{
  "capped": false,
  "live": 300,
  "meta": 307,
  "kernels": [
    {
      "ref": "thanh77/research-dataset",
      "title": "Research Dataset",
      "source": "live"
    },
    {
      "ref": "dorky1/time-series-forecasting",
      "title": "Time series forecasting",
      "source": "live",
      "kernel_id": "134697705"
    },
    {
      "ref": "rhoalpha/favorita-lgbm-pipeline",
      "title": "Favorita LGBM Pipeline",
      "source": "live",
      "kernel_id": "126346200"
    },
    {
      "ref": "takatakamaimai/3level16model264",
      "title": "3level16model264",
      "source": "live",
      "kernel_id": "121520036"
    },
    {
      "ref": "takatakamaimai/multiple-regression-analysis-ols-imai",
      "title": "Multiple regression analysis (OLS)_Imai",
      "source": "live",
      "kernel_id": "121271057"
    },
    {
      "ref": "takatakamaimai/lightgbm-imai",
      "title": "LightGBM_Imai",
      "source": "live",
      "kernel_id": "121267601"
    },
    {
      "ref": "manhdevuit/bigdata-lstm-lightgbm",
      "title": "bigdata-lstm&lightgbm",
      "source": "live",
      "kernel_id": "121163078"
    },
    {
      "ref": "pgnjams/ie212-datapreprocessing-final",
      "title": "IE212_DataPreprocessing_Final",
      "source": "live",
      "kernel_id": "121070595"
    },
    {
      "ref": "pgnjams/ie212-datapreprocessing-2",
      "title": "IE212_DataPreprocessing_2",
      "source": "live",
      "kernel_id": "121002458"
    },
    {
      "ref": "nghuxung/train-baseline-lstm-wavelet",
      "title": "Train Baseline + LSTM + Wavelet",
      "source": "live",
      "kernel_id": "120776131"
    },
    {
      "ref": "nghuxung/ie212-wavelet",
      "title": "IE212_Wavelet",
      "source": "live",
      "kernel_id": "119097360"
    },
    {
      "ref": "nghuxung/ie212-eda-p1",
      "title": "IE212_EDA_P1",
      "source": "live",
      "kernel_id": "117753653"
    },
    {
      "ref": "markterris/hw3-ts",
      "title": "hw3_ts",
      "source": "live",
      "kernel_id": "112603153"
    },
    {
      "ref": "rayyanmujtabamusab/final-note",
      "title": "final_note",
      "source": "live",
      "kernel_id": "112172914"
    },
    {
      "ref": "firmanmulti/demand-forecasting-favorita-grocery-sales",
      "title": "Demand Forecasting - Favorita Grocery Sales",
      "source": "live",
      "kernel_id": "106942291"
    },
    {
      "ref": "jacquesilly/tp-ml-favorita-5",
      "title": "tp-ml-favorita (5)",
      "source": "live",
      "kernel_id": "106806749"
    },
    {
      "ref": "diormbengue/pr-traitement",
      "title": "Prétraitement",
      "source": "live",
      "kernel_id": "106335120"
    },
    {
      "ref": "jacquesilly/eda-pr-vision-des-ventes",
      "title": "EDA_Prévision_des_ventes",
      "source": "live",
      "kernel_id": "106330182"
    },
    {
      "ref": "khadidiatoudiakhat/tp-ml-favorita",
      "title": "TP_ML_Favorita",
      "source": "live",
      "kernel_id": "106267925"
    },
    {
      "ref": "diormbengue/favorita-grocery-sales-forecasting",
      "title": "Favorita Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "106240203"
    },
    {
      "ref": "fatmaalzahramohamed/cs495-deeplearningfinalproject-flowx",
      "title": "CS495 – DeepLearningFinalProject – FlowX ",
      "source": "live",
      "kernel_id": "102109047"
    },
    {
      "ref": "quagghyy/prethesis-llmforecaster",
      "title": "PRETHESIS LLMFORECASTER",
      "source": "live",
      "kernel_id": "102080460"
    },
    {
      "ref": "quagghyy/note1",
      "title": "note1",
      "source": "live",
      "kernel_id": "102079835"
    },
    {
      "ref": "ikrammtimet/dl-project-flowx-data-preprocessing",
      "title": "DL Project : Flowx-Data preprocessing",
      "source": "live",
      "kernel_id": "99731857"
    },
    {
      "ref": "nguynquocduy24/midterm-bi",
      "title": "midterm_BI",
      "source": "live",
      "kernel_id": "98427321"
    },
    {
      "ref": "honggiaminh/favorita-code",
      "title": "favorita_code",
      "source": "live",
      "kernel_id": "98413340"
    },
    {
      "ref": "ehthniv/fork-of-machine-learning-k23416-sarima",
      "title": "Fork of Machine Learning_K23416_SARIMA",
      "source": "live",
      "kernel_id": "96646046"
    },
    {
      "ref": "ehthniv/machine-learning-k23416",
      "title": "Machine Learning_K23416",
      "source": "live",
      "kernel_id": "94679708"
    },
    {
      "ref": "iteddyyy/recsys",
      "title": "RecSys",
      "source": "live",
      "kernel_id": "83973796"
    },
    {
      "ref": "santiagomatamoros/practica1",
      "title": "Practica1",
      "source": "live",
      "kernel_id": "82653738"
    },
    {
      "ref": "uruchimavega/notebook11fa98164a",
      "title": "notebook11fa98164a",
      "source": "live",
      "kernel_id": "78334564"
    },
    {
      "ref": "uom200058l/favorita-grocery-sales-forecasting",
      "title": "Favorita Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "75504623"
    },
    {
      "ref": "thilaknakumaratunga/sales-forecasting-v1",
      "title": "Sales forecasting_v1",
      "source": "live",
      "kernel_id": "75451095"
    },
    {
      "ref": "uom200058l/notebook9df39a834b",
      "title": "notebook9df39a834b",
      "source": "live",
      "kernel_id": "75447336"
    },
    {
      "ref": "vravi7/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74855735"
    },
    {
      "ref": "evxdgh/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74800171"
    },
    {
      "ref": "silverfangs/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74763370"
    },
    {
      "ref": "verbatim479/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74759402"
    },
    {
      "ref": "yassmenhussinibrahim/ds-hw4",
      "title": "DS_HW4",
      "source": "live",
      "kernel_id": "74720841"
    },
    {
      "ref": "ruslantasmukhanov/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74706588"
    },
    {
      "ref": "viktorpetrovsky/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74683825"
    },
    {
      "ref": "matsveikasiakou/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74615878"
    },
    {
      "ref": "ssaammm/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74526108"
    },
    {
      "ref": "pavelnikolaichev/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74525492"
    },
    {
