Academic Performance Prediction Using Machine Learning

Developed an end-to-end machine learning solution to predict students' academic performance based on behavioral, demographic, and academic data. The project involved data preprocessing, exploratory data analysis, feature engineering, model training, and performance evaluation. Multiple classification algorithms were compared to identify the best-performing model. The final solution provides early predictions that can help educational institutions identify students at academic risk and support data-driven decision-making. Responsibilities Collected and analyzed student performance data. Cleaned and preprocessed the dataset. Performed Exploratory Data Analysis (EDA). Engineered meaningful features to improve model performance. Built and compared multiple machine learning classification models. Evaluated models using Accuracy, Precision, Recall, and F1-Score. Selected the best-performing model based on evaluation metrics. Visualized insights using charts and performance reports. Documented the complete machine learning workflow and project outcomes.