项目和企业组织中的社交网络代写 BCPM0049代写 R代写
250BCPM0049: Social Networks in Project and Enterprise Organisations Assessment Brief 项目和企业组织中的社交网络代写 Your task is to interpret a dataset of your choosing from a network analysi...
View detailsSearch the whole station
应用数学计算代写 Read these instructions carefully!!! This project involves predicting what happens to a mortgage loans that have been purchased by FNMA during the
This project involves predicting what happens to a mortgage loans that have been purchased by FNMA during the 3rd quarter of 2001.
The data has 30 predictor variables described in the file PredictorsDescription.csv”. All 30 of these variables are known to FNMA when the loans were acquired. Recently obtained performance information about all of the loans has become available relatively recently (in around 2021). Some of the loans performed as lender would expect: monthly payments made on time up until performance data stopped being collected. Other loans were problematic in that the holder of the mortgage may have foreclosed (payments stopped, home was taken by the lender, etc..) In many instances, the holder of the mortgage sold the home and paid the outstanding balance owed before the mortgage term (15 years, 30 years, etc..)
Every loan has a 12 digit loan id LID.
FORCLOSED – a Boolean variable that indicates whether or not foreclosure took place
NMONTHS – a variable giving the number of months that the mortgage remained on the books by the lender
A large data set has been randomly split into
TrainingData.csv – this one has all 30 predictors, a loan ID (LID), and the two performance variables (FORCLOSED, NMONTHS ) for 588,490 loans.
TestDataYremoved.csv – this one has only the 30 predictors and loan ID (LID) for a different set of loans (196,164 of them).
Your task is to use the 31 predictors to make predictions about performance for the loans in the test set.
1) Predict whether the loan will foreclose or not by supplying a Boolean value (True for foreclosure, False for non-foreclosure)
2) Predict the accuracy of your predictions in 1)
3) Predict NMONTHS – this should be a number but needn’t be an integer
4) Predict the accuracy of your predictions in 3).
o Uploading jupyter notebooks you used to do your calculations
o Answering 3 questions in a Blackboard final submission assessment (see Three Questions link)
o the true positive rate (TPR) for your predictions in 1) – see definitions below
o and the false positive rate (FPR) for your predictions in 1) – see definitions below
o the MAD (mean absolute deviation) between predicted and true values of NMONTHS i.e. the average of the absolute difference between your predicted value of NMONTHS and the true value of NMONTHS, averaged over all loans in the test dataset.
For foreclosure prediction (since I know the true status of each loan) I can fill a 2×2 table with counts of where each loan falls in the following table based on its true and predicted foreclosure status:
So once you submit your predictions the four numbers in the table should add up to 196,164. I can then calculate the quantities
TPR=TP/(TP+FN)
FPR=FP/(FP+TN)
and you should be striving to make TPR high and FPR low.
更多代写:CS美国代考推荐 雅思作弊被抓 英国统计代上网课 英文期刊论文代写 北美留学生sci论文代写 怎么写reflection
合作平台:essay代写 论文代写 写手招聘 英国留学生代写
BCPM0049: Social Networks in Project and Enterprise Organisations Assessment Brief 项目和企业组织中的社交网络代写 Your task is to interpret a dataset of your choosing from a network analysi...
View detailsMGT 226 ONLINE BUSINESS ANALYTICS 线上商业分析代写 Question 1 (2 points) Descriptive analytics encompasses summarization and visualization of historical data using tables, charts, Qu...
View detailsMT5863 Semigroup theory: Problem sheet 1 Definition and basic properties 半群理论代写 Let S be a semigroup, and let e, z, u ∈ S. Then: (i) e is a left identity if ex = x for all x ∈ S; (ii)...
View detailsMATH5806 Applied Regression Analysis Mid-session Test 应用回归分析作业代写 Note: • This assessment is due Thursday 30th June (Week 5), 2:30pm and must be uploaded to Moodle. You have 1.5 ho...
View details