Creating a custom locale based college recommendation system using K-means Clustering
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Original Presentation can be found here
In the era of machine learning and its diverse applications, recommendation systems stand out as a prominent and fascinating area of exploration. Recently, I had the privilege of presenting my research paper at the Virtual Multidisciplinary International Conference on Futuristic Trends for Sustainable Ecosystem. The paper delved into the development of a custom locale-based college recommendation system using K-means clustering, showcasing the immense potential of this approach in assisting prospective students with their higher education decisions.
The research paper provided an overview of the evolving landscape of machine learning and its significance in recommendation systems. By employing K-means clustering, a powerful unsupervised learning algorithm, the system effectively grouped colleges across the United States based on similarity indexes. Leveraging massive online data and the Foursquare application programming interface, the recommendation system presented users with universities that shared similarities with their preferred institutions.
This research not only demonstrated the practical application of clustering techniques in recommendation systems but also highlighted the value of personalized recommendations for prospective students. By enabling individuals to discover colleges similar to their preferences, the system empowers them to make informed decisions about their higher education journey. As the academic landscape continues to evolve, harnessing the power of machine learning in this domain holds the potential to revolutionize the way students explore and select colleges, ultimately shaping their future paths in a more meaningful and personalized manner.