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      "ref": "ashishpatel26/is-it-solusion",
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      "ref": "ashishpatel26/idea-that-works-better-than-data-leakage",
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      "ref": "nanomathias/genetic-programming-to-find-giba-s-property",
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      "ref": "titericz/the-property-by-giba",
      "title": "The Property by Giba",
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      "ref": "cenglish/santander-keras-1",
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      "source": "live",
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      "ref": "wentixiaogege/santander-46-features-add-andrew-s-feature-b337d2",
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      "source": "live",
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      "ref": "cedrickchee/the-easiest-way-to-use-pytorch-with-kaggle-s-gpu",
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      "ref": "ashishpatel26/sentander-value-challanges",
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      "title": "santad_label is present in row?",
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      "ref": "authman/eda-what-do-the-columns-mean",
      "title": "EDA: What Do The Columns Mean?",
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      "source": "live",
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      "source": "live",
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      "kernel_id": "1286294"
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      "ref": "sheboke93/santander-46-features-add-andrew-s-feature",
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      "ref": "lcltopismine/compare-two-method-of-feature-extraction",
      "title": "Compare two method of Feature Extraction",
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      "ref": "titericz/giba-countvectorizer-d-lb-1-43",
      "title": "Giba CountVectorizer :-D   -  LB: 1.43",
      "source": "live",
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      "ref": "arindamgot/stacked-ensemble-using-h2o-rmsle-1-39-local-cv",
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      "ref": "guoyiyang92/simple-feature-selection-categorical-continuous",
      "title": "Simple Feature Selection: Categorical & Continuous",
      "source": "live",
      "kernel_id": "1282962"
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      "title": "Benchmark model with LGBM [1.48]",
      "source": "live",
      "kernel_id": "1282561"
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      "source": "live",
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      "ref": "schlerp/bespoke-numpy-ann-for-regression",
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      "kernel_id": "1280227"
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    {
      "ref": "adarshchavakula/out-of-fold-oof-model-cross-validation",
      "title": "Out-of-Fold (OOF) Model Cross Validation",
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      "kernel_id": "1278482"
    },
    {
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      "title": "feature_scoring_vs_zeros",
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      "title": "tuning hyper parameters with scikit-optimize",
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      "kernel_id": "1276211"
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      "ref": "brendajohn1/santander-value-prediction-kek",
      "title": "Santander_Value_Prediction_Kek",
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      "ref": "bminixhofer/a-different-validation-technique",
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      "source": "live",
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      "ref": "khushal17adlakha/santander-lightgbm-rmse-and-cv",
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    {
      "ref": "rishgupta34/practice",
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      "source": "live",
      "kernel_id": "1265158"
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    {
      "ref": "meaninglesslives/vecstack-ensemble-10models",
      "title": "vecstack_ensemble_10Models",
      "source": "live",
      "kernel_id": "1263703"
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    {
      "ref": "leighplt/simple-pytorch-with-kaggle-s-gpu",
      "title": "Simple PyTorch with kaggle's GPU",
      "source": "live",
      "kernel_id": "1262279"
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      "title": "Basic analysis and pre-processing with Pandas",
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      "kernel_id": "1261745"
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      "ref": "hoiytoiy/train-test-data-differs-in-non-zeros-density",
      "title": "Train & test data differs in non-zeros density",
      "source": "live",
      "kernel_id": "1259804"
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    {
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      "title": "Atleast 162 categorical values in each column!!",
      "source": "live",
      "kernel_id": "1256967"
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    {
      "ref": "aditya1702/adversarial-val-lgbm-xgb-model-lb-1-40",
      "title": "Adversarial Val, LGBM+XGB Model[LB - 1.40]",
      "source": "live",
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      "ref": "amitkumarjaiswal/xgb-with-feature-engineering",
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      "source": "live",
      "kernel_id": "1252158"
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      "ref": "fenglilee/lr-and-lightgbm-prediction-analysis",
      "title": "LR  and lightGBM prediction analysis",
      "source": "live",
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      "ref": "famazon/adversarial-validation-set",
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      "kernel_id": "1251597"
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      "ref": "hmendonca/testing-features-with-shapley-values",
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      "source": "live",
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      "ref": "sggpls/public-sin-lb-1-38",
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      "ref": "analyticalmind/eda-for-santander-value-prediction",
      "title": "EDA for Santander Value Prediction",
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      "kernel_id": "1243690"
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      "ref": "longlongfeng/the-difference-between-pca-tsvd-experiment",
      "title": "The difference between PCA & tSVD - Experiment",
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      "kernel_id": "1242989"
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      "ref": "ashishpatel26/santendar-value-challange-using-lgbm-cv",
      "title": "Santendar value Challange using LGBM CV",
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      "kernel_id": "1240718"
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      "ref": "fabianaboldrin/pipeline-kernel-xgb-data-cleaning",
      "title": "Pipeline Kernel. xgb + data_cleaning",
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      "kernel_id": "1238858"
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      "ref": "ci3l4k/quick-overview-of-the-data",
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      "ref": "ashishpatel26/santander-value-challange-using-keras",
