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      "ref": "josephjosenumpeli/m5-forecasting-encoder-decoder-with-attention",
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      "ref": "gabzchua/m5-accuracy-poisson-tweedie",
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      "ref": "anshuls235/time-series-forecasting-eda-fe-modelling",
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      "ref": "rohitsingh9990/m5-feature-engineering-for-beginners",
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      "ref": "dantefilu/dnn-categorical-embeddings-optimized-day-to-day",
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      "ref": "marutama/back-to-predict-the-future-m5-eda-6-1",
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      "ref": "nictosi/m5-forecasting-exploration",
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      "source": "live",
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      "ref": "brunhs/m5-forecasting-introdutory-eda",
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      "ref": "yuta12345/kernel73abe5c36f-xgboosting",
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      "ref": "gopidurgaprasad/m5-encoder-decoder-pytorch",
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      "ref": "danofer/prophet-ts-starter-multiprocessing",
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      "ref": "adelizer/m5-forecasting-eda",
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      "source": "live",
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      "ref": "njain920/preliminary-eda",
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      "ref": "rikideguchi/m5-forecasting",
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      "ref": "mayer79/m5-forecast-dept-by-dept-and-step-by-step",
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      "ref": "rakshitlodha/m5-comp",
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      "ref": "saxinou/m5-forecasting-eda",
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    {
      "ref": "d19125435pranay/first-try-on-basic-random-forest-regression-model",
      "title": "First Try on basic Random Forest Regression Model",
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      "ref": "kyakovlev/m5-three-shades-of-dark-darker-magic",
      "title": "M5 - Three shades of Dark: Darker magic",
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      "ref": "fredcaram/m5-under-0-50-optimized",
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      "ref": "anyexiezouqu/magic-ensemble-top-kernels",
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      "ref": "albertobc/usage-of-xgboost",
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      "ref": "izotov/m5-under-0-50-optimized",
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      "ref": "kimurayut/m5-forecasting-eda-for-beginner",
      "title": "M5 Forecasting EDA for Beginner",
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      "kernel_id": "8936659"
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      "ref": "attol8/m5-eda-quarantined-edition",
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      "ref": "yassinealouini/seq2seq",
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      "ref": "jcolless/m5-rnn-wavenet-n-beats-approach",
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      "ref": "albertobc/m5-primera-submission-fructuosa",
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      "ref": "lucacarotenuto/kernel-last28days",
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      "ref": "ar2017/m5-forecasting-lightgbm",
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      "source": "live",
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      "ref": "chrisrichardmiles/weights-and-scales-for-the-past-2-years",
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      "ref": "eligeti/m5-plots",
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    {
      "ref": "shawncicoria/m5-forecaster-0-57330",
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      "kernel_id": "8911577"
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      "ref": "harupy/predict-day-by-day",
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      "ref": "chameleontk/rmse-and-wrmsse-of-a-submission",
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      "ref": "yassinealouini/trends-per-store",
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      "ref": "skylord/lgb-model-feature-importance",
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      "ref": "vgarshin/m5-catboost",
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    {
      "ref": "kimurayut/m5-forecasting-eda",
      "title": "【日本語】【初心者向け】M5 ForecastingのEDA",
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      "ref": "amtrema041996/m5-forecasting-cda",
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      "ref": "kuldeepdhadhwal/walmart-sales-eda",
