Kaggle Challenges

Keeping Customers Content

Kaggle Challenge
Industry:Banking
Topic:Customer Satisfaction

Dataset: Santander Customer Satisfaction dataset

Goal: Predict dissatisfied customers without detailed features and background.

Dataset: Anonymized dataset with 370 features for 76,020 customers

Solution: For the golden ensemble, Firefly Lab selected four Random Forest and one Gradient Boosting models. During preprocessing, Firefly Lab drew on Decision Tree and Logistic Regression for feature stacking, then engaged in feature selection for the Gradient Boosting model only, using the ExtraTrees Classifier.

Firefly Lab Results: 83.418%

Rank: Exceeded 1st   place of  5123 competing teams

(Firefly’s score outdistanced the winning entry by the amount that the winner exceeded the 2734th place entry.)

Data Scientist Time: 20 minutes