Regression Analysis Recipes: With Tools and Techniques to Solve Problems Using Python and R.

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

使用回歸分析工具在Python和R中解決問題。本書提供了使用熟悉的數據集（如Iris、波士頓房屋數據、King County房屋數據集等）在Python和R中進行問題解決的解決方案。

您將從介紹回歸分析的各種方法和執行探索性數據分析的技巧開始。接下來，您將回顧不同回歸技術的問題和解決方案，並建立模型以進行更好的預測。本書還解釋了使用線性回歸、隨機森林、決策樹和其他回歸方法建立基本模型的方法。最後，介紹了評估模型的方法，以及簡要介紹了繪圖方法。

每個示例都將幫助您理解數據科學中的各種概念。您將使用Python和R開發代碼，使用線性回歸、支持向量回歸、隨機森林回歸等回歸方法解決問題。本書還提供了獲取有關插補方法、主成分分析、變異度測量、CHI2、相關性、訓練和測試模型、異常值檢測、特徵重要性、獨熱編碼等詳細信息的步驟。

完成《回歸分析食譜》後，您將了解回歸分析工具和技術，並能夠在Python和R中解決問題。

您將學到什麼

• 使用Python和R對數據進行回歸分析


• 了解不同類型的回歸方法


• 使用Python和R對不同類型的數據集進行探索性數據分析，如異常值檢測、插補


• 回顧在回歸分析中使用的Python和R庫

本書適合對Python和R具有基本編程知識的軟件專業人士

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.

Geetha Subramanian 是一位持有特許會計師資格的專業人士，擁有7年以上的統計分析、數據分析、預算編制、預測和財務報告方面的經驗。她曾完成約翰霍普金斯大學的數據科學課程，並且在Python和R方面擁有超過五年的工作經驗。