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Machine Learning for Decision Sciences with Case Studies in Python [Hardback]

(PSG College of Technology, India), (KPMG LLP, Texas), (PSG College of Technology, Coimbatore, Tamil Nadu, India), (Amrita School of Engineering, Bengaluru, India)
  • Formāts: Hardback, 454 pages, height x width: 254x178 mm, weight: 957 g, 68 Tables, black and white; 255 Line drawings, black and white; 4 Halftones, black and white; 259 Illustrations, black and white
  • Izdošanas datums: 08-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 1032193565
  • ISBN-13: 9781032193564
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Machine Learning for Decision Sciences with Case Studies in PythonPalielināt
  • Formāts: Hardback, 454 pages, height x width: 254x178 mm, weight: 957 g, 68 Tables, black and white; 255 Line drawings, black and white; 4 Halftones, black and white; 259 Illustrations, black and white
  • Izdošanas datums: 08-Jul-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 1032193565
  • ISBN-13: 9781032193564
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032193564.html
Keywords:
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.



Features:











Explains the basic concepts of Python and its role in machine learning.





Provides comprehensive coverage of feature engineering including real-time case studies.





Perceives the structural patterns with reference to data science and statistics and analytics.





Includes machine learning-based structured exercises.





Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.

This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
Preface xv
Acknowledgment xvii
About the Authors xix
Introduction xxi
Chapter 1 Introduction
1(14)
1.1 Introduction to Data Science
1(1)
1.1.1 Mathematics
1(1)
1.1.2 Statistics
1(1)
1.2 Describing Structural Patterns
2(1)
1.2.1 Uses of Structural Patterns
2(1)
1.3 Machine Learning and Statistics
3(1)
1.4 Relation between Artificial Intelligence, Machine Learning, Neural Networks, and Deep Learning
4(2)
1.5 Data Science Life Cycle
6(2)
1.6 Key Role of Data Scientist
8(1)
1.6.1 Difference between Data Scientist and Machine Learning Engineer
8(1)
1.7 Real-World Examples
8(1)
1.8 Use Cases
9(6)
1.8.1 Financial and Insurance Industries
9(1)
1.8.1.1 Fraud Mitigation
9(1)
1.8.1.2 Personalized Pricing
10(1)
1.8.1.3 AML -- Anti-Money Laundering
10(1)
1.8.2 Utility Industries
11(1)
1.8.2.1 Smart Meter and Smart Grid
11(1)
1.8.2.2 Manage disaster and Outages
11(1)
1.8.2.3 Compliance
11(1)
1.8.3 Oil and Gas Industries
11(1)
1.8.3.1 Manage Exponential Growth
11(1)
1.8.3.2 3D Seismic Imaging and Kirchhoff
12(1)
1.8.3.3 Rapidly Process and Display Seismic Data
12(1)
1.8.4 E-Commerce and Hi-Tech Industries
12(1)
1.8.4.1 Association and Complementary Products
12(1)
1.8.4.2 Cross-Channel Analytics
12(1)
1.8.4.3 Event analytics
13(1)
Summary
13(1)
Review Questions
14(1)
Chapter 2 Overview of Python for Machine Learning
15(88)
2.1 Introduction
15(1)
2.1.1 The Flow of Program Execution in Python
15(1)
2.2 Python for Machine Learning
15(1)
