• Built anomaly detection system based on Twitter Anomaly Detection and Bayesian Change Point
• Developed anomaly detection alarm system pipeline using Python, R and SQL
• Imported data of donors to the system of NonProfitEasy and monitored data accuracy
• Cleaned raw data and performed visualizations using MS Excel
NYC Taxi Trip Analysis San Francisco, CA January 2017
House Prices: (Kaggle Competition) San Francisco, CA December 2016
• Implemented Machine Learning algorithms including Regression, Tree Methods and Ensemble Models in Python to predict house prices in Ames, Iowa based on 79 different features
• Explored possible feature transformations and interaction terms using tabplots and scatter plots in R
• Achieved top 7% in Kaggle ranking
Python, Jave, R, SQL, NoSQL, Apache Spark, MATLAB, Stata
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