An end-to-end Machine Learning web application that predicts a student's final score and success metrics based on various academic and behavioral features. This project is deployed on Hugging Face Spaces using Streamlit.
🚀 Features
Accurate Predictions: Utilizes a trained machine learning regression pipeline.
Interactive UI: Built with Streamlit for a smooth and user-friendly experience.
Robust Preprocessing: Uses serialized scaling and column transformation to ensure data consistency.
📁 Repository Structure
app.py: The main Streamlit application file handling user inputs and UI.
model.pickle: The trained Machine Learning model serialized using Pickle.
scale.pickle: The serialized StandardScaler instance used for feature scaling.
column.pickle: The serialized column transformer or list of feature columns to maintain structural alignment.
README.md: Model documentation and instructions.
🛠️ How It Works
User Input: The user provides student details (e.g., study hours, attendance, previous grades) via the Streamlit frontend.
Data Transformation:column.pickle aligns the features, and scale.pickle scales the numerical data to match the training distribution.
Inference: The processed data is fed into model.pickle to predict the final student score instantly.
💻 Local Installation & Setup
If you want to run this project locally, follow these steps:
