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Design and Implementation of a predictive model for students academic performance using machine learning

 Department: Computer Science  
 By: usericon olagok01  

 Project ID: 8891
   Rating:  (5.0) votes: 1
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   Price:₦5000
Abstract
The academic performance and retention of students are critical concerns for higher education institutions. Identifying students at risk of failure early enough for effective intervention remains a significant challenge, particularly in institutions like The Polytechnic Ibadan, which rely on reactive, manual systems. This study addresses this problem by designing and implementing a machine learning-based system to predict student academic performance proactively. The research leverages educational data mining techniques, utilizing the algorithm, Logistic Regression, on a dataset compiled from academic records. The methodology encompasses data collection, preprocessing, exploratory data analysis, model training, and evaluation. A comprehensive system analysis of the existing processes at The Polytechnic Ibadan was conducted, identifying key limitations such as delayed identification of at-risk students and inefficient use of available data. The proposed system aims to shift the paradigm from reactive to proactive by providing early warnings, enabling educators and advisors to implement timely support measures. By comparing multiple machine learning models, this research seeks to identify the most effective and generalizable predictor for student performance, thereby contributing to improved educational outcomes, resource allocation, and institutional decision-making....
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