Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models.
Running high-resolution physical models is computationally expensive and essential for many disciplines.
In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks.
Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources.
We conclude that tf-idf achieves better results than Word2Vec to model the dataset to feature vectors.