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: 2 days 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 (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-09T21:06:47.000Z (over 6 years ago)
- Last Synced: 2024-08-03T13:02:44.362Z (9 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
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