Integrating Machine Learning Models with Power BI for Predictive Analytics
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P111Keywords:
Predictive Analytics, Machine Learning Integration, Power BI, Business Intelligence, Azure Machine Learning, Data Visualization, Regression Models, Classification Models, Ensemble Learning, Enterprise AnalyticsAbstract
Their significant change to intelligent analytics systems following the ever-expanding volume of enterprise data and the rising need to support data-driven decision-making has caused the evolution of business intelligence (BI) systems. Traditional BI tools are based more on descriptive and diagnostic analytics whereby organizations would be in a position to learn past trends and present-day levels of performance. Nevertheless, with the introduction of predictive analytics that are now driven by Machine Learning (ML), the analysis has not only changed the established field but also left organizations with the ability to predict the future, identify anomalies, streamline processes, and even automate strategic decisions. The given paper is a detailed work on the incorporation of Machine Learning models with Microsoft Power BI to create scalable predictive analytics profiles that may be utilized in enterprises. The proposed structure illustrates such a framework that enables the integration of supervised and unsupervised ML algorithm such as Linear Regression, Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks with the Power BI using python and R scripting, Azure Machine learning services, and REST APIs. The study provides a pipeline that has an end-to-end pipeline, including the ingestion of data, preprocessing, feature engineering, model training, evaluation, deployment, and visualization. It focuses on the patterns of architectural designs that allow real-time and batch inferences in Power BI dashboards without compromising performance, scalability, and face governance. The research paper measures various model performance on performance in business data sets in sales prediction, customer churn prediction, and inventory management. Most commonly used evaluation metrics to compare the predictive capabilities include Accuracy, Precision, Recall, F1-score, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings indicate that ensemble models and gradient boosting algorithms perform better than the base statistical model in regression as well as classification. In addition, the endpoints of Azure ML guarantee the ability of modular deployment and operational scalability, whereas Power BI serves as the tool to improve interpretability by providing dynamic dashboards and KPI visualization. Also covered is security, data management and refresh constraints in power BI service environments. Difficulties in the areas of model retraining automation, the latency of updating the datasets, and computational limitations in Power BI Desktop are resolved based on the recommendations of the architectures. This study adds structured implementation model, comparative analysis and best-practice advice to organizations interested in operationalizing predictive analytics in BI settings. The results verify that the combination of the Machine Learning models and the Power BI allows the company to boost the intelligence of decisions to a higher level, making not just the fixed reporting systems informative but also predictive decision-support solutions.
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