2.2.1 Why Is Python Good for ML?
16(1)
2.3 Setting up Python
16(1)
2.3.1 Python on Windows
16(1)
2.3.2 Python on Linux
17(1)
2.3.2.1 Ubuntu
17(1)
2.4 Python Basics
17(58)
2.4.1 Python Operators
18(1)
2.4.1.1 Arithmetic Operators
18(1)
2.4.1.2 Comparison Operators
18(1)
2.4.1.3 Assignment Operators
18(1)
2.4.1.4 Logical Operators
18(1)
2.4.1.5 Membership Operators
19(1)
2.4.2 Python Code Samples on Basic Operators
19(1)
2.4.2.1 Arithmetic Operators
19(2)
2.4.2.2 Comparison Operators
21(1)
2.4.2.3 Logical Operators
22(1)
2.4.2.4 Membership Operators
23(1)
2.4.3 Flow Control
24(1)
2.4.3.1 If & elif Statement
24(1)
2.4.3.2 Loop Statement
25(1)
2.4.3.3 Loop Control Statements
26(1)
2.4.4 Python Code Samples on Flow Control Statements
26(1)
2.4.4.1 Conditional Statements
26(1)
2.4.4.2 Python if... else Statement
27(1)
2.4.4.3 Python if... elif... else Statement
28(1)
2.4.4.4 The For Loop
29(1)
2.4.4.5 The range() Function
29(2)
2.4.4.6 For Loop with else
31(1)
2.4.4.7 While Loop
31(1)
2.4.4.8 While Loop with else
32(1)
2.4.4.9 Python Break and Continue
32(1)
2.4.4.10 Python Break Statement
32(1)
2.4.4.11 Python Continue Statement
33(1)
2.4.5 Review of Basic Data Structures and Implementation in Python
34(1)
2.4.5.1 Array Data Structure
34(1)
2.4.5.2 Implementation of Arrays in Python
35(1)
2.4.5.3 Linked List
36(1)
2.4.5.4 Implementation of Linked List in Python
36(2)
2.4.5.5 Stacks and Queues
38(2)
2.4.5.6 Queues
40(1)
2.4.5.7 Implementation of Queue in Python
41(1)
2.4.5.8 Searching
42(2)
2.4.5.9 Implementation of Searching in Python
44(2)
2.4.5.10 Sorting
46(1)
2.4.5.11 Implementation of Bubble Sort in Python
47(1)
2.4.5.12 Insertion Sort
47(2)
2.4.5.13 Implementation of Insertion Sort in Python
49(2)
2.4.5.14 Selection Sort
51(1)
2.4.5.15 Implementation of Selection Sort in Python
52(1)
2.4.5.16 Merge Sort
52(1)
2.4.5.17 Implementation of Merge Sort in Python
53(1)
2.4.5.18 Shell Sort
54(1)
2.4.5.19 Quicksort
55(1)
2.4.5.20 Data Structures in Python with Sample Codes
55(3)
2.4.5.21 Python Code Samples for Data Structures in Python
58(10)
2.4.6 Functions in Python
68(1)
2.4.6.1 Python Code Samples for Functions
68(1)
2.4.6.2 Returning Values from Functions
68(1)
2.4.6.3 Scope of Variables
69(1)
2.4.6.4 Function Arguments
70(4)
2.4.7 File Handling
74(1)
2.4.8 Exception Handling
74(1)
2.4.9 Debugging in Python
75(1)
2.4.9.1 Packages
75(1)
2.5 Numpy Basics
75(17)
2.5.1 Introduction to Numpy
76(1)
2.5.1.1 Array Creation
76(1)
2.5.1.2 Array Slicing
77(1)
2.5.2 Numerical Operations
77(1)
2.5.3 Python Code Samples for Numpy Package
78(1)
2.5.3.1 Array Creation
78(4)
2.5.3.2 Class and Attributes of ndarray---.ndim
82(1)
2.5.3.3 Class and Attributes of ndarray---.shape
82(1)
2.5.3.4 Class and Attributes of ndarray---ndarray.size, ndarray.Itemsize, ndarray.resize
83(1)
2.5.3.5 Class and Attributes of ndarray---.dtype
83(1)
2.5.3.6 Basic Operations
84(1)
2.5.3.7 Accessing Array Elements: Indexing
85(3)
2.5.3.8 Shape Manipulation
88(2)
2.5.3.9 Universal Functions (ufunc) in Numpy
90(1)
2.5.3.10 Broadcasting
90(1)
