Regression analysis with Python : learn the art of regression analysis with Python

Content: Cover
 Copyright
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Table of Contents
 Preface
 Chapter 1: Regression --
The Workhorse of Data Science
 Regression analysis and data science
 Exploring the promise of data science
 The challenge
 The linear models
 What you are going to find in the book 
 Python for data science
 Installing Python
 Choosing between Python 2 and Python 3
 Step-by-step installation
 Installing packages
 Package upgrades
 Scientific distributions
 Introducing Jupyter or IPython
 Python packages and functions for linear models 
 NumPy
 SciPy StatsmodelsScikit-learn
 Summary
 Chapter 2: Approaching Simple Linear Regression
 Defining a regression problem
 Linear models and supervised learning
 Reflecting on predictive variables
 Reflecting on response variables
 The family of linear models
 Preparing to discover simple linear regression
 Starting from the basics
 A measure of linear relationship
 Extending to linear regression
 Regressing with StatsModels
 The coefficient of determination
 Meaning and significance of coefficients
 Evaluating the fitted values
 Correlation is not causation
 Predicting with a regression model Regressing with Scikit-learnMinimizing the cost function
 Explaining the reason for using squared errors
 Pseudoinverse and other optimization methods
 Gradient Descent at work
 Summary
 Chapter 3: Multiple Regression in Action
 Using multiple features
 Model building with Statsmodels
 Using formulas as an alternative
 The correlation matrix
 Revisiting gradient descent
 Feature scaling
 Unstandardizing coefficients
 Estimating feature importance
 Inspecting standardized coefficients
 Comparing models by R-squared
 Interaction models
 Discovering interactions
 Polynomial regression Testing linear versus cubic transformationGoing for higher-degree solutions
 Introducing underfitting and overfitting
 Summary
 Chapter 4: Logistic Regression
 Defining a classification problem
 Formalization of the problem: binary classification
 Assessing the classifier's performance
 Defining a probability-based approach
 More on the logistic and logit functions
 Let's see some code
 Pros and cons of logistic regression
 Revisiting Gradient Descend
 Multiclass Logistic Regression
 An example
 Summary
 Chapter 5: Data Preparation
 Numeric feature scaling
 Mean centering
 Standardization NormalizationThe logistic regression case
 Qualitative feature encoding
 Dummy coding with Pandas
 DictVectorizer and one-hot encoding
 Feature hasher
 Numeric feature transformation
 Observing residuals
 Summarizations by binning
 Missing data
 Missing data imputation
 Keeping track of missing values
 Outliers
 Outliers on the response
 Outliers among the predictors
 Removing or replacing outliers
 Summary
 Chapter 6: Achieving Generalization
 Checking on out-of-sample data
 Testing by sample split
 Cross-validation
 Bootstrapping
 Greedy selection of features 
 The Madelon dataset