------------------------------------------------- Tzu Han Hung (Vivian) CASE 2 1. Estimated profit by random selection Expected spending per catalog mailed = 0.053 * $103 = $5.46 Expected Gross Profit by random select= (5.46-2)*180‚000 = $622‚800 2. a) We applied partition to “All_data” sheet‚ and partition output is shown in “Data_Partition1” b) Logistic regression output can be seen in “LR_Output1”. Target variable is “purchase”. We select every variable except sequence_number(meaningless
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Title: “Data Mining: The Mushroom Database” Author: Hemendra Pal Singh* In this review “Data Mining: The Mushroom Database” is focuses in the study of database or datasets of a mushroom. The purpose of the research is to broaden the preceding researches by administer new data sets of stylometry‚ keystroke capture‚ and mouse movement data through Weka. Weka stands for Waikato environment for knowledge analysis‚ and it is a popular suite of machine learning software written in Java‚ developed at
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Introduction to Data Mining Summer‚ 2012 Homework 3 Due Monday June.11‚ 11:59pm May 22‚ 2012 In homework 3‚ you are asked to compare four methods on three different data sets. The four methods are: • Indicator Response Matrix Linear Regression to the Indicator Response Matrix. You need to implement the ridge regression and tune the regularization parameter. The material of this algorithm can be found in Page 103 to Page 106 in the book ”The Elements of Statistical Learning” (http://www-stat
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Mid Term Exam 15.062 Data Mining Problem 1 (25 points) For the following questions please give a True or False answer with one or two sentences in justification. 1.1 A linear regression model will be developed using a training data set. Adding variables to the model will always reduce the sum of squared residuals measured on the validation set. 1.2 Although forward selection and backward elimination are fast methods for subset selection in linear regression‚ only step-wise selection is guaranteed
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Use of Data Mining in Fraud Detection Focus on ACL Hofstra University Abstract This paper explore how business data mining software are used in fraud detection. In the paper‚ we discuss the fraud‚ fraud types and cost of fraud. In order to reduce the cost of fraud‚ companies can use data mining to detect the fraud. There are two methods: focus on all transaction data and focus on particular risks. There are several data mining software on the market‚ we introduce seven
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3. DATA MINING TECHNIQUES 3.1 NECESSITY OF DATA MINIING DATA Data is numbers or text which is a statement of a fact. It is unprocessed and stored in database for further analysis. Operational and transaction data such as cost and sales‚ is essential to modern enterprise’s internal environment. Non-operational data such as competitors’ sales and forecasting data‚ is responsible for analysis of external environment. INFORMATION Information is generated through data mining so that it becomes
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Systems The goal of the term project is to develop a useful and viable prediction or classification model based on data. You will need to develop a research question‚ which you refine further based on the availability of data. You may need to merge multiple data sets together. Process: • Each team of 2 or 3 students will work on a business problem involving data analysis with real data. The project will focus on classification and prediction methods we covered during the semester. • A presentation
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Case StudyWhat Can Businesses Learn From Text Mining?Text mining is the discovery of patterns and relationships from large sets of unstructured data – the kind of data we generate in e-mails‚ phone conversations‚ blog postings‚ online customer surveys‚ and tweets. The mobile digital platform has amplified the explosion in digital information‚ with hundreds of millions of people calling‚ texting‚ searching‚ “apping” (using applications)‚ buying goods and writing billions of e-mails on the go.Consumers
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Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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Excellence for Data Mining in Egypt By: Aref Rashad I- Introduction The convergence of computer resources connected via a global network has created an information tool of unprecedented power‚ a tool in its infancy. The global network is awash with data‚ uncoordinated‚ unexplored‚ but potentially containing information and knowledge of immense economic and technical significance. It is the role of data mining technologies arising from many discipline areas to convert that data into information
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