Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst 1st Edition by Dean Abbott 1118727967 9781118727966

Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst 1st Edition by Dean Abbott – Ebook PDF Instant Download/Delivery: 1118727967, 9781118727966

Full download Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst 1st Edition after payment

Product details:

ISBN 10: 1118727967
ISBN 13: 9781118727966
Author: Dean Abbott

Predictive Analytics shows tech-savvy business managers and data analysts how to use the techniques of predictive analytics to solve practical business problems.  It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish.  The author focuses on best practices—including tips and tricks—that are essential for successful predictive modeling. The author explains the theory behind the principles of predictive analytics in plain English; readers don’t need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the techniques chapters will begin with a description of the specific technique and how it relates to the overall process model for predictive analytics.  The depth of the description of a technique will match the complexity of the approach; the intent is to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.  For example, with decision trees, the primary algorithms (C5, CART and CHAID) will be described in qualitative terms (what are trees, what is a split), how they are similar and different (Gini vs. Entropy vs. chi-square tests), why one might use one technique over another, how one can be fooled by the models built using each algorithm (i.e., their weaknesses), what knobs one can adjust (depth, complexity penalties, priors, costs, etc.), and how to interpret the results. Each of the techniques is illustrated by hands-on examples, either unique to the task or as part of a more comprehensive case study.   The companion website will provide all of the data sets used to generate these examples, along with a free trial version of software, so that readers can recreate and explore the examples and case studies.   The book concludes with a series of in-depth case studies that apply predictive analytics to common types of business scenarios.  

Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst 1st Table of contents:

