Team Leader - Project Management
Heart Disease Prediction Analysis Using AIML.
As part of this project, I developed a predictive model to forecast the likelihood of heart disease based on a dataset of medical attributes. The key steps in this project included:
- Data Preprocessing: Cleaned and transformed raw healthcare data (e.g., age, blood pressure, cholesterol, exercise, smoking habits) to ensure it was suitable for modeling, including handling missing values, normalization, and feature selection.
- Model Development: Implemented multiple classification algorithms such as Logistic Regression, Decision Trees, and Random Forest to predict the presence of heart disease. Evaluated models using metrics like accuracy, precision, recall, and F1-score.
- Visualization and Insights: Used data visualization techniques to uncover patterns in the dataset, including the relationships between risk factors and heart disease.
- Model Optimization: Tuned hyperparameters using GridSearchCV to enhance model performance.
- Deployment and Reporting: Built an easy-to-understand report summarizing the results and insights. The final model was deployed as a tool for healthcare professionals to predict and prevent heart disease.
