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

Perform all common data preparations
Build sophisticated predictive models
Evaluate model quality with respect to different criteria
Deploy analytical predictive models

Prerequisite

Basic knowledge of computer programs and mathematics.

Overview

Business scenario
Analytics Taxonomy & Hierarchy
CRISP-DM & Data mining in the enterprise

Basic Usage

User interface
Creating and Managing RapidMiner repositories
Operators and processes
Storing data, processes, and results sets

EDA: Exploratory Data Analysis

Loading Data
Quick Summary Statistics
Visualizing Data & Basic Charting

Data Preparation

Basic Data ETL (Extract, Transform, and Load)
Data Types & Transformations of value types
Handling missing values
Handling attribute roles
Normalization and standardization
Filtering examples and attributes

Building Better Processes

Organizing
Renaming
Relative Path
Sub-processes
Building Blocks
Breakpoints

Predictive Model’s Algorithms

K-Nearest Neighbour
Correlations
Naive Bayes
Linear Regression
Rules
Decision Trees

Model Construction and Evaluation

Machine Learning Theory: Bias, Variance, Overfitting & Underfitting
Split and Cross Validation
Applying models
Optimization and Parameter Tuning
Splitting data
Evaluation methods & Performance criteria

Additional Workshops

Outlier Detection
Random Forests
Ensemble Modeling

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