Predictive Analysis of Heterogeneous Data – Techniques & Tools

Abstract

Data intensive processing and analysis has lead new research avenues to draw the attention of researchers and decision makers in the field of data and information sciences. The exponential rise in variety of data in today’s computerized era is laying new challenges for the society. Nevertheless, there are huge potentials and useful information present in the data. This undiscovered information is one of the most valuable assets for the data intensive scientific real-time applications. It is a vital factor for the efficient outcome and evolving advances in various technical aspects. Yet, a large number of sectors with varied data sources face difficulties with the variety of data. Processing this heterogeneous data is necessary to extract useful information, making it a decisive factor for the better survival of future with automation. Different Machine Learning techniques and tools are studied to perform predictive analysis of the continuous as well as categorical data.