Chapter 1 Overview of Predictive Analytics
What Is Analytics?
What Is Predictive Analytics?
Supervised vs. Unsupervised Learning
Parametric vs. Non-Parametric Models
Business Intelligence
Predictive Analytics vs. Business Intelligence
Do Predictive Models Just State the Obvious?
Similarities between Business Intelligence and Predictive Analytics
Predictive Analytics vs. Statistics
Statistics and Analytics
Predictive Analytics and Statistics Contrasted
Predictive Analytics vs. Data Mining
Who Uses Predictive Analytics?
Challenges in Using Predictive Analytics
Obstacles in Management
Obstacles with Data
Obstacles with Modeling
Obstacles in Deployment
What Educational Background Is Needed to Become a Predictive Modeler?
Chapter 2 Setting Up the Problem
Predictive Analytics Processing Steps: CRISP-DM
Business Understanding
The Three-Legged Stool
Business Objectives
Defining Data for Predictive Modeling
Defining the Columns as Measures
Defining the Unit of Analysis
Which Unit of Analysis?
Defining the Target Variable
Temporal Considerations for Target Variable
Defining Measures of Success for Predictive Models
Success Criteria for Classification
Success Criteria for Estimation
Other Customized Success Criteria
Doing Predictive Modeling Out of Order
Building Models First
Early Model Deployment
Case Study: Recovering Lapsed Donors
Overview
Business Objectives
Data for the Competition
The Target Variables
Modeling Objectives
Model Selection and Evaluation Criteria
Model Deployment
Case Study: Fraud Detection
Overview
Business Objectives
Data for the Project
The Target Variables
Modeling Objectives
Model Selection and Evaluation Criteria
Model Deployment
Summary
Chapter 3 Data Understanding
What the Data Looks Like
Single Variable Summaries
Mean
Standard Deviation
The Normal Distribution
Uniform Distribution
Applying Simple Statistics in Data Understanding
Skewness
Kurtosis
Rank-Ordered Statistics
Categorical Variable Assessment
Data Visualization in One Dimension
Histograms
Multiple Variable Summaries
Hidden Value in Variable Interactions: Simpson’s Paradox
The Combinatorial Explosion of Interactions
Correlations
Spurious Correlations
Back to Correlations
Crosstabs
Data Visualization, Two or Higher Dimensions
Scatterplots
Anscombe’s Quartet
Scatterplot Matrices
Overlaying the Target Variable in Summary
Scatterplots in More Than Two Dimensions
The Value of Statistical Significance
Pulling It All Together into a Data Audit
Summary
Chapter 4 Data Preparation
Variable Cleaning
Incorrect Values
Consistency in Data Formats
Outliers
Multidimensional Outliers
Missing Values
Fixing Missing Data
Feature Creation
Simple Variable Transformations
Fixing Skew
Binning Continuous Variables
Numeric Variable Scaling
Nominal Variable Transformation
Ordinal Variable Transformations
Date and Time Variable Features
ZIP Code Features
Which Version of a Variable Is Best?
Multidimensional Features
Variable Selection Prior to Modeling
Sampling
Example: Why Normalization Matters for K-Means Clustering
Summary
Chapter 5 Itemsets and Association Rules
Terminology
Condition
Left-Hand-Side, Antecedent(s)
Right-Hand-Side, Consequent, Output, Conclusion
Rule (Item Set)
Support
Antecedent Support
Confidence, Accuracy
Lift
Parameter Settings
How the Data Is Organized
Standard Predictive Modeling Data Format
Transactional Format
Measures of Interesting Rules
Deploying Association Rules
Variable Selection
Interaction Variable Creation
Problems with Association Rules
Redundant Rules
Too Many Rules
Too Few Rules
Building Classification Rules from Association Rules
Summary
Chapter 6 Descriptive Modeling
Data Preparation Issues with Descriptive Modeling
Principal Component Analysis
The PCA Algorithm
Applying PCA to New Data
PCA for Data Interpretation
Additional Considerations before Using PCA
The Effect of Variable Magnitude on PCA Models
Clustering Algorithms
The K-Means Algorithm
Data Preparation for K-Means
Selecting the Number of Clusters
The Kohonen SOM Algorithm
Visualizing Kohonen Maps
Similarities with K-Means
Summary
Chapter 7 Interpreting Descriptive Models
Standard Cluster Model Interpretation
Problems with Interpretation Methods
Identifying Key Variables in Forming Cluster Models
Cluster Prototypes
Cluster Outliers
Summary
Chapter 8 Predictive Modeling
Decision Trees
The Decision Tree Landscape
Building Decision Trees
Decision Tree Splitting Metrics
Decision Tree Knobs and Options
Reweighting Records: Priors
Reweighting Records: Misclassification Costs
Other Practical Considerations for Decision Trees
Logistic Regression
Interpreting Logistic Regression Models
Other Practical Considerations for Logistic Regression
Neural Networks
Building Blocks: The Neuron
Neural Network Training
The Flexibility of Neural Networks
Neural Network Settings
Neural Network Pruning
Interpreting Neural Networks
Neural Network Decision Boundaries
Other Practical Considerations for Neural Networks
K-Nearest Neighbor
The k-NN Learning Algorithm
Distance Metrics for k-NN
Other Practical Considerations for k-NN
Naïve Bayes
Bayes’ Theorem
The Naïve Bayes Classifier
Interpreting Naïve Bayes Classifiers
Other Practical Considerations for Naïve Bayes
Regression Models
Linear Regression
Linear Regression Assumptions
Variable Selection in Linear Regression
Interpreting Linear Regression Models
Using Linear Regression for Classification
Other Regression Algorithms
Summary
Chapter 9 Assessing Predictive Models
Batch Approach to Model Assessment
Percent Correct Classification
Rank-Ordered Approach to Model Assessment
Assessing Regression Models
Summary
Chapter 10 Model Ensembles
Motivation for Ensembles
The Wisdom of Crowds
Bias Variance Tradeoff
Bagging
Boosting
Improvements to Bagging and Boosting
Random Forests
Stochastic Gradient Boosting
Heterogeneous Ensembles
Model Ensembles and Occam’s Razor
Interpreting Model Ensembles
Summary
Chapter 11 Text Mining
Motivation for Text Mining
A Predictive Modeling Approach to Text Mining
Structured vs. Unstructured Data
Why Text Mining Is Hard
Text Mining Applications
Data Sources for Text Mining
Data Preparation Steps
POS Tagging
Tokens
Stop Word and Punctuation Filters
Character Length and Number Filters
Stemming
Dictionaries
The Sentiment Polarity Movie Data Set
Text Mining Features
Term Frequency
Inverse Document Frequency
TF-IDF
Cosine Similarity
Multi-Word Features: N-Grams
Reducing Keyword Features
Grouping Terms
Modeling with Text Mining Features
Regular Expressions
Uses of Regular Expressions in Text Mining
Summary
Chapter 12 Model Deployment
General Deployment Considerations
Deployment Steps
Summary
Chapter 13 Case Studies
Survey Analysis Case Study: Overview
Business Understanding: Defining the Problem
Data Understanding
Data Preparation
Modeling
Deployment: “What-If” Analysis
Revisit Models
Deployment
Summary and Conclusions
Help Desk Case Study
Data Understanding: Defining the Data
Data Preparation
Modeling
Revisit Business Understanding
Deployment
Summary and Conclusions

People also search for Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst 1st:

predictive analytics explained

data analyst predictive modeling

business analytics techniques

applied data science methods

predictive analytics lifecycle

Tags: Applied Predictive, Analytics Principles, Techniques, Professional Data Analyst, Dean Abbott