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Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard
| Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard |
| Autore | Johnson Ann <1965-2016, > |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : The MIT Press, , 2024 |
| Descrizione fisica | 1 online resource (272 pages) |
| Disciplina | 620.001/51 |
| Collana | Engineering studies |
| Soggetto topico |
Mathematical models - History
Engineering mathematics - History Predictive analytics Predictive control |
| Soggetto non controllato |
TECHNOLOGY & ENGINEERING / History
SCIENCE / Philosophy & Social Aspects SOCIAL SCIENCE / Future Studies |
| ISBN |
0-262-37905-8
0-262-37904-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910901897503321 |
Johnson Ann <1965-2016, >
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| Cambridge, Massachusetts : , : The MIT Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Harnessing the power of analytics / / Leila Halawi, Amal Clarke and Kelly George
| Harnessing the power of analytics / / Leila Halawi, Amal Clarke and Kelly George |
| Autore | Halawi Leila |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (153 pages) : illustrations (chiefly color) |
| Disciplina | 001.42 |
| Soggetto topico | Predictive analytics |
| ISBN | 3-030-89712-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction to Analytics and Data Science Chapter 2. Data Types Structure & Data Preparation Process Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance Chapter 5. Decision Trees & Ensemble Chapter 6. Regression Models Chapter 7. Neural Networks Chapter 8. Model Deployment |
| Record Nr. | UNINA-9910522570603321 |
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Halawi Leila
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Predictive Analytics with KNIME : Analytics for Citizen Data Scientists / / Frank Acito
| Predictive Analytics with KNIME : Analytics for Citizen Data Scientists / / Frank Acito |
| Autore | Acito Frank |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 001.42 |
| Soggetto topico | Predictive analytics |
| ISBN | 3-031-45630-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Chapter 1: Introduction to Analytics -- 1.1 The Growing Emphasis on Analytics -- 1.2 Applications of Analytics -- 1.3 The Citizen Data Scientist -- 1.4 The Analytics Process -- 1.5 Summary -- References -- Chapter 2: Problem Definition -- 2.1 Expert Views on Problem Definition -- 2.2 A Telecom Problem Definition Example -- 2.3 Defining the Analytics Problem -- 2.4 Structured Versus Unstructured Problems -- 2.5 Getting Started with Defining the Problem -- 2.6 Summary -- References -- Chapter 3: Introduction to KNIME -- 3.1 KNIME Features -- 3.2 The KNIME Workbench -- 3.3 Learning to Use KNIME -- 3.4 KNIME Extensions and Integrations -- 3.5 Data Types in KNIME -- 3.6 Example: Predicting Heart Disease with KNIME -- 3.7 Example: Preparation of Hospital Data Using KNIME -- 3.8 Flow Variables -- 3.9 Loops in KNIME -- 3.10 Metanodes and Components in KNIME -- 3.11 Summary -- Appendices -- Appendix 1: Integrating R into KNIME -- Appendix 2: Regex for Search Patterns -- Chapter 4: Data Preparation -- 4.1 Obtaining the Needed Data -- 4.2 Data Cleaning -- 4.3 Data Cleaning Nodes in KNIME -- 4.4 Missing Values -- 4.5 Dealing with Missing Values -- 4.6 Outliers -- 4.7 Feature Engineering -- 4.8 Example of Using KNIME for Data Preparation -- 4.9 Summary -- References -- Chapter 5: Dimensionality Reduction -- 5.1 Problems with Large Numbers of Variables -- 5.2 Approaches to Dimension Reduction -- 5.3 Principal Components Analysis -- 5.4 Example of Using PCA -- 5.5 Intuition and Algebra behind Principal Components -- 5.6 Summary -- References -- Chapter 6: Ordinary Least Squares Regression -- 6.1 Intuition About Simple Linear Regression -- 6.2 Multiple Regression -- 6.3 Building a Predictive Regression Model -- Variable Types in Regression -- Coding Nominal Variables -- Coding Ordinal Variables -- 6.4 Nonlinear Relationships.
