We have great pleasure in presenting First edition “Predictive Analytics”
written for students of PG courses. The related matters are written in a
simple and easily understandable.
This volume is an attempt to provide the students with thorough
understanding of Predictive Analytics. We have presented the subject
matter in a systematic manner with liberal use of charts and diagrams
where ever necessary so as to make it interesting and sustain students’
interest.
Contents –
Module – 1 Introduction to Predictive Analytics
Introduction
Predictive Analytics
Definition of Predictive Analytics
Significance of Predictive Analytics
Predictive vs. Descriptive vs. Prescriptive Analytics.
Overview of the Predictive Analytics Process
Applications in Business Case Studies from Various Industries (e.g., Finance, Marketing, Operations)
Discussion on the Impact of Predictive Analytics on Decision-making
Multiple Choice Question (MCQs)
Review Questions
References
Module – 2 Data Collection and Preparation
Introduction
Data Sources and Collection
Types of Data (Structured vs. Unstructured)
Data Collection Methods and Tools
Data Cleaning and Preparation
Handling Missing Data
Data Transformation and Normalization
Data Preparation Using Excel or Python/R for Data
Cleaning and Preparation
Multiple Choice Question (MCQs)
Review Questions
References
Module – 3 Statistical Foundations
Introduction
Statistical Concepts
Probability Distributions
Hypothesis Testing
Regression Analysis Basics Building Statistical Models
Simple Linear Regression
Multiple Linear Regression
Model Assumptions and Diagnostics
Multiple Choice Question (MCQs)
Review Questions
References
Module – 4 Predictive Modeling Techniques
Introduction
Regression Models:
Advanced Regression Techniques
(e.g., Polynomial, Ridge, Lasso Regression)
Model Evaluation Metrics (R², RMSE, MAE)
Classification Models:
Logistic Regression
Decision Trees and Random Forests
Model Evaluation Metrics
(Accuracy, Precision, Recall, F1 Score)
Time Series Analysis:
Components of Time Series Data
ARIMA Models
Multiple Choice Question (MCQs)
Review Questions
References
Module – 5 Machine Learning Basics
Introduction to Machine Learning
Supervised vs. Unsupervised Learning
Key Algorithms
(K Means Clustering, Support Vector Machines)
Model Evaluation and Validation
Cross-Validation Techniques
Bias-variance Trade-off
Multiple Choice Question (MCQs)
Review Questions
References
Module – 6 Big Data and Predictive Analytics
Introduction to Hadoop and Spark
Handling Large Datasets
Predictive Analytics Tools
Overview of Software (e.g., SAS, SPSS, Tableau)
Comparison of different Tools and their Applications
Ethical Issues in Predictive Analytics
Data Privacy and Security
Bias and Fairness in Predictive Models
Multiple Choice Question (MCQs)
Review Questions
References
