Product Sales Forecasting and Prediction Using Machine Learning Algorithm
Keywords:
Machine Learning, forecasting, predictive model, K-NN, Regression modelAbstract
Sales forecasting plays a crucial role for companies involved in various industries such as retailing, logistics, manufacturing, marketing, and wholesaling. The utilization of this approach enables companies to effectively allocate resources, accurately forecast sales revenue, and develop strategic plans that promote long-term organizational success. The conventional employment of statistical techniques for predicting sales at supermarkets has left numerous challenges unattended, leading to the development of models for prediction that exhibit suboptimal performance. The contemporary age of voluminous data, in conjunction with the availability of extensive computational resources, has rendered machine learning a preferred approach for predicting sales. This study demonstrates improved performance in predicting product sales from a specific store compared to existing machine learning algorithms. A K-Nearest Neighbor (K-NN) predictive model was constructed to anticipate sales for a business, specifically Big-Mart. The model's efficacy was then contrasted with that of Linear, Polynomial, and Ridge regression methodologies.
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Wang, Haoxiang. "Sustainable development and management in consumer electronics using soft computation." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 56.- 2. Suma, V., and Shavige Malleshwara Hills. "Data Mining based Prediction of D [3] Suma, V., and Shavige Malleshwara Hills. "Data Mining based Prediction of Demand in Indian Market for Refurbished Electronics." Journal of Soft Computing Paradigm (JSCP) 2, no. 02 (2020): 101- 110.
Crime Prediction using K Nearest Neighbour Algorithm by Akash Kumar, Aniket Verma,Gandhali Shinde,YashSukhdeve,Nidhi Lal published by 2020 International Conference on Emerging Trends in Information Technology and Engineering.
House Price Prediction Using Regressio Techniques: A Comparative Study by CH.Raga Madhuri, Anuradha G, M.VaniPujitha published by IEEE 6th International Conference on smart structures and systems 2019.
Deepa Rani Gopagoni, P V Lakshmi and Ankur Chaudhary, “Evaluatıng Machıne Learnıng Algorıthms For Marketıng Data Analysıs - Predıctıng Grocery Store Sales” https://www.researchgate.net/publication/344508907-2019.
Rising Odegua, “Applied Machine Learning for Supermarket Sales Prediction”, https://www.researchgate.net/publication/338681895, 2020.
Grigorios Tsoumakas, “A survey of machine learning techniques for food sales prediction”, Artif Intell Rev, https://doi.org/10.1007/s10462-018-9637-z-Springer-2018.
Yuta Kaneko and Katsutoshi Yada, “A Deep Learning Approach for the Prediction of Retail Store Sales”, IEEE 16th International Conference on Data Mining Workshops-2016.
Purvika Bajaj, Renesa Ray2, Shivani Shedge, Shravani Vidhate, Prof. Dr. Nikhilkumar Shardoor, “sales prediction using machine learning algorithms”, International Research Journal of Engineering and Technology (IRJET), Volume: 07 Issue: 06 | June 2020.
Elcio Tarallo, Getúlio K. Akabane, Camilo I. Shimabukuro, Jose Mello, Douglas Amancio, “Machine Learning in Predicting Demand for Fast-Moving Consumer Goods: An Exploratory Research”, https://www.researchgate.net/publication/338172563-2019.
Ranjitha and Spandana, “Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms”, Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021), IEEE Xplore Part Number: CFP21K74-ART; ISBN: 978-0-7381-1327-2.
Melvin Tom, Nayana Raju, Asha Issac,, Jeswin James, Rani Saritha R, “Supermarket Sales Prediction Using Regression”, International Journal of Advanced Trends in Computer Science and Engineering, Volume 10, No.2, March - April 2021.
Jie Yang and Sakgasit Ramingwong, “Analysis of Sales Influencing Factors and Prediction of Sales in Supermarket based on Machine Learning Technique”, Data Science and Engineering (DSE) Record, Volume 3, issue 1.
Rajesh, P. ., & Kavitha, R. . (2023). An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 96–105. https://doi.org/10.17762/ijritcc.v11i2s.6033
Kartika S. (2016). Analysis of “SystemC” design flow for FPGA implementation. International Journal of New Practices in Management and Engineering, 5(01), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/41
Pandey, J.K., Veeraiah, V., Talukdar, S.B., Talukdar, V., Rathod, V.M., Dhabliya, D. Smart city approaches using machine learning and the IoT (2023) Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, pp. 345-362.
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