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      "ref": "kanugeete/5-fold-classification-on-a-simple-neural-network",
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      "ref": "zfturbo/magic-feature-generator",
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      "ref": "jiweiliu/lb-0-9228-nn-weight-sharing-of-var-group",
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      "kernel_id": "3526755"
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      "ref": "elcaiseri/simple-gnb-and-pipeline-model-with-eda",
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      "ref": "sandeepkumar121995/santander-customer-transaction-prediction",
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      "kernel_id": "3524307"
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      "ref": "aawanghui/catboostalsoworks",
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      "ref": "offirinbar/santander-kaggle",
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      "ref": "subodanirodrigo/xgboost-for-santander-customer-transaction",
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      "kernel_id": "3522813"
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      "ref": "leonyang/blending-all",
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      "ref": "danmoller/funny-pattern-with-non-perfectly-round-numbers",
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      "ref": "christofhenkel/nn-with-magic-augmentation",
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      "ref": "matheuspush/santander-imbalanced-random-forest",
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      "kernel_id": "3520294"
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    {
      "ref": "kmezhoud/a-simple-xgboost-application",
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      "ref": "vikasmalhotra08/feature-binning-using-bayesian-blocks",
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      "kernel_id": "3517405"
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      "kernel_id": "3515484"
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      "ref": "tkanand/kernel286cf35111",
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      "kernel_id": "3515404"
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      "ref": "rsaund/sant-model-check",
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      "ref": "fuhang/weighted-kernel-naive-bayes-with-lasso-elimination",
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      "kernel_id": "3514586"
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      "ref": "infoabhitech/santander-customer-prediction-adaboost-smote",
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      "ref": "stevexyu/kfold-convolutional-neural-network",
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      "ref": "lzsraja/kernal-fe-gp-lgbm-keras",
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      "ref": "yoheiii/bayesian-optimization-for-santandar",
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      "ref": "dmacgirr/naive-bayes-normalized",
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      "ref": "nadare/catboost-with-gpu",
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      "ref": "taohoang/santander-magic-lgb-0-901-a2f5f9",
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      "ref": "dmacgirr/glm-on-balanced-sample-reduced-vars",
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      "ref": "tommyliucan/santander-eda",
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      "kernel_id": "3501205"
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    {
      "ref": "dmacgirr/balancing-target-variable-rpart",
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      "kernel_id": "3500963"
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    {
      "ref": "felipemello/why-your-model-is-overfitting-not-making-progress",
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      "kernel_id": "3498755"
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      "ref": "chizhu2018/augment-catv2",
      "title": "augment_catV2",
      "source": "live",
      "kernel_id": "3496431"
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    {
      "ref": "jatinpopli/project-santander-customer-transaction-predictions",
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      "kernel_id": "3496430"
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      "ref": "erseler/simplest-way-to-get-lb-score-0-887-gaussiannb",
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      "ref": "chizhu2018/lgb-sct-600",
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      "ref": "tasnimfatima/only-kmeansclustering-and-lgbm",
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      "ref": "niteshx2/keras-lgbm-aug-feature-eng-sampling-predic",
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      "kernel_id": "3491524"
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      "ref": "datahobbit/xgboost-hyperopt",
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      "kernel_id": "3489704"
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    {
      "ref": "mhviraf/what-a-decision-tree-cannot-see-ver-2",
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      "kernel_id": "3488902"
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      "ref": "kojimar/single-feature-prediction",
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      "ref": "tuanflash/simple-lgbm-model-add-feats-bayesopt",
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      "kernel_id": "3486839"
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      "ref": "dmacgirr/summary-df",
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      "ref": "frjo0083/lgbm-magic-sparse-3-fe",
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      "ref": "lordtt13/bi-time-series-model",
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      "ref": "scirpus/funky-gp-clustering",
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    {
      "ref": "ajnovice/santander-customer-transaction-prediction",
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      "ref": "naivelamb/multibranch-nn-baseline-magic-aug",
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      "ref": "mkowoods/gaussiannb-baseline-0-888-cv",
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      "ref": "dude431/is-there-any-magic-for-cat",
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      "ref": "bearstrikesback/yet-another-target-vs-vars",
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      "ref": "dpamgautam/santander-prediction",
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      "ref": "piyushm807/xgboost-basic-first-ever-kaggle-competition",
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      "ref": "m3yrin/santander-with-pfnet-autogbt",
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      "ref": "navarro380/santander-eda-keras-model-logistic-reg-lasso",
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      "kernel_id": "3463719"
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      "ref": "febilchacko/transaction-prediction-xgb-stratifiedkfold",
      "title": " Transaction Prediction- XGB + StratifiedKFold",
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      "kernel_id": "3463352"
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      "ref": "satyamsanu/santander-customer-transaction",
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      "ref": "adrianlievano/light-gbm-with-stratified-kfold",
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      "ref": "bobsaget/submission-file-for-santander",
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      "ref": "neibyr/simple-use-mean-target-encoding",
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      "ref": "mhviraf/there-are-no-categorical-features",
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      "ref": "ljjblackpig/eda-pca",
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      "kernel_id": "3458716"
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    {
      "ref": "trushk/fitting-gaussian-mixtures",
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      "ref": "stevexyu/decision-tree-baseline-for-santander",
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      "ref": "jiweiliu/pdf-of-original-and-magic-features",
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      "ref": "rgozun/ralph-gozun",
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      "ref": "whitebird/0-923-in-n-5-aug",
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      "ref": "coolaks/lgbm-kfold-feat-eng",
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      "ref": "coolaks/pytorch-complete-nn-santander-comp",
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      "ref": "dmacgirr/quick-anova-analysis-with-glm",
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      "kernel_id": "3449380"
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      "ref": "danmoller/modeling-features-with-many-normal-distributions",
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      "kernel_id": "3449070"
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    {
      "ref": "cherifabedrazek/santander-competition-first-interaction",
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      "ref": "super13579/lgbm-with-duplicate-flag-value-0-923",
