BN4201 Predictive Modeling for Business Syllabus:
BN4201 Predictive Modeling for Business Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVE:
➢ To equip students in understanding the basic concepts of Predictive analytics
➢ To familiarize students with various predictive modeling techniques.
UNIT – I INTRODUCTION TO PREDICTIVE ANALYTICS AND BASIC CONCEPTS
Introduction -Overview of Analytics and Predictive Analytics – Applications of Predictive Analytics in Business – Predictive Analytics Software (e.g., R, Python, SAS)- Supervised and Unsupervised Learning – Overview of Learning Types: Supervised vs. Unsupervised Introduction to Regression Models – Introduction to Classification Models – Basic Statistical Tools for Prediction – Descriptive Statistics, Inferential Statistics, Basic Statistical Techniques for Predictive Modeling
UNIT – II REGRESSION AND CLASSIFICATION MODELS
Regression Analysis – Simple and Multiple Regression, Iterative Regression Techniques – Classification Models – K-Nearest Neighbors (KNN) – Evaluation Techniques for Classification (Confusion Matrix, ROC Curve) – Model Evaluation & Validation – Metrics for Model Evaluation: MSE, Accuracy, Precision, Recall – Techniques for Model Validation
UNIT – III ADVANCED CLASSIFICATION TECHNIQUES AND ENSEMBLE METHODS
Ensemble Models – Overview of Ensemble Learning- Bagging and Boosting Techniques Bootstrapping – Introduction to Bootstrapping – Applications and Method- Advanced Classification Techniques – In-depth Study of
Advanced Classification Models
UNIT – IV CLUSTERING AND ASSOCIATION MODELS
Clustering Techniques – Introduction to Clustering – K-Means Clustering, Hierarchical Clustering – Association Models – Association Rules, Market Basket Analysis, Algorithms for Association Rules (e.g., Apriori, FP-Growth)
UNIT – V NEURAL NETWORKS AND DEEP LEARNING
Introduction to Neural Networks – Basics of Neural Networks, Multi-Layer Perceptrons (MLP) – Introduction to Deep Learning – Overview of Deep Learning, Basic Deep Learning Architectures – Applications of Deep Learning – Use Cases and Real-world Applications, Tools and Libraries for Deep Learning (e.g., Tensor Flow, Keras) – Review and Case studies, Comprehensive Review of Key Concepts – Case Studies
TOTAL: 45 PERIODS
COURSE OUTCOMES:
➢ Gain sufficient knowledge in Predictive modeling techniques and its usefulness in Business Analytics.
REFERENCES:
1. Dursun Delen, “Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners”, 2nd Edition, 2020 Pearson FT Press
2. “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009, Springer Series
3. “Statistical Methods for Business and Economics” by David R. Anderson, West Publishing
4. “Applied Regression Analysis and Generalized Linear Models” by John Fox, 2015, Sage Publications.
5. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall, 2011, Elsevier.
6. “Pattern Recognition and Machine Learning” by Christopher M. Bishop, 2006, Springer.
7. “Neural Networks and Deep Learning” by Michael Nielsen, Determination Press, 2015
8. “Ensemble Methods in Machine Learning” by Zhi-Hua Zhou, Chapman and Hall/CRC, 2012.
9. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Taylor and Francis.
10.”Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, O’Reilly Media, Inc.”, 2019.