The second part of the step-by-step walk-through to analyze and predict survival of heart failure patients. In the previous post, we looked at the heart failure dataset of 299 patients.
In the previous post, we looked at the heart failure dataset of 299 patients, which included several lifestyle and clinical features. That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models.
The motivating question is—_ ‘What are the chances of survival of a heart failure patient?’. _Through this walk-through, I try to answer this question while also giving a few insights on dealing with imbalanced datasets.
The code for this project can be found on my [GitHub_](https://github.com/ani-rudra-chan/Heart-Failure-Survival-Project.git) repository._
In the previous post, we saw that —
(Check out the previous [post_](https://medium.com/towards-artificial-intelligence/predicting-heart-failure-survival-with-machine-learning-models-part-i-7ff1ab58cff8) to get a primer on the terms used)_
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