2.5.3.11 Args and Kwargs
91(1)
2.6 Matplotlib Basics
92(2)
2.6.1 Creating Graphs with Matplotlib
93(1)
2.7 Pandas Basics
94(3)
2.7.1 Getting Started with Pandas
94(1)
2.7.2 Data Frames
95(1)
2.7.3 Key Operations on Data Frames
95(1)
2.7.3.1 Data Frame from List
95(1)
2.7.3.2 Rows and Columns in Data Frame
96(1)
2.8 Computational Complexity
97(1)
2.9 Real-world Examples
97(6)
2.9.1 Implementation using Pandas
98(1)
2.9.2 Implementation using Numpy
98(1)
2.9.3 Implementation using Matplotlib
98(1)
Summary
99(1)
Review Questions
100(1)
Exercises for Practice
101(2)
Chapter 3 Data Analytics Life Cycle for Machine Learning
103(42)
3.1 Introduction
103(1)
3.2 Data Analytics Life Cycle
104(41)
3.2.1 Phase 1 -- Data Discovery
104(3)
3.2.2 Phase 2 -- Data Preparation and Exploratory Data Analysis
107(3)
3.2.2.1 Exploratory Data Analysis
110(26)
3.2.3 Phase 3 -- Model Planning
136(3)
3.2.4 Phase 4 -- Model Building
139(1)
3.2.5 Phase 5 -- Communicating Results
140(1)
3.2.6 Phase 6 -- Optimize and Operationalize the Models
140(2)
Summary
142(1)
Review Questions
143(2)
Chapter 4 Unsupervised Learning
145(32)
4.1 Introduction
145(1)
4.2 Unsupervised Learning
145(2)
4.2.1 Clustering
147(1)
4.3 Evaluation Metrics for Clustering
147(3)
4.3.1 Distance Measures
148(1)
4.3.1.1 Minkowski Metric
149(1)
4.3.2 Similarity Measures
149(1)
4.4 Clustering Algorithms
150(1)
4.4.1 Hierarchical and Partitional Clustering Approaches
150(1)
4.4.2 Agglomerative and Divisive Clustering Approaches
150(1)
4.4.3 Hard and Fuzzy Clustering Approaches
150(1)
4.4.4 Monothetic and Polythetic Clustering Approaches
151(1)
4.4.5 Deterministic and Probabilistic Clustering Approaches
151(1)
4.5 k-Means Clustering
151(8)
4.5.1 Geometric Intuition, Centroids
151(1)
4.5.2 The Algorithm
152(1)
4.5.3 Choosing k
152(1)
4.5.4 Space and Time Complexity
153(1)
4.5.5 Advantages and Disadvantages of k-Means Clustering
153(1)
4.5.5.1 Advantages
153(1)
4.5.5.2 Disadvantages
153(1)
4.5.6 k-Means Clustering in Practice Using Python
154(1)
4.5.6.1 Illustration of the k-Means Algorithm Using Python
154(3)
4.5.7 Fuzzy k-Means Clustering Algorithm
157(1)
4.5.7.1 The Algorithm
158(1)
4.5.8 Advantages and Disadvantages of Fuzzy k-Means Clustering
158(1)
4.6 Hierarchical Clustering
159(6)
4.6.1 Agglomerative Hierarchical Clustering
159(2)
4.6.2 Divisive Hierarchical Clustering
161(1)
4.6.3 Techniques to Merge Cluster
161(2)
4.6.4 Space and Time Complexity
163(1)
4.6.5 Limitations of Hierarchical Clustering
163(1)
4.6.6 Hierarchical Clustering in Practice Using Python
163(1)
4.6.6.1 DATA.SET
164(1)
4.7 Mixture of Gaussian Clustering
165(4)
4.7.1 Expectation Maximization
166(2)
4.7.2 Mixture of Gaussian Clustering in Practice Using Python
168(1)
4.8 Density-Based Clustering Algorithm
169(8)
4.8.1 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
169(2)
4.8.2 Space and Time Complexity
171(1)
4.8.3 Advantages and Disadvantages of DBSCAN
171(1)
4.8.3.1 Advantages
171(1)
4.8.3.2 Disadvantages
171(1)
4.8.4 DBSCAN in Practice Using Python
172(2)
Summary
174(1)
Review Questions
