• Ajimol C
• Dr.R.Kavitha Jaba Malar

Data Mining is the process of extracting hidden knowledge from a large volume of data [7]. In this paper different types of classification algorithms including Naive Bayes, Logistic Regression, Random Forest and Support Vector Machine (SVM) algorithms have been comparatively tested to find the optimum algorithm for classification. Commonly a classification technique follows three approaches Machine Learning [9][10], Neural Network and Statistical for classification. This research analyzed the performance evaluation of popular classification algorithms used by machine learning systems. The main aim of the classification algorithm is to produce a good classification model which classifies the data accurately based on the training data set. Each classifier was tested with respect to accuracy, performance and execution time using the measured values. The experiments were carried out using the WEKA data mining tool, which includes the implementation of machine learning algorithms.

Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine (SVM), Weka Tool

"Performance evaluation of Classification algorithms based on Weather Forecasting Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.225-231, March-2019, Available :http://www.jetir.org/papers/JETIRAQ06046.pdf

"Performance evaluation of Classification algorithms based on Weather Forecasting Dataset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp225-231, March-2019, Available at : http://www.jetir.org/papers/JETIRAQ06046.pdf