Recommendations with IBM Project in Data Science Nano Degree
To be able to run and view this project. It's recommended to have the latest versions of the followings:
you need to install Jupyter Notebook to run and execute an Jupyter Notebook
For this project, I analyzed the interactions that users have with articles on the IBM Watson Studio platform, and made recommendations to them about new articles that would they will like.
The project consist from
- data folder that contains the data set csv's files:
- user-item-interactions.csv file: contain the data about users and articles interactions.
- articles_community.csv file: contain the data about articles.
- Recommendations_with_IBM.ipynb file:
Jupyter Notebook that carries projects parts:
I. Exploratory Data Analysis:
Where I explored the data I'm working with for the project.
II. Rank Based Recommendations:
Where I found the most popular articles simply based on the most interactions.
III. User-User Based Collaborative Filtering:
Looked at users that are similar in terms of the items they have interacted with and made recommendations based on this similarities.
IV. Matrix Factorization:
Used a machine learning approach to building recommendations. Using the user-item interactions build out a matrix decomposition. then used the decomposition to make perdiction about recommendations to users.
- Recommendations_with_IBM.html file: an html version of Recommendations_with_IBM.ipynb file
- project_tests.py file: python file that contains test carried in Recommendations_with_IBM.ipynb
- top_5.p, top_10.p and top_20.p files: hold's data related to project_tests.py
- user_item_matrix.p file: hold's user item interaction matrix loaded in Matrix Factorization part in Recommendations_with_IBM.ipynb.
Credit given to Udacity courses for code ideas and motivation , and to IBM Watson Studio platform for the data.
Author: NYRoomi