Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
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Updated
May 29, 2024 - Jupyter Notebook
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
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.
Drug Repurposing Datasets for Collaborative Filtering Methods. Notebooks and code to generate datasets for collaborative filtering-based drug repurposing.
In this repository, I have share notebook which contains recommender systems algorithms like apriori, collaborative filtering and SVD using surprise library of python.
A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise
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…
Python notebook for Book Recommendation System using collaborative filtering.
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.
Repository will contain the files and notebook for demonstrating the different recommendation systems using a memory based approach.
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.
Jupyter notebook file with recommendation methods for articles to users of the IBM Watson Studio platform.
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.
Implementation of Deep-learning techniques in pytorch
A collection of Jupyter notebooks on articles and material online related to recommender systems in python.
A python movie recommendation system created on jupyter notebook.
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