Why is the LogLoss value for my XGBoost model not improving?

Hey everyone! I am a beginner in machine learning and I currently working on a project with the goal to provide the most accurate prediction on the consumer choice based on safety features of cars. The benchmark LogLoss value is set at 1.210 and currently my model is at 1.2388. I have explored the following options but to no improvement in my LogLoss value.

1. Using alternative models such as Catboost, LightGBM
2. Using ensemble models alongside XGBoost
3. Performed Hyper parameter tuning for the existing parameters in XGBoost

Here is a bit more information on the current dataset that I am using.
- Total Rows: 21,565
- Total Columns: 113 (Categorical: 10, Numerical: 103)

Current Paramter values used:

objective = "multi:softprob",

num_class = 4,

eval_metric = "mlogloss",

eta = 0.05,

max_depth = 4,

min_child_weight = 5,

subsample = 0.8,

colsample_bytree = 0.8,

lambda = 1,

alpha = 0.1

I would like to gather your opinion on how I can further improve the LogLoss value of my model. Do let me know if I need to provide more information. Any help offered is greatly appreciated! Thank you in advance!