How to predict when an appointment will be scheduled? I have a dataset of tens of thousands of appointments. Appointments have a created date and scheduled date. Something like this:
ID   Created      Scheduled
1    08/01/2020   08/05/2020
2    08/01/2020   08/07/2020
3    08/02/2020   08/04/2020
...             

I'm trying to predict the probability of all possible schedule dates based on a created date in the future. So basically, if a customer created an appointment tomorrow (August 20th), what's the probability they would schedule their appointment for August 21th, August 22nd, August 23rd, etc. In theory, customers can create appointments into perpetuity but practically no one makes an appointment more than ~2 months in advance.
Some observations I've made are:

Customers prefer making appointments on weekends
Customers prefer making appointments during the last or first few days of the month
Most appointments are scheduled within 2 weeks of the day the customer creates the appointment

I've been struggling with this problem. I first tried to just look at how many days out a customer schedules their appointment. Something like 10% of the time it's the next day, 15% of the time it's 2 days out, etc. But that didn't account for how customers preferred making appointments on weekends and the start/end of the month. So it was incredibly inaccurate.
I'm frankly stumped about how to approach this problem. I would appreciate it if people have ideas on how I can go about this. Thank you! Please let me know if anything here is unclear.
Create new features specifying days of the week.
df['Created'] = pd.to_datetime(df['Created'])
df['Scheduled'] = pd.to_datetime(df['Scheduled'])
df['Scheduled_day_of_week'] = df['Scheduled'].dt.day_name()
df['Created_day_of_week'] = df['Created'].dt.day_name()

Create a new column day difference which will be the difference between the two days.
Then you can visualize for each day what would be the day difference.
Similarly you can try for the date.
This process is called Feature Engineering.