Regression with a Nonlinear Relationship -- Using a Polynomial Model -- Transformations to Deal with Nonlinearity -- 6.5 Evaluating Predictive Accuracy -- 6.6 Example Applications of Regression -- Predicting Home Prices -- Comparison of OLS and Stepwise Regression -- Using Regularization for Predictor Selection -- Regularization Formula -- Comparisons of Prediction Accuracy -- 6.7 Summary -- References -- Chapter 7: Logistic Regression -- 7.1 Intuition About Binary Logistic Regression -- 7.2 Modeling Probabilities -- 7.3 Estimating Logistic Regression Parameters -- 7.4 Example Using Simulated Data -- The Logistic Analysis Results -- Estimating Probabilities -- 7.5 The Nonlinearity of Logistic Regression Coefficients -- Change in X1 (−0.07 to +0.03) with X2 = 1.50 -- Change in X1 (+0.170 to +0.270) with X2 = 1.50 -- Change in X1 (−0.07 to +0.03) with X2 = 2.00 -- Change in X1 (−0.07 to +0.03) with X2 = 2.00 -- Changes in Odds, Log Odds, and Probability -- 7.6 Interpreting Logistic Results Using Log Odds -- 7.7 Evaluating Classification Models -- Confusion Matrices -- Which Metrics to Use with Confusion Matrices? -- ROC Curves -- Metrics with Multiple-Level Categorical Targets -- 7.8 Example: Predicting Employee Retention Using Logistic Regression -- Results for the Analysis of Employee Turnover -- 7.9 Predictor Interpretation and Importance -- An Approximate Method for Predictor Interpretation -- 7.10 Example: Predicting Heart Disease Using Logistic Regression -- Results for Heart Disease Prediction -- 7.11 Regularized Logistic Regression -- Applying Regularization to the Heart Disease Data -- Interpreting the Coefficients -- 7.12 Asymmetric Benefits and Costs -- 7.13 Multinomial Logistic Regression -- 7.14 Summary -- Appendix: Cohen's Kappa -- References -- Chapter 8: Classification and Regression Trees -- 8.1 Classification Trees. 8.2 Applications of Decision Trees -- 8.3 Developing Classification Trees -- 8.4 Growing Decision Trees Using Gini Impurity -- Demonstrating Gini Calculations -- 8.5 Pruning to Avoid Overfitting -- Pre-pruning -- Post-pruning -- Reduced Error Pruning -- Cost-Complexity Pruning -- Pruning Using the Minimum Description Length Principle -- Recommendations on Pruning -- 8.6 Missing Values in Decision Tree Analyses -- Ignoring Missing Data -- Imputation Techniques -- Using Machine Learning -- 8.7 Outliers in Classification Trees -- 8.8 Predicting Churn with a Classification Tree -- 8.9 Regression Trees -- 8.10 Example: Head Acceleration in a Simulated Motorcycle Accident -- 8.11 Strengths and Weaknesses of Decision Trees -- Strengths of Decision Trees -- Weaknesses of Decision Trees -- 8.12 Summary -- References -- Chapter 9: Naïve Bayes -- 9.1 A Thought Problem -- Analysis of the Thought Problem -- 9.2 Bayes' Theorem Illustrated -- Updating the Probabilities -- What Happens with More Predictors? -- 9.3 Illustration of Naïve Bayes with a "Toy" Data Set -- Calculations Needed for the Naïve Bayes' Model -- Results of Probability Calculations -- 9.4 The Assumption of Conditional Independence -- 9.5 Naïve Bayes with Continuous Predictors -- 9.6 Laplace Smoothing -- 9.7 Example of Naïve Bayes Applied to the Heart Disease Data -- Heart Disease Predictions with Naïve Bayes -- 9.8 Example of Naïve Bayes Applied to Detecting Spam -- Accuracy of SPAM/HAM Detection -- 9.9 Summary and Comments on Naïve Bayes -- References -- Chapter 10: k Nearest Neighbors -- 10.1 How kNN Works -- 10.2 A Two-Dimensional Graphic Example of kNN -- 10.3 Example Application of kNN to Diagnosing Heart Disease -- 10.4 kNN for Continuous Targets -- 10.5 kNN for Multiclass Target Variables -- 10.6 Summary -- References -- Chapter 11: Neural Networks. 