Best way to predict ranges of money? I am currently working on a project in which I have to develop a model to predict how much money other companies will make by using the services provided by my company. The money made is a type of tax return. The model should predict a range (e.g 100k$-200k$).
Basically, if I give the model the data of the target company, it will give me how much they can potentially make with us.
The data I have is the financial statements of the companies we worked with. This includes their general and financial data such as capital, number of employees, city, type of industry, and of course the amount of money they made with us.
After researching online, I found that most solutions are forecasting and time series based. While the data I have has the year in which we worked with the clients, I do not think this is the right approach for me. I experimented with decision trees and a regression model but I do not seem to get a good result.
Any tips on where to look to solve this problem?
If you have many available input parameters/features (financial data like balance, depreciations, tax for several years for several companies, the business areas where those companies are working in (e.g. telco, banks, media, etc) and you already have &quot;labelled&quot; data (I mean, you already have the &quot;tax&quot; saving for you previous customers).... well, a good start could be a standard process of feature selection and designing a neural network.