DATA MINING ANALYST FOR CLASSIFYING PLANT GROWTH DATA USING THE NAIVE BAYES METHOD

Abstract

Data mining is a powerful tool that involves extracting useful information from large datasets. In the context of plant growth classification, data mining can be used to analyze various factors, such as soil composition, climate conditions, and plant characteristics, to predict and classify plant growth patterns. In addition, data mining can also be used to predict potential pest outbreaks or disease outbreaks, allowing farmers to take proactive measures to protect their crops. The Naive Bayes algorithm is a popular machine learning technique that is widely used in data mining applications, including in the agricultural sector. One of its key strengths is its simplicity and ease of implementation, making it a practical choice for farmers looking to leverage data-driven insights. The application of the Naive Bayes method using Rapid Miner data mining for classifying plant growth data yielded an accuracy of 67.50%, demonstrating a moderate level of performance in distinguishing between different growth outcomes. The precision of the model was calculated at 72.00%, indicating that over half of the positive predictions (growth milestones classified as "yes") were correct. The recall was higher, at 75.00%, suggesting that the model successfully identified a majority of the actual positive cases. However, the AUC (Area Under the Curve) score of 0.685, These results suggest that while the Naive Bayes classifier is a useful tool for this task. while above random chance, reflects the model's limited ability to discriminate between positive and negative classes effectively.