      "ref": "hoanglongvn/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74522750"
    },
    {
      "ref": "giorgivelijanashvili/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74519798"
    },
    {
      "ref": "timuramangeldiyev/ds-hw4",
      "title": "DS_HW4",
      "source": "live",
      "kernel_id": "74515974"
    },
    {
      "ref": "danilakuroedov/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "74226267"
    },
    {
      "ref": "danilakuroedov/ds-4-hw",
      "title": "DS 4 HW",
      "source": "live",
      "kernel_id": "74214944"
    },
    {
      "ref": "kushaljain0/epam-store-kushal-jain",
      "title": "epam_store_kushal_jain",
      "source": "live",
      "kernel_id": "74076608"
    },
    {
      "ref": "katsiarynahurskaya/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "73910083"
    },
    {
      "ref": "anacrnkovi/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "73909371"
    },
    {
      "ref": "elizavetamoraru/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "73905696"
    },
    {
      "ref": "mmakemd/epam-store",
      "title": "epam_store",
      "source": "live",
      "kernel_id": "73367335"
    },
    {
      "ref": "shubham9056/shubham",
      "title": "shubham",
      "source": "live",
      "kernel_id": "69571247"
    },
    {
      "ref": "diqeddiq/notebooke41fff9e40",
      "title": "notebooke41fff9e40",
      "source": "live",
      "kernel_id": "61465751"
    },
    {
      "ref": "yllichn/eda-for-final-assignment",
      "title": "EDA for final assignment",
      "source": "live",
      "kernel_id": "60968389"
    },
    {
      "ref": "navid365/linear-regressionwith-feature-selection",
      "title": "Linear regressionwith Feature Selection",
      "source": "live",
      "kernel_id": "48820000"
    },
    {
      "ref": "wangfanya/grocery-prediction-with-neural-network",
      "title": "Grocery prediction with Neural Network",
      "source": "live",
      "kernel_id": "48785696"
    },
    {
      "ref": "navid365/eda-sales-superstore-dataset",
      "title": "EDA Sales-Superstore dataset",
      "source": "live",
      "kernel_id": "48643182"
    },
    {
      "ref": "navid365/eda-full-report-grocery-sales-dataset",
      "title": " EDA Full Report-Grocery Sales  Dataset",
      "source": "live",
      "kernel_id": "48499580"
    },
    {
      "ref": "hongxiaojunhan/basic-statistical-methods-on-favorita",
      "title": "Basic Statistical methods on Favorita",
      "source": "live",
      "kernel_id": "48495116"
    },
    {
      "ref": "yngghostt/prepare-favorita-dataset",
      "title": "prepare favorita dataset",
      "source": "live",
      "kernel_id": "47352821"
    },
    {
      "ref": "gregorykuznetsov/demand-kuznetsov-ga",
      "title": "demand_kuznetsov_ga",
      "source": "live",
      "kernel_id": "47190128"
    },
    {
      "ref": "davidkirkbrideblk/favorita-i",
      "title": "favorita-I",
      "source": "live",
      "kernel_id": "47157486"
    },
    {
      "ref": "saptarshi7470/lgbm-starter",
      "title": "LGBM Starter",
      "source": "live",
      "kernel_id": "47002803"
    },
    {
      "ref": "ravs20/favorita-data-preprocessing",
      "title": "Favorita (data preprocessing)",
      "source": "live",
      "kernel_id": "38960793"
    },
    {
      "ref": "ilegionnpy/proyecto-c-corporaci-n-favorita-grocery-sales-fo",
      "title": "Proyecto C - Corporación Favorita Grocery Sales Fo",
      "source": "live",
      "kernel_id": "38756282"
    },
    {
      "ref": "aerdem4/notebookaab184afb1",
      "title": "notebookaab184afb1",
      "source": "live",
      "kernel_id": "34862562"
    },
    {
      "ref": "thanhdat2601/notebook3695a45d49",
      "title": "notebook3695a45d49",
      "source": "live",
      "kernel_id": "30885344"
    },
    {
      "ref": "sharafmukhtar/read-over-100-million-rows-in-less-than-2-minutes",
      "title": "Read over 100 million rows in less than 2 minutes",
      "source": "live",
      "kernel_id": "25420324"
    },
    {
      "ref": "yixuliu/fork-of-corporaci-n-favorita-grocery-sales-forecas",
      "title": "Fork of Corporación Favorita Grocery Sales Forecas",
      "source": "live",
      "kernel_id": "24149048"
    },
    {
      "ref": "sandraezzat/memory-optimization-data-manipulation",
      "title": "Memory optimization &data manipulation",
      "source": "live",
      "kernel_id": "23689213"
    },
    {
      "ref": "sandraezzat/favorita-grocery-sales-forecasting",
      "title": "Favorita Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "23681231"
    },
    {
      "ref": "kseniadrokov/bgu-dl-2022b-dk-favorita-grocery-sales-forecasting",
      "title": "BGU-DL-2022b-DK_Favorita Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "23030032"
    },
    {
      "ref": "dmitrykhoteenkov/prophet-notebook",
      "title": "prophet_notebook",
      "source": "live",
      "kernel_id": "22910642"
    },
    {
      "ref": "saikamsivanireddy/new-notebook",
      "title": "New Notebook",
      "source": "live",
      "kernel_id": "21928340"
    },
    {
      "ref": "saikamsivanireddy/sales-forecast",
      "title": "Sales Forecast",
      "source": "live",
      "kernel_id": "21879045"
    },
    {
      "ref": "hemantnyadav/basic-statistical-methods-on-favorita",
      "title": "Basic Statistical methods on Favorita",
      "source": "live",
      "kernel_id": "17696916"
    },
    {
      "ref": "maximbragilovski/bgu-dl-2021b-zzm",
      "title": "BGU-DL-2021b- <ZZM>",
      "source": "live",
      "kernel_id": "17017071"
    },
    {
      "ref": "yashpiplani/eda-sales-forecasting",
      "title": "EDA & Sales Forecasting ",
      "source": "live",
      "kernel_id": "14258557"
    },
    {
      "ref": "emanuelepanizio/wip-favorita-unit-sales-forecasting",
      "title": "WIP: Favorita Unit Sales Forecasting",
      "source": "live",
      "kernel_id": "14244765"
    },
    {
      "ref": "jungeun0121/basic-eda",
      "title": "Basic EDA",
      "source": "live",
      "kernel_id": "13963157"
    },
    {
      "ref": "yundojeong/notebook24f42c4b27",
      "title": "notebook24f42c4b27",
      "source": "live",
      "kernel_id": "13879079"
    },
    {
      "ref": "hodtwito/bgu-dl2021-yhb",
      "title": "BGU-DL2021_YHB",
      "source": "live",
      "kernel_id": "13605390"
    },
    {
      "ref": "rajayushml/lgbm-starter",
      "title": "LGBM Starter",
