When you build up your portfolio, you should ask yourself why a specific project is interesting to you (and potentially to a broader audience). What advantage could it have to combine OR and ML, or why use one instead of the other?
In my opinion, these considerations add additional value to your portfolio, as they go beyond 'just' displaying your technical skills.
So, in addition to the ideas mentioned in previous posts, I could think of the following:
ML as input to OR: Find a problem relevant to industry, but too big to be solved in reasonable time using a MIP solver. Then use dimensionality reduction (e.g. principal component analysis) on the input data. Investigate the tradeoff between information loss and performance increase. You could also compare the optimal solution using reduced data to a heuristic solution using the original data.
OR to (better) solve ML problems: Find a machine learning problem and solve it using a MIP solver (or use other OR technique). The Bertsimas and King paper on linear regression might be a good point to start.
OR vs ML: Find a problem for which both OR and ML algorithms exist to solve it. Implement both and compare run time, solution quality, ease of implementation, etc.