Statistical Application Development with R and Python
Develop applications using data processing, statistical models, and CART
2nd Edition
Published on 13. January 2025
432 pages
978-1-78862-226-4 (ISBN)
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Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
File size
22,78 MB
ISBN-13
978-1-78862-226-4 (9781788622264)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Prabhanjan Narayanachar Tattar
Statistical Application Development with R and Python -
Book
08/2017
2nd Edition
Packt Publishing
€64.10
Shipment within 3-4 weeks
Person
Prabhanjan Narayanachar Tattar has a combined twelve years of experience with R and Python software. He has also authored the books A Course in Statistics with R, Wiley, and Practical Data Science Cookbook, Packt. The author has built three packages in R titled gpk, RSADBE, and ACSWR. He has obtained a PhD (statistics) from Bangalore University under the broad area of survival snalysis and published several articles in peer-reviewed journals. During the PhD program, the author received the young Statistician honors for the IBS(IR)-GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007) and also held a Junior and Senior Research Fellowship at CSIR-UGC.
Prabhanjan has worked in various positions in the analytical industry and nearly 10 years of experience in using statistical and machine learning techniques.
Prabhanjan has worked in various positions in the analytical industry and nearly 10 years of experience in using statistical and machine learning techniques.
Content
- Cover
- Copyright
- Credits
- About the Author
- Acknowledgment
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Data Characteristics
- Questionnaire and its components
- Understanding the data characteristics in an R environment
- Experiments with uncertainty in computer science
- Installing and setting up R
- Using R packages
- RSADBE - the books R package
- Python installation and setup
- Using pip for packages
- IDEs for R and Python
- The companion code bundle
- Discrete distributions
- Discrete uniform distribution
- Binomial distribution
- Hypergeometric distribution
- Negative binomial distribution
- Poisson distribution
- Continuous distributions
- Uniform distribution
- Exponential distribution
- Normal distribution
- Summary
- Chapter 2: Import/Export Data
- Packages and settings - R and Python
- Understanding data.frame and other formats
- Constants, vectors, and matrices
- Time for action - understanding constants, vectors, and basic arithmetic
- What just happened?
- Doing it in Python
- Time for action - matrix computations
- What just happened?
- Doing it in Python
- The list object
- Time for action - creating a list object
- What just happened?
- The data.frame object
- Time for action - creating a data.frame object
- What just happened?
- Have a go hero
- The table object
- Time for action - creating the Titanic dataset as a table object
- What just happened?
- Have a go hero
- Using utils and the foreign packages
- Time for action - importing data from external files
- What just happened?
- Doing it in Python
- Importing data from MySQL
- Doing it in Python
- Exporting data/graphs
- Exporting R objects
- Exporting graphs
- Time for action - exporting a graph
- What just happened?
- Managing R sessions
- Time for action - session management
- What just happened?
- Doing it in Python
- Pop quiz
- Summary
- Chapter 3: Data Visualization
- Packages and settings - R and Python
- Visualization techniques for categorical data
- Bar chart
- Going through the built-in examples of R
- Time for action - bar charts in R
- What just happened?
- Doing it in Python
- Have a go hero
- Dot chart
- Time for action - dot charts in R
- What just happened?
- Doing it in Python
- Spine and mosaic plots
- Time for action - spine plot for the shift and operator data
- What just happened?
- Time for action - mosaic plot for the Titanic dataset
- What just happened?
- Pie chart and the fourfold plot
- Visualization techniques for continuous variable data
- Boxplot
- Time for action - using the boxplot
- What just happened?
- Doing it in Python
- Histogram
- Time for action - understanding the effectiveness of histograms
- What just happened?
- Doing it in Python
- Have a go hero
- Scatter plot
- Time for action - plot and pairs R functions
- What just happened?
- Doing it in Python
- Have a go hero
- Pareto chart
- A brief peek at ggplot2
- Time for action - qplot
- What just happened?
- Time for action - ggplot
- What just happened?
- Pop quiz
- Summary
- Chapter 4: Exploratory Analysis
- Packages and settings - R and Python
- Essential summary statistics
- Percentiles, quantiles, and median
- Hinges
- Interquartile range
- Time for action - the essential summary statistics for The Wall dataset
- What just happened?
- Techniques for exploratory analysis
- The stem-and-leaf plot
- Time for action - the stem function in play
- What just happened?
- Letter values
- Data re-expression
- Have a go hero
- Bagplot - a bivariate boxplot
- Time for action - the bagplot display for multivariate datasets
- What just happened?
- Resistant line
- Time for action - resistant line as a first regression model
- What just happened?
- Smoothing data
- Time for action - smoothening the cow temperature data
- What just happened?
- Median polish
- Time for action - the median polish algorithm
- What just happened?
- Have a go hero
- Summary
- Chapter 5: Statistical Inference
- Packages and settings - R and Python
- Maximum likelihood estimator
- Visualizing the likelihood function
- Time for action - visualizing the likelihood function
- What just happened?
- Doing it in Python
- Finding the maximum likelihood estimator
- Using the fitdistr function
- Time for action - finding the MLE using mle and fitdistr functions
- What just happened?
- Confidence intervals
- Time for action - confidence intervals
- What just happened?
