---
title: Student Success Prediction
emoji: 🎓
colorFrom: blue
colorTo: indigo
sdk: streamlit
app_file: app.py
pinned: false
license: mit
---

# 🎓 Student Success Prediction

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

1. **User Input:** The user provides student details (e.g., study hours, attendance, previous grades) via the Streamlit frontend.
2. **Data Transformation:** `column.pickle` aligns the features, and `scale.pickle` scales the numerical data to match the training distribution.
3. **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:

1. **Clone the repository:**
   ```bash
   git clone [https://huggingface.co/spaces/amirsoahil101/Student_Success_Prediction](https://huggingface.co/spaces/amirsoahil101/Student_Success_Prediction)
   ```