174(3)
Chapter 5 Supervised Learning: Regression
177(42)
5.1 Introduction
177(1)
5.2 Supervised Learning -- Real-Life Scenario
177(1)
5.3 Types of Supervised Learning
178(3)
5.3.1 Supervised Learning -- Classification
178(1)
5.3.1.1 Classification -- Predictive Modeling
179(1)
5.3.2 Supervised Learning -- Regression
179(1)
5.3.2.1 Regression Predictive Modeling
180(1)
5.3.3 Classification vs. Regression
180(1)
5.3.4 Conversion between Classification and Regression Problems
181(1)
5.4 Linear Regression
181(38)
5.4.1 Types of Linear Regression
182(1)
5.4.1.1 Simple Linear Regression
183(1)
5.4.1.2 Multiple Linear Regression
184(2)
5.4.2 Geometric Intuition
186(1)
5.4.3 Mathematical Formulation
187(14)
5.4.4 Solving Optimization Problem
201(1)
5.4.4.1 Maxima and Minima
201(1)
5.4.4.2 Gradient Descent
202(3)
5.4.4.3 LMS (Least Mean Square) Update Rule
205(1)
5.4.4.4 SGD Algorithm
205(1)
5.4.5 Real-World Applications
206(1)
5.4.5.1 Predictive Analysis
206(2)
5.4.5.2 Medical Outcome Prediction
208(1)
5.4.5.3 Wind Speed Prediction
208(1)
5.4.5.4 Environmental Effects Monitoring
209(1)
5.4.6 Linear Regression in Practice Using Python
209(1)
5.4.6.1 Simple Linear Regression Using Python
209(3)
5.4.6.2 Multiple Linear Regression Using Python
212(3)
Summary
215(1)
Review Questions
215(4)
Chapter 6 Supervised Learning: Classification
219(132)
6.1 Introduction
219(1)
6.2 Use Cases of Classification
219(1)
6.3 Logistic Regression
219(17)
6.3.1 Geometric Intuition
220(2)
6.3.2 Variants of Logistic Regression
222(1)
6.3.2.1 Simple Logistic Regression
222(1)
6.3.2.2 Multiple Logistic Regression
223(1)
6.3.2.3 Binary Logistic Regression
223(1)
6.3.2.4 Multiclass Logistic Regression
224(1)
6.3.2.5 Nominal Logistic Regression
224(2)
6.3.2.6 Ordinal Logistic Regression
226(1)
6.3.3 Optimization Problem
226(1)
6.3.4 Regularization
226(1)
6.3.5 Real-World Applications
227(1)
6.3.5.1 Medical Diagnosis
227(1)
6.3.5.2 Text Classification
227(1)
6.3.5.3 Marketing
227(1)
6.3.6 Logistic Regression in Practice using Python
228(3)
6.3.6.1 Variable Descriptions
231(1)
6.3.6.2 Checking for Missing Values
231(3)
6.3.6.3 Converting Categorical Variables to a Dummy Indicator
234(2)
6.4 Decision Tree Classifier
236(43)
6.4.1 Important Terminology in the Decision Tree
236(1)
6.4.2 Example for Decision Tree
237(1)
6.4.3 Sample Decision Tree
238(1)
6.4.4 Decision Tree Formation
238(2)
6.4.5 Algorithms Used for Decision Trees
240(1)
6.4.5.1 ID3 Algorithm
240(1)
6.4.5.2 C 4.5 Algorithm
241(1)
6.4.5.3 CART Algorithm
241(1)
6.4.6 Overfitting and Underfitting
241(1)
6.4.6.1 Overfitting
241(1)
6.4.6.2 Underfitting
242(1)
6.4.6.3 Pruning to Avoid Overfitting
243(1)
6.4.7 Advantages and Disadvantages
244(1)
6.4.7.1 Advantages
244(1)
6.4.7.2 Disadvantages
244(1)
6.4.8 Decision Tree Examples
245(17)
6.4.9 Regression Using Decision Tree
262(4)
6.4.10 Real-World Examples
266(1)
6.4.10.1 Predicting Library Book
266(1)
6.4.10.2 Identification of Tumor
267(2)
6.4.10.3 Classification of Telescope Image
269(1)
6.4.10.4 Business Management
269(2)
