Classification or regression problem? I have a table with this features:
   'tourney_id', 'tourney_name', 'surface', 'draw_size', 'tourney_level',
   'tourney_date', 'match_num', 'winner_id', 'winner_seed', 'winner_entry',
   'winner_name', 'winner_hand', 'winner_ht', 'winner_ioc', 'winner_age',
   'winner_rank', 'winner_rank_points', 'loser_id', 'loser_seed',
   'loser_entry', 'loser_name', 'loser_hand', 'loser_ht', 'loser_ioc',
   'loser_age', 'loser_rank', 'loser_rank_points', 'score', 'best_of',
   'round', 'minutes', 'w_ace', 'w_df', 'w_svpt', 'w_1stIn', 'w_1stWon',
   'w_2ndWon', 'w_SvGms', 'w_bpSaved', 'w_bpFaced', 'l_ace', 'l_df',
   'l_svpt', 'l_1stIn', 'l_1stWon', 'l_2ndWon', 'l_SvGms', 'l_bpSaved',
   'l_bpFaced'

You can see that they are duplicate features with the difference that one half is for winners e.i winner_age and the other half for losers e.i loser_age. I not sure which approach to take as is not a classification problem in the sense that I don't have clear binary labels (of course I could categorize some of them) or regression because I don't have a clear target (each feature is both winner and loser). I'm trying to do some EDA first but in order to do it I have to have a clear understanding on which approach to take to start with in the first place.
If you are trying to predict the target of winner or loser, then it is a binary classification problem.
If you frame it as a binary classification, you'll have to reorganize the data as tidy data such that each row is an individual observation and each column is a feature.