PREDICTING YOUTUBE VIDEO VIEWS USING SUPERVISED REGRESSION MACHINE LEARNING

Predicting the popularity of online video content is an important machine learning problem because video views are influenced by multiple measurable characteristics such as engagement, publication timing, and platform metadata. This research presents a supervised learning approach for predicting the number of views received by trending YouTube videos in India. The study uses the INvideos.csv dataset from Kaggle, which contains 37,352 records and 16 original columns. The target variable is views, a continuous numerical quantity, making the task a regression problem. During preprocessing, identifiers and free-text fields were removed, while publication timestamps were transformed into year, month, weekday, and hour features. Missing records were removed, and the data was divided into training and testing sets using an 80:20 split. Four regression algorithms—Linear Regression, Decision Tree Regression, Random Forest Regression, and K-Nearest Neighbors Regression—were trained and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Random Forest produced the best performance, achieving an R² of 0.968, compared with 0.927 for Decision Tree, 0.922 for K-Nearest Neighbors, and 0.763 for Linear Regression. The results demonstrate that ensemble-based regression can effectively estimate YouTube video views from structured metadata and engagement indicators. However, because likes, dislikes, and comments may be recorded after publication, the model should be interpreted as an estimation of popularity using available metadata rather than a strictly pre-publication forecasting system.