Classification based on categorical features
Convert the categorical variables to binary, so the ML models can understand it. I would split match column into two columns each one has one country, then convert every country to a unique binary or a value. The model can then understand your approach
Dataset:




Team
Opponent
Home/Away
Ground
Match Date
Match Year
Match Period
Margin
Result




Country A
Country B
Away
Ground1
20-09-2016
2016
Sep-16
5 wickets
Lost


Country C
Country D
Away
Ground2
08-07-2012
2012
Jul-12
66 runs
Won




The target column for prediction is Result, which is a binary classification with values either Won or Lost
Aim is to predict whether the match result of a given team (team1), against an opponent (team2) at a given ground, is Won or Lost, so the input to the ML model from the user would be Team, Opponent, Ground.
The accuracy of the model is pretty low when only categorical columns are considered as input.
Any help on methods to train the ML model with just categorical variables..
Thanks!