Inspiration: We got inspired by seeing the challenge involved, which gave us the motivation to take up this challenge and perform some analysis in it which could be beneficial to the medical domain.
What it does: It starts with exploratory data analysis and finds some interesting correlation between data. Also, some models are prepared which gave us 83% accuracy and 90% accuracy for the test dataset.
How we built it: We built it using tableau and python. Also used API of CDC to get the area code, to drill it down further in data exploration. Where in python data cleaning, model building, and data acquiring were done. With tableu many correlations were founded.
Challenges we ran into: We got many challenges few of them were quiring and relating many public datasets. Finding a correlation between data and also doing predictive analysis
Accomplishments that we're proud of: We have got many interesting correlations between data, and accuracy of 86% in logistic regression and 97% in KNN.
What we learned: Using tableau, python and exploring data.
What's next for Data Analysis of Opioid Data-set(Cedar): Building a great predictive model
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