PLEASE HELP ASAP!!!!! i have excel files
ACCT 3130
You are required to turn in the adjusted data/excel files you used to answer the questions. Label each excel worksheet with the Question #s
Thus, you are required to turn in 4 files: Word file 2) Mortgage_2017 MT excel file Adjusted/Merged 3) RIA MT Excel file Adjusted 3) Loan Date MT Excel file Adjusted
Thus, you are required to turn in 4 files: 1) Mid-Term Exam - Supplemental Credit, Word file 2) Mortgage_2017 MT excel file Adjusted/Merged 3) RIA MT Excel file Adjusted 3) Loan Date MT Excel file Adjusted Use "Loan Data file to answer the following Questions la & 1b: The "Loan Data file includes AmountRequested, Month, Purpose, DebtToIncomeRatio, State, YearsofEmployment and Class columns. The data dictionary for the "Loan. Data file is available in Loan data data dictionary word file. Source: The data is obtained from LendingClub and is made available through the Data Analytics for Accounting textbook by Richardson, Teeter and Terrell (1st edition). Question 1a: (2 points) The DebtToIncomeRatio numbers are in decimal format for the APPROVE loans. For example, 0.2987 stands for 29.87% or .2589 stands for 25.89%. However, the information is in different format for the REJECT loans. For example, 25.58 stands for 25.58% for the REJECT loan. Convert the format of the DebtToIncomeRatio so that both APPROVE loans and REJECT loans would have similar format. Copy and paste the snapshot showing comparing APPROVE loan and REJECT loan converted DebtToIncomeRatio numbers (take screenshot where approve loans end and reject loans begin, or vice-versa): Question 1b: (2 points) Clean up the years of employment data under the YearQfEmployment column so that the data would be in numerical format. If the employment information is not available (n/a), then consider that the applicant has zero years of employment. Copy and paste the snapshot showing that the YearQfEmployment is in numerical format. Question 2: (3 points) Review the following classification results (BayesNet): Classifier Choose BayesNet -D- weka.classifiers.bayes.net.search.local.K2 ---P1-S BAYES -E weka.classifiers.bayes.net.estimate SimpleEstimator ---A 0.5 Classifier output LogScore ENTROPY: -87979.84301691004 LogScore AIC: -90150, 64381691004 Test options Use training set Supplied test set Cross-validation Folds 10 O Percentage split % 66 Set More options... Time taken to build model: 0.21 seconds === Stratified cross-validation === == Summary - Correctly Classified Instances 7473 Incorrectly Classified Instances 27 Kappa statistic 0.9928 Mean absolute error 0.0052 Root mean squared error 0.0578 Relative absolute error 1.0422 Root relative squared error 11.5939 Total Number of Instances 7500 99.64 0.36 % (Nom) Class Start Stop Result list (right-click for options) ) 17:26:07 - bayes.BayesNet === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure MCC 1.000 0.007 0.994 1.000 0.997 0.993 0.993 0.000 1.000 0.993 0.996 0.993 Weighted Avg. 0.996 0.004 0.996 0.996 0.996 0.993 ROC Area PRC Area Class 0.999 0.999 APPRO 0.999 1.000 REJEC 0.999 0.999 === Confusion Matrix === a b
