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Titlebook: Business Analytics for Professionals; Alp Ustundag,Emre Cevikcan,Omer Faruk Beyca Book 2022 The Editor(s) (if applicable) and The Author(s

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樓主: 小巷
31#
發(fā)表于 2025-3-26 23:56:31 | 只看該作者
978-3-030-93825-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
32#
發(fā)表于 2025-3-27 03:11:05 | 只看該作者
Descriptive Analyticstions, statistical inference, and Bayesian statistics are explained. Along with theory, practical applications on a sample data set are provided. Applications are performed using the following Python libraries: Pandas, Seaborn, and Statmodels.
33#
發(fā)表于 2025-3-27 08:00:57 | 只看該作者
Feature Engineeringmodel complexity and establish simple, accurate and robust models. Feature engineering is the process of using domain knowledge to extract input variables from raw data, prioritize them and select the best ones so that machine learning algorithms work well and model performance is improved.
34#
發(fā)表于 2025-3-27 11:46:41 | 只看該作者
Financial Analyticselping companies reduce risk and make more efficient financial decisions. Machine learning and advanced analytics are used in several financial applications such as fraud detection and prevention?systems, credit risk modelling, financial statement analysis,?algorithmic trading, robo-advisory systems?etc.
35#
發(fā)表于 2025-3-27 14:03:58 | 只看該作者
36#
發(fā)表于 2025-3-27 19:36:35 | 只看該作者
37#
發(fā)表于 2025-3-28 00:24:56 | 只看該作者
38#
發(fā)表于 2025-3-28 03:36:29 | 只看該作者
Neural Networks and Deep Learningking mechanism by artificially forming a neural network. In this book, artificial neural networks are referred to as neural networks. The principal idea of a neural network is to show transformation between input and output as connections between neurons in a sequence (arrangement) of layers (White
39#
發(fā)表于 2025-3-28 07:35:07 | 只看該作者
Feature Engineeringmodel complexity and establish simple, accurate and robust models. Feature engineering is the process of using domain knowledge to extract input variables from raw data, prioritize them and select the best ones so that machine learning algorithms work well and model performance is improved.
40#
發(fā)表于 2025-3-28 11:19:35 | 只看該作者
Prescriptive Analytics: Optimization and Modelingture using large amounts of data. In this process, prescriptive analytics combines the output of predictive analytics and uses artificial intelligence, optimization algorithms, and expert systems to provide adaptive, automated, constrained, time-bound, and optimal decisions, thus having the potentia
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