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      "source": "live",
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      "ref": "kyakovlev/m5-custom-features",
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      "source": "live",
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      "ref": "che4ako/best-try",
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      "source": "live",
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      "ref": "poedator/m5-under-0-50-optimized",
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      "ref": "gandalf1292/walmartforkedveryfstmodel",
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      "ref": "damienpark/m5-data-preprocessing-foods",
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      "ref": "jespermadsen/eda-p-m5-accuracy",
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      "ref": "ranand60/kernel-m5-forecasting-data-exploration",
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      "ref": "nxrprime/transformer-xl-intro-baseline",
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      "ref": "riow1983/m5-forecast-poisson-loss",
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      "source": "live",
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      "ref": "amtrema041996/m5-forecasting-eda",
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      "ref": "akashsuper2000/m5-forecast-accuracy",
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      "source": "live",
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      "ref": "randunugalhena/m-forecasting-var",
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      "ref": "sibmike/m5-longest-run-of-heads-for-out-of-stock-detection",
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      "ref": "davidistrati/enriched-data-m5",
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      "ref": "davidistrati/evaluation-of-nn-models",
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      "ref": "akashsuper2000/m5-forecast-v2-python",
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      "ref": "davidistrati/m5-forecasting-data-analysis",
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      "ref": "nklinzhiming/m5-forecast-v2-python",
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      "ref": "kneroma/m5-first-public-notebook-under-0-50",
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      "ref": "kkondo/a-simple-dashboard-for-m5-forecast-competition",
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      "ref": "sergioli212/m5-all-feature-engineering-ready-to-use",
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      "ref": "azat942wer/data-exploration-memory-optimization",
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      "ref": "mihirpavuskar/m5-forecasting-accuracy-basic-eda",
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      "ref": "nxrprime/preprocessing-fe",
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      "ref": "kneroma/m5-forecast-v2-python",
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      "ref": "danijelsubotic/dnn-with-categorical-embeddings-day-to-day",
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      "ref": "servietsky/m5-magic-blending",
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      "ref": "ragnar123/asymmetric-loss-functions-lgbm",
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      "ref": "mayer79/m5-forecast-poisson-loss-top-10",
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      "ref": "kthexaroulis/question-regarding-memory",
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      "ref": "tiagowutzke/competition-m5",
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      "ref": "alicemozkara/kernel75eeff245d",
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      "ref": "samiranand14/m5-forecasting-accuracy",
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      "source": "live",
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      "ref": "andreinovikov90/m5-walmart",
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      "ref": "omershect/m5-competiotn-data-exploratory",
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      "kernel_id": "8705775"
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    {
      "ref": "gopidurgaprasad/m5-forecasting-eda-lstm-pytorch-modeling",
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      "ref": "sibmike/m5-out-of-stock-feature-640x-faster",
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      "ref": "nxrprime/m5-forecaster-v2",
      "title": "M5 ForecasteR v2",
      "source": "live",
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      "ref": "whyjust/eda-for-m5-with-details",
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      "ref": "hotvar/jbrain-m5",
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      "ref": "kailex/m5-forecaster-v2",
