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TeamPunchParty

Data200s Group project

Firstly, shout out to my teammate Korede Ogundele, who wrote a significant amount of code in this repository and who I've always enjoyed working with.

In this project, we combined data analysis and machine learning techniques to model air quality across various US counties. Using Python libraries NumPy, Pandas, seaborn, matplotlib, and scikit-learn, our team constructed two linear regression models and successfully conducted a comprehensive analysis of emission data.

Links:

The project description page:

https://ds100.org/fa23/gradproject/

The google drive for dataset A originally provided to us

https://drive.google.com/drive/folders/1AVzJyX7yv9RufLUbGUD6DUDXXUsfW5W4

Our spreadsheet of initially brainstormed questions

https://docs.google.com/spreadsheets/d/1ntIW_GYvmMAgWO8m5aQh2oz8jWLRVaOj1m-uwFrHeok/edit#gid=113687052

Our document with our initial proposal

https://docs.google.com/document/d/1ciCfkGh4PehvJ21IM3Me0dYCzw-nH7uvAwNrqsUQs6c/edit

Checkpoint 1 google doc writeup:

https://docs.google.com/document/d/1itRKmAqeMe8nCv4MViYwHv5UWHMZBcLGdagjyv1heYc/edit

Checkpoint 2 google doc writeup:

https://docs.google.com/document/d/1sE2sYOxgZfOL1DPZ-x_wRYHt0eBoZIv1RK-uFJ_Mp70/edit?usp=sharing

dataset_A_preprocessing.ipynb

Originally copied from GHCN_data_preprocessing.ipynb in the dataset drive, we will likely tailor this to our needs.

Open questions:

See the "Emmissions questions" tab of the spreadsheet above. We can talk about transferring those questions to this readme or whatever long term organization we want.