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      "ref": "joe1995/weird-thing-search",
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      "ref": "praveenvijayarangan/santender-r-glm",
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      "ref": "geet9205/kernela542d5b38e",
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      "ref": "johnwei66/simple-xgb",
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      "kernel_id": "3442822"
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    {
      "ref": "mayankkestwal10/santander-catboost",
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      "source": "live",
      "kernel_id": "3441477"
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      "ref": "mkowoods/rfc-baseline",
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      "kernel_id": "3440473"
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      "ref": "scirpus/how-does-this-work-at-all",
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      "kernel_id": "3439867"
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    {
      "ref": "soumikdg/a-simple-clustering-based-pipe-line",
      "title": "A simple Clustering based pipe-line",
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      "kernel_id": "3439000"
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    {
      "ref": "muhammetcakmak/sml-algorithms-to-determine-customer-transaction",
      "title": "SML algorithms to determine customer transaction",
      "source": "live",
      "kernel_id": "3438612"
    },
    {
      "ref": "ebudia/simple-features-distributions",
      "title": "Simple features distributions",
      "source": "live",
      "kernel_id": "3436738"
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      "ref": "nroman/detecting-outliers-with-chauvenet-s-criterion",
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      "kernel_id": "3436732"
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      "ref": "ficklemaverick/feature-importances-using-random-forest",
      "title": "Feature Importances using Random Forest",
      "source": "live",
      "kernel_id": "3436228"
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    {
      "ref": "mauryaravi/santander-script",
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      "kernel_id": "3435552"
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    {
      "ref": "jingbinxu/eda-pca-simple-lgbm-on-kfold-technique-479fd8",
      "title": "EDA, PCA + Simple LGBM on KFold Technique 479fd8",
      "source": "live",
      "kernel_id": "3433819"
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    {
      "ref": "nekoumei/simple-eda-difference-in-dist-train-and-test",
      "title": "Simple EDA: difference in dist - train and test",
      "source": "live",
      "kernel_id": "3429274"
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      "ref": "josh24990/santander-customer-transaction-prediction",
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      "source": "live",
      "kernel_id": "3428434"
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    {
      "ref": "johnwei66/simple-logistic-regression",
      "title": "Simple Logistic Regression",
      "source": "live",
      "kernel_id": "3428236"
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      "ref": "hjd810/the-weighted-prediction-cross-validation-approach",
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      "kernel_id": "3428124"
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      "ref": "yassinealouini/are-ecdfs-better-than-histograms",
      "title": "Are ECDFs better than histograms?",
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      "kernel_id": "3427739"
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    {
      "ref": "pracas/decision-tree-visualisation",
      "title": "Decision_Tree_Visualisation",
      "source": "live",
      "kernel_id": "3427569"
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    {
      "ref": "carlospk/features-stats-dendogram",
      "title": "Features Stats Dendogram",
      "source": "live",
      "kernel_id": "3427518"
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    {
      "ref": "praxitelisk/santander-customer-transaction-prediction-eda-ml",
      "title": "Santander Customer Transaction Prediction EDA + ML",
      "source": "live",
      "kernel_id": "3427500"
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    {
      "ref": "pracas/z-score-lgb-2-leaves-augment",
      "title": "Z_Score_ LGB 2 leaves + augment",
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      "kernel_id": "3427330"
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    {
      "ref": "kmezhoud/ann-with-multi-layer-perception-keras-mlp",
      "title": "ANN with Multi_Layer Perception (Keras, MLP)",
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      "kernel_id": "3426781"
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    {
      "ref": "adamlouly/from-var-68-to-11-new-features",
      "title": "From var_68 to 11 new features",
      "source": "live",
      "kernel_id": "3426661"
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      "ref": "scirpus/i-am-sparsicus",
      "title": "I am Sparsicus",
      "source": "live",
      "kernel_id": "3426545"
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    {
      "ref": "marekwyborski/features-with-spikes",
      "title": "Features with spikes",
      "source": "live",
      "kernel_id": "3426501"
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    {
      "ref": "abdouljalil/auc-0-86-with-any-ml-model-just-maths",
      "title": "AUC 0.86 with any ML model, just maths !",
      "source": "live",
      "kernel_id": "3426008"
    },
    {
      "ref": "sandeepkumar121995/train-auc-0-930-valid-auc-0-917-lb-0-898",
      "title": "Train AUC 0.930, Valid AUC 0.917 (LB 0.898)",
      "source": "live",
      "kernel_id": "3425944"
    },
    {
      "ref": "robikscube/brainstorming-santander-features",
      "title": "Brainstorming Santander Features",
      "source": "live",
      "kernel_id": "3425909"
    },
    {
      "ref": "jiweiliu/fast-inplace-shuffle-for-augmentation",
      "title": "Fast inplace shuffle for augmentation",
      "source": "live",
      "kernel_id": "3425326"
    },
    {
      "ref": "bhaveshthaker/sctp-exploratory-data-analysis-eda-modeling",
      "title": "SCTP - Exploratory Data Analysis (EDA) & Modeling",
      "source": "live",
      "kernel_id": "3424779"
    },
    {
      "ref": "yusukesaito0141/target-distribution",
      "title": "target distribution",
      "source": "live",
      "kernel_id": "3424327"
    },
    {
      "ref": "bejeweled/h2o-santander-gridsearch",
      "title": "H2O Santander GridSearch",
      "source": "live",
      "kernel_id": "3424321"
    },
    {
      "ref": "silantev/santander-prediction-based-on-the-statistics",
      "title": "Santander prediction based on the statistics",
      "source": "live",
      "kernel_id": "3423729"
    },
    {
      "ref": "mauryaravi/snatander-customer-transaction-prediction",
      "title": "Snatander Customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3423704"
    },
    {
      "ref": "pracas/fast-pdf-correlation-matrix-how-many-groups",
      "title": "Fast PDF - correlation matrix - How Many Groups?",
      "source": "live",
      "kernel_id": "3423327"
    },
    {
      "ref": "vikram687/eda-santander-customer-transaction",
      "title": "EDA-Santander Customer Transaction",
      "source": "live",
      "kernel_id": "3422125"
    },
    {
      "ref": "jiweiliu/fast-pdf-calculation-with-correlation-matrix",
      "title": "Fast PDF calculation with correlation matrix",
      "source": "live",
      "kernel_id": "3421518"
    },
    {
      "ref": "darbin/hierarchical-clustering-approach",
      "title": "Hierarchical clustering approach",
      "source": "live",
      "kernel_id": "3413392"
    },
    {
      "ref": "isaranja/santander-eda-plotly",
      "title": "Santander : EDA plotly",
      "source": "live",
      "kernel_id": "3412377"
    },
    {
      "ref": "samfiddis/lgb-model",
      "title": "LGB Model",
      "source": "live",
      "kernel_id": "3409588"
    },
    {
      "ref": "amanoswal/kerneldfa1344a91",
      "title": "kerneldfa1344a91",
      "source": "live",
      "kernel_id": "3408903"
    },
    {
      "ref": "wlakinsson/santander-testing-pipeline",
      "title": "Santander - testing pipeline",
      "source": "live",
      "kernel_id": "3408576"
    },
    {
      "ref": "haimfeld87/thoughts-about-var-81",
      "title": "Thoughts about var_81",
      "source": "live",
      "kernel_id": "3407434"
    },
    {
      "ref": "miklgr500/nn-embedding",
      "title": "NN Embedding",
      "source": "live",
      "kernel_id": "3405488"
    },
    {
      "ref": "kaggle2007/santander-predictions-with-xgboost",
      "title": "Santander Predictions with XGBoost",
      "source": "live",
      "kernel_id": "3404410"
    },
    {
      "ref": "gilzamir/using-deep-learning-with-keras",
      "title": "Using Deep Learning with Keras",
      "source": "live",
      "kernel_id": "3404323"
    },
    {
      "ref": "aleksthegreat/auto-encoder-w-hidden-layers-nn",
      "title": "Auto Encoder w/ hidden layers + NN",
      "source": "live",
      "kernel_id": "3404320"
    },
    {
      "ref": "simongrest/finding-peaks-in-the-histograms-of-the-variables",
      "title": "Finding peaks in the histograms of the variables",