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      "source": "live",
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      "ref": "eligeti/quick-look-and-baseline-model-moving-average",
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      "ref": "renehiroki/walmart-sihga",
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    {
      "ref": "keitarokonishi/understand-m5-python",
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      "source": "live",
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      "title": "M5: EDA (Plotly) + LSTM Neural Network Vs. XGBoost",
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      "title": "M5 - 'Out of stock' feature",
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      "ref": "kyakovlev/m5-dark-magic",
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      "ref": "kyakovlev/m5-lags-features",
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      "ref": "akihirokkkkk/seq2seq",
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      "ref": "keitarokonishi/understand-m5-r",
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      "ref": "nxrprime/darker-magic",
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      "ref": "sumitai/m5-eda-using-pandas-profiling",
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      "ref": "kyakovlev/m5-simple-fe",
      "title": "M5 - Simple FE",
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      "kernel_id": "8596568"
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      "ref": "qcw171717/top-down-distribution-method",
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    {
      "ref": "koralturkk/m5-comptetition",
      "title": "M5-Comptetition",
      "source": "live",
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      "ref": "donghunkim/eda-profiling",
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      "source": "live",
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      "ref": "azmatsiddique/basic-m5-data-science",
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      "ref": "iicyan/japanese-m5-forecasting-sell-price-csv",
      "title": " [Japanese] M5 Forecasting 商品価格について sell_price.csv",
      "source": "live",
      "kernel_id": "8569386"
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      "ref": "nuka137/relation-between-sales-and-evnet-snap",
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      "ref": "iicyan/japanese-m5-forecasting-item-id",
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      "ref": "chizuchizu/we-don-t-need-to-use-transform",
      "title": "We don't need to use \"transform\"",
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      "ref": "lkatran/first-step-eda-and-baseline",
      "title": "First step - EDA and baseline",
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      "ref": "shinogi/time-series-visualize",
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      "kernel_id": "8548032"
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      "ref": "rohitsingh9990/m5-lgbm-fe",
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      "kernel_id": "8547625"
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      "ref": "iicyan/japanese-m5-forecasting",
      "title": "[Japanese] M5 Forecasting コンペの説明",
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      "kernel_id": "8542500"
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      "ref": "rodrigolima82/time-series-analysis-es-sarimax-xgb-lgbm",
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      "source": "live",
      "kernel_id": "8541962"
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      "ref": "alidoku/clustering-sales-data-and-simple-eda",
      "title": "Clustering Sales data and simple EDA",
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      "kernel_id": "8540824"
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      "ref": "iicyan/japanese-m5-sales-amount",
      "title": "[Japanese] M5 売上高について sales-amount",
      "source": "live",
      "kernel_id": "8537330"
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      "ref": "harupy/how-to-use-kaggle-api-as-a-python-library",
      "title": "How to use Kaggle API as a Python library",
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      "kernel_id": "8536401"
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      "ref": "robertburbidge/ensemble-starter-stochastic",
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      "source": "live",
      "kernel_id": "8535851"
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      "ref": "kuanlunchiang/simple-lstm-v0",
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      "kernel_id": "8533948"
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      "ref": "harupy/m5-pytorch-embeddings",
      "title": "m5-pytorch-embeddings",
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      "kernel_id": "8528264"