11.1 What Are Artificial Neural Networks? -- 11.2 The Learning Process for Artificial Neural Networks -- 11.3 Example of a Single-Layer Artificial Neuron -- 11.4 Example of a Multilayer Perceptron -- 11.5 Example Application of a Multilayer Perceptron with Multi-level Categorical Target -- 11.6 Considerations for Using Neural Nets -- 11.7 Example: Using Neural Nets to Predict Credit Status -- 11.8 Example: Neural Nets to Predict Used Car Prices -- 11.9 Summary -- References -- Chapter 12: Ensemble Models -- 12.1 Creating Ensemble Models -- 12.2 Ensemble Models Based on Decision Trees -- 12.3 Example of Ensemble Modeling with a Continuous Target -- 12.4 Example of Ensemble Modeling for a Binary Target -- 12.5 Summary -- References -- Chapter 13: Cluster Analysis -- 13.1 How Many Clusters Are There? -- 13.2 Recommended Steps in Running a Cluster Analysis -- 13.3 Hierarchical Cluster Analysis -- 13.4 k-Means Clustering -- 13.5 Density-Based Clustering -- 13.6 Fuzzy Cluster Analysis -- 13.7 Cluster Validation -- 13.8 Summary -- References -- Chapter 14: Communication and Deployment -- 14.1 Writing and Presenting the Final Report -- 14.2 Data Visualization -- 14.3 Deploying Predictive Models -- 14.4 Summary -- References -- Index. |
| Record Nr. | UNINA-9910768168903321 |
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Acito Frank
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| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Predictive intelligence for data-driven managers : process model, assessment-tool, IT-blueprint, competence model and case studies / / Uwe Seebacher
| Predictive intelligence for data-driven managers : process model, assessment-tool, IT-blueprint, competence model and case studies / / Uwe Seebacher |
| Autore | Seebacher Uwe G. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (275 pages) |
| Disciplina | 133.5 |
| Collana | Future of Business and Finance |
| Soggetto topico |
Predictive analytics
Business - Data processing Strategic planning |
| ISBN | 3-030-69403-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Reference -- Contents -- 1: Predictive Intelligence and the Basic Economic Principles -- 1.1 Where Do We Come from? -- 1.2 How Industrial Management Came About -- 1.3 The Separation of Ownership and Management -- 1.4 What Are the Current Challenges? -- 1.5 The Basic Economic Principles Are also Disrupted -- 1.6 What Role Does the Corona Pandemic Play? -- 1.7 What Do We Know? -- Further Reading -- 2: Predictive Intelligence at a Glance -- 2.1 What Is Predictive Intelligence? -- 2.2 The Maturity Model for Predictive Intelligence -- 2.3 The Predictive Intelligence Self-Assessment (PI-SA) -- 2.4 The Advantages of Predictive Intelligence -- 2.5 The Conclusion -- Further Reading -- 3: The Predictive Intelligence Ecosystem -- 3.1 Introduction -- 3.1.1 A/B Tests -- 3.1.2 Artificial Intelligence (AI) -- 3.1.3 Artificial Neural Network (ANN) -- 3.1.4 Association Analysis -- 3.1.5 Evaluation Metrics -- 3.1.6 Big Data -- 3.1.7 Business Analytics (BA) -- 3.1.8 Business Intelligence (BI) -- 3.1.9 Cloud Analytics -- 3.1.10 Cloud-Based Social Media Analytics (CSMA) -- 3.1.11 Cloud Sourcing -- 3.1.12 Clustering -- 3.1.13 Data Analysis -- 3.1.14 Data Cleansing -- 3.1.15 Date Lake -- 3.1.16 Data Mining -- 3.1.17 Data Science -- 3.1.18 Data Scientist -- 3.1.19 Deep Learning -- 3.1.19.1 Excursus -- 3.1.20 Descriptive Analytics -- 3.1.21 Descriptive Analysis -- 3.1.22 Descriptive Models -- 3.1.23 Exception Reporting -- 3.1.24 Extrapolation -- 3.1.25 Functional Models or Modeling -- 3.1.26 Hadoop Cluster -- 3.1.27 Harvesting -- 3.1.28 Principal Component Analysis (PCA) -- 3.1.29 In-Sample -- 3.1.30 k-Means-Clustering -- 3.1.31 k-Nearest Neighbors -- 3.1.32 Classification -- 3.1.33 Louvain Method -- 3.1.34 Machine Learning -- 3.1.35 Feature Extraction -- 3.1.36 Modeling -- 3.1.37 Model Monitoring -- 3.1.38 Sample Units -- 3.1.39 Neural Networks.