      "source": "live",
      "kernel_id": "3402939"
    },
    {
      "ref": "returnofsputnik/lgbm-count-differences-2",
      "title": "lgbm count differences 2",
      "source": "live",
      "kernel_id": "3402465"
    },
    {
      "ref": "returnofsputnik/lgbm-count-differences",
      "title": "lgbm count differences",
      "source": "live",
      "kernel_id": "3402315"
    },
    {
      "ref": "karangautam/oversampling-the-input",
      "title": "Oversampling the input",
      "source": "live",
      "kernel_id": "3402001"
    },
    {
      "ref": "returnofsputnik/number-of-unique-values-are-eerily-close",
      "title": "Number of unique values are eerily close",
      "source": "live",
      "kernel_id": "3401786"
    },
    {
      "ref": "fl2ooo/create-data",
      "title": "create_data",
      "source": "live",
      "kernel_id": "3401471"
    },
    {
      "ref": "josephgpinto/santander-lgbm-lgb-900",
      "title": "Santander LGBM -lgb .900",
      "source": "live",
      "kernel_id": "3400503"
    },
    {
      "ref": "lopost/santander-nn-v2",
      "title": "santander_nn_v2",
      "source": "live",
      "kernel_id": "3400231"
    },
    {
      "ref": "iggisv9t/autoencoder-for-data-structure-visualization",
      "title": "Autoencoder for data structure visualization",
      "source": "live",
      "kernel_id": "3400191"
    },
    {
      "ref": "miljan/overfitting-nn",
      "title": "Overfitting NN",
      "source": "live",
      "kernel_id": "3399992"
    },
    {
      "ref": "bruno16/lgb-2-leaves-augment",
      "title": "LGB 2 leaves + augment",
      "source": "live",
      "kernel_id": "3399435"
    },
    {
      "ref": "niteshx2/beginner-explained-lgb-2-leaves-augment",
      "title": "[Beginner Explained]LGB 2 leaves + augment",
      "source": "live",
      "kernel_id": "3398084"
    },
    {
      "ref": "gtambi/lgbm-parameter-optimization-bayesian",
      "title": "LGBM Parameter optimization - Bayesian",
      "source": "live",
      "kernel_id": "3397320"
    },
    {
      "ref": "denisco/logistic-experement-0-887",
      "title": "Logistic experement 0.887",
      "source": "live",
      "kernel_id": "3396881"
    },
    {
      "ref": "pracas/are-vars-mixed-up-time-intervals-cormat",
      "title": "Are vars mixed up time intervals? - cormat",
      "source": "live",
      "kernel_id": "3395266"
    },
    {
      "ref": "rifatjahanazad/exploring-the-data",
      "title": "Exploring the data",
      "source": "live",
      "kernel_id": "3394060"
    },
    {
      "ref": "ricksun/xgboost-stratifiedkfold-for-beginner",
      "title": "XGBoost + StratifiedKFold for beginner",
      "source": "live",
      "kernel_id": "3392429"
    },
    {
      "ref": "pipidog/simplest-model-naive-bayes-auc-0-89",
      "title": "Simplest Model, Naive Bayes, auc ~ 0.89",
      "source": "live",
      "kernel_id": "3392330"
    },
    {
      "ref": "sibmike/are-vars-mixed-up-time-intervals",
      "title": "Are vars mixed up time intervals?",
      "source": "live",
      "kernel_id": "3391460"
    },
    {
      "ref": "sibmike/subtracting-normality",
      "title": " Subtracting normality",
      "source": "live",
      "kernel_id": "3391455"
    },
    {
      "ref": "jganzabal/naive-bayes-vectorization-multiprocessing",
      "title": "Naive Bayes - Vectorization- Multiprocessing",
      "source": "live",
      "kernel_id": "3389660"
    },
    {
      "ref": "anki54/satander-customer-transaction-prediction",
      "title": "Satander Customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3388826"
    },
    {
      "ref": "nikitaomare/kernela4cb13745e",
      "title": "kernela4cb13745e",
      "source": "live",
      "kernel_id": "3388242"
    },
    {
      "ref": "rrathore01/two-different-distributions-in-data",
      "title": "Two different distributions in Data",
      "source": "live",
      "kernel_id": "3387427"
    },
    {
      "ref": "chloeycs/santander-eda-and-ml",
      "title": "Santander EDA and ML",
      "source": "live",
      "kernel_id": "3386534"
    },
    {
      "ref": "francoisdubois/correlation-matrix",
      "title": "correlation matrix",
      "source": "live",
      "kernel_id": "3386322"
    },
    {
      "ref": "prasad22s/kernelffd24dfd31",
      "title": "kernelffd24dfd31",
      "source": "live",
      "kernel_id": "3385962"
    },
    {
      "ref": "francoisdubois/timestamp-variables",
      "title": "timestamp variables",
      "source": "live",
      "kernel_id": "3384444"
    },
    {
      "ref": "harishreddy18/santander-customer-transaction-prediction",
      "title": "Santander customer transaction prediction",
      "source": "live",
      "kernel_id": "3384289"
    },
    {
      "ref": "balajisr/0-80-auc-with-a-single-feature",
      "title": "0.80+ AUC with a Single Feature",
      "source": "live",
      "kernel_id": "3383261"
    },
    {
      "ref": "ebudia/distributions-of-distributions",
      "title": "Distributions of distributions",
      "source": "live",
      "kernel_id": "3383243"
    },
    {
      "ref": "darbin/lgb-with-some-pca-features",
      "title": "LGB with some PCA features",
      "source": "live",
      "kernel_id": "3383099"
    },
    {
      "ref": "trushk/imbalanced-learn-for-handling-imbalanced-dataset",
      "title": "imbalanced-learn for handling imbalanced dataset",
      "source": "live",
      "kernel_id": "3380746"
    },
    {
      "ref": "arunsing/fork-of-santandar-eda-and-prediction",
      "title": "Fork of Santandar EDA and Prediction ",
      "source": "live",
      "kernel_id": "3380443"
    },
    {
      "ref": "amrmahmoud123/lgb-au",
      "title": "LGB  + au",
      "source": "live",
      "kernel_id": "3379970"
    },
    {
      "ref": "vv0x0x/santander-ridge",
      "title": "Santander Ridge",
      "source": "live",
      "kernel_id": "3378174"
    },
    {
      "ref": "darbin/lgb-on-pca-features",
      "title": "LGB on PCA features.",
      "source": "live",
      "kernel_id": "3376647"
    },
    {
      "ref": "mitjasha/lightgbm-with-blender-and-augment",
      "title": "lightgbm with blender and augment",
      "source": "live",
      "kernel_id": "3376371"
    },
    {
      "ref": "ttomas91/satander4-2-2-lgb-fake-train-aug",
      "title": "Satander4_2_2_lgb_fake_train_aug",
      "source": "live",
      "kernel_id": "3376225"
    },
    {
      "ref": "peterheindl/simple-model-stacking-using-mlxtend",
      "title": "Simple model stacking using mlxtend",
      "source": "live",
      "kernel_id": "3375623"
    },
    {
      "ref": "francoisdubois/some-features-could-be-probabilities",
      "title": "Some features could be probabilities  ?",
      "source": "live",
      "kernel_id": "3375231"
    },
    {
      "ref": "francoisdubois/fake-real-test-data-comparing-distrib",
      "title": "fake/real test data + comparing distrib",
      "source": "live",
      "kernel_id": "3373815"
    },
    {
      "ref": "subrahmanyamv/santander-sorted-features",
      "title": "Santander - Sorted Features",
      "source": "live",
      "kernel_id": "3372989"
    },
    {
      "ref": "benjibb/denoised-autoencoder-for-feature-engineering",
      "title": "Denoised Autoencoder for feature engineering",
      "source": "live",
      "kernel_id": "3371674"
    },
    {
      "ref": "mayank1261998/santander-avengers-ensemble",
      "title": "santander avengers ensemble",
      "source": "live",
      "kernel_id": "3371603"
    },
    {
      "ref": "b5strbal/lightgbm-naive-bayes-santander-0-900",
      "title": "LightGBM Naive Bayes - Santander - [0.900]",
      "source": "live",
      "kernel_id": "3370402"
    },
    {
      "ref": "matthew886/easy-nn-for-santander",
      "title": "Easy_nn_for_santander",
      "source": "live",
      "kernel_id": "3370297"
    },
    {
      "ref": "mks2192/list-of-fake-samples-and-public-private-lb-split",
      "title": "List of Fake Samples and Public/Private LB split",
      "source": "live",
      "kernel_id": "3369755"
    },
    {
      "ref": "mahyaret/santander-eda-using-xgboost-with-gpu-support",
      "title": "Santander EDA using XGBoost with GPU Support",
      "source": "live",
      "kernel_id": "3369591"
    },
    {
      "ref": "alexpetit12/getting-started-with-logistic-regression",
      "title": "Getting started with logistic regression",
      "source": "live",
      "kernel_id": "3369494"
    },
    {
      "ref": "nz0722/something-behind-var-21-var-22",
      "title": "Something Behind var_21 - var_22?",
      "source": "live",
      "kernel_id": "3368528"
    },
    {
      "ref": "hjd810/keras-lgbm-aug-feature-eng-sampling-prediction",
      "title": "Keras+LGBM  Aug+Feature Eng+Sampling Prediction",
      "source": "live",
      "kernel_id": "3367939"
    },
    {
      "ref": "nassiel/random-forest",
      "title": "Random Forest",
      "source": "live",
      "kernel_id": "3367555"
    },
    {
      "ref": "super13579/split-test-dataset",
      "title": "Split test dataset",
      "source": "live",
      "kernel_id": "3367502"
    },
    {
      "ref": "wlakinsson/santander-data-imbalance-fe",
      "title": "Santander - Data Imbalance: FE",
      "source": "live",
      "kernel_id": "3367113"
    },
    {
      "ref": "vaishnavinath/before-and-after-smote",
      "title": "Before and after SMOTE",
      "source": "live",
      "kernel_id": "3366592"
    },
    {
      "ref": "dgranuzzo/modified-naive-bayes-santander-0-899",
