The document discusses machine learning principles and applications, focusing on how Apache Spark can be utilized for data processing and predictive modeling in various fields like economics, healthcare, and advertising. It outlines types of machine learning, prevalent use cases, and specific model implementations such as logistic regression and random forests, along with performance metrics for evaluating models. Additionally, it highlights challenges faced in real-world applications and emphasizes the importance of data transformation and model training techniques.

1. Machine Learning with Big Data
using Apache Spark
Mukundan Agaram
Amit Singh

2. Agenda
Machine Learning Concepts1
Econometrics Model for Recession Prediction
Apache Spark Code Review
4
Platform & Data3
Prevalent Use Cases2
Other ML Concepts and Wrap Up
QA
5

3. What is Machine Learning
• Branch of AI
– Alan Turing – can machines think???
– “Field of study that gives Computers the ability to learn without
being explicitly programmed” – Arthur Samuel
• Learn from data
• Improve with experience
• Iteratively refine a model that can be used to predict outcomes of
questions based on previous learning

4. Types of Machine Learning
MachineLearning
Supervised
Regression
Interest rate
prediction
Classification Spam/No-spam
Unsupervised Clustering
Social Network
analysis
Recommender
Systems
Collaborative
Filtering
Netflix
recommendation

5. Prevalent Use Cases
• Spam Detection – Google Gmail
• Voice Recognition – Apple Siri
• Stock Trading
–High Frequency
–Recommendation Systems
–Algorithmic Trading
• Robotics
–Acquire skills – grasping objects, locomotion, automated driving and navigation
• Medicine and Healthcare
–Healthcare Analytics, Prediction based on Genomes, Health sensor analysis
• Advertising:
–Targeted Advertising based on interests and social media
• Retail and E Commerce:
–Frequency club cards, targeting coupons and promotions
–Recommendation Engines

6. Use Cases
• Gaming Analytics
– Predictive Analytics for Sports Games, Console based gaming profiles, upsell and
targeting in-app purchases and mods
• Internet of Things
– Large scale sensor data analysis for prediction, classification
• Social Network Analysis
– Facebook, LinkedIn
• Astronomy
– Galaxy formation

7. Languages and Platforms
• Apache Spark – MLlib
– Scala, Java, Python
• Mahout
• Python Libraries
– Scikit-learn, PyML, PyBrain, matplotlib
• R
– Open Source statistical programming language
• Matlab
• SAS
• Weka
• Octave
• Clojure

8. Apache Spark

9. Data Repositories
• UC Irvine Machine Learning Repository
• Infochimps
• Kaggle
• FRED – Federal Reserve Board in Kansas
• Many others...

10. Logistic Regression (Linear)

11. Logistic Regression (Non-Linear)

12. Logistic Regression

13. Model Design
• Data Collection
–Identify key inputs to the model
• Data Transformation and Curation
–Human ‘analyst’ should be able to view the data sets make predictions
–Data needs to be cleaned, scrubbed, transformed – normalized
–Generally most important step for any type of supervised learning
algorithm
• Review the data
• Visually make predictions of individual learning indicators

14. Sample Econometrics Model
• Objective: Predict Economic Conditions (Growth/Recession)
• Supervised Learning
• Widely used algorithms
• Logistic Regression
• SVM
• RandomForest (Decision Trees)
• Current Challenges
– Forecasts are either too early – 6-12 months before contraction starts
– Forecasts are too late and reported by NBER (National Bureau of Economic
Research) after recession has started
– Individuals and Corporations cannot plan effectively based on prevailing economic
conditions

15. Sample Econometrics Model
• Use ‘leading indicators’ for economic health
–Treasury Yield Curve between 10 year and 3 month (T10Y3M)
–Industrial Production (INDPRO)
–Unemployment insurance
–Market Returns – S&P500

