Heart-Failure-Prediction

About

This is a Mini-Project for SC1015 (Introduction to Data Science and Artificial Intelligence) focusing on Heart Failure Prediction.

Contributors

1. @yaomingng - Exploratary Data Analysis
2. @Kaiien01 - Decision Tree Uni-Variate/Multi-Variate
3. @CavinKavi - Random Forest

Problem Definition

Can we predict heart failure based on attributes? Which model is best to predict it?

Models Used

1. Decision Tree
2. Random Forest

Conclusion

1. Able to predict whether a patient will have chances of getting a heart disease based on his/her medical conditions.
2. To see what changes can be made to lower the chances of getting a heart disease.

What we learnt?

1. Making use of other classification techniques such as random forest classification to improve on the overall performance of the model.
2. Instead of removing NaN values, we can also replace it with median or mean depending on the situation.
3. Collaborating using GitHub
4. Accuracy Score

References

1. https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/
2. https://www.kaggle.com/code/durgancegaur/a-guide-to-any-classification-problem
3. https://www.w3resource.com/python-exercises/pandas/missing-values/python-pandas-missing-values-exercise-14.php