      "title": "Modified Naive Bayes - Santander - [0.899]",
      "source": "live",
      "kernel_id": "3365980"
    },
    {
      "ref": "yassinealouini/kernel-density-estimation",
      "title": "Kernel Density Estimation",
      "source": "live",
      "kernel_id": "3365859"
    },
    {
      "ref": "abhisingh879/santander-customertransaction-using-xgboost",
      "title": "Santander_CustomerTransaction using XGBoost",
      "source": "live",
      "kernel_id": "3365440"
    },
    {
      "ref": "jiazhuang/demonstrate-naive-bayes",
      "title": "demonstrate naive bayes",
      "source": "live",
      "kernel_id": "3364081"
    },
    {
      "ref": "hatunina/no-training-challenge",
      "title": "No training challenge ",
      "source": "live",
      "kernel_id": "3363340"
    },
    {
      "ref": "alexeych/strange-value-count-distributions",
      "title": "Strange value count distributions",
      "source": "live",
      "kernel_id": "3363251"
    },
    {
      "ref": "sandeep8530/pca-for-santander",
      "title": "PCA for santander",
      "source": "live",
      "kernel_id": "3360416"
    },
    {
      "ref": "jpison/transforming-from-gaussian-to-uniform-distribution",
      "title": "Transforming from Gaussian to Uniform Distribution",
      "source": "live",
      "kernel_id": "3359751"
    },
    {
      "ref": "yassinealouini/seeing-in-more-dimensions",
      "title": "Seeing in more dimensions?",
      "source": "live",
      "kernel_id": "3359270"
    },
    {
      "ref": "sandeep8530/keras-89-7-few-lessons-learnt",
      "title": "Keras-89.7. Few lessons learnt.",
      "source": "live",
      "kernel_id": "3358443"
    },
    {
      "ref": "yassinealouini/permutation-importance",
      "title": "Permutation Importance",
      "source": "live",
      "kernel_id": "3357787"
    },
    {
      "ref": "nakany15/geneticprogrammingfeatureengineering",
      "title": "GeneticProgrammingFeatureEngineering ",
      "source": "live",
      "kernel_id": "3356691"
    },
    {
      "ref": "yassinealouini/transforming-the-features",
      "title": "Transforming the features",
      "source": "live",
      "kernel_id": "3356138"
    },
    {
      "ref": "gokultalele/different-model-comparison-for-beginners",
      "title": "Different Model Comparison  For Beginners  ",
      "source": "live",
      "kernel_id": "3353977"
    },
    {
      "ref": "blackblitz/radius-neighbors-naive-bayes",
      "title": "Radius Neighbors Naive Bayes",
      "source": "live",
      "kernel_id": "3353538"
    },
    {
      "ref": "bejeweled/santander-eda",
      "title": "Santander EDA",
      "source": "live",
      "kernel_id": "3353491"
    },
    {
      "ref": "ilu000/santander-lgbm-detect-fake-train",
      "title": "Santander LGBM detect fake train",
      "source": "live",
      "kernel_id": "3351879"
    },
    {
      "ref": "jaredturner/sgd-classifier-no-probs",
      "title": "SGD Classifier - No Probs",
      "source": "live",
      "kernel_id": "3351866"
    },
    {
      "ref": "ilu000/unique-values-in-train-test-real-test-fake",
      "title": "Unique values in train / test_real / test_fake",
      "source": "live",
      "kernel_id": "3351590"
    },
    {
      "ref": "jesucristo/top-1-in-40-lines",
      "title": "top 1% in 40 lines",
      "source": "live",
      "kernel_id": "3351551"
    },
    {
      "ref": "cdeotte/modified-naive-bayes-santander-0-899",
      "title": "Modified Naive Bayes - Santander - [0.899]",
      "source": "live",
      "kernel_id": "3350111"
    },
    {
      "ref": "justfor/adversarial-validation-plus-ks-test",
      "title": "Adversarial Validation plus KS Test",
      "source": "live",
      "kernel_id": "3349879"
    },
    {
      "ref": "super13579/fork-of-fork-of-concate-nn-46ae3a",
      "title": "Fork of Fork of Concate NN 46ae3a",
      "source": "live",
      "kernel_id": "3349757"
    },
    {
      "ref": "shivani1711/eda-feature-eng-lgb-gpu-cb-gpu-ranking",
      "title": "EDA + Feature Eng + LGB(GPU) + CB(GPU) + Ranking",
      "source": "live",
      "kernel_id": "3348538"
    },
    {
      "ref": "atomkevich/denoising-autoencoder-sample-v1",
      "title": "denoising autoencoder sample v1",
      "source": "live",
      "kernel_id": "3348370"
    },
    {
      "ref": "wuliaokaola/santander-lb-0-869-no-need-to-train",
      "title": "Santander LB 0.869, No need to train",
      "source": "live",
      "kernel_id": "3347215"
    },
    {
      "ref": "pjt222/grouping-features-by-k-means",
      "title": "grouping features by k-means",
      "source": "live",
      "kernel_id": "3345864"
    },
    {
      "ref": "jiazhuang/z-value-as-features-to-train-nn-lgb-nb-models",
      "title": "Z-value as features to train NN/LGB/NB models",
      "source": "live",
      "kernel_id": "3344541"
    },
    {
      "ref": "rajeshcv/tensorflow-models",
      "title": "Tensorflow Models",
      "source": "live",
      "kernel_id": "3344239"
    },
    {
      "ref": "roydatascience/blender-of-0-901-solutions",
      "title": "Blender of 0.901 solutions",
      "source": "live",
      "kernel_id": "3343550"
    },
    {
      "ref": "tilii7/eliminate-features-recursively-cv",
      "title": "Eliminate features recursively + CV",
      "source": "live",
      "kernel_id": "3342172"
    },
    {
      "ref": "yag320/list-of-fake-samples-and-public-private-lb-split",
      "title": "List of Fake Samples and Public/Private LB split",
      "source": "live",
      "kernel_id": "3338898"
    },
    {
      "ref": "felipemello/boosting-creativity-towards-feature-engineering",
      "title": "Boosting creativity towards feature engineering",
      "source": "live",
      "kernel_id": "3338785"
    },
    {
      "ref": "frommer/dnn-santa",
      "title": "DNN Santa",
      "source": "live",
      "kernel_id": "3338094"
    },
    {
      "ref": "tomfranken/santander-simple-start",
      "title": "Santander Simple Start",
      "source": "live",
      "kernel_id": "3337961"
    },
    {
      "ref": "darbin/principal-component-analysis-approach",
      "title": "Principal component analysis approach",
      "source": "live",
      "kernel_id": "3337272"
    },
    {
      "ref": "triplex/more-unique-values-in-train-set-than-test-set",
      "title": "More unique values in train set than test set ?",
      "source": "live",
      "kernel_id": "3336686"
    },
    {
      "ref": "lbronchal/different-cross-validation-techniques",
      "title": "Different cross validation techniques",
      "source": "live",
      "kernel_id": "3336239"
    },
    {
      "ref": "painsama/testing-estimators-feature-engineering",
      "title": "Testing Estimators & Feature Engineering",
      "source": "live",
      "kernel_id": "3334871"
    },
    {
      "ref": "rahullalu/santander-ctp-feature-clustering",
      "title": "Santander CTP Feature Clustering",
      "source": "live",
      "kernel_id": "3334446"
    },
    {
      "ref": "matthew886/deep-learning",
      "title": "deep learning",
      "source": "live",
      "kernel_id": "3334365"
    },
    {
      "ref": "francoisdubois/distribution-of-target-by-variables",
      "title": "Distribution of target by variables ",
      "source": "live",
      "kernel_id": "3334313"
    },
    {
      "ref": "fdewes/lgbm-training-augmentation",
      "title": "LGBM + Training augmentation ",
      "source": "live",
      "kernel_id": "3332488"
    },
    {
      "ref": "jiazhuang/a-proof-of-synthetic-data",
      "title": "A proof of synthetic data",
      "source": "live",
      "kernel_id": "3331476"
    },
    {
      "ref": "aboost/predict-rmd",
      "title": "predict.Rmd",
      "source": "live",
      "kernel_id": "3331072"
    },
    {
      "ref": "isaranja/santander-lgb-ae",
      "title": "Santander : LGB & AE",
      "source": "live",
      "kernel_id": "3330687"
    },
    {
      "ref": "tomtillo/distribution-plots-of-all-variables",
      "title": "Distribution  plots of all variables",
      "source": "live",
      "kernel_id": "3330389"
    },
    {
      "ref": "qq313303303/magic-data-augment",
      "title": "Magic Data Augment",
      "source": "live",
      "kernel_id": "3329852"
    },
    {
      "ref": "interneuron/simply-sorting",
      "title": "simply sorting",
      "source": "live",
      "kernel_id": "3329271"
    },
    {
      "ref": "justlookinxd/baliz",
      "title": "Baliz",
      "source": "live",
      "kernel_id": "3328186"
    },
    {
      "ref": "tilii7/old-school-linear-svm",
      "title": "Old-school Linear SVm",
      "source": "live",
      "kernel_id": "3328015"
    },
    {
      "ref": "filipeborato/santander-predctions",
      "title": "Santander predctions",
      "source": "live",
      "kernel_id": "3326928"
    },
    {
      "ref": "jurajm/simple-and-quick-solution-with-naive-bayes",
      "title": "Simple and Quick solution with Naive Bayes",
      "source": "live",
      "kernel_id": "3326329"
    },
    {
      "ref": "jpison/moving-window-average-of-target-by-var-68",
      "title": "Moving window average of 'target' by 'var_68'",
      "source": "live",
      "kernel_id": "3326135"
    },
    {
      "ref": "returnofsputnik/frequentist-inference-approach-to-santander-round0",
      "title": "Frequentist Inference Approach to Santander round0",
      "source": "live",
      "kernel_id": "3325972"
    },
    {
      "ref": "dursunkoc/rapska",
      "title": "Rapska",
      "source": "live",
      "kernel_id": "3324891"
    },
    {
