Customer Profiling using Data Mining
- 1. DATA MINING SUBMITTED BY : SHUBHAM GUPTA, SUMAN CHATTERJEE, SIDDHARTH TIU SUBMITTED TO : Dr. A.C.S. Rao
- 2. 3 1. What is Data Mining Data mining is the process of discovering interesting patterns (or knowledge) from large amounts of data. The data sources can include databases, data warehouses, the Web, other information repositories, or data that are streamed into the system dynamically.
- 3. Why Data Mining Credit ratings/targeted marketing: Given a database of 100,000 names, which persons are the least likely to default on their credit cards? Identify likely responders to sales promotions Fraud detection Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular customer? Customer relationship management: Which of my customers are likely to be the most loyal, and which are most likely to leave for a competitor? :
- 4. Data mining Process of semi-automatically analyzing large databases to find patterns that are: valid: hold on new data with some certainity novel: non-obvious to the system useful: should be possible to act on the item understandable: humans should be able to interpret the pattern Also known as Knowledge Discovery in Databases (KDD)
- 5. Applications Banking: loan/credit card approval predict good customers based on old customers Customer relationship management: identify those who are likely to leave for a competitor. Targeted marketing: identify likely responders to promotions Fraud detection: telecommunications, financial transactions from an online stream of event identify fraudulent events Manufacturing and production: automatically adjust knobs when process parameter changes
- 6. Applications (continued) Medicine: disease outcome, effectiveness of treatments analyze patient disease history: find relationship between diseases Molecular/Pharmaceutical: identify new drugs Scientific data analysis: identify new galaxies by searching for sub clusters Web site/store design and promotion: find affinity of visitor to pages and modify layout
- 7. Data Mining Techniques Classification Clustering Regression Association Rules
- 8. Classification Models Neural networks Statistical models – linear/quadratic discriminants Decision trees Genetic models 8
- 10. Technique for Classification Decision-Tree Classifiers Job Income Job Income Income Carpenter Engineer Doctor Bad Good Bad Good Bad Good <30K <40K <50K>50K >90K >100K Predicting credit risk of a person with the jobs specified.
- 11. Decision trees Tree where internal nodes are simple decision rules on one or more attributes and leaf nodes are predicted class labels. Salary < 1 M Prof = teacher Good Age < 30 BadBad Good
- 12. Decision Trees A decision tree T encodes d (a classifier or regression function) in form of a tree. A node t in T without children is called a leaf node. Otherwise t is called an internal node. 12
- 13. Internal Nodes Each internal node has an associated splitting predicate. Most common are binary predicates. Example predicates: Age <= 20 Profession in {student, teacher} 5000*Age + 3*Salary – 10000 > 0 13
- 14. Leaf Nodes Consider leaf node t: Classification problem: Node t is labeled with one class label c in dom(C) Regression problem: Two choices Piecewise constant model: t is labeled with a constant y in dom(Y). Piecewise linear model: t is labeled with a linear model Y = yt + Σ aiXi 14
- 15. Example Encoded classifier: If (age<30 and carType=Minivan) Then YES If (age <30 and (carType=Sports or carType=Truck)) Then NO If (age >= 30) Then YES 15 Minivan Age Car Type YES NO YES <30 >=30 Sports, Truck
- 16. Why Decision Tree Model? Relatively fast compared to other classification models Obtain similar and sometimes better accuracy compared to other models Simple and easy to understand Can be converted into simple and easy to understand classification rules 16
- 17. Pros and Cons of decision trees · Cons - Cannot handle complicated relationship between features - simple decision boundaries - problems with lots of missing data · Pros + Reasonable training time + Fast application + Easy to interpret + Easy to implement + Can handle large number of features
- 18. Consumer Profiling Businesses need to effectively leverage available data to improve customer acquisition and retention. We will explore how analytics tools such as decision trees can help with customer acquisition.
- 19. EXAMPLE A manufacturer of home improvement equipment wants to identify which existing customers are best candidates for a new product they are developing. A decision tree such as the one shown below
- 21. Clustering Group Data into Clusters Similar data is grouped in the same cluster Dissimilar data is grouped in the same cluster How is this achieved ? K-Nearest Neighbor A classification method that classifies a point by calculating the distances between the point and points in the training data set. Then it assigns the point to the class that is most common among its k-nearest neighbors (where k is an integer).(2) Hierarchical Group data into t-trees
- 22. Regression “Regression deals with the prediction of a value, rather than a class.” (1, P747) Example: Find out if there is a relationship between smoking patients and cancer related illness. Given values: X1, X2... Xn Objective predict variable Y One way is to predict coefficients a0, a1, a2 Y = a0 + a1X1 + a2X2 + … anXn Linear Regression
- 23. Association Rules “An association algorithm creates rules that describe how often events have occurred together.” (2) Example: When a customer buys a hammer, then 90% of the time they will buy nails.
- 24. Advantages of Data Mining Provides new knowledge from existing data Public databases Government sources Company Databases Old data can be used to develop new knowledge New knowledge can be used to improve services or products Improvements lead to: Bigger profits More efficient service
- 25. Uses of Data Mining Sales/ Marketing Diversify target market Identify clients needs to increase response rates Risk Assessment Identify Customers that pose high credit risk Fraud Detection Identify people misusing the system. E.g. People who have two Social Security Numbers Customer Care Identify customers likely to change providers Identify customer needs
- 26. Relationship with other fields Overlaps with machine learning, statistics, artificial intelligence, databases, visualization but more stress on scalability of number of features and instances stress on algorithms and architectures whereas foundations of methods and formulations provided by statistics and machine learning. automation for handling large, heterogeneous data
- 27. THANK YOU