University of Illinois Urbana Champaign - Predictive Analytics and Data Mining 

Welcome to Predictive Analytics and Data Mining

Meet Professor Sridhar Seshadri

Rattle Installation Guidelines for Windows

Rattle Installation Guideline for MacOS

Rattle Interface for Windows

Introduction to Clustering

Applications of Clustering

How to Cluster

Introduction to K Means

Hierarchical (Agglomerative) Clustering

Measuring Similarity Between Clusters1

Real World Clustering Example

Clustering Practice and Summary

WEEK-2 Decision Trees

Introduction to Discriminative Classifiers

Model Complexity

Rule Based Classifiers

Entropy and Decision Trees

Classification Tree Example

Regression Tree Example

Introduction to Forests and Spam Filter Exercise

WEEK-3-Module 3: Rules, Rules, and More Rules

Introduction to Rules

K-Nearest Neighbor

K-Nearest Neighbor Classifier

Selecting the Best K in Rstudio

Bayes' Rule

The Naive Bayes Trick

Employee Attrition Example

Employee Attrition Example in Rstudio, Exercise, and Summary

WEEK-4 Model Performance and Recommendation Systems

Introduction to Model Performance

Classification Tree Example

True and False Negatives

Clock Example Exercise

Making Recommendations

Association Rule Mining

Collaborative Filtering

Recommendation Example in Rstudio and Summary