Discover the Potential of your Data with Machine Learning

Discover the Potential of
Your Data with Machine
Learning

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© Harbinger Systems | www.harbinger-systems.com

Presenters
Lalit Kumar
Business Analyst
Harbinger Systems
Gautam Mainkar
Data Analyst
Harbinger Systems

Agenda
• A Practical definition
• Why its important
• Using machine learning on enterprise data
– Types of business problems machine learning can solve
– How to categorize a problem- Regression, Clustering and
Classification
• Overview of key algorithms, tools and technologies
• Walk-through of real-world use cases

Machine Learning (ML) – A Practical Definition
A type of artificial intelligence
that provides computers with the ability to learn
without being explicitly programmed.
• Computer can infer rules inherent in data
• Computer adapts when exposed to new data

Why we Need it?
Comic by XKCD

Enterprise Data Hides Information
“There are things we know we know,
There are things we know we don't know.
But there are also things we don't know we don't
know”
- Donald Rumsfeld

What Constitutes a Machine Learning Problem?
Emphasis of machine learning is on
automatic methods
Devise learning algorithms that do the
learning automatically without human
intervention
Program by example: we don't care what
the machine does, as long as it does it
right
Result-oriented rather than process-
oriented

How can Machine Learning Add Value?
ML is a data driven approach
• Business knowledge isn’t necessary
ML is domain independent
• Same algorithms can be used across domains and in different use cases
ML creates flexible decision systems
• Creates robust systems that can adjust for changing systems without
human intervention

ML and Big Data
ML thrives with big data!
– Accuracy of algorithms increases with size of data
– Statistical approaches can treat big datasets much better than
traditional paradigms
– Decision making using ML can adapt to transactional data much better
Machine Learning Big Data

Fraud Detection: Did the user really do this login/make this purchase?
Product Recommendation: Will the user like this product?
Stock Trading: Will the stock go up or down?
Medical Diagnosis: Given some symptoms, what is the patient
suffering from?
Machine Learning Applications- Some Examples

How to Categorize the Problem?
Generally, machine learning problems looks to:
Identify a Value
Assign data points to a category
Discover similarities between two data points

Flowchart
Start
Sufficient
Data?
Sort into
category?
Predict a
value?
Refine Problem!
Labeled
Data
Clustering
Classification
Get more!
Regression

What to look for in algorithms:
Flexible across many use cases
Able to handle several input types
Accurate
Resistant to over-fitting/noise/error
Machine Learning Algorithms

Random Forest
Used for classification and regression
Works on small subsets of data and combines the result into the best estimate
XGBoost
Works on classification and regression
Starts off with a weak learner that improves over successive iterations
K-Means
Works on classification and clustering
Tries to find boundaries between data points for each individual variable
Machine Learning Algorithms

Tools and Technologies
Emphasis on tools which:
Can integrate with existing data architecture
Have a smooth learning curve
Simplify the process of analysis and prediction
Have an active community

Popular Machine Learning Tools
Python
Free, open-source, widely popular
Consolidates many important libraries in python, C
Has an active community
Disclaimer: Brand names, logos and trademarks used herein remain the property of their respective owners.

Popular Machine Learning Tools
R
Statistical computing language that simplifies complex
statistical operations
Large number of libraries available for extending
functionality (DB connectors, algorithm, visualization)
Disclaimer: Brand names, logos and trademarks used herein remain the property of their respective owners.

Scenario
Industrial MNC buys part assemblies from various suppliers
Supplier selection workflow is cumbersome and inadaptable
Create a system to predict supplier price quotes and simplify selection process
Price Prediction: Regression Problem

Data Available
Technical specifications and pricing parameters of past supplier quotes
Problem Type
Predict a numerical value (price quoted by supplier)
Numerical data (specs, prices, etc.)
Categorical data (part composition, payment options, etc.)

Algorithm Chosen: Random Forest
We are working with a mix of numerical and categorical variables
Large number of records but with relatively low dimensionality of features
(Overfitting is not a big risk)
We expect a complex relationship between features

Result
Predicted the quote price given by the supplier with a relatively low error rate
Simplified supplier selection workflow and opened avenues for complete
automation in future

Scenario
eLearning product is sold to universities, corporate and institution across
the globe
There is a need to improve conversion rate by targeted marketing
Create a system to sort prospects into a specific segment
Targeted Marketing: Classification Problem

Data Available
Historical data of purchases and customer data from CRM
Problem Type: Predict a Category (Customer Segment) Based on
Numerical data (payment records)
Categorical data (customer profile data, product purchased by data)

Algorithm Chosen: Gradient Boosting Machine (XGBoost)
A mix of numerical and categorical values
Extremely high dimensionality and size of data
Parallel processing capacities could be useful
Overfitting could be a problem

Result
Created customer segments; new prospects entering CRM are sorted into a
segment and marketing campaigns are targeted to a particular segment
Sales people are better equipped with insights

Scenario
News feed engine publishes varied news content for users
Some level of categorization is done by humans
There is a need to personalize and recommend articles
Create a system to discover similar articles based on content
Personalized News Feed: Clustering Problem

Data Available
Text content of the news articles
User’s reading history

Algorithm Chosen: K-Means Clustering
We are interested in sorting data points in an arbitrary series of clusters
No intrinsic metric for verifying the 'correctness' of a cluster, must be checked by
human oversight
We expect sorting to be accurate with more data

Result
Sorted articles into different clusters which are nominally identified by a label

Conclusion
• Amount of data available to enterprises is exploding
• In order to remain competitive, enterprises will have
to have mastery over their data
• Machine learning provides a powerful framework for
extracting meaning and actions from data

Q&A

Thank You!
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Discover the Potential of your Data with Machine Learning

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