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

FRUDRERA is an AI-powered recipe recommender that suggests recipes based on the ingredients detected in a photo of your fridge. It utilizes object detection and OCR to identify ingredients and recommend recipes accordingly.

SOEN471 Project - Team 10 - Winter 2024

Collaborative Filtering based on Google Analytics 360 data from BigQuery.

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.

제12회 산업통상자원부 공공데이터 활용 아이디어 공모전 최우수상🏆 | LLM 프롬프트 엔지니어링 | 추천 시스템 | 자연어 처리

Receive tailored suggestions for new reads based on your interests and books you have read before.

An overview of reccomendation systems in Python

Project for HackSC (The University of Southern California Hackathon)

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.

Diet Recommendation System using KNN and built with Python for backend, ReactJS for frontend, and Docker for fast deployment.

This is a collaborative filtering based books recommender system & a streamlit web application that can recommend various kinds of similar books based on an user interest.

Conducted Market Basket Analysis (MBA) on Amazon product dataset to enhance recommendations. Identified top-selling products and top products in each category using review count. Implemented asso- ciation rule mining for personalized recommendations. Evaluated effectiveness through metrics.

A Flask-based movie recommender system based on TF-IDF vectorization and cosine similarity.

🎵 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.

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.