MARC details
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08405nam a22002657a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20220412165804.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220222b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9788126567935 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.056312 |
| Item number |
BAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Bari, Anasse |
| 9 (RLIN) |
4612 |
| 245 ## - TITLE STATEMENT |
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| Title |
Predictive analytics for dummies |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Name of publisher, distributor, etc. |
Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. |
New Delhi |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
viii, 443 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
699.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Introduction Part 1: Getting Started with Predictive Analytics Chapter 1: Entering the Arena Exploring Predictive Analytics Mining data Highlighting the model Adding Business Value Endless opportunities Empowering your organization Starting a Predictive Analytic Project Business knowledge Data-science team and technology The Data Ongoing Predictive Analytics Forming Your Predictive Analytics Team Hiring experienced practitioners Demonstrating commitment and curiosity Surveying the Marketplace Responding to big data Working with big data Chapter 2: Predictive Analytics in the Wild Online Marketing and Retail Recommender systems Personalized shopping on the Internet Implementing a Recommender System Collaborative filtering Content-based filtering Hybrid recommender systems Target Marketing Targeting using predictive modeling Uplift modeling Personalization Online customer experience Retargeting Implementation Optimizing using personalization Similarities of Personalization and Recommendations Content and Text Analytics Chapter 3: Exploring Your Data Types and Associated Techniques Recognizing Your Data Types Structured and unstructured data Static and streamed data Identifying Data Categories Attitudinal data Behavioral data Demographic data Generating Predictive Analytics Data-driven analytics User-driven analytics Connecting to Related Disciplines Statistics Data mining Machine learning Chapter 4: Complexities of Data Finding Value in Your Data Delving into your data Data validity Data variety Constantly Changing Data Data velocity High volume of data Complexities in Searching Your Data Keyword-based search Semantic-based search Contextual search Differentiating Business Intelligence from Big-Data Analytics Exploration of Raw Data Identifying data attributes Exploring common data visualizations Tabular visualizations Word clouds Flocking birds as a novel data representation Graph charts Common visualizations Part 2: Incorporating Algorithms in Your Models Chapter 5: Applying Models Modeling Data Models and simulation Categorizing models Describing and summarizing data Making better business decisions Healthcare Analytics Case Studies Google Flu Trends Cancer survivability predictors Social and Marketing Analytics Case Studies Target store predicts pregnant women Twitter-based predictors of earthquakes Twitter-based predictors of political campaign outcomes Tweets as predictors for the stock market Predicting variation of stock prices from news articles Analyzing New York City's bicycle usage Predictions and responses Data compression Prognostics and its Relation to Predictive Analytics The Rise of Open Data Chapter 6: Identifying Similarities in Data Explaining Data Clustering Converting Raw Data into a Matrix Creating a matrix of terms in documents Term selection Identifying Groups in Your Data K-means clustering algorithm Clustering by nearest neighbors Density-based algorithms Finding Associations in Data Items Applying Biologically Inspired Clustering Techniques Birds flocking: Flock by Leader algorithm Ant colonies Chapter 7: Predicting the Future Using Data Classification Explaining Data Classification Introducing Data Classification to Your Business Exploring the Data-Classification Process Using Data Classification to Predict the Future Decision trees Algorithms for Generating Decision Trees Support vector machine Ensemble Methods to Boost Prediction Accuracy Naïve Bayes classification algorithm The Markov Model Linear regression Neural networks Deep Learning Part 3: Developing A Roadmap Chapter 8: Convincing Your Management to Adopt Predictive Analytics Making the Business Case Gathering Support from Stakeholders Presenting Your Proposal Chapter 9: Preparing Data Listing the Business Objectives Processing Your Data Identifying the data Cleaning the data Generating any derived data Reducing the dimensionality of your data Applying principal component analysis Leveraging singular value decomposition Working with Features Structuring Your Data Extracting, transforming and loading your data Keeping the data up to date Outlining testing and test data Chapter 10: Building a Predictive Model Getting Started Defining your business objectives Preparing your data Choosing an algorithm Developing and Testing the Model Going Live with the Model Chapter 11: Visualization of Analytical Results Visualization as a Predictive Tool Evaluating Your Visualization Visualizing Your Model's Analytical Results Visualizing hidden groupings in your data Visualizing data classification results Visualizing outliers in your data Visualization of Decision Trees Visualizing predictions Novel Visualization in Predictive Analytics Big Data Visualization Tools Tableau Google Charts Plotly Infogram Part 4: Programming Predictive Analytics Chapter 12: Creating Basic Prediction Examples Installing the Software Packages Installing Python Installing the machine-learning module Installing the dependencies Preparing the Data Making Predictions Using Classification Algorithms Creating a supervised learning model with SVM Creating a supervised learning model with logistic regression Creating a supervised learning model with random forest Comparing the classification models Chapter 13: Creating Basic Examples of Unsupervised Predictions Getting the Sample Dataset Using Clustering Algorithms to Make Predictions Comparing clustering models Creating an unsupervised learning model with K-means Creating an unsupervised learning model with DBSCAN Creating an unsupervised learning model with mean shift Chapter 14: Predictive Modeling with R Programming in R Installing R Installing RStudio Getting familiar with the environment Learning just a bit of R Making Predictions Using R Predicting using regression Using classification to predict Classification by random forest Chapter 15: Avoiding Analysis Traps Data Challenges Outlining the limitations of the data Dealing with extreme cases (outliers) Data smoothing Curve fitting Keeping the assumptions to a minimum Analysis Challenges Part 5: Executing Big Data Chapter 16: Targeting Big Data Major Technological Trends in Predictive Analytics Exploring predictive analytics as a service Aggregating distributed data for analysis Real-time data-driven analytics Applying Open-Source Tools to Big Data Apache Hadoop Apache Spark Chapter 17: Getting Ready for Enterprise Analytics Analytics as a Service Google Analytics IBM Watson Microsoft Revolution R Enterprise Preparing for a Proof-of-Value of Predictive Analytics Prototype Prototyping for predictive analytics Testing your predictive analytics model Part 6: The Part of Tens Chapter 18: Ten Reasons to Implement Predictive Analytics Chapter 19: Ten Steps to Build a Predictive Analytic Model Index |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Description Predictive Analytics For Dummies, 2e will help the you understand the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. You will learn how to incorporate algorithms through discovering data models, identifying similarities and relationships in your data, and how to predict the future through data classification. You will develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get stakeholder buy-in. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Management--Data processing |
| 9 (RLIN) |
5525 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Management--Mathematical models |
| 9 (RLIN) |
5526 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Data mining |
| 9 (RLIN) |
365 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Decision making--Mathematical models |
| 9 (RLIN) |
1481 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Chaouchi, Mohamed |
| 9 (RLIN) |
5527 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jung, Tommy |
| 9 (RLIN) |
5528 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
