Recommendation Systems with Python Machine Learning AI

Introduction

This is a project that builds recommender systems: Classification-based, Model-based Collaborative filtering systems and Content-based recommender systems. At the end I also evaluate which recommender performed the best

Data

1. Bank Marketing Data for Classification-based CF: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing#
2. MovieLens Data for Model-based CF: https://grouplens.org/datasets/movielens/100k/
3. mtcars Data for Content-based recommender: mtcars dataset source: Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

Recommender Systems

1. Classification-based Collaborative Filtering Systems

File: Classification_based_collaborative_filtering.ipynb - a notebook that demonstrates how calssification-based CF works powered by Logistic Regression classifier

2. Model-Based Collaborative Filtering Systems

File: Model-based collaborative filtering.ipynb - a notebook that demonstrates how model-based CF works powered by SVD Matrix Factorization

3. Content-Based Recommender Systems

File: Content based recommender systems.ipynb - a notebook that demonstrates how content based recommender system works powered by Nearest Neighbor Algo

References

https://www.linkedin.com/learning/building-a-recommendation-system-with-python-machine-learning-ai/welcome