This project is based on the zindi challange "Urban Air Pollution (https://zindi.africa/competitions/zindiweekendz-learning-urban-air-pollution-challenge). A predictive model for the PM2.5 value was designed using a stacked approach.
An input (X) and target (y) file (.csv) can be passed, resulting in an output showing an exemplary location-x-date table with predicted and real PM2.5 values. Also the RMSE is shown.
python main.py X y