Predictive analysis and modelling
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Transcript of Predictive analysis and modelling
• S – 36 LALIT MOHAN THURIMELLA
• S - 41 MANOJ KUMAR• S – 82 SUNIL KUMAR• S – 52 P SIVIAH• S - 94 SOMESH GILANI
INTRODUCTION
DEFINITION, DESCRIPTION & BUSINESS APPLICATIONS
DRIVERS FOR PREDICTIVE ANALYTICS
PREDICITVE ANALYTICS VS FORECASTING
PREDICTIVE MODELLING
GAZING AT FUTURE
FUTURE IN OUR HANDS
IS A DATA SCIENCE
A MULTIDISCIPLINARY SKILL SET ESSENTIAL FOR SUCCESS IN
BUSINESS, NONPROFIT ORGANIZATIONS & GOVERNMENT
INVOLVES SEARCHING FOR MEANINGFUL RELATIONSHIPS AMONG VARIABLES & REPRESENTING THOSE RELATIONSHIPS IN MODELS
RESPONSE VARIABLES
• THINGS WE ARE TRYING TO PREDICT
EXPLANATORY VARIABLES OR PREDICTORS
• THINGS WE OBSERVE, MANIPULATE, OR CONTROL THAT COULD RELATE TO THE RESPONSE
VARIABLES MODELS
REGRESSION
• PREDICTING A RESPONSE WITH MEANINGFUL MAGNITUDE
• QUANTITY SOLD, STOCK PRICE, OR RETURN ON INVESTMENT
CLASSIFICATION
• PREDICTING A CATEGORICAL RESPONSE
• WHICH BRAND WILL BE PURCHASED?
• WILL THE CONSUMER BUY THE PRODUCT OR NOT?
• WILL THE ACCOUNT HOLDER PAY OFF OR DEFAULT ON THE LOAN?
• IS THIS BANK TRANSACTION TRUE OR FRAUDULENT?
FORECASTING SALES FOR MARKET SHARE
FINDING A GOOD RETAIL SITE OR INVESTMENT
OPPORTUNITY
IDENTIFYING CONSUMER SEGMENTS AND TARGET MARKETS
ASSESSING THE POTENTIAL OF NEW PRODUCTS OR RISKS ASSOCIATED WITH
EXISTING PRODUCTS
USES
MOST ORGS APPLY PA TO CORE FUNCTIONS THAT PRODUCE REVENUE USE PA TO INCREASE
PREDICTABILITY
USE PA TO CREATE NEW REVENUE OPPORTUNITY
OF ORGS USE PA FOR CUSTOMER SERVICES
TOP 5 SOURCES OF DATA TAPPED FOR PA
SALES
MARKETINGCUSTOMER
PRODUCT
FINANCIAL
COMPANIES USE SOCIAL MEDIA
DATA
USE RESULTS OF PA FOR PRODUCT
RECOMMENDATIONS AND OFFERS
ASSERT THAT PA WILL HAVE MAJOR POSITIVE IMPACT ON THEIR ORG
OF ORG WHO USE PA HAVE REALIZED A COMPETITIVE ADVANTAGE
WITH REAL TIME PA YOU CAN MAKE SURE YOUR COMPANY DOESN’T MISS IT’S WINDOW OF OPPORTUNITY
CUSTOMER-RELATED ANALYTICS SUCH AS RETENTION ANALYSIS
AND DIRECT MARKETING
• PREDICT TRENDS
• UNDERSTAND CUSTOMERS
• PREDICT BEHAVIOUR
• PROVIDE TARGETED PRODUCTS
• COMPETITIVE DIFFERENTIATOR
• REDUCE FRAUDS
BUSINESS PROCESS REASONS
• PREDICTIVE ANALYTICS TO DRIVE BETTER BUSINESS PERFORMANCE
• DRIVE STRATEGIC DECISION MAKING
• DRIVE OPERATIONAL EFFICIENCY
• IDENTIFY NEW BUSINESS OPPORTUNITIES
• FASTER RESPONSE TO BUSINESS CHANGE
Based on survey: TDWI 2012
Based on survey: TDWI 2012
LACK OF UNDERSTANDING OF
PREDICTIVE ANALYTICS
TECHNOLOGY
LACK OF SKILLED PERSONNEL
INABILITY TO ASSEMBLE
NECESSARY DATA—INTEGRATION ISSUES
NOT ENOUGH BUDGET
BUSINESS CASE NOT STRONG ENOUGH
INABILITY TO ASSEMBLE
NECESSARY DATA—CULTURAL ISSUES
THE TECHNOLOGY IS TOO HARD TO USE
DECISION TREES
Process of predicting a future event based on historical data
Educated Guessing
Underlying basis of all business decisions Production
Inventory
Personnel
Facilities
FORECASTING
• Predict the next number
a) 3.7, 3.7, 3.7, 3.7, 3.7, ?b) 2.5, 4.5, 6.5, 8.5, 10.5, ?c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed.
A commonplace example might be estimation of some variable of interest at some specified future date.
• The term "forecasting" is used when it is a time seriesand we are predicting the series into the future. Hence"business forecasts" and "weather forecasts".
• Prediction is the act of predicting in a cross-sectionalsetting, where the data are a snapshot in time (say, aone-time sample from a customer database).
• Here you use information on a sample of records topredict the value of other records (which can be avalue that will be observed in the future).
• Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element.
• In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter.
• For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
• Prediction is generally more about classification problems. In sales, these could be at different stages of the customer lifecycle.
– At acquisition stage - Predict whether you could be my potential customer.
– At service stage - Predict whether you would buy my cross-sell/up-sell offer.
– At the retention stage - Predict whether you would remain my customer or not.
• Forecasting is more about understanding how my sales would be given the historic trend, seasonal effects (if at all) etc etc.
Both are very different and different predictive techniques are applied to solve each of the above problems.
Prediction is a generic term for gaining future knowledge on diverse aspects using diverse predictive techniques and diverse
methods (e.g. numeric forecasting, predicting purchase patterns,
predicting attrition causes in sales decline)
Forecasting is jut one of multiple predictive methods, usually referred to predicting the future state of a variable in a defined future time (sales revenue for the next X months, cost structure for the following year, etc.).
“Forecasting is about out-of-sample observations while prediction is about in-
sample observations”
…process by which a model is created or chosen to try to best predict the probability of an outcome
Predictive modelling is a process used in predictive analytics to create a statistical model of future behaviour
Fundamentals of Predictive Modelling• Data Collection• Data Extraction/transformation• Predictive Model• Business Understanding
Functionality Algorithm Applicability
Classification Logistic Regression
Decision Trees
Naïve Bayes
Support Vector Machine
Response Modeling
Recommending “Next likely product”
Employee retention
Credit Default modelling
Clustering Hierarchical K-means Customer segmentation
Association rules Apriori Market Basket analysis
Regression analysis to predict the result of a categorical dependent variable based on one or more predictors or independent variables
Useful to analyze and predict a discrete set of outcomes like
• success/failure of new product
• Likelihood of customer retention/loss
Logistic Regression, the connection between the categorical dependent variable and
the continuous independent variables is measured by changing the dependent
variable into probability scores
Y = b0 + b1x1 + b2x2 + ……………………….. + bkxk + E
Y = Dependent variable
b0 = Constant
b1 = Coefficient of variable X1
x1 = Independent Variable
E = Error Term
• Seven reasons you need predictive analytics today: Eric Segal, PhD• Predictive Analytics for Business Advantage. Fern Halper• www.predictionimpact.com• Wikipedia• www.slideshare.com