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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 below. You need to build classifiers using the techniques covered
in the workshops 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 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 show
'expert' use either KNIME or Python (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.
You need to write a short report describing how you
solved the problem and the results you found. See below
for the requirements for the report. You also need to attend a short oral defence 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
the oral defences will be given by email and in class. Using Kaggle
The Kaggle Competition will be available at a later
time. Here is the link:
https://www.kaggle.com/t/1029b1d1a4024845b3cb6ab37bbcf45b
Datasets
Below you will find 3 datasets: a training dataset for
training and optimising your model (it contains the target
values), an "unknown" dataset for the final model
assessment (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, "ID" and "label". These
datasets can also be found on the Kaggle competition
page under the "Data" tab.  Assignment3-TrainingDataset.csvLinks to an external
site.  Assignment3-UnknownDataset.csvLinks to an external
site.  a sample
submission kaggle_submission_sample.csvLinks to an
external site. The attribute description for the dataset is similar to that
from assignment 2: head_description.csvLinks to an
external site. The Kaggle competition link is here: see
below. https://www.kaggle.com/t/1029b1d1a4024845b3cb6ab
37bbcf45bLinks to an external site. Assessment
Assessment is real-time. This means that as soon as you
submit the file, Kaggle will assess the performance of your
classifier and provide you with 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 measure of performance reported by
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.