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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Sales forecasting using machine learning |
| Authors Name: | Hitesh SM , Yukthi A |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211590
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| Published Paper Id: | IJNRD2401051 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | An Intelligent Decision Analytical System necessitates the fusion of decision analysis and predictive methodologies. Within business frameworks, reliance on a knowledge base and the ability to predict sales trends holds paramount importance. The precision of sales forecasts profoundly influences business outcomes. Leveraging data mining techniques proves highly effective in unveiling concealed insights within vast datasets, thereby amplifying the accuracy and efficiency of forecasting. This study deeply examines and analyzes transparent predictive models aimed at refining future sales predictions. Conventional forecasting systems struggle with handling extensive data, often compromising the accuracy of sales forecasts. However, these challenges can be surmounted by employing diverse data mining techniques. The paper provides a succinct analysis of sales data and forecast methodologies, elaborating on various techniques and metrics crucial for accurate sales predictions. Through comprehensive performance evaluations, a well-suited predictive model is recommended for forecasting sales trends. The findings are encapsulated, emphasizing the reliability and precision of the adopted techniques for prediction and forecasting. The research identifies the Gradient Boost Algorithm as the optimal model, demonstrating superior accuracy in forecasting future sales trends |
| Keywords: | Data mining techniques, Machine Learning Algorithms, Prediction, Reliability, Sales forecasting |
| Cite Article: | "Sales forecasting using machine learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a443-a447, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401051.pdf |
| Downloads: | 000118759 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401051 Registration ID: 211590 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: a443-a447 Country: Bengaluru , Karnataka , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401051 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401051 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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