Regression Analysis Recipes: With Tools and Techniques to Solve Problems Using Python and R.
暫譯: 迴歸分析食譜：使用 Python 和 R 解決問題的工具與技術
Subramanian, Geetha
商品描述
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
Who This Book Is For
Software Professionals who have basic programming knowledge about Python and R
商品描述(中文翻譯)
使用迴歸分析工具來解決 Python 和 R 中的問題。本書提供使用熟悉的數據集（如 Iris、波士頓房價數據、金縣房屋數據集等）在 Python 和 R 中的問題解決方案。
您將從介紹各種迴歸分析方法和進行探索性數據分析的技術開始。接下來，您將回顧不同迴歸技術的問題和解決方案，並建立模型以提高預測準確性。本書還解釋了如何使用線性迴歸、隨機森林、決策樹和其他迴歸方法來構建基本模型。最後，將揭示評估模型的方法，並簡要介紹圖表。
每個範例將幫助您理解數據科學中的各種概念。您將在 Python 和 R 中開發代碼，使用迴歸方法（如線性迴歸、支持向量迴歸、隨機森林迴歸）來解決問題。本書還提供有關插補方法、主成分分析（PCA）、方差度量、CHI2、相關性、訓練和測試模型、異常值檢測、特徵重要性、獨熱編碼等的詳細步驟。
完成《迴歸分析食譜》後，您將理解迴歸分析工具和技術，並能在 Python 和 R 中解決問題。
您將學到什麼
- 使用 Python 和 R 對數據進行迴歸分析
- 理解不同類型的迴歸方法
- 使用 Python 和 R 進行探索性數據分析，如異常值檢測、對不同類型數據集的插補
- 回顧在迴歸分析中使用的 Python 和 R 的不同庫
本書適合誰
具有基本 Python 和 R 編程知識的軟體專業人員
作者簡介
Geetha Subramanian is a Chartered Accountant with 7+ years of experience in statistical analysis, data analytics, budgeting, forecasting, and financial reports. She has completed a data science course with John Hopkins University and has more than five years of experience working with Python and R.
作者簡介(中文翻譯)
Geetha Subramanian 是一位特許會計師，擁有超過 7 年的統計分析、數據分析、預算編制、預測和財務報告的經驗。她已完成約翰霍普金斯大學的數據科學課程，並擁有超過五年的 Python 和 R 的工作經驗。
