Regression Models Used in Data Mining and Practical Examples with R

In today's world, billions of data are produced every second. However, due to the abundance of data produced, today's age is called the "Big Data Age" or "Information Age". Transforming data into information in a qualified sense is of great importance in terms of both macro and micro aspects.
Data mining is the name given to the whole process of compiling, analyzing and converting data into qualified information output. Data mining applications in the literature; clustering, regression, classification or association rule inferences. In this respect, after the book titled "Clustering Algorithms Used in Data Mining and Applied Examples with R" published in 2020, this book on regression models was presented to your benefit. In this book, the concept of data mining has been defined in general, and then the models have been examined one by one. Hoping that this important resource will contribute to readers and researchers...
Topics covered in this book are as follows:
1. Introduction to Data Science and Machine Learning
2. Linear and Curvilinear (Polinomial) Regression Analysis
3. Decision Tree and Random Forest Regression Model
4. MARS Method
5. Support Vector Machines
6. XGBOOST Method
7. LightGBM and Catboost Algorithms
8. Artificial Neural Networks
9. ARIMA and LSTM Model
10. Convolutional Neural Networks (CNN)