[SOLVED] Cpsc 483 project 1

In this project you will familiarize yourself with Python and Jupyter by implementing a
rudimentary outlier detection algorithm.
The project may be completed individually, or in a pair of two (2) students as long as both
students are enrolled in the same section of the course.

Platforms
For this project (and, in general, for most machine learning or data science projects) you will
need a Jupyter notebook with Python 3. Jupyter allows you to create documents mixing text,
equations, code, and visualizations.

The Jupyter project itself recommends Anaconda if you intend to run notebooks locally on a
laptop or desktop computer. Alternatively you may use a cloud service such as Google Colab
that offers Jupyter notebooks online.

Libraries and Code
This project must be implemented in pure Python with the Python Standard Library, without
recourse to third-party libraries.
Code from A Whirlwind Tour of Python may be reused. All other code and the results of
experiments must be your own original work or the original work of other members of your team.

Dataset
The file participants.csv contains meeting attendance data reported by Zoom for the first
three weeks of a course. Each row contains the name of a student along with the number of
minutes that the student was logged in to the course Zoom meeting. (The names of students
have been changed to protect the innocent.)

Experiments
Run the following experiments in a Jupyter notebook, performing actions in code cells and
reporting results in Markdown cells.
1. Use the csv module to load and examine the dataset.
2. Find the quartiles for Week 1. You may wish to consult the Python Sorting HOW TO
document for help manipulating the data.
3. In order to record attendance, we want to find the students who logged into the Zoom
meeting but did not attend the entire lecture. In order to do this, we can look for outliers
in the data.
Tukey’s fences are a simple method to define outliers in terms of the interquartile range.
Use this method with k = 1.5 to find the outliers in the Week 1 attendance data.

4. Repeat experiments (2) and (3) for Weeks 2 and 3.
5. Consolidate your code from experiments (2) through (4) into a Python function named
tardy(). You may wish to define other functions as well.
This function should list the name and attendance statistics for any student whose
attendance in any week falls below the bottom fence for that week. Tardy students
should be listed in the same order as they appear in the original .csv file.

Submission
A Markdown cell at the top of the notebook should include project summary information as
described in the Syllabus for README files.
Since you may be actively editing and making changes to the code cells in your notebook, be
certain that each of your code cells still runs correctly before submission. You may wish to do
this by selecting Run All from the drop-down menu bar.

Submit your Jupyter .ipynb notebook file through Canvas before class on the due date.
If the assignment is completed by a pair, only one submission is required. Be certain to identify
the names of both students at the top of the notebook. See the following sections of the Canvas
documentation for instructions on group submission:
● How do I join a group as a student?
● How do I submit an assignment on behalf of a group?