Method for predicting winner of call for tenders

Introduction: Lately I've been looking into different machine learning methods to work around different business problems. By now I have a good, basic understanding of most regression and classification methods, and I'm able to use these methods to predict numeric values given other numeric values and/or simple categories (e.g. an employee's salary given age, years of experience and level of education) or a binary classification (e.g. will this employee leave the company based on the same variables).

What I'm looking for: However, I haven't found the right method for the problem I initially wanted to solve, which involves predicting a non-numeric, non-binary value from a mix of numeric and categorical data. I'm not looking for an in-depth explanation of how to solve the exact problem, but merely advise on which techniques/methods to look into. Ideally something that could be done with R.

The business problem: I have historical data on public tenders (i.e. public sector instutions buying goods/services from private contractors through calls for tenders). The data includes variables like:

• Orderer - i.e. who announced the tender (1 of ~150 municipalities/state insitutions)
• Type of procurement (1 or more of thousands of industrial classification codes)
• Estimated value of contract - A numeric value estimating the value of the contract (at a point before the winner is chosen).
• Winner - i.e. which contractor won the tender (1 of ~2000 private companies)

What I want to do is predict the winner of a tender given the three other variables. It's obviously not a regression problem, and the classification methods I know seem inadequate in handling the problem too. The data are clean and streamlined (no alternate spelling of the different orderer/contractor names, etc.). Any ideas about what to look into?

• You're going to have to elaborate on what "inadequate" means. What you are describing is a multi-class (i.e. not binary) classification problem. That admittedly gets difficult.
– CalZ
Sep 20, 2017 at 11:50
• Right. I guess what I mean is that I'm only familiar binary classification methods. What's more the task of encoding categorical data seems problematic, when there are so many levels in these categories. I.e. it's difficult to evaluate which parameters are significant for the model (looking at the P value). Sep 20, 2017 at 12:04