Lecture 7_Data Mining and Predictive Analytics.pptx -...

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1 Lecture 7 Data Mining and Predictive Analytics Dr Marten Risius E-mail: [email protected] Room: Joyce Ackroyd R516 BISM7233 Data Analytics for Business Semester 2, 2019
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2 Recap: Business Analytics Framework
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3 Agenda for today What is Data Mining Data types Predictive analytics Regression Classification Logistic Regression Decision Trees
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4 What is Data Mining?
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5 Activity Click on this link to access a dataset on passengers that boarded Titanic Titanic Dataset Review the dataset and propose an idea on how the dataset can be used.
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6 What is Data Mining? Data explosion problem The data is abundant. The data is being warehoused. The computing power is affordable. Drowning in data, but starving for knowledge! The competitive pressure is strong. Solution: data mining Extract interesting and useful knowledge from the data Detect and reduce fraudulent activities, identify customer buying patterns, reclaim profitable customers, identify trading rules from historical data, market-basket analysis, etc. Data Sources and Volumes are Increasing Traditional Analysis is cumbersome Automate the generation of Patterns
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7 Example: The Power of Data Mining
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8 A Definition : Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, valid, previously unknown, potentially useful and ultimately understandable) patterns or knowledge from large sets of data Prediction of meaningful trends and relationships Discovery of useful summaries of data Alternative names Data Analytics, Data Science, Knowledge discovery in databases (KDD), knowledge extraction, Data/Pattern analysis, Data Archeology, Data Dredging, Information Harvesting, Business Intelligence, AI etc.
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9 Data Mining applications Recommendations (eg Amazon) Loan decisions (finance) Predicting if a phone contract will be renewed Understanding WEB click patterns Developing Political Campaigns
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10 Methodology - CRISP DM Methodology for planning a data mining project Just as the BA Framework is a process for implementing analytics, CRISP DM is an approach for implementing data mining. Cross Industry Process for Data mining Note that data mining is an iterative process - Why?
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11 Association with BA Framework Solve 80% of the decision-making problems Solve 20% of the decision-making problems
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12 CRISP DM 12 1. Business Understanding Overview the process Data available and theorise activity 2. Data Understanding What data is available Understand data types and the role they play 3. Data Preparation Organise the data for modelling Do assumptions around the data materialise in our dataset? 4. Modelling Carry out data ming (and tune) 5. Evaluation Test in against production system 6. Deployment
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13 Two Goals of Data Mining Prediction forecast or deduce the value of an attribute (or attributes) based on values of other attributes Classification, regression techniques Description (Information)
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