I got a huge dataset, with 75 variables and over 800,000 observations. The target variable is "time to event", where the event is the withdrawal of the subject from a gambling website (days since joining). The independent variables vary, some are numeric, some categorical, for example: sum of winnings, gender, etc..I do not have censoring. My aim is to try and predict the number of days to withdrawal. The distribution of my target variable looks like this, and is clearly not normal:
This is a data mining problem from size of data point of view. How should I analyze this data? Which model should I use?