Study for Recommendation engine
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Updated
Mar 16, 2018 - Jupyter Notebook
Study for Recommendation engine
Use the graph features in SQL Server 2017 to perform market basket analysis and provide product recommendations for your users.
You can find my technical documents over here...
Budget Text Analysis for Seven Counties
Using XGboost to predict accommodation listing prices
Web-app and jupyter notebook, fully documented for Capstone of Udacity data science ND. Uses FunkSVD/handwritten gradient descent algorithm plus chi sq to determine recommendations for users based on demographic
Udacity Data Scientist Nanodegree Program.
Create A Recommendation Engine For Blog Articles
Built a Spotify recommendation system: fetched data from Spotify API, stored it on Google Cloud Storage, orchestrated with Airflow (Composer) to load into MongoDB Atlas, and developed a recommendation engine based on the data.
A recommendation engine that recommends books, movies, and articles based on data from Wikipedia and a user’s declared interests
Movie Recommendation Engine
android movie recommendations app
A recommendationn system for movies using Python and machine learning algorithms (k nearest neighbours, logistic regression). numpy. scikit-learn
Contains assignments, moocs, challenges, from recommendation engines, to deep learning
A lightweight recommendation engine for Ruby apps using Redis
Recommender system for food pairing
An anime recommendation engine powered by a pairwise similarity backend framework.
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