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.

DeepShelf is your AI-powered book buddy 📚🤖 — type what you’re in the mood for, and it finds the perfect novel using smart search + ranking ✨🔍

This project was done to fulfil the Machine Learning Terapan 2nd assignment submission on Dicoding. The domain used in this project is book recommendation.

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

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.

An AI-based inventory optimization system that leverages machine learning to predict demand, recommend menu items, and streamline stock management for restaurants and food service businesses.. — all deployed through a real-time Stream lit web app.

Creating an Product Recommender System with Apriori and FPGrowth.

MovieMinds - Connect with similar cinephiles

A Multi-Agent Deep Reinforcement Learning (MARL) based system that recommends research papers based on user-selected categories. Multiple DQN-trained agents collaboratively learn optimal policies to suggest relevant and diverse papers tailored to user preferences.

⚡ Blazing-fast graph processing engine in Rust with ML-powered optimization. 50-200x faster than Python/JS. Self-tuning algorithms, real-time anomaly detection, and sub-10ms queries on million-node graphs. 🚀🧠

All-in-one stealth OSINT reconnaissance tool for threat intel, bug bounty, and red teamers. metadata extraction, and parameter fuzzing included.

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

Simple and interpretable recommender system using cosine similarity between movie vectors.

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

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.

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

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).

Recommending videogames based on games and gamers similarity and segmenting gamers into groups of similar preferences