This project focuses on predicting the popularity of online news articles based on a variety of features such as the article's title length, the number of images, the number of videos, and more. The dataset used in this project is derived from the UCI Machine Learning Repository's Online News Popularity dataset.
The goal of the project is to develop an accurate predictive model using various machine learning techniques. The project begins with a comprehensive exploratory data analysis (EDA), which includes data cleaning, handling missing values, and understanding the distribution and correlation of features.
Overall, this project provides a comprehensive example of a machine learning workflow for predicting news popularity. The project files include all code used in the data preprocessing, model training, hyperparameter tuning, and evaluation stages of the project. Moreover, the detailed analysis of the project is written in the report(OnlineNewsPopularity-Prediction-report.pdf) present in the repo itself.