This repository contains the materials and resources for my final coursework project on building a machine learning model to predict the risk level of a Covid-19 patient based on their current symptoms, status, and medical history. The model was trained using the Adaboost and Logistic Regression algorithms and a Voting Classifier was used to boost the accuracy of the model. The model was then deployed on AWS using SageMaker.
The goal of this project is to build a machine learning model that can predict the risk level of a Covid-19 patient based on their current symptoms, status, and medical history. The model takes into account various factors such as age, sex, medical history, and current symptoms to make a prediction. The model was trained on a dataset containing patient information and was evaluated using various metrics such as accuracy and F1 score.
The project was implemented using Python and the following libraries:
- Scikit-learn for training and evaluating the machine learning model
- Pandas and Numpy for data manipulation and analysis
- Matplotlib and Seaborn for data visualization
- AWS SageMaker for deploying the model