🎓 Student-Academic-Performance

This project uses machine learning techniques to predict and classify student academic performance into three categories: High (H), Medium (M), and Low (L). It leverages behavioral and academic engagement data such as participation, resource usage, and absenteeism from a real-world dataset.

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🧩 Problem Statement

Academic institutions often lack predictive tools to identify students at risk of poor performance. The goal is to build a system that can classify students' academic performance using historical and behavioral data for early intervention and support.

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💡 Proposed Solution

• Classifies students into performance levels (High, Medium, Low)
• Trains multiple machine learning models for comparison
• Includes visual insights (EDA) and an interactive prediction interface
• Enables real-time prediction by entering custom student data

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🛠️ Technologies Used

• Python 3.9+
• Libraries:
  • pandas, numpy – Data handling
  • matplotlib, seaborn – Visualization
  • sklearn – Machine learning models and preprocessing
  • time, warnings – Runtime management

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📂 Dataset Overview

Dataset: AI-Data.csv

Features include:

• Demographics: Gender, Nationality, Place of Birth, Stage, Grade
• Academic Behavior: RaisedHands, VisitedResources, AnnouncementsView, Discussion
• Engagement: Parent Survey, Satisfaction, Absences
• Target: Class (H, M, L)

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🤖 Models Implemented

• Decision Tree Classifier
• Random Forest Classifier
• Perceptron (Linear Model)
• Logistic Regression
• Multi-layer Perceptron (Neural Network)

All models are trained, tested, and evaluated using accuracy, precision, recall, and F1-score metrics.

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