{"cells":[{"metadata":{},"cell_type":"markdown","source":"# What's this\n- I couldn't participate in this competition because I didn't have enough time\n- Therefore, I will read and summarize the highly rated notes for my future knowledge."},{"metadata":{},"cell_type":"markdown","source":"# EDA\n\n[Riiid: Comprehensive EDA + Baseline](https://www.kaggle.com/erikbruin/riiid-comprehensive-eda-baseline)\n[\nRiiid! Answer Correctness Prediction EDA. Modeling]https://www.kaggle.com/isaienkov/riiid-answer-correctness-prediction-eda-modeling\n- basicaly eda\n\n\n\n# Modeling\n[Competition API Detailed Introduction](https://www.kaggle.com/erikbruin/riiid-comprehensive-eda-baseline)\n- description of kaggle timeseries api\n\n\n\n[LGBM with Loop Feature Engineering](https://www.kaggle.com/its7171/lgbm-with-loop-feature-engineering)\n- Public Score 0.760\n- idea of looging lgbm in timeseries data to feature engineering\n\n\n[Riiid model LGBM](https://www.kaggle.com/ragnar123/riiid-model-lgbm)\n- LGBM model with loop feature engineering\n\n\n\n# preprocess\n[Tutorial on reading large datasets](https://www.kaggle.com/rohanrao/tutorial-on-reading-large-datasets)\n- this competition have large size dataset\n- in case of pandas, this will solved by making type when reading csv.\n\n[RIIID with blazing fast RID](https://www.kaggle.com/rohanrao/riiid-with-blazing-fast-rid)\n- using pythontable to convert pandas dataset for this large dataset.\n\n\n\n# Validation\n\n[CV Strategy](https://www.kaggle.com/its7171/cv-strategy)\n- CV as not cause time leaking\n\n\n\n\n\n[\nRiiid! Training and Prediction using a state](https://www.kaggle.com/markwijkhuizen/riiid-training-and-prediction-using-a-state)\n- [later to read]\n\n[Riiid! Training and Prediction using a state](https://www.kaggle.com/andradaolteanu/answer-correctness-rapids-xgb-lgbm)\n- [later to read]\n\n[Simple EDA and Baseline](https://www.kaggle.com/ilialar/simple-eda-and-baseline)\n- [later to read]\n\n[\nTime-series API (iter_test) Emulator](https://www.kaggle.com/its7171/time-series-api-iter-test-emulator)\n- [later to read]\n\n\n[SAKT with Randomization & State Updates](https://www.kaggle.com/leadbest/sakt-with-randomization-state-updates)\n- [later to read]\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}