An Exploratory Data Analysis and Machine Learning model
This Notebook contains scrupulous data analytics which involves:
- Extensive Data cleaning, wrangling, analysis and visualizations
- Building working Machine Learning models with high predictive capabilities
- Using proper computational algorithms and visualizations to derive insights from real-world Data ( i.e. Who is more likely to develop adverse reactions to vaccination )
- Involves an interactive session where we apply our Machine Learning Model to answer tough questions ( i.e. Given a case note report of individual patient bio-data and clinical history, we'd use our model to predict those who are likely to survive adverse reactions to COVID19 vaccination? )
- Key graphs on survivor demographics are in place
The datasets used in this repository comes from the following source - https://www.kaggle.com/ayushggarg/covid19-vaccine-adverse-reactions under a CC0: Public Domain license (https://creativecommons.org/publicdomain/zero/1.0/)