3.1.40 Out-of-Sample -- 3.1.41 Parameters -- 3.1.42 Predictive Analysis -- 3.1.43 Predictive Models or Modeling -- 3.1.44 Predictors -- 3.1.45 Prescriptive Analytics -- 3.1.46 Predictive Marketing -- 3.1.47 Procurement Intelligence -- 3.1.48 Random Forest -- 3.1.49 Regression Analysis -- 3.1.50 Regularization -- 3.1.51 Reinforcement Learning -- 3.1.52 Supervised Learning -- 3.1.53 Training Patterns -- 3.1.54 Unsupervised Learning -- 3.1.55 Validation -- 3.1.56 Variables -- 3.2 The Dynamics of the PI Ecosystem -- Further Reading -- 4: The Predictive Intelligence Maturity Model -- 4.1 Why Do We Need a PI Maturity Model? -- 4.2 How the PI Maturity Model Was Created? -- 4.2.1 The Game Begins -- 4.2.2 The Data Cube Is Created -- 4.3 The Three Dimensions of the PI Maturity Model -- 4.3.1 Validity of the Data -- 4.3.1.1 Therefore, Examine Whoever Binds Forever! -- 4.3.1.2 The Profit Lies in Purchasing! -- 4.3.2 Time Required for Analyses -- 4.3.3 Cost of the Data -- 4.4 The Predictive Intelligence Maturity Model (PIMM) -- 4.4.1 Stage 1: Reactive-Static Business Analytics -- 4.4.1.1 What Does Reactive Business Analytics Mean? -- 4.4.1.2 What Does Static Business Analytics Mean? -- 4.4.2 Level 2: Proactive-Situational Business Analytics -- 4.4.2.1 Why a Proactive Approach Is Such an Important Step? -- 4.4.2.2 Why Does It Have to Be Situational? -- 4.4.3 Level 3: Interactive-Dynamic Business Analytics -- 4.4.4 Stage 4: Dynamic Modeling Predictive Intelligence -- 4.4.4.1 What Does Predictive Intelligence Mean? -- 4.4.4.2 What Do ABM and Crawler Mean? -- 4.5 What Is the Decisive Factor? -- Further Reading -- 5: The Predictive Intelligence Self-Assessment -- 5.1 The Necessity for the PI-SA -- 5.2 What Are the Main Areas of the PI-SA? -- 5.2.1 The PI Potential Index -- 5.2.2 The Value Chain Index -- 5.2.2.1 The Different Markets -- 5.2.3 The Cost Efficiency Index. 5.2.4 The Structure Index -- 5.2.5 The Strategy Index -- 5.2.6 The Distribution Index -- 5.2.7 The PI Infrastructure Index -- 5.2.8 The PI Competence Index -- 5.3 The Evaluation of the PI-SA -- 5.4 Knowing Where You Are -- Further Reading -- 6: The Process Model for Predictive Intelligence -- 6.1 Every Path Begins with the First Step -- 6.2 The Preparation -- 6.2.1 Understanding the Batch Situation -- 6.2.2 Include the Allies -- 6.3 Phase 1: Reactive-Static Business Analytics -- 6.3.1 Where Does the Shoe Pinch? -- 6.3.2 Who Delivers What? -- 6.3.3 To Be or Not to Be -- 6.3.4 What a Basic Data Model Has to Achieve? -- 6.4 Phase 2: Proactive-Situational Business Analytics -- 6.4.1 Dashboards: Let´s Get Fancy! -- 6.4.2 It Is Time for Customer Data! -- 6.4.2.1 Why to Start from Zero Again! -- 6.4.2.2 How to Get to a CRM Quickly? -- 6.4.2.3 Why the Turbo Is Data Convergence? -- 6.4.3 With the Value Chain to the Relevant Market -- 6.4.3.1 How to Proceed? -- 6.4.4 How the Evaluations Are Becoming More and More Informative? -- 6.5 Phase 3: Interactive-Dynamic Business Intelligence -- 6.5.1 How a Key User Network Is Established? -- 6.5.2 How the 360 Data Panorama Is Achieved? -- 6.5.3 With Tertiary Processes into a Completely New Dimension -- 6.6 Phase 4: Dynamic-Modeling Predictive Intelligence -- 6.6.1 How Template-Based Intelligence Saves the Consultant? -- 6.6.2 With AI It Goes into the Cloud -- 6.7 What the Quintessence Is? -- Further Reading -- 7: The Predictive Intelligence TechStack (PITechStack) -- 7.1 Is Predictive Intelligence an IT Topic? -- 7.2 IT: Quo Vadis? -- 7.2.1 What Are the Benefits of Edge Computing? -- 7.2.2 With the Blockchain to the PI-Portal -- 7.2.3 Software-Based Virtualization Is on the Rise -- 7.3 Where IT Stands in 2030 -- 7.3.1 The Sale Is Dead, Because It Is About H2H -- 7.3.2 More Agile with Networks. 7.3.3 Full Speed Ahead into the Netflix Economy -- 7.3.4 From Big Data to Smart Data -- 7.3.5 From Resources to Smart Materials -- 7.4 What We Can Learn from the MarTechstack? -- 7.4.1 Why the Consulting Industry Is Disrupted? -- 7.4.2 What MarTech and SalesTech Teach Us? -- 7.4.3 Away from the ``Or´´ to the ``And,´´ and Yet Less Is More! -- 7.5 The Three Phases of the PITechStack -- 7.5.1 The Excelling -- 7.5.2 The Connecting -- 7.5.3 The Shopping -- 7.6 The PITechStack-Blueprint at a Glance -- 7.6.1 What Do Data and Information Applications Do? -- 7.6.2 Visualization and Processing Applications -- 7.6.3 Integration and Evaluation Applications -- 7.6.4 AdHoc Consultants -- 7.7 What Is The Environment of an Effective PITechStack? -- Further Reading -- 8: The Predictive Intelligence Team -- 8.1 Is Predictive Intelligence Also a Cathedral? -- 8.2 What Managers Need to Know -- 8.2.1 What Does It Mean to Live Predictive Intelligence? -- 8.2.2 How Can Data Be Managed as a Strategic Resource? -- 8.2.3 How Do You Build a Data-Driven Management System? -- 8.2.4 How to Create Competitive Advantages Through Predictive Intelligence -- 8.3 What the Perfect PI Team Looks Like -- 8.3.1 What Humanontogenetics Teaches Us -- 8.3.2 Why the Good Is so Obvious -- 8.3.3 Demand Determines the Dynamics -- 8.3.4 Why the PI Team Sets the Direction -- 8.4 The Predictive Intelligence Competence Model -- 8.4.1 Industry and Product Competence (IPC) -- 8.4.2 Analytical Thinking (AT) -- 8.4.3 Data Management -- 8.4.4 Data Exploration -- 8.4.5 Data Algorithmics -- 8.4.6 Data Visualization -- 8.4.7 Technology Management -- 8.4.8 Strategic Thinking -- 8.4.9 Leadership -- 8.5 The Predictive Intelligence Competence Matrix -- Further Reading -- 9: The Predictive Intelligence Case Studies -- 9.1 Why Predictive Intelligence Is Not Rocket Science. 9.2 Where Does the Case Study Company Come from? -- 9.2.1 Why the Situation Became Difficult -- 9.2.2 Instead of the Watering Can Ray Needs the Spear -- 9.2.3 You Do Not Have to Be Everybody´s Darling! -- 9.2.4 Start Small and Show Results -- 9.3 Showcase 1: Short-Term, Operational Predictive Intelligence -- 9.4 Showcase 2: Medium-Term Operational-Strategic Predictive Intelligence -- 9.5 Showcase 3: Long-Term Strategic-Tactical Predictive Intelligence -- 9.6 Why a Glass Ball Is Not Needed -- Further Reading -- 10: Why It Remains Exciting -- Further Reading -- Index. |
| Record Nr. | UNINA-9910483118303321 |
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Seebacher Uwe G.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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