Abstract:
The Student Performance Prediction System uses machine learning to help predict student
success based on factors like demographics, test preparation, and behavioral habits. Along
with students reading and writing scores, the system seeks to provide educators and
managers insightful analysis of data including gender, color, parental education level, and
lunch type that can assist identify students who might require more support before academic
issues get more intense. For the project, ridge regression was selected as it offers a nice
mix between still producing accurate predictions and simplicity of understanding. Reliable
predictions produced by the system were evaluated and may be applied to guide decisions
and interventions in actual learning environments. Following significant data protection
rules like GDPR and FERPA, we also ensured the system upholds students' privacy.
Although the present version offers insightful analysis, we intend to enhance the system by
adding additional data, investigating more sophisticated machine learning approaches, and
thus improving its general accuracy. In the end, this technique is meant to provide a more
customized and fair learning environment, so enabling kids to flourish and so preventing
undetected falling behind.
