A data-driven academic project that uses machine learning to analyze student attendance and assessment data, and predict overall academic performance.
Smart Attendance & Performance Prediction is an academic mini project created to explore how data and machine learning can support academic evaluation.
Instead of relying only on final exam scores, this system looks at attendance percentage, internal assessment marks, and assignment performance to predict whether a student is likely to pass or fail.
The goal is simple:
to show how early indicators can help identify students who may need academic support before it’s too late.
In many academic systems, student performance is evaluated only at the end of the semester.
This project demonstrates how predictive analysis can help:
- identify performance trends early
- reduce last-minute academic failures
- support data-based academic decisions
It’s a small project, but it reflects a real-world problem.
Student Academic Data
(attendance, internals, assignments)
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Machine Learning Logic (Python)
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Performance Prediction (Pass / Fail)
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Auto-generated PDF Report