Unlocking the Secrets of Online Engagement: A Deep Dive Into Website Traffic

Accurately predicting web traffic is crucial for optimizing website performance and understanding user behavior. This study investigates various machine learning and statistical models to forecast web traffic trends. We apply a range of algorithms, including XGBoost, Random Forest, SARIMA, ARIMA, Prophet, and Moving Average with Seasonal Index, to two distinct datasets: “Daily Website Visitors” and “Future Traffic to Wikipedia Pages.” The datasets are preprocessed and analyzed to uncover underlying trends and seasonal patterns, with models evaluated using key performance metrics such as MSE, RMSE, and MAPE. Our results demonstrate that the Moving Average model with Seasonal Index outperforms other models in terms of accuracy, achieving 95.87% accuracy on the “Daily Website Visitors” dataset and 98.23% accuracy on the “Future Traffic to Wikipedia Pages” dataset. These findings highlight the importance of leveraging advanced machine learning techniques alongside traditional statistical methods to improve web traffic forecasting, providing valuable insights for website optimization and digital marketing strategies.