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Data Mining By Jason Baltazar, Phil Cademas, Jillian Latham, Rachel Peeler & Kamila Singh.

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Presentation on theme: "Data Mining By Jason Baltazar, Phil Cademas, Jillian Latham, Rachel Peeler & Kamila Singh."— Presentation transcript:

1 Data Mining By Jason Baltazar, Phil Cademas, Jillian Latham, Rachel Peeler & Kamila Singh

2 What is Data Mining? Data Mining is data processing using sophisticated data search capabilities and statistical algorithms to discover patters and correlations in large preexisting databases. 2 Broad Categories: Supervised & Unsupervised

3 Unsupervised Data Mining “Descriptive Modeling” Uncover patterns and relationships among data No predetermined parameters Observations after analysis Used to assist in making business decisions

4 Cluster Analysis “Automated Data Mining” Used to discover the segments or groups within a customer data set Determine classes of similar customers that naturally fit together Demographics Segmented Markets Marketing and Advertising

5 Supervised Data Mining “Predictive Modeling” Set goals and parameters prior to data mining Concentration: only relevant patterns Predict outcomes Anomaly Detection, Classification & Prediction, Regression, Analysis

6 Anomaly Detection Models built to specify “normal” ranges of results Fraud Detection Tax, insurance, credit card industries Prevent Identity Theft Detect breaches in computer security PayPal 15% of all e-commerce in the U.S.

7 Classification & Prediction Most common data analysis tool “Who will buy what, and how much will they buy?” Credit analysis / Credit Scoring – Who are my “good credit risks?” Based on spending habits, income, and/or demographics Can be used in customer segmentation, business modeling, credit analysis, etc.

8 Classification & Prediction Human Resources Turnover analysis, employee development, recruiting, training, and employee retention Determine the “value” of employees Fill leadership/management positions from within the organization Groom and promote based on a set of predetermined skills, attitudes, and competencies

9 Regression Analysis Statistics applies to data to make predictions i.e. How product price and promotions affect sales Marketing, pricing, product positioning, sales forecasting, advertising, human relations, customer service Objectives: market response modeling and sales forecasting

10 Text Mining Text Mining is the process of automatically processing text and extracting information from it Presidential election

11 Text Mining Applications Security Applications Biomedical Applications Online Media Applications Academic Applications

12 Data Mining Advantages Helps to reduce costs Provides improved and more detail oriented service Increases market effectiveness Beneficial to all industries

13 Data Mining Disadvantages Privacy Issues Access to personal information Security Issues Insufficient security systems Misuse of information & inaccurate information

14 Insurance & Healthcare Target marketing Helps to develop different plans and policies

15 Mobile Communication Helps develop a variety of different cell phone plans Target marketing

16 Data Mining Privacy Who has access to consumer personal information CVS Pharmacy & Marketing Companies

17 Data Mining Ethics: Consumers How far is too far? Trustworthy? Data is being collected & used Opt out boxes What are some solutions that give consumers control? Access to databases that have their information The right to change what information is available

18 Data Mining Ethics: Businesses Help enhance overall customer satisfaction Profit enhancer? Violation of privacy Sometimes partnered with marketing companies They also have access to private information

19 Conclusion ANY QUESTIONS?

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