• G. V. L. Narayana
• Balla Sai Kiran
• Potnuru Hemanth
• Danda Akshay
• Battula Bala Surya Sai

Students encounter numerous challenges when trying to gain admission to their preferred college. The engineering admission process is often complicated, resulting in students joining colleges that may not match their true potential. To address this, a machine learning-based web application has been developed to predict the most suitable colleges for students. Utilizing algorithms such as K-Nearest Neighbors (KNN), Random Forest, and Decision Tree, the system analyzes historical data, including student ranks, gender, category, and admission trends, to generate personalized college recommendations. The backend, powered by Flask, integrates seamlessly with a user-friendly frontend, enabling students to input their details and receive real-time predictions. By providing data-driven insights, the application helps students make informed decisions, improving the accuracy of college selection and reducing the risk of poor career choices. Ultimately, this platform simplifies the admission process and assists students in securing admission to the best-suited institutions.

K-Nearest Neighbors classifier, Decision Tree Classifier, Random Forest Algorithm and Flask API

"Prediction of Engineering College Admissions Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.l358-l364, April-2025, Available :http://www.jetir.org/papers/JETIR2504B42.pdf

"Prediction of Engineering College Admissions Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppl358-l364, April-2025, Available at : http://www.jetir.org/papers/JETIR2504B42.pdf