      "ref": "samueltommzy/santander-customerprediction-model",
      "title": "Santander CustomerPrediction Model",
      "source": "live",
      "kernel_id": "3324473"
    },
    {
      "ref": "kmezhoud/tree-classification-by-recursive-partitioning",
      "title": "Tree Classification by Recursive partitioning",
      "source": "live",
      "kernel_id": "3322972"
    },
    {
      "ref": "iamrahulr2k/ludwig-experiment",
      "title": "ludwig_experiment",
      "source": "live",
      "kernel_id": "3321589"
    },
    {
      "ref": "omgrodas/lightgbm-with-data-augmentation",
      "title": "Lightgbm with data augmentation",
      "source": "live",
      "kernel_id": "3319789"
    },
    {
      "ref": "pnussbaum/santander-genetically-engineered-features-v07",
      "title": "Santander Genetically Engineered Features v07",
      "source": "live",
      "kernel_id": "3313181"
    },
    {
      "ref": "jacky5112/santander-customer-transaction-prediction-cnn",
      "title": "Santander Customer Transaction Prediction _ CNN",
      "source": "live",
      "kernel_id": "3313102"
    },
    {
      "ref": "ssslarry/2-layers-lightgbm",
      "title": "2 Layers LightGBM",
      "source": "live",
      "kernel_id": "3312948"
    },
    {
      "ref": "mueno115/randomly-shuffle-python",
      "title": "Randomly shuffle python",
      "source": "live",
      "kernel_id": "3311919"
    },
    {
      "ref": "boombasteek/nn-keras-scaler-cmp",
      "title": "NN_keras_scaler_cmp",
      "source": "live",
      "kernel_id": "3310841"
    },
    {
      "ref": "jesucristo/90-lines-solution-0-901-fast",
      "title": "90 lines solution 0.901 #Fast",
      "source": "live",
      "kernel_id": "3310138"
    },
    {
      "ref": "nandishsr/santander-customer-transaction-prediction",
      "title": "Santander customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3308611"
    },
    {
      "ref": "drdivya/santandar-baseline-model-light-gbm",
      "title": "Santandar baseline model (Light GBM)",
      "source": "live",
      "kernel_id": "3307848"
    },
    {
      "ref": "youlei0106/santander-customer-transaction-prediction-f2",
      "title": "Santander Customer Transaction Prediction_F2",
      "source": "live",
      "kernel_id": "3306670"
    },
    {
      "ref": "aawanghui/catboost",
      "title": "CatBoost",
      "source": "live",
      "kernel_id": "3306136"
    },
    {
      "ref": "danimelv/lr-over-sampling",
      "title": "LR - Over sampling",
      "source": "live",
      "kernel_id": "3304541"
    },
    {
      "ref": "selakavon/kernel476e3326b3",
      "title": "kernel476e3326b3",
      "source": "live",
      "kernel_id": "3303559"
    },
    {
      "ref": "jakubczakon/top-public-kernels-comparison",
      "title": "Top public kernels comparison",
      "source": "live",
      "kernel_id": "3300659"
    },
    {
      "ref": "ozanozgur/don-t-overfit-to-cv-oversampling-mean-encoding",
      "title": "Don't overfit to CV(Oversampling, mean encoding)",
      "source": "live",
      "kernel_id": "3300134"
    },
    {
      "ref": "zxspectrum/catboost-gpu",
      "title": "CatBoost GPU",
      "source": "live",
      "kernel_id": "3299499"
    },
    {
      "ref": "rsmits/cnn-1d-model",
      "title": "CNN 1D model",
      "source": "live",
      "kernel_id": "3299269"
    },
    {
      "ref": "karangautam/lightgbm-santander",
      "title": "LightGBM Santander",
      "source": "live",
      "kernel_id": "3298992"
    },
    {
      "ref": "bayuaji1997/bayes",
      "title": "Bayes",
      "source": "live",
      "kernel_id": "3298785"
    },
    {
      "ref": "mattburt07/santander-comp-simple-linear-nn-pytorch",
      "title": "Santander Comp - Simple Linear NN - PyTorch",
      "source": "live",
      "kernel_id": "3298417"
    },
    {
      "ref": "aritrase/pytorch-ff-nn",
      "title": "Pytorch_FF_NN",
      "source": "live",
      "kernel_id": "3297578"
    },
    {
      "ref": "danimelv/modelo-catboost-base",
      "title": "Modelo CatBoost Base",
      "source": "live",
      "kernel_id": "3297006"
    },
    {
      "ref": "gtambi/lgbm-augmentation",
      "title": "LGBM + Augmentation",
      "source": "live",
      "kernel_id": "3296545"
    },
    {
      "ref": "silantev/santander-prediction-using-gaussiannb",
      "title": "Santander Prediction using GaussianNB",
      "source": "live",
      "kernel_id": "3295490"
    },
    {
      "ref": "seriousran/dist-of-suspicious-vars-with-legendary-var-68",
      "title": "Dist. of suspicious vars with legendary var_68",
      "source": "live",
      "kernel_id": "3295485"
    },
    {
      "ref": "seriousran/0-and-1-distributions-in-each-features",
      "title": "0 and 1 distributions in each features",
      "source": "live",
      "kernel_id": "3293537"
    },
    {
      "ref": "anjum48/has-pca-or-svd-been-performed-on-the-dataset",
      "title": "Has PCA or SVD been performed on the dataset?",
      "source": "live",
      "kernel_id": "3287566"
    },
    {
      "ref": "yassinealouini/mystery-behind-var-68",
      "title": "Mystery behind var_68?",
      "source": "live",
      "kernel_id": "3285780"
    },
    {
      "ref": "gaurav146/customer-prediction-lgbm",
      "title": "Customer prediction ( lgbm )",
      "source": "live",
      "kernel_id": "3285560"
    },
    {
      "ref": "peterheindl/lgbm-parameter-tuning-with-bayesian-optimisation",
      "title": "LGBM Parameter Tuning with Bayesian Optimisation",
      "source": "live",
      "kernel_id": "3285072"
    },
    {
      "ref": "nakany15/undersample-bagging",
      "title": "UnderSample+Bagging",
      "source": "live",
      "kernel_id": "3284316"
    },
    {
      "ref": "zikazika/reusable-insightful-eda",
      "title": "Reusable/Insightful EDA",
      "source": "live",
      "kernel_id": "3283574"
    },
    {
      "ref": "samyak0112/kernel4e4a28744b",
      "title": "kernel4e4a28744b",
      "source": "live",
      "kernel_id": "3283392"
    },
    {
      "ref": "omgrodas/baseline-nn-with-fast-ai",
      "title": "Baseline NN with fast.ai",
      "source": "live",
      "kernel_id": "3283376"
    },
    {
      "ref": "garikhakobyan/lda-and-regression-analysis",
      "title": "LDA and Regression Analysis",
      "source": "live",
      "kernel_id": "3283266"
    },
    {
      "ref": "mailyousufkhan/santander",
      "title": "Santander",
      "source": "live",
      "kernel_id": "3282832"
    },
    {
      "ref": "lockeza/convolutional-nn-model",
      "title": "Convolutional NN model",
      "source": "live",
      "kernel_id": "3282562"
    },
    {
      "ref": "theoverfitter3139/lightgbm-with-gridsearch-starter-1",
      "title": "Lightgbm with GridSearch [Starter] #1",
      "source": "live",
      "kernel_id": "3282243"
    },
    {
      "ref": "darbin/random-solution-auc-0-5",
      "title": "Random solution (AUC = 0.5)",
      "source": "live",
      "kernel_id": "3281264"
    },
    {
      "ref": "yamashita/santander-transaction-stacking-1-0",
      "title": "Santander Transaction Stacking 1.0",
      "source": "live",
      "kernel_id": "3281218"
    },
    {
      "ref": "adnaiksachin25/dl-gbm-h2o",
      "title": "DL+GBM_h2o",
      "source": "live",
      "kernel_id": "3281207"
    },
    {
      "ref": "graf10a/overshuffling",
      "title": "Overshuffling",
      "source": "live",
      "kernel_id": "3280875"
    },
    {
      "ref": "roydatascience/santander-transaction-stacking-1-0",
      "title": "Santander Transaction Stacking 1.0",
      "source": "live",
      "kernel_id": "3280853"
    },
    {
      "ref": "evilpsycho42/effect-of-joint-distribution-on-targets-update",
      "title": "Effect of Joint Distribution on Targets, update !!",
      "source": "live",
      "kernel_id": "3280534"
    },
    {
      "ref": "amrmahmoud123/lightgbm-fe",
      "title": "Lightgbm+FE",
      "source": "live",
      "kernel_id": "3279272"
    },
    {
      "ref": "roydatascience/eda-pca-simple-lgbm-on-kfold-technique",
      "title": "EDA, PCA + Simple LGBM on KFold Technique",
      "source": "live",
      "kernel_id": "3277695"
    },
    {
      "ref": "mitjasha/854-easy-nn-for-santander",
      "title": "[.854]Easy_nn_for_santander",
      "source": "live",
      "kernel_id": "3277182"
    },
    {
      "ref": "sandeeppat/fastai-tabular-learner-baseline",
      "title": "FastAI - Tabular Learner BaseLine",
      "source": "live",
      "kernel_id": "3275882"
    },
    {
      "ref": "bejeweled/stats-features-lgbm",
      "title": "stats features lgbm",
      "source": "live",
      "kernel_id": "3275737"
    },
    {
      "ref": "jakubczakon/example-lightgbm-neptune-tracking",
      "title": "example lightgbm neptune tracking ",
      "source": "live",
      "kernel_id": "3274810"
    },
    {
      "ref": "yassinealouini/a-cup-of-tea",
      "title": "A cup of tea",
      "source": "live",
      "kernel_id": "3274655"
    },
    {
      "ref": "gorokhovnik/one-line-solution-what-for",
      "title": "one line solution (what for?)",
      "source": "live",
      "kernel_id": "3274629"
    },
    {
      "ref": "garikhakobyan/upgrade-on-ann",
      "title": "Upgrade on ANN",
      "source": "live",
      "kernel_id": "3273665"
    },
    {
      "ref": "garikhakobyan/fork-of-ann-in-the-go",
      "title": "Fork of ANN in the go",
      "source": "live",
      "kernel_id": "3273089"
    },
    {
      "ref": "elenabarbour/simple-random-forest-w-oversampling-using-smote",
