Country : Australia

Assignment Task

 


Scenario

 

This assignment is a practical data analytics project. 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 below.

 

You need to build classifiers using the techniques covered in the lectures to predict the class attribute. At the very minimum, you need to produce a classifier for each method we have covered. However, if you explore the problem very thoroughly (as you should do in the industry), preprocessing the data, looking at different methods, choosing their best parameters 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 report describing how you solved the problem and the results you found. See below for requirements.

Below you will find 3 datasets: 

 HousingDataset for training your model (it contains the target values), a UnknownDataset 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 file submitted to Kaggle should look like. In particular, you will need to set the column names in your submission file correctly - that is, "Row ID" and "Predict- Qualified".

 

Classification task

 

Build a classifier that classifies the “QUALIFIED” attribute - with 0 if it is not qualified (U), and 1 for qualified (Q). You can do different data pre-processing and transformations (e.g. grouping values of attributes, converting them to binary, etc.), 

providing explanations for why you have chosen to do that. You may need to split the HousingDataset into training and validation/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 R, Weka, Python, Orange, scikit-learn or other software. If you do this, though, please explain more about your classifier - and be sure that you are producing valid results!

You dont need to limit yourself to the classifiers we used in class, but if you do use other classifiers you need to describe them in your report and make sure you are producing valid results. A hint: usually it's not a case of having a  classifier that will produce good results. Rather, ;s a case of identifying or generating good features that can be used to solve the problem.
 
Assignment report and submission Report Your report should include the following information:

 

  1.  A description of the data mining problem;
  2.  The data preprocessing and transformations you did (if any);
  3.  How you went about solving the problem;
  4. Classification techniques used and summary of the results and parameter settings;
  5. The best classifier that you selected - the type, its performance, how it solved the problem (if it makes sense for that type of classifier), and reasons for selecting it;
  6.  Reflection: One page reflecting on your learning in the assignment. What did you learn about data mining and yourself as a result of doing the assignment? How would you approach the problem differently if you were to do it again? The more incisive and thoughtful your reflection is, the better your mark.


 

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  • Uploaded By : Roman
  • Posted on : November 21st, 2018
  • Downloads : 257

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