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A Machine Learning API with native redis caching and export import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.
This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.
Prediction of a readmission for a patient based on the Electronic Health Records (EHR) data. This project was done as part of a timed challenge with a time limit of 3 hours to work on this dataset. So, it is just a preliminary model using XGBoost algorithm with some basic data exploration for data processing.