Zimnat Insurance Recommendation Challenge
$5,000 USD
Can you predict which insurance products existing clients will want next?
1490 data scientists enrolled, 614 on the leaderboard
Customer servicePredictionStructured
1 July—13 September
75 days
which is best algorithim for this data

can anyone suggest me which one was the best algorthim works for this data

So far Catboost.

Catboost and Feed forward neural networks get you in the 0.027-0.028 range that is for me this was the case. Eventually Catboost trumped it. Make sure to run any of the NN algos on Google colab to use its higher GPU bandwidth. Otherwise it is very very taxing.

You can also use kaggke privit notebook with GPU, it works well. The data is available there.

LightGBM ended up working the best for me, but it required a lot more tweaking than Catboost did.

Can you share your code. Would like to see your approach and learn from it.

5-fold LGB gave me the best result

Lightgbm in dart mode had the best results according to cross validation. However, catboost had the best results according to public and private leaderboard. I should have put more weight to catboost in my ensemble to score higher. At first, I thought that my catboost model were overfitting to public leaderboard, but it wasn't the case.

could your share please your params? for me catboost and xgb perform worse.

@ssshch { 'boosting_type': 'dart', "max_depth":-1, "num_leaves":32, 'learning_rate': 0.1,"min_child_samples": 20, 'feature_fraction': 0.8,"bagging_freq":1,'bagging_fraction': 0.9,"lambda_l1":1,"lambda_l2":1}

However, I think you should be carefull with the number of iterations because dart mode does not support early stopping rounds.

@lcfstat, just asking-how do you get to see how your model is peforming from the private leaderboard?

@elijah-a-w Once the competision has finished, all participants can see their private leaderboard scores in the submissions menu.

My approach was sum of lgbm multiclass and binary problems.

one hot of categorical features, tfidf and tuning lambda_l2 increased my score.(+/- 40 place on public and privat)