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Predictive Analytics Using Soft Computing: A Case Study on Forecasting For Indian Automobile Industry

Jewel Murel D’Souza, Sheik Sana Begam, Santhosh Rebello

Predictive analytics is the branch of the advanced analytics, which encompasses a variety of statistical techniques from modeling, machine learning, statistics, and data mining that analyze current and historical facts to make predictions about unknown future events. Soft Computing will fit the predictive analytics using neural networks in a very efficient way by replacing all the other methods and produce forecasts as accurate as or better than those available from other statistical methods. Indian automobile industry has gone tremendous changein terms of sales, customer expectation and challenges.A quick decision making and a positive movetowards change is required as the market scenario can be highly dynamic and can cause change without giving much time to respond.This paper aims at providing a proposed neural network solution that can be used to predict the future sales on the base of historical data using Back Propagation algorithm. Especially we aim at finding which automobile company will be on a high scale of sales as per the customer’s capacity and desire. This forecasting helps firms to take strategic decisions to achieve their goals.