ML4EO/CGIAR_Yield_Estimation

Maize yields prediction in East African farms particularly Rwanda using satellite imagery data and machine learning models # CGIAR_Yield_Estimation Maize yields prediction in East African farms particularly Rwanda using satellite imagery data and machine learning models #### Tasks performed 1. Downloading and Loading the dataset of the zip files from Zindi or add them to the Google drive using the following links: ##### Test data: drive.google.com ##### Training data: drive.google.com Use the smaller files from Zindi (Train.csv, SampleSubmission.csv and bandnames.txt) uploaded by using the files tab. 2. Sampling from the images There are some hard-coded band indexes in the examples above that won't have made sense - how did we know which bands were which? There are 30 bands for each month. 3. Fitting a model The goal is to find a set of parameters that minimize the difference between the predicted output and the actual output for the training data.