6.4.10.5 Fault Diagnosis
271(1)
6.4.10.6 Healthcare Management
271(1)
6.4.10.7 Decision Tree in Data Mining
271(2)
6.4.11 Decision Trees in Practice Using Python
273(6)
6.5 Random Forest Classifier
279(27)
6.5.1 Random Forest and Their Construction
280(1)
6.5.2 Sampling of the Dataset in Random Forest
281(4)
6.5.2.1 Creation of Subset Data
285(1)
6.5.3 Pseudocode for Random Forest
286(1)
6.5.3.1 Pseudocode for Prediction in Random Forest
287(1)
6.5.4 Regression Using Random Forest
287(1)
6.5.5 Classification Using Random Forest
288(5)
6.5.5.1 Random Forest Problem for Classification - Examples
293(2)
6.5.6 Features and Properties of Random Forest
295(1)
6.5.6.1 Features
295(1)
6.5.6.2 Properties
296(1)
6.5.7 Advantages and Disadvantages of Random Forest
296(1)
6.5.7.1 Advantages
296(1)
6.5.7.2 Disadvantages
296(1)
6.5.8 Calculation of Error Using Bias and Variance
296(1)
6.5.8.1 Bias
296(1)
6.5.8.2 Variance
296(1)
6.5.8.3 Properties of Bias and Variance
297(1)
6.5.9 Time Complexity
297(1)
6.5.10 Extremely Randomized Tree
297(1)
6.5.11 Real-World Examples
298(1)
6.5.11.1 Machine Fault Diagnosis
298(1)
6.5.11.2 Medical Field
298(1)
6.5.11.3 Banking
299(1)
6.5.11.4 E-Commerce
300(1)
6.5.11.5 Security
300(1)
6.5.12 Random Forest in Practice Using Python
300(6)
6.6 Support Vector Machines
306(41)
6.6.1 Geometric Intuition
307(3)
6.6.2 Mathematical Formulation
310(2)
6.6.2.1 Maximize Margin with Noise
312(1)
6.6.2.2 Slack Variable ξi
312(3)
6.6.3 Loss Minimization
315(2)
6.6.4 Dual Formulation
317(3)
6.6.5 The Kernel Trick
320(1)
6.6.6 Polynomial Kernel
320(2)
6.6.6.1 Mercer's Theorem
322(1)
6.6.6.2 Radial Basis Function (RBF) Kernel
322(1)
6.6.6.3 Other Domain-Specific Kernel
323(1)
6.6.6.4 Sigmoid Kernel
323(1)
6.6.6.5 Exponential Kernel
323(1)
6.6.6.6 ANOVA Kernel
323(1)
6.6.6.7 Rational Quadratic Kernel
323(1)
6.6.6.8 Multiquadratic Kernel
323(1)
6.6.6.9 Inverse Multiquadratic Kernel
323(1)
6.6.6.10 Circular Kernel
324(1)
6.6.6.11 Bayesian Kernel
324(1)
6.6.6.12 Chi-Square Kernel
324(1)
6.6.6.13 Histogram Intersection Kernel
324(1)
6.6.6.14 Generalized Histogram Intersection Kernel
324(1)
6.6.7 Nu SVM
324(1)
6.6.8 SVM Regression
325(1)
6.6.9 One-Class SVM
326(1)
6.6.10 Multiclass SVM
326(1)
6.6.10.1 One against All
326(1)
6.6.10.2 One against One
327(1)
6.6.10.3 Directed Acyclic Graph SVM
327(1)
6.6.11 SVM Examples
328(13)
6.6.12 Real-World Applications
341(1)
6.6.12.1 Classification of Cognitive Impairment
341(1)
6.6.12.2 Preprocessing
342(1)
6.6.12.3 Feature Extraction
342(1)
6.6.12.4 SVM Classification
342(1)
6.6.12.5 Procedure
342(1)
6.6.12.6 Performance Analysis
343(1)
6.6.12.7 Text Categorization
343(1)
6.6.12.8 Handwritten Optical Character Recognition
344(1)
6.6.12.9 Natural Language Processing
344(1)
6.6.12.10 Cancer Prediction
345(1)
6.6.12.11 Stock Market Forecasting
345(1)
6.6.12.12 Protein Structure Prediction
346(1)
6.6.12.13 Face Detection Using SVM
346(1)
6.6.13 Advantages and Disadvantages of SVM
347(1)
6.7 SVM Classification in Practice Using Python
347(4)
6.7.1 Support Vectors
347(1)
