Predictive Analytics

Overview

Modern enterprises capture significant amounts of data about its customers, suppliers, and partners. The challenge, however, is to transform this vast data repository into actionable business intelligence. This course introduces predictive analytics tools that can provide valuable business insights. Analysis tools include decision trees, neural networks, market basket analysis, and discriminant analysis. Both data cleaning and analyses will be discussed and applied to sample data.

Syllabus

Module 1: R Basics

Welcome to Predictive Analytics! Module 1 introduces the R programming environment and the basics of writing code in R.

Module 2: kNN

In Module 2, you will learn the basics of Classification and understand the working of the kNN classifier.

Module 3: Naive Bayes

To effectively learn Naive Bayes classification, this module will cover both the theoretical foundations and the practical implementation in R.

Module 4: Decision Trees

In order to understand the working of the Decision Tree as a classifier , we will need to grasp how this algorithm makes decisions and classifies new data points based on patterns it learned from training data.

Module 5: ANNs

This module focused on "Using Artificial Neural Networks (ANNs) as a classifier" aims to provide a comprehensive understanding of how these powerful, biologically inspired models can be applied to categorize data.

Module 6: SVMs

This module, "Classification using Support Vector Machines (SVMs)", will equip you with a deep understanding of this powerful machine learning algorithm and its application in classifying data.

Module 7: Clustering

This module on "Clustering" aims to introduce you to the powerful world of unsupervised learning, where the goal is to discover inherent groupings within unlabeled data.

Module 8: Associative Rule Mining

Mining frequent item sets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.

Summative Course Assessment

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.