Business Analytics with R and Python (AI for Risks)

✍ Scribed by David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 206
• Edition: 2024
• Category: Library

✦ Synopsis

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 on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

✦ Table of Contents

Preface
Contents
1 Data Mining in Business
1.1 Introduction
1.2 Requirements for Data Mining
1.3 Business Data Mining
1.3.1 Frequent Itemset Mining
1.3.2 Customer Relationship Management
1.3.3 Bankruptcy Prediction
1.3.4 Fraud Detection
1.4 Summary
2 Data Mining Processes
2.1 KDD
2.2 CRISP-DM
2.2.1 Business Understanding
2.2.2 Data Understanding
2.2.3 Data Preparation
2.2.4 Modeling
2.2.5 Evaluation
2.2.6 Deployment
2.3 SEMMA
2.3.1 Step 1 (Sample)
2.3.2 Step 2 (Explore)
2.3.3 Step 3 (Modify)
2.3.4 Step 4 (Model)
2.3.5 Step 5 (Assess)
2.4 Model Controls
2.5 Evaluation of Model Results
2.5.1 Example Model
2.5.2 Cost Metrics
2.5.3 Other Measures
2.6 Summary
References
3 Data Mining Software
3.1 R
3.2 Rattle
3.3 Python
3.3.1 Installing Python
3.3.2 Running Python
3.4 Summary
4 Association Rules
4.1 Methodology
4.2 Demonstration Dataset
4.2.1 Fit
4.2.2 Lift
4.3 The Apriori Algorithm
4.4 Association Rules from Software
4.4.1 Association Rules in Rattle
4.4.2 R Code
4.4.3 Python Code
4.5 Conclusion
Reference
5 Cluster Analysis
5.1 K-Means Clustering
5.1.1 A Clustering Algorithm
5.1.2 Loan Data
5.2 Clustering Methods Used in Software
5.3 Example Cases
5.3.1 Churn Clustering Model
5.3.2 Credit Risk Assessment Model
5.4 Software
5.4.1 Portuguese Bankruptcy Dataset
5.4.2 Rattle K-Means Clustering
5.4.3 R Clustering
5.4.4 R Code
5.4.5 Python Clustering
5.5 File BostonHousingKaggle.csv
5.5.1 R Code
5.6 Summary
References
6 Regression Algorithms in Data Mining
6.1 Regression Models
6.2 Forecasting S&P 500
6.2.1 R Code for Simple Regression
6.2.2 Python Code for Simple Regression
6.3 ARIMA Modeling
6.3.1 R Code for ARIMA
6.3.2 Python Code for ARIMA
6.4 Multiple Regression
6.4.1 R Code for Multiple Regression
6.4.2 Python Code for Multiple Regression
6.5 Stepwise Regression
6.5.1 R Code for Stepwise Regression
6.6 Logistic Regression
6.6.1 Tests of the Regression Model
6.6.2 Software Demonstrations of Logistic Regression
6.6.3 R Code
6.6.4 Python Code
6.7 Summary
7 Classification Tools
7.1 Classification Models
7.1.1 Regression
7.1.2 Decision Trees
7.1.3 Random Forest
7.1.4 Extreme Boosting
7.1.5 Support Vector Machines
7.1.6 Neural Networks
7.2 Bankruptcy Data Set
7.3 Logistic Regression
7.3.1 R Code Logistic Regression
7.3.2 Python Code Logistic Regression
7.4 Support Vector Machines
7.4.1 R Code SVM
7.4.2 Python Code SVM
7.5 Neural Networks
7.5.1 R Code Neural Network
7.5.2 Python Code Neural Network
7.6 Decision Trees
7.6.1 R Code Decision Tree
7.6.2 Python Code Decision Tree
7.7 Random Forests
7.7.1 R Code Random Forest
7.7.2 Python Code Random Forest
7.8 Boosting
7.8.1 R Code XGBoost
7.8.2 Python Code Gradient Boosting
7.9 Comparison
7.10 Loan Default Prediction Model
7.11 Summary
References
8 Variable Selection
8.1 Taiwan Bankruptcy Data
8.1.1 Correlation
8.1.2 Logistic Regression Variable Significance
8.1.3 Entropy
8.1.4 Information Content from Random Forest Models
8.1.5 Control Models Using All 94 Variables
8.2 Example Variable Selection Case
8.3 Value of Variable Reduction
References
9 Dataset Balancing
9.1 Bankruptcy Datasets
9.2 Balancing
9.3 Process
9.4 Data
9.4.1 Poland Data
9.4.2 Taiwan Data
9.4.3 Slovak Data
9.4.4 U.S. Data
9.5 Results
9.6 Example Credit Card Fraud Detection Case
9.7 Conclusions
References
Index