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This repository contains a web application that integrates with a music recommendation system, which leverages a dataset of 3,415 audio files, each lasting thirty seconds, utilising a Locality-Sensitive Hashing (LSH) implementation to determine rhythmic similarity, as part of an assignment for the Fundamental of Big Data Analytics (DS2004) course.

License: BSD 3-Clause "New" or "Revised" License

Jupyter Notebook 99.18% Python 0.48% HTML 0.34%
ann approximate-nearest-neighbors big-data data-science flask-application locality-sensitive-hashing lsh music music-information-retrieval music-recommendation

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I'm a 21-year-old data science aficionado hailing from the vibrant city of Islamabad, Pakistan. 🌆 Armed with a love for all things data and artificial intelligence, I'm on a mission to conquer the world of analytics with my scientific prowess. 👨🏼‍💻



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