https://danielhanbitlee.github.io/opioid_mortality/

Using Python, AWS, Jupyter notebook, pandas, numpy, scikit-learn, xgboost, matplotlib, seaborn, and other packages, performed data wrangling, data storytelling, exploratory data analysis, and in-depth analysis and predictions for 2016 US county level opioid overdose mortality rate using algorithms such as random forest, stochastic gradient boosting, LASSO, ridge regression, and multiple linear regression.
https://danielhanbitlee.github.io/opioid_mortality/

Last synced: about 1 month ago
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Using Python, AWS, Jupyter notebook, pandas, numpy, scikit-learn, xgboost, matplotlib, seaborn, and other packages, performed data wrangling, data storytelling, exploratory data analysis, and in-depth analysis and predictions for 2016 US county level opioid overdose mortality rate using algorithms such as random forest, stochastic gradient boosting, LASSO, ridge regression, and multiple linear regression.

• Host: GitHub
• URL: https://danielhanbitlee.github.io/opioid_mortality/
• Owner: danielhanbitlee
• Created: 2018-09-12T00:24:01.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-10-09T21:06:47.000Z (almost 6 years ago)
• Last Synced: 2024-04-30T15:32:44.605Z (5 months ago)
• Language: Jupyter Notebook
• Homepage: https://danielhanbitlee.github.io/opioid_mortality/
• Size: 14.2 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:

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