  Establishing a new business may involve Knowledge acquisition in various
areas, from personal to business and marketing sources. This task is
challenging as it requires examining various data islands to uncover hidden
patterns and unknown correlations such as purchasing behavior, consumer buying
signals, and demographic and socioeconomic attributes of different locations.
This paper introduces a novel framework for extracting and identifying
important features from banking and non-banking data sources to address this
challenge. We present an attention-based supervised feature selection approach
to select important and relevant features which contribute most to the
customer's query regarding establishing a new business. We report on the
experiment conducted on an openly available dataset created from Kaggle and the
UCI machine learning repositories.
