Sheeba Kizhakkayil is part of the Operations and Delivery Excellence group of HiTech Business unit at Tata Consultancy Services (TCS). Based out of TCS' Delhi Office, she is responsible for monitoring and analysing the project performance and provide support to management in ensuring experience of certainty to TCS clients. Sheeba Kizhakkayil has been involved with Software and IT for the last 19 years in various roles, including project management and deployment of process models such as ISO and CMMI. She is a CMMI assessor for Services and qualified for Dev assessment as well.
Using Quantitative Techniques to Improve Performance
Application of Machine Learning for Predictive Modelling
with Pavan Kumar Damaraju
Machine Learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Some of the common areas where machine learning (ML) techniques applied are Fraud Detection , Spam email filtering, Classification of articles in Google news, etc. In this paper, we are presenting a case study on the application of an ML approach to recognize the parameters that cause an anomaly in a multivariate process, with a view to both prediction as well as diagnosis of the root cause. The quality of the approach has been assessed based on the high levels of precision, recall and ultimate overall accuracy.