Movie Recommendation Using Matrix Factorization.

This is a repository for our CE7454 Deep Learning for Data Science Project, Group 07

Movies recommendation and rating prediction using collaborative filtering.

Analysis of Google PlayStore reviews for “League of Legends: Wild Rift” and “Mobile Legends: Bang Bang”

To predict if a customer will like a movie or not depending on his past ratings and preferences using RBMs.

for beginners tutorial

An Amazon product recommender system that predicts product ratings and review helpfulness based on linear regression and latent-fact model.

Implemented a model that is capable of predicting a restaurant rating taking into account several factors such as reviews and restaurant facilities. Analysis of review is done based on NLP techniques that include polarity analysis, TF-IDF which are all followed by pre-processing.

Worked on building a predictive model by considering multicollinearity and other Machine learning concepts related to  factors or variables using R programming. 

A Python Package for Benchmarking Collaborative Filtering Algorithms in Recommendation

Predicting Amazon ratings based on reviews by Text Classification using the Naive Bayes Algorithm.

Dash app on Render about a Dashboard for Sales and Rating Prediction

Netflix data challenge hosted by PRML course in IITM, we secured 5th position as team Goodfellas

Solutions to the programming assignments to the Machine Learning Course offered by Stanford University

电子科技大学 2020 级《信息检索》课程代码。

This project is an implementation of simple rating prediction systems for items from user

Kaggle competition for a UCSD graduate level CSE class " Recommenders System"

The Glicko-2 rating system for Scala and Scala.js

Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.