🎓 Student Score Prediction ML App

• Gender
• Race/Ethnicity
• Parental Level of Education
• Lunch Type
• Test Preparation Course
• Reading and Writing Scores

🚀 How it works

1. User enters student details in the Streamlit interface.
2. Input data is passed to the PredictPipeline from src/pipeline/predict_pipeline.py.
3. A trained ML model (with CatBoost/XGBoost/Scikit-learn) predicts the final score.
4. The prediction is displayed in real-time on the app.

🛠️ Tech Stack

• Python (scikit-learn, CatBoost, XGBoost, dill)
• Streamlit (for web app deployment)
• Pandas & NumPy (for data handling)
• Matplotlib & Seaborn (for visualization during development)

📦 Installation