6.7.2 What Is a Hyperplane?
348(1)
Summary
349(1)
Review Questions
349(2)
Chapter 7 Feature Engineering
351(22)
7.1 Introduction
351(1)
7.2 Feature Selection
352(3)
7.2.1 Wrapper Methods
353(1)
7.2.1.1 Forward Selection
353(1)
7.2.1.2 Backward Elimination
353(1)
7.2.1.3 Exhaustive Feature Selection
354(1)
7.2.2 Featured Methods
354(1)
7.3 Factor Analysis
355(2)
7.3.1 Types of Factor Analysis
355(1)
7.3.2 Working of Factor Analysis
355(1)
7.3.3 Terminologies
356(1)
7.3.3.1 Definition of Factor
356(1)
7.3.3.2 Factor Loading
356(1)
7.3.3.3 Eigenvalues
356(1)
7.3.3.4 Communalities
356(1)
7.3.3.5 Factor Rotation
356(1)
7.3.3.6 Selecting the Number of Factors
356(1)
7.4 Principal Component Analysis
357(2)
7.4.1 Center the Data
357(1)
7.4.2 Normalize the Data
357(1)
7.4.3 Estimate the Eigen decomposition
357(1)
7.4.4 Project the Data
357(2)
7.5 Eigenvalues and PCA
359(2)
7.5.1 Usage of eigendecomposition in PCA
359(2)
7.6 Feature Reduction
361(1)
7.6.1 Factor Analysis Vs. Principal Component Analysis
362(1)
7.7 PCA Transformation in Practice Using Python
362(2)
7.8 Linear Discriminant Analysis
364(4)
7.8.1 Mathematical Operations in LDA
365(3)
7.9 LDA Transformation in Practice Using Python
368(5)
7.9.1 Implementation of Scatter within the Class (Sw)
368(1)
7.9.2 Implementation of Scatter between Class (Sb)
369(2)
Summary
371(1)
Review Questions
371(2)
Chapter 8 Reinforcement Engineering
373(16)
8.1 Introduction
373(1)
8.2 Reinforcement Learning
373(3)
8.2.1 Examples of Reinforcement Learning
375(1)