      "title": "Simple Random Forest w/ Oversampling using SMOTE",
      "source": "live",
      "kernel_id": "3271009"
    },
    {
      "ref": "sarmat/eda-and-preprocessing-example",
      "title": "EDA and preprocessing example ",
      "source": "live",
      "kernel_id": "3267777"
    },
    {
      "ref": "arnabdan/nn-dropouts-early-stopping-acc-loss-plots",
      "title": "NN-dropouts-early-stopping-acc-loss-plots",
      "source": "live",
      "kernel_id": "3267489"
    },
    {
      "ref": "peterheindl/lightgbm-random-parameter-search-auc-threshold",
      "title": "LightGBM random parameter search & AUC threshold",
      "source": "live",
      "kernel_id": "3266535"
    },
    {
      "ref": "itslek/automl-easy-with-h2o-santander-transaction-v3",
      "title": "AutoML Easy with H2O {Santander Transaction} v3",
      "source": "live",
      "kernel_id": "3265670"
    },
    {
      "ref": "binilg/santander-compact-solution-8-lines-will-do",
      "title": "Santander Compact Solution (8 lines will do)",
      "source": "live",
      "kernel_id": "3265536"
    },
    {
      "ref": "karangautam/correcting-mistake",
      "title": "Correcting Mistake",
      "source": "live",
      "kernel_id": "3262296"
    },
    {
      "ref": "super13579/lgbm-model-only",
      "title": "LGBM model ONLY",
      "source": "live",
      "kernel_id": "3261622"
    },
    {
      "ref": "dmacgirr/naive-bayes-not-normalized",
      "title": "Naive Bayes - not normalized",
      "source": "live",
      "kernel_id": "3260986"
    },
    {
      "ref": "jiweiliu/lgb-2-leaves-augment",
      "title": "LGB 2 leaves + augment",
      "source": "live",
      "kernel_id": "3260936"
    },
    {
      "ref": "ronrickd/lasso-logistic",
      "title": "Lasso+Logistic",
      "source": "live",
      "kernel_id": "3260555"
    },
    {
      "ref": "ranjoranjan/kernel83f071aec1",
      "title": "kernel83f071aec1",
      "source": "live",
      "kernel_id": "3259609"
    },
    {
      "ref": "reidempey/santander-classification-problem-w-tyler",
      "title": "Santander Classification Problem - w/ Tyler",
      "source": "live",
      "kernel_id": "3259011"
    },
    {
      "ref": "djordjenedeljkovic/santander-customer",
      "title": "Santander Customer",
      "source": "live",
      "kernel_id": "3257918"
    },
    {
      "ref": "mhviraf/santander-compact-solution-14-lines-will-do",
      "title": "Santander Compact++ Solution (14 lines will do)",
      "source": "live",
      "kernel_id": "3257690"
    },
    {
      "ref": "society765/kernel-xgboost-cv",
      "title": "kernel_xgboost_cv",
      "source": "live",
      "kernel_id": "3256993"
    },
    {
      "ref": "hireme/using-only-one-feature-for-prediction",
      "title": "using only ONE feature for prediction ",
      "source": "live",
      "kernel_id": "3255919"
    },
    {
      "ref": "adnaiksachin25/santander-simple-h2o-deep-learning-model",
      "title": "Santander_Simple_H2o deep learning model",
      "source": "live",
      "kernel_id": "3255596"
    },
    {
      "ref": "adarsh415/santander-customer-transaction-tensorflow",
      "title": "Santander Customer Transaction  Tensorflow",
      "source": "live",
      "kernel_id": "3254459"
    },
    {
      "ref": "ymatioun/santander-model-one-feature-at-a-time",
      "title": "Santander: model one feature at a time",
      "source": "live",
      "kernel_id": "3254347"
    },
    {
      "ref": "xubujie/hint-for-categorize-features",
      "title": "Hint for categorize features ??",
      "source": "live",
      "kernel_id": "3254005"
    },
    {
      "ref": "youlei0106/kernel5c5f0b715c",
      "title": "kernel5c5f0b715c",
      "source": "live",
      "kernel_id": "3253816"
    },
    {
      "ref": "bearstrikesback/adversarial-validation-plus-ks-test",
      "title": "Adversarial Validation plus KS Test",
      "source": "live",
      "kernel_id": "3253272"
    },
    {
      "ref": "laplaceplanet/just-params-0-900",
      "title": "Just Params 0.900",
      "source": "live",
      "kernel_id": "3251958"
    },
    {
      "ref": "emand2pac/woe-decomposition-lgb",
      "title": "WOE+decomposition+LGB",
      "source": "live",
      "kernel_id": "3249974"
    },
    {
      "ref": "plasticgrammer/santander-customer-transaction-gaussiannb",
      "title": "Santander Customer Transaction: GaussianNB",
      "source": "live",
      "kernel_id": "3248888"
    },
    {
      "ref": "nholloway/comparing-resampling-techniques-and-xgboost",
      "title": "Comparing Resampling Techniques and XGBoost",
      "source": "live",
      "kernel_id": "3247963"
    },
    {
      "ref": "niyipop/simple-2-layer-nn-in-pytorch",
      "title": "Simple 2-Layer NN in PyTorch",
      "source": "live",
      "kernel_id": "3247575"
    },
    {
      "ref": "sionek/randomly-shuffled-data-also-works",
      "title": "Randomly Shuffled Data Also Works",
      "source": "live",
      "kernel_id": "3246833"
    },
    {
      "ref": "abbbhishekkk/santandar-customer-transactions-prediction",
      "title": "Santandar Customer Transactions prediction",
      "source": "live",
      "kernel_id": "3246201"
    },
    {
      "ref": "wwu651/auto-feature-engineering-lgb",
      "title": "Auto-feature Engineering + LGB",
      "source": "live",
      "kernel_id": "3245811"
    },
    {
      "ref": "returnofsputnik/what-are-the-most-common-numbers",
      "title": "What are the most common numbers?",
      "source": "live",
      "kernel_id": "3244865"
    },
    {
      "ref": "samueltommzy/ctprediction-model",
      "title": "CTPrediction Model",
      "source": "live",
      "kernel_id": "3244678"
    },
    {
      "ref": "apj1995/lightgbm0-9",
      "title": "LightGBM0.9",
      "source": "live",
      "kernel_id": "3240603"
    },
    {
      "ref": "akashsharma/santander2",
      "title": "Santander2",
      "source": "live",
      "kernel_id": "3237611"
    },
    {
      "ref": "jomaxx/area-under-the-roc-curve-explained",
      "title": "area under the ROC curve explained",
      "source": "live",
      "kernel_id": "3236665"
    },
    {
      "ref": "vishalbajaj2000/santander-lightgbm-xgb-feature-interactions",
      "title": "Santander - LightGBM + XGB Feature Interactions",
      "source": "live",
      "kernel_id": "3236545"
    },
    {
      "ref": "returnofsputnik/study-santander-clustering-1-to-30-kmeans",
      "title": "study santander clustering: 1 to 30 kmeans",
      "source": "live",
      "kernel_id": "3234212"
    },
    {
      "ref": "sandeepkumar121995/magic-parameters",
      "title": "Magic Parameters !!",
      "source": "live",
      "kernel_id": "3234113"
    },
    {
      "ref": "ubaid01/kernel8d8c92de47",
      "title": "kernel8d8c92de47",
      "source": "live",
      "kernel_id": "3233973"
    },
    {
      "ref": "adnaiksachin25/santander-lgbm-216-features",
      "title": "Santander_LGBM_216_features",
      "source": "live",
      "kernel_id": "3233931"
    },
    {
      "ref": "roydatascience/eda-pca-lgbm-santander-transactions",
      "title": "EDA, PCA + LGBM : Santander Transactions",
      "source": "live",
      "kernel_id": "3233321"
    },
    {
      "ref": "jesucristo/30-lines-starter-solution-fast",
      "title": "30 lines  Starter Solution #FAST",
      "source": "live",
      "kernel_id": "3232686"
    },
    {
      "ref": "graf10a/santander-eli5-weights-for-lightgbm",
      "title": "Santander: ELI5 weights for LightGBM",
      "source": "live",
      "kernel_id": "3228278"
    },
    {
      "ref": "tahirmuslimscientist/best-feature-engineering-techniques-for-ml",
      "title": "Best Feature Engineering techniques for ML",
      "source": "live",
      "kernel_id": "3227845"
    },
    {
      "ref": "charlessuhartanto/looking-at-data",
      "title": "Looking at data",
      "source": "live",
      "kernel_id": "3224541"
    },
    {
      "ref": "rmadhnakv2/3-layer-ann",
      "title": "3 layer ann",
      "source": "live",
      "kernel_id": "3223963"
    },
    {
      "ref": "arulrajj/gridsearchcv",
      "title": "gridsearchcv",
      "source": "live",
      "kernel_id": "3223668"
    },
    {
      "ref": "marcospcsj/kernel-cod-valid-cruzada-lgbm",
      "title": "kernel_cod_valid_cruzada_lgbm",
      "source": "live",
      "kernel_id": "3223558"
    },
    {
      "ref": "adarsh415/solving-santander-customer-transaction-problem",
      "title": "Solving Santander Customer Transaction  Problem",
      "source": "live",
      "kernel_id": "3222488"
    },
    {
      "ref": "adnaiksachin25/santander-208-features",
      "title": "Santander_208_features",
      "source": "live",
      "kernel_id": "3221810"
    },
    {
      "ref": "jazivxt/blender",
      "title": "Blender",
      "source": "live",
      "kernel_id": "3220685"
    },
    {
      "ref": "brandenkmurray/randomly-shuffled-data-also-works",
      "title": "Randomly Shuffled Data Also Works",
      "source": "live",
      "kernel_id": "3220587"
    },
    {
      "ref": "kamalchhirang/simple-lightgbm-with-good-parameters",
      "title": "Simple LightGBM with Good Parameters",
      "source": "live",
      "kernel_id": "3220485"
    },
    {
      "ref": "lavanyadml/santander-ls",
      "title": "Santander LS",
      "source": "live",
      "kernel_id": "3220090"
    },
    {
      "ref": "tandonarpit6/santander-customer-lgb-neural-network",
      "title": "Santander Customer - LGB , Neural Network",
      "source": "live",
      "kernel_id": "3219642"
