In this article, I present a project on Crop Yield Prediction and Irrigation Optimization using deep learning techniques.
Deep learning is a powerful approach for multivariate analysis, especially when dealing with complex datasets with many variables. This technique can capture intricate patterns in the data, providing a robust solution for problems involving multiple factors and interactions.
The purpose of this project is to provide a complete example of how to apply deep learning in a practical scenario, step by step, covering everything from data preparation to model building and evaluation.
We will explore each stage together, focusing on strategic decisions and technical justifications that support model development.
If you enjoy this article and find the content useful, please leave your feedback and give 👏. This helps to value the work I am doing here and allows more people to access this knowledge. Let’s get started…
Data Dictionary:
The data we are working with in this project is fictitious and was created to demonstrate how to…
Be the first to comment