Student Performance Predict

Developed a supervised machine learning classification project for predicting student performance. The project included data cleaning and preprocessing, exploratory data analysis and visualization, feature selection, and training and evaluating multiple classification models, including Logistic Regression, SVM, KNN, and Decision Tree. The best-performing model was selected for prediction and integrated into an interactive interface for student performance prediction.