      "source": "live",
      "kernel_id": "11602919"
    },
    {
      "ref": "cuge1995/cannot-work-with-zip",
      "title": "cannot work with zip ",
      "source": "live",
      "kernel_id": "10237753"
    },
    {
      "ref": "devendratapdia/corporacion-favourita-grocery-sales-focasting",
      "title": "Corporacion Favourita Grocery Sales Focasting",
      "source": "live",
      "kernel_id": "9906213"
    },
    {
      "ref": "tanzy96/ben-s-lgbm-notebook-2",
      "title": "Ben's LGBM Notebook 2",
      "source": "live",
      "kernel_id": "3513925"
    },
    {
      "ref": "vaillaud/favorita-reverse-engineering-lgbm",
      "title": "Favorita reverse engineering LGBM",
      "source": "live",
      "kernel_id": "3242507"
    },
    {
      "ref": "nguyentp/rekaggle-favorita-timeseries-feature-engineering",
      "title": "rekaggle-favorita-timeseries-feature-engineering",
      "source": "live",
      "kernel_id": "3056377"
    },
    {
      "ref": "vinaykumar2491/imflash217-bulldozers",
      "title": "imflash217_bulldozers",
      "source": "live",
      "kernel_id": "2708613"
    },
    {
      "ref": "rihadv/predicting-transactions-fb-prophet-tutorial",
      "title": "Predicting Transactions - FB Prophet Tutorial",
      "source": "live",
      "kernel_id": "2298895"
    },
    {
      "ref": "thawatchai2018/predicting-transactions-fb-prophet-tutorial",
      "title": "Predicting Transactions - FB Prophet Tutorial",
      "source": "live",
      "kernel_id": "2123669"
    },
    {
      "ref": "fengsi/fast-ai-ml-lecture-3",
      "title": "fast.ai ML Lecture 3",
      "source": "live",
      "kernel_id": "2116215"
    },
    {
      "ref": "timetraveller98/testing-pandas-read-csv-performance",
      "title": "Testing pandas.read_csv performance",
      "source": "live",
      "kernel_id": "1913917"
    },
    {
      "ref": "mormukut/top-1-on-leaderboard",
      "title": "Top 1% on LeaderBoard",
      "source": "live",
      "kernel_id": "1343346"
    },
    {
      "ref": "socherbyc/forecast",
      "title": "Forecast",
      "source": "live",
      "kernel_id": "1102703"
    },
    {
      "ref": "anuj3305/kernel20508a3f18",
      "title": "Kernel20508a3f18",
      "source": "live",
      "kernel_id": "962259"
    },
    {
      "ref": "jihyeseo/favorita-grocery-eda",
      "title": "favorita grocery eda",
      "source": "live",
      "kernel_id": "958205"
    },
    {
      "ref": "johnsonchen1989/fork-of-fork-of-shopping-for-insights-favorita-e",
      "title": "Fork of Fork of Shopping for Insights - Favorita E",
      "source": "live",
      "kernel_id": "746712"
    },
    {
      "ref": "ksrinivas02/shopping-for-insights-favorita-eda-c62c1e",
      "title": "Shopping for Insights - Favorita EDA c62c1e",
      "source": "live",
      "kernel_id": "693504"
    },
    {
      "ref": "zazulam/comprehensive-python-and-d3-js-favorita-ana-0ec32d",
      "title": "Comprehensive Python and D3.js Favorita ana 0ec32d",
      "source": "live",
      "kernel_id": "620029"
    },
    {
      "ref": "elloumi/test-1",
      "title": "test 1",
      "source": "live",
      "kernel_id": "550515"
    },
    {
      "ref": "jtrotman/store-wise-plots-of-promo-code-by-day-item-white",
      "title": "Store-wise plots of promo code by day/item - white",
      "source": "live",
      "kernel_id": "544692"
    },
    {
      "ref": "jtrotman/store-wise-pixel-plots-of-promo-code-by-day-item",
      "title": "Store-wise pixel plots of promo code by day/item",
      "source": "live",
      "kernel_id": "541945"
    },
    {
      "ref": "kelexu/5-neural-network-average-public-504-private-516",
      "title": "5 neural network average Public 504, Private 516",
      "source": "live",
      "kernel_id": "541197"
    },
    {
      "ref": "shixw125/1st-place-nn-model-public-0-507-private-0-513",
      "title": "1st Place NN Model(public:0.507, private:0.513)",
      "source": "live",
      "kernel_id": "540961"
    },
    {
      "ref": "shixw125/1st-place-lgb-model-public-0-506-private-0-511",
      "title": "1st Place LGB Model(public:0.506, private:0.511)",
      "source": "live",
      "kernel_id": "540956"
    },
    {
      "ref": "viczyf/lgbm-model-private-score-0-515",
      "title": "LGBM Model private score 0.515",
      "source": "live",
      "kernel_id": "540892"
    },
    {
      "ref": "mattsherar/pytorch-gru",
      "title": "PyTorch GRU",
      "source": "live",
      "kernel_id": "540857"
    },
    {
      "ref": "deepdreamer/a-silver-solution-33rd",
      "title": "A silver solution(33rd) ",
      "source": "live",
      "kernel_id": "540826"
    },
    {
      "ref": "byronvinu/lstm-network-for-regression",
      "title": "LSTM Network for Regression",
      "source": "live",
      "kernel_id": "540446"
    },
    {
      "ref": "carlosefrain17/notebook5bff396417",
      "title": "Notebook5bff396417",
      "source": "live",
      "kernel_id": "540431"
    },
    {
      "ref": "aharless/analyze-2nd-week-sales",
      "title": "analyze 2nd week sales",
      "source": "live",
      "kernel_id": "539269"
    },
    {
      "ref": "aharless/second-week-sales",
      "title": "second week sales",
      "source": "live",
      "kernel_id": "539229"
    },
    {
      "ref": "web8341/grocery-analysis-v4",
      "title": "Grocery Analysis V4",
      "source": "live",
      "kernel_id": "538750"
    },
    {
      "ref": "sbongo/tensorflow-nn-drop-out-batch-norm-lb-0-515",
      "title": "TensorFlow NN+Drop out+Batch Norm(LB 0.515)",
      "source": "live",
      "kernel_id": "538047"
    },
    {
      "ref": "stkbailey/pre-processing-the-oil-timeseries",
      "title": "Pre-processing the oil timeseries",
      "source": "live",
      "kernel_id": "529853"
    },
    {
      "ref": "azzabiala/predict-sales-grocery-rf-gbr",
      "title": "Predict Sales (Grocery ) - RF - GBR",
      "source": "live",
      "kernel_id": "529826"
    },
    {
      "ref": "danieleewww/ensemble-of-ma-lgbm-catboost-models-lb-0-512",
      "title": "Ensemble of MA, LGBM, CatBoost models (LB 0.512)",
      "source": "live",
      "kernel_id": "529631"
    },
    {
      "ref": "aviatorx/shopping-for-insights-favorita-eda",
      "title": "Shopping for Insights - Favorita EDA",
      "source": "live",
      "kernel_id": "521151"
    },
    {
      "ref": "aharless/lgbm-one-step-ahead-with-proper-validation",
