Problem 1
Use the data set Default.csv which has 10000 observations on the following 4 variables:
• default – A factor with levels No and Yes indicating whether the customer defaulted on
their debt• student – A factor with levels No and Yes indicating whether the customer is a student
• balance – The average balance that the customer has remaining on their credit card
after making their monthly payment• income – Income of customer
Apply logistic regression, linear discriminant analysis, quadratic discriminant analysis and Knearest neighbor classification methods to predict customers that are likely to default in
DefaultPredict.csv dataset.Problem 2
Use the Lecture_7_data.zip file.
Certain words like “and,” “the,” and “of,” are very common in all English sentences and are not
very meaningful in deciding spam/nonspam status, so these words have to be removed from
the emails. Download a list of stop words from http://www.ranks.nl/resources/stopwords.html
and remove them from the emails (word list).Do the following;
• Transform a message body into a set of the words.
• Combine the words across messages into a bag of words.• Tally the frequencies in the training data of words in spam and ham separately to
estimate the probability of a word appears in a message given it is spam (or ham) from
the proportion of spam (or ham) messages containing that word.• Estimate the likelihood that a new test message is spam (or ham) given its contents, i.e.,
given the message’s words compute the naïve Bayes version of the log likelihood ratio.• Find a threshold for the log likelihood ratio, where a message with a value above the
threshold is classified as spam. Choose this threshold by examining the error rates for
the test data.Compare the classification results obtained by removing the stop words with the case when
you don’t remove words from analysis.Problem 3
Follow the example in Lecture 5 R code to analyze the pair trading strategy for several pairs
taken from the following list: PEP, KO, DPS. Include transaction costs of 0.02% (or 2 basis
points) for each transaction. Discuss the results
2, Assignment, FE-582, solved, –
[SOLVED] Fe-582 – assignment 2
$25
File Name: Fe-582_–_assignment_2.zip
File Size: 216.66 KB
Only logged in customers who have purchased this product may leave a review.
Reviews
There are no reviews yet.