Predictive Analytics For The Modern Enterprise (Record no. 359837)

MARC details
000 -LEADER
fixed length control field 05455 a2200157 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241021122205.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241021b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355428912
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
100 ## - MAIN ENTRY--PERSONAL NAME
Author Ali N.A.
245 ## - TITLE STATEMENT
Title Predictive Analytics For The Modern Enterprise
Remainder of title :A Practitioner's Guide To Designing And Implementing Solutions
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. SPD
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent 341
520 ## - SUMMARY, ETC.
Summary, etc. Table of Contents<br/>Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix<br/>1. Data Analytics in the Modern Enterprise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>The Evolution of Data Analytics 1<br/>Different Types of Data Analytics 7<br/>Descriptive Analytics 8<br/>Diagnostic Analytics 9<br/>Predictive Analytics 10<br/>Prescriptive Analytics 11<br/>Knowledge Acquisition, Machine Learning, and<br/>the Role of Predictive Analytics 13<br/>Tools, Frameworks, and Platforms in the Predictive Analytics World 16<br/>Languages and Libraries 17<br/>Services 18<br/>Conclusion 21<br/>2. Predictive Analytics: An Operational Necessity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23<br/>The Move from “Data Producing” to “Data Driven” 23<br/>Challenges to Using Predictive Analytics 26<br/>People 27<br/>Data 28<br/>Technology 29<br/>Vertical Industry Use Cases for Predictive Analytics 31<br/>Finance 31<br/>Healthcare 34<br/>Automotive 36<br/>Entertainment 38<br/>Conclusion 44<br/>v<br/>3. The Mathematics and Algorithms Behind Predictive Analytics. . . . . . . . . . . . . . . . . . . . . 45<br/>Statistics and Linear Algebra 46<br/>Regression 53<br/>What Is Regression Analysis? 56<br/>Regression Techniques 59<br/>R-squared and P-value 67<br/>Selecting a Regression Model 70<br/>Decision Trees 72<br/>Training Decision Trees 75<br/>Using Decision Trees to Solve Regression Problems: Regression Trees 80<br/>Tuning Decision Trees 82<br/>Other Algorithms 84<br/>Random Forests 84<br/>Neural Networks 86<br/>Support Vector Machines 93<br/>Naive Bayes Classifier 98<br/>Other Learning Patterns in Machine Learning 105<br/>Conclusion 109<br/>4. Working with Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111<br/>Understanding Data 111<br/>Data Preprocessing and Feature Engineering 114<br/>Handling Missing Data 114<br/>Categorical Data Encoding 115<br/>Data Transformation 116<br/>Outlier Management 116<br/>Handling Imbalanced Data 117<br/>Combining Data 118<br/>Feature Selection 119<br/>Splitting Preprocessed Data 120<br/>Understanding Bias 120<br/>The Predictive Analytics Pipeline 122<br/>The Data Stage 122<br/>The Model Stage 123<br/>The Serving Stage 124<br/>Other Components 126<br/>Selecting the Right Model 127<br/>Conclusion 128<br/>5. Python and scikit-learn for Predictive Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129<br/>Anaconda and Jupyter Notebooks 129<br/>NumPy in Python 132<br/>vi | Table of Contents<br/>Introduction to NumPy 133<br/>Generating Arrays 137<br/>Array Slicing 139<br/>Array Transformation 140<br/>Other Array Operations 141<br/>Exploring a Business Example Using Pandas 142<br/>Pandas in Python 146<br/>Import and View Data 147<br/>Visualize the Data 149<br/>Data Cleaning and Modification 153<br/>Reading from Different Data Sources 155<br/>Data Filtering and Grouping 161<br/>Scikit-learn 167<br/>Training and Predicting with a Linear Regression Model 168<br/>Using a Random Forest Classifier 175<br/>Training a Decision Tree 177<br/>A Clustering Example (Unsupervised Learning) 182<br/>Conclusion 183<br/>6. TensorFlow and Keras for Predictive Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185<br/>TensorFlow Fundamentals 186<br/>Linear Regression Using TensorFlow 193<br/>Data Preparation 194<br/>Model Creation and Training 196<br/>Predictions and Model Evaluation 200<br/>Deep Neural Networks in TensorFlow 202<br/>Conclusion 211<br/>7. Predictive Analytics for Business Problem-Solving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213<br/>Prediction-Based Optimal Retail Price Recommendations 213<br/>Using a Simple Linear Regression Model 216<br/>Using a Polynomial Regression Model 221<br/>Using Multivariate Regression 233<br/>An Introduction to Recommender Systems 246<br/>Building Recommender Systems Using surprise scikit in Python 250<br/>Credit Card Fraud Classification 267<br/>Credit Card Fraud Baseline Analysis Using Artificial Neural Networks 268<br/>Credit Card Fraud Weighted Analysis Using Artificial Neural Networks 285<br/>Credit Card Analysis with Multiple Hidden Layers<br/>in the Artificial Neural Network 289<br/>Conclusion 292<br/>Table of Contents | vii<br/>8. Exploring AWS Cloud Provider Services for AI/ML. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293<br/>To Cloud or Not to Cloud 293<br/>Exploring AWS SageMaker 295<br/>Prerequisites 296<br/>Data Ingest and Exploration 297<br/>Data Transformation 304<br/>Model Training and Prediction 314<br/>Cleanup 316<br/>Exploring Amazon Forecast 317<br/>Import Data 319<br/>Train the Predictor 321<br/>Create a Forecast 322<br/>What-If Analysis 324<br/>Cleanup 326<br/>Conclusion 327<br/>9. Food for Thought. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329<br/>A Few More Use Cases 331<br/>Navigation and Traffic Management 331<br/>Credit Scoring 333<br/>The Social Impact of Predictions 334<br/>Conclusion 336<br/>Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337<br/>viii | Table of Contents
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     Cummins College of Engineering for Women Pune Cummins College of Engineering for Women Pune 15/10/2024 115 1200.00   006.312 ALI CCEP-BK-67512 15/10/2024 1600.00 15/10/2024 Books