I developed a machine learning model to predict student academic performance using Random Forest Regression. The project involved comprehensive data preprocessing, including handling missing values and encoding categorical variables to prepare the dataset for analysis. I employed Random Forest Regression due to its robustness and ability to handle complex datasets with high dimensionality. The model was evaluated using Mean Squared Error (MSE) to ensure accurate predictions of students' final grades based on various features, such as attendance and prior academic records.