I am tried to solve a MILP problem using python pulp and The solution is infeasible. So, I want to find where infeasibility is coming and want to relax it or remove it to find feasible solution. it is difficult to check manually in the LP file bcz large number of constraints are present. So How I can handle this issue?
I went through some articles they mentioned that check manually in the LP file but it is very difficult to do manually for a huge number of variables/constraints.
It is giving just infeasibility
I use some rule of thumbs to check infeasibility.

Always start with a small data set that you can inspect more manually.
After relax all integer variables. If this relaxation is infeasible, your problem is linear infeasible. You might have constraints saying stuff like x > 3 and x < 2;
If the linear relaxation is feasible, then deactivate each constraint once. Frequently you find some obvious constraints being infeasible, such as sum(i,x_i) = 1. But if you deactivate one by one, you may find that another more complex constraint set is causing infeasibility, and there you might investigate better.
Identifying infeasibility constraint and relaxing/removing it in Python using Pulp?