markielokie/anomaly-detection

Project on anomaly detection using statistical learning.

Python for Data Analysis: Presentation Assignment

Term: Spring 2022

• Team: Group 4

• Project title: Anomaly detection using statistical learning for identifying possible heart attacks

• Team members:

  • Jiachen Hu (jh4494@columbia.edu)
  • Jiayi Wang (jw4028@columbia.edu)
  • Marcus Loke (ml4636@columbia.edu)
  • Qinyun Luo (ql2400@columbia.edu)
  • Rohit Kundurthi (rk3141@columbia.edu)
  • Xin Li (xl3171@columbia.edu)
  • Yafei Wen (yw3764@columbia.edu)
  • Yinjie Dai (yd2597@columbia.edu)
  • Yunzi Ma (ym2826@columbia.edu)
  • Zheyu Xu (zx2347@columbia.edu)

• Project summary: This project seeks to use the anomaly detection (seasonal ESD) functions from the GitHub repo by Nacho Navarro, which is the Python code of the technique that Jordan Hochenbaum, Owen S. Vallis and Arun Kejariwa at Twitter Inc. have created. We attempt to use the seasonal ESD function to flag out anomalies in the human heartrate, leading to better detection and timely prevention of a heart attack.

• Technologies used: Python

Following suggestions by RICH FITZJOHN (@richfitz). This folder is orgarnized as follows.

proj/
├── lib/
├── data/
├── doc/
├── figs/
└── output/

Please see each subfolder for a README file.