Business
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PAPPT.pptx
Predictive Analytics - An overview
Agenda
Introduction to Big Data.
What is Analytics?
Overview of Predictive
Analytics
Techniques.
Business Applications
of Predictive Analytics.
Predictive Analytics Tools in Market.
What is Predictive Analytics?
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
techniques.
the
help of data mining
Apply observed patterns to unknowns in the Past, Present or Future.
Things That Happen On Internet Every Sixty Seconds
Things That Happen Every Sixty Seconds
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.”
What is Analytics?
Data Analysis: OSEMN Process
OSEMN is an acronym that rhymes with “awesome”
Obtain Data
Scrub Data
Explore Data
Model Data
iNterpret Results
Predictive Analytics Process Cycle
Common Predictive Analytics Methods
•
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
(Contd.)
•
Association rule mining:
Mining items occurring together.
•
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.
Business Applications
Multi-channel
of
Predictive
Analytics
Renewable
Energy
Smarter Healthcare
Finance
sales
Factory Failures
Telecom
Traffic Control
Spam Filters
Fraud and Risk
Retail: Churn
Manufacturing
Trading Analytics
Business Applications (Contd.)
•
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.)
•
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.)
•
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.
