这是一篇来自澳洲的关于探索性数据分析和数据清理的数据代写
- Assessment Resources
Before you start writing your code, you will need to read the following materials:
Datasets: Please find your own datasets (<student_id>_dirty.csv, <student_id>_outlier.csv, <student_id>_missing.csv) placed in “input data” folder in Gdrive.
The supplementary files provide further information needed to complete the assignment.
While using Jupyter Notebook, you should consider the use of different cells to make notes as you go. However, be precise and do not include things that are not relevant to the tasks in your notebook.
Standards for commenting code are available online (e.g. https://www.python.org/dev/peps/pep-0008/#comments). You must ensure that you can clearly explain what your code does with clear comments.
The work required to finish this assessment should be your own. If you use resources elsewhere, make sure that you properly acknowledge them in your notebook. You may need to review the FIT citation style tutorial to make you’re familiar with appropriate citing and referencing for this assessment. Also, review the demystifying citing and referencing for help.
- How to Submit
Once you have completed your work, take the following steps to submit your work.
You need to submit two files:
- <student_id>_ass2.zip: In total, there are 4 files to be included in the zip file. Please refer to assignment specs for details.
- <student id> ass2.py: This should be an export of your jupyter notebook file in py format and it will be used for plagiarism checks.https://lms.monash.edu/mod/assign/view.php?id=10646893
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- student_id _ass2.py: This should be an export of your jupyter notebook file in py format and it will be used for plagiarism checks.
Click the Add Submission button below to submit and upload your assignment. Please do remember to accept the submission statement! Only the submitted assignments will be marked. Those shown as a draft won’t be marked!
If you need further guidance, please ask on the ED discussion platform.
Submission status
Attempt number This is attempt 1.
Submission
status No attempt
Grading status Not marked
Due date Thursday, 6 October 2022, 11:55 PM
Time remaining 33 days 4 hours
Last modified –
Submission
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