Vijaykumar Adamapure

MachinePulse.

Predictive Analytics - An overview

Introduction to Big Data.

What is Analytics?

Overview of Predictive Analytics Techniques.

Business Applications of Predictive Analytics.

Predictive Analytics Tools in Market.

Agenda

Gartner Hype Cycle

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.”

Survey on Big Data Adoption Stages

What is Analytics?

OSEMN is an acronym that rhymes with “awesome”

Data Analysis: OSEMN Process

Obtain Data

Scrub Data

Explore Data

Model Data

iNterpret Results

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?

Predictive Analytics Process Cycle

• 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

• 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.)

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

Business Applications of Predictive Analytics

Factory Failures

Finance Smarter HealthcareMulti-channel

sales

Telecom

Manufacturing

Traffic Control

Trading Analytics Fraud and Risk

Renewable Energy

Spam Filters

Retail: Churn

• 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.

Business Applications (Contd.)

Predictive Analytics Tools in Market