PREDICTIVE ANALYTICS USING ADVANCED MACHINE LEARNING FRAMEWORK 2 DAY WORKSHOP

WORKSHOP AGENDA

DAY 1

DAY 2

Module: Define the Problem

This module is intended to define the business problem, convert that into an analytical problem and define the key steps involved in the analytical exercise. This module will also cover the data collection step.

Module: Data Preparation

Concepts to be covered:

1.Default Value Treatment
2.Outlier Treatment
3.Missing Value Treatment

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Module: Feature Extraction

Concepts to be covered:

1.Forms of Derived Variables
2.Derived Variable Creation

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Practice Session 1

Hands-on practical session to apply the concepts learnt in the Data Preparation and Feature Extraction modules

Module: Dimension Reduction

Concepts to be covered:

1.Principal Component Analysis (PCA)
2.Variable Clustering 
3.Information Value (IV)

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Practice Session 2

Hands-on practical session to apply the concepts learnt in the Dimension Reduction module

Module: Application of Logistic Regression

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Module: Application of Decision Tree

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Practice Session 3

Hands-on practical session to apply the concepts of Logistic Regression and Decision Tree

Module: Application of Gradient Boosting

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Module: Application of Naïve Bayes

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Module: Application of Artificial Neural Network

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of relevant ATH functions

Module: Application of Support Vector Machine

Concepts to be covered:

1. Application of the technique

2. Interpretation of Results

Analyttica TreasureHunt’s Feature Showcase: Demo of ATH functions

Practice Session 4

Hands-on practical session to apply the concepts of Gradient Boosting, Naïve Bayes, ANN & SVM