[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127251-en":3,"doc-seo-127251-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127251,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","BMSP-ML - big mart sales prediction using different machine learning techniques","Big mart retail chains face month-to-month sales variations that complicate planning and inventory control. This study predicts store sales by analyzing historical sales across different stores, focusing on patterns in factors influencing demand. The workflow imputes missing values, applies feature engineering, splits data into train and test sets, and trains multiple machine learning regression models. Evaluation using RMSE shows the random forest model outperforms ridge regression, linear regression, and decision tree approaches, indicating stronger predictive accuracy for monthly sales.","BMSP-ML: big mart sales prediction using different machine  \nlearning techniques  \nRao Faizan Ali1, Amgad Muneer2, Ahmed Almaghthawi3, Amal Alghamdi4, Suliman Mohamed Fati5,  \nEbrahim Abdulwasea Abdullah Ghaleb2  \n1Department of Computer Science, Faculty of Software Engineering, University of Management and Technology, Lahore, Pakistan 2Department of Computer and Information Sciences, Faculty of Science and Information Technology, Universiti Teknologi  \nPETRONAS, Seri Iskandar, Malaysia  \n3Department of Computer Science, Collage of Science and Art at Mahayil, King Khalid University, Muhayel Aseer, Saudi Arabia 4Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah ,  \nJeddah, Saudi Arabia  \n5Department of Information System, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia  \nArticle history:  \nReceived Aug 24, 2021 Revised Nov 4, 2022 Accepted Dec 4, 2022  \nKeywords:  \nLinear regression Machine learning Random forest Ridge regression Sales prediction  \nCorresponding Author:  \nVariations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE) .  \nThis is an open access article under the CC BY-SA license.  \nAmgad Muneer  \nDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS 32610 Seri Iskandar, Perak, Malaysia  \n[Email: muneeramgad@gmail.com](Email: muneeramgad@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe sale is a lifeline of every business sales prediction that significantly impacts companies. Accurate predictions benefit the organization to maintain the standard and increase the company's lifestyle by using different strategies [1] . Typically, a prediction is based on the knowledge of previous studies with a deep focus on the conditions and then applies various factors, including customer's taste, culture, marketplace, and many more. In short, we can say that our prediction depends upon the previous study results [2] . Every business needs to be good in profit, and profit does not mean that stock sales are at maximum but also avoid the extra stock. Every retailer must maintain the stock according to the requirements and check the flaws and drawbacks that lead the sales down. Therefore, the proposed study deals with the same problem by predicting the store's sales [3] .  \nTherefore, this paper divides the prediction into different phases, including preprocessing the data. In the first section, we explore and impute the missing values of the data by using statistics, then we use  \nfeature engineering to explore more data and are ready to pass in models. Splitting the data into train and test variables with some ratio is better. Scikit-learn gives us the reliability to split the data into the desired ratio. This process gives a benefit to avoiding over and to underfit. After, we applied machine learning models to predict the results. We call the regressor of each method and then predict and compare it with the test values. 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