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31250-Introduction to Data Analytics

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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 need to build classifiers using the techniques covered in the lecture 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. 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 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 32130. 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. 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 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 sets 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 “QuoteConversion_Flag” 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 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. 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.

31250-Introduction to Data Analytics

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