A DSCI 525 project by Group 3 to predict rainfall in NSW, Australia based on big datasets

Our goal is to develop and deploy cloud-based ensemble machine learning model for future rainfall prediction in NSW, Australia. The datasets we used contain rain (mm/day) over time observed or computed by different models, retrieved on figshare. The datasets were loaded and combined together using pandas and dask, and underwent exploratory data analysis using both python and R. The data will then be used for big data machine learning model building and deployment to predict future rainfall in Australia.

Python 3.8.3

re == 2.2.1
requests == 2.25.1
json == 2.0.9
pandas == 1.2.3
dask == 2021.3.1
rpy2 == 3.4.3
pyspark == 3.1.1
s3fs == 0.6.0
joblib == 1.0.1
matplotlib == 3.4.1
sklearn == 0.0
json5 == 0.9.5
urllib3 == 1.26.4
flask == 1.1.2

R 4.0.2

arrow == 3.0.0
dplyr == 1.0.3

We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab. UBC MDS DSCI 525 Group III:

• Mitchie Yiqi Zhao @shoebillm
• Jianru Deng @jianructose
• Aishwarya Gopal @AishwaryaGopal12
• Aditya Bhatraju @adibns

CMIP6 Experimental Design and Organization https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6

Pangeo Coupled Model Intercomparison Project Phase 6 https://pangeo-data.github.io/pangeo-cmip6-cloud/

SILO - Australian Climate Data https://www.longpaddock.qld.gov.au/silo/