Data Mining and Business Forecasting: Enhancing Decision-Making through Intelligent Data Analytics
Keywords:
Business forecasting, Clustering, Data mining, Deep learning, Machine learning, Predictive analyticsAbstract
The sheer volume, variety, and complexity of commercial information demand improved analytical approaches that can support accurate forecasting and timely managerial planning. This work presents a hybrid data mining/business forecasting approach that is developed to extract hidden patterns from complex information sets and increase the predictive accuracy of business prediction. It integrates data pre-processing, variable selection, clustering, nonlinear learning, and business forecasting techniques to address challenges in data quality, high dimensionality, nonlinear relationships, and temporal dependencies. The system can use classification, regression, association rule mining, neural networks, ensemble learning, gradient boosting, LSTM-based forecasting, and time-series analysis techniques, with hybrid models combining different models to complement each other and make the best use of each. The proposed setup is based on data pre-processing, conceptualization, clustering and association analysis, predictor selection, model building and validation, comparison with traditional individual modelling, and the use of suitable forecasting and classification performance measures. The setup seems to assure better, more accurate, and more reliable business prediction compared to a standard, single course of action. The feature selection and hybrid modelling reduce the effects of redundant and non-representative information, reducing the model complexity. Empirical application has demonstrated the capacity of the system to improve forecasting, customer mining, risk calculations, and many other data mining-based business processes. The general system can be seen as a practical approach to the data analysis and business forecasting domain. It offers flexibility and reliability that may contribute to improving demand forecasts, reducing uncertainty and bidding risk, and increasing the operational efficiency of the institution.
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