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

不整齊

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.

膠狀

APEX

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.

crucial

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.

climax

Correction to: Business Analytics with R and Python,

共同時(shí)代

Commodious

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

痛打

FOLLY

Charitable

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.