Applied Random Forest Regression Analysis

Developed a regression-based machine learning model to predict task and completion durations using ensemble learning techniques. The project focused on understanding algorithmic behavior, feature importance, and performance evaluation through regression metrics. By applying Random Forest Regression, the solution captures non-linear relationships within the data and demonstrates how ensemble methods improve prediction accuracy and robustness across complex datasets.