Store sales prediction with data science

Introduction

Now a day, forecasting demand of retail sales network found to be challenging task, especially while integrating the orders from online and physical stores generates the abundance of data. Those data needs to be stored, analyzed, processed, understood and become ready to be acted in very short period of time. The challenge becomes even bigger for the third party logistics operators as they are mainly responsible for the retaining, storing and breaking of the inbound quantity from the suppliers, consolidation and picking up of the retail orders and finally organize and plans shipments on the daily basis. To provide the effective solution to resolve the issues such that advent of data science can be found effective. The data science employs the scientific methods, algorithms, processes and systems for extraction of the knowledge from the data and uses those data to take the effective decisions. The connection of the retail tends to develop in rapid manner, the retailer needs to analyze the data and develop a scenario for customer. Thus, the customers were influenced by the tricks that are made by the retailers. In order to create the effective idea, the retailers needs to process large number of customer data. The implications of the data science can be found helpful in gaining the effective insights about the customers and emerging market trends by the analysis of those data. The analysis on the purpose and problem faced in this project were analyzed in the current research study. The evaluation of those problems can be found helpful in determination of effective solution.

Background of the study

The main goal of every store is to make the profit and it is made possible when large number of goods sold and turnover rate becomes high. The major challenge in increasing sales totally depends on the ability of the manager to forecast the sales pattern and to know when to order inventories and to plan for the staffs and manpower. One of the valuable assets of the supermarket will be data of the customers. With the help of those data, the modeling of the important patterns as well as variables will be made with assistance of the machine learning algorithms and it can be able to generate the accurate forecast sales rate.

There are various techniques exists for forecasting the sales rate of the stores and many of the retail stores dependent on the traditional statistical models. The application of those models does not found effective now days and thus effective models needs to be applied. However, the machine learning solution emerge as important area in the data science and forecasting power and predictive nature were high in compared with the other methods and techniques. In order to correctly predict the future event, machine learning models needs to be trained from the existing patterns and it will be used for prediction of the instances. The accurate forecasting model will significantly increase the revenue of the retail stores and it is greatly improvise the profit of the firm by gaining deeper insights about the interests of the customers.

Project description

The project developed on the basis of the data science technology such as machine learning. The application of models of the machine learning tends to enhance the sales rate by means of effective prediction. The project mainly done for prediction of the machine learning models that suits for the sales forecasting process and selection of the machine learning models can be done with the consideration of the mean absolute error (Dataiku, 2021). The three models are now chosen such as K-Nearest Neighbor, Random Forest and Gradient Boosting and comparative study were made between them to select the effective ML models. The determination of the training and validation score was made for the chosen models to predict the effectiveness. The performance metric that are chosen for the evaluation of the effectiveness of the machine learning model such as mean absolute error. The employment of the machine learning models can be helpful to estimate the future sales rate on the basis of the past data. The predicted values were analysed with test data and accuracy will be estimated. On the basis of the accuracy rate, the conclusion of the model that is better for the sales prediction can be made. The model that is chosen will be simply applied for determination of the sales forecasting process (Rawat, 2020).

Problem statement

One of the biggest challenges and problems faced by the stores such as how to mine the vast information of the customers and features of the products to become economical advantage. The various aspects of market based analysis were studied with usage of academic literature such as customer interest and purchasing patterns of the customers in multi store environment for improvising the sales rate (Pavlyshenko, 2018). Thus the market basket analysis found to be effective solution in providing the retailer about the information on the sales rate. The market basket analysis provides the effective frequent item set with assistance of the association rules. There exists various machine learning models and application of those models can be found effective in analysis of the customer interests and purchasing behavior of the customers can be leveraged. The sales forecasting were found to be major problem in the retail stores and it can be totally overcome with the usage of the machine learning models.

