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What is Predicting Student Dropout Risk with KNN about?

This project focuses on predicting student dropout using a dataset of 5000 students preparing for the JEE exam, employing Exploratory Data Analysis (EDA), K-Nearest Neighbors (KNN) for classification, and K-Means clustering for risk categorization. Key findings indicate that lower academic scores, fewer study hours, and psychological issues correlate with higher dropout rates, while the KNN model achieved an accuracy of about 80%. The K-Means clustering identified three performance groups, aiding in the vis

Author

vpranavrajan

Language

EN