What would be the most appropriate neural network to train with a dataset like this?:
id,timestamp
B6Q_6XYb5p65QG3,1304474548
iSjl6kmliNfBFoI,1295997524
Uy9oyE2nzJxNWJZ,1256104029
QM5-eotmsdkPhf5,1134644428
4mcrT4f_wtSy_ru,1261067627
Sb4Eg5z12ESarN3,1647054017
WvwKiPE2AvcQFjI,1534320056
ekufrTSThZ3sJNW,1389277113
uJApLEvULs03tf4,1493811764
b4hcxn3qXVA4wd_,1239339894
y1OjJhKRkTSkven,1130146654
JeaF_DMH-fFGS4T,1425254914
KUgf-3FKes1IKQX,1447501927
6CMVPYsPXV7MOET,1298337269
AC3xm_8KCpTWE1i,1396932104
fvDNW4YLRYTzaDK,1537370404
Ni7Bg1tBG17UOLC,1401677745
O-6vRfrO7g6Zqz5,1277357594
37fk5yq9XQrOPFf,1635309809
G7UwXLgnp_PlYZ7,1257546306
The idea is to make a program that, when passing an id that it doesn't know, returns the corresponding timestamp (or an approximate one).
Another question I have is, how should I normalize the dataset for training (from what I understand, numerical datasets are normally used)?