A video search engine application in flask using Neo4j, MongoDB and MySQL.

This JavaScript engine recommends openings based on individual play style. Users can specify whether they are looking for an aggressive/passive opening or an open/closed game. They can also specify whether they like gambits and how deep into theory they'd like to go. Chess opening descriptions are scraped from Wikipedia.

Students performance system to help students improve their academic performance

A yelp recommendation system based on user classification and review emotional analysis

An recomendation system that uses portfolio optimization and LSTM stockmarket prediction to give user recommendation on investing

A Machine-Learning based Recommendation system for club football personnel to identify underperforming players of a given football club and provide appropriate younger replacements from other clubs.

Project repo to create a website that recommends a skincare routine based on a user's mood at the time.

Content Based Movie Recommendation System using flask.

A website where you can list the ingredients available with you, and the application will tell you what you can prepare with those ingredients. Option of different cuisines will be provided,where u can choose from, and the recipe will be provided in a few seconds. Useful for youngsters or amateurs in cooking, who can't decide what to prepare. Ke…

Providing Movie Recommendations and Critique on the reviews given to it by other people.

This is a movie recommendation system project that I developed to put into practice some Machine Learning techniques, so the goal is for the user to choose a movie that have already been watched and receive the recommendation of new movies related to the chosen one.

Movie recommendation system

Implementação de um sistema de recomendação da disciplina Teoria dos Grafos.

Crop Recommendation is a web application that suggest/recommend the farmer to which crop to grown his/her field.

This is the code for the movie recommendation app built for Microsoft Engage 2022 project.

A simple book recommender system made with NodeJS and mongoDB

Movie Recommendation System base on Content Based Filtering

Movie Recommendation Anytime Anywhere

This was a project for the course web technology for semester 5.