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      "kernel_id": "1236408"
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      "ref": "ashishpatel26/hyper-parameter-tuning-code-for-lightgbm",
      "title": "Hyper parameter Tuning code for LightGBM",
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      "kernel_id": "1235245"
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      "ref": "sggpls/santander-pipeline-kernel-xgb-fe-lb1-38",
      "title": "Santander Pipeline Kernel, xgb + fe [LB1.38]",
      "source": "live",
      "kernel_id": "1234287"
    },
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      "ref": "ksnithya/my-first-ml-code",
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      "source": "live",
      "kernel_id": "1233401"
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      "ref": "blastchar/santander-value-prediction-challenge-xgboost-1",
      "title": "Santander Value Prediction Challenge - Xgboost 1",
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      "kernel_id": "1232415"
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      "ref": "sahilsharmagur/lb1-41",
      "title": " [LB1.41]",
      "source": "live",
      "kernel_id": "1232303"
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      "ref": "scirpus/santander-gp-clustering-ii",
      "title": "Santander GP Clustering II",
      "source": "live",
      "kernel_id": "1232154"
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      "ref": "seiya1998/lgbm-with-random-projection-and-aggregate-lb-1-41",
      "title": "LGBM with Random Projection and Aggregate[LB 1.41]",
      "source": "live",
      "kernel_id": "1231179"
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      "ref": "vykhand/get-better-feature-names-and-some-mini-eda",
      "title": "Get better Feature names, and some mini-EDA",
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      "kernel_id": "1231025"
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      "ref": "asydorchuk/save-98-of-ram",
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      "kernel_id": "1230040"
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      "ref": "lightsalsa/ensemble-of-lgbm-and-xgb",
      "title": "Ensemble of LGBM and XGB",
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      "kernel_id": "1228517"
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      "ref": "glazacvetaneba/preprocessing-new-single-model-baseline-1-43",
      "title": "Preprocessing + new single model baseline [1.43]",
      "source": "live",
      "kernel_id": "1227450"
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      "ref": "sgorlick/columnar-decomposition-techniques",
      "title": "Columnar Decomposition Techniques",
      "source": "live",
      "kernel_id": "1227424"
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    {
      "ref": "improvnps/zero-finder",
      "title": "Zero_Finder",
      "source": "live",
      "kernel_id": "1226565"
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      "ref": "kevinbonnes/adversarial-validation",
      "title": "Adversarial validation",
      "source": "live",
      "kernel_id": "1225805"
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      "ref": "lightsalsa/lgbm1",
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      "source": "live",
      "kernel_id": "1224986"
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      "ref": "nanomathias/feature-engineering-benchmarks",
      "title": "Feature Engineering Benchmarks",
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      "kernel_id": "1224788"
    },
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      "ref": "ianchute/geometric-mean-of-each-row-lb-1-55",
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      "kernel_id": "1224382"
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      "ref": "seiya1998/light-gbm-with-ridge-and-basic-aggregates",
      "title": "Light GBM with RIdge and Basic Aggregates",
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      "kernel_id": "1223958"
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      "ref": "hmendonca/training-data-analyzes-time-series",
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      "kernel_id": "1222763"
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      "ref": "devaraju/simple-eda-xgb-and-lgb",
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      "kernel_id": "1222554"
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      "ref": "cttsai/bayesian-optimized-xgboost",
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      "source": "live",
      "kernel_id": "1221122"
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      "ref": "amarjeet007/random-forest-feature-engineering-lightgbm",
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      "kernel_id": "1220686"
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      "ref": "shaz13/distributions-pca-analysis-beginner-friendly",
      "title": "Distributions & PCA Analysis [Beginner Friendly]",
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      "kernel_id": "1220388"
    },
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      "ref": "hmendonca/keras-regressor-neural-network",
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      "source": "live",
      "kernel_id": "1217790"
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    {
      "ref": "danofer/ensembling",
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      "source": "live",
      "kernel_id": "1216914"
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      "ref": "foochg/deep-learning-for-santander-value-prediction",
      "title": "Deep Learning for Santander Value Prediction",
      "source": "live",
      "kernel_id": "1214851"
    },
    {
      "ref": "tanreinama/catboost-stackedae-with-mxnet-meta-1-40lb",
      "title": "CatBoost, StackedAE with MXNet, Meta [1.40LB]",
      "source": "live",
      "kernel_id": "1214722"
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      "ref": "haimfeld87/randomforest-with-50-features",
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      "source": "live",
      "kernel_id": "1213719"
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      "ref": "malteklane/santander-rfr",
      "title": "Santander RFR",
      "source": "live",
      "kernel_id": "1211876"
    },
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      "ref": "scirpus/santander-poor-mans-tsne",
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      "ref": "psbhat/simple-random-forest-kernel-for-a-beginner",
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      "kernel_id": "1208894"
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      "ref": "tc171995/santander-value-prediction-insights-from-the-eda",
      "title": "Santander Value Prediction - Insights from the EDA",
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      "kernel_id": "1208885"
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      "ref": "ashishpatel26/preprocessing-lightgbm-xgboost",
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      "kernel_id": "1208792"
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      "ref": "clair14/we-love-our-clients",
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      "kernel_id": "1208757"
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      "ref": "abhilashawasthi/fs-lasso-hyperparamtuning-hyperopt",
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      "kernel_id": "1208704"
