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      "ref": "gauridargar/american-express-eda-by-gauri",
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      "ref": "dungdao706/amex-deep-learning-deepctr-autoint-xdeepfm",
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      "ref": "youheitomio/gpu-version-amex-lgbm-by-youhei-tomio",
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      "ref": "pham0030/ibm-coursera-clf-final-amex-default-prediction",
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      "ref": "duanchenliu/baseline-exploration",
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      "ref": "faseeh001/amex-data-analysis",
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      "ref": "junjitakeshima/amex-try-to-improve-lgbm-starter-eng",
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      "ref": "ryandpark/notebookbe778ae9f0",
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    {
      "ref": "sshikamaru/american-express-default-prediction",
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    {
      "ref": "pallavijagtap/american-express-default-prediction-9th-aug",
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      "ref": "awaldeep/submission-for-xgboost-optuna-baseline",
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      "ref": "ishaan45/amex-feature-engineering-ranking",
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      "ref": "duanchenliu/xgboost-starter-0-793",
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      "ref": "adorachang/data-exploring-adora",
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      "kernel_id": "28223772"
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    {
      "ref": "dkraynak/amex-basic-logit-with-feather-data",
      "title": "AMEX - Basic Logit with Feather Data",
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    {
      "ref": "santosh1974/amex-first",
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    {
      "ref": "jonaswm/improving-lightgbm-model-with-cleanlab",
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    {
      "ref": "what5up/amex-lstm-pytorch-lightning-training",
      "title": "⚡ AMEX LSTM [PyTorch-Lightning] ⚡ TRAINING",
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      "kernel_id": "28209514"
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    {
      "ref": "what5up/amex-lstm-pytorch-lightning-inf-0-717",
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      "ref": "jonaswm/autogluon-automl",
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      "kernel_id": "28207743"
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    {
      "ref": "devsubhash/amex-eda-default-prediction",
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      "kernel_id": "28206699"
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    {
      "ref": "narendra/amex-gru-train",
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      "kernel_id": "28205364"
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    {
      "ref": "madhuban17/amex-reading-data",
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    {
      "ref": "girishkumarsahu/american-express-default-prediction-eda",
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      "ref": "rosicky1234/simple-logistic-regression",
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    {
      "ref": "nandakishorejoshi/amex-pre-and-post-model-predictive-analysis",
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    {
      "ref": "datauma/credit-card-default-prediction-amex",
      "title": "Credit card Default Prediction - AMEX",
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      "ref": "ryumdra/amex-date-customers",
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      "ref": "raddar/deanonymized-days-overdue-feat-amex",
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      "ref": "danofer/amex-data-preprocesing-feature-engineering",
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      "ref": "pyagoubi/amex-eda-evolvement-of-numeric-features-over-time",
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      "ref": "seraquevence/ensemble-weighted-average-iii-0-797",
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      "kernel_id": "28122702"
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    {
      "ref": "sarang210/american-express-correlations",
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      "source": "live",
      "kernel_id": "28112854"
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      "ref": "abdellatifsassioui/create-pickeld-data-from-50-gb-to-6gb",
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      "ref": "vivek61/amex-eda-model-building",
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      "kernel_id": "28085549"
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      "ref": "jagdmir/american-express-default-prediction-eda",
      "title": "American Express Default Prediction - EDA",
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    {
      "ref": "junjitakeshima/amex-simple-lgbm-starter-for-beginner-en",
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      "kernel_id": "28083084"
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    {
      "ref": "duanchenliu/data-exploring-dl",
      "title": "Data Exploring_DL",
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      "kernel_id": "28077979"
    },
    {
      "ref": "ragnar123/amex-lgbm-dart-cv-0-7963",
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      "kernel_id": "28077463"
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    {
      "ref": "pvtrmalli/amex-default-prediction-eda-lgbm",
      "title": "💳💳Amex default prediction --💳💳 EDA && LGBM💳💳",
      "source": "live",
      "kernel_id": "28072970"
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    {
      "ref": "asapbabass/american-express-analyse-de-donn-es",
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      "kernel_id": "28072861"
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    {
      "ref": "stautxie/amex-default-prediction-model-in-r-part-2",
      "title": "AMEX Default Prediction - Model in R (Part 2)",
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      "kernel_id": "28069068"
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    {
      "ref": "datajmcn/xgboost-explainer-testing-model-studio",
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      "ref": "susnato/amex-borutashap-feature-selection",
      "title": "AMEX - BorutaShap Feature Selection ",
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      "kernel_id": "28058434"
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    {
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      "source": "live",
      "kernel_id": "28054037"
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    {
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      "source": "live",
      "kernel_id": "28049357"
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    {
      "ref": "raddar/understanding-na-values-in-amex-competition",
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      "kernel_id": "28041815"
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    {
      "ref": "datajmcn/amex-eda-time-series-transformations",
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      "kernel_id": "28038527"
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    {
      "ref": "pavelvod/amex-eda-even-more-insane-time-patterns-revealed",
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      "source": "live",
      "kernel_id": "28036879"
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    {
      "ref": "yamaich/amex-nacounts-train-and-test",
      "title": "AmEx_NAcounts(train and test)",
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      "kernel_id": "28035618"
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    {
      "ref": "ahmedali058/amex-default-predection",
      "title": "Amex Default predection",
      "source": "live",
      "kernel_id": "28028233"
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    {
      "ref": "raj401/visualize-the-learningratescheduler",
      "title": "Visualize the LearningRateScheduler!!!",
      "source": "live",
      "kernel_id": "28019843"
    },
    {
      "ref": "gzguevara/compute-new-features-based-on-risk-variables",
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      "source": "live",
      "kernel_id": "28017013"
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    {
      "ref": "hinepo/amex-tabnet-training",
      "title": "AMEX - TabNet training",
      "source": "live",
      "kernel_id": "28010386"
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    {
      "ref": "ryanhartz/notebook5459c2a14f",
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      "source": "live",
      "kernel_id": "28002078"
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    {
      "ref": "nickimpark/amex-prediction",
      "title": "AMEX Prediction",
      "source": "live",
      "kernel_id": "28000733"
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    {
      "ref": "bhavikardeshna/hyperparameter-optimization-optuna",
      "title": "Hyperparameter Optimization || Optuna",
      "source": "live",
      "kernel_id": "27997416"
    },
    {
      "ref": "aaron874/amex-competition-eda-and-model-selections",
      "title": "Amex Competition: EDA and Model Selections",
      "source": "live",
      "kernel_id": "27993198"
    },
    {
      "ref": "pavelvod/amex-eda-revealing-time-patterns-of-features",
      "title": "AMEX EDA: Revealing time patterns of features",
      "source": "live",
      "kernel_id": "27982943"
    },
    {
      "ref": "narendra/amex-xgboost-baseline",
      "title": "Amex Xgboost baseline",
      "source": "live",
      "kernel_id": "27979624"
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      "ref": "beezus666/ensemble-weighted-average",
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      "ref": "sravanneeli/train-file-pckl-creation-from-parquet-files",
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      "ref": "apoorvbhardwaj/amex-hyperparams-optimisation-using-optuna",
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      "ref": "datajmcn/baseline-model-xgboost",
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      "ref": "rriceice/tensorflow-gru-starter-0-790",
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