• Savita Sangam
• Pooja Ghude
• Mansi Ganpat Padekar
• Pradip Alam

This research paper presents a machine learning-based approach for predicting sales in the retail sector, focusing on Big Mart as a case study. The study explores various steps involved in data preprocessing, analysis, and model training using the XGBoost Regressor algorithm. The proposed method aims to accurately forecast sales, aiding in inventory management and strategic decision-making. Through extensive experimentation and evaluation, the effectiveness of the model in predicting sales for Big Mart is demonstrated. Sales forecasting is a critical task for businesses to anticipate future demand, allocate resources efficiently, and optimize operations. Traditional methods often rely on historical data and manual analysis, which may not capture the complexities and dynamic nature of modern markets. In recent years, machine learning techniques have emerged as powerful tools for sales forecasting, leveraging advanced algorithms to uncover patterns and trends in data. Furthermore, we present a case study demonstrating the implementation of a machine learning-based sales forecasting system for a retail company. The case study involves preprocessing sales data, engineering informative features, selecting appropriate machine learning models, and evaluating the performance of the forecasting system. We discuss the insights gained from the case study and the practical implications for businesses seeking to adopt machine learning in sales forecasting.

This research paper presents a machine learning-based approach for predicting sales in the retail sector, focusing on Big Mart as a case study. The study explores various steps involved in data preprocessing, analysis, and model training using the XGBoost Regressor algorithm. The proposed method aims to accurately forecast sales, aiding in inventory management and strategic decision-making. Through extensive experimentation and evaluation, the effectiveness of the model in predicting sales for Big Mart is demonstrated. Sales forecasting is a critical task for businesses to anticipate future demand, allocate resources efficiently, and optimize operations. Traditional methods often rely on historical data and manual analysis, which may not capture the complexities and dynamic nature of modern markets. In recent years, machine learning techniques have emerged as powerful tools for sales forecasting, leveraging advanced algorithms to uncover patterns and trends in data. Furthermore, we present a case study demonstrating the implementation of a machine learning-based sales forecasting system for a retail company. The case study involves preprocessing sales data, engineering informative features, selecting appropriate machine learning models, and evaluating the performance of the forecasting system. We discuss the insights gained from the case study and the practical implications for businesses seeking to adopt machine learning in sales forecasting.

"Sales Forecast Prediction & Recommendation System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.61-68, June-2024, Available :http://www.jetir.org/papers/JETIRGH06010.pdf

"Sales Forecast Prediction & Recommendation System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp61-68, June-2024, Available at : http://www.jetir.org/papers/JETIRGH06010.pdf