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      "ref": "leviyu/gcrevenue-feature-extraction-with-multiprocess",
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      "ref": "burritodan/gstore-pythonic-flatten-only-hits-column",
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      "ref": "usmanabbas/flatten-hits-and-customdimensions",
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      "ref": "nakapoor/simple-data-analysis-on-ga-customer-revenue",
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      "ref": "youhanlee/features-which-distinguish-train-from-test",
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      "ref": "pendu777/simple-exploration-baseline-ga-customer-revenue",
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      "ref": "ceshine/wip-visitor-level-data-visualization",
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      "kernel_id": "1806634"
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    {
      "ref": "kaushikishar/google-analytics-ki-20181005-v4",
      "title": "Google Analytics KI 20181005 V4",
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      "kernel_id": "1803325"
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    {
      "ref": "steveandthedogs/sb-googleanalyticscustomerrevenueprediction-v2",
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      "kernel_id": "1802974"
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      "ref": "scirpus/explaining-disparity-between-cv-and-the-lb",
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      "ref": "ahbon888/lgb-xgb-catboost-ensemble",
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      "ref": "scirpus/feature-fest-with-genetic-programming",
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      "ref": "arjundas/first-try",
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      "source": "live",
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      "ref": "lituokobe/ga-customer-revenue-prediction-data-exploration",
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      "ref": "vaibhav2201/teach-lightgbm-to-sum-predictions",
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      "kernel_id": "1793316"
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      "ref": "vaibhav2201/1-43-plb-feature-engineering-best-model-combined",
      "title": "1.43_PLB_Feature_Engineering_Best Model Combined",
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      "ref": "nikitsoftweb/lightgbm-feature-engineering-kfold",
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    {
      "ref": "jaytalati87/which-encoding-is-good-for-time-validation-1-4417",
      "title": "Which encoding is good for time-validation?-1.4417",
      "source": "live",
      "kernel_id": "1793206"
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      "ref": "mukesh62/lgb-fe-groupkfold-cv-xgb",
      "title": "LGB + FE+ GroupKFOLD+CV + XGB",
      "source": "live",
      "kernel_id": "1793107"
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      "ref": "xavierbourretsicotte/feature-engineering-analysis-on-tableau",
      "title": "Feature engineering Analysis on Tableau",
      "source": "live",
      "kernel_id": "1792381"
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    {
      "ref": "xavierbourretsicotte/kernel-for-file-processing-2",
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      "ref": "georsara1/covariate-shift-check-up-updated",
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      "kernel_id": "1792048"
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      "ref": "santoshgaikwad/santosh-google-competition-kernel",
      "title": "Santosh - Google Competition Kernel",
      "source": "live",
      "kernel_id": "1791293"
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    {
      "ref": "yakolle/gs-lgb-tune-param-idf-reboot",
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      "kernel_id": "1791163"
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      "ref": "yakolle/gs-lgb-indi-tune-params-idf-reboot",
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      "source": "live",
