├── .idea ├── ActiveUserPrediction.iml ├── inspectionProfiles │ └── Project_Default.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── README.md ├── blending └── blending_v1.py ├── catboostpy ├── catboost_test.py ├── catboost_v7.py ├── cb_v8.py └── cb_v9.py ├── dataanalysispy ├── data_analysis.py ├── eda_v1.ipynb └── get_global_file.py ├── dataprocesspy ├── __pycache__ │ └── data_process_v7.cpython-36.pyc ├── create_data.py ├── create_feature_v3_nonp.py ├── create_feature_v3_parallel.py ├── data_process.py ├── data_process_v9.py ├── kuaishou_stats2.csv └── ts_feature_calculators.py ├── featureselection ├── feature_importance_mi.csv ├── feature_importance_shap.csv ├── feature_selection.py └── keep_features.py ├── hardcodedpy ├── hard_approach.py ├── hardcode_approach.py ├── hardcode_approach_v2.py ├── hardcode_approach_v3.py ├── merge_approach.py └── new_merge.py ├── lgbpy ├── kuaishou_stats.csv ├── lgb_model.py └── lgb_v16.py ├── lrpy ├── lr_v1.py └── lr_v2.py ├── model └── engines.py ├── nnpy ├── dnn.py ├── f1_keras.py ├── nn_model.py ├── nn_v1.py └── nn_v2.py ├── paper ├── Modeling and Predicting the Active video-viewing time in a large-scale e-learning system.pdf ├── The Prediction of Booking Destination on airbnb dataset.pdf ├── Using Deep Learning to Predict Customer Churn in a mobile telecommunication newwork.pdf ├── field-aware fatorization machine for CTR prediction.pdf └── predicting airbnb user's desired travel destination.pdf ├── photos ├── 16count.JPG ├── 23count.JPG ├── 23count3.JPG ├── 24count.JPG ├── 24count3.JPG ├── count2.JPG ├── describe.JPG ├── outlier1.JPG ├── registerday_count.JPG ├── sample.JPG └── value_count.JPG ├── quick_test.py ├── rfpy └── rf_v1.py ├── rulepy └── hardcode_approach.py ├── statsfile ├── kuaishou_stats.csv └── kuaishou_stats2.csv ├── svmpy └── svm_v1.py ├── test.py └── utilspy ├── calculate.py ├── create_data.py ├── kpca.py ├── kuaishou_stats2.csv ├── ts_feature_calculators.py ├── util_analysis.py ├── 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