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      "ref": "saivarunk/lgb-baseline-using-encoded-features-auto-encoder",
      "title": "LGB Baseline using Encoded features (Auto Encoder)",
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      "kernel_id": "1207954"
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      "ref": "bguberfain/stratified-folds",
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      "source": "live",
      "kernel_id": "1207375"
    },
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      "ref": "vasilis73/satander-analysis",
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      "ref": "shravankoninti/pca-pls-rf-my-first-kaggle",
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      "kernel_id": "1206283"
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      "ref": "nanomathias/linear-regression-with-elastic-net",
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      "kernel_id": "1206045"
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      "ref": "longyin2/eda-pipeline-for-anonymous-problems-baseline",
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      "ref": "scirpus/cut-and-run",
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      "source": "live",
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    },
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      "ref": "bw0282/sandbox",
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      "ref": "nanomathias/distribution-of-test-vs-training-data",
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      "source": "live",
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      "ref": "prashantkikani/santad-dl-approch",
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      "source": "live",
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      "ref": "aritrase/santander-deeplearning",
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      "source": "live",
      "kernel_id": "1199470"
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      "ref": "ishida/santander-value-lightgbm-for-beginner",
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      "ref": "kailex/santander-eda-features",
      "title": "Santander: EDA + features",
      "source": "live",
      "kernel_id": "1197565"
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      "ref": "fabianaboldrin/base-xgb-1-41",
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      "ref": "pierreco/modeling-with-pca",
      "title": "Modeling with PCA",
      "source": "live",
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      "ref": "samratp/aggregates-sumvalues-sumzeros-k-means-pca",
      "title": "Aggregates + SumValues + SumZeros + K-Means + PCA",
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      "ref": "alexpengxiao/preprocessing-model-averaging-by-xgb-lgb-1-39",
      "title": "preprocessing, model averaging by xgb + lgb [1.39]",
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      "ref": "imsayantan/decoding-id-column-possible-time-series-analysis",
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      "kernel_id": "1195255"
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      "ref": "bbennett36/lightgbm-poisson-shap-only-50-features-1-67",
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      "kernel_id": "1194859"
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      "ref": "siddhartha14/base-xgb-1-47-5a76d2",
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      "source": "live",
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      "ref": "gautamramuvel/xgb-cv-and-gridsearchcv",
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      "source": "live",
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      "ref": "prashantkikani/ensembling-has-always-been-the-answer",
      "title": "Ensembling has always been the answer",
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      "ref": "hsankesara/santander-first-attempt",
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      "source": "live",
      "kernel_id": "1191301"
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    {
      "ref": "adhilhazari/love-is-the-answer-a77168",
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      "ref": "scirpus/how-low-can-you-go",
      "title": "How Low can you go?",
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      "ref": "aritrase/santander-reg",
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      "source": "live",
      "kernel_id": "1190568"
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      "ref": "ashishpatel26/fork-of-santander-value-prediction-xgb-lightgbm-ca",
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      "source": "live",
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      "ref": "tanreinama/histgram-of-all-columns",
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      "ref": "mortido/digging-into-the-data-time-series-theory",
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      "ref": "vasilis73/descriptive-satander",
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      "source": "live",
      "kernel_id": "1186475"
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      "ref": "curtarelli/just-another-boring-day",
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      "ref": "ns3115/factor-analysis-santander-value-predict",
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      "kernel_id": "1185545"
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      "ref": "ashishpatel26/santander-value-prediction-xgb-lightgbm-catboost",
      "title": "Santander Value Prediction XGB-LIGHTGBM-CATBOOST",
      "source": "live",
      "kernel_id": "1184579"
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      "ref": "cttsai/gbm-blended",
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      "kernel_id": "1184054"
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      "ref": "arkaung/potato-data-analysis-time-series-data",
      "title": "Potato Data Analysis -> Time series data?",
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      "kernel_id": "1183698"
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      "ref": "cttsai/xgb-starter-w-seed-diversity",
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      "ref": "antoniom85/modelo-keras-simple",
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      "source": "live",
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      "ref": "powerofzero/xgboost-with-best-features-feature-importance",
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      "ref": "mbkinaci/xgboost-with-best-features-feature-importance",
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      "ref": "nicapotato/lgbm-cv-tuning-and-seed-diversification",
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      "ref": "zaburo/neural-network-starter",
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      "ref": "nicapotato/parameter-search-visualization-dr-extratree",
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      "ref": "blastchar/santander-value-prediction-challenge-h2o-step-1",
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      "ref": "praveen96/model-decision-tree-starter",
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      "ref": "shivamb/dataset-decomposition-techniques",
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      "ref": "yekenot/baseline-with-decomposition-components",
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}