Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Project Description:
A farmer seeking help to select the best crop for his field. Due to budget constraints, the farmer could only afford to measure two out of the four essential soil measures:
- Nitrogen content ratio in the soil
- Phosphorous content ratio in the soil
- Potassium content ratio in the soil
- pH value of the soil
This is a classic feature selection problem, where the objective is to pick the most important features that could help predict the crop accurately.
Project Tasks:
In this project, Two techniques for feature selection are applied to solve the farmer's problem. The project shows valuable insights into how machine learning can solve real-world agricultural problems.