31250 Introduction to Data Analytics

Assignment 3: Data Mining in Action

Data Mining代写 This assignment is a practical data analytics project that follows on from the data exploration you did in assignment 2.

Due date                                      Friday, 11.59 pm, 7 June 2019

Marks                                      Out of 100, weighted to 50% of your final mark.

Submission format                                      A report in PDF (preferable) or MS Word Doc and an oral defence.

Filename                                      ida_a3_xx.pdf / ida_a3_xx.doc at UTSonline and ida_a3_xx.csv at Kaggle

where xx is your student id.

Report format                                      Around 10-12 pages with the information described below. Use 11 or 12 point Times or Arial fonts.

Submit to                                      UTS Online assignment submission button.

                                                          Please, make sure to call the filenames as described above.

Scenario Data Mining代写

This assignment is a practical data analytics project that follows on from the data exploration you did in assignment 2.

You will be acting as a data scientist at a consultant company and you need to make a prediction on a dataset. The dataset can be found on the UTSOnline site as well as on the Kaggle website (using the link that we will send to you by email).

You will be given one of the classifiers studied in class to work with.Data Mining代写

You need to build a classifier using that technique to predict the class attribute. At the very minimum, you need to produce a classifier using that method for the data. However, if you explore the problem very thoroughly (as you should do in industry), preprocessing the data, looking at different methods, choosing their best parameter settings and identifying the best classifier in a principled and explainable way, then you should be able to get a better mark.

If you choose to use KNIME and you show ‘expert’ use (i.e. exploring multiple classifiers, with different settings, choosing the best in a principled way and being able to explain why you built the model the way you did), this will attract a better mark. If you choose to use R or Python to build, optimise and test different models, this will also attract better marks.Data Mining代写

You need to write a short report describing how you solved the problem and the results you found. See below for requirements.

You also need to attend a short oral defense of your classifier of around 5 minutes where you show the classifier (e.g. using the KNIME workflow or Python/R code) and answer some questions about it. Details about oral defences will be given by email and in class.

Kaggle Data Mining代写

For this assignment you will use the Kaggle website (kaggle.com) to download your assignment data and also submit to your assignment solution. The report itself will be submitted on UTSOnline as for the other assignments. We will provide you a link by email, which contains an invitation for the project. You need to use the link to access the project, because the project is private only  for 31250. Sharing the competition with anyone irrelevant to the subject is strictly prohibited. When you submit to Kaggle you will need to make a login to Kaggle using your UTS email address. You won’t be  able  to  submit  to  the competition if you use a non-UTS email address.

You will find 3 datasets: a training set for training your model (it contains the target values), a test set for testing the model (it does not have the target values – you need to predict them) and a submission sample which shows you what the submission file to Kaggle should look like.

The assessment is real time. Data Mining代写

This means that as soon as you submit the file, Kaggle will assess the accuracy of your system based on AUC and provide you the result.You can submit multiple times, but Kaggle has a limit for the number of times you can do this per day.

Do not use the accuracy or AUC reported from Kaggle as a measure of your test error in the final competition and optimise to it. This is because Kaggle has two measures: a public measure, which it reports to you, and a private measure, which it keeps hidden. Instead, develop several models and estimate the test error yourself before submitting to Kaggle. Remember that your estimate of test error is just that: an estimate. The actual private measure will probably be a little bit different.

Data Mining代写
Data Mining代写

Data sets Data Mining代写

You can find the training dataset and the test dataset to evaluate the accuracy of your classifier on the Kaggle website using the link that we will provide to you by email.

Classification Task

Build a classifier that classifies the “Final_Y” attribute. You  can  do  different data pre-processing and transformations (e.g. grouping values of attributes, converting them to binary, etc.), providing explanation why you have chosen to do that. You may need to split the training set into a training, validation and test sets to accurately set the parameters and evaluate the quality of the classifier.

You can use KNIME to build classifiers. Feel free to use any other tool such as other classifiers in R, Weka, Python, Orange, scikit-learn or other pieces of software. If you do this, though, please explain more about your classifier – and be sure that you are producing valid results! You don’t need to limit yourself to the classifiers we used in class, but if you do you need to describe about them in your report and make sure you are producing valid results. Data Mining代写

A hint: usually it’s not a case of having a ‘better’ classifier that will produce good results. Rather, it’s a case of identifying or generating good features that can be used to solve the problem.

Assignment report Data Mining代写

In your report include the following information (10-12 pages):

  • The data mining problem, inputs, output;
  • The data preprocessing and transformations you did (ifany).
  • How you went about the problem
  • Classification techniques used and summary of the results and parametersettings
  • Theactual classifier that you selected – the type, its performance, how it solved the problem (if it makes sense for that kind of classifier), and reasons for selecting

On average each student will require between 24 and 36 hours to complete this assignment.


There will be a class prize for the submission that is at the Top 3 places in      the assignment scoreboard on Kaggle.

Assessment Data Mining代写

This assignment is assessed as individual work.

The report contributes up to 30 marks out of the 50. The marking criteria can be found on UTSOnline.

The oral defense contributes up to 20 marks of the 50. At the oral defense students need to submit their report to the examiner and answer questions about their solution showing the workflow (in KNIME) or the working code in Python, R or other tools. Students receive either 0, 10, 15 or 20 marks in the following way.

Students showing the baseline classifier they were assigned in KNIME and are able to satisfactorily answer questions about it will receive 10 out of 20. Data Mining代写

Students showing an investigation with many classifiers using Python/ R/KNIME, with basic data preprocessing, parameter estimation and model evaluation, will receive 15 marks out of 20.

Students  showing  an  in-depth  investigation  using  Python/R/KNIME  (multiple classifiers with valid data preprocessing, parameter estimation and model evaluation) will receive 20 marks out of 20.

Students who fail the oral defense will be permitted to undertake it once again. If they pass, they will receive a maximum of 10 out of 20.

Relationship to Objectives Data Mining代写

This assignment addresses subject objectives 3, 4, 5 and 6.

Return of Assignments

Marks for the oral defense will be received a short time following the defense itself. The mark for the written report will be given within 3 weeks of submission. Feedback on the report will be given only for students requesting it. Emails will be sent when marking is complete.

Academic Standards Data Mining代写

All text in your assignment should be paraphrased into your own words and referenced using the Harvard referencing style. Please refer to the Subject Outline for details about penalties for Academic Misconduct.

Late Penalties

Refer to the Subject Outline for details of the Late Penalty that may be applied to submitted work unless prior arrangements have been made with the subject coordinator.

Special Consideration Data Mining代写

You may apply for special consideration (SC) due to unforeseen circumstances, either before or after the due date, at http://www.sau.uts.edu.au/assessment/ consideration/online.html. The three basic reasons for SC are health, family, or work problems; “I haven’t finished yet” is not a valid reason. You must provide documentary evidence to support your claim, such as a doctor’s certificate, a statutory declaration, or a letter from your employer.


The assignments may be checked through the Turnitin ® Plagiarism Prevention system, for identifying unoriginal material, copied (without reference to the source) from an electronic source on the Internet, electronic libraries, other assignments. Data Mining代写

Data Mining代写
Data Mining代写

更多其他: prensentation代写 Case study代写 Academic代写 Review代写 Resume代写 Case study代写  心理学论文代写 哲学论文代写 计算机论文代写 毕业论文代写 论文代写

合作平台:天才代写 幽灵代写  写手招聘