🎓 AI-Powered Student Performance Prediction System

This project is a web application built with Streamlit that predicts student pass/fail outcomes based on their internal assessment marks and attendance. The goal is to provide an early warning system for educators to identify and support at-risk students before their final exams.

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## Features ✨

• Interactive Web Interface: A user-friendly frontend that allows for easy interaction.
• Flexible File Upload: Supports both CSV and Excel file formats for student data.
• Real-Time Prediction: Trains a Logistic Regression model and generates predictions instantly after data is uploaded.
• Clear Results: Displays the model's accuracy, a full list of student predictions, and a visual summary.
• Data Visualization: Automatically generates a bar chart showing the total number of students predicted to pass versus fail.

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## Installation ⚙️

To set up and run this project on your local machine, please follow these steps.

1. Prerequisites:

   • Make sure you have Python 3.7 or higher installed on your system.

2. Clone the Repository (or Download Files):

   • Download all the project files (including app.py, requirements.txt, and the data files) into a single folder on your computer.

3. Install Required Libraries:

   • Open your terminal or command prompt.
   • Navigate to the project folder where you saved the files.
   • Run the following command to install all necessary libraries from the requirements.txt file:

     pip install -r requirements.txt

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## How to Run the Application 🚀

1. Ensure you are in the project's root directory in your terminal.
2. Run the following command to launch the Streamlit application:

   python -m streamlit run app.py

3. The application will automatically open in a new tab in your default web browser.
4. Use the sidebar to upload the three data files (attendance, IA1, and IA2).
5. Click the "Run Prediction" button to see the results.

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