Predict whether or not a user will visit the library tomorrow using historical data
First and foremost, you need to reformat your data into what's called a balanced panel structure. For each day in your training data, each user should have a record for that day associated with an indicator variable for whether or not they visited. If every record in your training data corresponds to a visit, you're not giving those classifiers much to work with. You're model needs to know what a user's status looks like on days when they don't visit to be able to discriminate between days when they will and won't visit. The indicator variable, denoting "this user visited today", is the target of your prediction. Not the date or time column, if that's what you were trying.

Additionally, you're going to need to do some feature engineering. This process comprises the bulk of time and creativity expended on most data science projects, and the amount of effort you will put in will make or break your project. For each record in the data set, you should calculate things like:


What is the day of the week/month/year?
how many times did the user visit on this day of the week in the last k days?
how many times did the user visit on weekdays/weekends in the last k days?
How many days has it been since the users last visit?
How many times has the user visited the library in the last k days?
how many times has the user visited the library and paid a fine in the last k days?
How many users visited in the last k days?
Is the library open this day (e.g. it could be a national holiday)?
Are the public schools currently in session? On holiday? In finals?
How many days since the beginning of the most recent semester?
How many days until the semester ends?


Better yet, if you're not limited to what you've shown us...


How many books are currently in the users possession? How many are late?
If the user visited today, how much would they owe in fines?
how many books did they check out in their last visit?
how many books have they checked out in the last k days?
how long has this person been a member at the library?
has their membership expired?
in the last k years, have they allowed their membership to expire?
have they recently updated their address?
How much have they donated in the last k years?
What fraction of books checked out in the last 5 years by this user have belonged to each dewey decimal class?


These are just some ideas off the top of my head. Don't let yourself be limited by my suggestions.

If you really only have the data you've shown us, a project you can try that would be more likely to give you good results would be to predict just the number of people who will visit the library on a particular day, rather than which actual people. If this sounds like it might be useful to you, check out Poisson models.