  The challenge of imbalanced soil nutrient datasets significantly hampers
accurate predictions of soil fertility. To tackle this, a new method is
suggested in this research, combining Uniform Manifold Approximation and
Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO).
The main aim is to counter the impact of uneven data distribution and improve
soil fertility models' predictive precision. The model introduced uses Sparse
Attention Regression, effectively incorporating pertinent features from the
imbalanced dataset. UMAP is utilized initially to reduce data complexity,
unveiling hidden structures and important patterns. Following this, LASSO is
applied to refine features and enhance the model's interpretability. The
experimental outcomes highlight the effectiveness of the UMAP and LASSO hybrid
approach. The proposed model achieves outstanding performance metrics, reaching
a predictive accuracy of 98%, demonstrating its capability in accurate soil
fertility predictions. Additionally, it showcases a Precision of 91.25%,
indicating its adeptness in identifying fertile soil instances accurately. The
Recall metric stands at 90.90%, emphasizing the model's ability to capture true
positive cases effectively.
