An AI-powered educational analytics system that predicts student exam performance based on behavioral and lifestyle factors and provides personalized recommendations using Google Gemini AI.
Student Performance Prediction is an EdTech project that leverages Machine Learning and Generative AI to analyze students' study habits and lifestyle patterns and predict their academic performance.
The system not only estimates exam scores but also generates personalized suggestions related to:
- Study planning
- Time management
- Lifestyle balance
- Sleep optimization
- Mental well-being
- 📈 Student exam score prediction using Machine Learning
- 🤖 AI-generated personalized study recommendations
- 📊 Exploratory Data Analysis (EDA) and visualization
- 🖥 Interactive Streamlit web application
- 🔗 Flask backend integration
- 🧠 Google Gemini AI assistance
- 📉 Model comparison and evaluation
- Streamlit
- Flask
- Python
- Scikit-Learn
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Google Gemini API
The dataset contains approximately 1100 student records with attributes such as:
- Age
- Study Hours Per Day
- Social Media Usage
- Netflix Hours
- Attendance Percentage
- Sleep Hours
- Exercise Frequency
- Mental Health Rating
Target Variable:
Exam Score
Three regression models were trained and evaluated:
| Model | R² Score | RMSE |
|---|---|---|
| Linear Regression | 0.804 | 7.10 |
| Random Forest Regressor | 0.790 | 7.34 |
| Decision Tree Regressor | 0.720 | 8.47 |
✅ Linear Regression
It achieved an R² score of 0.804, explaining approximately 80% of the variance in student performance.
After prediction, the application sends the student's data to Google Gemini AI, which generates:
- Study routine suggestions
- Mental wellness guidance
- Time management tips
- Sleep optimization advice
- Recommendations for students with part-time jobs
Streamlit UI
↓
Flask Backend
↓
Machine Learning Model
↓
Prediction Result
↓
Google Gemini AI
↓
Personalized Suggestions
- Data Collection
- Data Preprocessing
- Exploratory Data Analysis
- Feature Scaling
- Model Training
- Performance Evaluation
- Streamlit Frontend Development
- Flask Backend Integration
- Gemini AI Recommendation Generation
The application allows users to:
- Input lifestyle and academic information.
- Predict expected exam scores.
- Receive AI-powered academic guidance.
- Improve study habits through personalized recommendations.
- Add academic history and GPA analysis
- Real-time progress tracking
- Multi-semester performance prediction
- Mental health assessment integration
- Institution-level analytics dashboard
Priyanka Kar Master of Computer Application (MCA) Swami Vivekananda University West Bengal, India
Machine Learning • Educational Data Mining • Streamlit • Flask • Scikit-Learn • Generative AI • Google Gemini • Student Analytics • EdTech