Table of Contents Introduction 4 Objectives 6 Literature Review 7 Framework 9 Data Analysis 14 Qualitative Analysis 14 Qualitative Analysis 16 Research Methodology 17 Conclusion 20 Bibliography 21
APPENDIX – I
APPENDIX – II
APPENDIX – III
APPENDIX – IV
APPENDIX - V

Introduction
Predictive analytics has its origin from a famous saying: Past performance is the basic indicator of future results. It looks at historical cases and builds models which can then be applied to benefit present scenarios or predict future scenarios. Predictive Analytics is the best way for a business to predict customer responses in the future. It provides solutions for businesses facing main problems like ‘What segment of potential consumers will respond best to our message’ and ‘how can I stop my customers from leaving, and why am I losing them?’(Curtis, 2010).
Predictive analytics is not just for providing a solution for a business problem but involves techniques mainly to improve the focus of company towards customers and customers towards company. The magnificence of predictive analytics is that a business characteristically perceives a win-win situation. In other words, a business not only benefits from higher returns but also gets to save on cost (Colin, 2009). Predictive analytics is becoming a competitive necessity and an important aspect of many types of business, particularly in this type of economy where an organization is trying to increase its efficiency and at the same time maintain and grow the business.
The choice of this topic comes from the fact that prediction is always interesting and challenging. One needs to analyse the history in depth to predict accurately. From the business point of view, predicting and taking decisions for the future growth of organization is the most challenging. And the same intuition led us for the selection of this