Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms
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International Conference on Computer Vision and Image Processing
CVIP 2019: Computer Vision and Image Processing pp 421–432 Cite as
A Comparative Study of Big Mart Sales Prediction
- Gopal Behera 9 &
- Neeta Nain 9
- Conference paper
- First Online: 29 March 2020
Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))
Nowadays shopping malls and Big Marts keep the track of their sales data of each and every individual item for predicting future demand of the customer and update the inventory management as well. These data stores basically contain a large number of customer data and individual item attributes in a data warehouse. Further, anomalies and frequent patterns are detected by mining the data store from the data warehouse. The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. In this paper, we propose a predictive model using Xgboost technique for predicting the sales of a company like Big Mart and found that the model produces better performance as compared to existing models. A comparative analysis of the model with others in terms performance metrics is also explained in details.
- Machine learning
- Sales forecasting
- Random forest
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Beheshti-Kashi, S., Karimi, H.R., Thoben, K.D., Lütjen, M., Teucke, M.: A survey on retail sales forecasting and prediction in fashion markets. Syst. Sci. Control Eng. 3 (1), 154–161 (2015)
Article Google Scholar
Bose, I., Mahapatra, R.K.: Business data mining-a machine learning perspective. Inf. Manage. 39 (3), 211–225 (2001)
Chu, C.W., Zhang, G.P.: A comparative study of linear and nonlinear models for aggregate retail sales forecasting. Int. J. Prod. Econ. 86 (3), 217–231 (2003)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combing content-based and collaborative filters in an online newspaper (1999)
Google Scholar
Das, P., Chaudhury, S.: Prediction of retail sales of footwear using feedforward and recurrent neural networks. Neural Comput. Appl. 16 (4–5), 491–502 (2007). https://doi.org/10.1007/s00521-006-0077-3
Domingos, P.M.: A few useful things to know about machine learning. Commun. ACM 55 (10), 78–87 (2012)
Langley, P., Simon, H.A.: Applications of machine learning and rule induction. Commun. ACM 38 (11), 54–64 (1995)
Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 1 (1), 14–23 (2011)
Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications. Wiley, New York (2008)
Ni, Y., Fan, F.: A two-stage dynamic sales forecasting model for the fashion retail. Expert Syst. Appl. 38 (3), 1529–1536 (2011)
Punam, K., Pamula, R., Jain, P.K.: A two-level statistical model for big mart sales prediction. In: International Conference on Computing, Power and Communication Technologies (GUCON), pp. 617–620. IEEE (2018)
Ribeiro, A., Seruca, I., Durão, N.: Improving organizational decision support: detection of outliers and sales prediction for a pharmaceutical distribution company. Procedia Comput. Sci. 121 , 282–290 (2017)
Shrivas, T.: Big mart dataset@ONLINE, June 2013. https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14 (3), 199–222 (2004). https://doi.org/10.1023/B:STCO.0000035301.49549.88
Article MathSciNet Google Scholar
Smyth, B., Cotter, P.: Personalized electronic program guides for digital TV. AI Mag. 22 (2), 89 (2001)
Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes (1996)
Xia, M., Wong, W.K.: A seasonal discrete grey forecasting model for fashion retailing. Knowl.-Based Syst. 57 , 119–126 (2014)
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Malaviya National Institute of Technology Jaipur, Jaipur, India
Gopal Behera & Neeta Nain
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Correspondence to Gopal Behera .
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Malaviya National Institute of Technology, Jaipur, Rajasthan, India
Santosh Kumar Vipparthi
Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Balasubramanian Raman
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Behera, G., Nain, N. (2020). A Comparative Study of Big Mart Sales Prediction. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_37
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DOI : https://doi.org/10.1007/978-981-15-4015-8_37
Published : 29 March 2020
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SALES PREDICTION MODEL FOR BIG MA RT. Nikita Malik *, Karan Singh 2. 1 Assistant Professor, MSI. 2 Student, MSI. Janakpuri, New Delhi. 1 *[email protected], 9971633991. 2 karan01921202017 ...
Predicting sales accurately is essential for effective inventory management, supply chain optimization, and revenue maximization. In this research paper, we propose a comprehensive study of various machine learning regression algorithms to predict Big Mart sales. We compare the performance of Linear Regression, Decision Trees, Random Forests ...
The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. In this paper, we proposed a predictive ...