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    {
      "ref": "sh1ngg/arboretum-baseline",
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      "source": "live",
      "kernel_id": "8526808"
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      "ref": "sebastiancarino/m5-forecasting-accuracy-r",
      "title": "M5 Forecasting - Accuracy (R) ",
      "source": "live",
      "kernel_id": "8525598"
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    {
      "ref": "yasagure/don-t-need-rem-memory-again",
      "title": "Don't need rem_memory again",
      "source": "live",
      "kernel_id": "8521603"
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    {
      "ref": "rohitsingh9990/m5-forecasting-eda-feature-engineering",
      "title": "M5 Forecasting  - EDA + Feature Engineering",
      "source": "live",
      "kernel_id": "8515195"
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    {
      "ref": "leonzz/m5-forecasting-arima",
      "title": "m5 forecasting - arima",
      "source": "live",
      "kernel_id": "8514403"
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    {
      "ref": "alfonsocarta/m5-statistical-benchmarks-with-python-classes",
      "title": "M5 Statistical Benchmarks with Python classes",
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      "kernel_id": "8511938"
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    {
      "ref": "chrisrichardmiles/m5-backtest-workflow-made-easy",
      "title": "M5: Backtest Workflow Made Easy",
      "source": "live",
      "kernel_id": "8511835"
    },
    {
      "ref": "debojit23/m5-base-modelling",
      "title": "M5-base-modelling",
      "source": "live",
      "kernel_id": "8509853"
    },
    {
      "ref": "anirbansen3027/m5-forecasting-exhaustive-eda-beginner",
      "title": "M5 Forecasting Exhaustive EDA Beginner",
      "source": "live",
      "kernel_id": "8505736"
    },
    {
      "ref": "ykitaaa/m5-forecasting-starter-data-exploration",
      "title": "M5 Forecasting  - Starter Data Exploration（自分用） 📉",
      "source": "live",
      "kernel_id": "8504101"
    },
    {
      "ref": "thomasadorfer/m5-preliminary-eda",
      "title": "M5 Preliminary EDA",
      "source": "live",
      "kernel_id": "8496356"
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    {
      "ref": "zachary3141/cnn-sma",
      "title": "CNN+SMA",
      "source": "live",
      "kernel_id": "8493376"
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    {
      "ref": "harupy/automatic-logging-of-lightgbm-training-with-mlflow",
      "title": "Automatic logging of LightGBM training with MLflow",
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      "kernel_id": "8492349"
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    {
      "ref": "chrisrichardmiles/numpy-pandas-challenge-current-leader-0-67402",
      "title": "Numpy, Pandas Challenge: current leader 0.67402",
      "source": "live",
      "kernel_id": "8488722"
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    {
      "ref": "tnmasui/m5-wrmsse-evaluation-dashboard",
      "title": "M5 - WRMSSE Evaluation Dashboard",
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      "kernel_id": "8488019"
    },
    {
      "ref": "robertburbidge/statistical-benchmarks-wrmsse-stochastic-ensemble",
      "title": "Statistical Benchmarks WRMSSE Stochastic Ensemble",
      "source": "live",
      "kernel_id": "8484814"
    },
    {
      "ref": "akashsuper2000/m5-baseline",
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      "source": "live",
      "kernel_id": "8472382"
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    {
      "ref": "akashsuper2000/m5-forecast-keras-with-categorical-embeddings-v2",
      "title": "M5 Forecast: Keras with Categorical Embeddings V2",
      "source": "live",
      "kernel_id": "8472093"
    },
    {
      "ref": "chrisrichardmiles/simple-model-avg-last-28-days-grouped-by-weekday",
      "title": "Simple model: avg last 28 days grouped by weekday",
      "source": "live",
      "kernel_id": "8471498"
    },
    {
      "ref": "lilsummer877/visualize-the-effect-of-events-and-festivities",
      "title": "Visualize the effect of events and festivities 🎉 ",
      "source": "live",
      "kernel_id": "8471136"
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    {
      "ref": "girmdshinsei/for-beginner-r-lightgbm-japanese",
      "title": "for_beginner(R/LightGBM/Japanese)",
      "source": "live",
      "kernel_id": "8455652"
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    {
      "ref": "cindyway/exploratory-data-analysis",
      "title": "Exploratory Data Analysis",
      "source": "live",
      "kernel_id": "8453967"
    },
    {
      "ref": "chizuchizu/the-idea-that-halves-running-time-faster-code",
      "title": "The idea that halves running time(faster code)",
      "source": "live",
      "kernel_id": "8453050"
