Basic Movie Recommendation Web Application using user-item collaborative filtering.

Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate recommendations🤩

An end-to-end restaurant recommendation system built with Flask and Python. This project showcases a fully functional web application, hosted on Heroku, that helps users discover the best dining options based on their preferences.

Auto Complete / Suggestion feature using Trie data structure

A recommender engine based on Collaborative Filtering of the games available on the Steam Game Store

A platform to read and share 📚 with other users, the platform tracks users by collecting there data and exposing the collected data as an API

Recommendation System with IBM Watson

Experimental Design & Recommendations Project of Udacity Data Scientist Nanodegree

This project is a part of the Data Scientist Nanodegree by Udacity. It features an important collaboration with IBM, the provider of the dataset. The aim is to develop a recommendation engine and suggest new articles to the IBM Watson Community users.

Auto Complete / Suggestion feature using Trie data structure

Library Information System

Built article recommendation engines for IBM Watson's platform users. Used collaborative filtering, content-based, ranking and knowledge-based techniques.

An audio book recommendation engine for Audible users.

Docker webapp on django - Parisian Culture - Datamuse

Rank-based, collaborative filtering and matrix factorisation techniques for Recommendation Engine for IBM Watson Studio platform

EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.

A modern, responsive web application that delivers personalized content recommendations based on user preferences and behavior. This interactive recommendation system allows users to discover content tailored to their interests through category selection, tag filtering, and customizable content parameters.

This repository analyze user interactions with articles on the IBM Watson Studio platform and develop recommendation systems to suggest new articles that align with their interests.

P&G Hack - Recommendation platform

Here is the Repository contains three Data Science projects which is Disaster_Response_Pipeline, Recommendation_with_IBM, Write a Data_Science Blog Post.