      "title": "LGBM One Step Ahead with Proper Validation",
      "source": "live",
      "kernel_id": "520895"
    },
    {
      "ref": "ericperbos/favorita-fastai",
      "title": "Favorita fastai",
      "source": "live",
      "kernel_id": "518890"
    },
    {
      "ref": "sbongo/lgbm-xgb-lr-weighted-average-lb-0-514",
      "title": "LGBM+XGB+LR Weighted Average (LB 0.514)",
      "source": "live",
      "kernel_id": "518664"
    },
    {
      "ref": "sjoly123/lgbm-one-step-ahead-c8de0f-adj",
      "title": "LGBM One Step Ahead c8de0f adj",
      "source": "live",
      "kernel_id": "517915"
    },
    {
      "ref": "tunguz/lgbm-one-step-ahead-c8de0f",
      "title": "LGBM One Step Ahead c8de0f",
      "source": "live",
      "kernel_id": "516927"
    },
    {
      "ref": "tunguz/regularized-greedy-forest-starter-lb-0-544",
      "title": "Regularized Greedy Forest Starter (LB 0.544)",
      "source": "live",
      "kernel_id": "516617"
    },
    {
      "ref": "aerdem4/ensemble-of-ma-lgbm-catboost-models-lb-0-512",
      "title": "Ensemble of MA, LGBM, CatBoost models (LB 0.512)",
      "source": "live",
      "kernel_id": "516265"
    },
    {
      "ref": "dongxu027/ensemble-of-ma-lgbm-catboost-models-lb-0-513",
      "title": "Ensemble of MA, LGBM, CatBoost models (LB 0.513)",
      "source": "live",
      "kernel_id": "515956"
    },
    {
      "ref": "aharless/bojan-s-favorita-xgboost-from-prepared-data",
      "title": "Bojan's Favorita XGBoost from prepared data",
      "source": "live",
      "kernel_id": "515530"
    },
    {
      "ref": "aharless/bojan-s-favorita-catboost-from-prepared-data",
      "title": "Bojan's Favorita Catboost from prepared data",
      "source": "live",
      "kernel_id": "514825"
    },
    {
      "ref": "sbongo/another-look-at-forecasting-gridsearch-arima",
      "title": "Another look at Forecasting(GridSearch+ARIMA)",
      "source": "live",
      "kernel_id": "514585"
    },
    {
      "ref": "tunguz/catboost-starter-lb-0-517",
      "title": "CatBoost Starter (LB 0.517)",
      "source": "live",
      "kernel_id": "514500"
    },
    {
      "ref": "abdulelah1/notebook3c28b168c6",
      "title": "Notebook3c28b168c6",
      "source": "live",
      "kernel_id": "514059"
    },
    {
      "ref": "miguelcpsbrito/supermarket-predictions",
      "title": "Supermarket predictions",
      "source": "live",
      "kernel_id": "513035"
    },
    {
      "ref": "dragondeldesierto/corporacion-favorita-leaderboard-small-analysis",
      "title": "Corporacion Favorita Leaderboard small analysis",
      "source": "live",
      "kernel_id": "511693"
    },
    {
      "ref": "dragondeldesierto/onpromotion-basic-stats-in-r",
      "title": "onpromotion basic stats in R",
      "source": "live",
      "kernel_id": "511295"
    },
    {
      "ref": "tunguz/xgboost-one-step-ahead-lb-0-519",
      "title": "XGBoost One Step Ahead (LB 0.519)",
      "source": "live",
      "kernel_id": "511234"
    },
    {
      "ref": "andygoo/analysis-of-aggregated-item-series",
      "title": "Analysis of aggregated item series",
      "source": "live",
      "kernel_id": "511017"
    },
    {
      "ref": "bdeng3/intuitive-interactive-eda-plotly-geojson",
      "title": " Intuitive & Interactive EDA (Plotly+Geojson)",
      "source": "live",
      "kernel_id": "509758"
    },
    {
      "ref": "danieleewww/lgbm-one-step-ahead",
      "title": "LGBM One Step Ahead",
      "source": "live",
      "kernel_id": "509584"
    },
    {
      "ref": "senkin13/lstm-starter",
      "title": "LSTM Starter",
      "source": "live",
      "kernel_id": "509196"
    },
    {
      "ref": "houxudan63/lgbm-modification",
      "title": "LGBM modification",
      "source": "live",
      "kernel_id": "507098"
    },
    {
      "ref": "aharless/lightgbm-including-unsold-items",
      "title": "LightGBM including unsold items",
      "source": "live",
      "kernel_id": "505774"
    },
    {
      "ref": "aharless/preparing-data-ii",
      "title": "Preparing data II",
      "source": "live",
      "kernel_id": "505494"
    },
    {
      "ref": "vsedelnik/log-ma-dow-means-promo-coeff-lb-0-526",
      "title": "Log MA, DOW Means, promo coeff (LB: 0.526)",
      "source": "live",
      "kernel_id": "504722"
    },
    {
      "ref": "aharless/stage-3-validation-and-submission",
      "title": "Stage 3. Validation and Submission",
      "source": "live",
      "kernel_id": "504213"
    },
    {
      "ref": "ashfaqeee/grocery-prediction",
      "title": "Grocery Prediction",
      "source": "live",
      "kernel_id": "504002"
    },
    {
      "ref": "sjoly123/lgbm-one-step-ahead",
      "title": "LGBM One Step Ahead",
      "source": "live",
      "kernel_id": "503974"
    },
    {
      "ref": "aharless/stage-2b-the-second-half",
      "title": "Stage 2b: the second half",
      "source": "live",
      "kernel_id": "503043"
    },
    {
      "ref": "aharless/stage-2a-the-first-half",
      "title": "Stage 2a: the first half",
      "source": "live",
      "kernel_id": "502925"
    },
    {
      "ref": "songkevinc/favorita-feature-extraction-and-data-cleaning",
      "title": "Favorita: Feature Extraction and Data Cleaning",
      "source": "live",
      "kernel_id": "501464"
    },
    {
      "ref": "larui529/reproducing-heads-or-tails-s-eda-in-python",
      "title": "Reproducing Heads or Tails's EDA in Python",
      "source": "live",
      "kernel_id": "500234"
    },
    {
      "ref": "aharless/nn-starter-using-lingzhi-ceshine-data",
      "title": "NN starter using Lingzhi/Ceshine data",
      "source": "live",
      "kernel_id": "498082"
    },
    {
      "ref": "aharless/lgbm-from-prepared-favorita-data",
      "title": "lgbm from prepared favorita data",
      "source": "live",
      "kernel_id": "498000"
    },
    {
      "ref": "aharless/preparing-data-for-lgbm-or-something-else",
      "title": "Preparing data for LGBM (or something else)",
      "source": "live",
      "kernel_id": "497933"
    },
    {
      "ref": "tunguz/lgbm-one-step-ahead",
      "title": "LGBM One Step Ahead",
      "source": "live",
      "kernel_id": "497782"
    },
    {
      "ref": "vrtjso/lgbm-one-step-ahead",
      "title": "LGBM One Step Ahead",
      "source": "live",
      "kernel_id": "496704"
    },
    {
      "ref": "aharless/xgb-using-ceshine-s-data",
      "title": "XGB using Ceshine's data",
