Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179586,
        author = {Amulya M Murthy and Ananya H and Chinmayee N and Hitha S and Shashidhara H V},
        title = {EDU tour – career recommendation system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7350-7354},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179586},
        abstract = {Today, students have limitless choices when it 
comes to academic and career options. Given the lack of 
automated, tailored support, many students are left to 
navigate the complex world of career decisions on their 
own, leading to undesirable outcomes. In this project, we 
develop an AI-based web application that forecasts 
individualized career paths using a Long Short-Term 
Memory (LSTM) neural network through a Flask 
application. The system is built to provide instant, 
scalable, and backed by data career suggestions in a sleek 
and intuitive platform.},
        keywords = {education technology, student interests,  Flask, LSTM, deep learning, career prediction.},
        month = {May},
        }