In this project I have implemented a k-means clustering algorithm to explore the similarities and differences in people's tastes in movies based on how they rate different movies. This algorithm is a simplistic recommendation engine that shows the most basic idea of "collaborative filtering".
The data I have used comes from the wonderful MovieLens user rating dataset.
This project is from Udacity Machine Learning Engineer Nanodegre.
For more info see simple-recommendation-engine.ipynb file.
This project requires Python and the following Python libraries installed:
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.
simple-recommendation-engine.ipynb: notebook file with coding and explanation about the project.
ml-latest-small: File with a dataset that describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service.
helper.py: File with auxiliar methods used in notebook file.
In a terminal or command window, navigate to the top-level project directory simple-movies-recommendation-engine/ (that contains this README) and run the following command:
jupyter notebook simple-movies-recommendation-engine.ipynbThis will open the Jupyter Notebook software and project file in your browser.