Machine Learning Model Performance in Dry and Wet Seasons
Published: 7 May 2024| Version 1 | DOI: 10.17632/nj5t3p9mgy.1
Contributor:
Andrew ToluTaiwoDescription
These codes implements machine learning models such as Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) with geospatial and non-geospatial data to predict volume of water consumed by poor urban households in dry and wet seasons
Files
Categories
Machine Learning, Geospatial Data Repository, Water Consumption