  In many real-world datasets, rows may have distinct characteristics and
require different modeling approaches for accurate predictions. In this paper,
we propose an adaptive modeling approach for row-type dependent predictive
analysis(RTDPA). Our framework enables the development of models that can
effectively handle diverse row types within a single dataset. Our dataset from
XXX bank contains two different risk categories, personal loan and agriculture
loan. each of them are categorised into four classes standard, sub-standard,
doubtful and loss. We performed tailored data pre processing and feature
engineering to different row types. We selected traditional machine learning
predictive models and advanced ensemble techniques. Our findings indicate that
all predictive approaches consistently achieve a precision rate of no less than
90%. For RTDPA, the algorithms are applied separately for each row type,
allowing the models to capture the specific patterns and characteristics of
each row type. This approach enables targeted predictions based on the row
type, providing a more accurate and tailored classification for the given
dataset.Additionally, the suggested model consistently offers decision makers
valuable and enduring insights that are strategic in nature in banking sector.
