A Comparative Study of Supervised Machine Learning Models: Decision Tree Classifier and Linear Regression on Banana Growth Prediction

This study presents a comparative analysis of two predictive modeling approaches Decision Tree Classifier and Linear Regression to access their effectiveness in classifying and predicting banana plant growth. Experimental results showed that both models achieved 100% accuracy with the Decision Tree providing perfect classification and Linear Regression yielding an R² score of 1.00. The results suggest a strong correlation between the selected features and the growth variable. The small dataset size likely contributed to the perfect fit, indicating the need for a larger sample size to validate the models robustness in real-world applications.The study highlights that Decision Trees are ideal for categorical classification, while Linear Regression offers interpretable insights into numerical growth relationships.