Problem Statement

Identifying patients who are in danger of or are suffering from Coronary heart disease.

Approach

EDA:

Through hypothesis testing for age where greater the age of the patient,The more likely they get heart disease. Similarly the Chest pain for the patient is determined.

The final model is given to the classification model to predict if the patient is likely to get heart disease. Hyper parameter tuning is done to improve the performance metrics of the model and to remove any biases in the model.

Conclusion

Inference from the study is that the ensemble model gave the best result when compared to simple classification models. The model is able to help doctors in accurately identify patients who are suffering from heart disease to help patients in leading a healthier life.