A python movie recommendation system created on jupyter notebook.

Python notebook for Book Recommendation System using collaborative filtering.

Jupyter notebook file with recommendation methods for articles to users of the IBM Watson Studio platform.

Drug Repurposing Datasets for Collaborative Filtering Methods. Notebooks and code to generate datasets for collaborative filtering-based drug repurposing.

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.

Euphoric Fiddler is a bunch of random experiments and scripts in data preprocessing and image filtering. It includes some notebooks on recommendations based on category and collaborative filtering. None of the code is optimised for production and is largely used as a reference to quick scripts and as a playground.

The complete recommender system using both Collaborative FIltering and Content based filtering approaches, in addition to a web crawler, an API and the main website.

In this repository, I have share notebook which contains recommender systems algorithms like apriori, collaborative filtering and SVD using surprise library of python.

Implementation of Deep-learning techniques in pytorch

A collection of Jupyter notebooks on articles and material online related to recommender systems in python.

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

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

Machine Learning Model to detect hidden malwares and phase changing malwares.It predicts the date of the next probable attack of the malware and its extent.It deals with the change in network traffic flow.It is developed in Python in Jupyter notebook.

Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to…