Predictive Modeling for Agriculture
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🌱 Predictive Modeling for Agriculture | Machine Learning Project
I’m excited to share a new Machine Learning project I completed: Predictive Modeling for Agriculture*🚜🌱
🎯 Project Objective
The goal was to solve a real-world **Feature Selection problem:
A farmer can only afford to measure **one** soil property. Which single soil measurement would provide the strongest predictive performance for classifying crop types?
I evaluated four soil features individually:
🧪 Nitrogen (N)→ 14.77%
🧪 Phosphorous (P)→ 17.95%
🧪 Potassium (K)→ 24.55% 🏆
🌱 pH → 9.77%
🤖 Machine Learning Approach
* Data Exploration & Preparation
* Feature Selection
* Train/Test Split
* Logistic Regression
* Model Prediction
* Accuracy Evaluation
* Comparison of individual features
🏆 Key Result
Potassium (K) achieved the highest predictive performance among the four individual soil measurements, with an accuracy of *24.55%*.
This project gave me practical experience with Supervised Learning, Classification, Feature Selection, Logistic Regression, and Model Evaluation using Scikit-learn.
I’m continuing to strengthen my Machine Learning skills by building practical projects and applying what I learn to real-world problems. 🚀
🔗 **Project & Code:**