Accomplished predictive modeling project for agriculture

Today I've just completed a 'predictive modeling project for agriculture' on Datacamp!

In this project, I developed a model to predict optimal crops based on soil characteristics (N, P, K, pH).

Here's a glimpse into my journey:

1️⃣ Started with data visualization to understand the relationships between soil metrics and crop suitability. While insightful, selecting the best feature wasn't straightforward from visuals alone.

2️⃣ Pivoted to a more systematic approach using machine learning:

- Implemented logistic regression with multi-class classification

- Evaluated each feature's predictive performance using F1 scores

- This method provided a clear, quantitative way to identify the most influential soil characteristic for crop prediction

Key learnings:

✅ The importance of combining visual and statistical methods in data analysis

✅ Practical application of multi-class logistic regression in agricultural contexts

✅ Using F1 scores to evaluate feature performance in imbalanced datasets

This project deepened my understanding of data science techniques and their real-world applications in agriculture. I am excited to apply these skills to future projects!

Project Link: https://lnkd.in/gCgpXizB

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