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January 26, 2022
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Using survey data combined with publicly available financial data, our team of data scientists created a model to identify the premium for companies in our sample that successfully scale AI, controlling for various characteristics of the companiesWhe..
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One of the most successful methods for creating AI has been to adopt Data Science practices to shape data and create machine learning models based on techniques such as linear regression to predict a constant value. Some machine learning algorithms a..
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Getting started with data science can be a daunting task. Identifying and understanding the appropriate methods and techniques presents one challenge, while producing the code necessary to implement those methods and techniques presents another chall..
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This paper explores churn prediction for savings account customer, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models, considering the imbalance characteristics of customer ..
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January 24, 2022
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Oracle Retail Big Data Cloud Service can help, regardless of the answers to these top data questions: Which architecture is most appropriate – a Data Warehouse or a Data Lake? Is the analytical value known or presumed? Should the data be loaded..
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Due to escalating healthcare costs, accurately predicting which patients will incur high costs is an important task for payers and providers of healthcare. High-cost claimants (HiCCs) are patients who have annual costs above $250,000 and who represen..
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In 2006, the National Academy of Medicine established the Roundtable on Evidence-Based Medicine for the purpose of providing a trusted venue for national leaders in health and health care to work cooperatively toward their common commitment to effect..
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This paper discusses how R and SAS can be used to predict bank failures and the benefit that it would have to company's futures. By using stable forecasting, investors can predict the future bank failures. By using these softwares, organization..
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Forecast Sales using ARIMA and SARIMA
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Location wise normalized sales and independent features for a retail giant
