.