      "source": "live",
      "kernel_id": "496499"
    },
    {
      "ref": "josephspear/favorita-grocery-sales-forecasting-eda",
      "title": "Favorita Grocery Sales Forecasting EDA",
      "source": "live",
      "kernel_id": "495841"
    },
    {
      "ref": "hemantnyadav/one-product-random-forest",
      "title": "One Product Random Forest",
      "source": "live",
      "kernel_id": "492854"
    },
    {
      "ref": "ericdfr/compare-submissions",
      "title": "Compare submissions",
      "source": "live",
      "kernel_id": "492470"
    },
    {
      "ref": "aharless/quick-sanity-check",
      "title": "Quick sanity check",
      "source": "live",
      "kernel_id": "491872"
    },
    {
      "ref": "aharless/ceshine-s-lgb-w-val-data-included-in-training",
      "title": "Ceshine's LGB w val data included in training",
      "source": "live",
      "kernel_id": "491446"
    },
    {
      "ref": "npa02012/ceshine-s-lgbm-starter-in-r-lb-0-529",
      "title": "Ceshine's LGBM Starter - in R (LB 0.529)",
      "source": "live",
      "kernel_id": "489626"
    },
    {
      "ref": "aharless/the-danger-of-far-out-of-sample-ts-forecasts",
      "title": "The danger of far out-of-sample TS forecasts",
      "source": "live",
      "kernel_id": "488291"
    },
    {
      "ref": "andygoo/dataset-make",
      "title": "Dataset_make",
      "source": "live",
      "kernel_id": "486405"
    },
    {
      "ref": "scirpus/transform-target-to-error-space",
      "title": "Transform target to error space",
      "source": "live",
      "kernel_id": "485503"
    },
    {
      "ref": "singhpari/notebook5754646f19",
      "title": "Notebook5754646f19",
      "source": "live",
      "kernel_id": "483651"
    },
    {
      "ref": "aharless/dissecting-ceshine-lee-s-lgbm-kernel",
      "title": "Dissecting Ceshine Lee's LGBM kernel",
      "source": "live",
      "kernel_id": "482970"
    },
    {
      "ref": "aharless/ok-but-how-do-we-know-which-had-the-best-score",
      "title": "OK, but how do we know which had the best score?",
      "source": "live",
      "kernel_id": "482863"
    },
    {
      "ref": "dragondeldesierto/snaive-forecast-frequency-7",
      "title": "snaive forecast (frequency = 7)",
      "source": "live",
      "kernel_id": "482669"
    },
    {
      "ref": "nappon/create-network-graph-to-plan-join-strategy",
      "title": "Create network graph to plan join strategy",
      "source": "live",
      "kernel_id": "482287"
    },
    {
      "ref": "leonpaul93/initial-eda-and-memory-reduction",
      "title": "Initial EDA and memory reduction",
      "source": "live",
      "kernel_id": "472058"
    },
    {
      "ref": "djcjlll/5-day-data-challenge-day-1",
      "title": "5 Day Data Challenge: Day 1",
      "source": "live",
      "kernel_id": "470819"
    },
    {
      "ref": "vrtjso/ensemble-of-public-kernels",
      "title": "Ensemble of Public Kernels",
      "source": "live",
      "kernel_id": "469364"
    },
    {
      "ref": "gouravg2/comprehensive-python-and-d3-js-favorita-analytics",
      "title": "Comprehensive Python and D3.js Favorita analytics",
      "source": "live",
      "kernel_id": "467989"
    },
    {
      "ref": "ceshine/lgbm-starter",
      "title": "LGBM Starter",
      "source": "live",
      "kernel_id": "467163"
    },
    {
      "ref": "shubhs1211/primary-eda",
      "title": "Primary EDA",
      "source": "live",
      "kernel_id": "465997"
    },
    {
      "ref": "noklamchan/understanding-the-data-table-relationships",
      "title": "Understanding the Data, table relationships",
      "source": "live",
      "kernel_id": "465006"
    },
    {
      "ref": "shubhs1211/predict-grocery-sales-3-models",
      "title": "Predict Grocery sales 3 models",
      "source": "live",
      "kernel_id": "464137"
    },
    {
      "ref": "anujis60/primary-eda",
      "title": "Primary EDA",
      "source": "live",
      "kernel_id": "464044"
    },
    {
      "ref": "cbrioso/optimal-weights-of-prior-sales-lb-0-644",
      "title": "Optimal weights of prior sales  LB 0.644",
      "source": "live",
      "kernel_id": "462277"
    },
    {
      "ref": "prabhulingayya/data-exploration-and-analysis",
      "title": "Data Exploration and Analysis",
      "source": "live",
      "kernel_id": "461542"
    },
    {
      "ref": "shubhs1211/predict-sales-xgboost",
      "title": "Predict sales XGBoost",
      "source": "live",
      "kernel_id": "460362"
    },
    {
      "ref": "ben519/paulo-pinto-s-log-mas-ported-to-r-lb-0-529",
      "title": "Paulo Pinto's log MAs ported to R (LB: 0.529)",
      "source": "live",
      "kernel_id": "458608"
    },
    {
      "ref": "gmanjunath/xgboost-predictions",
      "title": "XGBoost Predictions....",
      "source": "live",
      "kernel_id": "458210"
    },
    {
      "ref": "cbrioso/lightgbm-with-cv",
      "title": "LightGBM with CV",
      "source": "live",
      "kernel_id": "457787"
    },
    {
      "ref": "rawi707/favorita-analysis-via-regression-models",
      "title": "Favorita Analysis via Regression Models",
      "source": "live",
      "kernel_id": "455827"
    },
    {
      "ref": "gaojianj96/item-mean-calculation",
      "title": "Item_Mean_Calculation",
      "source": "live",
      "kernel_id": "455808"
    },
    {
      "ref": "kiranganji99/log-ma-and-day-of-week-means-in-r-lb-0-532",
      "title": "Log MA and Day of week means in R (LB: 0.532)",
      "source": "live",
      "kernel_id": "455072"
    },
    {
      "ref": "jaime9/using-the-media-as-estimator-of-the-sales-simple",
      "title": "Using the media as estimator of the sales (Simple)",
      "source": "live",
      "kernel_id": "453348"
    },
    {
      "ref": "konradb/extending-date-ranges",
      "title": "Extending date ranges",
      "source": "live",
      "kernel_id": "452847"
    },
    {
      "ref": "nit458/holiday-data-cleaning-and-merging-with-store",
      "title": "holiday_data_cleaning_and_merging_with_store",
      "source": "live",
      "kernel_id": "450361"
    },
    {
      "ref": "smcnish71/min-of-two-ma-kernels-lb-0-528",
      "title": "Min of two MA kernels (LB: 0.528)",
      "source": "live",
      "kernel_id": "446088"
    },
    {
      "ref": "melwinjose/mean-prediciton-0-908-20-lines",
      "title": "Mean Prediciton [0.908] <20 Lines",
      "source": "live",
      "kernel_id": "445796"
    },
    {
