PUMPKIN SEED PREDICTION USING VOTING CLASSIFIER: A COMPARATIVE ANALYSIS WITH DECISION TREE, LOGISTIC REGRESSION, AND SVM

G. Ravi Kumar; G. Thippanna; D. William Albert

Authors

G. Ravi Kumar Assistant Professor, Dept. of Computer Science, Rayalaseema University, Andhra Pradesh, India. Author
G. Thippanna Professor, Dept. of CSE, Ashoka Women’s Engineering College, Andhra Pradesh, India. Author
D. William Albert Professor, Dept. of CSE, Ashoka Women’s Engineering College, Andhra Pradesh, India. Author

Keywords:

ML Algorithms, Decision, Tree, Regression, SVM

Abstract

In this study, we explore the prediction accuracy of pumpkin seed quality using a Voting Classifier in comparison with three popular machine learning algorithms: Decision Tree, Logistic Regression, and Support Vector Machine (SVM). We conducted experiments on a labeled dataset containing features related to pumpkin seeds to classify their quality as either good or bad. The Voting Classifier combines the predictions of multiple models to enhance overall accuracy. The results demonstrate the effectiveness of the Voting Classifier in improving accuracy and precision compared to individual algorithms.

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