      "kernel_id": "1791075"
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      "ref": "ashufet/kernel-for-saving-files-without-cat-encoding",
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      "ref": "kabure/exploring-the-consumer-patterns-ml-pipeline",
      "title": "Exploring the Consumer Patterns + ML Pipeline ",
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      "kernel_id": "1790499"
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    {
      "ref": "usmanabbas/flatten-columns-remove-columns-before-normalizing",
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      "kernel_id": "1789711"
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      "ref": "rochellesilva/make-it-simple-look-at-data-create-model-in-r",
      "title": "Make it simple: Look at data & Create Model in R",
      "source": "live",
      "kernel_id": "1789225"
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    {
      "ref": "yakolle/gs-lgb-tune-param-idf",
      "title": "gs-lgb-tune-param-idf",
      "source": "live",
      "kernel_id": "1788760"
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    {
      "ref": "artemklevtsov/fetch-competition-data-with-r",
      "title": "Fetch competition data with R",
      "source": "live",
      "kernel_id": "1788725"
    },
    {
      "ref": "aks709/data-cleaning-and-elimination-of-unnecessary-data",
      "title": "Data cleaning and Elimination of unnecessary data",
      "source": "live",
      "kernel_id": "1788428"
    },
    {
      "ref": "yakolle/gs-lgb-indi-tune-params-idf",
      "title": "gs-lgb-indi-tune-params-idf",
      "source": "live",
      "kernel_id": "1788241"
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    {
      "ref": "diogomenezesborges/full-depth-eda-analysis",
      "title": "Full Depth EDA Analysis",
      "source": "live",
      "kernel_id": "1788212"
    },
    {
      "ref": "nakapoor/simple-missing-data-analysis-gacrp",
      "title": "Simple missing data analysis :  GACRP",
      "source": "live",
      "kernel_id": "1787919"
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    {
      "ref": "prashantkikani/ensembling-fe-is-the-answer",
      "title": "Ensembling + FE is the answer",
      "source": "live",
      "kernel_id": "1787533"
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    {
      "ref": "nikitpatel/lightgbm-with-best-hyperparameter-tuning",
      "title": "LightGBM with Best Hyperparameter tuning ",
      "source": "live",
      "kernel_id": "1784031"
    },
    {
      "ref": "ravann/2-working-on-dataset",
      "title": "2. Working on dataset",
      "source": "live",
      "kernel_id": "1782451"
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    {
      "ref": "xavierbourretsicotte/light-gbm-wip",
      "title": "Light GBM WIP",
      "source": "live",
      "kernel_id": "1781526"
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    {
      "ref": "scirpus/teach-lightgbm-to-sum-predictions-with-gp",
      "title": "Teach_LightGBM_to_Sum_Predictions with GP",
      "source": "live",
      "kernel_id": "1779819"
    },
    {
      "ref": "xavierbourretsicotte/lgbm-error-analysis-negative-downsampling",
      "title": "Lgbm Error Analysis - Negative downsampling ?",
      "source": "live",
      "kernel_id": "1779662"
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    {
      "ref": "ravann/1-step-by-step-format-data-to-columnar-format",
      "title": "1. Step by Step - format data to columnar format",
      "source": "live",
      "kernel_id": "1775363"
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    {
      "ref": "loganathanspr/eda-ga-customer-revenue-prediction",
      "title": "EDA - GA Customer Revenue Prediction",
      "source": "live",
      "kernel_id": "1773732"
    },
    {
      "ref": "xaviermaxime/kernel-for-saving-files",
      "title": "Kernel for saving files",
      "source": "live",
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    {
      "ref": "smasar/tutorial-preprocessing-processing-evaluation",
      "title": "Tutorial (Preprocessing, Processing, Evaluation)",
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      "kernel_id": "1770945"
