STUDENT PERFORMANCE PREDICTION SYSTEM FINAL YEAR PROJECT REPORT

Abstract

Machine learning uses hidden relationships and patterns of data for predicting future trends. Companies use data mining to turn raw data into meaningful information by discovering underlying patterns of data. By using software to look for patterns in large batches of data, organizations and different universities can learn more about their stakeholders and specifically for the students to develop better study strategies, greater results, and lower failure rates. Over the last decade, improvements in processing power and speed have enabled us to automate traditional, time- consuming data analysis. The more complex the data sets collected; the more insights can be gained. We aim to build a data-driven website that uses data mining and machine learning techniques to predict student’s performances according to their historic data. We will first identify student attributes that can be used to predict their performance through analysing available research work. Later, we will collect the data about students’ attributes through a data collection form. The data will be processed by using state of the art data mining methods and by training regression and classification machine learning models. The best performing machine learning model will be used in the website to predict student performance. The website will also enable its users to recommend resources (e.g., books, tutorials, and research papers) based on their experiences while studying a course. It will also enable students to offer their services for understanding a topic or problem in a course