2. Students should design, implement and test classification algorithms to achieve the best
classification performance on the given data set.
3. Every group should work on this project independently and submit a project report online
through Canvas before the due date. The report should include at least the following sections:
Section 1: Introduction (provide a brief description of the project including background on
machine learning, the data set description, the goals to achieve, a summary of the methods
used and what you have achieved).
Section 2: Methods (provide a detailed description of the methods used and how they are
Section 3: Results and Analysis (describe the performance indices used, the performance
evaluation methods and the classification results in the forms of charts, graphs and tables)
Section 4: Conclusions (present the conclusions based on the results)
Section 5: Appendix (include your python code and running results here)
Group members should work together in this project. Please specify the contributions of each
member to the project at the end of Section 4.
4. At least two different classification methods covered in this course should be used. One of the
following three methods must be used: Support Vector Machine method, Artificial Neural
Networks method and Random Forest method. The performance of the methods used should
be compared and results should be presented in the project report. On top of the basic version
of the methods, if you implement any additional measures (such as data cleaning,
normalization, hyperparameter optimization, etc.) to improve the performance of the
classification methods, please provide descriptions of those measures in your project report.
5. The programming language should be Python. The machine learning package that can be used is
sklearn. No other machine learning packages are allowed. But other supporting packages such
as NumPy, SciPy, Pandas and matplotlib can be used.
6. Submit runnable Python code of the project with your report. The instructor may run your
program to verify the results.
7. The final project will be graded based on the submitted report (the presentation quality of the
8. The number of pages of the report should be limited to 7 pages excluding the Appendix section.
The report must be in pdf format.
9. The data set is the red wine quality data set from UCI Machine Learning Repository:
https://archive.ics.uci.edu/ml/datasets/wine+quality. Please download winequality-red.csv from
the data folder of the site. The data set and description are also available on Canvas.
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