Student Performance Predictor
ML-Powered Academic Performance Prediction
A machine learning application that predicts student academic performance using relevant academic and demographic data. Built to understand and implement the complete machine learning workflow from raw data to deployed predictions.
Overview
The project covers the full ML lifecycle — data collection, preprocessing, exploratory analysis, feature engineering, model training, evaluation, and prediction. It demonstrates how machine learning can be applied to educational data to identify students who may need additional support.
The Problem
Educational institutions often identify struggling students too late — after they have already fallen behind. Early prediction of performance can enable timely intervention and support.
The Solution
Built a supervised machine learning model trained on academic data including study hours, attendance, previous scores, and other relevant features to predict student performance outcomes.
Features
Tech Stack
📈 Demonstrates complete ML workflow on real educational data
Architecture
What I Learned
This project gave me hands-on experience with the complete machine learning workflow. I learned how data quality directly impacts model performance, how to approach feature engineering thoughtfully, and how to evaluate models beyond just accuracy.