This is a classification problem that aims to predict whether a patient is likely to die in the hospital or not based on various features related to the patient and their medical history.
The dataset used for this problem is provided in the file hospital_deaths_train.csv. It contains 3,250 samples and 116 features, including patient demographics, vital signs, laboratory results, and medications.
The objective of this problem is to build a machine learning model that can preprocess and accurately predict whether a patient is likely to die in the hospital or not based on the provided features.
We have several evaluation metric used for this problem. Which include: accuracy, precision, Recall, F1-score, ROC AUC and MSS