      "ref": "rjcampa/simple-oil-price-imputation",
      "title": "Simple Oil Price Imputation",
      "source": "live",
      "kernel_id": "443606"
    },
    {
      "ref": "youngguy131/devfest",
      "title": "devfest",
      "source": "live",
      "kernel_id": "443209"
    },
    {
      "ref": "kimkihyun/devfest-7210db",
      "title": "devfest 7210db",
      "source": "live",
      "kernel_id": "443199"
    },
    {
      "ref": "ggoals/devfest",
      "title": "devfest",
      "source": "live",
      "kernel_id": "443163"
    },
    {
      "ref": "rexzhang2013/sales-prediction-practise",
      "title": "Sales Prediction Practise",
      "source": "live",
      "kernel_id": "442563"
    },
    {
      "ref": "kaggleslayer/grocery-prediction-with-neural-network",
      "title": "Grocery prediction with Neural Network",
      "source": "live",
      "kernel_id": "439560"
    },
    {
      "ref": "hanania/mean-item-sales-benchmark",
      "title": "\"Mean Item Sales Benchmark\"??",
      "source": "live",
      "kernel_id": "439541"
    },
    {
      "ref": "robinhoo1973/supportive-data-preprocess-with-low-memory",
      "title": "Supportive Data Preprocess with Low Memory",
      "source": "live",
      "kernel_id": "438755"
    },
    {
      "ref": "paulorzp/log-ma-with-special-days-lb-0-529",
      "title": "Log MA with special days (LB:0.529)",
      "source": "live",
      "kernel_id": "438603"
    },
    {
      "ref": "nimesh280/ma-forecasting-with-holiday-effect-lb-0-529",
      "title": "MA forecasting with holiday effect (LB: 0.529) ",
      "source": "live",
      "kernel_id": "437464"
    },
    {
      "ref": "hemantnyadav/multilayer-peceptron-without-oil-csv",
      "title": "Multilayer Peceptron without oil.csv",
      "source": "live",
      "kernel_id": "436467"
    },
    {
      "ref": "towever/devfest",
      "title": "devfest",
      "source": "live",
      "kernel_id": "436377"
    },
    {
      "ref": "loveall/all-csv-files-a-glance",
      "title": "All CSV files @ a glance",
      "source": "live",
      "kernel_id": "433996"
    },
    {
      "ref": "captcalculator/variation-on-dseverything-s-ets-starter-script",
      "title": "Variation on DSEverything's ETS Starter Script",
      "source": "live",
      "kernel_id": "433773"
    },
    {
      "ref": "sudosudoohio/average-per-month-then-caculate-mean-of-four-ends",
      "title": "Average per month then caculate mean of four ends",
      "source": "live",
      "kernel_id": "432864"
    },
    {
      "ref": "daniluvatar/persisten-forecast-performance-baseline-notebook",
      "title": "Persisten Forecast Performance Baseline (Notebook)",
      "source": "live",
      "kernel_id": "432667"
    },
    {
      "ref": "daniluvatar/persistent-forecast-performance-baseline",
      "title": "Persistent Forecast Performance Baseline",
      "source": "live",
      "kernel_id": "432452"
    },
    {
      "ref": "loveall/nine-regressors-gokagglers",
      "title": "Nine regressors: GoKagglers",
      "source": "live",
      "kernel_id": "432291"
    },
    {
      "ref": "hemantnyadav/randomforestregressor-without-oil-csv",
      "title": "RandomForestRegressor without oil.csv",
      "source": "live",
      "kernel_id": "432266"
    },
    {
      "ref": "sunnybangale/finding-overlap-between-train-and-test-datasets",
      "title": "Finding overlap between train and test datasets",
      "source": "live",
      "kernel_id": "431488"
    },
    {
      "ref": "gauravlingwal007/forecasting-grocery-sales",
      "title": "Forecasting Grocery Sales ",
      "source": "live",
      "kernel_id": "430598"
    },
    {
      "ref": "loveall/grocery-eda-dirty-xgboost-arima-ets-prophet-gk",
      "title": "Grocery EDA  Dirty XGBoost, Arima,ETS,Prophet_GK",
      "source": "live",
      "kernel_id": "430446"
    },
    {
      "ref": "loveall/favorita-analytics",
      "title": "favorita-analytics",
      "source": "live",
      "kernel_id": "430421"
    },
    {
      "ref": "npa02012/mean-baseline-lb-59-translated-to-r",
      "title": "Mean Baseline (LB .59) - Translated to R!",
      "source": "live",
      "kernel_id": "430177"
    },
    {
      "ref": "rosejk/ml-project",
      "title": "ML_Project",
      "source": "live",
      "kernel_id": "430066"
    },
    {
      "ref": "sohinibhattacharya86/predict-grocery-sales-rf-xgb",
      "title": "Predict Grocery Sales - RF - XGB",
      "source": "live",
      "kernel_id": "429266"
    },
    {
      "ref": "ericdfr/new-items-in-stores",
      "title": "new items in stores",
      "source": "live",
      "kernel_id": "428462"
    },
    {
      "ref": "tarobxl/how-the-test-set-is-split-lb-0-532",
      "title": "How the Test Set is split? (LB: 0.532)",
      "source": "live",
      "kernel_id": "428336"
    },
    {
      "ref": "tkm2261/bigquery-sql-reproducing-paulo-pinto-s-lb-0-529",
      "title": "BigQuery SQL reproducing Paulo Pinto's LB: 0.529",
      "source": "live",
      "kernel_id": "428200"
    },
    {
      "ref": "paulorzp/log-ma-and-days-of-week-means-lb-0-529",
      "title": "Log MA and  Days of Week Means (LB: 0.529)",
      "source": "live",
      "kernel_id": "427635"
    },
    {
      "ref": "tarobxl/ma-for-each-day-lb-0-537",
      "title": "MA for each day - LB 0.537",
      "source": "live",
      "kernel_id": "427145"
    },
    {
      "ref": "rahavendranmohandass/notebook033d0568ec",
      "title": "Notebook033d0568ec",
      "source": "live",
      "kernel_id": "426232"
    },
    {
      "ref": "cjolley/xgboost-model-in-r-with-puzzling-results",
      "title": "xgBoost model in R (with puzzling results!)",
      "source": "live",
      "kernel_id": "426185"
    },
    {
      "ref": "paulorzp/log-means-and-medians-to-predict-new-itens-0-546",
      "title": "Log Means and Medians to Predict New Itens ~0.546",
      "source": "live",
      "kernel_id": "425980"
    },
    {
      "ref": "tauseefkhan/holiday-events-analysis-work-in-progress",
      "title": "Holiday Events Analysis (Work In Progress)",
      "source": "live",
      "kernel_id": "425948"
    },
    {
      "ref": "nishant4k/time-series-lb-0-558",
      "title": "Time_Series LB 0.558",
      "source": "live",
      "kernel_id": "425804"
    },
    {
      "ref": "tkm2261/bigquery-kaggle-tutorial-with-lb-0-59-and-0-546",
      "title": "BigQuery & Kaggle Tutorial with LB: 0.59 and 0.546",
