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.
A composition of Machine Learning Projects in python using algorithms in supervised, unsupervised, and deep learning.
This project is a skincare recommendation system that uses webcam detection, image analysis, or manual input to identify skin concerns and suggest suitable products from a dataset.
MovieMinds - Connect with similar cinephiles
SOEN471 Project - Team 10 - Winter 2024
Collaborative Filtering based on Google Analytics 360 data from BigQuery.
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.
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)
Provide book reccomendations using local LLMs, ensuring higher accuracy and reliability by cross referencing LLM with exisiting book database.
Indian food reccomendor
An overview of reccomendation systems in R
Web Application for Movie Reccomendations using FAISS
제12회 산업통상자원부 공공데이터 활용 아이디어 공모전 최우수상🏆 | LLM 프롬프트 엔지니어링 | 추천 시스템 | 자연어 처리
🎵 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.
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