AI Summary & Key Takeaways
AI InsightThe results suggestthat CRT method can considerably improve both prediction accuracy and computational time of PCA-DNNmodel, while displaying a good generalization ability.
📌 3 Key Takeaways
Özet
High dimensionality and skewness are two intrinsic characteristics of real estate dataset that affects the priceprediction accuracy of deep neural network (DNN). The objective of this study is to investigate the effect ofskewness in prediction accuracy of combined principal component analysis (PCA) with DNN (PCA-DNN)model. This research follows a threefold approach over a high dimensional and positively skewed real estateprice dataset. Firstly, data distribution is to conform with normality using three conventional skewnessreduction techniques, namely as square root transformation (SRT), cube root transformation (CRT), andlogarithmic transformation (LT) methods. Secondly, the high dimensionality of original, SRT, CRT and LTskewed datasets are to be reduced using PCA. Thirdly, price prediction accuracy of PCA-DNN model overdatasets with different skewness levels are to be compared by observing their error values. The results suggestthat CRT method can considerably improve both prediction accuracy and computational time of PCA-DNNmodel, while displaying a good generalization ability. Despite CRT method, SRT and LT methods resultedin high error values and overfitting issues, respectively.