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Predictive Modeling for Agriculture

Step into the fascinating world of agriculture with this use case! Discover how multi-class machine learning is being utilized to assist farmers in cultivating the perfect crop each season. Through hands-on experience with actual scenarios, you'll learn the art of applying supervised ML and feature selection techniques to solve real-world problems.

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1 Tasks1,500 XP

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Project Description

A farmer reached out to you as a machine learning expert seeking help to select the best crop for his field. Due to budget constraints, the farmer explained that he 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

The expert realized that this is a classic feature selection problem, where the objective is to pick the most important features that could help predict the crop accurately. Can you help him?

Project Tasks

  1. 1
    In this project, you will be introduced to two techniques for feature selection and apply them to the farmer's problem. By working on this project, you will gain valuable insights into how machine learning can solve real-world agricultural problems.


Python Python


Machine LearningProgramming
George Boorman HeadshotGeorge Boorman

Curriculum Manager, DataCamp

George is a Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.
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