      "source": "live",
      "kernel_id": "425297"
    },
    {
      "ref": "johnmisquita/sales-forecaster",
      "title": "Sales forecaster",
      "source": "live",
      "kernel_id": "425213"
    },
    {
      "ref": "ekami66/tables-merging",
      "title": "Tables merging",
      "source": "live",
      "kernel_id": "424329"
    },
    {
      "ref": "alijs1/prophet-example-for-time-series-predictions",
      "title": "Prophet example for time-series predictions",
      "source": "live",
      "kernel_id": "423196"
    },
    {
      "ref": "domcastro/log-moving-averages-forecasting-lb-0-547",
      "title": "Log Moving Averages Forecasting (LB=0.547)",
      "source": "live",
      "kernel_id": "423013"
    },
    {
      "ref": "sidshady/transactions-per-year-per-month-comparison",
      "title": "Transactions per year per month comparison",
      "source": "live",
      "kernel_id": "421521"
    },
    {
      "ref": "marcos19araujo/the-best-products-to-be-reach",
      "title": "The best products to be reach",
      "source": "live",
      "kernel_id": "421048"
    },
    {
      "ref": "ceshine/parse-holiday-data",
      "title": "Parse Holiday Data",
      "source": "live",
      "kernel_id": "420955"
    },
    {
      "ref": "paulorzp/log-moving-averages-forecasting-lb-0-546",
      "title": "Log Moving Averages Forecasting (LB=0.546)",
      "source": "live",
      "kernel_id": "419934"
    },
    {
      "ref": "ldreano/read-train-csv-with-a-condition",
      "title": "Read train.csv with a condition",
      "source": "live",
      "kernel_id": "418931"
    },
    {
      "ref": "tusharraman/sales-forecast",
      "title": "Sales Forecast ",
      "source": "live",
      "kernel_id": "416609"
    },
    {
      "ref": "ceshine/simple-knn-baseline",
      "title": "Simple kNN Baseline",
      "source": "live",
      "kernel_id": "416496"
    },
    {
      "ref": "rloury/test-with-neural-network",
      "title": "Test with Neural Network ",
      "source": "live",
      "kernel_id": "416417"
    },
    {
      "ref": "thepathofd/a-first-kaggle-part-1-forecasting-store-47",
      "title": "A first Kaggle - Part 1 - Forecasting store #47",
      "source": "live",
      "kernel_id": "415168"
    },
    {
      "ref": "franciscadias/weekends-at-favorita-eda-analysis",
      "title": "Weekends at Favorita - EDA Analysis",
      "source": "live",
      "kernel_id": "414984"
    },
    {
      "ref": "loveall/read-data-for-low-memory-usagesk",
      "title": "Read data for low memory usageSK",
      "source": "live",
      "kernel_id": "414200"
    },
    {
      "ref": "clarkchong/fan-fei-s-grocery-understanding-lightgbm",
      "title": "Fan Fei's Grocery (Understanding LightGBM)",
      "source": "live",
      "kernel_id": "414168"
    },
    {
      "ref": "paulorzp/mean-log-with-pandas-only-lb-0-558",
      "title": "Mean Log with Pandas only (LB: 0.558)",
      "source": "live",
      "kernel_id": "413440"
    },
    {
      "ref": "rajuspartan/store-wise-and-product-category-wise-analysis",
      "title": "Store wise and Product category wise analysis",
      "source": "live",
      "kernel_id": "413386"
    },
    {
      "ref": "domcastro/mean-baseline-lb-59",
      "title": "Mean Baseline (LB ~ .59)",
      "source": "live",
      "kernel_id": "413299"
    },
    {
      "ref": "deeplake/favorita-grocery-data-analysis-model-test",
      "title": "Favorita Grocery - Data analysis & model test",
      "source": "live",
      "kernel_id": "412140"
    },
    {
      "ref": "noordk/week-by-week-data",
      "title": "Week By Week Data",
      "source": "live",
      "kernel_id": "411441"
    },
    {
      "ref": "kunalkotian/easily-load-train-csv-w-o-crash-save-feather-file",
      "title": "Easily Load train.csv w/o Crash, Save Feather File",
      "source": "live",
      "kernel_id": "411397"
    },
    {
      "ref": "rloury/sales-prevision-lstm",
      "title": "Sales prevision - LSTM",
      "source": "live",
      "kernel_id": "410682"
    },
    {
      "ref": "deenaliz/master-dataset-with-basic-eda-ready-for-analysis",
      "title": "Master dataset with basic EDA ready for analysis",
      "source": "live",
      "kernel_id": "410646"
    },
    {
      "ref": "loveall/shopping-for-insights-favorita-eda",
      "title": "Shopping for Insights - Favorita EDA",
      "source": "live",
      "kernel_id": "410129"
    },
    {
      "ref": "anjukan/exploratory-data-analysis-for-sales-forecasting",
      "title": "Exploratory Data Analysis for Sales Forecasting",
      "source": "live",
      "kernel_id": "409942"
    },
    {
      "ref": "ekami66/python-eda-with-few-interactive-graphs",
      "title": "Python EDA with few interactive graphs",
      "source": "live",
      "kernel_id": "409768"
    },
    {
      "ref": "ericdfr/transactions",
      "title": "Transactions ?",
      "source": "live",
      "kernel_id": "409495"
    },
    {
      "ref": "tsdaemon/mapreduce-for-batch-processing-of-a-big-dataset",
      "title": "MapReduce for batch processing of a big dataset",
      "source": "live",
      "kernel_id": "407391"
    },
    {
      "ref": "gmanjunath/favorita-analytics",
      "title": "favorita-analytics",
      "source": "live",
      "kernel_id": "406239"
    },
    {
      "ref": "sakira/what-to-buy-from-the-grocery",
      "title": "What to buy from the Grocery?",
      "source": "live",
      "kernel_id": "405694"
    },
    {
      "ref": "loveall/grocery-eda-dirty-xgboost-arima-ets-prophet",
      "title": "Grocery EDA  Dirty XGBoost, Arima,ETS,Prophet",
      "source": "live",
      "kernel_id": "404933"
    },
    {
      "ref": "loveall/mean-baselinesk-lb-59",
      "title": "Mean BaselineSK (LB ~ .59)",
      "source": "live",
      "kernel_id": "403929"
    },
    {
      "ref": "jagangupta/interactive-favorita-visualizations-dashboards",
      "title": "Interactive Favorita visualizations | Dashboards",
      "source": "live",
      "kernel_id": "403767"
    },
    {
      "ref": "captcalculator/a-very-extensive-favorita-exploratory-analysis",
      "title": "A Very Extensive Favorita Exploratory Analysis",
      "source": "live",
      "kernel_id": "403486"
    },
    {
      "ref": "tkm2261/eval-metric-and-evaluating-last-year-sales-bench",
      "title": "Eval Metric and Evaluating Last Year Sales Bench",
      "source": "live",
      "kernel_id": "403423"
