Forecasting ticket sales and city I am learning data science. I have the following dataset for train tickets:

1. order_date_meduim

order,date,medium
95062,2017-09-11,35
171081,2017-07-05,39
122867,2017-08-18,39
107186,2017-11-23,
171085,2017-09-02,

2. order_ordercityA_ordercityB [some order has only 1 ordercity, I think ordercity means here which city is something like source and destination]

order,ordercityA,ordercityB
81773,4,11
105838,4,
76153,24,18
93058,12,
11623,24,3
3070,24,3

3. order_ticketcount,ticketclass

order,ticketcount,ticketclass
246783,1,pax
1693998,2,pax
1958576,1,other
673681,1,pax
1593899,1,pax
194035,1,pax


I need to forecast the ticket sales for a week and also the ordercity with medium of booking. 

As I am new, could someone give a possible answer about how to create a prediction model that could predict the sales for 1 week? Also, I doubt the data is time-series data. 

I code in Python.
You have got yourself a time series forecasting problem. And with multiple input variables it is called multivariate time series forecasting.

What Is Time Series Forecasting?

You can start with EDA on your data and find out if you can see any trend or seasonality. ( You might need to add or update your current features to get underlying trend/seasonality )

After EDA, you can start looking into following models, all of them are the go-to for time series prediction problem:


Classical, Statistical 


ARMA for stationary data 
ARIMA for data with a trend - Refer
SARIMA for data with seasonality
Holt-Winters Forecasting - Refer
Theta method - Refer
Fourier Transformation - Refer

Machine Learning 


Quantile Regression Forest(QRF)
Support Vector Regression(SVR)
Recurrent Neural Networks(RNNs) (LSTM)



If you are not comfortable with Statistics then I would advise you to start with LSTMs for forecasting - Refer