Concordia Institute for Information Systems Engineering INSE 6220 — Fall 2021 Advanced Statistical Approaches to Quality Final Course Project — Due Date: December 7, 2021 Purpose: The purpose of the...

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Concordia Institute for Information Systems Engineering INSE 6220 — Fall 2021 Advanced Statistical Approaches to Quality Final Course Project — Due Date: December 7, 2021 Purpose: The purpose of the INSE 6220 term project is for students to apply advanced statistical quality control tools and techniques that are discussed in class throughout the semester using Python. The term project must be done individually. Instructions: You must submit your project report and Jupyter Notebook electronically through the Moodle learning management system: https://moodle.concordia.ca • Make sure to write your name and ID number on the first page of your project report. • E-mail submissions of the project will NOT be accepted. • The due date is December 7, 2021, before midnight. Potential Data Sets: You should be able to identify a good potential data set based on other work within your research group, or within your laboratory, or from the literature, or previous experience. This will be an excellent way to have relevant data for use in the project. Some data sets are available at: https://mldata.org/repository/data/ http://archive.ics.uci.edu/ml/datasets.html https://www.kaggle.com/datasets http://www.statsci.org/datasets.html https://github.com/awesomedata/awesome-public-datasets https://github.com/plotly/datasets Final Written Report (due on December 7, 2021): You will turn in a written comprehensive report about your work, together with the Jupyter Notebook you pro- duced for your project. The written report should be between 7 and 10 pages in length, formatted in two columns per page format. You may use IEEE templates for Microsoft Word or LaTeX available at: https://www.ieee.org/conferences/publishing/templates.html Sample Project Report and Jupyter Notebook are available on Moodle. Evaluation: The project report consists of two parts, and constitutes 35% of your course grade as follows: • 15% for applying and analyzing the results of principal component analysis on your data set. • 10% for applying and analyzing the results of any machine learning algorithm on your data set: https://pythonprogramming.net/machine-learning-tutorial-python-introduction/ https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ https://towardsdatascience.com/predict-employee-turnover-with-python-da4975588aa3 https://github.com/susanli2016/Machine-Learning-with-Python • 10% for the Jupyter Notebook.