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Sales forecasting is essential for a number of sectors including manufacturing, marketing, wholesale, retail, logistics, and other sectors. This approach allows companies to efficiently allocate resources, accurately forecast sales revenue, and develop strategic plans that support long-term organizational plans success. This article examines forecasts for the clothing industry. Clothing sales data from January 2021 to December 2023 is used for analyzing. Python is a preferred option because of its adaptability and simplicity of use when processing complex sales data and making accurate inferences about future trends. The article is focused on improving businesses using sales forecasting. The study used XGBoost and linear regression, two well-known machine learning methods, to forecast sales of the clothing industry. It includes linear model, XGBoost algorithm, and data flow diagram representations. Sales forecasting is essential for efficient inventory control, resource allocation, as well as strategic decision-making in the ever-changing retail and e-commerce environments. Future sales patterns can be effectively predicted by utilizing existing techniques like linear regression and machine learning algorithms like XGBoost. This article examines how these models work in Python to predict sales and evaluate the results.

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Leveraging Python for Accurate Sales Prediction in the Apparel Industry Insights into Techniques: Exploring XGboost and Linear Regression Model

  • 会议论文

  • 发表日期：

    2025年

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  • N. Jayanthi,
  • S. Subendiran,
  • S. Prabhu,
  • S. Kavin Babu,
  • D. Umamaheswari

摘要

Sales forecasting is essential for a number of sectors including manufacturing, marketing, wholesale, retail, logistics, and other sectors. This approach allows companies to efficiently allocate resources, accurately forecast sales revenue, and develop strategic plans that support long-term organizational plans success. This article examines forecasts for the clothing industry. Clothing sales data from January 2021 to December 2023 is used for analyzing. Python is a preferred option because of its adaptability and simplicity of use when processing complex sales data and making accurate inferences about future trends. The article is focused on improving businesses using sales forecasting. The study used XGBoost and linear regression, two well-known machine learning methods, to forecast sales of the clothing industry. It includes linear model, XGBoost algorithm, and data flow diagram representations. Sales forecasting is essential for efficient inventory control, resource allocation, as well as strategic decision-making in the ever-changing retail and e-commerce environments. Future sales patterns can be effectively predicted by utilizing existing techniques like linear regression and machine learning algorithms like XGBoost. This article examines how these models work in Python to predict sales and evaluate the results.