This project aims to predict student academic performance using educational data analytics and machine learning techniques. The objective is to identify key academic and behavioral factors influencing performance and assist institutions in improving learning outcomes and student support systems.
Collect educational datasets containing student marks, attendance, assignments, and demographic data.
Clean and preprocess the dataset by handling null values and encoding categorical variables.
Perform exploratory data analysis to understand score distributions and correlations.
Identify significant features affecting student performance.
Split data into training and testing sets.
Implement machine learning algorithms such as linear regression, decision trees, or random forest.
Train models and evaluate them using accuracy, precision, recall, and RMSE metrics.
Compare multiple models to select the best-performing approach.
Analyze prediction results and interpret influencing factors.
Visualize predicted vs actual performance using graphs.
Prepare detailed documentation covering system architecture, algorithms, and findings.
