Machine Learning to predict risk of items I'm trying to find out what I need to research and start learning to try and apply machine learning to this problem:


  In multiple offices I have 20 chairs, all of these chairs will need to have a risk assessment carried out, as the chairs become older I want to understand when they become more at risk because of other factors such as; age, usage, last time assessed and areas level of risk. I then want to be able to come up with a simple risk score of each individual chair.


I have done some research and I think it points towards using a machine learning technique such as supervised learning with a neural network but I'm uncertain. Given my scenario what should I be looking to start learning and doing?

Furthermore I believe that from my limited knowledge that providing all of the values I'm using are the same across other items I could apply and scale out this model for other items?



Further info;

As I'm familliar with Javascript I'm choosing to use synaptics.js to create a neural network and I'm going to be generating sample data inside a node server. I'm going to be using the following for my datasets that will be used as my model for learning from:


  Age (days to years), Location(low, med, high), Office(low, med, high), Last Risk Assessment Date (days-> years capped by twenty), Daily use (Low, Medium, High) and quality(low, med, high).


All of the above will be given an expected outcome manually so that I can attempt to figure out a way for the neural network to process the above and come to the same conclusion I did.

With the above labels I will be adding a weighting that will then be used to classify the risk level it should be associated to.

The factors I'm looking for the neural network to understand is that given my factors a chair with a lot of high factors will likely be placed in a higher outcome value of say 1-4 for risk factors. I'm then hoping I can use this learning to be applied to multiple other things other than chairs and increase the labels the network can use to learn from as I go along.
This sounds like a homework problem and those are frowned upon on this web site; so I'm going to be cautious about helping you and just give you general guidance:

1 - be very, very careful about picking models before you do anything else. That is a bit of Researcher Bias and you should try to avoid that; let the data tell you where to go, not the other way around. 

2 - you have to think about what "factors" you can use to describe each chair. How old is it? What color is it? Does it have wheels or not? Some of these features will have an effect on your models, others will not. But it should be your goal to start with a long list of factors and then try to model them out and see which ones have an effect on your results. 

Start putting a list of factors together and then you will be in a better position to continue your studies.