Recommendations with IBM

Project Motivation

For this project, I was interested in analyzing the interactions that users have with articles on the IBM Watson Studio platform, and making recommendations to them about new articles they would like. The project was divided into the following tasks:

• Exploratory Data Analysis
• Rank Based Recommendations
• User-User Based Collaborative Filtering
• Matrix factorization

Installations

• NumPy
• Pandas
• Seaborn
• Matplotlib
• Wordcloud
• Pillow
• Pickle
• No additional installations beyond the Anaconda distribution of Python and Jupyter notebooks.

File Descriptions

• data
  • articles_community.csv # articles
  • user-item-interactions.csv # user item interactions data
• Recommendations_with_IBM.ipynb # Jupyter notebook
• Recommendations_with_IBM.ipynb # html of jupyter notebook
• top_5.p, top_10.p, top_20.p
• README.md

Acknowledgement

This analysis is benefited from the Udacity instructor and mentor team's help and support.