Advanced visualization concepts
Information Visualization

What We Are Going to Learn

• Traditional Analytics methods
  • Clustering
  • Regression
  • Classification
  • Dimensionality reduction
  • Recommendation systems

Clustering

http://scikit-learn.org/stable/modules/clustering.html#clustering

When to Use It

• When you want to find similar items
• Depends on your distance metric
• When you have too many items and you want to aggregate

Examples

• Customer segmentation
• Grouping experiment outcomes

Regression

http://scikit-learn.org/stable/modules/linear_model.html#passive-aggressive-algorithms

When to Use It

• Present (identify/compare) tendency
• Predict values

Regression

Examples

• Stock prices
• Drug response

What car to buy?

User: person buying a car

Task: What's the best car to buy?

Data: all cars on sale

Normal procedure

Ask friends and family

Problem

That's inferring statistics from a sample n = 1

Better approach

Data-based decisions

Jeep Willys

• Colombia bought many Jeeps after the war
• They are the a sort of mountain taxi
• There is a trend to pimp them up

Classification

When to Use It

• Present (identify/compare) tendency
• Present (identify/compare) groups
• Aggregate
• Predict values

Examples

• Photo categorization
• Sentiment analysis
• Spam filtering

Dimensionality Reduction

When to Use It

• Attribute filtering
• Categorize documents (topic modeling)

Recommendation Systems

When to Use It?

• Large catalog with user preference history
• If you like "A" and "B", maybe you will like "C"

Types

• Collaborative filtering
• Content- based systems
• Hybrids

Examples

• Amazon
• Facebook
• Google
• Yahoo
• Netflix Prize

How to Use the Algorithms

• R
• scikit-learn
• Tensorflow (Google)
• Caffe
• Amazon ML
• Spark MLlib
• Graphlab

What We Learned

• Traditional Analytics methods
  • Clustering
  • Regression
  • Classification
  • Dimensionality reduction
  • Recommendation systems