Ai student success prediction

Student-sucess-prediction 🎓 AI Student Success Prediction System An AI-powered system that predicts whether a university student is likely to Graduate, Stay Enrolled, or Drop Out using machine learning and provides intelligent explanations using Google Gemini API.

This project helps universities identify students at risk early so they can provide academic or financial support and improve student retention.

📌 Project Overview Student dropout is a major challenge for universities worldwide. Many students leave their studies due to academic difficulties or financial problems.

This project uses machine learning and generative AI to analyze student data and predict academic outcomes. The system also generates human-readable explanations and recommendations to help university administrators support students effectively.

🚀 Features 📊 Predicts student outcome:

Graduate Enrolled Dropout 🤖 AI explanation using Google Gemini API

📈 Machine Learning model trained using XGBoost

🖥 Interactive dashboard built with Streamlit

🧠 Provides insights about academic and financial risk factors

🛠 Technologies Used Python Scikit-learn XGBoost Streamlit Pandas Joblib Google Gemini API 📂 Project Structure predictive_customer_outreach/ │ ├── app/ │ └── streamlit_app.py # Streamlit dashboard application │ ├── model/ │ ├── student_success_xgb_model.pkl │ ├── label_encoder.pkl │ └── feature_columns.pkl │ ├── data/ │ └── data.csv # Dataset used for training │ ├── notebook/ │ └── student_success_prediction.ipynb # Model training notebook │ ├── requirements.txt # Project dependencies ├── .gitignore # Files ignored by Git └── README.md # Project documentation ⚙️ How the System Works User enters student information in the dashboard The trained machine learning model analyzes the input data The model predicts the student's academic outcome Gemini AI generates an explanation of the prediction The system provides recommendations for university support 📊 Input Features Used The model analyzes several academic and financial indicators:

Age at enrollment Admission grade Previous qualification grade 1st semester units enrolled 1st semester units approved 2nd semester units approved Debtor status Scholarship holder Tuition fees up to date Gender These features help the model understand student academic performance and financial risk factors.

🧠 Machine Learning Model The prediction model is trained using XGBoost, a powerful gradient boosting algorithm widely used for structured datasets.

The model is saved as a .pkl file and loaded into the Streamlit application for real-time predictions.

🤖 AI Explanation with Gemini The system integrates Google Gemini API to generate:

Simple explanation of predictions Key factors influencing student outcomes Practical recommendations for universities This makes the system more interpretable and useful for decision-makers.

▶️ Running the Project

1. Clone the repository git clone https://github.com/vivekkumar326/ai-student-sucess-prediction
2. Navigate to the project cd ai-student-success-prediction
3. Create virtual environment python3 -m venv venv source venv/bin/activate
4. Install dependencies pip install -r requirements.txt
5. Run the Streamlit dashboard streamlit run app/streamlit_app.py 🎯 Project Impact This system can help universities:

Detect students at risk of dropping out Improve student retention Provide early academic intervention Support data-driven educational decisions 📌 Future Improvements Add feature importance visualization Implement SHAP explainability Deploy the system as a cloud-based web application Integrate with university student information systems 👨‍💻 Author Vivek Kumar

Machine Learning Project – Student Success Prediction System

Built With

• api
• gemini
• github
• joblib
• jupyetrnotebook
• ml
• model
• numpy
• pandas
• python
• scikit-learn
• streamlit
• xgboost