Practical Data Analytics Project & Python Report Writing - IT Assignment Help

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.

 Dataset

Below you will find 3 datasets: a 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

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