data mining 1

Total 5 questions – Each question 200 words – 1000 words total; No plagiarism please (Please see attached document for questions/See below)

1.Choose the area of your preference, whatever you would like to describe in a dataset and explain using data mining.For example: actresses/actors, food, movies, sports, music bands, or anything you want.

Create a data file in .arff format containing about 20 entries, each described by

about 4 attributes, with the last attribute containing your preference (class attribute), e.g.

@relation food

@attribute calories numeric

@attribute taste {sweet, sour, bitter, salty}

@attribute course {appetizer, main, dessert, drink}

@attribute vegetarian {yes, no}

@attribute like_it {yes, no}

@data

100, sweet, dessert, yes, yes%icecream

80, bitter, drink, yes, yes%beer

2, sweet, dessert,yes, no%cake

Compare 3 algorithms for classification of your data: decision trees, a classification or an association rule learner, and naive Bayes. For each algorithm check what the error is (which algorithm can explain your personal liking the best), and observe the generated rules (do they tell you anything interesting?).

2.Use the following learning schemes to compare the training set and 10-fold stratified cross-validation scores of the labor data (in labor_neg_nominal.arff):

•k-nearest neighbours (IBk) with decision trees (j48.J48)

•k-nearest neighbours (IBk) with decision trees j48.J48 with option -M 3, so that each leaf has at least 3 instances.

A)What does the training set evaluation score tell you? B)What does the cross-validation score evaluate?

C)Which one of these models would you say is the best?Why?

3.Use the following learning schemes to analyze the Titanic data (in titanic.arff).

C4.5- weka.classifiers.j48.J48

Association rules-weka.associations.apriori

Decision List- weka. Classifiers.PART

A)What is the most important descriptor (attribute) in titanic.arff?

B)How well were these methods able to learn the patterns in the dataset? Quantify your

answer?

C)Compare the training set and 10-fold cross-validations scores of the methods.

D)Would you trust these models?Did they really learn what was important to survive the

Titanic disaster?

E)Which one would you trust more, even if just very slightly? Why?

4.Choose one of the following three files: soybean.arff, autoprice.arff, hungarian, zoo.arff or zoo2_x.arff and use any two schemas of your choice to build and compare the models. Which one of the models would you keep?Why?

5.Use the Association rule learner APRIORI method to find the association rule in the Weather.nominal data set.How many rules did it produce?How large are the item sets? What was the largest one? What happened when you increased/decreased the confidence level? What about the number of rules?What happens when you increase the confidence parameter to 2?Why?

Place your order
(550 words)

Approximate price: $22

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more
error: Content is protected !!