This project developed two wine recommendation models using the XWines dataset, employing collaborative filtering and content-based techniques. It leveraged Python, Numpy, Pandas, Jupyter Notebook, VSCode, and Scikit-learn.

A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.

A notebook for movie and TV show recommendations using Boolean and TF-IDF methods. Get personalized suggestions based on text descriptions and choose the method that suits your preferences.

This is a repository that contains a jupyter notebook that has a movie recommender. You can use that for your reference to build applications for Movie Recommendation System.

A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise

This Notebook Recommends Movies by finding correlation based on user rating of each movie

In the IBM Watson Studio, there is a large collaborative community ecosystem of articles, datasets, notebooks, and other A.I. and ML. assets. Users of the system interact with all of this. This is a recommendation system project to enhance the user experience and connect them with assets. This personalizes the experience for each user.

This Notebook Recommends whether a user will be able to pay back loan or not. This system could be used by banks before giving loans to costumers

This Notebook Recommends Restaurants based on popularity

In this repository I'm implementing PyTorch based Deep Neural Networks from basic ANN to Advanced Graph Neural Networks. Please suggest if you have any ideas

Notebooks on using transformers for sequential recommendation tasks

In this notebook I have tried to use the data provided by Netflix and implement two recommender systems.

Music Recommender System

Repository of OpenClassrooms' AI Engineer path, project #9 : create a books recommandation system, integrate and deploy it as a mobile app

Repository will contain the files and notebook for demonstrating the different recommendation systems using a memory based approach.

Data Science Project for Udacity's Data Scientist Program. Using Python in Jupyter Notebook.

Using Python

Jupyter notebooks from recommendation systems classes

Machine_Learning_Techniques_Implementation notebooks. - Implement all ML techniques in python using SKLearn on different datasets. - Simple recommendation system - spam email classifier - Classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews.

Notebook for Data Science - Machine Learning