Regression Analysis Recipes
With Tools and Techniques to Solve Problems Using Python and R.
Buch in deiner Nähe kaufen
Beschreibung
Use regression analysis tools to solve problems in Python and R. This book provides problem-solving solutions in Python and R using familiar datasets such as Iris, Boston housing data, King County House dataset, etc.
You'll start with an introduction to the various methods of regression analysis and techniques to perform exploratory data analysis. Next, you'll review problems and solutions on different regression techniques with building models for better prediction. The book also explains building basic models using linear regression, random forest, decision tree, and other regression methods. It concludes with revealing ways to evaluate the models, along with a brief introduction to plots.
Each example will help you understand various concepts in data science. You'll develop code in Python and R to solve problems using regression methods such as linear regression, support vector regression, random forest regression. The book also provides steps to get details about Imputation methods, PCA, variance measures, CHI2, correlation, train and test models, outlier detection, feature importance, one hot encoding, etc.
Upon completing Regression Analysis Recipes, you will understand regression analysis tools and techniques and solve problems in Python and R.
You will:
- Perform regression analysis on data using Python and R
- Understand the different kinds of regression methods
- Use Python and R to perform exploratory data analysis such as outlier detection, imputation on different types of datasets
- Review the different libraries in Python and R utilized in regression analysis
Use regression analysis tools to solve problems in Python and R. This book provides problem-solving solutions in Python and R using familiar datasets such as Iris, Boston housing data, King County House dataset, etc.
You'll start with an introduction to the various methods of regression analysis and techniques to perform exploratory data analysis. Next, you'll review problems and solutions on different regression techniques with building models for better prediction. The book also explains building basic models using linear regression, random forest, decision tree, and other regression methods. It concludes with revealing ways to evaluate the models, along with a brief introduction to plots.
Each example will help you understand various concepts in data science. You'll develop code in Python and R to solve problems using regression methods such as linear regression, support vector regression, random forest regression. The book also provides steps to get details about Imputation methods, PCA, variance measures, CHI2, correlation, train and test models, outlier detection, feature importance, one hot encoding, etc.
Upon completing Regression Analysis Recipes, you will understand regression analysis tools and techniques and solve problems in Python and R.
What You'll Learn
- Perform regression analysis on data using Python and R
- Understand the different kinds of regression methods
- Use Python and R to perform exploratory data analysis such as outlier detection, imputation on different types of datasets
- Review the different libraries in Python and R utilized in regression analysis
Software Professionals who have basic programming knowledge about Python and R
Covers different libraries in Python and R used in regression analysis
Covers more than 100 recipes on regression analytics with Python and R
The book uses standard codes which can be replicated across different datasets
Autor*in
Geetha Subramanian
Themen in »Regression Analysis Recipes«
Data Science Regression Analysis Python R Supervised learning Decision tree regression Random forest regression Linear Regression Support Vector Ridge Regression Polynomial Regression