Show Me Potential Customers Data Mining Approach Leila Etaati

10 years experience in SQL server PhD students in Information System Department, Business School University of Auckland Lecturer and Tutor of BI and database System design in University of Auckland i

Solving Real-World Challenges with MS Data Mining (DEMO) Descriptive AnalysisPredictive AnalysisEnhance.NET Application with Data Mining Exploring Data Mining Algorithms Introduction to Microsoft Data Mining Solution Introduction to Data Mining Agenda

Introduction to Data Mining

Data mining : Marketing Department

DM Types of Analysis?

Data Mining Life Cycle

Introduction to Microsoft Data Mining Solution

Mining Structure

Train and Test

Microsoft Decision Tree Algorithm A descriptive and predictive algorithm Accept both discrete and continues attributes, needs a key column, input column It employs feature selection technique to guide the selection Who is going to buy the bike Age>40Have 0 child Have 1-2 Childs Have 3 or more Childs Age<=40Have 0 Childs Have 1-2 Childs Have 3 or more Childs Age Number of Child at home

Clustering Algorithm  Categorize items in groups with similar attribute values  employs K-means algorithm and Expectation Maximization (EM)  Mostly descriptive but can be predictable

Microsoft Naïve Bayes Algorithm A classification Algorithm Uses Bayesian technique for categorization. It is useful for finding attributes that effects on generating a result, such as finding prospective buyers of a product.(descriptive)

Association Rule Identifies association between attributes. One of the most common usage of this is to do a market basket analysis with this algorithm

Time Series For time based analysis. Such as predicting sales for next couple of months.

Bike Buyers Number of Car at Home Buying the Bike

Demo: Microsoft Decision Tree, Clustering and Naïve Bayes Leila Etaati

Content Type Discrete data values are separate such as colour values: Red, Yellow, and Blue Continuous data values are continues; such as Age, or salary. Cyclical data values are in a cyclic order, such as days of week. Ordered data values are in a sequential order; such as days of month. Discretized data values are continues, but bucketed into categories and as a result behave as discrete.

Accuracy Charts Lift Chart Profit Chart Classification Matrix Cross Validation

Demo: Finding the Best Algorithm Leila Etaati

Demo: Prediction with DMX Leila Etaati

DEMO: Enhance.NET Application with Data Mining

CREATE MINING MODEL TravelBudgetPrediction ( traveller_ID long KEY, Year TEXT DISCRETE, Quarter TEXT DISCRETE, mode TEXT DISCRETE, country TEXT DISCRETE, purpose TEXT DISCRETE, package TEXT DISCRETE, Age TEXT DISCRETE, Sex TEXT DISCRETE, Duration TEXT DISCRETE, Visits long DISCRETE, Nights long DISCRETE, Spend long DISCRETE PREDICT) USING MICROSOFT_DECISIONTREE;

DMX Code with.Net (predict the travel Budget)

Demo: Microsoft Association Rule Leila Etaati

Summary Solving Real-World Challenges with MS Data Mining (DEMO) Descriptive AnalysisPredictive AnalysisEnhance.NET Application with Data Mining Exploring Data Mining Algorithms Introduction to Microsoft Data Mining Solutions Introduction to Data Mining

References to Study More  Data Mining Tutorials in MSDN:  Data Mining Algorithms in MSDN:  Data Mining with SQL Server 2008 Book:

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