Sharchat Recsys 2023 Challange - Recommendations Squad

The best model is xgb_all_feat and the corresponding code in src/xgb_all_cat_num.ipynb

List of the experiment results:

Model	Val Loss	Test Loss	Sharchat Timestap	Description
xgb_all_feat	5.968829287	6.37396	24/05/2023 05:09	Hyper parameter turing with optuna
f1_xgb_catb_best		6.375778	27/05/2023 15:33	Combined XGB and Catboost with f1 formula
xgb_all_feat_xgb_chain	5.99	6.385457	25/05/2023 16:06	Xgb on top of another Xgb model
catboost_all_feat	5.739692312	6.461268	25/05/2023 11:42	Catboost model with all the feature with optuna
xgb_stacked_kfold_logistic	Nan	6.611657	05/06/2023 16:34	XGB Stacked kflod Logistic Regression
xgb_calibrated_logistic		6.61368	26/05/2023 14:55	Calibrated XGB with logistic regression
xgb_stack_all_cat_num_xgb	6.026	6.64367	25/05/2023 06:20	Stack all the above row 1,2 and 3 optuna with xgb
xgb_num_cat_all_avg		6.690628	25/05/2023 16:22	Simple average of all the xgb of row 1
xgb_cat_feat	6.248026244	6.927283	24/05/2023 05:18	Hyper parameter turing with optuna while using only Categorical Features
xgb_float_all_feat	5.79	7.254	27/05/2023 21:11	Converted all the categorical values to probablites
xgb_num_feat	6.245909454	7.401632	24/05/2023 05:18 ,24/05/2023 05:19	Hyper parameter turing with optuna while using only Numerical Features
xgb_stack_all_cat_num	7.68580268	9.910395	25/05/2023 04:10	Stack all the above row 1,2 and 3 optuna with logistice regression model
xgb_full_data	Nan		27/05/2023 22:29	Validation data is also mixed with whole data but dates is not used