juliisstudy/predict_popularity_news

Predicting the Popularity of Online News Machine Learning Project

Predicting the Popularity of Online News Machine Learning

About the Project

The project aims to experiment the classification models that were claimed in the paper, A Proactive Intelligent Decision Support System (IDSS) for Predicting the Popularity of Online News. The four models:

• Random Forest (RF)
• Support Vector Machine (SVM) with a Radial Basis Function (RBF)
• K-NearestNeighbors (KNN)
• Naive Bayes (NB)

I tested the different sets of data attributes: all attributes, the attributes selected by CfsSubsetEval, and the attributes selected on the paper. The experimental results jusified the claims in the paper. **Random Forest (RF)** has the highest prediction accuracy rate.

Built With

• Python
• sklearn library