Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification

Recent developments in analytical technologies helped in developing applications for real-time problems faced by industries. These applications are often found to consume more time in the training phase. To reduce this pre-treatment of data in the training phase is pointed out to be an appropriate methodology. Normalization is the best technique for pre-processing data before the training phase of application. Normalization using metrics with criteria is found to be very important to attain good predictive results with less amount of time. This paper depicts the improvement in predictive accuracies with the help of normalization techniques. Various criteria needed to achieve such data normalization are also described. In this paper, will be having a glance on three different machine learning classifier i.e., Radial SVM, KNN, Sigmoid SVM and seven different standardization techniques i.e., StandardScaler, Scale, RobustScaler, QuantileTransform, PowerTransform, MinMaxS caler and MaxAbsS caler.