1. Predictive Analytics:
From Data to Foresight
Summer School 2015
Nirmal Fernando
Associate Technical Lead, WSO2 Inc.

2. Predictive Analytics
Extract information from existing
data sets to determine patterns
and predict future outcomes and
trends.
It does not tell you what will
happen in the future.
It forecasts what might happen in the future with an acceptable
level of reliability.
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source: http://insidebigdata.com/2014/08/25/salespredict-
marketo-partner-using-predictive-analytics/

3. Where is it used?
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4. Ticket to success
o Guiding front-line decisions and
actions via transmuting data into
predictive visions and intuitions.
o Customer requirements and steps to
increase profitability and retention.
o Boosting productivity of people, assets and processes
o Eliminating threats and frauds before they can hamper the
image and reputation of the company.
o Assessing the social media impact of your products in the
market.
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5. o Matured
o Less expensive
o More approachable
o Easy to make use of
Unlocking the potential of “Big Data”
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6. Machine Learning
Machine learning is the science of getting
computers to act without being explicitly
programmed.
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7. Categorization
o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning
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8. Categorization
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o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning

9. Supervised Learning
Machine learning task of inferring a function from labeled
training data.
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source: http://www.astroml.org/sklearn_tutorial/general_concepts.html

10. Supervised Learning - Classification
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11. Supervised Learning - Regression
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12. Supervised Learning - Algorithms
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Regression
o Linear Regression
o Lasso Regression
o Ridge Regression
Classification
o Logistic Regression
o Support Vector Machine
o Decision Tree
o Random Forest
o Naive Bayes

13. Unsupervised Learning
Machine learning task of inferring a function from unlabeled
training data. The algorithm tries to find similarities among the
objects in question.
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source: http://www.astroml.org/sklearn_tutorial/general_concepts.html

14. Unsupervised Learning - Clustering
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15. o Manage and explore your data
o Analyze the data using machine learning algorithms
o Build machine learning models
o Compare and manage generated machine learning models
o predict using the built models
Powered by Apache Spark and Apache Spark MLlib.
Key words,
o ML Project: a logical grouping of set of machine learning analyses you
would perform on a selected dataset.
o ML Analysis: holds a pre-processed feature set, a selected machine
learning algorithm and its calibrated set of hyper-parameters.
WSO2 Machine Learner
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16. Manage your datasets...
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17. Explore your data..
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18. Manage ML projects...
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19. Generate and manage ML models
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20. Finding the best model
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21. Other features...
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o Fast and scalable machine learning
o Every operation exposed using a REST API
o Easy to use User Interface
o Use generated models in WSO2 ESB and WSO2 CEP for
prediction
Future,
o Deep learning algorithms
o NLP techniques
o Data pre-processing techniques

22. Let’s try to solve a real world
problem
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23. Problem description
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source: https://www.kaggle.com/c/otto-group-product-classification-challenge

24. Dataset
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o 93 features
o for 200,000+ products
o id - an anonymous id unique to a product
o feat_1, feat_2, ..., feat_93 - the various features of a product
o target - the class of a product. There are 9 most important
product categories (like fashion, electronics, etc.)

25. Objective
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Build a predictive model
which is capable of
categorizing a given
product into
one of the categories.

26. Towards the solution..
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28. Summary
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o Discussed predictive analytics
o Learnt what machine learning is
o Got to know widely-used machine learning techniques
o Glanced at WSO2 Machine Learner product features
o Solved a real-world machine-learning problem using WSO2
Machine Learner

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30. Contact us !