This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications. Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT. Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1

1. Vijaykumar Adamapure
MachinePulse.
Predictive Analytics - An overview

2.  Introduction to Big Data.
 What is Analytics?
 Overview of Predictive Analytics Techniques.
 Business Applications of Predictive Analytics.
 Predictive Analytics Tools in Market.
Agenda

3. Gartner Hype Cycle

4. Things That Happen On Internet Every Sixty Seconds

5. Things That Happen Every Sixty Seconds

6. The 5 V's of Big Data
“Big data is high-volume, high-velocity and high-variety information assets
that demand cost-effective, innovative forms of information processing for
enhanced insight and decision making.”

7. Survey on Big Data Adoption Stages

8. What is Analytics?

9. OSEMN is an acronym that rhymes with “awesome”
Data Analysis: OSEMN Process
Obtain Data
Scrub Data
Explore Data
Model Data
iNterpret Results

10. Predictive analytics is the practice of extracting insights from the existing
data set with the help data mining, statistical modeling and machine
learning techniques and using it to predict unobserved/unknown events.
 Identifying cause-effect relationships across the variables from the
historical data.
 Discovering hidden insights and patterns with the help of data mining
techniques.
 Apply observed patterns to unknowns in the Past, Present or Future.
What is Predictive Analytics?

11. Predictive Analytics Process Cycle

12. • Regression:
Predicting output variable using its cause-effect relationship with
input variables. OLS Regression, GLM, Random forests, ANN etc.
• Classification:
Predicting the item class. Decision Tree, Logistic Regression, ANN,
SVM, Naïve Bayes classifier etc.
• Time Series Forecasting:
Predicting future time events given past history. AR, MA, ARIMA,
Triple Exponential Smoothing, Holt-Winters etc.
Common Predictive Analytics Methods

13. • Association rule mining:
Mining items occurring together. Apriori Algorithm.
• Clustering:
Finding natural groups or clusters in the data. K-means, Hierarchical,
Spectral, Density based EM algorithm Clustering etc.
• Text mining:
Model and structure the information content of textual sources.
Sentiment Analysis, NLP
Common Predictive Analytics Methods (Contd.)

14.  Need to check predictive model’s out of sample performance.
 Model Assessment: Hit Rate, Gini Coefficient, K-S Chart, Confusion
Matrix, ROC Curve, Lift Chart, Gain Chart etc.
Evaluating Predictive Models

15. Business Applications of Predictive Analytics
Factory Failures
FinanceSmarter Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading Analytics Fraud and Risk
Renewable
Energy
Spam Filters
Retail: Churn

16. • Supply Chain:
Simulate and optimize supply chain flows to reduce inventory.
• Customer Profiling:
Identify high valued customers and retain their loyalty.
• Pricing:
Identify the optimal price which will increase net profit.
• Human Resources:
Best Employees selection for particular tasks at optimal
compensation. Employee churn retention.
Business Applications (Contd.)

17. • Renewable Energy:
Energy forecasting, electricity price forecasting, Predictive
Maintenance, Operational cost minimization.
• Financial Services:
Approval of credit cards/ loan applications based on credit scoring
models, Options pricing, Risk analysis etc.
• E-Commerce:
Identify cross-sell and upsell opportunities, increase transactions
size, maximize campaign's response based CRM data.
Business Applications (Contd.)

18. • Product Quality Control:
Detect product quality issues in advance and prevent them.
• Revenue Performance:
Identify key drivers of revenue generation and optimization of
revenue.
• Fraud and Crime Detection:
Detect fraud , criminal activity, insurance claims, tax evasion and
credit card frauds.
• HealthCare:
Identify prevalence of particular disease to a patient based health
conditions.
Business Applications (Contd.)

19. Predictive Analytics Tools in Market

20. Thank you!