8.3 How RL Differs from Other ML Algorithms?
376(1)
8.3.1 Supervised Learning
376(1)
8.4 Elements of Reinforcement Learning
376(3)
8.4.1 Policy
376(1)
8.4.2 Reward Signal
377(1)
8.4.3 Value Function
377(1)
8.4.3.1 Examples of Rewards
377(1)
8.4.4 Model of the Environment
378(1)
8.4.5 The Reinforcement Learning Algorithm
378(1)
8.4.6 Methods to Implement Reinforcement Learning in ML
379(1)
8.5 Markov Decision Process
379(2)
8.5.1 Preliminaries
379(1)
8.5.2 Value Functions
380(1)
8.6 Dynamic Programming
381(8)
8.6.1 Policy Evaluation
382(1)
8.6.2 Policy Improvement
383(2)
8.6.3 Policy Iteration
385(1)
8.6.4 Efficiency of Dynamic Programming
385(1)
8.6.5 Dynamic Programming in Practice using Python
386(1)
Summary
387(1)
Review Questions
387(2)
Chapter 9 Case Studies for Decision Sciences Using Python
389(54)
9.1 Use Case 1 -- Retail Price Optimization Using Price Elasticity of Demand Method
389(12)
9.1.1 Background
389(1)
9.1.2 Understanding the Data
390(10)
9.1.3 Conclusion
400(1)
9.2 Use Case 2 -- Market Basket Analysis (MBA)
401(11)
9.2.1 Introduction
401(1)
9.2.2 Understating the Data
401(11)
9.2.3 Conclusion
412(1)
9.3 Use Case 3 -- Sales Prediction of a Retailer
412(7)
9.3.1 Background
412(1)
9.3.2 Understanding the Data
413(5)
9.3.3 Conclusion
418(1)
9.4 Use Case 4 -- Predicting the Cost of Insurance Claims for a Property and Causalty (P&C) Insurance Company
419(11)
9.4.1 Background
419(1)
9.4.2 Understanding the Data
419(11)
9.5 Use Case 5 -- E-Commerce Product Ranking and Sentiment Analysis
430(13)
9.5.1 Background
430(1)
9.5.2 Understanding the Data
431(10)
Summary
441(1)
Review Questions
442(1)
Appendix: Python Cheat Sheet for Machine Learning 443(6)
Bibliography 449(4)
Index 453
Dr. S. Sumathi is working as a Professor in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 30 years. Her research interests include Neural Networks, Fuzzy Systems and Genetic Algorithms, Pattern Recognition and Classification, Data Warehousing and Data Mining, Operating systems and Parallel Computing. She is the author of more than 40 papers in refereed journals and international conferences. She has authored books with reputed publishers such as Springer and CRC Press.

Dr. L. Ashok Kumar was a Postdoctoral Research Fellow from San Diego State University, California. He is a recipient of the BHAVAN fellowship from the Indo-US Science and Technology Forum and SYST Fellowship from DST, Govt. of India. His current research focuses on integration of Renewable Energy Systems in the Smart Grid and Wearable Electronics. He has 3 years of industrial experience and 19 years of academic and research experience. He has published 167 technical papers in International and National journals and presented 157 papers in National and International Conferences. He has authored 10 books with leading publishers like CRC, Springer and Elsevier. He has completed 26 Government of India funded projects, and currently 7 projects are in progress.

Dr. Suresh Rajappa PhD PMP MBA is seasoned senior IT management consulting professional with 25 years experience leading large global IT programs and projects in IT Strategy, Finance IT (FINTECH) Transformation Strategy, BI and data warehousing / Data Analytics and Management for multiple fortune 100 clients across diverse industries, generating millions of dollars to top and bottom lines. Successful recruiting and leading onshore/offshore cross-cultural teams to deliver complex enterprise-wide solutions within tight deadlines and budgets. Highly effective at breaking down strategic program/project initiatives into tactical plans and processes to achieve aggressive customer goals. Excel at leveraging strategic partnerships, global resources, process improvements, and best practices to maximize project delivery performance and ROI. Inspirational, solution-focused leader with exceptional ability managing multimillion-dollar P&Ls/budgets and change management initiatives. As an adjunct professor, Dr. Suresh Rajappa teaches data science for graduate and doctoral students at PSG College of technology. His Industry specializations include Utility, Finance (Banking and Insurance) and HiTech Manufacturing. He is a frequent speaker Microsoft PASS conferences, SAP Financials and SAP TechEd conferences on Data Analytics related topics. He also teaches Data Analytics and IT Project Management for Undergraduate and Graduate level students. He is also key note speaker in International Conference on Artificial Intelligence, Smart Grid and Smart City Applications.

Dr. Surekha Paneerselvam is an Assistant Professor (Sr. Gr) in the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India with 20 years of experience in teaching, industry and research. She has published 35 papers in International and National journals and conferences. She has authored 7 books with leading publishers such as CRC Press and Springer. Her research interests include Control Systems, Computational Intelligence, Machine Learning, Signal and Image Processing, Embedded Systems, Real time operating systems, and Virtual Instrumentation.