DropGuard: Predictive Student Retention Website

DropGuard: Engineering Student Retention Analysis Project Overview: Our project, DropGuard, focuses on analyzing the retention or dropout rates of engineering students in India. Using machine learning (ML), we aim to predict whether a student will continue their education or drop out based on various factors specific to the Indian context. We have an ML model trained on Google Colab for this purpose. While we prefer using our trained model, we are open to the development or integration of a new model that fits our requirements. Project Requirements: We need a comprehensive and interactive website with the following key features: 1]Student Retention Analysis: Predict the retention or dropout status for individual students using the ML model. Provide detailed insights into each prediction, highlighting the factors contributing to the outcome. 2]Dataset Management: Enable the registration of new students and update their information directly on the website. Automatically add new entries to the dataset for real-time analysis. 3]Data Visualization: Visualize the overall dataset with graphs and charts. Include individual-level visualizations for each student's data and retention prediction. If possible, integrate BI tools or advanced visualization software for enhanced data representation. 4]User Interface and Experience: The website should be user-friendly, visually appealing, and highly interactive. Incorporate features like intuitive navigation, responsive design, and modern styling to create an engaging experience. 5]Deployment: Deploy the website on a cloud platform (e.g., Google Cloud, AWS, Azure) to ensure easy accessibility. The deployed website must be capable of integration with our college’s app/website. Deliverables: 1]A Fully Functional Website: Meeting all the requirements mentioned above, with all necessary integrations and optimizations. 2]Comprehensive Research Report: A detailed document outlining the problem statement, methodology, dataset analysis, model performance, and key findings of the project. 3]Technical Documentation: A guide explaining the website's architecture, code structure, ML model integration, and steps to update or maintain the system. 4]Presentation Deck: A professionally designed presentation highlighting the project goals, implementation process, results, and key takeaways. 5]Step-by-Step Guidance: Instructions on running and deploying the website, tailored for beginners in deployment and hosting. #Additional Notes: We are open to creative suggestions for improving the functionality or aesthetics of the website. The project is highly focused on delivering an impressive, data-driven tool that can be showcased to academic mentors and evaluators.