Student Performance Prediction System
Overview
The Student Performance Prediction System is a machine learning project that predicts student academic performance based on various factors such as study time, attendance, parental education, and other socio-academic features.
The system uses supervised machine learning algorithms to analyze historical student data and predict performance outcomes. A Flask-based web application allows users to input student details and receive real-time predictions.
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Problem Statement
Educational institutions often struggle to identify students who may require additional academic support.
This project aims to use machine learning to predict student performance, enabling early intervention and better academic planning.
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Solution
A predictive machine learning model was built using historical student data. The model analyzes important academic and socio-demographic factors to estimate student performance.
The trained model is integrated into a Flask web application, allowing users to input student data and instantly receive predictions.
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Key Features
• Student Performance Prediction Predicts student academic outcomes using machine learning.
• Real-time Predictions Flask web interface allows users to input data and get predictions instantly.
• Data Preprocessing Pipeline Includes data cleaning, encoding, and feature scaling.
• Multiple ML Algorithms Tested Different models were evaluated to select the best-performing one.
• Model Evaluation Performance measured using standard regression metrics.
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Tech Stack
Programming Language • Python
Machine Learning • Scikit-learn • Pandas • NumPy
Data Visualization • Matplotlib • Seaborn
Backend • Flask
Tools • Jupyter Notebook • VS Code • Git