Midterm
Machine Learning Foundations with Python (90-803)
Spring 2023
This is an individual test. No communication with other students is allowed. Do not post questions about the midterm in Piazza; this assignment is under exam conditions. You are only allowed to ask clarification questions.
Note: even a 1-minute later (whatever the reason) will result in a 0. Plan ahead and deliver ahead of time!
From the datasets provided, you will be required to complete a series of tasks; please read carefully through them.
Part 2: Classification
Dataset: WFH_WFO_dataset.csv
Dataset description: https://www.kaggle.com/datasets/anninasimon/predict-if-people-prefer-wfh-verses-wfo-data
Target variable: Target (WFH=1 | WFO=0)
- WFH = Work from Hom
- WFO - Work from the Office
- Perform any necessary cleaning steps
- Draw at least two plots that will give you insight into your data and your next steps (this can be a correlation matrix, the distribution of a particular variable, or a density plot, among others). Include an explanation of why these plots are relevant and how they will help you with your models.
- Perform any necessary feature engineering steps (explain and justify).
Classify the Target variable (WFH vs. WFO)
- Fit three different models to your dataset
- Show that you fitted three different models
- Properly tune all your models
- Model Selection - Choose the best model for your data
- Validate all your models and compare the performance between models
Once you have your final model, answer the following questions:
- Does your classifier have a higher difficulty classifying a particular class? Explain.
- How good is the classification of your final model?
- Choose a specific performance metric to report to answer the previous question. Justify why this particular metric would be more relevant to this dataset.
The submission for this section should be MidtermMLFP_S23_Classification_YourNameHere.ipynb
General Midterm Notes
- You can decide if you drop any columns, or missing values, but you have to document them and give reasons for them. The reasons cannot be "there were too many variables", "it was too difficult" ,"seemed like an obvious column to drop" or similar.
- You can add subsections to your file if it helps to make it easier/clearer to understand.
- Make each step of the required tasks clear to us, that way, we can easily award you well-deserved points!
- Remember to use both code and markdown cells, and include comments in your code (we can't read your process off from your mind!).
- Only use the python libraries, techniques, and knowledge learned from Weeks 1-7.
- Please keep all dataset inside the
datafolder.
