Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The purpose of this project is to construct prediction model using various machine learning algorithms and to document the end-to-end steps using a template. The Online News Popularity dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.
INTRODUCTION: This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the article’s popularity level in social networks. The dataset does not contain the original content, but some statistics associated with it. The original content be publicly accessed and retrieved using the provided URLs.
Many thanks to K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 – Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal, for making the dataset and benchmarking information available.
ANALYSIS: The baseline performance of the ten algorithms achieved an average accuracy of 59.95%. Three algorithms (Bagged CART, AdaBoost, and Stochastic Gradient Boosting) achieved the top three accuracy scores after the first round of modeling. After a series of tuning trials, Stochastic Gradient Boosting turned in the top result using the training data. It achieved an average accuracy of 67.38%. Using the optimized tuning parameter available, the Stochastic Gradient Boosting algorithm processed the validation dataset with an accuracy of 66.89%, which was just slightly worse than the training data.
CONCLUSION: For this iteration, the Stochastic Gradient Boosting algorithm achieved the top-tier training and validation results. For this dataset, Stochastic Gradient Boosting should be considered for further modeling or production use.
Dataset Used: Online News Popularity Dataset
Dataset ML Model: Binary classification with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity
The HTML formatted report can be found here on GitHub.