Dataset transformation: Integrating the reverse effect of value and share to individual data

Practically,transforming dataset is not a transient approach,yet it is an incremental one by considering a set of computational procedures to achieve a targeted purpose. this study attempts to propose an approach to transform a dataset by integrating the reverse impact of the data value and share of an individual data to its corresponding data point. the model building is conducted by discovering a relational understanding between an individual data and its value and share among parallel data points within an identical limit. the relationship of individual data is then taken to produce an adjusted individual data,and hence results in a transformed dataset. a trial is conducted over a time-series dataset to understand its practical effect. looking at the procedures,this study offers a practically general measure to understand the reverse impact of data value and share to deliver a transformed dataset for diverse purposes.