- Doing it in Python
- Hypothesis testing
- Binomial test
- Time for action - testing probability of success
- What just happened?
- Tests of proportions and the chi-square test
- Time for action - testing proportions
- What just happened?
- Tests based on normal distribution - one sample
- Time for action - testing one-sample hypotheses
- What just happened?
- Have a go hero
- Tests based on normal distribution - two sample
- Time for action - testing two-sample hypotheses
- What just happened?
- Have a go hero
- Doing it in Python
- Summary
- Chapter 6: Linear Regression Analysis
- Packages and settings - R and Python
- The essence of regression
- The simple linear regression model
- What happens to the arbitrary choice of parameters?
- Time for action - the arbitrary choice of parameters
- What just happened?
- Building a simple linear regression model
- Time for action - building a simple linear regression model
- What just happened?
- Have a go hero
- ANOVA and the confidence intervals
- Time for action - ANOVA and the confidence intervals
- What just happened?
- Model validation
- Time for action - residual plots for model validation
- What just happened?
- Doing it in Python
- Have a go hero
- Multiple linear regression model
- Averaging k simple linear regression models or a multiple linear regression model
- Time for action - averaging k simple linear regression models
- What just happened?
- Building a multiple linear regression model
- Time for action - building a multiple linear regression model
- What just happened?
- The ANOVA and confidence intervals for the multiple linear regression model
- Time for action - the ANOVA and confidence intervals for the multiple linear regression model
- What just happened?
- Have a go hero
- Useful residual plots
- Time for action - residual plots for the multiple linear regression model
- What just happened?
- Regression diagnostics
- Leverage points
- Influential points
- DFFITS and DFBETAS
- The multicollinearity problem
- Time for action - addressing the multicollinearity problem for the gasoline data
- What just happened?
- Doing it in Python
- Model selection
- Stepwise procedures
- The backward elimination
- The forward selection
- The stepwise regression
- Criterion-based procedures
- Time for action - model selection using the backward, forward, and AIC criteria
- What just happened?
- Have a go hero
- Summary
- Chapter 7: Logistic Regression Model
- Packages and settings - R and Python
- The binary regression problem
- Time for action - limitation of linear regression model
- What just happened?
- Probit regression model
- Time for action - understanding the constants
- What just happened?
- Doing it in Python
- Logistic regression model
- Time for action - fitting the logistic regression model
- What just happened?
- Doing it in Python
- Hosmer-Lemeshow goodness-of-fit test statistic
- Time for action - Hosmer-Lemeshow goodness-of-fit statistic
- What just happened?
- Model validation and diagnostics
- Residual plots for the GLM
- Time for action - residual plots for logistic regression model
- What just happened?
- Doing it in Python
- Have a go hero
- Influence and leverage for the GLM
- Time for action - diagnostics for the logistic regression
- What just happened?
- Have a go hero
- Receiving operator curves
- Time for action - ROC construction
- What just happened?
- Doing it in Python
- Logistic regression for the German credit screening dataset
- Time for action - logistic regression for the German credit dataset
- What just happened?
- Doing it in Python
- Have a go hero
- Summary
- Chapter 8: Regression Models with Regularization
- Packages and settings - R and Python
- The overfitting problem
- Time for action - understanding overfitting
- What just happened?
- Doing it in Python
- Have a go hero
- Regression spline
- Basis functions
- Piecewise linear regression model
- Time for action - fitting piecewise linear regression models
- What just happened?
- Natural cubic splines and the general B-splines
- Time for action - fitting the spline regression models
- What just happened?
- Ridge regression for linear models
- Protecting against overfitting
- Time for action - ridge regression for the linear regression model
- What just happened?
- Doing it in Python
- Ridge regression for logistic regression models
- Time for action - ridge regression for the logistic regression model
- What just happened?
- Another look at model assessment
- Time for action - selecting iteratively and other topics
- What just happened?
- Pop quiz
- Summary
- Chapter 9: Classification and Regression Trees
- Packages and settings - R and Python
- Understanding recursive partitions
- Time for action - partitioning the display plot
- What just happened?
- Splitting the data
- The first tree
- Time for action - building our first tree
- What just happened?
- Constructing a regression tree
- Time for action - the construction of a regression tree
- What just happened?
- Constructing a classification tree
- Time for action - the construction of a classification tree
- What just happened?
- Doing it in Python
- Classification tree for the German credit data
- Time for action - the construction of a classification tree
- What just happened?
- Doing it in Python
- Have a go hero
- Pruning and other finer aspects of a tree
- Time for action - pruning a classification tree
- What just happened?
- Pop quiz
- Summary
- Chapter 10: CART and Beyond
- Packages and settings - R and Python
- Improving the CART
- Time for action - cross-validation predictions
- What just happened?
- Understanding bagging
- The bootstrap
- Time for action - understanding the bootstrap technique
- What just happened?
- How the bagging algorithm works
- Time for action - the bagging algorithm
- What just happened?
- Doing it in Python
- Random forests
- Time for action - random forests for the German credit data
- What just happened?
- Doing it in Python
- The consolidation
- Time for action - random forests for the low birth weight data
- What just happened?
- Summary
- Index
