Intelligent Sales Forecasting Technique Application

Abstract

The primary objective of this study was to design and implement a machine learning-based sales forecasting system to enhance the production capacity and sales trajectory. The deployed model features a web-based interface that allows users to input parameters to generate predictions. The application of an intelligent forecasting technique, namely a machine learning model, significantly contributed to determining the optimal manufacturing output for a specific product in this study. The data analysis was conducted utilizing statistical software known as Tableau. The machine learning algorithm employed for constructing the model was the multiple linear regression model, which is particularly well-suited for trend analysis. The supplied dataset was utilized to train and test a supervised machine learning model, which was subsequently deployed on a local web server. Furthermore, a database system was effectively implemented to facilitate data storage, retrieval, and manipulation. The model was evaluated using two commonly employed metrics, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE), within the Jupyter notebook environment. The resulting evaluation scores were 2.364858669808942 for RMSE and 1.7610409547966064 for MAE. These metrics were deemed effective in accurately predicting outcomes and efficiently presenting results.