    },
    {
      "ref": "ashishpatel26/updated-bayesian-lgbm-xgb-cat-fe-kfold-cv",
      "title": "Updated Bayesian + LGBM_XGB_CAT+ FE + Kfold + CV",
      "source": "live",
      "kernel_id": "1768678"
    },
    {
      "ref": "xaviermaxime/light-gbm-with-simple-engineered-features",
      "title": "Light GBM with simple engineered features",
      "source": "live",
      "kernel_id": "1767397"
    },
    {
      "ref": "knly10/ga-revenue-prediction",
      "title": "GA Revenue Prediction",
      "source": "live",
      "kernel_id": "1762696"
    },
    {
      "ref": "xavierbourretsicotte/localizing-utc-time-eda-and-walkthrough",
      "title": "Localizing UTC time: EDA and walkthrough",
      "source": "live",
      "kernel_id": "1761724"
    },
    {
      "ref": "yakolle/gs-get-src-data",
      "title": "gs-get-src-data",
      "source": "live",
      "kernel_id": "1761461"
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    {
      "ref": "yulinzxc/eda-feature-tool-lightgbm",
      "title": "EDA + Feature Tool + LightGBM",
      "source": "live",
      "kernel_id": "1761241"
    },
    {
      "ref": "mithrillion/non-stationarities-in-feature-time-series",
      "title": "Non-stationarities in Feature Time Series",
      "source": "live",
      "kernel_id": "1759896"
    },
    {
      "ref": "igauty/1-exploratory-data-analysis",
      "title": "1. Exploratory Data Analysis",
      "source": "live",
      "kernel_id": "1759173"
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    {
      "ref": "prashantkikani/teach-lightgbm-to-sum-predictions-fe",
      "title": "Teach_LightGBM_to_Sum_Predictions + FE",
      "source": "live",
      "kernel_id": "1758082"
    },
    {
      "ref": "yassinealouini/start-here-features-engineering-pipeline",
      "title": "[Start here] Features Engineering Pipeline",
      "source": "live",
      "kernel_id": "1757068"
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    {
      "ref": "brucezhaor/kernel-test",
      "title": "kernel-test",
      "source": "live",
      "kernel_id": "1756784"
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    {
      "ref": "ogrellier/teach-lightgbm-to-sum-predictions",
      "title": "Teach_LightGBM_to_Sum_Predictions",
      "source": "live",
      "kernel_id": "1756010"
    },
    {
      "ref": "thesoum/google-analytics-data-cleaning-lightgbm-model",
      "title": "Google Analytics Data Cleaning & LightGBM Model",
      "source": "live"
    },
    {
      "ref": "tatianass/gbm-google-store-revenue",
      "title": "GBM - Google Store Revenue",
      "source": "live",
      "kernel_id": "1754935"
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    {
      "ref": "murrrue/revenue-rate-and-cond-revenue-mean",
      "title": "revenue rate and cond. revenue mean...",
      "source": "live",
      "kernel_id": "1754480"
    },
    {
      "ref": "mtodisco10/feature-engineering-time-since-last-session",
      "title": "Feature Engineering: Time Since Last Session",
      "source": "live",
      "kernel_id": "1754068"
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    {
      "ref": "mithrillion/semi-declarative-feature-cleaning-and-enginnering",
      "title": "Semi-Declarative Feature Cleaning and Enginnering",
      "source": "live",
      "kernel_id": "1751255"
    },
    {
      "ref": "mithrillion/geonetwork-feature-conflict-resolution",
      "title": "GeoNetwork Feature Conflict Resolution",
      "source": "live",
      "kernel_id": "1751178"
    },
    {
      "ref": "prashantkikani/rstudio-humble-try-for-autoencoders",
      "title": "rstudio Humble Try for AutoEncoders",
      "source": "live",
      "kernel_id": "1744483"
    },
    {
      "ref": "kartik31/garcb-prdiction-lgb",
      "title": "GARCB Prdiction + LGB",
      "source": "live",
      "kernel_id": "1741182"
    },
    {
      "ref": "afaist/preliminary-data-processing",
      "title": "Preliminary data processing",
      "source": "live",
      "kernel_id": "1735834"
    },
    {
      "ref": "tyburam/googleanalytics-randomforestregression",
      "title": "GoogleAnalytics-RandomForestRegression",
      "source": "live",
