Seybold Report ISSN: 1533-9211
Dr. Manisha Maddel1
Assistant Professor ,Indira School of Business Studies, Pune , India
manisha.maddel@indiraisbsmba.edu.in
Dr. Vidya Gavekar2
Associate professor, Suryadatta Institute Of Business Management and Technology, vidyagavekar@gmail.com
Vol 17, No 10 ( 2022 ) | DOI: 10.5281/zenodo.7157418 | Licensing: CC 4.0 | Pg no:1662-1673 | Published on: 07-10-2022
Abstract
In recent years, machine learning and statistical prediction models have been used successfully to solve business problems. This is mostly due to the exponential growth of consumer data and how easy it is to get access to computer power. Visualizing and making sense of this data to help consulting organisations make better business decisions is hard and has room for improvement. One reason why these companies are so successful is that they can accurately estimate how long a job will take and, by extension, how much money it will cost. The goal of this study is to do two things: first, figure out which machine learning and statistical methods are best for predicting internal costs in a consulting firm; and second, make a user interface with visual analytics that shows the results of these methods and helps people make decisions in an interactive way. Through the research, both of these goals will be met. The customer relationship management (CRM) database that the consulting firm kept was used to get the information needed for this study. The database had information about several consulting projects and covered a period of twelve years. In the past, we've used statistical linear models and machine learning decision trees to do research. We proposed models based recurrent neural networks .to analytical decision making in businesses and the role of machine learning and artificial intelligence.
Keywords:
analytical decision , machine learning , artificial intelligence.