sapphirine Goto Github PK

-icmltitle-amazon-product-recommendation-assistant

-oscar-award-analysis

Oscar Award analysis

201512-56visualization-of-spatial-temporal-patterns-of-tweeting-behavior

Time and Geographical analysis of twitter

201512-72

BDA Fall 2015 Final - Group 201512-72

201605-45

Stateful algorithms/UI to process streaming stock activity and news.

201612-46investmentassistant

201705-3-never-lost-multicamera-face-detection-and-tracking

201705-3-Never-lost: Xucheng Chen (xc2360) and Lingyu Zhang (lz2494)

2019-aspect-analysis

201912-04-consumption-level-prediction-of-gstore-customers

201912-11-videos-to-tfrecords

Library to convert video files to TFRecords using Apache Beam.

201912-12-entity-sentiment-analysis-reddit

Final project code for Timotius Kartawjiaya, Fernando Troeman, and Hritik Jain

201912-13-video-object-segmentation

video object segmentation

201912-14-trending-topics-sentiment-analysis-of-twitter

Sapphirine/201912-14-Trending-Topics-Sentiment-Analysis-of-Twitter

201912-15-airbnb-price-analysis-around-the-world

Final project of group 201912-15.

201912-16-better-name-more-money

Final Project Code for Zihe Wang, Qianhui Yu, and Duanyue Yun

201912-18-sentiment-analysis-for-stock-market-predictions

A big data perspective on Nike social sentiment and stock price.

201912-20-music-emotion-recognition

Music is a medium to express emotion. According to literature, music emotion can be quantified continuously as valence and arousal (VA) density distribution on a 2-D space. However, these data are hard to retrieve as they require intense human effort to manually label songs, especially the number of songs become enormous. The goal of this project is to reproduce a model proposed by Chin, Y.-H. et.al (2018), to predict VA density for a new song based on those densities for training songs as well as audio features of both new and training songs. This will help save human labeling effort on new songs in the future. Furthermore, a prototype of content-based music recommender system is built to demonstrate the usability of the algorithm.

201912-20-nyc-motor-vehicle-collisions-analysis

201912-21-nyc-airbnb-data-analysis-visualization-and-price-prediction

201912-23

Prediction and Analysis Based on English Premier League

201912-24-customized-movie-recommendation-system

201912-27-pubmed-database-analysis-and-trend-visualization

201912-28-ny_state_inpatient_healthcare_analysis

This is the final project for big data analytics by Justin Yong Sik Cho, Yuan-Fang Lin, and Jing Qian.

201912-3-understanding-clouds-from-satellite-images

This repo is for the final project of Fall 2019 EECS 6893 BIG DATA ANALYTICS course in Columbia University, contributed by Hongbo Du(hd2452), Jiajia Chen(jc5029), Yuqin Xu(yx2478)

201912-30_game-for-you-

201912-31-the-emotion-pattern-on-art

The code of EECS6893 Fall2019 Final Project Code

201912-38-amazon-phone-data-analysis

201912-39-get-your-entertainment-recommendations-based-on-your-selfie

201912-40

201912-42