🏥 Multiple Disease Prediction

• Website | Github
• Tech Stack: Python, Streamlit, Pandas, Numpy, Matplotlib, Seaborn, Scikit Learn, Github Actions, Docker, AWS EC2
• Tech Stack: Python, Streamlit, Pandas, Numpy, Scikit Learn, Github Actions, Docker, AWS EC2
• Algorithms Used: Diabetes(Support Vector Machine), Heart Disease(Logistic Regression), Parkinson’s Disease(Support Vector Machine)
• Model Accuracy: Diabetes(77%), Heart Disease(78%), Parkinson’s Disease(87%)
• Automated CI/CD Pipeline: GitHub Actions workflow with testing, Docker builds, and AWS EC2 deployment

Customer Segmentation

• Website | Github
• Tech Stack: Python, Streamlit, Pandas, Numpy, Matplotlib, Seaborn, Scikit Learn
• Algorithms Used: K-Means Clustering (Elbow Method)
• Statistical summary of customer data, Distribution analysis of key features, Correlation analysis between variables, Visualization of customer patterns are done through EDA
• Purchasing patterns and customer preferences which can provide actionable insights for marketing and sales teams and enable targeted campaigns and personalized customer experiences