🎓 AI Student Performance Analytics Platform
An AI-powered web application that predicts student math scores and provides interactive analytics dashboards to understand academic performance patterns.
The system uses machine learning models trained on student performance data to analyze relationships between different academic factors and predict student outcomes.
🚀 Live Demo
🔗 Access the deployed application here
👉 https://ai-student-performance-kagaxfmtgwugvkjwqkyfrz.streamlit.app/
📌 Project Overview
The AI Student Performance Analytics Platform is designed to analyze and predict student academic performance using machine learning.
The application allows users to input student academic attributes and predict the Math Score based on learned patterns from historical data.
The system also provides interactive visualizations that help users explore performance trends and insights.
✨ Key Features
🎯 Predict Student Math Scores using Machine Learning
📊 Interactive analytics dashboard
📈 Data visualization using Plotly
🔐 User authentication system
🕓 Prediction history tracking
📥 Download prediction results
🌐 Web application built using Streamlit
🧠 Machine Learning Models
The following machine learning models were implemented and evaluated:
Model Description Linear Regression Predicts scores using linear relationships Decision Tree Captures nonlinear patterns and decision rules Random Forest Ensemble learning method for improved accuracy Model Performance Model MSE R² Score Linear Regression 30.28 0.87 Decision Tree 71.54 0.69 Random Forest 35.82 0.84
✅ Linear Regression achieved the best performance with the highest R² score.
📊 Input Features
The prediction model uses the following inputs:
📖 Reading Score
✍️ Writing Score
👩🎓 Gender
🍱 Lunch Type
Output
🎯 Predicted Math Score
🛠 Tech Stack Programming Language
Python
Framework
Streamlit
Machine Learning
Scikit-learn
Data Processing
Pandas
NumPy
Visualization
Plotly
Database
SQLite
Version Control
Git & GitHub
📂 Project Structure
AI-Student-Performance
│
├── app.py
├── model.pkl
├── dataset
│ └── StudentsPerformance.csv
├── requirements.txt
├── README.md
└── database.db
If you want to run the project on your own system, follow these steps.
1️⃣ Clone the Repository git clone https://github.com/anuskagupta123/AI-Student-Performance.git 2️⃣ Navigate to the Project Folder cd AI-Student-Performance 3️⃣ Install Dependencies pip install -r requirements.txt 4️⃣ Run the Streamlit Application streamlit run app.py
The application will open in your browser.
📈 Example Prediction Input
Reading Score: 75 Writing Score: 80 Gender: Female Lunch Type: Standard
Output
🎯 Predicted Math Score: 78
The prediction is generated using the trained machine learning model based on patterns learned from the dataset.
📊 Dataset
This project uses the Students Performance Dataset, which contains academic scores and demographic factors of students.
Dataset attributes include:
Gender
Reading Score
Writing Score
Math Score
Lunch Type
Test Preparation Course
Parental Level of Education
👩💻 Author
Anuska Gupta Computer Science Student | Machine Learning Enthusiast
🔗 GitHub https://github.com/anuskagupta123
⭐ Future Improvements
Add deep learning models
Improve model accuracy
Add teacher/admin analytics dashboards
Add more academic features
Deploy using Docker and cloud infrastructure