Targeting Uplift
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Targeting Uplift
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Author : René Michel
language : en
Publisher: Springer Nature
Release Date : 2019-09-09
Targeting Uplift written by René Michel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-09 with Business & Economics categories.
This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context.
Targeting Using Augmented Data In Database Marketing
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Author : Bettina Hüttenrauch
language : en
Publisher: Springer
Release Date : 2016-06-10
Targeting Using Augmented Data In Database Marketing written by Bettina Hüttenrauch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-10 with Business & Economics categories.
This study delivers insights on which external sources – e.g. website click behavior, surveys, or social media data – can and cannot be used for data augmentation. A case study is performed to test the suitability of different sources in order to create a generalized practical guide for data augmentation in marketing. Data augmentation is a beneficial tool for companies to use external data, if the internal data basis for targeting is not sufficient to reach the customers with the highest propensity. This study shows that augmenting data from feasible sources leads to significant conversion lifts.
Cracking Data Science Case Study Interview Data Features Models And System Design
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Author : Sai Dheeraj Gummadi
language : en
Publisher: Sai Dheeraj Gummadi
Release Date :
Cracking Data Science Case Study Interview Data Features Models And System Design written by Sai Dheeraj Gummadi and has been published by Sai Dheeraj Gummadi this book supported file pdf, txt, epub, kindle and other format this book has been release on with Antiques & Collectibles categories.
What will this book teach you? How to approach a data science problem from scratch – Learn to ask the right questions, define business goals, and determine the data needed. Feature thinking made practical – Understand what features to engineer for each use case and why. Designing the right ML solution – Learn which models to use, how to evaluate them, and how to interpret results. End-to-end analytics frameworks – Go beyond ML with strong descriptive, diagnostic, and inferential techniques. System design for data science – Learn how to productionize your ML work with pipelines, feedback loops, and model serving. What’s inside the book? Foundations (Sections 1–3): Introduction to Data Science, Analytics, and Machine Learning Real-world analogies and industry insights Clear differentiation of when to use analytics vs. ML Retail & eCommerce: Churn Prediction, Demand Forecasting Recommendation Systems, Customer Segmentation Price Elasticity using A/B Testing Finance & Fintech: Credit Scoring, Fraud Detection Portfolio Risk, CLTV Prediction Smart Transaction Routing Supply Chain & Operations: Inventory & Route Optimization Supplier Reliability, Backorder Forecasting Anomaly Detection in Logistics Marketing & Sales: Campaign Uplift, Dynamic Pricing Sales Forecasting, Attribution Modeling Sentiment Analysis Why is this book different? This is the only book that connects business understanding to data and system design. Each use case includes: Business Objective & Key Questions Data Requirements & Assumptions Analytical & ML Solutioning Feature Engineering Insights Modeling Approaches Business Metrics & Evaluation Production-Ready System Design Who is this book for? Whether you're a fresh graduate, a data enthusiast looking to break into data science, a working professional transitioning from BI/Analytics to ML, or a product manager or consultant who needs a structured framework—this book is designed for you. It eliminates fluff and delivers exactly what interviewers and real-world business challenges demand: clarity, structure, and actionable solutions.
Enterprise Artificial Intelligence Transformation
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Author : Rashed Haq
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-23
Enterprise Artificial Intelligence Transformation written by Rashed Haq and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-23 with Business & Economics categories.
Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
Predictive Analytics
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Author : Eric Siegel
language : en
Publisher: John Wiley & Sons
Release Date : 2013-02-19
Predictive Analytics written by Eric Siegel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-19 with Business & Economics categories.
In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. Why early retirement decreases life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death, including one health insurance company. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward -- but that can be predicted in advance? Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics. This book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.
Predictive Analytics For Dummies
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Author : Anasse Bari
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-31
Predictive Analytics For Dummies written by Anasse Bari and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-31 with Business & Economics categories.
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
Advances In Data Mining Applications And Theoretical Aspects
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Author : Petra Perner
language : en
Publisher: Springer
Release Date : 2016-06-27
Advances In Data Mining Applications And Theoretical Aspects written by Petra Perner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-27 with Computers categories.
This book constitutes the refereed proceedings of the 16th Industrial Conference on Advances in Data Mining, ICDM 2016, held in New York, NY, USA, in July 2016. The 33 revised full papers presented were carefully reviewed and selected from 100 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control, industry, and society.
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases
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Author : Rosa Meo
language : en
Publisher: Springer Nature
Release Date : 2024-12-31
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases written by Rosa Meo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.
The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.
Target Reservoirs For Co2 Miscible Flooding
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Author : John H. Goodrich
language : en
Publisher:
Release Date : 1980
Target Reservoirs For Co2 Miscible Flooding written by John H. Goodrich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Carbon dioxide categories.
Applied Data Science Using Pyspark
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Author : Ramcharan Kakarla
language : en
Publisher: Springer Nature
Release Date : 2024-12-01
Applied Data Science Using Pyspark written by Ramcharan Kakarla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.
This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks. In Chapters 1, 2, and 3, we will begin by setting up the environment and covering the basics of PySpark, focusing on data manipulation. Chapter 4 delves into the art of variable selection, demonstrating various techniques available in PySpark. In Chapters 5, 6, and 7, we explore machine learning algorithms, their implementations, and fine-tuning techniques. Chapters 8 and 9 will guide you through machine learning pipelines and various methods to operationalize and serve models using Docker/API. Chapter 10 will demonstrate how to unlock the power of predictive models to create a meaningful impact on your business. Chapter 11 introduces some of the most widely used and powerful modeling frameworks to unlock real value from data. In this new edition, you will learn predictive modeling frameworks that can quantify customer lifetime values and estimate the return on your predictive modeling investments. This edition also includes methods to measure engagement and identify actionable populations for effective churn treatments. Additionally, a dedicated chapter on experimentation design has been added, covering steps to efficiently design, conduct, test, and measure the results of your models. All code examples have been updated to reflect the latest stable version of Spark. You will: Gain an overview of end-to-end predictive model building Understand multiple variable selection techniques and their implementations Learn how to operationalize models Perform data science experiments and learn useful tips