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I am working on highly imbalanced dataset and trying to increase accuracy(metric: roc_auc) of my model which is hovering around 82-83%. This is part of an internal ML competition and people who are at the top have accuracy around 85-88%.

I am just wondering what else I can do to improve my model's accuracy. Any suggestion or tips would be appreciated.

Details of training dataset :

The training data shape is : (166573, 14)

https://i.stack.imgur.com/l0cHL.png

Distribution of features :

As you can see, only the first 4 columns go to different max values.
Rest of the columns have either 1 or 0 value (max: 1, min: 0)

https://i.stack.imgur.com/l0cHL.png

Scaling features :

X['scaled_distance']= sc.fit_transform(X['distance'].values.reshape(-1,1))
X['scaled_visit_count'] = sc.fit_transform(X['visit_count'].values.reshape(-1,1))
X['scaled_tier'] = sc.fit_transform(X['tier'].values.reshape(-1,1))

Null Handling :

train['tier'].fillna(round(train['tier'].mean(),2),inplace=True)

At last, I have tried different models (Xgboost, Random Forest with SMOTE, lightbgm etc..) I have got best results with lightbgm with some tuned parameters..

lgbm.fit(X_train,y_train)
LGBMClassifier(bagging_fraction=0.8, bagging_freq=15, boosting_type='gbdt',
 class_weight=None, colsample_bytree=1.0, feature_fraction=0.5,
 importance_type='split', is_unbalance=True, learning_rate=0.01,
 max_depth=7, min_child_samples=20, min_child_weight=0.001,
 min_split_gain=0.0, n_estimators=520, n_jobs=-1, num_leaves=40,
 objective=None, random_state=10, reg_alpha=0.0, reg_lambda=0.0,
 silent=True, subsample=1.0, subsample_for_bin=200000,
 subsample_freq=0)

Please refer full code here :
https://github.com/PraveenKS30/ML/blob/master/Surge2019/Surge%20Pre%20Machine%20Learning.ipynb

I am not sure what else I can do to improve my accuracy.. Should I preprocess in different way ? Should I try neural network now?
Please suggest.