Cardiac arrest, most commonly known as a heart attack is a dangerous and life-threatening condition as millions of people die from it every year. One of the most common causes of death in human beings, cardiac arrest can be caused due to a number of factors. It can be a consequence of our lifestyle or can be the cause of other medical conditions or illnesses. This hackathon challenges the data science community to use the given data set to predict whether a patient is under a potential risk of having a cardiac arrest.
Given are 12 distinguishing factors that can provide insight into whether a patient may fall under the risk of having a heart attack. Your objective as a data scientist is to build a machine learning model that can predict if a patient is likely to have a cardiac arrest or not.
The unzipped folder will have the following files.
- Train.csv – 889 observations.
- Test.csv – 382 observations.
- Sample Submission – Sample format for the submission.
Target Variable: UnderRisk
The datasets will be made available for download on May 28th, Thursday at 9 am IST.
Below are the file formats for the provided data
The submission must contain the probabilities of the target classes no(0) and yes(1).
No Reviews found for this hackathon.