The objective of this Zindi competition was to create a machine learning model capable of predicting the humidity for a particular plot in the next few days, using data from the past. A part of the challenge is to design algorithms that are resilient and can be trained with incomplete data (e.g. missing data points) and unclean data (e.g. lot of outliers).
This resulting model will enable farmers to anticipate water needs and prepare their irrigation schedules.