I have a table with 1 000 projects from past. Each has approxiametly 150 attributes (e.g. project manager, initial year of project, team members, budget, investors in project (yes or no) and a lots of other, place of project, ..). Some attributes for some projects are missing and there is just "no available" info. Each project has assigned value, whether it was successful or not. That's the key indicator.
I have to perform analysis to find relevant factors for these projects, based on their potential to become successful. What's the best way to do that?
Here's an example with the attribute "initial year of project". There is 1 000 project in the table. 600 projects were successful finished and 400 were not. From all 1 000 projects, 900 has available information about "initial year of the project" (so, 100 projects have "no available" info in this attribute). These are values:
- year 2010 - 100 projects were started - 50 of them were successful, 50 unsuccessful
- year 2011 - 200 projects were started - 150 of them were successful,50 unsuccessful
- year 2012 - 300 projects were started - 100 of them were successful,200 unsuccessful
- year 2013 - 100 projects were started - 20 of them were successful, 80 unsuccessful
- year 2014 - 200 projects were started - 170 of them were successful, 30 unsuccessful
I thought, that the correct way to find the most relevant factors is to quantify the dependency ratio of each attribute. So, for attribute "initial year of project" with the data above, I made a computation like this: 0.85*0.22*0.9 (because 170/200 = 0.85; 200/900=0.22;900/1000 = 0.9)
Is it correct? How would you solve this?