AI-Based Approach for Retail Sale Forecasting

In the fast-paced world of retail, accurate sales forecasting plays a crucial role in optimizing inventory management, planning marketing strategies, and making informed business decisions. Traditional forecasting methods often struggle to capture the complexities and dynamic nature of retail sales patterns. However, with the advent of Artificial Intelligence (AI), retailers now have a powerful tool at their disposal to improve the accuracy and efficiency of their sales forecasting processes. The AI-based approach for retail sale forecasting involves leveraging advanced algorithms and techniques to analyse historical sales data, customer behaviour, market trends, and other relevant factors. This study presents an AI-based approach for retail sale forecasting, starting with data collection from Kaggle, a popular platform for open datasets. The collected data undergoes pre-processing steps, including handling missing values, outliers, and categorical data. Three regression models, namely Linear Regressor (LR), Ridge Regressor (RR), and Random Forest Regressor (RF), are constructed to predict sales. The models are evaluated using common evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2). The results reveal that the RF model outperforms the LR and RR models, providing better accuracy and capturing a larger portion of the variability in the sales data. These findings highlight the significance of AI -based approaches, particularly the RF Regressor, in improving retail sale forecasting accuracy and supporting decision-making processes.