Downloads: 2 | Views: 54 | Weekly Hits: ⮙1 | Monthly Hits: ⮙2
Research Paper | Business Management | India | Volume 13 Issue 4, April 2024
Predictive Modeling in Business Analytics: Leveraging AI & Machine Learning
Manikanta Konkathi
Abstract: Even though business analytics is not a new concept in the corporate world it has been remodeled and updated to keep up with the modern - day competition that exists in present businesses. One such crucial update that business analytics has received is artificial intelligence. Artificial intelligence (AI) is a significant technology with the potential to revolutionize many aspects of our lives. Applications of machine learning (ML) within BA have proliferated in recent years, revolutionizing the process of business decision - making. The traditional business analytics methods focus on statistical approaches towards assessing business performance. However, the predictions made from these methods are highly uncertain. In the present work, we focus on developing a robust prediction model over past sales information using ML techniques. Artificial Neural Networks (BRNN) and Random Forest Models have been chosen in the present study to compare the best ML model for prediction. The accuracy of the model is validated using a 10 - fold Cross Validation (CV) approach. After comparing both the models, BRNN was seen to be more accurate than random forest model by a minute difference. The feature importance from the ML based models is used for suggesting the critical goods for improving the sales in future. Through this study, we would like to signify the role of ML based techniques in BA. The findings will be focused on outlined in terms of the dependability and accuracy of effective prediction and forecasting techniques.
Keywords: prediction model, sales forecasting, Artificial Intelligence, machine learning, Random Forest, Bayesian regularization neural networks, root mean square error, Tableau sample superstore, Rstudio
Edition: Volume 13 Issue 4, April 2024,
Pages: 875 - 889