    },
    {
      "ref": "paulorzp/one-line-mean-log-lb-0-622",
      "title": "One Line Mean Log (LB: 0.622)",
      "source": "live",
      "kernel_id": "403403"
    },
    {
      "ref": "heyt0ny/read-data-for-low-memory-usage",
      "title": "Read data for low memory usage",
      "source": "live",
      "kernel_id": "403393"
    },
    {
      "ref": "ceshine/mean-baseline-lb-59",
      "title": "Mean Baseline (LB ~ .59)",
      "source": "live",
      "kernel_id": "402991"
    },
    {
      "ref": "nakapoor/finding-best-selling-products",
      "title": "Finding best selling products",
      "source": "live",
      "kernel_id": "402937"
    },
    {
      "ref": "armaan/comprehensive-favorita-analysis-groupby-d3",
      "title": "Comprehensive Favorita analysis - Groupby & D3",
      "source": "live",
      "kernel_id": "402054"
    },
    {
      "ref": "nilzone/eda-for-grocery-sales-forecasting",
      "title": "EDA for Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "401991"
    },
    {
      "ref": "dongxu027/time-series-ets-starter-lb-0-556",
      "title": "Time Series ETS Starter (LB 0.556)",
      "source": "live",
      "kernel_id": "401986"
    },
    {
      "ref": "jeru666/simple-exploration-visualization-notebook",
      "title": "Simple Exploration & Visualization Notebook",
      "source": "live",
      "kernel_id": "401865"
    },
    {
      "ref": "nilzone/eda-on-grocery-sales-forecasting",
      "title": "Exploratory Data Analysis on ",
      "source": "live",
      "kernel_id": "401844"
    },
    {
      "ref": "jeru666/all-csv-files-a-glance",
      "title": "All CSV files @ a glance",
      "source": "live",
      "kernel_id": "401575"
    },
    {
      "ref": "joconnor/eat-the-benchmark-lb-0-669",
      "title": "Eat The Benchmark (LB 0.669)",
      "source": "live",
      "kernel_id": "401495"
    },
    {
      "ref": "kausik123/corporaci-n-favorita-grocery-sales-forecasting",
      "title": "Corporación Favorita Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "401436"
    },
    {
      "ref": "victorgrobberio/visualizing-smelling-and-feeling-business-data",
      "title": "Visualizing, Smelling and Feeling Business Data",
      "source": "live",
      "kernel_id": "401416"
    },
    {
      "ref": "ranandiitk/analysis-grocery-sales-forecasting",
      "title": "ANALYSIS - Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "401271"
    },
    {
      "ref": "arthurtok/comprehensive-python-and-d3-js-favorita-analytics",
      "title": "Comprehensive Python and D3.js Favorita analytics",
      "source": "live",
      "kernel_id": "401213"
    },
    {
      "ref": "viveksrinivasan/reading-random-subset-of-rows-in-train-csv",
      "title": "Reading Random Subset Of Rows In train.csv",
      "source": "live",
      "kernel_id": "400951"
    },
    {
      "ref": "bala2728/predicting-the-sales",
      "title": "Predicting the Sales",
      "source": "live",
      "kernel_id": "400880"
    },
    {
      "ref": "juanumusic/tidy-notebook-and-deep-analysis",
      "title": "Tidy Notebook and Deep Analysis",
      "source": "live",
      "kernel_id": "400759"
    },
    {
      "ref": "hireme/do-you-like-random",
      "title": "Do you like random? :)",
      "source": "live",
      "kernel_id": "400323"
    },
    {
      "ref": "ambarish/grocery-eda-dirty-xgboost-arima-ets-prophet",
      "title": "Grocery EDA  Dirty XGBoost, Arima,ETS,Prophet",
      "source": "live",
      "kernel_id": "400282"
    },
    {
      "ref": "asindico/sales-forecasting-eda",
      "title": "Sales Forecasting EDA",
      "source": "live",
      "kernel_id": "400262"
    },
    {
      "ref": "jagangupta/memory-optimization-and-eda-on-entire-dataset",
      "title": "Memory optimization and EDA on entire dataset",
      "source": "live",
      "kernel_id": "400212"
    },
    {
      "ref": "paulorzp/one-line-median-lb-0-650",
      "title": "One Line Median (LB 0.650)",
      "source": "live",
      "kernel_id": "400104"
    },
    {
      "ref": "rwexler/eda-for-grocery-sales-forecasting",
      "title": "EDA for Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "400051"
    },
    {
      "ref": "puremath86/clean-up-on-aisle-4",
      "title": "Clean up on aisle 4!",
      "source": "live",
      "kernel_id": "400001"
    },
    {
      "ref": "headsortails/shopping-for-insights-favorita-eda",
      "title": "Shopping for Insights - Favorita EDA",
      "source": "live",
      "kernel_id": "399956"
    },
    {
      "ref": "saivarunk/eda-on-grocery-sales-forecasting",
      "title": "EDA on Grocery Sales Forecasting",
      "source": "live",
      "kernel_id": "399905"
    },
    {
      "ref": "inversion/dataframe-with-all-date-store-item-combinations",
      "title": "Dataframe with all Date-Store-Item Combinations",
      "source": "live",
      "kernel_id": "399868"
    },
    {
      "ref": "inversion/last-year-s-sales-benchmark",
      "title": "Last Year's Sales Benchmark",
      "source": "live",
      "kernel_id": "399856"
    },
    {
      "ref": "clarkchong/fan-fei-s-grocery-understanding-mean-baseline",
      "title": "Fan Fei's Grocery (Understanding Mean Baseline)",
      "source": "live",
      "kernel_id": "256299"
    },
    {
      "ref": "lkkadiri/a-very-extensive-favorita-exploratory-analysis",
      "title": "",
      "source": "meta",
      "kernel_id": "412702"
    },
    {
      "ref": "ailobe/fastai-ml1-lesson1-rf",
      "title": "",
      "source": "meta",
      "kernel_id": "1389108"
    },
    {
      "ref": "leeweimin/fast-ai-lesson-3-favorita-notes",
      "title": "",
      "source": "meta",
      "kernel_id": "2662953"
    },
    {
      "ref": "ashishpsharma/kernel743bad8c17",
      "title": "",
      "source": "meta",
      "kernel_id": "8214213"
    },
    {
      "ref": "azminetoushikwasi/regression-eda-stacking-ensembling",
      "title": "",
      "source": "meta",
      "kernel_id": "29631911"
    },
    {
      "ref": "ravs20/training-10-100-100000",
      "title": "",
      "source": "meta",
      "kernel_id": "39363261"
    },
    {
      "ref": "saptarshi7470/log-ma-and-days-of-week-means-lb-0-529",
      "title": "",
      "source": "meta",
      "kernel_id": "47002579"
    },
    {
      "ref": "woodsyosh/ml-project-bis",
      "title": "",
      "source": "meta",
      "kernel_id": "58281375"
    }
  ],
  "errors": []
}