# What is an appropiate machine learning algorithm for my problem?

I was having an idea for a software, which would make use of machine learning, and I started to code it. I got stuck at selecting the algorithm, since I'm not familiar with this field.

My use-case is:

For teaching, I'm collecting Wi-Fi data, and measure signal strength, like:

My network, 86%
Neighbours network, 34%
Other network, 23%


Then I assign 2 numbers to it, each between -1 and +1, so for example: (-0.65, 0.43)

For the evaluation part, I give similar samples:

My network, 68%
Neighbours network, 27%
Other network, 36%


Then expect the algorithm to return something like: (-0.63, 0.41)

I'm thinking it would be neural networks or pattern recognition, but these are really broad terms. I was hoping someone could tell me which algorithm(s) would cope well with this problem.

• I don't understand the question. How do you go from your 3 percentages to the two numbers between -1 and +1 ? – sqrt Nov 7 '15 at 11:15
• By teaching the algorithm. – Daniel Zolnai Nov 7 '15 at 12:09
• So you want to predict ground coordinates as function of signal strengths? You could also predict room as function of signal strengths. That might be a little easier for a start. Walls etc. will reduce the signal in a non-linear fashion so a non-linear model would probably be best. Neural-nets with one input for each wifi plus one or two hidden layers and two outputs may work well. You may need 500 training samples. – Soren Havelund Welling Nov 7 '15 at 13:07