    },
    {
      "ref": "kfkabdg/edaaa",
      "title": "ウォルマートEDAaa",
      "source": "live",
      "kernel_id": "8442367"
    },
    {
      "ref": "chizuchizu/wanna-make-fast-running-code",
      "title": "wanna make fast-running code",
      "source": "live",
      "kernel_id": "8438715"
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    {
      "ref": "dhananjay3/wrmsse-evaluator-with-extra-features",
      "title": "WRMSSE Evaluator with extra features",
      "source": "live",
      "kernel_id": "8436471"
    },
    {
      "ref": "girmdshinsei/for-japanese-beginner-with-wrmsse-in-lgbm",
      "title": "for_Japanese_beginner(with WRMSSE in LGBM))",
      "source": "live",
      "kernel_id": "8436261"
    },
    {
      "ref": "qcw171717/other-naive-forecasts-submission-score",
      "title": "Other Naive Forecasts & Submission Score",
      "source": "live",
      "kernel_id": "8435286"
    },
    {
      "ref": "janzenliu/fork-how-important-are-the-holidays",
      "title": "[Fork] How important are the holidays?",
      "source": "live",
      "kernel_id": "8433432"
    },
    {
      "ref": "nonserial/m5-accuracy-mlr3learners-lightgbm-r-package",
      "title": "M5 accuracy: mlr3learners.lightgbm R package",
      "source": "live",
      "kernel_id": "8432375"
    },
    {
      "ref": "kamalnaithani/m5-eda-and-forecasting-model",
      "title": "M5 EDA and forecasting Model",
      "source": "live",
      "kernel_id": "8431166"
    },
    {
      "ref": "harupy/simple-baseline-using-average-over-past-years",
      "title": "Simple baseline using average over past years",
      "source": "live",
      "kernel_id": "8426841"
    },
    {
      "ref": "prabhavsingh/m5-analysis-eda",
      "title": "M5 ANALYSIS + EDA",
      "source": "live",
      "kernel_id": "8426267"
    },
    {
      "ref": "nxrprime/m5-simple-holt-winters-and-lgbm",
      "title": "📅📅M5: Simple Holt-Winters and LGBM📅📅",
      "source": "live",
      "kernel_id": "8420227"
    },
    {
      "ref": "philwebb59/how-important-are-the-holidays",
      "title": "How important are the holidays?",
      "source": "live",
      "kernel_id": "8418601"
    },
    {
      "ref": "kailex/m5-forecaster-0-57330",
      "title": "M5 ForecasteR [0.57330]",
      "source": "live",
      "kernel_id": "8414563"
    },
    {
      "ref": "philwebb59/how-important-are-snap-days",
      "title": "How important are SNAP days?",
      "source": "live",
      "kernel_id": "8411075"
    },
    {
      "ref": "aykhanpy/m5-analysis",
      "title": "M5 Analysis",
      "source": "live",
      "kernel_id": "8408366"
    },
    {
      "ref": "sujoyde/m5-exploration",
      "title": "M5_Exploration",
      "source": "live",
      "kernel_id": "8403545"
    },
    {
      "ref": "j0000shchen/walmarts-m5",
      "title": "Walmarts M5",
      "source": "live",
      "kernel_id": "8401135"
    },
    {
      "ref": "sourabhdattawad/getting-started-basics-of-time-series-forecasting",
      "title": "Getting Started: Basics of time series forecasting",
      "source": "live",
      "kernel_id": "8397029"
    },
    {
      "ref": "mayer79/m5-forecast-keras-embeddings-with-r",
      "title": "M5 Forecast: Keras Embeddings with R",
      "source": "live",
      "kernel_id": "8390974"
    },
    {
      "ref": "tomfromwales/2020q1-m5-eda",
      "title": "2020Q1:M5 - EDA",
      "source": "live",
      "kernel_id": "8386599"
    },
    {
      "ref": "timetraveller98/m5-snap-categorical-feature-analysis",
      "title": "M5 - SNAP categorical feature analysis",
      "source": "live",
      "kernel_id": "8386596"
    },
    {
      "ref": "hiteshsom/m5-accuracy-feature-engg-lightgbm",
      "title": "M5-Accuracy - Feature Engg - LightGBM ",
      "source": "live",
      "kernel_id": "8379698"
    },
    {
      "ref": "aditya48/m5-forecasting-accuracy",
      "title": "M5 Forecasting-Accuracy",
      "source": "live",
      "kernel_id": "8378120"
    },
    {
      "ref": "rahulvv/detect-yearly-trends-m5-forecasting-challenge",
      "title": "Detect yearly trends - M5 Forecasting challenge",
      "source": "live",
      "kernel_id": "8378089"
    },
    {
      "ref": "philwebb59/how-important-are-weekends",
      "title": "How important are weekends?",
      "source": "live",
      "kernel_id": "8374128"
    },
    {
      "ref": "ukveteran/prophet-jma-m5-forecasting-practice",
      "title": "Prophet: JMA - M5 Forecasting - Practice",
      "source": "live",
      "kernel_id": "8372102"
    },
    {
      "ref": "richamishra01/beginner-s-eda-on-walmart-m5-forecasting",
      "title": "Beginner's EDA on Walmart M5 forecasting",
      "source": "live",
      "kernel_id": "8367965"
    },
    {
      "ref": "mayer79/m5-forecast-attack-of-the-data-table",
      "title": "M5 Forecast: attack of the data.table",
      "source": "live",
      "kernel_id": "8366749"
    },
    {
      "ref": "damienpark/question-for-m5-eda",
