Classification of Fertiliser Type Based on Soil Minerals Using Voting Classification Over Decision Tree

To estimate the type of fertilizer based on soil minerals using voting classifier. For forecasting accuracy %, a Voting Classifier with sample size 10 and Decision Tree was iterated at various times. A supervised learning algorithm is Tree. It constructs “forest” an array decision trees, typically trained to use “bagging” method. Novel Classification predictive model that learns from several models predicts output (class) result representing greatest likelihood being chosen class. The produced substantial results 96 percent accuracy, compared 94% for classifier showed statistical evidence p=0.001 (p<0.05). most effective classifies more than