Define
Problem
Raw Data Collection
Data Pre-Processing 1


Find Missing Data
Reduce Noise
Exploratory Data Analysis
Split Data to Training Set
Test Data
Data Pre-Processing 2
 Select Features
 Scale Features
 Dimensional Reduction
Select Algorithm
Final
Model
Development
Refine
Optimize Parameters
Data Post-Processing

Performance Metrics
Final Model
New Data Set
Predictions