Ranjitha P, Spandana.M "Predictive Analysis for Big Mart Sales U sing Machine Learning. Algorithms" Proceedings of the Fifth International Conference on Intelligent Computing and Control ...
In this paper, prediction of sales of a product from a particular outlet is performed via a two-level approach that produces better predictive performance compared to any of the popular single model predictive learning algorithms. The approach is performed on Big Mart Sales data of the year 2013. Data exploration, data transformation and ...
This paper developed a prediction model that will forecast product sales at a particular shop using numerous datasets. This study is able to get findings with a required degree of accuracy using the method employed to create a comprehensive model. Additionally, this information can be used to take decisions to additionally foster arrangements.
Currently, supermarket run-centres, Big Marts keep track of each individual item's sales data in order to anticipate potential consumer demand and update inventory management. Anomalies and general trends are often discovered by mining the data warehouse's data store. For retailers like Big Mart, the resulting data can be used to forecast future sales volume using various machine learning ...
The resultant data can be used for predicting future sales volume with the help of different machine learning techniques for the retailers like Big Mart. In this paper, we propose a predictive model using Xgboost technique for predicting the sales of a company like Big Mart and found that the model produces better performance as compared to ...
This research endeavors to harness the power of machine learning algorithms to conduct predictive analysis for Big Mart sales, a well-established retail chain with a wide footprint. The objective of this study is to develop robust predictive models capable of forecasting sales patterns, identifying influential factors, and ultimately aiding in ...
A predictive model was developed using Xgboost, Linear regression, Polynomial regression, and Ridge regression techniques for forecasting the sales of a business such as Big-Mart, and it was discovered that the model outperforms existing models. Currently, supermarket run-centres, Big Marts keep track of each individual item's sales data in order to anticipate potential consumer demand and ...
IJCRT2106802 International Journal of Creative Research Thoughts (IJCRT) www.ijcrt.org g674 BIG MART SALES PREDICTION USING MACHINE LEARNING Rohit Sav, Pratiksha Shinde, Saurabh Gaikwad ... 1. Title: - A Forecast for Big Mart Sales Based on Random Forests and Multiple Linear Regression (2018) Author: - Kadam, H., Shevade, R., Ketkar, P. and ...
In this study, exploratory machine learning approaches are used to forecast big-box store sales using Big Mart Sales data from the year 2013. In this study, exploratory machine learning approaches are used to forecast big-box store sales. In general, sales forecasting is crucial for advertising, merchandising, warehousing, and production, and it is done in a variety of organizations.
Nowadays shopping malls and Big Marts keep the track of their sales data of each and every individual item for predicting future demand of the customer and update the inventory management as well. These data stores basically contain a large number of customer data and individual item attributes in a data warehouse.
We are addressing the problem of big mart sales prediction or forecasting of an item on customer's future demand in different big mart stores across ... of the paper. Different prediction methods give different performance predictions when used for daily fresh food sales forecasting. ... International Journal of Research Publication and ...
different machine learning techniques for the retailers like Big Mart. In this paper, we propose a predictive model using XG ... 9.4 prediction of sales 55 9.6 performance values of proposed 56 model . 4 CHAPTER 1 INTRODUCTION Big Mart is a big supermarket chain, with stores all around the country and its current ...
This paper investigates the viability of predict sales in a small-scale retail supermarket, and evaluates the performance of linear regression, random forest, and gradient boosting method in the ...
Big Mart Sales. Dr S.B.Thorat, Mr Amol Suryawanshi Director1, ... improve their predictions of Big Mart sales by shop and product using computer-generated data. A lot of organisations are largely dependent on data, and precise market forecasting is ... DogoRangsang Research Journal UGC Care Group I Journal ISSN: 2347-7180 Vol-13 Issue-01 June ...
Taking various aspects of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be employed to take decisions to improve sales. : Machine Learning is a category of algorithms that allows software applications to become more accurate in predicting outcomes without being ...
The example of Big Mart, a one-stop shop, is explored in this study. Predicting the sale of different types of items and understanding the effects of different items on the sale of goods Results with high levels of accuracy are obtained using different features of the data collected from Big Mart and the method used to improve the forecast model.
This methodology is primarily used by shopping marts, groceries, Brand outlets etc. The data analysis applied to the predictive machine learning models provides a very effective way to manage sales, it also generously contributes to better decisions and plan strategies based on future demands.This approach is.
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In this paper, the case of Big Mart, a one-stop-shopping- center, has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the ...