- Megan Steinmasel
- Michael Belmear
- Rohan Kamat
- The Online News Popularity project focuses on analyzing factors influencing the popularity of online news articles using the Online News Popularity dataset from UCI's machine learning repository, containing attributes extracted from news articles published by Mashable over two years. Stakeholders such as content creators, publishers, marketers, advertisers, and consumers stand to benefit from insights gained, informing content creation, marketing strategies, ad targeting, and content recommendations. Methodologically, the project employs hypothesis testing and correlation analysis to investigate feature relationships and their impact on article popularity. Bootstrapping techniques generate simulated samples for estimating confidence intervals and calculating p-values to assess statistical significance. Additionally, correlation matrices and heatmaps visualize attribute relationships, aiding in the identification of key factors driving online news article popularity.
- To read the official project report, view the news-popularity-documentation.pdf
- To see the data mining techniques employed using Python, view the final.ipynb