      "kernel_id": "1735138"
    },
    {
      "ref": "yakolle/gs-flatten-data-analysis",
      "title": "gs-flatten-data-analysis",
      "source": "live",
      "kernel_id": "1734227"
    },
    {
      "ref": "nikitsoftweb/mapping-user-flow-lb-1-447",
      "title": "Mapping User Flow - LB 1.447",
      "source": "live",
      "kernel_id": "1733450"
    },
    {
      "ref": "steveandthedogs/sb-googleanalyticscustomerrevenueprediction-data",
      "title": "SB_GoogleAnalyticsCustomerRevenuePrediction_Data",
      "source": "live",
      "kernel_id": "1728939"
    },
    {
      "ref": "depmountaineer/tabularize-and-basic-clean",
      "title": "Tabularize and Basic Clean",
      "source": "live",
      "kernel_id": "1728284"
    },
    {
      "ref": "youhanlee/which-encoding-is-good-for-time-validation-1-4417",
      "title": "Which encoding is good for time-validation?-1.4417",
      "source": "live",
      "kernel_id": "1727777"
    },
    {
      "ref": "juliojuse/simple-exploration-baseline-ga-customer-revenue",
      "title": "Simple Exploration+Baseline - GA Customer Revenue",
      "source": "live",
      "kernel_id": "1727103"
    },
    {
      "ref": "ravikiransm/test1-lgb-catboost",
      "title": "test1-LGB-CATBOOST",
      "source": "live",
      "kernel_id": "1725829"
    },
    {
      "ref": "yakolle/gs-data-analysis",
      "title": "gs-data-analysis",
      "source": "live",
      "kernel_id": "1725203"
    },
    {
      "ref": "ashishpatel26/light-gbm-with-cross-validation-approach",
      "title": "Light GBM with Cross Validation Approach",
      "source": "live",
      "kernel_id": "1723946"
    },
    {
      "ref": "vkgpt11/lgbm-with-feature-eng-and-cleaning-lb-1-4420",
      "title": "LGBM with Feature Eng and Cleaning LB-1.4420",
      "source": "live",
      "kernel_id": "1722064"
    },
    {
      "ref": "hyperc/google-analytics-customer-revenue-prediction-eda",
      "title": "Google Analytics Customer Revenue Prediction EDA",
      "source": "live",
      "kernel_id": "1720938"
    },
    {
      "ref": "ajaykgp12/lgb-with-simple-time-related-features",
      "title": " LGB  with simple  time related features",
      "source": "live",
      "kernel_id": "1720187"
    },
    {
      "ref": "pkactuary/parse-json-formatted-features",
      "title": "Parse JSON formatted features",
      "source": "live",
      "kernel_id": "1718996"
    },
    {
      "ref": "cican17/gstore-prediction-bayesian-optimization",
      "title": "Gstore Prediction Bayesian Optimization",
      "source": "live",
      "kernel_id": "1716697"
    },
    {
      "ref": "chocozzz/bayesian-optimization-for-lightgbm",
      "title": "Bayesian Optimization for LightGBM",
      "source": "live",
      "kernel_id": "1716151"
    },
    {
      "ref": "ogrellier/create-extracted-json-fields-dataset",
      "title": "create_extracted_json_fields_dataset",
      "source": "live",
      "kernel_id": "1716125"
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    {
      "ref": "samstiyer/parsing-the-json-columns-in-r-tidy-approach",
      "title": "Parsing the JSON Columns in R - Tidy Approach",
      "source": "live",
      "kernel_id": "1715084"
    },
    {
      "ref": "jpmiller/go-with-the-flow-lb-1-447",
      "title": "Go with the Flow - LB 1.447",
      "source": "live",
      "kernel_id": "1714249"
    },
    {
      "ref": "ogrellier/using-classification-for-predictions",
      "title": "using_classification_for_predictions",
      "source": "live",
      "kernel_id": "1714132"
    },
    {
      "ref": "artgor/lgb-and-feature-generation",
      "title": "LGB and feature generation",
      "source": "live",
      "kernel_id": "1712006"
    },
    {
      "ref": "laleh83/gstore-revenue-prediction-laleh-s-version",
      "title": "GStore Revenue Prediction - (Laleh's version)",
      "source": "live",
      "kernel_id": "1711658"
    },
    {
      "ref": "chocozzz/customer-revenue-eda-time-series",
      "title": "Customer Revenue :  EDA + Time series",
      "source": "live",
      "kernel_id": "1710910"
    },
    {
      "ref": "dhuwaaragesh/eda-and-cleaning",
      "title": "EDA and Cleaning",
