AI-Based Decision Support System for Startup Investment Analysis

Abstract

Software startups businesses create
inventive, programming escalated items
or administrations. Such imaginativeness
converts into vulnerability in regards to a
matching requirement for an item from
expected clients, addressing a potential
determinant justification for startup
disappointment. Research has shown that
trial and error, a methodology in light of
the utilization of tests to direct a few parts
of programming advancement, could
further develop these organizations'
prosperity rate by encouraging the
assessment of presumptions about clients'
necessities previously fostering an
undeniable item. By and by,
programming new companies are not
involving trial and error true to form. In
this review, we researched the
explanations for such a jumble among
hypothesis and practice. To accomplish it,
we played out a subjective review study of
106 failed software