Abstract
The main objective behind doing this work is that the future is autonomous and these systems improve analytics in cloud by providing “machine to human” environments. Oracle Analytics Cloud introduces the ability to “explain” an attribute in context of the other attributes and metrics in the dataset! Uncover what drives your results, be transparent with your findings, and analyze key segments of customer behavior. Hence with the growing amount of data in today’s world, an autonomous database would be perfect to handle it. The complete methodology adopted focuses on data preparation using the explain and other features of Oracle and then analyzing using different visuals after cleaning the data to get insights followed by creating different machine learning models and a small sample of natural language processing model. This entire paper focuses on bringing out the insights of sales that was further used to forecast the same for future, hence helping the company to strategize its sales all over. Through this paper, I have created several models which are further used on the autonomous database to answer the queries. Through this paper, I gained vast knowledge regarding various utilities of Oracle and how the technology is emerging which can help me in my future works. Oracle Analytics Cloud is seeking to go toe-to-toe with Microsoft (Power BI), Tableau and Qlik in particular. Why? All three vendors’ offerings are popular for BI and analytics. Moreover, they address many of the same areas as Oracle’s offering, including visual analysis and discovery, machine learning-driven data prep, and augmented analytics in the form of natural language queries. The tools used where Oracle Analytics Cloud, Oracle Analytics Desktop, Sql loader.
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Jain, T., Agarwal, M., Kumar, A., Verma, V.K., Yadav, A. (2022). Building Machine Learning Application Using Oracle Analytics Cloud. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_35
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