A composition of Machine Learning Projects in python using algorithms in supervised, unsupervised, and deep learning.

An overview of reccomendation systems in Python

Project for HackSC (The University of Southern California Hackathon)

Reading Recommendation System: This project implements K Nearest Neighbor (kNN) Collaborative Filtering to build a book recommender system based on a publicly available dataset.

M.Sc. Courses in Data Science, including Machine Learning, Deep Learning, Statistics and Data Analysis, and Recommendation Systems.

Indian food reccomendor

An overview of reccomendation systems in R

Building a Custom Vector Search Engine with Weaviate : The project discusses the architecture of Weaviate, an open-source vector database and provides a tutorial implementation of a custom vector search engine using Weaviate Cloud Service(WCS).

By using a dataset sourced from IMDb taken from the kaggle.com site. This system can provide video game recommendations based on their genre.

Personalized smoking recommendations based on Collaborative Filtering.

Exploring Bloom embeddings as a compression technique for recommendation algorithms. Aimed at reducing the size of large input and output dimensionalities to enhance training and deployment efficiency on devices with limited hardware. This project evaluates Bloom embeddings using various hash functions and compares them with alternative methods.

🎵 Unlock the Future of Music with Predictive Analysis!

CineSuggest," an advanced movie recommender powered by machine learning, removes uncertainty in film selection, employing data-rich algorithms for personalization.