Dated: 29/12/2012 Contents Business objectives: The database corresponds to the sinking of the titanic on April the 15th 1912. It is part of a database containing the passengers and crew who were aboard the ship‚ and various attributes correlating to them. The purpose of this task is to apply the methodology of CRISP-DM and follow the phases and tasks of this model. Using the classification method in rapid miner and both the decision tree and KNN algorithms‚ I will create a training
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:- a) They help us to catch the customers but does-not helps us to catch the employees within the Company. Ans 2:- Jaeger use the Data Mining applications which catch the thieving employees within the Company. Hence those employee which gave more discount in billing‚etc could be easily caught. With the help of Data Mining‚ the whole company data from different branches can be centralized which help in tracking and maintaining the stock. Ans 3:- With the help of Data Mining‚ the company
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Learning and Data Mining Overview: Efficient asset allocation through statistical learning methods and comparison of methods for the creation of an index tracking ETF (Exchange traded fund) Datasets: The datasets are chosen from the website of the book “Statistics and Data Analysis for Financial Engineering” by David Ruppert. The book is mentioned as one of the references for this course. The two data sets chosen are 1. Stock_FX_Bond.csv 2. Stock_FX_Bond_2004_to_2006.csv The data includes
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan‚ Steinbach‚ Kumar © Tan‚Steinbach‚ Kumar Introduction to Data Mining 4/18/2004 1 Why Mine Data? Commercial Viewpoint O Lots of data is being collected and warehoused – Web data‚ e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions O Computers have become cheaper and more powerful O Competitive Pressure is Strong – Provide better‚ customized services for an edge (e.g
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Recommended Systems using Collaborative Filtering and Classification Algorithms in Data Mining Dhwani Shah 2008A7PS097G Mentor – Mrs. Shubhangi Gawali BITSC331 2011 1 BITS – Pilani‚ K.K Birla Goa INDEX S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. Topic Introduction to Recommended Systems Problem Statement Apriori Algorithm Pseudo Code Apriori algorithm Example Classification Classification Techniques k-NN algorithm Determine a good value of k References Page No. 3 5 5 7 14 16 19 24 26 2
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MBA 503.01 – Data Analysis and Decision Making Spring 2013 M ondays & Wednesdays : 1 0 : 00 a .m. – 1 1 : 2 0 a .m. H arriman Hall Room 10 8 M . Shane Higuera ‚ Ed.D. E - Mail: s hane@ sbawebsite.net T elephone & T ext : (631) 8 07 - 7904 Goals of the Course This course is an introduction to data analysis and decision making in business. In your career‚ you will often face situations in which a clear understanding of statistical thinking and decisionmaking methodology will be essential
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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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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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regression model to testing and validation dataset (output is in “LR_Output2”‚ “LR_Testscore2”‚ and “LR_ValidLiftChart2”). In testcore sheet‚ we can see the probability output we generated for each row from test data. Below shows the regression model and scoring summary. 3. a) the data of purchaser only is in “Purchasers_only” sheet b) Partition is shown in “Data_Partition2” sheet c) Multiple Linear regression output can be seen in “MLR_Output1”. Target variable is “spending”. We select every
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The importance of data for operations management and decision making In order to be able to make well guided decisions‚ one needs well based facts and therefore one is in continuous need of quality data. The same goes for operations management; data of substance is a must to run a company in its optimal levels of efficiency‚ effectiveness and capacity. The five levels of Data Quality Maturity according to Gartner are Aware‚ Reactive‚ Proactive‚ Managed and Optimized. Using these levels and applying
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