Python-Based Predictive Modeling for Agriculture: A Human-Centered Approach to Land Suitability Assessment

The future of agriculture depends on our ability to make informed, data-driven decisions about land use and crop management. This paper presents a comprehensive, humancentered approach to predicting land suitability using machine learning, with a focus on interpretability and practical impact. Leveraging a rich dataset of soil nutrient measurements, we develop and evaluate a Random Forest-based predictive model that classifies land as low, medium, or highly suitable for cultivation. Our methodology emphasizes transparency, from data preprocessing and feature engineering to model evaluation and real-world implications. The results demonstrate that integrating diverse soil features and prioritizing model interpretability can empower farmers, agronomists, and policymakers to optimize resource allocation, improve yields, and promote sustainable agricultural practices. We discuss the challenges of class imbalance, the importance of feature selection, and the need for future research that bridges the gap between advanced analytics and everyday agricultural decision-making.