      "title": "Question for M5(EDA)",
      "source": "live",
      "kernel_id": "8346917"
    },
    {
      "ref": "klose1996/mmmmm5",
      "title": "MMMMM5",
      "source": "live",
      "kernel_id": "8345927"
    },
    {
      "ref": "radstallion/sequential-prep-visualization-de-trend",
      "title": "Sequential Prep ,Visualization, de-trend",
      "source": "live",
      "kernel_id": "8343990"
    },
    {
      "ref": "qcw171717/naive-baseline",
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      "source": "live",
      "kernel_id": "8343592"
    },
    {
      "ref": "steverab/m5-forecasting-competition-gluonts-template",
      "title": "M5 Forecasting Competition GluonTS Template",
      "source": "live",
      "kernel_id": "8342969"
    },
    {
      "ref": "mobinmirarab/m5-forecasting-accuracy",
      "title": "M5 Forecasting - Accuracy",
      "source": "live",
      "kernel_id": "8342805"
    },
    {
      "ref": "xiaojiu1414/lightbgm-master-data-fold-index-nodrop",
      "title": "lightbgm-master-data-fold-index-NoDrop",
      "source": "live",
      "kernel_id": "8336221"
    },
    {
      "ref": "resistance0108/digging-in-correlation-between-sales-of-items",
      "title": "Digging in correlation between sales of items",
      "source": "live",
      "kernel_id": "8336193"
    },
    {
      "ref": "anyexiezouqu/eda-chinese",
      "title": "中文数据探索：EDA—Chinese",
      "source": "live",
      "kernel_id": "8329533"
    },
    {
      "ref": "l0new0lf/m5-competition-eda-models",
      "title": "M5 Competition : EDA + Models 📈 ",
      "source": "live",
      "kernel_id": "8326666"
    },
    {
      "ref": "lukegray515/m5-extensive-data-exploration",
      "title": "M5 Extensive Data Exploration",
      "source": "live",
      "kernel_id": "8326428"
    },
    {
      "ref": "ssatagop/basic-eda",
      "title": "Basic EDA",
      "source": "live",
      "kernel_id": "8325101"
    },
    {
      "ref": "brunoborges95/m5-time-series-forecasting-using-mapa-sarimax",
      "title": "M5 Time Series Forecasting using MAPA SARIMAX",
      "source": "live",
      "kernel_id": "8323664"
    },
    {
      "ref": "georsara1/fbprophet-try2-python-multiprocessing",
      "title": "FBprophet try2 (Python Multiprocessing)",
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      "kernel_id": "8320614"
    },
    {
      "ref": "chariots17/ensemble-starter",
      "title": "Ensemble Starter",
      "source": "live",
      "kernel_id": "8316990"
    },
    {
      "ref": "williamhuybui/holiday-s-visualization",
      "title": "Holiday's visualization",
      "source": "live",
      "kernel_id": "8314555"
    },
    {
      "ref": "anushakarthik1991/m5-competition-eda-models-0-867",
      "title": "M5 Competition : EDA + Models 📈 0.867",
      "source": "live",
      "kernel_id": "8314028"
    },
    {
      "ref": "harupy/m5-baseline",
      "title": "m5-baseline",
      "source": "live",
      "kernel_id": "8303068"
    },
    {
      "ref": "ajax0564/keras-with-embeddings",
      "title": "Keras with Embeddings",
      "source": "live",
      "kernel_id": "8302589"
    },
    {
      "ref": "sampath1310/m5-eda-interactive",
      "title": "M5 EDA Interactive",
      "source": "live",
      "kernel_id": "8302539"
    },
    {
      "ref": "lakshyarawal/eda-for-accuracy-m5",
      "title": "EDA for Accuracy - M5",
      "source": "live",
      "kernel_id": "8302339"
    },
    {
      "ref": "tnmasui/m5-forecasting-lstm-w-custom-data-generator",
      "title": "M5 Forecasting - LSTM w/ Custom Data Generator",
      "source": "live",
      "kernel_id": "8300626"
    },
    {
      "ref": "ryuichiyanagisawa/m5-forecasting-very-simple-eda",
      "title": "M5 Forecasting-very-simple-EDA",
      "source": "live",
      "kernel_id": "8299163"
    },
    {
      "ref": "aniketkatyal/m5-deep-dive-into-the-data",
      "title": "M5 || Deep dive into the Data",
      "source": "live",
      "kernel_id": "8293688"
    },
    {
      "ref": "zachary3141/m5-beginner-eda-cnn",
      "title": "M5 beginner EDA + CNN",
      "source": "live",
      "kernel_id": "8290699"
    },
    {
      "ref": "lebroschar/aggregate-forecast-eda",
      "title": "Aggregate Forecast EDA",
      "source": "live",
      "kernel_id": "8288930"
    },
    {
      "ref": "grapestone5321/m5-forecasting-accuracy-sample-submission",
      "title": " M5 Forecasting-Accuracy-sample_submission",
      "source": "live",
      "kernel_id": "8288619"
    },
    {
      "ref": "kei96kag/m5-forecasting-eda-data-understading-by-r",
      "title": "M5_Forecasting EDA : Data Understading by R",
      "source": "live",
      "kernel_id": "8288362"
    },
    {
      "ref": "mayer79/m5-forecast-keras-with-categorical-embeddings-v2",
      "title": "M5 Forecast: Keras with Categorical Embeddings V2",
      "source": "live",
      "kernel_id": "8285722"
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