      "source": "live",
      "kernel_id": "1709465"
    },
    {
      "ref": "mithrillion/finding-useful-information-in-missingness",
      "title": "Finding Useful Information In Missingness",
      "source": "live",
      "kernel_id": "1708793"
    },
    {
      "ref": "oneraghavan/google-analytics-customer-revenue-eda-baseline",
      "title": "Google analytics Customer Revenue EDA , baseline",
      "source": "live",
      "kernel_id": "1706829"
    },
    {
      "ref": "chocozzz/ensemble-public-kernels",
      "title": "Ensemble public  kernels",
      "source": "live",
      "kernel_id": "1705189"
    },
    {
      "ref": "prashantkikani/howmuch-train-test-data-are-different",
      "title": "Howmuch train & test data are different?",
      "source": "live",
      "kernel_id": "1705135"
    },
    {
      "ref": "yassinealouini/eda-and-benchmark-model-101",
      "title": "EDA and Benchmark Model 101",
      "source": "live",
      "kernel_id": "1704503"
    },
    {
      "ref": "sz8416/lb-1-4439-gacr-prediction-eda-lgb-baseline",
      "title": "[LB 1.4439] GACR Prediction: EDA + LGB Baseline",
      "source": "live",
      "kernel_id": "1703628"
    },
    {
      "ref": "ashishpatel26/permutation-importance-feature-imp-measure-gacrp",
      "title": "Permutation Importance: Feature Imp Measure GACRP",
      "source": "live",
      "kernel_id": "1702457"
    },
    {
      "ref": "prashanththangavel/unique-session-counts-helps-sure-lb-1-6647",
      "title": "Unique session counts  helps ? Sure! LB : 1.6647",
      "source": "live",
      "kernel_id": "1699953"
    },
    {
      "ref": "scirpus/a-bit-of-gp-clustering",
      "title": "A bit of GP Clustering",
      "source": "live",
      "kernel_id": "1699792"
    },
    {
      "ref": "tatianass/google-customer-revenue-eda-with-tableau",
      "title": "Google Customer Revenue: EDA with Tableau",
      "source": "live",
      "kernel_id": "1699724"
    },
    {
      "ref": "rahullalu/gstore-eda-lgbm-baseline-1-4260",
      "title": "GStore EDA (LGBM Baseline 1.4260)",
      "source": "live",
      "kernel_id": "1697272"
    },
    {
      "ref": "gowrishankarin/interactive-eda-using-plotly",
      "title": "Interactive EDA using Plotly",
      "source": "live",
      "kernel_id": "1696818"
    },
    {
      "ref": "qwe1398775315/eda-lgbm-bayesianoptimization",
      "title": "EDA + LGBM + BayesianOptimization",
      "source": "live",
      "kernel_id": "1695320"
    },
    {
      "ref": "ogakulov/feature-engineering-step-by-step",
      "title": "Feature Engineering Step by Step",
      "source": "live",
      "kernel_id": "1694473"
    },
    {
      "ref": "wesleyjr01/ga-challenge-xgboost-permutation-importance",
      "title": "GA Challenge - XGboost + Permutation Importance",
      "source": "live",
      "kernel_id": "1689174"
    },
    {
      "ref": "mithrillion/fixing-conflicts-in-the-geonetwork-attributes",
      "title": "Fixing Conflicts in the geoNetwork Attributes",
      "source": "live",
      "kernel_id": "1686486"
    },
    {
      "ref": "mithrillion/gstore-revenue-data-preprocessing",
      "title": "GStore Revenue Data Preprocessing",
      "source": "live",
      "kernel_id": "1685545"
    },
    {
      "ref": "chocozzz/lightgbm-version-fork-basic-xgb-for-gstore",
      "title": "LightGBM version Fork basic xgb for GStore",
      "source": "live",
      "kernel_id": "1685212"
    },
    {
      "ref": "prashantkikani/rstudio-deep-learning-approach",
      "title": "rstudio Deep Learning approach",
      "source": "live",
      "kernel_id": "1684656"
    },
    {
      "ref": "eminem1147/simple-exploration-baseline-ga-customer-revenue",
      "title": "Simple Exploration+Baseline - GA Customer Revenue",
      "source": "live",
      "kernel_id": "1684431"
    },
    {
      "ref": "statchaitya/general-eda-of-google-data",
      "title": "General EDA of google data",
      "source": "live",
      "kernel_id": "1682839"
    },
    {
      "ref": "ezornow/gstore-skeleton",
      "title": "gstore_skeleton",
      "source": "live",
      "kernel_id": "1681906"
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