data comprises over fifty anonymized health characteristics linked to three age-related conditions. Your goal is to predict whether a subject has or has not been diagnosed with one of these conditions -- a binary classification problem.
train.csv :- The training set.: Id :- Unique identifier for each observation. AB-GL :- Fifty-six anonymized health characteristics. All are numeric except for EJ, which is categorical. Class A binary target :- 1 indicates the subject has been diagnosed with one of the three conditions, 0 indicates they have not. test.csv :- The test set. Your goal is to predict the probability that a subject in this set belongs to each of the two classes. greeks.csv :- Supplemental metadata, only available for the training set. Alpha Identifies the type of age-related condition, if present. A No age-related condition. Corresponds to class 0. B, D, G The three age-related conditions. Correspond to class 1. Beta, Gamma, Delta Three experimental characteristics. Epsilon :- The date the data for this subject was collected. Note that all of the data in the test set was collected after the training set was collected. sample_submission.csv :- A sample submission file in the correct format.