Chikodili-Chukwu/ML-Covid-Project

Using Predictive ML models to predict Covid trends in Africa **COVID-19 Spread in Africa Using Machine Learning** This project analyzes COVID-19 cases and deaths across African countries from 2020 to 2022. It uses exploratory data analysis, K-Means clustering, PCA, Decision Tree Regression, and Random Forest Regression to study regional patterns and predict COVID-19 mortality. The dataset was obtained from Our World in Data and filtered to include African countries. **Tools Used** • Python • Jupyter Notebook • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn **Project Steps** 1. Cleaned and filtered the dataset 2. Compared COVID-19 cases and deaths across African regions 3. Used K-Means clustering to group similar countries 4. Used PCA to visualize the clusters 5. Built Decision Tree and Random Forest regression models 6. Evaluated the models using MSE, RMSE, and R² **Results** Model RMSE R2 Score Decision Tree Regressor 1.1321 0.67849 Random Forest Regressor 1.3257 0.5524 The Decision Tree Regressor performed better and explained about 67% of the variation in COVID-19 deaths. The clustering results also showed that countries with similar COVID-19 outcomes were not always located in the same region.