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Data Mining
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Agenda
• What is Data Mining?
• Definition
• CRISP-DM
• Modeling Techniques
• How models are built and used?
• Data warehouse Vs Data mining
• Applications Of Data Mining
• Case Study - Telecom
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What is Data Mining?
• Interactive and iterative process
• To find underlying relationships and features in data
• Knowledge driven
• Enhance the value of existing information resources
• Predicting valuable information
• Data or knowledge discovery
• To predict future trend and behavior
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Definition
The process of finding previously unknown patterns
and trends in databases and using that information
to build predictive models.
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CRISP - DM
• CRISP-DM stands for CRoss Industry Standard
Process for Data Mining
• It is a industry and tool neutral data mining process
model
• This Process makes large data mining projects faster,
cheaper and more reliable
Check CRIP-DM’s home page for more details www.crisp-dm.org
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Phases in CRISP - DM
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Modeling Techniques
Predictive Models
• Neural Nets
• Rule Induction
• Linear and Logistic Regression
Example
Credit card Company can rate the customer before
issuing Credit card by using the predictive models.
Customer’s applications which are rated low by the
predictive models are rejected.
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Modeling Techniques
Association Rules
• APPRIORI
• GRI
Example
A leading Super market applies association
techniques in its transaction database and finds out items
which are often purchased together and comes up with new
bundled offer to promote its other non selling items.
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Modeling Techniques
Clustering Techniques
• Kohonen Networks
• K-Means
• Two Step
Example
A Insurance company groups similar customers
based on various parameters and launches special
promotional offers targeting each segment and achieves
higher response rate.
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How Models are built and used?
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Data Mining Process
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Data Warehouse Vs Data Mining
Data Warehouse
Data Mining
Contains historical
Process of finding hidden
information stored in the
information from large
form of relational database datasets.
which are acquired from
different sources.
Present information can
be acquired.
Future information can be
predicted.
Provides answers to
questions like
“Who is purchasing our
products?”
Provides answers to
questions like
“Who is not purchasing
our products?”
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Applications of Data Mining
• Market Segmentation
• Customer churn
• Fraud Detection
• Direct Marketing
• Interactive Marketing
• Market basket Analysis
• Trend Analysis
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Case Study - Telecom
Objective:
To retain the customer base for a telecom company by
creating a model using Neural network algorithm which will
predict which customers are probable churners
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Case Study - Telecom
Data Preparation
• Use the service usage data which contains summarized
information about present as well as past customers
• Prepare the data by removing missing values and deriving
new fields
• Partition the data as Training and Testing data
• Select only important fields as input fields with respect to
output field
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Case Study - Telecom
Modeling
• Apply Neural Network Algorithm to Training data and build
the model
• After creating the model apply Testing data in order to verify
the models accuracy
Deployment
• Deploy the final model to the current customer database
• The model Predicts ‘n’ no. of customers are probable
Churners in its current customer database
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Case Study - Telecom
End Result
The telecom company reacts immediately by sending
lucrative offers through SMS and other means to these
probable churners by then it prevents its customers from
losing to its competitors
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Finally….
Data mining lets you be Proactive rather than Retrospective
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