    },
    {
      "ref": "aawanghui/santander-nn",
      "title": "Santander NN",
      "source": "live",
      "kernel_id": "3216641"
    },
    {
      "ref": "swarnim97/simple-neural-network",
      "title": "Simple Neural Network",
      "source": "live",
      "kernel_id": "3215323"
    },
    {
      "ref": "maarklam/logistic-regression-vs-neural-network",
      "title": "Logistic Regression vs Neural Network",
      "source": "live",
      "kernel_id": "3215249"
    },
    {
      "ref": "sreekarchidurala/santander",
      "title": "Santander ",
      "source": "live",
      "kernel_id": "3214744"
    },
    {
      "ref": "jesucristo/santander-magic-lgb-0-901",
      "title": "Santander Magic LGB 0.901",
      "source": "live",
      "kernel_id": "3212328"
    },
    {
      "ref": "tturan27/santander",
      "title": "Santander",
      "source": "live",
      "kernel_id": "3211969"
    },
    {
      "ref": "jpnewmenji/data-exploration",
      "title": "Data Exploration",
      "source": "live",
      "kernel_id": "3211110"
    },
    {
      "ref": "super13579/lgbm-model-catboost",
      "title": "LGBM model + CATBOOST",
      "source": "live",
      "kernel_id": "3209110"
    },
    {
      "ref": "kazutak/lgbm-santander-customer-transaction-prediction",
      "title": "LGBM - Santander Customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3203583"
    },
    {
      "ref": "qinhui1999/denoising-autoencoder-sample-v1",
      "title": "denoising autoencoder sample v1",
      "source": "live",
      "kernel_id": "3202585"
    },
    {
      "ref": "cttsai/santander-ctp-catboost-w-statistical-features",
      "title": "Santander CTP CatBoost  w/ Statistical Features",
      "source": "live",
      "kernel_id": "3200915"
    },
    {
      "ref": "ajithvajrala/santander-customer-transaction-prediction",
      "title": "Santander Customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3199219"
    },
    {
      "ref": "kensengds2019/santander-eda",
      "title": "Santander EDA",
      "source": "live",
      "kernel_id": "3199114"
    },
    {
      "ref": "davidmarkalbert/santander-eda-to-model-comparison",
      "title": "santander-eda-to-model-comparison",
      "source": "live",
      "kernel_id": "3197990"
    },
    {
      "ref": "jinudaniel/lightgbm-xgboost-and-catboost",
      "title": "LightGBM, XGBoost and CatBoost",
      "source": "live",
      "kernel_id": "3196943"
    },
    {
      "ref": "m3yrin/simple-eda-lightgbm-prediction",
      "title": "Simple EDA + lightGBM Prediction",
      "source": "live",
      "kernel_id": "3196288"
    },
    {
      "ref": "danimelv/modelo-xgboost",
      "title": "Modelo XGBoost",
      "source": "live",
      "kernel_id": "3196092"
    },
    {
      "ref": "wanderdust/santander-customer-satisfaction-pablo-jorge",
      "title": "Santander Customer Satisfaction Pablo, Jorge",
      "source": "live",
      "kernel_id": "3195361"
    },
    {
      "ref": "nz0722/santander-k-means-new-features-and-lgb",
      "title": "Santander k-means new features and LGB",
      "source": "live",
      "kernel_id": "3194970"
    },
    {
      "ref": "arunsing/santandar-eda-and-prediction",
      "title": "Santandar EDA and Prediction ",
      "source": "live",
      "kernel_id": "3192730"
    },
    {
      "ref": "mstamatis/feature-interactions-fm-nffm-5fold-updating",
      "title": "Feature Interactions(fm/nffm-5Fold)(updating)",
      "source": "live",
      "kernel_id": "3191598"
    },
    {
      "ref": "tezdhar/getting-started-santander",
      "title": "Getting started Santander",
      "source": "live",
      "kernel_id": "3190433"
    },
    {
      "ref": "prayanshratan/santander-customer",
      "title": "Santander Customer ",
      "source": "live",
      "kernel_id": "3188674"
    },
    {
      "ref": "sohaibanwaar1203/diminsionality-reduction-and-smote-sampling",
      "title": "Diminsionality Reduction and SMOTE SAMPLING",
      "source": "live",
      "kernel_id": "3185601"
    },
    {
      "ref": "rednivrug/dae-with-lightgbm",
      "title": "DAE with lightgbm",
      "source": "live",
      "kernel_id": "3184137"
    },
    {
      "ref": "yamashita/santander-eda-and-prediction-1b2dff",
      "title": "Santander EDA and Prediction 1b2dff",
      "source": "live",
      "kernel_id": "3184022"
    },
    {
      "ref": "huangzhiyong/a-simple-lgb-base-on-undersampler",
      "title": "a simple lgb base on UnderSampler",
      "source": "live",
      "kernel_id": "3181692"
    },
    {
      "ref": "aleksthegreat/help-with-autoencoder-vae",
      "title": "Help with autoencoder/vae",
      "source": "live",
      "kernel_id": "3181375"
    },
    {
      "ref": "mingaliev/santander-lgb-new-features-rank-mean-10-folds",
      "title": "Santander LGB + new features + rank mean 10 folds",
      "source": "live",
      "kernel_id": "3180684"
    },
    {
      "ref": "brandenkmurray/nothing-works",
      "title": "Nothing Works",
      "source": "live",
      "kernel_id": "3179538"
    },
    {
      "ref": "returnofsputnik/does-binning-bimodal-variables-improve",
      "title": "Does binning bimodal variables improve ?",
      "source": "live",
      "kernel_id": "3179274"
    },
    {
      "ref": "harshel7/transaction-prediction-ensemble-nn-lgbm-cat",
      "title": "Transaction Prediction (Ensemble NN+LGBM+CAT)",
      "source": "live",
      "kernel_id": "3178915"
    },
    {
      "ref": "scirpus/santander-gp",
      "title": "Santander GP",
      "source": "live",
      "kernel_id": "3178793"
    },
    {
      "ref": "redroy44/catboost-hyperopt",
      "title": "catboost + hyperopt",
      "source": "live",
      "kernel_id": "3178255"
    },
    {
      "ref": "ranjoranjan/simple-lightgbm-0-899-lb",
      "title": "Simple LightGBM 0.899 LB",
      "source": "live",
      "kernel_id": "3176173"
    },
    {
      "ref": "vikash1a2b3c/transaction-prediction-using-catboost-and-kfold",
      "title": "transaction prediction using catboost and kfold ",
      "source": "live",
      "kernel_id": "3175474"
    },
    {
      "ref": "krithi07/logistic-to-lightgbm-for-beginners",
      "title": "Logistic to LightGBM for Beginners ",
      "source": "live",
      "kernel_id": "3175079"
    },
    {
      "ref": "tarunpaparaju/santander-data-visualization",
      "title": "Santander Data Visualization",
      "source": "live",
      "kernel_id": "3172303"
    },
    {
      "ref": "udaygurugubelli/tf-dnn",
      "title": "TF:DNN",
      "source": "live",
      "kernel_id": "3171315"
    },
    {
      "ref": "sicongfang/eda-feature-engineering",
      "title": "EDA & Feature Engineering ",
      "source": "live",
      "kernel_id": "3170646"
    },
    {
      "ref": "mingaliev/simple-xgbm-899",
      "title": "Simple XGBM 899",
      "source": "live",
      "kernel_id": "3169377"
    },
    {
      "ref": "danimelv/logistic-regression-base",
      "title": "Logistic Regression Base",
      "source": "live",
      "kernel_id": "3168887"
    },
    {
      "ref": "mingaliev/simple-lightgbm-0-898-lb",
      "title": "Simple LightGBM 0.898 LB",
      "source": "live",
      "kernel_id": "3168224"
    },
    {
      "ref": "graf10a/data-serialization",
      "title": "data_serialization",
      "source": "live",
      "kernel_id": "3167758"
    },
    {
      "ref": "mingaliev/simple-xgboost-0-889-on-public",
      "title": "Simple XgBoost 0.889 on public",
      "source": "live",
      "kernel_id": "3167155"
    },
    {
      "ref": "drsergio/save-intermediate-results-for-stacking",
      "title": "Save intermediate results for stacking",
      "source": "live",
      "kernel_id": "3166819"
    },
    {
      "ref": "dalip98/santandar-customer-prediction-using-decision-tree",
      "title": "Santandar Customer Prediction using Decision Tree",
      "source": "live",
      "kernel_id": "3164413"
    },
    {
      "ref": "ankitdhall97/basic-models",
      "title": "Basic Models",
      "source": "live",
      "kernel_id": "3164086"
    },
    {
      "ref": "gunesevitan/santander-customer-transaction-eda-fe-lgb",
      "title": "Santander Customer Transaction - EDA / FE / LGB",
      "source": "live",
      "kernel_id": "3163498"
    },
    {
      "ref": "t26wtnb/adabound-optimizer-dnn-v-s-adam-experiment",
      "title": "AdaBound Optimizer DNN + v.s. Adam Experiment",
      "source": "live",
      "kernel_id": "3160355"
    },
    {
      "ref": "ruby33421/quick-start-0-9-lgb-with-new-features",
      "title": " Quick start @ 0.9 - LGB with New Features",
      "source": "live",
      "kernel_id": "3158672"
    },
    {
      "ref": "pnussbaum/santander-statistics-v04",
      "title": "Santander-Statistics v04",
      "source": "live",
      "kernel_id": "3156613"
    },
    {
      "ref": "krishnakesavan/r-sample-keras-5-fold-cv",
      "title": "R  sample Keras 5 fold CV",
      "source": "live",
      "kernel_id": "3156496"
    },
    {
      "ref": "tankist88/xgb-with-k-fold",
      "title": "XGB with k-Fold",
      "source": "live",
      "kernel_id": "3155471"
    },
    {
      "ref": "samfiddis/simple-keras-model",
      "title": "Simple Keras Model",
      "source": "live",
      "kernel_id": "3155023"
    },
    {
      "ref": "debjoydas/customer-transaction",
      "title": "Customer_transaction",
      "source": "live",
      "kernel_id": "3154559"
    },
    {
      "ref": "luancaius/santander-transactions-v1",
      "title": "Santander Transactions - v1",
