Regression Analysis Recipes
Apress (Verlag)
978-1-4842-7804-8 (ISBN)
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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
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.
Chapter 1: Supervised Learning AlgorithmsChapter Goal: Provide a brief understanding of different Regression analysis techniquesNo of pages: 18Sub -Topics: 1.Regression2.Polynomial Regression3.Support Vector Regression4.Decision Tree Regression5.Random Forest Regression
Chapter 2: Exploratory Data AnalysisChapter Goal: This chapter covers questions related to Exploratory Data Analysis i.e., Correlation, Outliers and other topicsNo of Pages: 20Sub-Topics: Python - sort using attrgetterR - Formatting datePython - CorrelationPython - Sorting based on indexPython - IndexingR - Compute attribute importanceR - Mean, median, modePython - Impute missing dataPython - Impute with mask optionPython - Mean, median, modeR - OutliersPython - OutliersR - IQRPython - IQRPython - Impute with mean, median and modeR - Impute using mice packageR - Impute with mean and median
Chapter 3: Linear RegressionChapter Goal: This chapter covers questions related to linear regression i.e., prediction with linear regression, forward and backward selection.No of Pages: 26Sub-Topics: Python - Prediction with linear regressionPython - Linear regression with PCAR - Prediction with linear modelPython - Linear regression with forward selectionPython - Linear modelR - Linear regression and scalePython - OLS with t-stat and p-valuesPython - OLS and Identify variables with significant relationshipPython - OLS and confidence intervalPython - OLS and significant variablesPython - Print slope and P-valueR - Linear Regression model with forward selectionR - Linear Regression and degree of freedomPython - Linear regression and MSECalculate Slope and interceptPython - Linear regression with backward elimination
Chapter 4: Random ForestChapter Goal: This chapter covers questions related to random forest regression i.e., prediction with random forest, feature importance.No of Pages: 20Sub-Topics: R - Prediction with random forestR - Random forest with a fixed number of treesR - Bagging modelR - Stacking algorithmPython - Accuracy of random forest modelR - Random forest tree structurePython - Random forest and learning curveR - Random forest and feature importanceR - Random forest with optimal mtry valuePython - Random forest using out of bag estimatePython - Random forest and feature importance
Chapter 5: Decision TreeChapter Goal: This chapter covers questions related to decision tree i.e., prediction with decision tree, feature importance and recursive feature selection.No of Pages: 15Sub-Topics: Python - Prediction with decision TreePython - Recursive featureR - Decision tree and RMSEPython - Decision tree and cross validationPython - Decision tree and feature importanceR - Decision tree and feature importancePython - Decision tree and adaboostR Stochastic gradient boosting
Chapter 6: Support Vector Chapter Goal: This chapter covers questions related to support vector regression i.e., prediction with support vector.No of Pages: 10Sub-Topics: R - Prediction with support vector regressionPython - Print support vector regression model fit statusR - Support vector regression and number of support vectorsPython - Support vector regression and number of support vectors
Chapter 7: Polynomial, Lasso and Ridge RegressionChapter Goal: This chapter covers questions related to polynomial regression i.e., prediction with polynomial regression, Lasso and Ridge regressionNo of Pages: 24Sub-Topics:
R - Prediction with polynomial regressionR - Polynomial regression with degree 5R - Polynomial regression and confidence intervalPython - Ridge regression and R-squarePython - Lasso regression and MSER- Ridge Regression and coefficient matrixR- Ridge Regression and K fold cross validationPython - Ridge regression and model scorePython - Ridge regression and the regularization parameterR - LassoR - Lasso with K fold cross validationPython - Lasso and model scorePython - Lasso and regularization parameterPython - Polynomial regression with recursive feature selection method
Chapter 8: Principal Component Analysis and CHI2Chapter Goal: This chapter covers questions related to principal component analysis and CHI2i.e., Eigen values, Eigen vectors, Chi valueNo of Pages: 14Sub-Topics: Python - PCA with MinMaxScaler
Python - Eigen vector and standard scalerPython - Eigen values and correlation matrixPython - Chi-square contingencyPython - PCA and variance ratioPython - Variance inflation factorPython - Chi valueR - PCA with principal componentsPython - Correlation coefficients, Eigen valuesPython - PCA and explained variance ratio
Chapter 9: Evaluation MetricsChapter Goal: This chapter covers questions related to evaluation metrics used in machine learning i.e., mean squared error, root Mean squared error.No of Pages: 18Sub-Topics: R - Linear regression and MSER - Support vector regression and RMSER - Linear regression and RMSER- Ridge regression and MSEPython - Ridge regression and RMSER - Lasso and MSEPython - Lasso and RMSER - Linear regression and RMSE, MAEPython - Linear regression and Model scorePython - Linear regression and MSE
Chapter 10: Plots and Other CodesChapter Goal: This chapter covers questions related to the creation of plots and distance calculation using Euclidean and Manhattan techniques. No of Pages: 14 Sub-Topics: Python - Jaccard indexPython - BoxplotPython - HeatmapR - Bar plotPython - Function to compute Manhattan distancePython - Euclidean distancePython - Manhattan distancePython - Word splitPython - Function to print textPython - Printing from text
Appendix: The appendix contains links for datasets used in this book
| Erscheint lt. Verlag | 14.10.2022 |
|---|---|
| Zusatzinfo | V, 195 p. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Data Science • Decision tree regression • linear regression • Polynomial Regression • Python • R • random forest regression • Regression Analysis • Ridge regression • supervised learning • Support vector |
| ISBN-10 | 1-4842-7804-6 / 1484278046 |
| ISBN-13 | 978-1-4842-7804-8 / 9781484278048 |
| Zustand | Neuware |
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