Data Analyst, Master of Urban Spatial Analytics @UPenn, Bachelor of Urban Management @Nankai University.
Looking forward to making a more convenient, real and peaceful world using data with humanity.
View My LinkedIn Profile
There are two main parts in this project, the first part focus on aging level prediction with multiple features extracted from the Health, Nutrition, and Population Statistics dataset and the second part focus on time series prediction for birth rate in 20 years.

In this project, house price prediction for Zillow is carried out with multi-resource commercial and socio-economic data. Feature engineering is an important part of the project, which helps achieve a satifying accuracy and generalizability.

Faced with the more and more serious heroin overdose in Cincinnati, we trained a model based on Poisson Regression to predict overdose in each area, which is helpful for the Public Health Department to allocate medical resources efficiently.

A series of web-based maps show the evaluation of the safety and convenience of each neighborhood in Chicago based on crime data and local amenities. Residents especially students could choose a reasonable and livable neghborhood to move in, and find local amenities easily!

A Power BI version of neighbordhood livability report for Chicago. There are more understandable plots and striking key information!

In this project, we build a prediction model based on Random Forest to predict the temperature if we convert a specific propotion of vacant lands to green space in Philadelphia! We want to use data and model to make the environment better!


Page template forked from evanca