Project Overview:

• Developed classification model by applying Random Forest Classifier, Logistic Regression, LightGBM, and XGBoost techniques to 32MM labeled training dataset and 20MM unlabeled test dataset with 23 attributes related to online ads

• Gained optimal model by Grid Search cross-validation, reached log loss of 0.408 on test data for model performance