Paper Title
AI-Driven Placement Prediction and Insights

Abstract
A comprehensive, data-driven platform called "AI-Driven Placement Prediction and Insights" was created to help academic institutions and students better understand and improve placement preparedness. The system combines a number of contemporary technologies to provide precise placement forecasts and useful information. Flutter Web was used in the development of the user interface, making it responsive and fluid. Primarily, the platform utilizes a Random Forest algorithm-based machine learning model that was trained on a variety of student characteristics, such as academic performance (10th, 12th, and graduation grades), quiz scores, technical scores (out of 20), logical reasoning, verbal ability, quantitative aptitude, the number of projects and internships completed, and mastery of critical skills like SQL, DSA, Java, Python, C++, ML, AI, GenAI, Tableau, ReactJS, NodeJS, and more. Real-time predictions based on the most recent student inputs are ensured by exposing the model through a Flask API that is implemented on Render. The creation of comprehensive Power BI dashboards is made possible by the system's safe management and storage of all data. These dashboards help administrators and students identify placement trends, skill gaps, and overall performance data by providing relevant visuals. To ensure scalability and accessibility, the complete system is integrated into the Flutter Web application, and Firebase Hosting is used for the final release. This project demonstrates the usefulness of AI and ML in the educational field as well as the ability of full-stack integration to provide insightful, engaging, and significant solutions. Keywords - Placement Analysis, Placement Prediction, Machine Learning, Data Visualization, Data-Driven