The analysis on the problem stated can be found as major importance to resolve the issues faced by the retail industries. The data scientists revealed that the deployment of the machine learning model can be effective in prediction of the future sales rate. The forecasted values would be examined with test data, and then the accuracy will be calculated.  Based on the value of accuracy the two models the author will decide which is the best model for the prediction of sales. The determination of the sales rate can improvise the revenue rate with usage of the effective machine learning models (Benjamin Schreck, 2016).

Aim and objectives

The research aim is to investigate the sales prediction of the customers on retail stores prior to certain period which is well in advance. This is helpful in determining the sales productivity and could make new strategies to eliminate the risks of business process. The aim of this article is to predict the future sales of the company Big-mart by observing the past values of sales information. Detailed study was given by the author using ML models like linear regression, K-neighbours, XGBoost, Random forest models. The prediction of data that includes item weight, Item of fat content , visibility, type of item, MRP, establishment of outlet and size of the outlet and location of the outlet etc.

Objectives

• To make a review on various ML models and to find out the best model in sales prediction
• To analyze the data and make synthesis of statistical information obtained from datasets
• From the analysis, discussions and recommendation to use the best model for sales prediction (Akshay Krishna, 2018)

Scope of the study

The research mainly focused to predict the daily sales rate for six weeks and applying the effective machine learning models for the prediction process. There are 3,000 retail stores present in European countries. In this project, we are going to select the 1,115 stores for prediction of the sales rate as per the performance metrics. The analysis of the historical data obtained from the Rossmann stores can be found helpful in prediction of the sales rate in weekly or monthly basis. The scope of carry out the respective project is to increase the sales prediction rate with employment of the machine learning models and techniques. Here we are going to predict the daily sales for 6 weeks well in advance. The marketing conditions gets analysed with the implied machine learning models and techniques and sales forecasting will be done with aid of those models (Tanya Charan Pahadi, 2020). The elimination of the risks by taking the inappropriate decisions can be completely eliminated with assistance of the machine learning models. The prediction of the sales rate in the various countries can be forecasted with the applied machine learning models.

Significance of the study

The significance of the study made on this project is to make the analysis on the machine learning models that are going to be applied for the increased revenue of the retail stores. The chosen models will forecast the sales rate in effective manner. The sales forecast found to be major challenge in the retail stores and it can be overcome with the study made. The application of the machine leaning model can largely helpful for the retailers and business managers to take the effective decisions to improvise the business productivity rate. The overstocking and under stocking risks can be completely eliminated with proper resource estimation. The best moving product also determined by means of the machine learning models and sales rate improvised by thorough analysis. Thus the study made can enhance the sales rate by determination of the effective ML models. The forecasting of the events was made by analysis of the patterns that are gathered from the existing databases.

References

Akshay Krishna, A. V. A. A. C. H., 2018. Sales-forecasting of Retail Stores using Machine Learning Techniques. s.l., IEEE- International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS).

Benjamin Schreck, K. V., 2016. What Would a Data Scientist Ask? Automatically Formulating and Solving Predictive Problems. IEEE, Volume 9, pp. 1-9.

Dataiku, 2021. Forecasting Sales with Machine Learning, s.l.: https://www.dataiku.com/learn/samples/forecasting-sales/.

Pavlyshenko, B. M., 2018. Machine-Learning Models for Sales Time Series Forecasting. Data – MDPI, 2019(4), pp. 1-11.

Rawat, K., 2020. Rossmann Store Sales Prediction, s.l.: https://medium.com/analytics-vidhya/rossmann-store-sales-prediction-998161027abf.

Tanya Charan Pahadi, P. R. a. A. V., 2020. Retail Store Sale Prediction. Easy chair, pp. 1-8.

Krishna, M. B. a. V. A., 2016. A framework of smart homes connected devices using Internet of Things. Noida, he 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noid.

Mi Jeong Kim, M. E. C. H. J. J., 2019. Developing Design Solutions for Smart Homes Through User-Centered Scenarios. frontiers.in, pp. 1-20.