標(biāo)題: Titlebook: Business Analytics with R and Python; David L. Olson,Desheng Dash Wu,Majid Nabavi Book 2024 The Editor(s) (if applicable) and The Author(s [打印本頁(yè)] 作者: 磨損 時(shí)間: 2025-3-21 18:50
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書(shū)目名稱Business Analytics with R and Python網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Business Analytics with R and Python網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Business Analytics with R and Python被引頻次
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書(shū)目名稱Business Analytics with R and Python讀者反饋
書(shū)目名稱Business Analytics with R and Python讀者反饋學(xué)科排名
作者: 柔美流暢 時(shí)間: 2025-3-21 23:32 作者: Addictive 時(shí)間: 2025-3-22 00:35
David L. Olson,Desheng Dash Wu,Majid NabaviProvides a comprehensive review of data mining analytics.Gives review of real management applications.Presents demonstration with publicly available datasets作者: GENRE 時(shí)間: 2025-3-22 05:43
AI for Riskshttp://image.papertrans.cn/b/image/192792.jpg作者: 即席演說(shuō) 時(shí)間: 2025-3-22 12:27 作者: 劇本 時(shí)間: 2025-3-22 16:02
The Feminine Voice in Philosophyetail with examples of loading and opening these software systems. The graphical user interface (GUI) Rattle (part of the R system) is used throughout the book along with example R (R Studio) and Python (Anaconda and Jupyter lab) interfaces.作者: Rankle 時(shí)間: 2025-3-22 17:04
https://doi.org/10.1007/978-981-97-4772-6Descriptive Data Mining; Prescriptive Data Mining; AI and Predictive Data Mining; Data Visualization; As作者: indenture 時(shí)間: 2025-3-22 22:03
978-981-97-4774-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: growth-factor 時(shí)間: 2025-3-23 02:04
https://doi.org/10.1007/978-3-030-44421-1This chapter introduces the book, beginning with discussion of knowledge management. The requirements for data mining studies are reviewed. The most common business data mining applications are presented. The chapter ends with an overview of the contents of the remaining chapters.作者: 不溶解 時(shí)間: 2025-3-23 07:38
The Feminine Voice in PhilosophyThe data mining process from problem identification to study implementation is presented. The major systems (KDD, CRISP-DM and SEMMA) are described. The process of evaluating model results for different types of data is described.作者: Type-1-Diabetes 時(shí)間: 2025-3-23 10:09 作者: 思考 時(shí)間: 2025-3-23 17:28
The Feminine Voice in PhilosophyCluster analysis is described using the K-means algorithm. Datasets are used to demonstrate cluster analysis representing the important applications of churn, loan application analysis, and real estate evaluation. Rattle is demonstrated on all datasets, with R and Python code provided.作者: 發(fā)電機(jī) 時(shí)間: 2025-3-23 18:02
https://doi.org/10.1007/978-94-011-3174-2Regression algorithms are described, beginning with simple regression and moving on to autoregressive integrated moving average time series forecasting, multiple regression, stepwise regression, and logistic regression. Rattle is demonstrated on all datasets, with R and Python code provided.作者: 破裂 時(shí)間: 2025-3-23 23:50 作者: FLAGR 時(shí)間: 2025-3-24 05:58
https://doi.org/10.1007/978-94-011-3174-2The issue of variable selection is presented. Four different machine learning approaches are presented to reduce the number of variables in classification modeling. They are demonstrated with a bankruptcy data file. The value of variable reduction is discussed.作者: FUSE 時(shí)間: 2025-3-24 08:31 作者: cipher 時(shí)間: 2025-3-24 12:16 作者: CLOWN 時(shí)間: 2025-3-24 17:15
Data Mining Processes,The data mining process from problem identification to study implementation is presented. The major systems (KDD, CRISP-DM and SEMMA) are described. The process of evaluating model results for different types of data is described.作者: Junction 時(shí)間: 2025-3-24 20:48 作者: 典型 時(shí)間: 2025-3-24 23:24 作者: 不整齊 時(shí)間: 2025-3-25 03:47
Regression Algorithms in Data Mining,Regression algorithms are described, beginning with simple regression and moving on to autoregressive integrated moving average time series forecasting, multiple regression, stepwise regression, and logistic regression. Rattle is demonstrated on all datasets, with R and Python code provided.作者: 膠狀 時(shí)間: 2025-3-25 10:06 作者: APEX 時(shí)間: 2025-3-25 12:19
Variable Selection,The issue of variable selection is presented. Four different machine learning approaches are presented to reduce the number of variables in classification modeling. They are demonstrated with a bankruptcy data file. The value of variable reduction is discussed.作者: crucial 時(shí)間: 2025-3-25 17:14
Dataset Balancing,Many classification data mining studies involve highly skewed data, such as bankruptcy and medical issues (both of which are hoped to be rare). This can lead to statistical issues. Methods for dataset balancing are discussed and demonstrated on four different bankruptcy data files. They are also applied to a credit card fraud detection dataset.作者: climax 時(shí)間: 2025-3-25 20:49
Correction to: Business Analytics with R and Python,作者: 共同時(shí)代 時(shí)間: 2025-3-26 02:48 作者: Commodious 時(shí)間: 2025-3-26 04:27
2731-6327 vailable datasets.This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses作者: 痛打 時(shí)間: 2025-3-26 08:53 作者: FOLLY 時(shí)間: 2025-3-26 15:15 作者: Charitable 時(shí)間: 2025-3-26 17:03
Data Mining Software,etail with examples of loading and opening these software systems. The graphical user interface (GUI) Rattle (part of the R system) is used throughout the book along with example R (R Studio) and Python (Anaconda and Jupyter lab) interfaces.作者: 辯論 時(shí)間: 2025-3-26 23:36
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