16. Data Transformation and Curation
-3.
-1.5
0.
1.5
3.
2/2/1986 7/2/1988 12/2/1990 5/2/1993 10/2/1995 3/2/1998 8/2/2000 1/2/2003 6/2/2005 11/2/2007 4/2/2010 9/2/2012 2/2/2015 7/2/2017
Treasury Yield Curve - Normalized
T10Y3M Recession (1/0 - Y/N)

17. Data Transformation and Curation
-13.5
-9.
-4.5
0.
4.5
2/2/1986 7/2/1988 12/2/1990 5/2/1993 10/2/1995 3/2/1998 8/2/2000 1/2/2003 6/2/2005 11/2/2007 4/2/2010 9/2/2012 2/2/2015 7/2/2017
Industrial Production - Normalized
Frequency: MonthlyINDPRO
Frequency: MonthlyRecession (1/0 - Y/N)

18. Data Transformation and Curation
-0.875
-0.4375
0.
0.4375
0.875
1.3125
2/2/1986 9/2/1988 4/2/1991 11/2/1993 6/2/1996 1/2/1999 8/2/2001 3/2/2004 10/2/2006 5/2/2009 12/2/2011 7/2/2014 2/2/2017
Unemployment Insurance - Normalized
Frequency: MonthlyCCSA
Frequency: MonthlyRecession (1/0 - Y/N)

19. Data Transformation and Curation
-3.
-1.5
0.
1.5
3.
2/2/1986 9/2/1988 4/2/1991 11/2/1993 6/2/1996 1/2/1999 8/2/2001 3/2/2004 10/2/2006 5/2/2009 12/2/2011 7/2/2014 2/2/2017
S&P500 - Normalized
Frequency: MonthlyS&P500 - Norm
Frequency: MonthlyRecession (1/0 - Y/N)

20. Data Plots in Spark Shell
• Data Plots in Spark Shell (Demo)

21. Bias versus Variance

22. Model Training and Testing
• Model Data should be divided into
Training
Cross Validation
Testing set
• Splitting into these 3 helps improve model performance in
real world by eliminating bias and variance and helps get
model closer to optimal results
• More features does not necessarily mean a better prediction
• MLlib provides API to help with these operations

23. Model Performance Measurements
• Precision
• Recall
• F1 Score
• Confusion Matrix

24. Model Performance Measurements
• Precision
 How often does our algorithm have false positives
 = true positives / # predicted positive
 = true positives / (true positive + false positive)
 High precision is good (i.e. closer to 1)
 You want a big number, because you want false positive
to be as close to 0 as possible

25. Model Performance Measurements
• Recall
 How sensitive is our algorithm?
 Of all patients in set that actually have cancer, what
fraction did we correctly detect
 = true positives / # actual positives
 = true positive / (true positive + false negative)
• High recall is good (i.e. closer to 1)
• You want a big number, because you want false negative to
be as close to 0 as possible

26. Model Performance Measurements
F1Score (fscore)
• = 2 * (PR/ [P + R])
• Fscore is like taking the average of precision and recall
giving a higher weight to the lower value

27. Model Results and Code Review
• Logistic Regression
• SVM
• Random Forest

28. Apache Spark MLlib
• Algorithms Supported:
–Linear SVM
–Logistic Regression SGD
–Classification and Regression Tree
–K-Means Clustering
–Recommendation versus alternating mean squares
–Singular Value Decomposition
–Linear Regression with L1 and L2 Regularization
–Multinomial Naïve Bayes
–Basic Statistics
–Feature Transformations

29. Unsupervised Learning
• K Means Clustering
– Customer Segmentation
– Social Network Analysis
– Computer Data Center Analysis
– Astronomical Galaxy formations
• Recommendation Engines

30. Unsupervised Learning – K Means

31. Recommender Systems
• Class of information filtering system that
predicts the ‘rating’ or ‘preference’ user
would give to an item
• Examples:
– NetFlix
– Amazon
– Apple Genius

32. Recommender
• Collaborative Filtering
– User-User
– Item-Item

33. Recommender Systems

34. Q/A