Unit 4 Stretch Task: Regression ML

Note

Canvas: U4: Stretch Task — Regression ML
Type: Stretch Task (1 pt, complete/incomplete)
Copilot: Allowed for syntax lookup; disallow for answer generation.

Background

The clean air act of 1970 was the beginning of the end for the use of asbestos in home building. By 1976, the U.S. Environmental Protection Agency (EPA) was given authority to restrict the use of asbestos in paint. Homes built during and before this period are known to have materials with asbestos You can read more about this ban.

The state of Colorado has a large portion of their residential dwelling data that is missing the year built and they would like you to build a predictive model that can classify if a house is built pre 1980.

Colorado gave you home sales data for the city of Denver from 2013 on which to train your model. They said all the column names should be descriptive enough for your modeling and that they would like you to use the latest machine learning methods.

Client Request

The Client is a state agency in Colorado that is responsible for the health and safety of its residents. They have a large portion of their residential dwelling data that is missing the year built and they would like you to build a predictive model that can classify if a house is built pre 1980.

Data

URL: dwellings_ml.csv (ml ready)
Optional URL: dwellings_neighborhoods_ml.csv (ml ready)
Informational URL: dwellings_denver.csv (not cleansed)
Information: Data description

Readings

• All regressor algorithms in scikit-learn (skim)
• How to choose a good evaluation metric for your Machine learning model (you can start in section 11 - Evaluating for regression problems)
• Optional Review: Polars User Guide, Joins

Optional/Alternative Reading

Note

• Polars Definitive Guide, Chap 14, Joining and Concatenating
• Polars Cookbook, Chap 8 Reshaping and Tidying Data, Joining DataFrames
• Another overview and description of pros and cons for popular classifiers and regression models
• Boosted algorithms in scikit-learn

Stretch Questions

Skills: regression models, metrics beyond accuracy

1. Join the dwellings_neighborhoods_ml.csv data to the dwelling_ml.csv on the parcel column to create a new dataset. Duplicate the code for the model you recommended in the previous taskn above and update it to use this data. Explain the differences and if this changes the model you recomend to the Client.

2. Can you build a model that predicts the year a house was built? Note this is a regression ML model, not a classifier. Report appropriate evaluation metrics for the model. Explain the model and the evaluation metrics you used to determine if the model is good.

Submission / Deliverables:

No template is provided for this assignment. You must create your own file as part of the task. Answer the questions in this assignment. Each answer should include a written description of your results, code cells with comments, charts and/or tables.

NoteWhere to submit

Your instructor will advise you — or it will be evident in Canvas — whether to submit a rendered .html file, or a link to the rendered file on GitHub Pages (gh-pages). Do not submit the URL to the GitHub .qmd file.

NoteHow to submit (GitHub Pages)

When you have completed the report and are ready to submit, render the project into HTML and publish it to GitHub Pages. Follow these steps:

1. Have this assignment’s template/quarto file open in VS Code and nothing else
2. Click the Preview button in VS Code (top right of the screen)
   1. This renders the project so you can review it
   2. Confirm everything displays as you would like it to
   3. How you see it is how it is viewed for grading
   4. If there is an error in any cell, the rendering stops and you will need to fix the error before rendering again (if you get stuck post your error in Slack)
3. Once the report is confirmed, close the preview and open the GitHub Desktop application
4. Confirm you are in the correct repository (top left corner)
5. Confirm you are on the Main branch (top left corner — never change off Main)
6. Type a summary of the changes in the Summary box
7. Click Commit to main (blue button, bottom left)
8. Click Push origin (blue button, middle right)
   1. This pushes your changes to GitHub
   2. The publish.yml workflow renders the project into HTML files
   3. The HTML files are published to the gh-pages branch
   4. The URL of the published project is in the deployment section on GitHub
      1. In GitHub Desktop, click Open in GitHub to navigate to the repository
      2. Click the Actions tab and confirm there were no errors in rendering
      3. Open the deployment section on the main repo page to find the URL
      4. Navigate to the URL and confirm it displays as you intended
      5. Copy the URL and submit it in Canvas