# How to predict a preferred route based on weather and distance

I want to train a neural network to predict what my favourite home-work route will be for a particular day. I have these features for routes on a day:

temperature, humidity, congestion, distance, duration


I have come up with this concept of training/testing a network:

//
// training features result in route 1,2 or 3
//
Network.train([30,10,12,20,12] , 1)
Network.train([20,10,22,20,14] , 3)
Network.train([23,10,2,20,10] , 2)
Network.train([20,10,22,20,12] , 2)

//
// On a new day, predict which route the user is most likely to take:
//
var route = Network.test([25,8,12,22,12])


My question is: is this a viable approach? Can I make relevant predictions this way if I have enough training data?

Can I generate an outcome between 1 and 3 this way?

• Thinking out loud: perhaps distance and duration shouldn't be taken into consideration since they are actually part of the route – Kokodoko Apr 9 at 15:36