Finance Projection Using Data Mining Algorithms on SSAS and Python Data Science Libraries

Suhas Hanumanthaiah

doi:10.5281/zenodo.14980005

Finance Projection Using Data Mining Algorithms on SSAS and Python Data Science Libraries

Author(s)	Suhas Hanumanthaiah
Country	United States
Abstract	This paper compares Microsoft SQL Server Analysis Services and Python for financial forecasting, evaluating their ease of use, available algorithms, performance, scalability, and cost. SSAS offers a user-friendly interface within the Microsoft ecosystem, simplifying implementation for users familiar with the platform. However, its algorithm range is limited compared to Python's extensive libraries like Scikit-learn, TensorFlow, and PyTorch, which provide greater flexibility for advanced techniques like deep learning and ensemble methods. Python's flexibility allows for custom preprocessing, feature engineering, and model deployment, but requires programming expertise and computational resources. While SSAS can handle large datasets, its performance can be limited for complex models and real-time forecasting, especially in high-frequency trading or low-latency applications. Optimization techniques and efficient database design are crucial for maximizing SSAS performance. Python's performance depends on efficient coding and adequate hardware resources. Cost-wise, SSAS necessitates a SQL Server license, while Python is open-source but requires hardware investment. Personnel expertise is another factor, with Python demanding specialized data science skills. The optimal choice depends on the forecasting task complexity, data characteristics, available resources, and team expertise. SSAS is suitable for simpler tasks within the Microsoft environment, while Python is preferred for complex projects requiring advanced algorithms and scalability. Future research should focus on developing specialized financial forecasting algorithms, integrating diverse data sources, and improving the scalability and interpretability of complex machine learning models for enhanced accuracy and reliability in financial projections.
Field	Engineering
Published In	Volume 10, Issue 2, July-December 2019
Published On	2019-10-03
Cite This	Finance Projection Using Data Mining Algorithms on SSAS and Python Data Science Libraries - Suhas Hanumanthaiah - IJAIDR Volume 10, Issue 2, July-December 2019. DOI 10.5281/zenodo.14980005
DOI	https://doi.org/10.5281/zenodo.14980005
Short DOI	https://doi.org/g868mz

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Finance Projection Using Data Mining Algorithms on SSAS and Python Data Science Libraries

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