UNIT-I:

What Is Predictive Data Analytics- Predictive Data Analytics Tools- What Is Machine Learning- How does Machine Learning Works- The Predictive Data Analytics Project Lifecycle: CRISPDM- Data to Insights to Decisions: Converting Business Problems into Analytics Solutions-Assessing Feasibility-Designing the Analytics Base Table-Designing and Implementing Features-Different Types of Data-Different Types of Features-Handling Time-Implementing Features

UNIT-II:

            Data Exploration: The Data Quality Report- Getting to Know the Data- Identifying Data Quality Issues- Handling Data Quality Issues- Advanced Data Exploration: Visualizing Relationships between Features – Measuring Covariance and Correlation- Data Preparation: Normalization- Binning- Sampling

UNIT-III:

Information-based Learning: Fundamentals- Decision Trees- Shannon᧙s Entropy Model- Information Gain- Standard Approach: The ID3 Algorithm- Extensions and Variations: Alternative Feature Selection and Impurity Metrics- Handling Continuous Descriptive Features- Predicting Continuous Targets- Tree Pruning- Model Ensembles

UNIT-IV:

Similarity-based Learning: Big Idea- Fundamentals- Standard Approach: The Nearest Neighbor Algorithm -Extensions and Variations. Probability-based Learning: Standard Approach: The Naive Bayes Model- Error-based Learning: Fundamentals- Standard Approach: Multivariable Linear Regression with Gradient Descent

UNIT-V:

            The Art of Machine Learning for Predictive Data Analytics: Different Perspectives on Prediction Models- Choosing a Machine Learning Approach- A Descriptive Statistics and Data Visualization for Machine Learning: Central Tendency- Descriptive Statistics for Categorical Features- Populations and Samples- Data Visualization

TEXT(S)

1. John D. Kelleher, Brian Mac Namee, Aoife D᧙Arcy ,᧜Fundamentals of Machine Learning For Predictive Data Analytics᧝, 2015, Illustrated edition, The MIT Press, ISBN-10 ᧏ : ᧎ 0262029448 ISBN-13 ᧏ : ᧎ 978-0262029445

REFERENCE MATERIALS

1. Andreas Wichert , Luis Sa-Couto, ᧜Machine Learning ᧔ A Journey to Deep Learning᧝, 2021, World Scientific Publishing Co Pte Ltd, ISBN-10: ᧎9811234051, ISBN-13 : 978-9811234057.
2. Ralph Winters, ᧜Practical Predictive Analytics ᧜ 2017, Packt Publishing Limited, ISBN-10 : ᧎1785886185, ISBN-13 : ᧎978-1785886188
3. An***e Bari, Mohamed Chaouchi , Tommy Jung, ᧜Predictive Analytics For Dummies (For Dummies Series)᧝, 2014, John Wiley & Sons Publisher, ISBN-10 ᧏ : ᧎ 1118728963, ISBN-13 ᧏ : ᧎ 978-1118728963.

E-RESOURCES

1. https://maru***ech.com/machine-learning-predictive-analytics/
2. https://www.sas.com/en_gb/insights/articles/analytics/a-guide-to-predictive-analytics-and-machine-learning.html
3. https://www.techtarget.com/searchenterpriseai/feature/Machine-learning-and-predictive-analytics-work-better-together
4. https://www.bmc.com/blogs/machine-learning-vs-predictive-analytics/