I have a task that is probably related to data analysis or even neural networks.
We have a data source of our partners, job portal. The source values are arrays of different attributes related to the particular employee:
- His\her gender,
- Age,
- Years of experience,
- Portfolio (number of the projects done),
- Profession and specialization (web design, web programming, management etc.),
- many other (around 20-30 totally)
Every employee has it's own salary (hourly) rate. So, mathematically, we have some function
F(attr1, attr2, attr3, ...) = A*attr1 + B*attr2 + C*attr3 + ...
With unknown coefficient. But we know the result of the function for the specified arguments (let's say, we know that a male programmer with 20 years of experience and 10 works in portfolio has a rate of $40 per hour).
So we have to find somehow these coefficients (A, B, C...), so we can predict the salary of any employee. This is the most important goal.
Another goal is to find which arguments are most important - in other words, which of them cause significant changes to the result of the function. So in the end we have to have something like this: "The most important attributes are years of experience; then portfolio; then age etc.".
There may be a situation when different professions vary too much from each other - for example, we simply may not be able to compare web designers with managers. In this case, we have to split them by groups and calculate these ratings for every group separately. But in the end we need to find 'shared' arguments that will be common for every group.
I'm thinking about neural networks because it's something they may deal with. But I'm completely new to them and have totally no idea what to do.
I'd very appreciate any help - which instruments to use, what algorithms, or even pseudo-code samples etc.
Thank you very much.
