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.

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

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 ✨🔍

SOEN471 Project - Team 10 - Winter 2024

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.

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.

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.

MovieMinds - Connect with similar cinephiles

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

Creating an Product Recommender System with Apriori and FPGrowth.

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

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

An overview of reccomendation systems in Python

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.

Developed a movie recommendation system by sourcing data from the New York Times Article Search and The Movie Database APIs. Extracted, merged, and cleaned data to create a comprehensive dataset, enabling users to find movie reviews and related titles based on their preferences.

I developed a simple content-based recommendation system that suggests movies to users based on their preferences. Users can enter a movie they like, and the system recommends other movies with similar genres. This project helped me understand the basics of recommendation systems and content-based filtering techniques.

🎵 Unlock the Future of Music with Predictive Analysis!

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.

A Movie Recommendation System to recommend movies to users based on their preferences and past interactions.