Heart Disease Prediction
تفاصيل العمل
Data preprocessing & cleaning – making the dataset ready for analysis Dimensionality reduction with PCA – simplifying data while keeping critical information Feature selection – identifying the most important predictors for heart disease Supervised learning models – building and testing classification models Unsupervised learning techniques – exploring patterns and hidden structures in the data Hyperparameter tuning – fine-tuning models for better accuracy Interactive UI with Streamlit – creating a user-friendly interface to interact with predictions Deployment – taking the model from local development to a live environment
مهارات العمل
بطاقة العمل
طلب عمل مماثل
