Abstract
The era of data-driven decision-making has ushered in a compelling need for businesses, particularly those in their early stages, to leverage advanced technologies for handling and extracting insights from diverse and dynamic datasets. This abstract explores the application of deep learning as a transformative tool in managing early-stage business data. Early-stage business data encompasses a wide array of information, including market research, customer behavior, financial records, and operational metrics. Effectively harnessing this data can offer a competitive advantage by informing strategic choices and guiding business growth. Deep learning, a subset of artificial intelligence, has demonstrated remarkable capabilities in processing and deriving meaningful patterns from complex, unstructured, and high-dimensional data. In this research paper, we have identified the problems related to the processing of early-stage business data. We have also presented a case study to implement of all phases of data handling using python. Implementation includes data uploading, preprocessing, model creation, model validation, and testing. By leveraging deep learning techniques, businesses can unlock the untapped potential within their early-stage data, leading to more informed and strategic decisions, improved operational efficiency, and a competitive edge in the marketplace.
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Priyanshu, Pratap, A., Chopra, B.U.K., Fatima, S., Verma, P. (2024). Use of Deep Learning to Handle Early-Stage Business Data. In: Kaiser, M.S., Singh, R., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fifth International Conference on Trends in Computational and Cognitive Engineering. TCCE 2023. Lecture Notes in Networks and Systems, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-97-1923-5_11
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DOI: https://doi.org/10.1007/978-981-97-1923-5_11
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