Student Performance Analysis and Prediction System

A comprehensive web application built with Streamlit for analyzing and predicting student academic performance using machine learning models. The system provides dashboards for professors and students, performance insights, and predictive analytics.

Features

• Role-Based Access: Separate login portals for professors and students.
• Interactive Dashboards: Visualize academic data with charts and statistics.
• Performance Prediction: Predict SGPA, Percentage, and Total Marks using pre-trained ML models.
• Data Analysis: Explore semester-wise performance trends and course analytics.
• MongoDB Integration: Secure storage and retrieval of student records.
• Automated Reporting: Generate test reports and prediction results.

Installation

1. Clone the Repository

   git clone https://github.com/username/student-performance-analysis.git

2. Install Dependencies

pip install -r requirements.txt

3. Set Up MongoDB

• Install MongoDB locally and start the service on mongodb://localhost:27017.
• Create a database named college_db with collections professors and students.

4. Run the Application

   streamlit run main.py