      "source": "live",
      "kernel_id": "3152959"
    },
    {
      "ref": "shubham311994/santander-new",
      "title": "Santander_new",
      "source": "live",
      "kernel_id": "3152500"
    },
    {
      "ref": "sushrutshitoot/santander-draft-v2-eda-lgb-nn",
      "title": "Santander Draft v2 - EDA + LGB + NN",
      "source": "live",
      "kernel_id": "3151769"
    },
    {
      "ref": "igauty/sctp-ml",
      "title": "SCTP_ML",
      "source": "live",
      "kernel_id": "3151744"
    },
    {
      "ref": "fionyip/new-player-logistic-reg-rforest-xgboost",
      "title": "New player : Logistic reg, RForest , XGBoost",
      "source": "live",
      "kernel_id": "3150890"
    },
    {
      "ref": "justlookinxd/lightgbm-hyperparameter-lb-0-899",
      "title": "Lightgbm hyperparameter LB 0.899",
      "source": "live",
      "kernel_id": "3150707"
    },
    {
      "ref": "nick2logan/basicmodels-originaldata",
      "title": "basicmodels_originaldata",
      "source": "live",
      "kernel_id": "3150394"
    },
    {
      "ref": "tomehta/adding-features-and-evaluation",
      "title": "Adding Features and evaluation",
      "source": "live",
      "kernel_id": "3147612"
    },
    {
      "ref": "canonwu/save-intermediate-results-for-stacking",
      "title": "Save intermediate results for stacking",
      "source": "live",
      "kernel_id": "3144973"
    },
    {
      "ref": "akhileshrai003/logistic-regression-with-new-features-feather",
      "title": "Logistic regression with new features + feather",
      "source": "live",
      "kernel_id": "3144101"
    },
    {
      "ref": "mikelkn/how-to-oversample-for-beginners-lb-score-0-788",
      "title": "How to oversample; for Beginners: LB Score 0.788",
      "source": "live",
      "kernel_id": "3143835"
    },
    {
      "ref": "marcocarnini/lightgbm-model-optimizing-learning-reate",
      "title": "Lightgbm model - Optimizing learning_reate",
      "source": "live",
      "kernel_id": "3141941"
    },
    {
      "ref": "stryker0710/what-if-features-transactions",
      "title": "What if features=transactions",
      "source": "live",
      "kernel_id": "3141818"
    },
    {
      "ref": "bhupendramishra2504/santander-submission",
      "title": "Santander Submission",
      "source": "live",
      "kernel_id": "3141505"
    },
    {
      "ref": "asrsaiteja/eda-santander-customer-transaction-prediction",
      "title": "EDA - Santander Customer Transaction Prediction",
      "source": "live",
      "kernel_id": "3139520"
    },
    {
      "ref": "karangautam/everything-overfits",
      "title": "Everything Overfits",
      "source": "live",
      "kernel_id": "3139313"
    },
    {
      "ref": "ytochii/dnn-linear-batchnormalization-using-chainer",
      "title": "DNN(Linear +BatchNormalization)  Using Chainer ",
      "source": "live",
      "kernel_id": "3139134"
    },
    {
      "ref": "ibozkurt79/santander-lightgbm-a-boost-with-bayesiansearch",
      "title": "Santander LightGBM a boost with BayesianSearch",
      "source": "live",
      "kernel_id": "3139109"
    },
    {
      "ref": "krishnakesavan/r-sample-catboost-5-fold-cv",
      "title": "R sample catboost 5 fold CV",
      "source": "live",
      "kernel_id": "3138797"
    },
    {
      "ref": "santhoshkatta/nn-scaled-encoded-early-stopping-loss-acc-plots",
      "title": "NN-scaled-encoded-early stopping-loss&Acc plots",
      "source": "live",
      "kernel_id": "3136530"
    },
    {
      "ref": "tomehta/feature-engineering-eda-and-lgbm",
      "title": "Feature Engineering , EDA and LGBM",
      "source": "live",
      "kernel_id": "3136254"
    },
    {
      "ref": "poppins/lgbm-extended-features-optimized",
      "title": "LGBM extended features optimized",
      "source": "live",
      "kernel_id": "3135782"
    },
    {
      "ref": "shrutimechlearn/santander-customer-transaction-pca-and-nb",
      "title": "Santander customer transaction, PCA and NB",
      "source": "live",
      "kernel_id": "3134932"
    },
    {
      "ref": "mohanamurali/naive-baye-stacked-with-xgboost-bayesian-optim",
      "title": "Naive Baye stacked with XGboost bayesian optim",
      "source": "live",
      "kernel_id": "3132317"
    },
    {
      "ref": "silverstone1903/xgboost-baseline",
      "title": "XGBoost Baseline ",
      "source": "live",
      "kernel_id": "3131672"
    },
    {
      "ref": "super13579/pytorch-nn-cyclelr-k-fold-0-897-lightgbm-0-899",
      "title": "Pytorch NN CycleLR+K fold(0.897) & LightGBM(0.899)",
      "source": "live",
      "kernel_id": "3130604"
    },
    {
      "ref": "vmvadivel/basic-xgboost-based-solution",
      "title": "Basic xgboost based solution",
      "source": "live",
      "kernel_id": "3129681"
    },
    {
      "ref": "graf10a/logistic-regression-with-new-features-feather",
      "title": "Logistic regression with new features + feather",
      "source": "live",
      "kernel_id": "3127602"
    },
    {
      "ref": "apurvasinha2003/light-gbm-with-eda",
      "title": "Light GBM with EDA",
      "source": "live",
      "kernel_id": "3127163"
    },
    {
      "ref": "kaggle2007/simple-nn-with-accuracy-and-loss-plotting",
      "title": "Simple NN with Accuracy and Loss Plotting",
      "source": "live",
      "kernel_id": "3126980"
    },
    {
      "ref": "nick2logan/dimentionality-reduction",
      "title": "dimentionality_reduction",
      "source": "live",
      "kernel_id": "3126890"
    },
    {
      "ref": "jprusso/deep-learning",
      "title": "Deep Learning",
      "source": "live",
      "kernel_id": "3124672"
    },
    {
      "ref": "axel81/nn-model-using-fastai-1-0-lb-score-0-879",
      "title": "NN model using FastAI 1.0 LB Score  0.879",
      "source": "live",
      "kernel_id": "3123104"
    },
    {
      "ref": "marcospcsj/simple-xgboost-test",
      "title": "simple xgboost test",
      "source": "live",
      "kernel_id": "3122780"
    },
    {
      "ref": "peterheindl/variable-importance-with-r-and-glmnet",
      "title": "Variable importance with R and glmnet",
      "source": "live",
      "kernel_id": "3121734"
    },
    {
      "ref": "silvia09/sctp-test",
      "title": "SCTP_TEST",
      "source": "live",
      "kernel_id": "3121013"
    },
    {
      "ref": "blackblitz/gaussian-mixture-naive-bayes",
      "title": "Gaussian Mixture Naive Bayes",
      "source": "live",
      "kernel_id": "3119498"
    },
    {
      "ref": "jomaxx/feature-importances-mutual-information",
      "title": "Feature Importances (Mutual Information) ",
      "source": "live",
      "kernel_id": "3118775"
    },
    {
      "ref": "yujirokita/adversarial-validation",
      "title": "Adversarial Validation?",
      "source": "live",
      "kernel_id": "3117204"
    },
    {
      "ref": "zakraicik/ensemble-gnb-xgboost-lb-0-895",
      "title": "Ensemble - GNB +  XGBoost (LB = 0.895)",
      "source": "live",
      "kernel_id": "3116135"
    },
    {
      "ref": "vishalbajaj2000/santander-lgbm-baseline",
      "title": "Santander - LGBM Baseline",
      "source": "live",
      "kernel_id": "3115058"
    },
    {
      "ref": "envym3/santander-customer-transaction-prediction-2",
      "title": "Santander Customer Transaction Prediction 2",
      "source": "live",
      "kernel_id": "3114062"
    },
    {
      "ref": "raushan000/kernela5ec979588",
      "title": "kernela5ec979588",
      "source": "live",
      "kernel_id": "3113036"
    },
    {
      "ref": "rahul516/santander-decisiontree",
      "title": "santander decisiontree",
      "source": "live",
      "kernel_id": "3112742"
    },
    {
      "ref": "jialinzhang/linearregression-oversampling",
      "title": "LinearRegression+Oversampling",
      "source": "live",
      "kernel_id": "3111813"
    },
    {
      "ref": "jialinzhang/bayesianridge-oversampling",
      "title": "BayesianRidge+Oversampling",
      "source": "live",
      "kernel_id": "3111708"
    },
    {
      "ref": "jialinzhang/lr-oversampling",
      "title": "LR+Oversampling",
      "source": "live",
      "kernel_id": "3111342"
    },
    {
      "ref": "jialinzhang/xgb-oversampling",
      "title": "Xgb+Oversampling",
      "source": "live",
      "kernel_id": "3111227"
    },
    {
      "ref": "sandan0o0/lgb-regression",
      "title": "LGB_regression",
      "source": "live",
      "kernel_id": "3111007"
    },
    {
      "ref": "igauty/sctp1",
      "title": "SCTP1",
      "source": "live",
      "kernel_id": "3110389"
    },
    {
      "ref": "jacksonisaac/initial-eda-fastai-tabular-default-baseline",
      "title": "Initial EDA + FastAI Tabular Default Baseline",
      "source": "live",
      "kernel_id": "3109875"
    },
    {
      "ref": "viewside/kernel1480a081ac",
      "title": "kernel1480a081ac",
      "source": "live",
      "kernel_id": "3108886"
    },
    {
      "ref": "dromosys/gpu-accelerated-lightgbm-full",
      "title": "GPU-accelerated LightGBM full",
      "source": "live",
      "kernel_id": "3108572"
    },
    {
      "ref": "jsarda/predict-3-0",
      "title": "Predict 3.0",
      "source": "live",
      "kernel_id": "3106608"
    },
    {
      "ref": "ymatioun/santander-linear-model-with-additional-features",
      "title": "Santander linear model with additional features",
      "source": "live",
      "kernel_id": "3106138"
    },
    {
      "ref": "harshpyati/kernel65d73a8fcc",
      "title": "kernel65d73a8fcc",
      "source": "live",
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