Duration

2 days

Course Objective

This two days course prepare analyst to take the knowledge gained and apply it to their own respective data mining problems, solving them quickly and easily. The lessons learnt will be applicable to areas such as customer analytics, targeted marketing, social media analytics, fraud detection, predictive maintenance, resource management, etc. This course is suggested for analysts and data scientists.

Audience

This course is suggested for analysts and data scientists.

Training Methodology

Hands-on exercise, lecture, group discussion, and case study.

What Will You Learn

Deploy analytical predictive models
Utilize more complex functionality of RapidMiner Studio
Apply more sophisticated analytical approaches

Prerequisite

RapidMiner & DataScience : Foundations

Overview

Business case
Intro course review
Loading new data

EDA: Exploratory Data Analysis

Multiple sources
Joins & Set Theory
Understanding new attributes

Data Preparation

Advanced Data ETL (Extract, Transform, and Load)
Aggregation & Multi-level aggregation
Pivot & De-Pivot
Calculated values
Regular Expressions
Changing value types
Feature Generation and Feature Engineering
Loops
Macros

Predictive Model’s Algorithms

Support Vector Machines
K-Means Clustering
Neural Networks
Logistic Regression

Model Construction and Evaluation

Advanced performance criteria
ROC plots
Comparison between models
Sampling
Weighting
Feature Selection: Forward Selection
Feature Selection: Backward Elimination
Validation of preprocessing and preprocessing models
Optimization & Logging results

Additional Workshops

Principal Components Analysis
Logistic Regression
Performance (Cost) Model Optimization

Programme scope, delivery arrangements and suitability are confirmed during enquiry.