SPICON 2016 presentation

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Machine Learning & Predictive Analytics
Thomas V Joseph
Head Data Science Practice
Quadrant 4 System Corporation

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The path we will tread today ………..
Unravelling the Machine Learning Paradigm
• What is machine learning
• Dynamics of machine learning
• Machine Learning in daily lives
Machine Learning Process in Action
• Machine Learning engagement process @ Quadrant 4
• Machine Learning in action : Case study on battery
failure prediction

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What is Machine Learning ??
Let us go back in time to revisit the equation of a line which we learned in high school
Intercept : C
Slope : m
X
Y
Equation of a Line : Y = MX + C
• Y : Dependent variable
• X : Independent Variable
• C & M are the parameters of the line
• Knowing the parameters we will be able to predict Y for any new values of X
X1
Y1
Business Context of the above equation
• Y : Sales
• X : Any variable that affects sales … say : Advertisement budget
• We want to predict future sales (Y) based on our planned investment (X)
• We can do prediction of Sales if we estimate or learn the parameters
The essence of Machine Learning is to estimate or learn the unknown parameters from available data

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Machine Learning : Learning Parameters – Setting the context
How do we learn parameters which enable us to do prediction ? We use Data !!!!!!!!!!!
Independent
Variables
X’s
Dependent Variable
Y
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13C
Learn all parameters
C M1 M2 M3 1
crim
zn
Indu
X Medv.

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Learning Parameters – Optimisation Dynamics : Gradient Descent
1 0.1 0.1 0.1 1
crim
zn
Indu
X Medv. =Medv.- Error
Assumed Parameters Values of each variable Estimated Y Real Y
Average
Error
Each
Parameter
+
or
-
X Variable
= Updated
Parameter
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13C
Optimized parameters corresponding to minimum error

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Machine Learning
Supervised
Regression
Predicting Sales
Classification
Image Recognition
Unsupervised
Clustering/
Association mining
Product
Associations
Reinforcement Learning
Recommender Systems
Movie Recommendation
in Netflix
Machine Learning – Types

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Machine Learning – Algorithms
Source : https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png

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Machine Learning in Daily Lives
Face Book Tagging
Application of Image recognition / Classification
Algorithms
Source :Google Images
Amazon Recommendations
Recommendation Engines / Collaborative filtering
Source :Google Images

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Data Science Process in Action @ Quadrant 4

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Machine Learning Journey
Source :http://www.kdnuggets.com/2016/10/big-data-science-expectation-reality.html

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Predictive Models for Battery Failure – Business Case
Design Bench Discovery Engine Solution Toolkit Value Realization
Business Problem
• Identification of failed
cases( Time instance &
battery type).
• Predicting the propensity for
failure.
Levers & Data Points
• Data Points
• Conductance
• Discharge voltage
• Discharge current
• Terminal voltage
• Temperature
• Model
• Massaging the data points
into data frames for
analysis.
Inferential Exploration
• Following the conductance
trail.
• Exploratory analysis using
an aggregating metric.
• Identification of trends
pointing to the problem.
• Introduction of other
variables like voltage and
current.
• Establishing relationship
between conductance and
voltage.
• Intuition for feature
engineering.
Feature Engineering
• Introduction of new
features related to voltage
and conductance
• Feature transformation
• Preparation of training,
validation and test sets
Modelling
• Initial unsupervised model
for validating the feature
space.
• Spot check of the ML
model
• Training the model
• Model evaluation ,fine
tuning and deployment of
the model
Outcome
• Agility in response
• Optimized Cost of
maintenance
• Reduction in down time
• Customer satisfaction

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Inferential Analysis and Predictive Analytics for School District, TX
Business
Discovery
Inferential
studies and
Feature
Engineering
Model building
and deployment1
Derive key business drivers and influencing factors
• Model Selection ( From
suitable classification/
regression models.)
• Training the model
• Model fine tuning
• Baseline metrics
• Model deployment onto
existing product
• Trends of key variables
across schools and
causal analysis
• Inter relationship
between variables
1
2
3
Predict potential student drop out. Inferential studies on factors affecting achievement
• Feature engineering which
includes
• Transformation of
existing features
• Introducing new
features

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Corporate Information

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Our Services

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Data Science - Predictive Analytics Process
Design Workbench
Identify business issues
Scope out project / PoC
Frame the analytic problem
Recommend tools/technologies
Discovery Engine
Facilitate data discovery
and Data diagnostics
Identify key drivers
High level solution framework
Solution Toolkit
Model training and validation
Select final robust solution
Value Realization
Value realization through predictive
analytics
Build data Products
Dashboard capabilities