PioSte / PM_dataset

My educational project with Pandas and time series modeling

Intro

I'm doing this project in order to get some practical experience with Pandas and time series data modeling. Hence, the work is ongoing, the processing is chaotic and the approach might not be the best.

And the dataset is…

PM2.5 Data of Five Chinese Cities Data Set

Data

Data Set Information:

The time period is between Jan 1st, 2010 to Dec 31st, 2015. Missing data are denoted as NA.

Attribute Information:

No: row number
year: year of data in this row
month: month of data in this row
day: day of data in this row
hour: hour of data in this row
season: season of data in this row
PM: PM2.5 concentration (ug/m^3)
DEWP: Dew Point (Celsius Degree)
TEMP: Temperature (Celsius Degree)
HUMI: Humidity (%)
PRES: Pressure (hPa)
cbwd: Combined wind direction
Iws: Cumulated wind speed (m/s)
precipitation: hourly precipitation (mm)
Iprec: Cumulated precipitation (mm)

The goal

PM prediction based on weather (forecast).

What to do with missing data?

Currently imputed with the scikit-learn IterativeImputer.