jcmaxim Goto Github PK

ar-ecard

greeting cards with AR effect [ios App]

data-mining

Analytical Data Mining - Amazon.com large scale of reviews parsing and clustering

extractbrowser

extract browser family from user agents

freeml

Data Science Resources (Mostly Free)

git-test

hangzhou_house_knowledge

2017年买房经历总结出来的买房购房知识分享给大家，希望对大家有所帮助。买房不易，且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to everyone.

netflix

:movie_camera: :bar_chart: Machine learning algorithms for Netflix recommendations (Caltech CS/CNS/EE 156b, Spring 2015)

netflix-prize

The code I used to get in the top #150 in the Netflix Prize

netflixprize

Automatically exported from code.google.com/p/pankratz-cs373-netflix

samr

An entry to kaggle's 'Sentiment Analysis on Movie Reviews' competition

sentiment_analysis

svdrecommendersystem

将SVD应用于推荐系统中的评分预测问题

test-harness

text-clustering

Identify all the unique words that appear in the “review/text” field of the reviews in the dataset (http://snap.stanford.edu/data/web-FineFoods.html). Clean the data and remove stopwords then identify top 500 frequent words. Using these 500 words vectorize all reviews. Cluster the vectorized reviews into 10 clusters using k-means. From each centroid, select the top 5 words that represent the centroid (i.e., the words with the highest feature values)