Framingham Heart Study: EDA and Classification Model

• Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Project Focus: Exploratory data analysis and predicting heart disease risk using a decision tree classifier
• GitHub Repository: Project Link

This project involves an extensive exploratory data analysis (EDA) of the Framingham Heart Study dataset and the development of a decision tree classifier to predict the risk of heart disease. Key highlights of the project include:

• Data Cleaning and Preprocessing: Addressed missing values, outliers, and feature scaling to prepare the dataset for analysis and modeling.
• EDA Insights:
  • Examined the impact of key factors like age, cholesterol levels, blood pressure, and smoking habits on heart disease risk.
  • Visualized correlations using heatmaps, histograms, and pair plots to uncover hidden patterns in the data.
  • Identified trends in gender-specific risk factors for cardiovascular diseases.
• Model Development: Built a decision tree classifier achieving 90% accuracy on the test set by optimizing hyperparameters and feature selection.
• Evaluation: Assessed model performance using metrics such as precision, recall, F1-score, and ROC-AUC to