The evaluation metric for this competition is [Area Under the Curve (AUC)](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5). AUC - ROC curve is a performance measurement for the classification problem at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0s as 0s and 1s as 1s.


## Submission Format
For every index in the dataset, submission files should contain two columns: Id and Predicted probability between [0,1]. The file should contain a header and have the following format: Id, Predicted 1,0.3 2,0.8 3,0.4 4,0.7 etc.