GitHub repository: sherkhan15/Forest-Cover-Normalized-UnNormalized-Predictor-with-ANN
Primary language: Jupyter Notebook
Created: 2019-10-03T02:14:00Z
Description: Context This dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado. All observations are cartographic variables (no remote sensing) from 30 meter x 30 meter sections of forest. There are over half a million measurements total!  Content This dataset includes information on tree type, shadow coverage, distance to nearby landmarks (roads etcetera), soil type, and local topography.  Acknowledgement This dataset is part of the UCI Machine Learning Repository, and the original source can be found here. The original database owners are Jock A. Blackard, Dr. Denis J. Dean, and Dr. Charles W. Anderson of the Remote Sensing and GIS Program at Colorado State University.  Inspiration Can you build a model that predicts what types of trees grow in an area based on the surrounding characteristics? A past Kaggle competition project on this topic can be found here. What kinds of trees are most common in the Roosevelt National Forest? Which tree types can grow in more diverse environments? Are there certain tree types that are sensitive to an environmental factor, such as elevation or soil type?