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Feel free to add some more.\n\n## EDA\n\n### Image input dataset (aka *deep learning* competition)\n- [Anokas - Exploratory Data Analysis](https://www.kaggle.com/anokas/data-science-bowl-2017/exploratory-data-analysis-4)\n\n\n### Non-image input dataset (aka *xgboost* competition)\n- [SRK - Simple Exploration Notebook - Sberbank](https://www.kaggle.com/sudalairajkumar/sberbank-russian-housing-market/simple-exploration-notebook-sberbank)\n- [Angela - Detailed Data Exploration in Python](https://www.kaggle.com/xchmiao/house-prices-advanced-regression-techniques/detailed-data-exploration-in-python)\n- [BeyondBeneath - Geolocation visualisations](https://www.kaggle.com/beyondbeneath/talkingdata-mobile-user-demographics/geolocation-visualisations)\n- [Pedro Marcelino - Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/house-prices-advanced-regression-techniques/comprehensive-data-exploration-with-python)\n- [Alan (AJ) Pryor, Jr. - Detailed Cleaning/Visualization (Python)](https://www.kaggle.com/apryor6/santander-product-recommendation/detailed-cleaning-visualization-python)\n- [Philipp Schmidt - Quora EDA \u0026 Model selection (ROC, PR plots)](https://www.kaggle.com/philschmidt/quora-question-pairs/quora-eda-model-selection-roc-pr-plots)\n\n## Technics :\n- [Anisotropic - Introduction to Ensembling/Stacking in Python](https://www.kaggle.com/arthurtok/titanic/introduction-to-ensembling-stacking-in-python)\n\n- [Kyle HounslowA - Method for Finding Leaked Images in Test Set](https://www.kaggle.com/kylehounslow/a-method-for-finding-leaked-images-in-test-set)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvfdev-5%2Fawesome-kaggle-kernels","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvfdev-5%2Fawesome-kaggle-kernels","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvfdev-5%2Fawesome-kaggle-kernels/lists"}