California-Environmental-Conditions-Dataset
OpenML dataset with id 43606
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102431/California-Environmental-Conditions-Dataset.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of classes: 0
Number of features: 19 (numeric: 16, symbolic: 0 and in total binary: 0 )
Number of instances: 128,125
Number of instances with missing values: 116
Number of missing values: 138
Context Explore an environmental conditions dataframe scraped from CIMIS weather stations using a selenium chromedriver. With California's wildfires setting records in 2020, it is worthwhile to explore factors that may contribute to creating at risk environments. This dataset was used in conjunction to building an XGBoost Classifier to accurately predict probability for fire given environmental condition features. Following my Fire Risk Analysis project. Content 262 Station Id's correspond to California weather station IDs. Approximately 14 numerical features for exploratory data analysis. Advanced users can keep date feature for time series analysis. Target column corresponds to fires on the respective observation date, in the observation region. Acknowledgements
CIMIS: https://cimis.water.ca.gov/Default.aspx
Inspiration
What additional features would be valuable in determining fire risk?
What features are most important for specific models in determining target?
Is there an accurate LSTM to determine feature predictions?
" to determine fire risk in the future?
This page was built for dataset: California-Environmental-Conditions-Dataset
